Patent Application
20250141755
This disclosure relates generally to data processing with respect to manipulation of physical, digital, and abstract resources—e.g., physical or digital web-objects—of decentralized data networks, such as documents, graphical user interfaces, application programing interfaces, digital entities, physical entities, hybrid entities, etc. that are resources of the Internet, the Internet of Things (IoT), or the Internet of Everything (IoE). More particularly, this disclosure focuses primarily on to identifying, interpreting, clustering, representing, reasoning, associating, and manipulating contextually and structurally complex and dynamic web-resources in order to provide a truly self-contained intelligent system wherein various networks of consistent and persistent interoperable sub-systems that are capable of performing complex web-based tasks may be formed. However, the disclosure transcends the field of focus and overlaps with a wide range of research fields from artificial general intelligence to cognitive science or from ontology to epistemology.
Keywords: Artificial General Intelligence, Ontologies, Complex Systems, Black-box Systems, Knowledge-based Systems, Fuzzy Theory, Fuzzy Knowledge Representation and Reasoning, Control Theory, Fuzzy Control Systems, Uncertainty Representation and Reasoning, Uncertainty Management in Data Processing, Dynamic-Uncertainty, Dynamically-Fuzzy Semantic Relations based on Analogies, Dynamically-Fuzzy Semantic Networks and Reasoning based on Analogies, Transforming-Objects, Transforming-Concepts and their Analogical Relations.
The primary purpose of this disclosure is to provide a technological basis for the formation of a global network of interoperable automated agents that operate throughout the Web with the ability to interact with web-resources stably, sustainably, and independently—i.e., without any support from the providers of the resources such as integration APIs—in order to perform complex web-based tasks—from improving services to industrial automation, or from data mining to evaluation of information—at a level comparable to human experts. This goal is also in line with the most ambitious yet unrealized goal of the Semantic Web—the general purpose of which is to make Internet data machine-readable thus processable. Accordingly, both semantic technologies and semantic web technologies, especially, the ones related to ‘semantic extraction’ (i.e., automatically extracting structured information—or meaning—from unstructured data such as natural language texts, machine executable scripts, images, audios, videos, or any combination thereof such as user interfaces which comprise many types of unstructured data as components) and ‘uncertainty reasoning’ (i.e., methods designed for representing and reasoning with knowledge when Boolean truth values are unknown, unknowable, or inapplicable) predominantly define the state-of-the-art in the field of this disclosure.
In essence, state-of-the-art methodology—for making Internet data machine-readable and thus processable—rely on the development of ontologies related to web content, i.e., web-resources, which are any identifiable resources (physical, digital, or abstract) present on or connected to the World Wide Web. In this way, machines can process knowledge itself through those ontologies, using processes similar to human deductive reasoning and inference. Besides utilizing semantic extraction, Semantic Web utilizes technologies such as Resource Description Framework (RDF), Web Ontology Language (OWL), and Extensible Markup Language (XML) which in coordination provide machine-readable descriptions that supplement or replace the content of web documents. Thus, content may manifest itself as descriptive data—describing the structure of the knowledge about said content—stored in web-accessible databases such as in the form of web annotations, or as markup within documents.
The general consensus with respect to the primary challenges for the Semantic Web is: i) vastness, i.e. the challenge of processing extremely big data; ii) vagueness, i.e., imprecise concepts, such as tall, short, hot, cold, etc.; iii) uncertainty, i.e., precise concepts with uncertain predicate values, such as the probability that ‘A is B’ is Y % instead of ‘A is B’; iv) inconsistency, i.e., logical contradictions that inevitably arise in the process of developing and/or combining large ontologies; and v) maliciousness, i.e., intentionally misleading the consumer of the information by the producer of the information. W3C Incubator Group for Uncertainty Reasoning for the World Wide Web (URW3-XG) final report further combines these together under the single heading of ‘uncertainty’ to encompass a variety of aspects of imperfect knowledge, including incompleteness, inconclusiveness, vagueness, ambiguity, and others. More concretely, ‘representing and reasoning’ with ‘uncertainty and vagueness’ in ontologies, i.e., ‘uncertainty representation and reasoning’, is accepted to be the most challenging problem of the Semantic Web. Commonly applied approaches to uncertainty representation and reasoning include probability theory, possibility theory, fuzzy theory, and theory of belief functions.
It is obvious that if said ontologies can be developed then machines may semantically interpret and thus process web content. On the other hand, it is controversial whether said ontologies can ever be developed—especially deep enough to be useful. Or,—more interestingly—if developed, whether they can be maintained. The main cause of the latter—which is the problem that the current disclosure focuses on—is the chaotic nature of web content, i.e., uncertainty induced by the dynamicity of web-resources or in short dynamic-uncertainty,—a relatively underestimated or underemphasized problem. Indeed, web-resources—even static ones—may be subject to simple or complex alterations, i.e., adjustments, modifications, transformations, etc., that may occur progressively or abruptly both contextually and structurally, without any notification. Essentially, any automated reasoning system in an effort to deliver on the promise of the Semantic Web has to first and foremost deal with the challenge of representing and reasoning with ‘uncertainty and vagueness induced by the dynamicity of web-resources’ in ontologies regardless of the other imperfections of the knowledge itself. Therefore, state-of-the-art methodology—for making Internet data machine-readable and thus machine-processable—is problematic at the core.
Indeed, state-of-the-art methodology is proven to be only successful when applied to ‘stable’ web content, i.e., relatively static web-resources that provide adequate real-time information—such as through integration APIs—with respect to contextual and/or structural alterations whenever they occur. For example, Wikipedia has been successful in terms of Semantic Web compatibility. However, web-resources are mostly dynamic and complex both contextually and structurally, and most websites do not provide APIs for integration or what is provided may not be adequate and/or permanent. In general, websites have neither the ability nor the desire to cooperate. Furthermore, even absolutely static resources can be manually altered drastically. For example, a digital web-resource such as a static user interface can be altered by its provider both contextually and structurally. Or, even a physical web-resource connected to IoT such as a standard vehicle with a unique ID can be altered by the owner dramatically by extreme overhauling. In this context, for the execution of a certain task, for example, associating an agent with a web-resource—that is even assumed to be static—based on the existing ontologies may well lead to instability in the process. Thus, in practice, once a resource interacted with is altered, associations are often lost or incorrectly re-established and executed.
In conclusion, the Web is no more ‘a web of documents’—as once described by Tim Berners-Lee in 2009—but a web of complex and dynamic interfaces provided in order to transceive complex and dynamic data or information to perform complex and dynamic processes during interactions with humans or machines. Moreover, these interfaces are evolving further to become personally adaptive intelligent interfaces that provide personalized experiences to their human or machine users. For example, adaptive websites provide dynamically altering web pages—both contextually and structurally—according to each corresponding user, such as based on web search history and real-time sentiment analysis. On the other hand, as stated before, even completely static web environments can be altered manually both structurally and contextually in time and the level of these alterations can be dramatic—especially if the intention is adversarial. Therefore, it is essential to assume that all web environments, i.e., all web-resources, are chaotic/unstable in nature. In this context, the fundamental problem with the state-of-the-art is its attempt to develop ontologies of inherently unstable content. Indeed, it would only be an engineering problem to provide said capabilities to any system that operates on environments that are guaranteed to be in an absolutely steady state, such as the database of a search engine where the collected internet data is stored via web crawling and scrapping for processing.
The current disclosure provides methods and systems that can manage the dynamic-uncertainty induced by resources of decentralized data networks, in order to ensure the stability and sustainability of task-oriented automated operations, such as operations conducted through intelligent agents. In contrast to the state-of-the-art object-based processing, the methods and systems enable object-aware processing—which is essentially context-aware processing—to ensure the establishment of stable and sustainable associations with physical and digital web-objects, while enabling those objects to be processed dynamically with full adaptability in response to contextual and structural alterations in real-time. Thus, the disclosure provides—both human and machine—users with the ability to develop and deploy modular systems capable of engaging any conceivable physical or digital interaction with the resources of a data network, such as dynamically linking and manipulating clusters of complex web-objects to execute complex tasks in complex and dynamic web environments—most importantly—stably and sustainably.
Decentralized data networks—especially the Internet, the Internet of Things, or the Internet of Everything in general—have a tremendous potential that is currently idle or in its infancy in terms of independently operating task oriented interoperable automated agents—despite the groundbreaking advancements in AI. Disclosed methodology, when combined with the state-of-the-art methodologies in AI, may well lead to the formation of constantly growing dynamic networks of modular, interoperable, intelligent agents throughout the Web—in a truly stable, sustainable and self-contained manner, such as without any API support—which is indeed the ultimate goal of the Semantic Web. However, although this was the initial goal that was set out, the solutions developed have reached a scope far beyond the scope of that goal. The complexity of the problem has led to a radical and far-reaching solution. Below is a metaphorical representation of the essence of the problem that is being solved by the disclosed methods and systems.
In mythology, folklore and speculative fiction, shapeshifting is the ability to physically transform oneself through unnatural means, while gaining the features of the transformed entity. In this context, a shapeshifter is a person or being with the ability to change its physical form—along with the features that come with those forms—at will. For example, if a shapeshifting snake transforms into a bird, then it gains all the abilities that a bird possesses such as the ability to fly. Or if a shapeshifting bird transforms into an x-ray machine than it gains all the features that an x-ray machine possesses while losing the former abilities. Furthermore, a shapeshifter may also transform into currently unknown—in between—beings. Such as an x-ray machine that also has the features of a gaming console. Shapeshifting process can be intermittent or continuous, rapid or slow. While some shapeshifters follow certain rules during their transformation processes and they are limited with respect to the scope of the transformations (such as Optimus Prime of Transformers), others do not follow any rules, and they can transform into all sorts of things (such as Aku of Samurai Jack).
Now, consider a hospital, where the doctors, nurses, caregivers who work in it are shapeshifters. Even the hospital itself including the equipment are shapeshifters. Fortunately, the director of the hospital is a normal human being who is in charge of everything and also responsible about the well-being of the patients who are also usually normal humans, but sometimes there may be patients who are shapeshifters as well. Just like any hospital, it must be ensured that: patients are diagnosed correctly; surgeries are performed flawlessly; the equipment work properly, the employees work in harmony, etc. The only tools that the director has are the agents at his disposal, who also act as a bridge between him and the rest of the hospital. Now consider the problem: ‘how such a hospital that is full of chaos and all kinds of dynamic-uncertainties can be managed by the director with the help of his agents stably and sustainably’—but—solely based on physical and mathematical principles without any surreal magical tricks.
The current disclosure provides a radical solution to this challenging problem by extending the borders of fuzzy-theory and ontologies by introducing the concept of ‘transforming-objects, transforming-concepts and their analogue relations through relational-bandwidths’ under the disclosed ontological model ‘Dynamically-Fuzzy Semantic Relations based on Analogies’ which constitutes the ontological basis of ‘Methods and Systems for Object-Aware Fuzzy-Processing based on Analogies’. ‘Dynamically-Fuzzy Semantic Relations based on Analogies’ also comprises a novel knowledge representation and reasoning model ‘Dynamically-Fuzzy Semantic Networks and Inference based on Analogies’ that incorporates reasoning by analogies through relational-bandwidths based on a ‘Generalized Similarity Inference Rule’ that is derived and introduced. Ultimately, ‘Dynamically-Fuzzy Semantic Networks and Inference based on Analogies’ is an extension of the conventional ‘Fuzzy Semantic Networks’ that involves all types of semantic relations, i.e., crisps, fuzzy, analogically-fuzzy, where each relation type is essentially a special case of the relation type that the model asserts—thus may be considered as a super-ontological-model comprising all fundamental models. Essentially, the methodology disclosed herein, i.e., Methods and Systems for Object-Aware Fuzzy Processing based on Analogies, is built in accordance with the principles that are set by said overarching ontological model.
In essence, ‘Object-Aware Fuzzy Processing based on Analogies’ consists of the following core processes: A) Defining systems involving dynamic-uncertainty—such as black-box systems—as physical or conceptual entities that transform between states that they possess, wherein each state that an entity may possess—including the respective rules regarding their manifestations—is defined by the contextual properties and boundaries that the corresponding system is subject to; and B) Establishing associations with said transforming entities based on analogies—i.e., semantic similarities—according to reference contexts—i.e., sets of concepts that set the rules and conditions to which a system is subject—through relational-bandwidths—i.e., domains of relations defined by similarity thresholds—wherein entities are considered associated with each other. In contrast to the state-of-the-art methodologies based on fuzzy semantic relations which enable entities to be associated through relational-degrees, the disclosed methodology enables entities to be associated through relational-bandwidths, which enables the establishment of dynamic associations to families of analogous entities defined according to relevant contexts. In this regard, disclosed methodology not only enables the establishment of associations between dynamically uncertain entities, but also enables the establishment of associations between entities whose existence are not yet known, such as objects that are not yet discovered or concepts that are not yet innovated.
To exemplify the process A, consider defining the class that Optimus Prime is a member of as a transforming-concept that transforms between infinitely many states wherein each state involves a concept that may exist within the contextual boundaries set by the concepts: i) Robot Phase: The set of all possible nuclear-fusion-powered, intelligent, humanoid-robot concepts that do not violate the rules of physics; ii) Vehicle Phase: The set of all possible combustion-engine driven wheel-based freightage vehicle concepts that do not violate the rules of physics; iii) Transition Phase: The set of all possible concepts that comprise any combination of the properties of said concepts, i.e., above robot (i) and vehicle (ii), while never exceeding the transformational rate of change, i.e., the slope, ±tan π/8, without violating the rules of physics. N.B.: Transformational rate of change between consecutive manifestations of a transforming-object may be represented by
wherein STO(t)˜TO(t−ϵ)/C
For more information on this matter, see Q.V. the sections S4 and S5.
To exemplify the process B, consider establishing a dynamic association between a stable-object and a transforming-object that is known to be functionally compatible with said stable-object for at least one of its manifestations, i.e., a manifestation that establishes a harmonious working system with acceptable operational characteristics. Let the stable-object be a certain ‘induction motor stator’ and the transforming-object be its rotor that is identified to transform in the context of ‘induction motor rotors that are mechanically compatible with said stator’, i.e., installable and mechanically rotatable however electromagnetically may or may not be compatible. Further, in order to simplify the system, consider the whole system, i.e., both the rotor and the stator, as a transforming-object, i.e., a transforming-motor. Now, consider the task of identifying at least one other compatible rotor—satisfying the operational characteristics of the initial configuration within certain tolerances—that the transforming-rotor—and thus the transforming-motor—may further manifest, however without utilizing any methodology that is primarily based on ‘trial and error’ or primarily based on ‘utilization of knowledge-bases’.
In essence, according to the embodiments, the disclosed methodology achieves this goal by i) conceptualizing a root-motor (OB) to be used as an analogical reference from the initial working rotor-stator configuration according to the task, i.e., eliminating the non-essential properties in the context of satisfying the operational characteristics of the initial configuration; ii) determining a base-motor-context (CB) to be used as the contextual reference in the process of similarity comparison between the root-motor (OB) and the nth manifestation of the transforming-motor (TOn) according to the task, i.e., determining the contextual properties and boundaries that the induction motors are subject to particularly in the context of said task; iii) identifying the lower (αL) and the upper (αH) similarity thresholds, i.e., the bandwidth of relationship, according to the disclosed ontological model ‘αL≤STO
As a result of the above process, compatible rotors may be identified, without utilizing any methodology that is primarily based on trial-and-error or primarily based on utilization of knowledge-bases, but mainly based on ‘perception of analogies’—which may be a gateway to the computational formulization of human intuition. Therefore, disclosed methodology may revolutionize knowledge-based systems including hybrids systems, i.e., knowledge-based systems that also comprise statistical-learning methodologies, by introducing further context-awareness to those systems, similar to that of the Attention Mechanism that revolutionized deep-learning.
Note1: The quality of the disclosed methodology mainly relies on the determination of the base-context (CB) and identification of the lower (αL) and the upper (αH) similarity thresholds, i.e., the bandwidth of relationship or relational bandwidth, wherein especially the latter is a very complex process that also involves statistical learning methods. Note2: In the conventional fuzzy semantic relations, defining the degree of a relation as a variable—such as a function of time—does not automatically define the presence of a transforming entity in the system. In other words, such modifications do not necessarily convert the fuzzy semantic relation into a dynamically-fuzzy semantic relation. For example, consider, dynamically altering degrees of relations between causes-and-effects in a system as further events occur in the system, e.g., according to Bayesian principles. Nevertheless, even such systems may also be defined and processed as transforming-entities within the scope of to the disclosed methodology. Note3: In the Transformers universe, Autobots and Deceptions are depicted as simpler systems in terms of the transition phase.
Said methodology has an enormous potential for also explaining the essence of things, such as explaining the underlying mechanisms behind the causes and effects of physical or digital events, due to the fact that each analogy inherently represents a universal fact within a reference context rather than a proposition or a cause-and-effect relationship. To exemplify, consider the below—highly simplified—example regarding the behavior of a dice observed by flattened, i.e., almost two-dimensional, creatures that live on a completely flat world, who do not possess a complete three-dimensional perception under the influence of a very strong gravity.
Assume that those creatures observe the behavior of the dice and construct a conventional, i.e., non-analogical, semantic network based on those observations. Accordingly, the semantic network involves seven nodes such that one of them represents the dice as a black-box system that produces an output and the each of the remaining six nodes represents a corresponding one of the individual sides—or faces—of that dice, wherein each edge that connects the dice to a face conveys the expected value of observing the corresponding face with respect to each appearance, i.e., roll, of the dice and which is identified to be ⅙ for all faces. It is clear that neither the individual relations nor the whole network, does not expose much about the underlying mechanisms that regulate the phenomenon in question—even claiming some sort of symmetry existing in the system cannot be properly supported. Thus, the creatures cannot go further than speculating about the underlying mechanism based on the information provided by the semantic network.
On the contrary, consider those creatures extending the semantic network wherein each face pair of the dice is further compared based on analogies in the context of their shapes and sizes, i.e., geometrical properties. This time, the newly added edges between the faces, conveys further information of ‘exact geometrical similarity’, i.e., symmetry, between all six faces and which is among the most essential information with respect to theorizing the underlying mechanism that causes the observed outcomes of the dice. Moreover, if the creatures can manage to slice the dice into many cross-sections along the direction of the normal-vector of a face, they may both reveal the full geometrical properties of the dice—by comparing the similarities of the cross-sections in the context geometrical properties—and the role of center of mass in the system—by comparing the similarities of the cross-sections in the context of distribution of mass. Thus, by reasoning through the extended semantic network, they may establish a general theory regarding the underlaying mechanism of all polyhedral dices with respect to their behavior under the influence of a unidirectional gravitational field—beyond explaining the behavior of the cubical dice in question.
The world is full of shape shifters that transform gradually or rapidly. The World Wide Web, quantum mechanics, economical interactions, human relations, etc. are all solid examples of complex systems involving not only uncertainty but also dynamic-uncertainty. The disclosed methodology provides a concrete solution to process and manage such uncertainties in such complex systems in accordance with the capabilities that it possesses—such as establishing associations between entities whose existence may or may not known, e.g., objects that are not yet discovered or concepts that are not yet innovated. Consequently, the disclosed methodology—which bypasses utilization of knowledge-bases or trial-and-error as primary methodologies and leverages ‘perception of analogies’ instead—provides a fundamental technology, which also lays the groundwork for further research and development that may drive further major breakthroughs across diverse fields of science and technology.
N.B.: To further exemplify the need for processing complex systems involving dynamic-uncertainty: Consider managing 3rd party automated agents that operate on hostile web environments that constantly induce dynamic-uncertainty into the process by complex and dynamic transformations. Or, consider simulating quantum-mechanical systems that involve sub-quantum-mechanical systems that induce dynamic-uncertainty by dynamically interfering each other. Or consider analyzing a socio-economical system—such as the global stock market—that involves individuals and corporations who induce dynamic-uncertainty by alterations in their behavior.
Re-Defining of Some of the Core Concepts Due to the Inadequacy of the Current Definitions without Affecting the General Methodology:
Entities: An entity is either ‘a conceptual entity’ or ‘a physical entity’ or ‘a hybrid entity that involves both conceptual and physical properties in a certain proportion’, wherein ‘conceptual entities’ are subject to the ‘rules of the governing concepts that they are included in’ (e.g., mathematical concepts of the mathematics), ‘physical entities’ are subject to the ‘laws of physics’ (e.g. physical events of the physical universe/s) and ‘hybrid entities’ are subject to both the ‘rules of the governing concepts that they are included in’ and the ‘laws of physics’. Exemplification: As an example of a hybrid entity, consider the case of a digital simulation of a physical event. A digital simulation is—generally—the outcome of a process that involves both conceptual entities such as algorithms and physical entities such as processors. Furthermore, the degree of approximation to reality of a simulation is determined by both the quality of algorithms and the capacity of the processing hardware involved. In this context, at least some of the digital entities may be considered as hybrid entities that are subject to the laws of computation involving both the rules of mathematics and the laws of physics. For purely physical and purely conceptual entities—again in the context of computation—a mechanical computer such as the ‘Antikythera Mechanism’, and an abstract machine such as the ‘Turing Machine’ may be considered respectively. N.B.: As a counter argument, it can be further proposed that a digital simulation process may not be that different than a mechanical simulation process such as the operation of the Antikythera Mechanism’ once started. However, this may not be a valid argument for all cases. Consider a digital simulator that involve conceptualization of the outcomes—such as in the form of a feedback loop—during a simulation process. In such a case the outcome is a product of both conceptual entities—such as a decision based on the conceptualization of an event—and physical entities—such as the electrons that interact with the semi-conductors in the transistors to execute that decision. If one fails, the outcome is affected thus both entities must be evaluated as a whole, thus considering digital entities as either conceptual or hybrid makes sense.
Objects: In accordance with the above definition of the core concept of ‘entities’, an object is either a full-object, or a semi-object, or a virtual-object, wherein a full-object is a pure physical entity, a semi-object is a hybrid entity, and a virtual-object is a pure conceptual entity that represents either a physical entity, or a conceptual entity, or a hybrid entity—while considering all related concepts such as ‘instances’ aka ‘individuals’ accordingly. Thus, ‘digital entities’ must be considered as digital-objects that involve either purely conceptual properties, or—in between—hybrid properties in certain proportions. Said definition of ‘objects’ obviously deviates from common informatics terminology (such as the OOP terms) that defines an object only as a pure concept that represents a physical or conceptual entity. On the other hand, defining ‘objects’ as ‘purely conceptual only’ while totally ignoring the concept of ‘semi-objects’ thus ignoring the concept of ‘digital-objects that may involve both physical and conceptual properties at the same time indissociably’ may be considered a deficiency in the context of a field of research. For example, this may be a very critical issue especially when dealing with qubits instead of binary bits in quantum computing, such as while programing an application for a quantum computing system beyond a certain level of complexity. Or in the process of constructing the theory of everything especially in the context of the concept of ‘information’ for example when dealing with fundamental assumptions such as ‘conservation of information’. In conclusion, i) an object may be purely physical, or purely conceptual, or hybrid—with certain proportions of both; ii) if the exact nature of the object cannot be inferred from the context, it must be specified accordingly; iii) only conceptual objects may have instances since all physical and hybrid objects are unique entities in the universe and a unique entity may not have further instance of itself; and iv) a digital-object may be purely conceptual or hybrid, but not purely physical, which provides the transition from the physical to the abstract. N.B.: It may be even further proposed to define ‘digital-objects’ as entities that cannot be purely conceptual either if any supporting evidence emerges about this matter. In such a case, for example the OOP term ‘object’ may be replaced with the term conceptual-object. N.B.: As mentioned above, none of these definitional alterations affect the essence of the disclosed methods, systems, principles, etc.
Web-Resources and Web-Objects: A ‘web-resource’ is any identifiable resource (physical, digital or abstract) present on or connected to the World Wide Web or any similar structure of a data network, while a ‘web-object’ is any object that is a ‘resource’ or ‘component of a resource’ of the Web. Thus, the context of web-resources is not bounded with the context of the World Wide Web and similarly the context of web-objects is not bounded with the context of web-page elements.
This disclosure includes the following sections: S1) Description of a Novel Web-Based Interaction Framework ‘Superimposed Interaction Framework’ (SIF): S1.1) Modular Sub-Systems Created by Super-Objects; S1.2) An Example of the Envisioned Infrastructure to Realize the Promises of SIF, ‘Semantic Web Infrastructure for Superimposed Interactions’ (SWISI); S1.3) Statement of the Fundamental Problems that the Framework Involves, i.e., the Problem of Ensuring the Stability and Sustainability of Interactions; S1.4) Statement of the Design Constrains and Design Parameters based on the Problem Statement. S2) Description of the Methods and Systems for ‘Object-Aware Fuzzy Processing based on Analogies’ involving only Digital Web-Objects: S2.1) A High-Level Architecture of an Exemplary System Processing Digital Web-Objects; S2.2) Methods for Processing Digital Web-Objects. S3) Description of the Methods and Systems for ‘Object-Aware Fuzzy Processing based on Analogies’ Involving Physical and Digital Web-Objects: S3.1) A High-Level Architecture of an Exemplary System Processing Physical and Digital Web-Objects; S3.2) Extending the Methods for Processing Digital Web-Objects to Process Physical and Digital Web-Objects. S4) Description of a Novel Ontological Model ‘Dynamically-Fuzzy Semantic Relations based on Analogies’ Comprising Transforming-Objects, Transforming-Concepts, and their Analogue Relations: S4.1) A Novel Knowledge Representation and Reasoning Model ‘Dynamically-Fuzzy Semantic Networks and Inference based on Analogies’; S4.1.1) Principles of Transforming-Objects, Transforming-Concepts and Methods for Semantic Similarity Comparison; S4.1.2) ‘Dynamically-Fuzzy Semantic Networks and Inference based on Analogies’ Continued; S4.2) Similarity Functions of Transforming-Objects and Transforming-Concepts; S4.3) Generalized Association Rule between Super-Objects and Web-Objects in the context of Dynamically-Fuzzy Semantic Relations based on Analogies: S4.3.1) Methods for Identifying the Context (CB), Root-Object (OB), and Similarity Threshold (ST) of a Super-Object in the context of Dynamically-Fuzzy Semantic Relations based on Analogies; S4.3.2) Determinability of Critical Boundaries and the Optimal Value of Similarity Threshold in Finite and Infinite Sets; S4.3.3) Methods for Deciding on the Value of Similarity Threshold; S4.3.4) Recap of the Process 2710; S4.3.5) Methods for Identifying Associations Between Objects and Super-Objects; S4.3.6) Strategies related to Conditions that are Partially Satisfied due to Missing, Incompatible, or Insufficient Components of Associated Objects and/or Loss of Components of Associated Objects During a Process; S4.3.7) Partial Execution of a User Generated Application Encapsulated in a Super-Object; S4.3.8) Revisiting Transforming-Objects with respect to Processes 2700 and 2800. S5) Description of the Methods and Systems for ‘Object-Aware Fuzzy Control based on Analogies’—A PID Control System to Control a Transforming-Object based on the Manipulation of said Transforming-Object: S5.1) Feedback Process; S5.2) Comparator; S5.3) Compensator; S5.4) Main-Process. S6) Converting a Method Involving T-Objects to a Method Involving T-Concepts. S7) Recap of the Processes related to ‘Dynamically-Fuzzy Semantic Relations based on Analogies’ in the context of Super-Objects. S8) Comparison of the Disclosed Ontological Model ‘Dynamically-Fuzzy Semantic Relations based on Analogies’ with the State-of-the-Art and Disclosure of Additional Methods and Principles: S8.1) Comparison of ‘Similarity Fuzzy Semantic Relations’ [Castro et al., 2022] with the disclosed ontological model ‘Dynamically-Fuzzy Semantic Relations based on Analogies’; S8.2) Similarity Inference in the Context of the Disclosed Ontological Model ‘Dynamically-Fuzzy Semantic Relations based on Analogies’—‘Generalized Similarity Inference Rule’; S8.3) Derivation of the ‘Generalized Similarity Inference Rule’ from the ‘Similarity Inference Rule’; S8.4) Conclusions with respect to Comparison of the Disclosed Ontological Model with the State-of-the-Art and Disclosure of Additional Methods and Principles.
The superimposed interaction framework promises the formation of interoperable modular sub-systems based on the establishment of dynamic associations between super-objects and web-objects. In essence, a super-object is a user generated set of instructions, i.e., a user generated application, that process/manipulate the web-object to which it is associated. Super-objects together with web-objects constitute the building blocks in the framework of Superimposed Interactions. More concretely, in the process of establishing associations to form modular sub-systems, if super-objects are conjunctional building blocks, then web-objects are reference building blocks, i.e., a base for reference. In this context, any web-object (digital or physical) existing in or connected to a data network may be used as a building block. By definition, a web-object can be any component of a web-resource from a single element, i.e., an elementary object, to a cluster of interrelated elements including the web-resource itself as a whole. Thus, any web-resource e.g., any web page, API, or a physical entity connected to said data network, can be included in any process partially or wholly as a web-object along with super-objects (provided that integration APIs or similar services are not used for any process related to ensuring stability and sustainability). In this context, super-objects that are directly associated with web-objects constitute the most fundamental layer of the superimposed interactions and their activity is considered as first-order interactions with web-objects or—shortly—they are considered as first-order super-objects. For example, a first-order super-object may be programmed to dynamically process particular images in dynamic websites in order to convert them from 2D to stereo 3D.
Similarly, super-objects that are directly associated with first-order super-objects are considered as second-order super-objects. For example, a second-order super-object may be programmed in association with the above mentioned first-order super-object—that converts 2D images to 3D—to enable users to annotate sections of 3D images with intelligent 3D post interfaces that can positionally organize the display of 3D annotations. However, if the adjunct super-object directly processes the web-object itself or its surrounding environment—such as adapting to the background to provide an improved user experience—then it is considered a first-order super-object. In principle, super-objects that are directly associated with n−1st order super-objects are considered as nth order super-objects and their activity is considered as nth order interactions with web-objects. For example, a third-order super-object may be programmed in association with the above mentioned second-order super-object—that enable users to annotate sections of 3D images—to further process the annotations or annotated sections, such as to collect statistical data.
Furthermore, super-objects may be used as junction points and users may interact with each other through first or higher order super-objects that act as network links. For example, a super-object may be programmed to dynamically process certain articles on news websites to identify and indicate inconsistencies or fallacies, and to respond critically according to the revisions that publishers make—if they do so. Further, an adjunct super-object may be programmed in association with the first order super-object to enable users—including the publisher and the reviewer—to communicate with each other, such as through adaptive post & comment interfaces, regarding the claimed fallacies right on the spot. In this context, the Superimposed Interaction Framework (SIF) also paves the way for web-wide social networking without borders—which herein referred to as Superimposed Networking. On the whole, the framework provides a multi-layered interaction environment, in which advanced modular sub-systems, e.g., automated agents, may be developed and deployed in association with any web-object—or any cluster of web-objects—of a data network in any imaginable way.
According to an embodiment, the super-object 1118 is associated with the video player interface 1104a (shaded in grey on the web page 1104) of the online video sharing website in order to dynamically integrate advanced features to the interface to be effective within the entire website, such as a color corrector or a 2D to 3D convertor, while collecting statistical data regarding the content played, such as viewing rates per-video. The super-object 1114 is associated with the link preview interface 1102b along with the search engine interface 1102a (both shaded in grey on the web page 1102) of the online review publishing website in order to dynamically manipulate the interfaces to use them as a source for retrieving metadata about services and products including their ratings and reviews. Because both the super-objects 1114 and 1118 also act as APIs of 1102a, 1102b, and 1104a, the super-object 1128 is developed and associated with both of them as a universal port to collect data and information in regard to 1102 and 1104a. In this context, 1118 is further modified to utilize the data and information provided by 1128 in order to process and display corresponding ratings and reviews of the videos that are being played. Similarly, 1114 is further modified also to utilize the data and information provided by 1128 in order to process and include the statistics collected by 1118. Meanwhile, said video sharing and review publishing websites decide to use 1128 to improve their services.
The super-object 1120 is associated with the core page of a particular video, thus it is also associated with the link preview of said video appearing on the page 1104 (shaded in grey)—as a preference of its developer. The super-object 1120 comprises only non-executable content, i.e., comprises no instructions, thus serves similarly to a conventional annotation, such as a post, comment, or tag. The super-objects 1116 and 1122 are individually associated with the API 1110 which is a service provided by the video sharing website that publishes the web page 1104. The super-object 1122 particularly acts as a universal API, i.e., provides API conformity among different conventions, in regard to the API 1110. The super-object 1124 is developed and associated with 1118, 1120 and 1122 in order to integrate the features of 1118 and 1122 while using the content shared in 1120 as a data input, such as to initiate a sub-process, etc. The super-object 1126 is developed and associated with the super-object 1124 in order to further enhance its capabilities. Meanwhile, the super-object 1130 is associated with various graphical user interfaces 1106a, 1106b, 11088, and 1108b spread over two web pages of another website (shaded in grey on web pages 1106 and 1108), in order to dynamically manipulate the interfaces as a whole. Essentially, these are dispersed segments of a previously singular graphical user interface with which the super-object 1130 is associated. In addition, the super-objects 1136 and 1138 are developed and associated with the super-object 1130 in order to enhance and expand its abilities by utilizing the API 1112 provided by the same website and the cluster of APIs 1140 provided by a third-party website, according to the embodiment.
To recap, within the context of Superimposed Interaction Framework (SIF), super-objects may be associated with complex and dynamic web-objects—such as a structurally altering GUI, or a contextually altering HTML table in a structurally altering GUI, or an API, or combinations thereof—in complex and dynamic web environments—such as an adaptive website—to perform complex and dynamic tasks within the scope of accessible data/information—such as a dynamic task about data mining and analytics—stably and sustainably. Once super-objects are deployed in association with web-objects—which is herein referred to as first-order super-objects—users may further deploy higher-order super-objects in association with the first-order super-objects while interacting with each other through any super-object that act as a network link—such as through messaging interfaces provided. As a result, within the scope of the disclosed framework, an infinite array of associations may be established between or through web-objects in order to perform an infinite array of operations in relation to the corresponding web-objects. Thus, any identifiable web-object, such as a GUI, a table, an article, an API, etc. or any identifiable cluster of web-objects, such as a combination of GUIs, tables, articles, APIs, etc., may become a potential building block—besides the user-generated super-objects—for constructing complex modular sub-systems—such as, creating cascade data processing structures via super-objects that act as connectors and processors—in order to perform complex operations—similar to the concept of electronic sound generation by modular synthesis.
‘Semantic Web Infrastructure for Superimposed Interactions’ or ‘SWISI’ is envisioned to be an intelligent intermediary powered by the disclosed methods and systems in order to realize the interaction mechanism that the superimposed interaction framework offers, i.e., interaction based on super-objects. SWISI is envisioned to operate autonomously between nodes of a data network, especially focusing on decentralized data networks where generally there is no coalition—and therefore no coordination—between server nodes, such as the Internet. In this context, SWISI may directly provide a coalition between client nodes through the client-side application of the system—such as embedded in browsers—in coordination with the server-side application. On the other hand, SWISI may indirectly provide a coalition between server nodes by controlling the interactions between client nodes and server nodes in real-time, through the client-side application of the system in coordination with the server-side application. During this—inherently challenging—process, SWISI may utilize data/information accessible through browsers of client nodes, e.g., web pages and APIs,—but—without resorting to any support for integration from server nodes, such as utilizing integration APIs. Theoretically, SWISI may provide a full coalition between all nodes of a data network within the scope of all accessible data and information through browsers—or similar intermediaries. In this context, SWISI may act as a universal and highly capable ‘dynamic integration API’ between nodes of a data network without the need of any API support from any node for integration.
Web-resources have evolved from relatively static documents into dynamic interfaces and are becoming increasingly complex both contextually and structurally. Indeed, increasing the dynamicity in layout and content inherently enhances the efficiency of human-machine interactions by providing tailored experiences. For example, consider a GUI of an adaptive website that can configure itself in real-time according to the needs of its individual users, such as enhancing the users' ability to interact with the GUI itself, by smart alterations in terms of layout and features based on the feedback about the efficiency of the past and ongoing interactions. On the other hand, in contrast to the adaptive websites—which are obviously at the complex side of the spectrum—even the simplest static web documents can alter dramatically in time manually and these alterations can be complex. Furthermore, interaction possibilities can be infinitely many and they can be also extremely complex. To illustrate the complexity of the problem of ‘ensuring stability and sustainability of interactions between super-objects and web-objects’ the simplest possible case of ‘annotating a static HTML page with very basic components’ can be considered. Indeed, web annotations are among the simplest interaction options with web-resources since they require only the establishment of associations between web-resources—such as between a user generated content e.g. a comment and a web-object—without the additional operations unique to super-objects.
As an exemplary case, annotating a static ‘article page’ consisting only of elementary web-objects, i.e., web elements, namely, a header and a footer—as components of the page—, a heading, a couple of paragraphs, a picture, and a video—as components of the article—can be considered. For example, users can annotate components of the page—in whole or in part—such as, annotating the footer, the video, one of the paragraphs, or a sentence in one of the paragraphs, etc. Or users can annotate arbitrary clusters in the page such as, annotating the heading, the video, and the footer as a group selected randomly. Or users can annotate meaningful or purposeful clusters of the page such as, the article as a whole, i.e., the cluster including all web-objects related to the article such as the heading, the paragraphs, the picture, and the video, but, not extraneous content, such as ads or other links scattered in the article. Furthermore, users can annotate the objects of the page contextually, which can vary greatly from ‘exact contents’ to ‘roles and functions’ of singular or clustered elementary web-objects. For example, users can annotate the article based on its content, or they can annotate the article regardless of its content. Or, users can annotate the objects of the page structurally, such as annotating the frame of a GUI or the scrollbar of a table, etc. Consequently, as can be seen, despite the simplicity of the page, the interaction possibilities are quite rich.
On the other hand, contextual and structural alterations—which can occur manually in a static page—further increase the complexity of the problem. For example, contents of the web-objects can be altered slightly or drastically, and/or existing objects can be deleted, and new objects can be added. Further, relative positions of the elementary objects (i.e., elements) can be altered slightly or drastically, such as the layout of the components or the layout of the whole page can be altered. Further, object identifiers can be altered along with the previous alterations, and as a result, object model of the web page—such as the Document Object Model (DOM)—can be altered completely. Thus, the page can be altered deeply both contextually and structurally in various levels, and all of these alterations can occur simultaneously and rapidly. For example, the paragraphs of the article displayed in the page can be modified grammatically, or attributes of the image and the video—such as the resolution and format—can be altered. Further, the article itself can be rearranged so that the number of paragraphs can increase or decrease, or the image belonging to the article can be replaced with another one within the same context. Further, a new paragraph, image, or video can be added to the article that are out of context, such as hidden advertisement content meticulously inserted as if it was part of the article. Further, the position of the new paragraph, image, or video within the article can be altered, thus the structure of the article can be altered. In conclusion, as can be seen, despite the staticity and simplicity of said page, alteration possibilities—albeit manual—are quite rich, and these examples could be multiplied further.
On the whole, both the interactions and alterations that can occur even in a simple web document can be very diverse and challenging. Besides,—as mentioned before—web-resources have evolved from static documents to highly capable dynamic user interfaces and the scope of the interaction capabilities aimed to be provided by super-objects is far more complex than web annotations. In principle, the problem of ‘maintaining associations with resources and adapting corresponding execution procedures to alterations’, i.e., the problem of ‘ensuring the stability and sustainability of interactions’, increase exponentially as the dynamicity and complexity of web-resources and/or alterations increase.
In essence, the difficulty of ensuring the stability and sustainability of the interactions of a super-object depends on the task, environment, and alterations, which is a complex control problem involving multi-layered ontological problems, i.e., problems concerning existence and existential assumptions of bonds between super-objects and web-objects. For example, in what context will the association be established; in what conditions will this association be preserved or terminated; if the association is preserved, how the task will be performed according to the current state. In this context, ensuring the stability and sustainability of a super-object may be defined briefly as ‘controlling how that super-object interacts with exactly what’ and ‘maintaining that interaction despite contextual and structural alterations by adapting to said alterations during encounters in real-time’. Furthermore, besides prospective interactions between interactive web-objects and super-objects, some web-objects—interactive or not—may react with intelligent metamorphoses or transfigurations to the super-objects that they are associated with—for example—to destabilize the associations. For example, contextual and/or structural alterations related to a GUI and its surrounding environment may be adversarial interventions designed to destabilize the super-object with which the GUI is associated. In this context, a number of assumptions have been identified:
Consequentially, said assumptions can be ultimately combined and represented by a generalized assumption to constitute the fundamental axiom of the framework: ‘Web-objects are assumed to always undergo unpredictable transformations in-between manifestations’. Or more concretely: ‘a web-object is postulated to be a transforming-object—or a t-object in short—which is an object that is assumed to transform unpredictably between manifestations, appearances, or observations’. Indeed, web-objects can be intentionally programmed to alter unpredictably both contextually and structurally at each manifestation, thus can be programmed to transform unpredictably between manifestations—just like magical shape-shifters or transformers.
To recap, web-resources have evolved from static pages to dynamic user interfaces, and they are evolving further to intelligent user interfaces that are capable of adapting to their users individually, providing personalized experiences. As a result, most of the web-resources are individualized, structurally complex and subject to frequent alterations both contextually and structurally. Furthermore, adversarial attempts of websites such as making specially designed alterations in web pages as an attempt to create confusion by/regarding contextual and structural alterations in the web page is a potential threat. Consequently, according to the—above-stated-generalized assumption, i.e., ‘a web-object is an object that is assumed to transform unpredictably between manifestations, appearances, or observations’, the key design parameters follows as: ‘ensuring the stability and sustainability of interactions (i.e., ‘controlling how super-objects interact with exactly what’ and ‘maintaining those interactions despite alterations’) i) in real-time, ii) in any environment (e.g., rapidly and/or drastically altering complex web environments) iii) within any scenario (e.g., adversarial attempts of websites in order to destabilize the operations of the system) iv) in a self-contained manner (e.g., not utilizing integration APIs or similar services provided by websites)’.
In this context: i) Any approach based solely relying on pre-scanning, pre-analyzing, pre-organizing, or archiving web-resources as a primary method for the processes regarding establishing & maintaining associations, adaptation, etc. is incompetent and must be eliminated. For example, keeping track of the states and activities of web-resources by data crawling or scraping and utilizing the collected information in order to recover intended previous versions and/or identifying the correct representations of altered web-resources may provide a historical record that omits relevant details/information. Instead, each visited web-resource must be analyzed individually for each client at each viewing/rendering cycle. ii) Any approach based solely relying on collaboration with websites as a primary method in operations regarding establishing & maintaining associations, adaptation, etc. is incompetent and must be eliminated. Instead, the system must be able to be self-sufficient, i.e., self-contained, by using observable, reachable, retrievable data, and information of web-resources. In fact, the tools provided, such as integration APIs, are often insufficient or no tools are provided at all. Besides, even if a fully competent integration API is provided, its continuity cannot necessarily be guaranteed. iii) Any approach based solely relying on image processing, such as page view analyses based on computer vision algorithms, is incompetent and must be eliminated. To exemplify this argument, a web-object designed to perform a complex function—such as a graphical user interface (GUI) that includes forms and tables designed to operate some sort of complex interactive operation with users—may be considered. In such a scenario, the system must essentially analyze the related codes/scripts in order to identify the complete process besides analyzing visual features and aspects of the GUI. Furthermore, said GUI can be altered in such a way that it does not maintain its ‘structural integrity’ anymore. For example, the GUI can be divided into structurally different sub-clusters that are also positioned discretely in the page, which as a whole perform the same operation. Or the simplistic case of a video, such as an educational video shared in an educational website may also be considered. In such a case, depending on the process to be performed by the super-object to be associated, it can be essential to analyze the video content partially or wholly. Furthermore, said video can be altered in such a way that it does not maintain its ‘contextual integrity’ anymore. For example, the video can be re-edited without any reliable metadata regarding the context of the alteration. In such a scenario, the system must also analyze the altered video content in order to extract the context of the most recent version. Instead, each visited web-resource must be analyzed in-depth—both contextually and structurally including machine-readable content such as HTML and JavaScript code—for each client at each viewing/rendering cycle individually.
Note1: A ‘design parameter’ is a ‘qualitative and/or quantitative aspect of a physical and/or functional characteristic of a system that is input to its design process’. Note2: A ‘design constraint’ is a limitation or restriction in the design process imposed by internal and external factors. Note3: Regarding the first assumption, web-objects—which may be digital, physical or combinations thereof—are assumed to be observable therefore ensuring the stability and sustainability of first order interactions is assumed to be feasible in terms of observability without utilizing integration APIs. Note4: According to the W3C standards, by definition, a web-resource is always identifiable by its identifier—such as its URI. However, a resource identifier may not be able to reliably convey information about the true nature—such as conceptual semantics—of a web-resource even if intended so—for example due to unpredictable alterations that may occur in the resource itself. Furthermore, it is virtually impossible to proactively identify and convey the contextual semantics of a web-resource—even an absolutely static one—that exists in an unpredictably variable environment—for example due to alterations in other resources that coexist and interact with the resource itself. Therefore, it can be deduced that epistemologically, except being a claim of the existence of an entity, resource identifiers—such as URIs—are generally useless.
In the following sections, the description of the methods and systems presented progresses gradually from associating non-executable—passive—super-objects (i.e., user generated contents) with digital web-objects to associating fully functional executable super-objects (i.e., user generated applications) with digital web-objects—including the adaptational and executional processes. Subsequently, the disclosure expands from interaction with digital objects to interaction with all kinds of objects. In this context, generalized methods and systems involving digital and physical web-objects are described and exemplified. Following that, a novel ontological model comprising transforming-objects, transforming-classes, and their analogue relations is described and exemplified. Lastly, further methods for ‘object-aware fuzzy processing based on analogies’ in the context of the disclosed ontological model is described and exemplified.
S2) Methods and Systems for Object-Aware Fuzzy Processing Based on Analogies involving Digital Web-Objects—Interaction with Digital Web-Resources:
As a special case in terms of interaction with digital web-resources and excluding physical and abstract resources that are not represented by digital objects, according to various embodiments, visual objects, i.e., visual web-objects, are used as reference building blocks for associating super-objects since the majority of web-resources on the Web are graphical user interfaces, e.g., web pages. Nevertheless, any non-visual object that can exist in a web page—e.g., a script or markup—is comprised as the contents of visual objects—e.g., as the content of a component of a web page or as the content of the whole page itself.
Visual objects are objects on a web page that form the structure of the web page. According to Kudělka et al. (2010); Visual objects are independent in their content, have an ability to be classified in the meaning of their purpose, and identifiable in terms of labelling a certain physical part of the web page. Visual objects carry some information to a user and as a whole perform certain functions. A visual object generally performs one or more of the four basic functions: A visual object may be i) informative (e.g., may provide some basic semantic content to users), ii) navigational (e.g., may have a hyperlink to guide users to another object), iii) interactive (e.g., may have an interactive tool for users to communicate with the system), iv) decorative (e.g., may include elements for beautifying a page). Ref: Kudělka et al., 2010, Visual Similarity of Web Pages. In: AINSC volume 67, Springer. On the other hand, a web-object is essentially either a web element or a cluster of interrelated web elements, wherein a web element is an elementary or fundamental object that is not composed of other objects. In this context, a visual object is essentially either a visual element or a cluster of interrelated visual elements. More concretely, an elementary visual object is a visual element, and a meaningful cluster of elementary visual objects is a cluster of interrelated visual elements—wherein a meaningful cluster of elementary objects is not an arbitrary cluster but a cluster with a definable/identifiable role and/or function. In this context, visual elements are the visual building blocks of a web page, and any visual element of a web page may be used as a reference for associating super-objects.
Visual elements are defined to facilitate the description of the systems and methods of the current subject matter. According to various embodiments, a visual element is an elementary visual field that includes ‘content’ and ‘visual aspects’ of a web page element. For example, in the case of rendering hypertext markup language (HTML) and cascading style sheets (CSS), when laying out a document (i.e., web page), the browser's rendering engine may represent each HTML element as a rectangular box according to the standard CSS basic box model. While ‘HTML element’ provides the content, CSS determines the position and properties (size, border size, background color, etc.) of these example boxes. In principle, each CSS box (i.e., visual element field) with its content (i.e., content of visual element) is a visual building block (i.e., visual element) of a web page. Accordingly, every HTML element may correspond to a visual element, which may be a—singular—CSS box (i.e., visual element field) that includes the content of the corresponding HTML element (e.g., English text, markup text, hyperlink, image, audio, video etc.). Further, the same may apply for other markup languages supporting CSS, such as XHTML, XML, XUL, SVG, etc. Regarding the positioning of visual elements; In a web page, a visual element may have a fixed position (e.g., relative to canvas) or may be positioned relative to other visual elements (e.g., CSS structures). For example, a cluster of interrelated visual elements (e.g., a GUI object) may be positioned in a framing visual element, and that framing visual element may be positioned relative to another visual element (e.g., container of the header). In the case of rendering HTML and CSS, the layout of a web page may be specified by CSS. A rendering engine may interpret each style sheet and calculate precise graphical coordinates of CSS boxes (i.e., visual element fields) for the visual representation.
To elaborate on the characteristics of a visual element, according to the embodiments, content of a visual element may be textual, or visual, or audial, or combinations thereof. Alternatively, or in addition, a visual element may include no content at all, i.e., a visual element with null content. A content may be included to a visual element by various methods, for example by transclusion, i.e., inclusion of the content by reference, or embedding the raw data of the content into markup. Textual content of a visual element is character content that may be: i) text for humans (e.g., a paragraph of an article), and/or ii) text for machines (e.g., markup/script). Visual content of a visual element is any visual content excluding text (e.g., an image, an image sequence, a video without sound, etc.). Audial content of a visual element is any audial content (e.g., an audio file, a radio stream, etc.). A visual element may simultaneously contain textual, visual, and audial contents individually (e.g., a visual element may contain an image, an embedded invisible hyperlink, and a text simultaneously), or a visual element may contain textual, visual, and audial contents in a single pack (e.g., a single multimedia file containing text, audios, images, animations, videos, etc.). On the other hand, although a visual element with null content has no content at all, its field (i.e., visual element field) has properties such as, size and shape of the field, color and thickness of the border, color of the background fill, etc.
Note1: Although a textual content can be physically classified as a visual content, it is separated for clarity and simplicity. Note2: Besides the standard visual and audial interfaces, a computer system may also involve special interfaces such as, haptic or olfactive interfaces, and accordingly a visual element may also include other types of content according to data to be decoded. Note3: The web-resource interaction system is not limited with the use of visual elements as references. Embodiments may use any element of a web page as a reference for associating super-objects. However, based on the assumption that the use of visual elements may allow the visualization of the disclosed mechanisms more clearly, some of the example embodiments described herein solely use visual elements and/or clusters of visual elements as references for associating super-objects. Note4: Elements, i.e., elementary objects that are not composed of other objects, may vary from framework to framework. For example, while for CSS, each style sheet including its content—obviously excluding any other contained/framed style sheet and its content, if any—may be defined as a visual element, in another framework each individual pixel may be defined as a visual element. Note5: Especially in the case of physical elements, an element, i.e., elementary object, may be an integral part of another element or cluster of elements. For example, 3D printing technology enables production of objects with complex integrated components, such as internal components of rocket engine nozzles or silencers. In the context of the described methods and systems, objects can contain integrated elements, and such elements do not constitute an obstacle for the operations from a methodological point of view.
Expanding upon this introduction, a detailed description of the web-resource interaction system with respect to digital web-resources is presented through various examples, description and embodiments provided herein. According to various embodiments, the system includes a browser-based application at the client-side, that works in collaboration with an application server and a database server at the server-side. Client-side application, i.e., the client, in collaboration with the server-side application, i.e., the server, may perform processes within the system for enabling interaction of super-objects and/or users with elements of web pages and/or with other super-objects and/or users, on a data network. As described herein,
The server 102 of the system 100 includes—or has access to—the database 104 (including the database server), the application server 106, and two separate web servers 108a and 108b. The data network 110 may include gateways, routers, other servers, and clients, etc. which are not shown. The system 100 includes N number of system users (112a, 112b, 112c, 112d) and their clients (114a, 114b, 114c, 114d), respectively. For example, the client 114a is the client of the system user 112a. The system 100 also includes a plurality of network users. Four of the M number of network users (116a, 116b, 116c, 116d) are shown in
Network user 116a is connected to the server 102. Network user 116a may be browsing web pages containing user inputs of the system 100 or may be interacting with particular user inputs of a system user. Network user 116b is connected both to the server 102 and the web page 120a. Network user 116c is connected to the web page 120c only and not related with the system 100. Network user 116d is connected to the network 110 but not connected to the system 100 or any other web page shown.
Client 114a is connected to the server 102 and the web page 120c of the website server 118c. The web page 120c is a social media user page which is open to a closed subgroup only. While the client 114a has permission to reach to the web page 120c, the server 102's access is prohibited. The system 100 scans (probes) the web page 120c indirectly through the client 114a, i.e., the system uses the client 114a as a data link between the server 102 and the web page 120c. Thus, the server 102 may scan the source code of the web page 120c and/or communicate with the website server 118c to retrieve information about hyperlinks and visual elements associated with the web page 120c, such as 122e or 122f.
Client 114b is connected to the server 102 and the web page 120b of the website server 118b. The web page 120b is accessible by any network user including the system users (e.g., a news website). The server 102 is connected directly to the web page 120b known to be unrestricted for all network users with intent to reduce the data transfer load of the client 114b. In this case the system 100 scans (probes) the web page 120b directly. Thus, the server 102 may scan the source code of the web page 120b and/or communicate with the website server 118b to retrieve information about hyperlinks and visual elements associated with the web page 120b, such as 122c and 122d.
Client 114c is connected to the server 102, the web page 120b of the website server 118b, and the web page 120d of the website server 118c. The web page 120d is another user page which is not accessible by all network users. On the other hand, the web page 120b is accessible by any network user including the server 102. While the server 102 is indirectly connected to the restricted web page 120d through the client 114c, it is connected directly to the web page 120b, known to be unrestricted for all network users. Thus, the server may scan the source code of the web pages 120b and 120d and/or communicate with the website servers 118b and 118c to retrieve information about hyperlinks and visual elements 122c, 122d, 122g and 122h associated with the web pages 120b and 120d.
The system user 112d and the corresponding client 114d are not connected to the network 110. They are passive members of the system 100.
Within the communication structure of the system 100, and via the execution of the example methods 200, 300, 400 and 500 explained below, system users 112a to 112d may associate user inputs with the visual elements 122a to 122h of the web pages 120a to 120d and set up a superimposed social network on the web pages of the data network 110 through user inputs that act as network links. Similarly, system users 112a to 112d may reach to any user input that is associated with the objects of web pages of the data network 110. However, a network user who is not a system user may also connect to the system through a client that acts as a proxy server. In one embodiment, one of the clients of the system is used as a proxy server at the system, in which some of the network users, who are not system users, are connected to the system through this client and immediately become system users.
To elaborate the concept of superimposed networking: Any system user, who is connected to the data network 110 via a client, may be interconnected with other system users through the data network 110 via communication protocols (e.g., TCP/IP), and various user interfaces. In this context, user inputs possessing communication modules and interfaces may connect users with each other for communication, and user inputs may act as network links of an overlay social network on web-resources of a data network. For example, users may communicate through commenting, messaging, reacting interfaces deployed on user inputs, while adding some of the authors of the user inputs and/or comments on the user inputs to their follow list or friends list and join each others social network. Therefore, system users may set up an overlay social network on web pages and connect with each other across the data network that the web-resource interaction system is included in. Accordingly, each user generated content or application that is associated with objects of web pages, may become a junction point connecting users. Furthermore, a system user may connect to the publisher/developer (e.g., author) of a user input through the corresponding user input via particular interfaces. For example, a user who is an author may have a unique user identifier stored in the database record of his/her user input, which is associated with one or more visual element/s of a web page, such that rendering of the web page displays the user identifier as available to another user who may provide a connection request to the web page (e.g. through a system's user interface displayed on the web page) to initiate communication (e.g. P2P communication). Furthermore, interaction through a user input may be allowed to all users or may be restricted for use by a closed subgroup only, thus, sub social groups may be formed.
For brevity, the web server 108a and the application server 106 are together hereinafter referred to as ‘server’, and the database server with the database 104 are together hereinafter referred to as ‘database’.
The process begins with the manifestation/presentation of a web page by the web browser of a system user. At 202, for the rendered web page, the client requests a web page analysis and a database query from the server to identify visual elements that are associated with user inputs. Next, based on the received request, the server queries the database to determine a connection method in order to communicate with the web page server and scan the web page (204). In some embodiments, the server may connect directly to the target web page over the data network. In yet other embodiments, the server may connect indirectly to the target web page via linking up through the client, i.e., using client as a data link. Indirect connection is always reliable, because server may not have access to the target web page (e.g., the target web page may be a social media user page accessible to a closed subgroup only), or may have limited access, or there may be some restrictions for ordinary network users or for the system user itself. On the other hand, for various reasons such as reducing the data transfer load of the system user, the server may directly connect to the web pages known to be unrestricted for all network users (e.g., news websites, government websites, e-commerce websites, etc.). For this purpose, the system may use an URL list of unrestricted websites and may decide between direct or indirect connection by comparing the URL of the target web page with the URLs of the websites, i.e., domain names, in the list. In one embodiment, the server scans the unrestricted website list (UWL) for a match with the URL of the target website (204). Unrestricted website list (UWL), which includes the URLs of the websites that are known to be unrestricted for all network users (e.g., CNN.com, BBC.com, etc.), may be generated and updated manually and/or by software algorithms working under the system.
Next, the server decides the connection method (206). If the target web page is unrestricted then the server connects to the target web page directly over the network to scan the web page directly (208). If the target web page is restricted, then the server connects to the target web page indirectly through the client to scan the web page indirectly (210). According to an embodiment, the server prefers to connect to any web page server indirectly only, because the indirect connection method provides a reliable communication mechanism for all cases and may be preferred if extra data transfer load of the client is not a concern.
At 212, based on the source code (which may specify the content, layout, and structure of the web page, and may define the meaning of each element) and/or any information retrievable from the web page server, the server scans the rendered web page and analyzes each visual element to identify distinctive information about its content, position, and properties, in order to generate a visual element identifier. Content of a visual element may be a text, an image, an audio, a video, a hyperlink, an application etc., and the source code may contain the content itself (e.g., a container including a text and a hyperlink) or the identifier/locator of the content for retrieving it from the website server (e.g., a container including an image or a video). Position of a visual element on a web page is the relative position of the visual element according to other visual elements. Properties of a visual element varies, e.g., size, shape, border width, background color, etc. of the visual element field. Content and properties of a visual element are solid references for identification in most of the cases. However, in some cases, relative position information may be the only reference to identify a visual element since position of a visual element is always distinctive,—as long as content, and/or layout, and/or structure of the web page does not alter in a way to invalidate it. For example, there may be multiple visual elements with identical properties and content. On the other hand, there may be cases where the position itself may not be sufficient to identify a visual element. For example, layout of web page may be changed in time. Accordingly, a visual element of a web page may be identified more accurately by referencing all variables (i.e., content, relative position, and properties) simultaneously, or one of them individually, or in any combination that fits the needs of the application.
Next, the server analyzes the resource identifiers (e.g., URLs) of the hyperlinks that are associated with visual elements—if any—determined at 212, and identifies the core resource identifiers (e.g., core URLs) (214). Core resource identifiers are resource identifiers of hyperlinks isolated from the additional extensions, e.g., subsequently added auxiliary extensions by search engines.
Next, for each visual element, the server generates a uniform visual element identifier (UVEI) that includes distinctive information about content—if any—, information about distinctive properties—if any—, and relative position information of the visual element identified at 212 including exact location information (e.g., URL) of the web page (216). The server also assigns an ID for each uniform visual element identifier (UVEI) generated. In one embodiment, a uniform visual element identifier (UVEI) of a visual element includes: i) A sample of the content, an attribute of the content (e.g., the media type) and the associated URI of a hyperlink—if any—determined at 212, as the content information (CI′); ii) Resource identifier of the web page (e.g., URL) and relative position information of the visual element with respect to other visual elements determined at 212, as the location information (LI), iii) Information about the properties of the visual element field determined at 212, as the property information (PI). While distinctive information about the position and location, i.e., ‘relative position’ and ‘exact location’, always exists (at least in the moment of rendering), ‘distinctive information about content’ and ‘information about distinctive properties’ of a visual element may or may not exist. In this context, for example, a uniform visual element identifier (UVEI) within the CSS structure may include: i) ‘null content’ as content information (CI′); ii) CSS properties such as style sheet dimensions and color, as property information (PI); iii) URL of the web page and the relative position information of the style sheet, as the location information (LI). In such a case, LI is the only distinguishing parameter if there are other elements with exactly the same PI, i.e., CSS properties, in the source. According to another embodiment, uniform visual element identifier (UVEI) may also include core resource identifier (e.g., core URL) of the associated hyperlink isolated at 214, for example, i) Core URL of the hyperlink K of visual element Z; ii) URL of web page Y of website X, and relative position information of visual element Z on web page Y; iii) Property A and B of visual element Z, according to the embodiment.
Next, based on the uniform visual element identifiers (UVEI) generated at 216, the server queries the database for user input records that are involving visual elements and/or hyperlinks—if any—of the web page (218). Thus, the server queries the database for identifying matches between new UVEIs (i.e., nUVEIs) and recorded UVEIs (i.e., rUVEIs) of the user input records that are including the locator (e.g., URL) of the web page in the rUVEIs, based on various comparison methods such as relative position comparison and/or comparison based on similarity analysis of contents, etc. Further, based on the uniform cluster function identifiers (UCFIs) generated at 306, the server may also query the database for user input records that are involving visual element clusters of the web page. In this context, for example, based on a functional similarity comparison, the server queries the database for identifying matches between new UCFIs (i.e., nUCFIs) and recorded UCFIs (i.e., rUCFIs) of the user input records that are associated with the web page. Process 300 may be executed after the execution of 216 where the UVEIs are generated, thus, following the execution of process 300, process 218 may be executed, according to the embodiments.
In some embodiments, only one visual element may be associated with a user input if the user input is a user generated content, while one or more visual elements may be associated with a user input if the user input is a user generated software application. In this scenario, for user generated contents, the server may include contents partially in the UVEIs as the content information (CI′) by a predefined sampling procedure, for each UVEI generated at 216, and the server may execute a similarity comparison between the sampled contents included in the recently generated UVEIs (nUVEIs) at 216 and the sampled contents included in the recorded UVEIs (rUVEIs) of the rendered web page, which may represent interacted visual elements in the rendered web page. In other words, user input records may be stored to include the UVEIs of interacted visual elements which a user has interacted with to add user input thereto, e.g., into the database record of a user input. In addition, the server may also query database records for a match between the hyperlinks detected at 212 and interacted web pages, i.e., web pages associated with one or more user inputs. In this process, core URLs of the hyperlinks determined at 214, are compared with the URLs of the interacted web pages recorded in the database. Within an example scenario, for user generated software applications, the database may be also queried according to the cluster information (CI)—that is identified in process 300 according to positional and functional relationship of visual elements—besides the uniform visual element identifiers (UVEI) of the interacted visual elements. In this process, for each record of a user input associated with the web page, recently extracted cluster information (CI) is compared with the cluster information (CI) (or the portion related with the interacted cluster) that is recorded in the database in process 400, i.e., rUVEIs belonging to an interacted cluster are compared with each set of nUVEIs that belong to each identified cluster.—It should be noted herein that this comparison technique may be optimized by various sub-methods, such as preliminary image analyses based on computer vision techniques in order to eliminate irrelevant cluster pairs in the process of similarity comparison. In the process of identifying interacted clusters, cluster information (CI)—along with other information obtained at 300 such as, subjective function type (SFT), etc. is used as a reference, in order to maintain the associations in case of occurrence of contextual and/or structural alterations in interacted clusters. Alternatively, or in addition, based on the uniform cluster function identifiers (UCFIs) generated at 306, the server may also query the database for user input records that are involving visual element clusters of the web page, i.e., querying the database for identifying matches between new UCFIs (i.e., nUCFIs) and recorded UCFIs (i.e., rUCFIs) of the user input records that are associated with the web page, based on a functional similarity comparison, according to the embodiments. In various embodiments, both user generated contents and software applications may be associated with singular visual elements, such as images, videos, paragraphs, or with clusters of interrelated visual elements, such as, link previews, social media posts, media players, tables, lists, articles, headers, footers, other predefined clusters etc.
Next, for each matching user input—if any—, the server generates an input metadata shell message (IMSM) containing input metadata (IMD), i.e., metadata of the user input, and uniform visual element identifier/s (nUVEI/s) of the corresponding visual element/s of the rendered web page (220).
Next, the server forwards input metadata shell messages (IMSM) to the client—if any (222). At this phase, no other information about the content of the user inputs is sent to the client. Thus, the process gains speed by decreasing the data transfer load. According to an embodiment, input metadata (IMD) contains brief information about the associated user input stored in the user input records of the database, e.g., publisher, heading, summary, popularity score, rank etc. of the user input. Next, the client receives and buffers input metadata shell messages (IMSM)—if any (224).
Next, for each input metadata shell message (IMSM) received, the client associates input metadata (IMD) with the corresponding visual element/s of the web page being rendered for the user (226).
Next, the client visually displays input markers (IM) on the web page in connection to the corresponding visual elements and/or clusters of visual elements that are associated with the user inputs—if any—in order to indicate the availability of the user inputs for user access (228). According to various embodiments, user input markers (IM) may be transparent, miniature, and/or inconspicuous icons marking discrete visual elements and/or clusters of visual elements of the web page in a manner which does not impede the usage of the web page. In one embodiment, the client places input markers (IM) at only certain points within visual element fields, e.g., top right corner of a visual element field. Thus, input markers (IM) may be integrated with the web page's sense of balance and esthetics due to their association with visual element fields. In case of a cluster of interrelated visual elements,—which are identified in the process 300—, the marker may be placed at a predetermined position, such as a top right corner, of the visual element field that is enclosing the group—if any. Else, the client may create a virtual frame that is enclosing the cluster of interrelated visual elements and mark it by the same way. Moreover, appearance of the input markers (IM) in an embodiment may vary and particular input markers (IM) may be used for particular user inputs. For example, in embodiments, while ordinary user comments that are associated with a visual element are marked with a circular input marker (IM), expert user comments associated with the same visual element are marked with a triangular input marker (IM) separately. Regarding the method of manipulation, client may change the source code of a page to include markings, or the browser may be programmed to mark the relevant positions without changing the source code. In case of HTML, the client may utilize DOM (Document Object Model) of the web page to manipulate the web page without changing the source code, according to the embodiments.
The process terminates after the execution of 228 unless the page is an infinite scrolling web page. In case of an infinite scrolling web page, for every additional manifestation of the web page the process loops between 212 to 228, i.e., all sub processes are executed in sequential order starting at 212 and ending at 228. As a result of the process 200, the client, in collaboration with the server, identifies and marks visual elements and/or clusters of visual elements that are associated with user inputs—if any—, according to an embodiment.
To elaborate on the process 200, i.e., the procedures to identify visual elements that are associated with user inputs in a rendered web page, and the primary function/purpose of the uniform visual element identifier (UVEI) concept: According to the embodiments, there may be two phases of identifying visual elements, and whenever a web page is rendered by a browser of a client, these procedures may be executed specifically for that client: Phase 1) ‘Analyzing a web page that a user is viewing, and identifying each visual element; for each visual element, generating a uniform visual element identifier (UVEI) that includes distinctive information about content—if any—, information about distinctive properties—if any—, relative position information of the corresponding visual element (e.g., with respect to neighboring visual elements), and exact location information (e.g., URL) of the web page where the corresponding visual element belongs to. An UVEI identified in the phase 1 may also be called as ‘new UVEI’ or ‘nUVEI’, which is a temporary reference of a visual element particular to the viewing session of a rendered web page of a client. An nUVEI becomes a recorded UVEI (i.e., rUVEI) and becomes stationary, if it is recorded in the database (e.g., in a field of a user input record) to associate a user input with the corresponding visual element. Phase 2) ‘Based on uniform visual element identifiers (UVEIs) of visual elements identified in phase 1 (i.e., nUVEIs), i) identifying visual elements that were previously interacted by comparing nUVEIs with rUVEIs stored in the database, and/or ii) identifying role/s of each visual elements and/or iii) identifying clusters of interrelated visual elements, and/or iv) identifying visual elements that are available for interaction. According to the embodiments, for each visual element, a uniform visual element identifier (UVEI) is generated in each viewing session of a client, because a stable reference—independent of source code dictated by website servers—is required, since content, and/or layout and/or structure of a web page (i.e., source code) may be altered—manually (e.g., by website admins) and/or automatically (e.g., by executing scripts/programs)—in progress of time, and such alterations may result in loss of association between the interacted visual elements and their corresponding user inputs associated therewith and stored in the database records. For example, element attributes may be changed in such a way that alteration invalidates a locator strategy. Accordingly, methods such as anchoring based on element IDs, or executing conventional fragment identification systems, may not work properly. Moreover, alterations can be done deliberately to jam such systems by more advanced procedures. For example, content of some visual elements of a web page may be altered in such a way that altered content (e.g., pictures, videos, etc.) may be identifiable only by the detailed analysis of the content itself. Consequently, any system depending on the identification of interacted visual elements based on source code of web pages is inevitably prone to loss of associations or false associations between the time the web page elements are interacted, and corresponding web pages are re-rendered. For example, associations may be lost, or user inputs may be associated with irrelevant visual elements. In principle, the UVEI serves as a stable reference to maintain that association by holding/containing any possible distinctive information about content and properties, and also relative position information of each visual element with respect to other visual elements. Thus, the UVEI creates a base for various locating/identifying strategies and procedures.
To elaborate on the subject of distinctive information about content of a visual element: According to the embodiments, distinctive information about content of a visual element may be the type of the content (e.g., media type), and/or general properties of the content (e.g., type, size, etc.), and/or the content itself,—and which can be considered a reliable reference for most of the cases if the extra data processing load is not a concern. Accordingly, a uniform visual element identifier (UVEI) of a visual element may include the content—of the visual element—itself partially or wholly as a reference for comparison. For example, if the content of a visual element is a text, a hyperlink, a script, or an image, then it may be included in UVEI directly. For larger files (e.g., audio or video files) where the data processing load may be a concern, general properties of the content (e.g., type, size, etc.) may be used in combination with the properties of the visual element. Furthermore, content—of a visual element—may be included in its UVEI partially, and sampling techniques may be used for estimation/approximation of the content, such as interpolation. On the other hand, not all visual elements do possess distinctive content. For example, in a web page, there may be multiple visual elements with identical content.
To elaborate on the subject of distinctive information about the properties of a visual element: According to the embodiments, distinctive information about properties of a visual element may be any property (e.g., shape, size, background, padding, border, margin size, etc.) of the visual element that are unique among all visual elements of a web page (e.g., a unique background color, size, font, etc.). For example, the size of a canvas element of a web page is a distinctive property for most of the cases since it contains all visual elements of a web page. On the other hand, not all visual elements possess distinctive properties. For example, in a web page, there may be multiple visual elements with identical properties. In the case of CSS, properties of a visual element field is as follows: animation properties, background properties, border properties, color properties, dimension properties, generated content properties, flexible box layout properties, font properties, list properties, margin properties, multi-column layout properties, outline properties, padding properties, print properties, table properties, text properties, transform properties, transitions properties, visual formatting properties, etc.
To elaborate on the subject of relative position information of a visual element: According to the embodiments, ‘relative position information’ and ‘exact location information’ of a visual element includes position of the visual element relative to other visual elements in a rendered web page, and the URL of the rendered web page that it belongs to. Unlike content and properties of a visual element, relative position information is always distinctive as long as content and/or structure of the web page does not alter in a way to invalidate it. In one embodiment, positioning rules of visual elements included in the string of relative position information of UVEIs are similar to the ones in the style sheets of CSS.
To elaborate on the subject of distinctive information in general: According to the embodiments, distinctive information about content, information about distinctive properties, relative position information, and exact location information that are all included in UVEI may be used in several combinations for associating/identifying interacted visual elements, according to needs. In all cases, ‘exact location information’ is essential,—even if the relative position of the visual element alters—, since it includes locator (URL) of the web page that the visual element belongs to. On the other hand, there can be special cases where content, properties, and relative position information of a visual element—all together—cannot be sufficient to identify the visual element depending on the level of alteration of content, and/or layout and/or structure of a web page. For example, there can be multiple visual elements with identical properties and content in a web page, where relative positions of visual elements alter frequently. Accordingly, maintaining associations in between interacted web page elements and their corresponding user inputs may not be possible in some special cases.
To elaborate on the possible identifying strategies: According to various embodiments, content of visual elements (which may be stored in the database within a corresponding UVEI generated by the system), may be used partially or wholly (e.g., depending on data size) as the primary reference for locating/identifying visual elements that are interacted, besides (or in addition to) the URL of the web page that the visual elements belong to. In this regard, content of a visual element may be included—partially or wholly—in the corresponding UVEI as the distinctive information about content. In embodiments, for associating user inputs with visual elements, and identifying visual elements that are associated with user inputs in web pages that are viewed by users; the system collects content fragments from visual elements that include media files, such as images, videos, audios, documents, etc. by appropriate sampling procedures particularly selected according to content properties (e.g., media type, size, format, etc.) of each visual element, and include collected content fragments to the corresponding UVEIs as the distinctive information about content, in order to reduce the data processing load. In this process, for content located in website servers (e.g., documents, videos, audios, or any kind of streaming media) the system fetch data from website servers for sampling (e.g., by sampling the first ‘one second’ of videos, or by sampling small segments from PDF documents, etc.). When the system queries the database for identifying interacted visual elements of a web page, recently generated UVEIs are compared with the recorded UVEIs of the said web page according to their content type (e.g., video with video, image with image, text file with text file, etc.). In this process, the system compares content fragments with each other according to their similarities, and for each tested pair calculates the similarity rate—or resemblance rate—based on the similarity analysis. If similarity percentage of a pair exceeds a predefined similarity threshold, then the system associates the pair, according to the embodiment. In such embodiments, various statistical similarity measures may be used as a base for similarity analysis of data sets, and various algorithms may be preferred or developed depending on the type of content (e.g., image, audio, video, text, etc.) of visual elements. Furthermore, even semantic similarity comparison may be used in some specific applications.
To elaborate on the subject of analyzing and identifying interacted visual elements, a hypothetical example is provided as following: In one embodiment, a system user SU interacts with a dynamic web page that he/she is viewing, by associating respective user inputs as APP1, POST1, and POST2, with a text T, which is the content of visual element VE1, with an image P, which is the content of visual element VE2, and with a video V, which is the content of visual element VE3. APP1 which is associated with the VE1 may be a translator program that translates text from one language to another and dynamically processes/manipulates the visual element in order to display the translated version of the original text. POST1 and POST2 which are associated with VE2 and VE3 respectively, are social media posts including pictures and ideas of SU about the content of the corresponding visual element. A database record is generated and stored by the system for each user input APP1, POST1 and POST2, wherein each record contains content information CI′, property information PI and relative position information LI of VE1, VE2 and VE3 in their corresponding UVEIs respectively. Properties information PI may be all available information about the properties of VE1, VE2, and VE3. Image P and text T may be included as a whole to their corresponding database records in their corresponding UVEIs respectively,—due to their relatively small data sizes—, and video V may be included partially (e.g., by collecting samples from the source file of the video, which are fetched from the website server),—due to its relatively large file size—, as content information CI′. For example, in this process, the system may associate each post or application with the corresponding visual element in the rendered web page by recording the posts or applications with corresponding UVEIs to the database. In the first attempt, the website server alters the layout and structure of the said web page deliberately to jam the system including formal alterations in the source code, such as altering element IDs, content identifiers etc. As a result, relative positions and properties of VE1, VE2 and VE3 becomes altered including corresponding element IDs, content IDs etc., and they become the derivatives DVE1, DVE2, and DVE3 of the original visual elements (except the contents). When SU—or any other system user-views the web page (e.g., when the web page is rendered), the system generates new UVEIs (nUVEIs) of DVE1, DVE2, DVE3 and compares them with the user input database records involving UVEIs (rUVEIs) that include the URL of the said web page. In this process, the system compares the original text T with other texts, image P with other images and video V with other videos (by comparing sampled fragments) of the web page based on recorded UVEIs (rUVEIs) and recent nUVEIs. Based on the query, recorded UVEIs of VE1, VE2 and VE3, match with the recent nUVEIs of DVE1, DVE2, and DVE3 on content basis with a predetermined similarity level, such as a 100% similarity. Consequently, based on the similarity comparison of the content, the system successfully associates SU's user inputs with the corresponding visual elements in the rendered web page, and visually marks them. By selection of these markers, SU—or any other system user—may reach to the corresponding metadata. In the second attempt, the website server alters the content of DVE1 by slightly changing the text T (thus becomes T′), alters the content of DVE2 by slightly degrading the image quality of P—by processing the image—(thus becomes P′), and finally modifies DVE3 by transforming the visual element into a dynamic one that alters video content randomly in progress of time from a list that include N number of different videos. In addition to that, the website server also alters the layout and structure of the web page including radical alterations in the source code. As a result, relative positions, properties and contents of DVE1, DVE2 and DVE3 becomes altered and they become the derivatives DDVE1, DDVE2, and DDVE3. When SU—or any other system user—views the web page, the system generates new UVEIs (nUVEIs) of DDVE1, DDVE2, DDVE3 and compares them with the user input database records involving UVEIs (rUVEIs) that include the URL of the said web page. In this process, the system compares the original text T with other texts, image P with other images and video V with other videos. Based on the query, i) recorded UVEI of VE1, partially match with the recent UVEI of DDVE1, on content basis with X % similarity, ii) recorded UVEI of VE2, partially match with the recent UVEI of DDVE2, on content basis with Y % similarity, iii) recorded UVEI of VE3, does not match with the recent UVEI of DDVE3 initially but thereafter with 100% similarity, based on the comparison of the sampled fragments of video V (included in the UVEI of VE3) with altering video content in any visual element of the web page during the viewing. Consequently, based on the similarity comparison of the content, the system successfully associates SU's user inputs with the corresponding visual elements and visually marks them, because the similarity rates X and Y exceed the predefined similarity threshold. On the other hand, the system visually marks the dynamic visual element DDVE3 whenever video V is loaded and deletes the marker whenever video V is altered with another video. By selection of these markers, SU—or any other system user—may reach to the corresponding metadata. In the third attempt, the web page server alters the content of DDVE1 by significantly changing the text T′ (thus becomes T″) and alters the content of DDVE2 by significantly degrading the image quality of P′ (thus becomes P″). Text T″ and image P″ diverge from originals in such a degree that similarity rates fall below the predefined similarity threshold. Consequently, while keeping the association of visual element DDVE3 with video V, the system terminates the association of visual elements DDDVE1 and DDDVE2 with text T and image P respectively, according to the embodiment.
According to the embodiments, the process of identifying/locating visual elements of web pages does not have to rely on any ID based on the source code of web pages (e.g., utilizing namespaces, etc.). If preferred, uniform visual element identifiers (UVEIs) of visual elements do not include IDs assigned by website servers such as fragment identifiers, content identifiers, etc. Methods and systems presented within the current disclosure include embodiments designed on the basis that source codes of web pages—including any ID assigned by website servers—can be deliberately changed to confuse, and/or damage, and/or jam such systems. Alternatively, or in addition, the web-resource interaction system may also include processing IDs based on the source codes and is not limited to the methods presented.
Uniform visual element identifier (UVEI) is similar to URL, which is a reference to a web-resource that specifies its location on a data network, and a mechanism for retrieving it. According to various embodiments, uniform visual element identifier (UVEI) is also a reference to a visual element that specifies its location on a data network, and a mechanism for retrieving it. However, conceptually the difference between conventional URIs and UVEIs is radical. Firstly, each UVEI is generated by analyses of the corresponding visual element in order to extract distinctive information about its content, properties, and position within a framework of universal standards, and the UVEI of a visual element may be recorded as rUVEI and used as a reference for comparison of semantic similarities with the distinctive information of each visual element extracted subsequently as nUVEIs when the corresponding source (e.g., a web page that the visual element belongs to) is re-rendered in order to identify a match. Secondly, semantic similarities may be compared on the basis of content, role, function, etc., i.e., context, thus, a visual element may be linked according to content, role, function, etc., i.e., context. In this regard, the UVEI mechanism described herein may be defined as a fuzzy logic referencing mechanism based on pure analogies (i.e., a comparison between one thing and another), by which ‘a distinctive representation identified by the analyses of one thing’ (i.e., an analogue) is compared with ‘a distinctive representation identified by the analyses of another thing’ (i.e., another analogue) based on semantic similarities (analogies) in order to associate/link the former with the latter.
The above-listed elaborations with respect to the process 200 is not limited with the embodiments provided, thus many more embodiments and implementations are possible.
The process begins after the execution of 216, in which the server generates a uniform visual element identifier (UVEI) for each visual element of the rendered web page. At 302, for the rendered web page, based on the uniform visual element identifiers (UVEIs) the server analyzes the visual elements to identify the role/function of each visual element, i.e., extracting basic operational function/s (BOF) described in the elaborations regarding 300 and the basic role (e.g., subjective function type classification) of each visual element.
According to various embodiments subjective function type is classified/categorized as main content, side content and auxiliary element. In this context, main content is the main subject of a web page, e.g., the subject matter presented for consideration of the viewers. For example, in a news web page, news articles, news videos, link previews of other news article pages or videos etc. are main contents. Side content is the subaltern subject of a website such as ads, extraneous announcements, or any other extraneous content. Finally, auxiliary element is any visual element without a subjective function. For example, visual elements that are graphic components of GUI elements are auxiliary elements, such as containers—like canvases and frames—, buttons, sliders, text fields, list boxes, icons, navigation links, search boxes, lines, special shapes, etc. are auxiliary elements. Subjective function type (SFT) does not overlap among the visual elements. A visual element is either main content, or side content, or auxiliary element. In embodiments, to identify subjective function type (SFT) of the visual elements of a web page, the server identifies only the side contents and auxiliary elements. The remaining visual elements of the web page fall into the main content class. In this regard, identifying any 2 of the 3 classes/categories is sufficient in order to identify corresponding subjective function type (SFT) of each visual element of a web page, according to the embodiments.
Next, the server analyzes each visual element to identify cluster information (CI) of visual elements (304). Cluster information (CI) is information about the relationship of a visual element with other visual elements in a rendered web page and used as a reference to group interrelated visual elements of the rendered web page. For example, a group of visual elements within a framing visual element (e.g., a container) are identified as positionally interrelated visual elements. Moreover, by frames within frames, or groups within groups, cluster information may also include ranks of interrelationship. For example, a search field and a search button lined up next to each other may be a first order cluster, while the entire navigation bar containing them besides some link buttons may be a second order cluster. In this context, the highest rank of interrelationship may correspond to all visual elements of the web page framed/enclosed by the canvas, i.e., the web page itself. According to the embodiments, analysis of visual elements to identify cluster information (CI) is based on relative positional relationship of visual elements (e.g., a group of visual elements that is framed by another visual element), or functional relationship of visual elements (e.g., a group of visual elements serving for a particular purpose regardless of their positions), or ‘relative positional and functional’ relationship of visual elements together (e.g., a group of visual elements lined up next to each other serving for a particular purpose).
In embodiments, the server may utilize DOM (document object model) of the rendered web page to analyze parent, child, and sibling node relations in order to identify the cluster information (CI) according to positional relationship of visual elements. Further, the server may utilize the classification of each visual element as ‘basic operational function/s’ (BOF) and ‘subjective function type’ (SFT) including any other information determined at 302, in order to identify the cluster information (CI) according to functional relationship of visual elements. Further, the server may also utilize the cluster information (CI) determined at 304 in a feedback loop structure, to identify the subjective function type (SFT) of previously unidentified visual elements, according to the embodiments. In various embodiments, cluster information (CI), identified according to ‘positional+functional’ relationship of visual elements, includes IDs of uniform visual element identifiers (UVEIs), where each cluster of ‘first degree relative visual elements’ is designated by a first order cluster ID—that is assigned for each first order cluster (i.e., first order sub-cluster), including information about the functional relationship of the visual elements—within the first order cluster—that is predicted or determined based on the interbedded process of 302 and 304 as discussed with reference to
Next, based on the uniform visual element identifiers (UVEIs) generated at 216; their corresponding basic operational functions (BOF) and basic roles (SFT), and their corresponding cluster information extracted at 302 and 304, the system may further analyze each identified cluster of interrelated visual elements to extract their high-level function/s (e.g., a standard video player with a color corrector and 2D to stereoscopic 3D convertor, or a search box with a search button within a container) (306). In this step, high-level functions of identified clusters may be extracted, for example, the high-level functions of high to low rank clusters may be ‘a header’ as the N−1st rank, ‘a search box with a search button within a container in the header’ as the N−2nd rank, ‘only the search box with the search button’ as the N−3rd rank, and ‘only the search box’ alone as the N−4th rank (0th rank for this case) respectively, where the Nth rank cluster (4th rank for this case) represents the whole web page. According to various embodiments, the system may further perform additional semantic analyses for the extraction of high-level function/s of each cluster of interrelated visual elements. For example, via utilizing various artificial intelligence algorithms based on—such as—natural language processing, machine learning, pattern recognition, etc. Thus, even more accurate, detailed, and comprehensive UCFIs may be extracted. Upon the extraction of the high-level function/s of a cluster, the system generates a ‘uniform cluster function identifier’ (UCFI) that includes/represents the extracted function/s of the corresponding cluster and assigns the extracted/interpreted function/s, i.e., UCFI, as an analogue reference to be used for establishing associations with a cluster of interrelated visual elements besides the UVEIs of the visual elements belonging to the cluster. Further, the system may also utilize the uniform cluster function identifiers (UCFIs) in a feedback loop structure to identify—especially—functional relationship of visual elements whose functional relationship with other visual elements is previously unidentified. For example, a pair of distantly positioned but functionally related clusters (similar to that of header-footer relationship) may be identified in a web page. Thus, via utilizing the process 306, functional relationship of distantly positioned visual elements may be identified at 304, and further this information extracted at 304 may lead to the extraction of additional information at 306, and so forth.
Next, the server generates a page information file (PIF) containing uniform visual element identifiers (UVEIs) generated at 216 including their corresponding basic operational functions (BOF) and basic roles (SFT), and their corresponding cluster information (CI) extracted at 302 and 304; and the uniform cluster function identifiers (UCFIs) extracted at 306 (308). More precisely, page information file (PIF) may include information obtained in processes 302, 304, and 306 including i) basic operational function/s of each visual element; ii) basic role, i.e., subjective function type (SFT), of each visual element; iii) cluster information (CI) of each visual element—identified according to positional and/or functional relationship of visual elements—in order to extract clusters of interrelated visual elements; iv) the uniform cluster function identifiers (UCFIs) as contextual references of clusters of interrelated visual elements and v) any other information may further be extracted or preferred. In this context, a client may use the information provided by a page information file (PIF) to reach all information extracted from the web page—so far—and may use this information for further analyses and/or as a further reference (e.g., via storing the PIF in the database and utilizing it later for constructing a reference that represents the initial state of the rendered web page). Thus, the client may identify for example the availability for interaction, according to a special (e.g., customized) or generalized predefined filtering criteria.
The process terminates after the execution of 308 unless the page is an infinite scrolling web page. In case of an infinite scrolling web page, for every additional manifestation of the web page the process loops between 302 to 308, i.e., all sub processes are executed in sequential order starting at 302 and ending at 308. As a result of the process 300, the client in collaboration with the server, classifies each visual element of a web page, and identifies each cluster of interrelated visual elements including corresponding function of each, thus provides information to the system, such as for enabling/disabling visual elements and/or clusters of visual elements for interaction according to a set of predefined interaction rules (i.e., identifying web-objects that are available to receive one or more user inputs), according to an embodiment.
To elaborate on the subjective function type (SFT): According to the embodiments, subjective function type (SFT) is a non-overlapping predetermined classification/categorization of each visual element in a web page according to its role. In an example, the predetermined classifications/categorization of the subjective function types may be based on three discrete types: i) main content, ii) side content, and iii) auxiliary element. According to this classification/categorization; an ‘auxiliary element’ is a visual element that possesses only operational function/s without a subjective function, while a ‘main content’ or a ‘side content’ is a visual element that possesses a subjective function besides its operational function/s, in which any additional function beyond operational functions is considered subjective. Essentially, this is a classification of 2 discrete—and naturally existing—parent types of page elements: i) elements without a subjective function; and ii) elements involving a subjective function besides their operational function/s.
A web page is an interface whose main purpose is to convey information to a user and receive information from the user. In this context, an auxiliary element is an element that provides only operational help and support in the process of conveying and receiving information without possessing a subjective function. For example; a check box for interaction, a frame grouping elements for ease of perception, an icon for attention, a background texture for decoration, a menu button or a search field for navigation are visual elements without a subjective function, and accordingly are auxiliary elements. However, a heading of a news item, a paragraph of an article, an image of a product or an add, a video of a movie, an audio of a musical piece, an application for gaming, a link preview of another web page, or a document including an e-book, are contents of visual elements possessing functions beyond operational functions about the web page and/or website that they belong to (e.g., content for the consideration of viewer/s), and accordingly are main or side contents. In this regard, while a visual element that functions as a navigation button including the name of the link location is an auxiliary element, a similar visual element that functions as a navigation button including a line of poetry or a mathematical formula—besides the name of the link location—is not, since the additional information that it is carrying is not operational, but for the consideration of the viewers, and accordingly, such a visual element is qualified as main or side content.
To elaborate on the principles to identify subjective function type (SFT): Similar to the visual objects as defined hereinbefore, a visual element may perform one or more of the four basic functions in a web page, besides a primal function: A visual element may be navigational, interactive, decorative, informative, or combinations thereof, and in addition to these four basic functions, a visual element inherently—and inevitably—performs an architectural/structural function by taking part in the formation of a web page,—just like a brick of a wall. These basic functions also define the said operational functions: i) Navigational: A navigational visual element helps viewers to navigate (e.g., through a hyperlink) in a single page and/or multiple pages of a website including links to external web pages. A navigational visual element may include visual/audial/textual content related to navigational processes including markup/script; ii) Interactive: An interactive visual element helps viewers to interact (e.g., through a form) with the web page, and/or with the web page server. An interactive visual element may include visual/audial/textual content related to interactive processes including markup/script; iii) Decorative: A decorative visual element carries content for beautifying a page. A decorative visual element may include visual/audial/textual content with decorative features including markup/script (e.g., a script for fetching images from the server); iv) Informative: An informative visual element provides informative content to users. An informative visual element may include visual/audial/textual content with informative features including markup/script (e.g., a script for fetching text from the server). N.B.: According to the embodiments, in order to simplify the process 302, informative content used in navigational and/or interactive processes without possessing a subjective function are excluded, and considered as content with navigational and/or interactive features (e.g., purely operational GUI elements such as, addresses, icons, etc.); v) Architectural: An architectural/structural visual element helps viewers to perceive layout and content of a web page more accurately, easily and enjoyably by separating segments, framing interrelated visual elements, etc. Only visual elements with null content are purely architectural (e.g., containers like frames, separators, canvases, etc.), and inherently, every visual element is also architectural by taking part in the formation of the web page. And lastly, a visual element may also possess more than one of these functions simultaneously. For example, a navigational visual element may also be decorative by involving an image for beautifying the page, while being informative by involving a text relevant with the link location, e.g., the summary of the content of the link location.
In principle, informative, navigational, interactive, decorative and architectural functions are all operational functions. However, informative function differs from the rest, because only informative content (e.g., a paragraph or an image) may involve a subjective function, and if this is so, then the visual element is considered as main or side content, else it is auxiliary element. In the context of operational and subjective functions, it can be deduced that any visual element with navigational, and/or interactive, and/or decorative, and/or architectural function/s cannot involve any sort of subjective function, i.e., only a visual element with informative function may involve a subjective function. Although this deduction is important in the process of identifying SFT classes/categories, some navigational, interactive, and decorative visual elements may also possess informative content and thus, informative function, as described in examples herein. Therefore, in various embodiments, functional analysis of each content of a visual element is made according to the basic operational functions, in order to identify their functional relation with the respective visual element.
According to the embodiments, for identifying subjective function type (SFT) of each visual element in a web page that a user is viewing, example rules are listed based on the definitions and deductions presented about SFT classification/categorization: i) Basic operational functions of visual elements are: informative, navigational, interactive, decorative, architectural functions; A visual element may possess one or more of these functions simultaneously, and it always possesses an architectural function; ii) Any additional function of a visual element beside its basic operational function/s is considered subjective, and therefore the visual element is considered subjective, i.e., possessing a subjective function; iii) An auxiliary element is a visual element without a subjective function; A ‘main content’, or a ‘side content’ is a visual element that involves a subjective function besides its operational function/s; iv) Any visual element with null content (i.e., without any textual/visual/audial content) is auxiliary element; v) Only informative content may involve a subjective function; vi) An informative visual element is an auxiliary element as long as its content does not involve any sort of subjective function; vii) Any visual element whose function is ambiguous is considered decorative, and therefore considered as auxiliary element.
To elaborate on the process 302 to identify subjective function type (SFT), and the process 304 to identify cluster information (CI), an exemplary interbedded procedure is presented according to the embodiments: In embodiments, for identifying roles, such as main contents, side contents and auxiliary elements of a web page, the following sub-processes are executed in the process 302; In step 302/1: each visual element that includes textual/visual/audial content,—including related markup/script—is identified by the system analyzing content properties and/or content itself (e.g., for markup/script), based on the uniform visual element identifier (UVEI) of each visual element. If a visual element includes textual/visual/audial content then it is flagged as ‘unidentified element’; else, it is flagged as ‘auxiliary element’. In this step, visual elements with null content are identified (e.g., containers, separators, frames, etc.), and which are considered purely architectural. Although they are null in content, they possess properties such as, size, border size, border color, background color, etc. In one embodiment, the system—roughly—estimates the possible roles of visual elements with null contents by comparing their properties and relative positions with the known web page layouts when rendered.
In step 302/2, among the ‘unidentified elements’ that are flagged in step 302/1, each visual element with navigational and/or interactive features without possessing a subjective function is identified by the steps: For each visual element among the ‘unidentified elements’ that are flagged in step 302/1; 302/2.1) detecting textual content—including markup/script—related with the execution of navigational and/or interactive processes; 302/2.2) detecting visual content that is a component of a navigational and/or interactive user interface (GUI), 302/2.3) detecting audial content that is a component of a navigational and/or interactive user interface (AUI); 302/2.4) detecting textual content that is a component of a navigational and/or interactive user interface (GUI); Following the execution of the steps regarding functional analysis 302/2.1 to 302/2.4, executing process 304 for identifying cluster information (CI); 304/1) i) analyzing the position of each detected visual element relative to other visual elements of the web page, ii) analyzing the properties of each detected visual element, and iii) based on the detected roles, relative positions, and properties identifying related visual elements for each detected visual element and grouping the interrelated ones; 302/2.5) for each detected visual element, deciding whether the visual element is auxiliary element or not, based on the steps 302/2.1, 302/2.2, 302/2.3, 302/2.4, 304/1 and by utilizing the information obtained in the step 302/1. Flagging each visual element as ‘unidentified element’ or ‘auxiliary element’ according to the decision.
In one embodiment, for each visual element flagged as ‘unidentified element’ at 302/1; Regarding the step 302/2.1, textual content including markup is analyzed to detect a hyperlink associated with the visual element for detecting a navigational feature (e.g., URL), and any script associated with the visual element is analyzed for detecting an interactive feature (e.g., JavaScript); Next, the system executes the steps 302/2.2, or 302/2.3, or 302/2.4, or combinations thereof according to the content of the analyzed visual element, (whether or not a navigational and/or interactive feature is detected). Regarding the step 302/2.2, visual content is compared with known graphic components of navigational and interactive GUI elements of web pages such as, buttons, search fields, special shapes, icons (like play, pause record icons)—if any. Further, if the visual element is detected as navigational and/or interactive at the step 302/2.1, then a similarity comparison may be also executed in between the visual content and known graphic components of GUI elements for identifying the purpose of unique images, e.g., special icons, logos, avatars, etc. Regarding the step 302/2.3, audial content is compared with known audial components of navigational and interactive AUI elements of web pages such as, audio like clicks, buzzers, etc.—if any; Regarding the step 302/2.4, textual content is analyzed in order to detect text used only as a component of the GUI that it belongs to, such as an address, an instruction, etc.—if any; Regarding the step 304/1, the position of each detected visual element relative to other visual elements of the web page is analyzed, and based on the detected roles and relative positions, related visual elements for each detected visual element is identified. Accordingly, framing visual elements, i.e., containers, and framed visual elements are identified as groups of interrelated visual elements—if any; Regarding the step 302/2.5, for each detected visual element, based on 302/2.1, based on 302/2.2, or 302/2.3, or 302/2.4 or combinations thereof, and based on 304/1 the system evaluates the obtained information and decides whether the analyzed visual element is auxiliary element or not. For example, based on the features of the visual elements that are enclosed by a container, and based on the relative position of the framing container, the system may predict the purpose of the enclosed group (e.g., a navigation bar, a header, a footer, a social media post, etc.) and may deepen its evaluation for the role detection of the visual elements within the enclosed group. For a visual element that is detected as possessing navigational and/or interactive features in 302/2.1, possible outcomes are as follows: i) if the content (visual, and/or audial, and/or textual) is a component of a navigational and/or interactive user interface, then the visual element is flagged as auxiliary element. ii) if any of the content (visual, and/or audial, and/or textual) is not a component of a navigational and/or interactive user interface, then the visual element is flagged as ‘unidentified element’. For a visual element that is detected as ‘not possessing’ navigational and/or interactive features in 302/2.1, possible outcomes are as follows: i) if the content (visual, and/or audial, and/or textual) is a component of a navigational and/or interactive user interface, then—because its function is ambiguous—the visual element is flagged as auxiliary element; ii) if any of the content (visual, and/or audial, and/or textual) is not a component of a navigational and/or interactive user interface, then the visual element is flagged as ‘unidentified element’; according to the embodiment.
In step 302/3, among the ‘unidentified elements’ that are flagged in step 302/2, each visual element with decorative features without possessing a subjective function is identified by the steps: For each visual element among the ‘unidentified elements’ that are flagged in step 302/2; 302/3.1) detecting visual content with decorative features; 302/3.2) detecting audial content with decorative features; 302/3.3) detecting textual content with decorative features; Following the execution of the steps regarding functional analysis 302/3.1 to 302/3.3, executing the process 304 for identifying cluster information (CI); 304/2) i) analyzing the position of each detected visual element relative to other visual elements of the web page, ii) analyzing the properties of each detected visual element, and iii) based on the detected roles, relative positions, and properties identifying related visual elements for each detected visual element and grouping the interrelated ones; 302/3.4) for each detected visual element, deciding whether the visual element is auxiliary element or not, based on the steps 302/3.1, 302/3.2, 302/3.2, 304/2, and by utilizing the information obtained in the steps 302/1 and 302/2. Flagging each visual element as ‘unidentified element’ or ‘auxiliary element’ according to the decision.
Various strategies may be applied for the detection of visual, audial and textual content with decorative features, without possessing a subjective function. One strategy may be comparing positions of the visual content relative to other visual elements containing visual content for detection of overlapping sections. For example, any visual content that is positioned under another visible content may be considered as background image, and thus decorative. Similarly, a background music may be considered as decorative. Another strategy may be using size and position factors together for identifying images like logos, icons, etc. For example, a visual element containing a relatively small sized image positioned at the top left of a web page without possessing navigational or interactive features may be considered as an ornamental image with pure decorative features. In some cases, separating decorative content from informative ones may require more effort, e.g., due to the similarities of contents. Semantic analysis of textual, visual, and audial contents may be used in such cases.
In one embodiment, for each visual element flagged as ‘unidentified element’ at 302/2; Regarding the step 302/3.1, visual elements including images or image sequences with overlaying visual elements including visible content upon them are detected, and also other images like icons, logos, etc. are detected based on their relative positions and their properties like size, shape, etc.; Regarding the step 302/3.2, audio used for decorative purposes such as background music, etc. are detected; Regarding the step 302/3.3, text used for decorative purposes such as, a background fill, ornamental patterns etc. are detected; Regarding the step 304/2, the position of each detected visual element relative to other visual elements of the web page is analyzed, and based on the detected roles and relative positions, related visual elements for each detected visual element are identified. Accordingly, framing visual elements, i.e., containers, and framed visual elements are identified as groups of interrelated visual elements—if any; Regarding the step 302/3.4, for each detected visual element, based on 302/3.1, or 302/3.2, or 302/3.3 or combinations thereof, and based on 304/2, and also utilizing the information obtained by the previous steps 302/1 and 302/2, the system evaluates all obtained information and decides whether the analyzed visual element is auxiliary element or not. For example, based on the features of the visual elements that are enclosed by a container, and based on the relative position of the framing container, the system may predict the purpose of the enclosed group (e.g., a header with a background picture or a canvas with ornamental patterns etc.) and may deepen its evaluation for the role detection of the visual elements within the enclosed group. For a visual element that is processed by the step 302/3, possible outcomes are as follows: i) if the visual element possesses only decorative function (i.e., visual, and/or audial, and/or textual content is decorative), then the visual element is flagged as ‘auxiliary element’; ii) if the visual element possesses only navigational and/or interactive function/s with a decorative function, then the visual element is flagged as ‘auxiliary element’; iii) else, the visual element is flagged as ‘unidentified element’; according to the embodiment.
In step 302/4, among the remaining ‘unidentified elements’ that are flagged in step 302/3, each visual element with informative features without possessing a subjective function is identified by the steps: For each visual element among the ‘unidentified elements’ that are flagged in step 302/3; 302/4.1) detecting visual content with informative features without a subjective function; 302/4.2) detecting audial content with informative features without a subjective function; 302/4.3) detecting textual content with informative features without a subjective function; Following the execution of the steps regarding functional analysis 302/4.1 to 302/4.3, executing the process 304 for identifying cluster information (CI); 304/3) i) analyzing the position of each detected visual element relative to other visual elements of the web page, ii) analyzing the properties of each detected visual element, and iii) based on the detected roles, relative positions, and properties identifying related visual elements for each detected visual element and grouping the interrelated ones; 302/4.4) for each detected visual element, deciding whether the visual element is auxiliary element or not, based on the steps 302/4.1, 302/4.2, 302/4.3, 304/3, and by utilizing the information obtained in the steps 302/1, 302/2, and 302/3. Flagging each visual element as ‘main or side content’ or ‘auxiliary element’ according to the decision.
Various strategies may be applied for the detection of visual, audial, and textual content with informative features without possessing a subjective function. According to the processes 302/1, 302/2 and 302/3, architectural, navigational and/or interactive, decorative visual elements including ambiguous ones (which are flagged as decorative) are identified and flagged as auxiliary elements. In addition, at 302/3, ‘navigational and/or interactive+decorative’ visual elements are also identified (based on 302/2) and flagged as auxiliary elements. Any remaining visual element that is flagged in step 302/3 as ‘unidentified element’ possesses an informative function, —with or without other basic operational functions—, and may or may not have a subjective function. These visual elements may be: ‘informative’, ‘informative+navigational and/or interactive’, ‘informative+decorative’, or ‘informative+navigational and/or interactive+decorative’. One strategy may be a rough elimination based on content type. Unlike text and images, most of the multimedia files (e.g., videos, audios, image sequences etc.) are main or side contents, and they may be flagged directly as main or side content. However, it is still possible to encounter with a video, audio or multimedia file in a web page that is used purely for operational purposes, e.g., an instructional video about an interactive feature of the web page, or an audio guide for navigating within the website. For precise identification of such content, semantic analysis may be a requirement. On the other hand, in steps 302/1, 302/2 and 302/3, since all operational functions of visual elements are identified (as architectural, navigational, interactive, and decorative respectively and the rest as informative), including clusters of interrelated visual elements (with the exception of the ones belonging to informative visual elements, which are being determined in this step), more practical approaches are also possible. In this context, one strategy may be identifying positional and functional relationship of the content with the cluster that it belongs to, based on: i) the properties of the content such as, size for images or number of characters for text; ii) previously identified function/s of the visual elements that are members of the cluster; iii) predicted or identified function of the cluster as a whole. For example, a relatively short text—that is purely informative without navigational and/or interactive features—positioned on the header may be considered as the name or label of the web page or website, and thus, informative content without a subjective function. However, a relatively long text positioned on the header may be considered as informative content with a subjective function. For example, it can be a quote or a message from the author of the web page. Further, clusters of visual elements with distinctive features may be defined to the system. For example, tables may be defined to the system, in which the content of a data cell element is informative with a subjective function, while the content in a column or row header (e.g., names of the variables) is informative without a subjective function. Furthermore, the system may identify the functions of higher rank clusters by prediction based on the identified functions of visual elements, identified functions of lower rank clusters, and consideration of the positional and functional relationship of lower rank clusters according to the know higher rank clusters with distinctive features. For example, a container including, a link preview with a commenting interface may be considered as a social network post. In general, this strategy may be applied for identifying text and/or image content of articles, social media posts, comments, comments related with the social media posts, etc. And may be supported with semantic analysis of contents.
In one embodiment, for each visual element flagged as ‘unidentified element’ at 302/3; Regarding the step 302/4.1, each visual element including an image is analyzed according to its content properties (e.g., size and shape), its positional and functional relationship with the cluster that it belongs to and predicted or determined function of the cluster as a whole—if available. Based on the analyses, each informative content without a subjective function is detected. All video and multimedia files, and all image sequences are considered as possessing a subjective function; Regarding the step 302/4.2, each visual element including a relatively small sized audio file is analyzed based on semantic analysis. Based on the analyses, each informative content without a subjective function is detected. The rest of the audio files—including the audio data of video files—are considered possessing a subjective function; Regarding the step 302/4.3, each visual element including text is analyzed according to its content properties (e.g., number of characters, words, sentences, etc.), its positional and functional relationship with the cluster that it belongs to and predicted or determined function of the cluster as a whole—if available. Based on the analyses, each informative content without a subjective function is detected; Regarding the step 304/3, the position of each detected visual element relative to other visual elements of the web page is analyzed, and based on the detected roles and relative positions, related visual elements for each detected visual element is identified. Accordingly, framing visual elements, i.e., containers, and framed visual elements are identified as groups of interrelated visual elements—if any; Regarding the step 302/4.4, for each detected visual element, based on 302/4.1, or 302/4.2, or 302/4.3 or combinations thereof, and based on 304/3, and also utilizing the information obtained by the previous steps 302/1, 302/2 and 302/3, the system evaluates all obtained information and decides whether the analyzed visual element is ‘main or side content’ or auxiliary element. For example, based on the features of the visual elements that are enclosed by a container, and based on the relative position of the framing container, the system may predict the purpose of the enclosed group and may deepen its evaluation for the role detection of the visual elements within the enclosed group. For a visual element that is processed by the step 302/4, possible outcomes are as follows: i) if the informative content of the visual element does not possess a subjective function (i.e., visual, and/or audial, and/or textual content without a subjective function) then the visual element is flagged as ‘auxiliary element’; ii) if the informative content of the visual element does possess a subjective function (i.e., visual, and/or audial, and/or textual content with a subjective function) then the visual element is flagged as ‘main or side content’; according to the embodiment.
In step 302/5, among the remaining visual elements that are flagged as ‘main or side content’ in step 302/4, each visual element with side content (i.e., extraneous content) is identified by the steps; For each visual element among the ‘main or side contents’ that are flagged in step 302/4; 302/5.1) detecting visual extraneous content; 302/5.2) detecting audial extraneous content; 302/5.3) detecting textual extraneous content; Flagging each detected visual element as ‘side content’ and flagging the rest as ‘main content’. Since the cluster information (CI) is identified for all visual elements of the web page in the previous steps 302/1, 302/2, 302/3 and 302/4, and since the final decision step is needles, process 304 and the final step are eliminated.
Various strategies may be applied for the detection of side content, i.e., extraneous content. One strategy may be detecting third party websites, e.g., by analyzing download/upload activities. Another strategy may be detecting ads based on the industrial standards that are being used, such as standardized banner sizes, or add fields etc. Another strategy may be semantic analysis of textual, visual, and audial contents, which may be considered as the most reliable but the costliest in terms of the data processing load.
In one embodiment, for each visual element flagged as ‘main or side contents’ at 302/4; Regarding the step 302/5.1, image and field size of the visual element is compared with known industry standards and flagged as suspicious if it matches. Next, download/upload activities of the—suspicious—visual element may be inspected based on the related markup/script and fetched data. As a result, third party ad resource and visual extraneous content is detected; Regarding the step 302/5.2, audial extraneous content is detected by detecting 3rd party streaming transmissions; Regarding the step 302/5.3, textual extraneous content is detected by semantic analysis; Regarding the outcome of the step 302/5, among the visual elements that are flagged as ‘main or side contents’ each visual element with ‘side content’ is identified, and accordingly each visual element with ‘main content’ is identified; according to the embodiment. In addition to all these, ‘main content’ may be sub-classified/sub-categorized and be used for further filtering of visual elements for interaction. In one embodiment, content published by the website (e.g., columns, articles, news items, etc.) and content published by the viewers (e.g., comments, etc.) are identified and only website's content is filtered for interaction (e.g., based on functional analysis, cluster information, etc.). In another embodiment, link previews of other web pages, and dynamic data displayed in tables (e.g., fetched data from the database of the website displayed in a HTML table upon a request) are identified and filtered out (e.g., based on functional analysis, cluster information, etc.).
To summarize the processes 302 and 304, which are interbedded according to the embodiment; In step 302/1, visual elements with null content, i.e., purely architectural visual elements, are identified and the remaining visual elements may be flagged as ‘unidentified element’ and filtered for further evaluation. At this stage, ‘unidentified elements’ to be processed may include visual elements with navigational features, and/or interactive features, and/or decorative features, and/or informative features, or—as a special case—ambiguous features—which are considered as decorative—, with or without a subjective function. In step 302/2, visual elements with navigational and/or interactive features without possessing a subjective function are identified—if any. Furthermore, by the step 304/1 cluster information (CI) of the said visual elements are identified, and thus, clusters of interrelated visual elements regarding the said visual elements are determined—if any. In this process, any visual element containing a visual or audial or textual content that is not related with navigational and/or interactive functions is flagged as ‘unidentified element’ and filtered for further evaluation. In step 302/3, visual elements with decorative features without possessing a subjective function are identified—if any. Furthermore, by the step 304/2 cluster information (CI) of the said visual elements are identified, and thus, clusters of interrelated visual elements regarding the said visual elements are determined—if any. In this process, any visual element containing a visual or audial or textual content that is not related with the decorative functions is flagged as ‘unidentified element’ and filtered for further evaluation. In step 302/4, visual elements with informative features without possessing a subjective function are identified—if any—, and the rest of the visual elements are flagged as ‘main or side content’. Furthermore, by the step 304/3 cluster information (CI) of the said visual elements are identified, and thus, clusters of interrelated visual elements regarding the said visual elements are determined—if any. In step 302/5, each visual element with side content is identified—if any—, and accordingly remaining ones are flagged as main content. As a result, the system identifies basic operational function/s of each visual element, while identifying any subjective function classified/categorized as main or side content, and determines clusters of interrelated visual elements, according to the embodiment. In one embodiment, in order to speed up the process 302/1 to 302/5, for the uniform web pages of websites (e.g., Twitter, Instagram, Facebook etc.), before executing 302/1, common objects of web pages are identified such as, header, footer, local navigation boxes, scan columns, etc., by comparing the web pages of the website. Next, based on the common visual elements, the system—roughly—estimates the possible roles of the visual elements by comparing their properties and relative positions in the rendered web page with known web page layouts.
The above-listed elaborations with respect to the process 300 is not limited with the embodiments provided, thus many more embodiments and implementations are possible.
The process begins with the request of a system user from the client to associate a user input with a visual element of a rendered web page. At 402, the client inquiries whether the rendered web page is scanned, i.e., processed by 200 and 300, or not. If the web page is not scanned, then the process 200 and the process 300 may be executed respectively at 404 before 406 is executed. Else, 406 is executed immediately after.
At 406, the client requests the page information file (PIF) of the rendered web page generated at 300 in order to contextually identify each visual element and cluster of interrelated visual elements via UVEIs and Cis including corresponding roles/functions of each, such as basic operational function/s (BOF), basic roles (SFT), and/or high-level function/s of each cluster of interrelated visual elements (UCFI), etc. Next, based on the received request, the server forwards the page information file (PIF) to the client (408), and the client receives and buffers the page information file (PIF) (410).
Next, based on the page information file (PIF), the client decides on the interactions, i.e., identifying the availability of each visual element and/or each cluster of interrelated visual elements for interaction according to a predefined filtering criteria of the system and flag the UVEIs as ‘available’ in the PIF, which is buffered in the client. More precisely, based on the role/function of each visual element and their interrelationship including the high-level functions of each corresponding cluster determined at 300, the client (or the server in another configuration) filters visual elements for interaction according to a predefined filtering criteria of the system, (e.g., as only main content, only side content, only auxiliary elements, or any combination thereof for associating user generated contents, and/or as clusters of interrelated visual elements according to their ‘positional’, or ‘positional+functional’ relations for associating user generated software applications), and enables filtered visual elements for interaction, while disabling the remaining visual elements (412). Filtering criteria of the embodiments may vary. For example, for associating user generated contents, in order to focus on contextual information of web pages, e.g., news, articles, comments, videos, social media posts, etc., only visual elements including main content may be available for interaction. According to various embodiments, for associating a user generated content, the system may allow selection of only one visual element among the filtered ones. However, for associating a user generated software application, the system may allow selection of a cluster of visual elements among the filtered ones, and thus, may associate a user generated software application with one or more visual elements. In this process, the system may utilize the interrelated visual elements that are identified and grouped at 300,—where the relationship may be positional, and/or ‘positional+functional’, and/or any other predefined relationship criteria—, and filters them for interaction, according to a predefined filtering criteria. For associating user generated software applications, predefined filtering criteria may be filtering only clusters with ‘positional+functional’ relations. However, predefined filtering criteria may be more specific about the clusters to be filtered for interaction. For example, some particular clusters with distinctive features may be predefined in the system,—which may be specific to certain websites or may be used in general—such as, link previews, social media posts, headers, footers, menu boxes, navigation bars, tables, commenting interfaces, etc., and they may be identified by analyzing the previously detected roles/functions and relations at 300 (
Next, the client indicates each of the visual elements and/or clusters of interrelated visual elements that are available to receive one or more user inputs—determined at 412—by a visual indicator, e.g., by highlighting the available (filtered) visual element field (414). Thus, the system user may select a visual element and/or a cluster of interrelated visual elements for associating a user input. In embodiments for associating user generated contents, the system highlights the available visual element fields in green, and for associating user generated software applications the system highlights the available visual element fields in blue.
Next, the client receives a user input for a visual element or a cluster of interrelated visual elements that is available to receive one or more user inputs (416). In various embodiments, system users create user inputs via an input interface that is particularly designed for generating user inputs. In embodiments, for enabling users to generate software applications, a software development tool is provided. According to the embodiments, the software development tool isolates a selected cluster, and indicates each sub-cluster according to the ranks of interrelationship within the selected cluster (similar to a DOM tree, where the document is the selected cluster), wherein a system user may include additional content—including scripts—, and/or alter the properties of visual elements, and/or add new visual elements, and/or define the fields or portions of the fields of visual elements to be dynamically processed/manipulated by the script that he/she is developing. In another embodiment, based on a user's selection of an available cluster, the system displays a list of—system executable—software applications that are particularly developed for the selected cluster of interrelated visual elements. Thus, the user may select an already existing user generated software application to process/manipulate a cluster.
Next, the client generates a post shell message (PSM) that includes all information pertaining to the user input and the uniform visual element identifier/s (UVEI) of the corresponding visual element/s including each corresponding subjective function type (SFT) and corresponding cluster information (CI) (418). In various embodiments, post shell message (PSM) also includes other information obtained in processes 302, 304, and 306 such as, basic operational function/s (BOF) of each visual element, and uniform cluster function identifier (UCFI) of each cluster of interrelated visual elements, etc.
Next, the client transmits the post shell message (PSM) to the server (420), and the server receives and buffers the post shell message (PSM) (422).
Next, in the case of establishing an association with a singular visual element, the server associates the user input with both the corresponding visual element and the hyperlink that is associated with the visual element—if any—by generating a database record that includes the user input (i.e., the content of user input such as images, texts, scripts, etc.), uniform visual element identifier (UVEI) of the corresponding visual element including its corresponding basic operational function/s (BOF), subjective function type (SFT) and corresponding cluster information (CI) and any other information preferred (424). In the case of establishing an association with a cluster of interrelated visual element, the system may associate the user input with the corresponding cluster of interrelated visual elements by generating a database record that includes the user input (i.e., the content of user input such as images, texts, scripts, etc.), the uniform cluster function identifier (UCFI) of the corresponding visual element cluster, each uniform visual element identifier (UVEI) of the visual elements that belong to the cluster including each corresponding basic operational function/s (BOF), subjective function type (SFT), and cluster information (CI), and any other information preferred (424).
In one embodiment, the server appoints a unique resource identifier (e.g., an URL) for the user input as the database ID and creates a database record for the user input under this ID where the server records all information according to the database fields of the user input such as UVEI, BOF, SFT, CI, UCFI of the associated visual element or the associated cluster of interrelated visual elements, contents of the user input, further interactions with the user input such as comments, etc. In this scenario database record of the user input may be updated whenever the user input is edited, or comments are added. In another embodiment, for each user input, a dedicated web page that includes the content of the user input may be generated also, and the web page may be addressed with the appointed resource identifier that is used as the database ID. In various embodiments, only one visual element may be associated with a user input if the user input is a user generated content, while one or more visual elements may be associated with a user input if the user input is a user generated software application. In addition to that, in case of user generated contents, subjective function type (SFT) and cluster information (CI) may be temporary references used for each viewing session of a client, and thus they are not recorded in the database and not included to post shell message (PSM). On the other hand, in case of user generated software applications, subjective function type (SFT) and cluster information (CI) may be permanent references used against contextual and/or structural alterations of interacted clusters of web pages/websites, and thus they may be included to post shell message (PSM) and recorded in the database. Thus, positional and functional relationship of visual elements within an interacted cluster may be used as a reference to identify the cluster if it alters, and further, may be used for adapting the associated software applications to the existing state of the altered cluster. For example, the structure/layout of uniform social media posts (e.g., post interfaces) of a social media website may alter in a way that relative positions of all sub-clusters such as, ‘author information’, ‘main content’, ‘reaction interface’, ‘commenting interface’ may completely change. Furthermore, content and properties of the visual elements, such as icons, shapes, background colors, etc., may also alter completely. However, the system may still identify the altered ‘post interface’ based on the analysis of the initial state and recent state according to the positional and functional relationship within the clusters of the pages of the said social media website, according to the embodiments.
Next, for the user input, the server generates an input metadata shell message (IMSM) containing input metadata (IMD), i.e., metadata of the user input, and uniform visual element identifier/s (UVEI) of the corresponding visual element/s (426).
Next, the server forwards input metadata shell message (IMSM) to the client (428), and the client receives and buffers input metadata shell message (IMSM) (430).
Next, for the input metadata shell message (IMSM) received, the client associates input metadata (IMD) with the corresponding visual element or cluster of interrelated visual elements of the web page (432).
Next, the client displays an input marker (IM) on the web page in connection to the corresponding visual element and/or cluster of interrelated visual elements that is associated with the user input (434).
The process terminates after the execution of 434. As a result of the process 400, the client, in collaboration with the server, associates a user input with a visual element and/or a cluster of interrelated visual elements that are available to receive one or more user inputs, according to an embodiment.
The process begins with the selection of an input marker (IM) of a visual element or a cluster of interrelated visual elements—of a rendered web page—that is associated with one or more user inputs, by the system user. In one embodiment, system user selects the input marker (IM) by clicking a pointing device. At 502, the client receives the selection.
Next, based on the received selection and input metadata shell messages (IMSM) received and buffered at 224 and/or 430, the client displays input metadata (IMD) of each user input that is associated with the selected visual element or cluster of interrelated visual elements as a list in connection to the corresponding/selected input marker (IM) (504). In one embodiment, N number of input metadata (IMD) is listed in rows for N number of corresponding user inputs. The user browses metadata list that is ranked according to various ranking algorithms for display on the web page. The list scrolls downward if needed. Input metadata (IMD) contains summary info such as heading, rank, popularity score, publisher etc., and the system user may decide whether to reach content pertaining to a user input or not according to metadata.
Next, the client receives selection of input metadata (IMD) of a user input from the metadata list (506). In one embodiment, a system user selects one of the input metadata (IMD) from the metadata list manually by a pointing device.
Next, the client requests content pertaining to the user input corresponding to the selected input metadata (IMD) from the server (508).
Next, based on the received request, the server retrieves the requested content pertaining to the user input corresponding to the selected input metadata (IMD) from the database and forwards it to the client (510).
Next, the client receives and buffers the content pertaining to the user input corresponding to the selected input metadata (IMD) (512).
Next, if the user input is user generated content, then the client displays the content pertaining to the user input in connection to the corresponding input metadata (IMD), else the client executes program instructions of the user generated software application in connection to the corresponding visual element and/or cluster of interrelated visual elements (514). As a result of the process 500, the client, in collaboration with the server, displays metadata and content of user inputs in connection to the corresponding visual elements and/or clusters of interrelated visual elements, and/or displays metadata of user generated software applications and executes the selected ones according to an embodiment.
According to various embodiments, the process 900 begins with the rendering of a web page—partially or wholly—by the web browser of a system user.
Process A—902—includes the interbedded steps of analyzing, classifying, and clustering visual elements of the rendered portions of a web-resource that a user is viewing. In this process, the system analyses rendered visual elements for extracting corresponding UVEIs to be used as an analogue reference of the corresponding respective visual element for further analyses such as for classifying visual elements (i.e., elementary web-objects such as images, videos, audios, texts, etc.) into one or more of a plurality of predetermined classes (e.g., basic operational functions and subjective function types) and clustering interrelated visual elements (i.e., meaningful clusters of elementary web-objects, such as GUIs, forms, tables, lists, articles, etc.) according to their positional and/or functional relations in rank orders, including extracting/interpreting the role/function of each cluster in rank orders.
In the first sub-step of 902, the system analyzes each visual element individually, including related markup/script and fetched content (such as web images, videos, audios, etc.) to collect distinctive information about the visual element (i.e., to extract the basic context) and generates a ‘uniform visual element identifier’ UVEI for each of the identified visual elements, which uniquely identifies a corresponding one of the visual elements. More precisely, the system associates with each of the UVEIs, information regarding the web page, distinctive information and/or properties of content of the corresponding one of the visual elements, and a relative position information of the corresponding one of the visual elements. As a result, the system assigns a UVEI to each visual element (i.e., elementary visual object) to be used both as a base for further analysis and as a reference (e.g., an analogue) for establishing associations between super-objects and web-objects.
Next, based on the UVEIs generated, the system analyzes the visual elements for classifying each of the visual elements into one of the basic operational functions classified as, for example, navigational, interactive, informative, decorative, structural, or combinations thereof. In this process, the system may further classify each visual element into one of the basic roles, e.g., subjective function types classified as ‘main content’ and ‘auxiliary element’. Subjective function type (SFT) is a non-overlapping classification of each visual element according to its basic role in a page. In this context, further sub-classes/sub-categories may be created according to the operational needs. For example, according to the embodiments, ‘main content’ may also be sub-categorized as ‘main content’ and ‘side content’ per se. According to the classification mechanism of the system: i) all elements possess one or more basic operational function/s; ii) an ‘auxiliary element’ is an element that possesses only operational function/s, i.e., having only an operational role without a subjective role; iii) a ‘main content’ or a ‘side content’ is an element that also possesses a subjective function besides operational function/s, i.e., conveying a subject matter, i.e., a matter presented for consideration of the viewers. For example, in YouTube™, a scroll bar or a play button may be identified as an auxiliary element, a video or a link preview of a video may be identified as a main content, while an ad or any other extraneous content may be considered as side content. As a result, the system classifies each visual element (i.e., each elementary visual object) into, for example, one or more of the basic operational functions and further classifies each visual element into, for example, only one of the subjective function types, i.e., one of the basic roles.
Further, in this process, based on the classification of the basic operational functions and utilizing the extracted information regarding the classification of the subjective function types, the system analyzes respective relative positions and respective functional relations of the elements with other elements, in order to cluster functionally interrelated elements in rank orders and extract the function of each cluster. In this context, unlike conventional segmentation of elements, clustering includes all ranks of interrelationship. For example, a search field and a search button lined up next to each other serving for the same purpose is a first order cluster, while the entire navigation bar containing these two elements besides a couple of link buttons is a second order cluster and so forth. Accordingly, the highest order cluster corresponds to all visual elements of a web page framed/enclosed by the canvas, i.e., the web page itself. Further, the system extracts high-level function/s of each identified cluster of interrelated visual elements and assigns a ‘uniform cluster function identifier’ (UCFI) for each as an analogue reference to be used for establishing associations with a cluster of interrelated visual elements—in addition to the set of UVEIs that belongs to the cluster.
Referring again to Process A: i) Uniform cluster function identifier (UCFI) may also be used for individual visual elements by considering each visual element as a zeroth order cluster. ii) The process of ‘classifying and clustering’ are interbedded processes, i.e., the information obtained in one process is used to obtain information in the other and vice versa, as shown with a loop in the flowchart 300 (
As a result of Process A, i.e., the process of analyzing, classifying, and clustering web-objects, the system determines/interprets at a contextual level ‘what exactly the web-objects are’ in the rendered web page. Following the execution of Process A, the processes B and D may be initiated and executed independent of each other. In some examples, the processes B and D may be initiated simultaneously and executed independent of each other.
Process B—904—includes the step of deciding on interactions with respect to the rendered portions of the web-resource that the user is viewing. In this process, based on i) each UVEI generated; ii) the role of each visual element identified; iii) each cluster of interrelated visual elements identified including the corresponding UCFI; and iv) the predefined set of interaction rules that is set according to the operational and implementational needs; the system controls user interaction with web-objects—in respect of the development and deployment process of super-objects—by recommending interaction options and enabling/disabling web-objects for interaction according to the selected interaction type. The predefined set of interaction rules is a library of operational rules that is set for ‘how a user can interact with which visual element or cluster of interrelated visual elements based on an interaction type’. In other words, for each interaction type—which represents a unique interaction option for either associating contents, or associating applications—special and general rules are assigned according to the interaction needs and based on the criteria of ensuring the stability and sustainability. For example, as a general rule, interaction with a cluster of randomly selected visual elements is prohibited—due to the violation of the stability/sustainability criteria—, unless a determinable function of the cluster is introduced into the system by the user. Or, for example, as a special rule, only visual elements with particular roles—such as only main content and side content—may be enabled for interaction. In this context, the system recommends interaction options to a user, and based on the preferences of the user, the system enables visual elements and/or clusters of interrelated visual elements for interaction and disables the rest (e.g., filtering visual elements for interaction) according to the corresponding interaction rules and indicates each available visual element or cluster of interrelated visual elements, e.g., with a visual indicator, for the selection of the user.
Referring again to Process B: i) Interaction rules that can beset are infinitely many and take shape individually according to the needs of each implementation possibility. ii) In order to reduce the processing load of the system, Process B may be initiated with a receival of an interaction request of the user.
As a result of Process B, i.e., the process of deciding on interactions, the system decides ‘how the user can interact with web-objects in respect of the development and deployment process of super-objects’ in the rendered web page. Following the execution of Process B, Process C may be initiated if the user wishes to associate a content or application that is available/ready for association.
Process C—908—includes the step of establishing associations. In this process, based on the receipt of the user's selection of a visual element or a cluster of interrelated visual elements, the system associates the prospective super-object developed by the user with the selected visual element or cluster of interrelated visual elements by generating a database record that includes the analogue reference of the visual element or cluster of interrelated visual elements being interacted with. In this context, for each super-object, a unique database record is generated, containing a locator field, a uniform cluster function identifier (UCFI) field, a content field, an author field, and/or any other field, etc. Analogue references are recorded under the locator field and uniform cluster function identifier (UCFI) field—or under any other field that is created for this purpose. If interacted web-object is a visual element, then the UVEI of the interacted visual element is recorded under the locator field, and the uniform cluster function identifier (UCFI) field remains empty—unless it is preferred to treat the interacted visual element as a cluster, i.e., a cluster that includes a single element, itself. If an interacted web-object is a cluster of interrelated visual elements, then each of the UVEIs belonging to the interacted cluster are recorded under the locator field, while extracted function of the interacted web-object is recorded under the uniform cluster function identifier (UCFI) field. Finally, the content of the super-object (e.g., general contents and/or executable contents such as a set of instructions for the system for execution) and information about the author are recorded under content field and author field respectively. In some embodiments, extracted basic operational functions and basic roles of visual elements are also included in the database records of super-objects to be used as further references. In this context, both basic operational function/s and basic role of a visual element are used as a secondary (supportive) reference for the establishment of an association, according to the embodiments.
In various embodiments, the primary reference used for establishing an association with a visual element is a UVEI, while the primary reference used for establishing an association with a cluster of interrelated visual elements is the extracted function of the cluster, i.e., the uniform cluster function identifier (UCFI), along with the UVEIs of the visual elements belonging to the cluster. According to other embodiments, uniform cluster function identifier (UCFI) is used as the only reference for establishing an association with a cluster. For example, a cluster identified as a GUI having the primary function of being a ‘video player’ with the secondary functions identified as being a color corrector and a 2D to stereoscopic 3D convertor. On the other hand, the UVEIs of the visual elements belonging to the cluster may be used only as a reference for the adaptation process between the initial state of the cluster—i.e., the contextual and structural state of the cluster when the super-object was associated with it—and the most recent state of the cluster—i.e., the contextual and structural state of the cluster when the web-resource that the cluster belongs to is re-visited/re-rendered. For example when the abovementioned GUI (video player) is re-visited; via i) constructing a reference that represents the initial state of the cluster based on the rUVEIs, ii) constructing a reference that represents the most recent state of the cluster based on the nUVEIs, iii) comparing the constructed references to identify the differences between the states, and iv) considering a predefined set of adaptation rules; the system may identify the adaptation procedures, thus the system may not only identify an altered cluster that was previously interacted, but may also identify how to dynamically process/manipulate the most recent cluster, according to the embodiments.
In addition, hybrid referencing is also possible via using both the reference of a visual element and the reference of a cluster of interrelated visual elements simultaneously to establish an association, such as, referencing a GUI with a certain function (e.g., a video interface) that includes a particular content (e.g., a particular video, such as ‘2001: A Space Odyssey’). Last but not least, analogue reference of a cluster of interrelated visual elements—which inherently may correspond to a set of analogue references to be chosen in various combinations for each super-object & web-object pair due to the very nature of contextuality—may be often a composite of multiple attributes, functions, roles, etc. For example, relative position or any feature of a web-object may also be used as a part of the reference. In this context, the system may suggest different combinations of references to the user and/or allow users to compose custom references for establishing associations. Further, non-existent features at the initial state of an interacted web-object may also be added to the reference. Furthermore, web-objects that do not yet exist may be introduced to the system in advance and based on such references the system may be programmed for possible interactions that may occur in the future. Therefore, the presented referencing mechanism herein,—which is in principle based on analogue references and analogies—, provides an extremely wide variety of association possibilities. In this context, the scope of a reference may be broadened up or narrowed down via subtraction/addition of further contexts. Furthermore, via addition of specific information to references, such as fragment Is of web-resources, the references may be too narrowed down such that the analogue referencing mechanism described herein may act like a conventional referencing mechanism. In this context, the analogue referencing mechanism described herein is not an alternative to the conventional referencing mechanisms but a technological advancement that may also provide the solutions of conventional mechanism while enhancing the stability and sustainability of associations dramatically in extremely complex and dynamic web environments.
Referring again to Process C: i) According to the embodiments, ‘a visual element’ or ‘a cluster of interrelated visual elements’ may also be associated with ‘a visual element’ or ‘a cluster of interrelated visual elements’ based on the same principles and methods that are applied for associating a super-object with a web-object. ii) Because each UVEI also includes the relative position information of the corresponding visual element with respect to other visual elements, it may be possible for the system to construct a comprehensive reference for the initial state of a cluster based on recorded UVEIs (rUVEIs). On the other hand, cluster information (CI) may also be utilized in this process and cluster information (CI) of each visual element of an interacted cluster may also be included in the database record of the corresponding super-object if preferred, e.g., by adding a sub-field to the locator field of the database record structure 800 and storing thereto. iii) Once an association is identified between a super-object and a cluster of interrelated visual elements, in order to compare the initial state of the cluster with the most recent state, the system may construct a reference for the initial state of the cluster based on rUVEIs and construct a reference of the most recent state of the cluster based on nUVEIs. The reference for the initial state may be constructed since a UVEI (i.e., a uniform visual element identifier generated based on analyses and interpretation of the visual element) include contextual information ready for executing the process of comparison since each UVEI includes distinctive information extracted based on analyses, such as distinctive information about content of the corresponding visual element. iv) Constructing the initial state or most recent state of a cluster based on UVEIs is actually constructing a reference for each state for the process of comparison in order to identify the adaptation procedures to the most recent state rather than constructing the actual clusters themselves, since a UVEI includes only distinctive information—identified via analyses—rather than the whole content in most of the cases.
As a result of Process C, i.e., the process of establishing associations, the system associates a super-object with one or more web-objects in the rendered web page. Following the execution of Process C, the process terminates.
Process D—910—is executed following the execution of Process A. Process D includes the step of identifying already established associations, i.e., identifying each association between existing super-objects and web-objects and adapting to contextual/structural alterations of the web page content that may have occurred between the initial state and the most recent state of each associated web-object. In this process, based on the comparison of recently extracted analogue references with previously recorded analogue references (which comparison methods are described in the following sections), the system identifies associations between web-objects, i.e., visual elements or clusters of interrelated visual elements, and super-objects, i.e., user generated contents and/or applications. In this context, the system queries the database for records of super-objects that are associated with the web page and identifies each of them—if any. After the completion of the extraction of each reference of each visual element and each cluster of interrelated visual elements of the rendered web page in Process A, the system initiates the comparison process of recently extracted references with previously recorded references. For visual elements, the system compares each recorded uniform visual element identifier (rUVEI) with each recently generated uniform visual element identifier (nUVEI)—preferably between elements with similar properties in order to speed up the process. The system may also utilize the identified classes of visual elements, such as basic operational functions, subjective function types, etc., to optimize the process. For clusters of interrelated visual elements, the system compares each recorded ‘uniform cluster function identifier’ (rUCFI)—recorded under the cluster reference field of a super-object—with each of the recently extracted uniform cluster function identifiers (nUCFIs). Next, for a corresponding or matching couple (rUCFI and nUCFI), if the super-object is active, i.e., an application, then, based on the recorded uniform visual element identifiers (rUVEIs) the system constructs a representation of the initial state of the cluster (i.e. the contextual and structural representation of the interacted web-object at the instant when the association with the corresponding super-object is established for the first time) and compares it with the representation of the current state (i.e., the contextual and structural representation of the interacted web-object at the instant when the association with the corresponding super-object is re-established upon being subsequently rendered or viewed by another user) constructed based on the recently generated uniform visual element identifiers (nUVEIs) in order to identify a procedure to adapt the respective execution procedures of the associated application to the most recent state according to a predefined set of adaptation rules. In this context, the system may identify a structurally altered cluster (e.g., a GUI) with a steady function, and adapt itself to the recent structure within a contextually and structurally altered web page.
On the other hand, since the references (UVEIs and UCFIs) are collected/extracted/identified under different conditions they cannot be expected to match exactly for most of the cases. Therefore, the system evaluates the semantic similarity levels of the references and decide whether there is a match. For example, the system may collect content fragments from visual elements that include media files, such as images, videos, audios, documents, etc.—by appropriate sampling procedures particularly selected according to the content properties such as media type, size, format, etc. of each visual element—and include the collected content fragments to the corresponding uniform visual element identifiers (UVEIs) as the distinctive information about content. In such a case, the system may compare content fragments with each other according to their similarities, and for each compared pair of collected content fragments for UVEIs calculates the similarity rate—or resemblance rate—based on the similarity analysis. If similarity percentage of a pair of collected content fragments for UVEIs exceeds a predefined similarity threshold, then the system associates the pair of collected content fragments and therefore the corresponding nUVEIs and rUVEIs. Accordingly, various statistical similarity measures may be used as a base for similarity analysis of datasets and various algorithms may be preferred or developed depending on the type of content (e.g., image, audio, video, text, etc.) of elements. Further, semantic similarity comparison of UVEIs, UCFIs, and/or parts thereof may be based on advanced AI algorithms. For example, functional similarities of two clusters may be compared (e.g., comparing the ‘social media post interfaces’ of Facebook™ and Twitter™, which have functional similarities at a certain level). In this context, the system may identify both a contextually and structurally altered cluster (e.g., a GUI) with slightly altered functions in such a degree that comparing the recently extracted UCFIs (nUCFIs) with UCFIs stored in the database as recorded UCFIs (rUCFIs) may result in exceeding a predefined threshold of similarity (i.e., identifying functionally analogous clusters and matching them based on a predefined threshold of functional similarity), thus the system may associate a super-object with the intended cluster and further may adapt itself to the most recent state of the cluster both contextually and structurally for execution—based on the comparison of the constructed representation of the initial state (via the corresponding rUVEIs) and constructed representation of the most recent state (via the corresponding nUVEIs) and also according to the predefined set of adaptation rules.
The predefined set of adaptation rules is a library of operational rules that is set as regulatory for the system to be used in the process of ‘adapting the executional procedures of a super-object that is initially developed for the corresponding root-object’ to ‘the most recent state or to a viable analogue of that root-object’. For example, once an association is established between a super-object and a web-object in a target environment, based on the comparison of the initial and most recent state representations that are constructed from the corresponding rUVEI/s and nUVEI/s, adaptational rules are applied in the process of modifying the executional instructions—conveyed by that super-object—for the current state. In essence, each adaptation rule is set in accordance with both the root-object (e.g., in accordance with its content, structure, role/function, etc.) and the instructions to be executed (i.e., the user generated application developed) to dynamically process/manipulate that web-object. The adaptation rules of a user generated application in relation to the web-object to be dynamically processed/manipulated by the application is set in the process I, i.e., the process of generating contents and developing applications. According to various embodiments, adaptation rules are set individually for each application in accordance with the web-object to be dynamically processed/manipulated by the application. The process of adapting to a web-object is significantly more complex and challenging for clusters of visual elements (e.g., GUIs) than that of singular visual elements. In this process, based on the extracted information in the processes A→B→C and A→D, the system identifies possible contextual and/or structural alteration scenarios of a web-object to which a user generated application is associated. Next, in cooperation with the developer user, the system semi-automatically determines each adaptation rule that corresponds to an identified alteration scenario. For example, in the case of integrating a functional thumbs down button to a social media post interface, the system determines N number of permutations of structural alterations and accordance with input from the user, determines the position of the thumbs down button and the counter for each permutation. In this process, the user may introduce the positioning condition to the system of the thumbs down button and counter set, i.e., the sub-cluster. An exemplary instruction may be: define ‘thumbs up button and its counter’ as sub-cluster A (i.e., the already existing sub-cluster in the GUI); define ‘thumbs down button and its counter’ as sub-cluster B (i.e., the sub-cluster to be integrated by the system); if possible, position sub-cluster B to the right of sub-cluster A as the first choice; or position sub-cluster B to the left of sub-cluster A as the second choice; else (i.e., if not possible) identify the most similar layout arrangement of the GUI among the N number of layout permutations previously determined and position sub-cluster B accordingly. It is important to note that for complex clusters with many elements and features, such as graphical user interfaces (e.g., a social media post interface), even without the addition of any further elements into, total number of permutations of structural alterations (e.g., alterations in the layout of a GUI without the addition or reduction of visual elements) can be relatively large. Further, the cluster can be structurally divided into sub-clusters, each having different roles/functions and distant positions from each other. Furthermore, the total number of combinations of contextual alterations (e.g., addition of features or alteration of contents including a web image content of an auxiliary element such as a play/pause button icon) can be many, such as infinitely many. In this context, artificial intelligence algorithms may be further developed in accordance with the processes described herein in order to meet the operational needs. For example, based on prior operations with respect to adaptation of execution procedures, a training model—for training machine learning algorithms—may be developed.
Referring again to Process D: i) Adaptation rules that can be set may be infinitely many or less and may take shape individually or in groups according to each web-object and each application to dynamically process/manipulate that web-object. In this context, it is believed that the description—including the examples given-regarding the concept of ‘setting and utilizing an adaptation rules library’ fully enables implement of the systems and methods described herein without undue experimentation. ii) As shown in
As a result of Process D, i.e., identifying already established associations, the system identifies each web-object associated with at least one super-object—if any—and further adapts itself to the most recent state of the interacted web-object/s, and thus the system may execute the associated applications—properly—in the rendered web page. Following the execution of Process D, Process E may be initiated.
Process E—912—includes the step of displaying metadata of associated super-objects in a web page. In this process, based on identified associations in the web page that the user is viewing and based on ‘user specific’ recommendations of the system, the system displays metadata of super-objects for user's selection. For super-objects that are identified as available according to the recommendations, the system displays metadata in relation with the associated web-objects through intelligent interfaces for the selection of the user. In this context, the system may sort available super-objects according to their quality score, popularity, suitability, etc. and the highest-ranking ones may be displayed. Although the system may always display metadata of passive super-objects, as a special case for the active super-objects that have been previously associated, the system may automatically execute the applications without displaying any metadata and bypass this step in some examples.
As a result of Process E, i.e., displaying metadata of super-objects, the system enables the user to select super-objects to be display or executed based on the user specific recommendations of the system in the rendered web page. Following the execution of Process E, Process F—914—and/or Process G—916—may be initiated depending on, for example, the selection/s and/or instructions of the user.
Process F—914—includes the step of displaying passive super-objects, i.e., a super-object that has a null set of instructions. In this process, based on the user's selection of a preview of a passive super-object from a metadata list, the system displays the selected passive super-object through intelligent interfaces in relation with the web-object that the passive super-object is associated with. The system may be controlled to avoid interference with the content and structure of the web page, and instead only display markers that indicate the existence of associated metadata of passive super-objects and smart interfaces that convey user generated contents if preferred or required by the user and/or system. Such content manipulation controls may be configured in the system based on global system wide settings, country wide settings, statewide settings, city wide settings, content provider level settings, website level settings, web page level settings, web page element level settings or any other granularity and/or criteria that selectively avoids interference with the content and structure of web pages. For example, the system may be set to allow interference with the content and structure for a first country, uniform resource locator (URL), or web page and may be set to prohibit such activity for another country, URL and/or web page. In other words, the system may be configured to selectively allow or not allow direct content integration in web pages in order to maintain and control any legal, ethical, and/or fair use conditions that may be desired or present. In this context, for example, users may be prohibited to replace a web page content with a user generated one including replacement of advertisements.
As a result of Process F, i.e., displaying passive super-objects, the system enables users to view a user generated content that is selected from a metadata list that is related to a web-object in the rendered web page. Following the execution of Process F, Process H—918—may be initiated.
Process G—916—includes the step of executing super-objects. In this process, based on the adaptation procedures identified in Process D,—which are identified in order to adapt the respective execution procedures developed for the corresponding root-object (i.e., the initial state of the corresponding element or element cluster) to the most recent state—the system executes each super-object that is either selected from a metadata preview or already associated into the web page by the user.
As a result of Process G, i.e., executing super-objects, the system executes each selected user generated application that is developed to dynamically process/manipulate the associated web-object by adapting to the most recent state. Following the execution of Process G, Process H may be initiated.
Process H—918—includes the step of enabling interactions with/through super-objects. In this process, the system enables the user to connect and interact with other users through the super-objects that act as network links. For example, the user may interact with other users through commenting interfaces integrated with passive super-objects—similar to that of social media posts—and further may communicate with other users independently, for example, via private messengers operating on a peer-to-peer (P2P) basis. In this context, the system provides users with the ability to create a webwide social network upon web-resources through super-objects, thus providing a boundless and versatile social networking alternative, i.e., the superimposed networking, to the conventional social networking alternatives provided mostly by the monopolistic websites such as Facebook™, Twitter™, Instagram™, etc. Furthermore, Process H enables further interactions with super-objects, such as enabling users to further associate user generated contents and applications, i.e., second-order super-objects, with first-order super-objects via utilizing the algorithms described herein—if needed—and/or other external applications and/or algorithms.
As a result of Process H, i.e., enabling further interactions with/through super-objects, the system i) enables the user to connect and interact with other users through super-objects and to interact with super-objects themselves via associating second-order super-objects with them in a rendered web page.
Process I—920—includes the step of generating contents and developing applications. In this process, the system enables the user to generate contents and develop applications to be associated with web-objects. In the process of generating contents, the system provides the user with tools to create, arrange, and edit texts, images, videos, audios, documents, etc. that may be separate and distinct from a web page and/or may be included in a web page. The processes to create, arrange, and edit texts, images, videos, audios, documents, etc., may be conventional processes, such as being similar to that of generating social media posts. In the process of developing applications, the system provides a set of tools and frameworks (e.g., a superimposed software development framework) in order to assist the user—who may be an expert web developer or a non-expert layman—to develop applications that dynamically process/manipulate visual elements and/or clusters of interrelated visual elements. In this context, based on the user's expertise level in programming, and also for example, his/her occupation, socio-economic class, fields of interest, etc. the system may share application examples—if any—according to the web-objects that the user wishes to process/manipulate, and further may assist him/her in the development process. Further, the system may utilize algorithms of explainable AI to help the user to understand and interpret the decisions or predictions made by the system. Thus, the user may debug and improve application performance. Furthermore, the system may utilize hybrid intelligence workflow patterns for the co-creation, deployment, and adoption. Thus, users' engagement, willingness to adopt and willingness to co-create may be increased. Furthermore, utilizing hybrid intelligence, the system, in collaboration with the user, may set adaptation rules individually for each application developed in accordance with the web-object to be processed/manipulated by the application—as elaborated in Process D.
Referring again to Process I: The superimposed software development framework (SSDF) is an envisioned framework to be provided by the system in order to facilitate the development of superimposed software applications. SSDF may leverage capabilities of the system (i.e., all operations performed based on the processes 902, 904, 906, 908, 910, 912, 914, 916, 918, etc.) such as the interpretations of web-objects based on analyzing, classifying, clustering, referencing, comparing, etc. In principle SSDF utilizes both the interpretations of the system such as UVEIs, BOFs, SFTs, Cis, UCFIs, and rule libraries such as system-defined interaction rules, in order to identify programming options for each identified visual element and/or cluster of interrelated visual elements in a web page. In this context, one of the interesting features of the SSDF is ‘contextual programing’ which emerges based on the above-mentioned facilities provided by the system. Contextual programing is a high-level programing approach which may enable both expert and non-expert (layman) users to develop user generated applications that process/manipulate web-objects. Alternatively, or in addition, such user generated applications may be retrieved from an applications library of pre-stored user generated applications. Such an applications library may be for access by a particular user, a group of users, and/or all users. Automated programming by the system of such user generated applications may include AI (artificial intelligence) algorithm-based population, by the system, of the user generated applications with webpage specific information obtained by the system's interpretation of web-objects and based on analyzing, classifying, clustering, analogue referencing, etc. For example, SSDF may recommend the user to adapt from the application library and/or develop an application for a particular GUI (e.g., a video player interface, a social media post interface, etc.) regardless of any web source and may automatically adapt the developed application for any web-resource that includes similar GUIs that are within the proximity of a certain similarity rate—or resemblance rate—that is set by, for example, the system, or the user, or the system and the user collaboratively together. Thus, the system may customize a developed application for a particular web page the user wishes to associate with the developed application according to the interpretation, by the system, of the web-objects of the particular web page based on AI, where the AI is further trained by the collaborative interaction of the user with the system during adaptation/development of the developed application for the particular web page. Alternatively, or in addition, the user may introduce to the system the function/s of a previously unidentified cluster (e.g., a table with certain functions) and the system/user may develop and/or adapt an application/program that process and manipulate similar clusters. Further, SSDF may use previously developed user generated application(s) from the library as samples/examples to identify and recommend programing possibilities to the users by utilizing AI algorithms (such as machine learning models) that may be specially developed for this purpose and further refined/taught based on the user interaction with the system—in respect of both development and deployment process of super-objects and interactions with deployed super-objects. Furthermore, SSDF may record each corresponding development and deployment process of user generated applications—that are developed in SSDF—to accumulate a big data of software development and deployment processes and may process the big data by specially designed advanced AI algorithms to extract information for conceptualization of software development ideas. Moreover, the system may automatically adapt such developed and associated applications according to changes in web pages as discussed herein.
As a result of Process I, i.e., generating contents and developing applications, the system enables the user—even if he/she is a layman—to develop web-based applications that dynamically process/manipulate web-objects,—thus enables the user to become a service provider—while providing a conventional tool for generating contents.
The steps A→B and A→D are automatically re-executed if the visited web page is subsequently rendered. However, the steps C, E, F, G, and H may be selectively re-executed according to the demand of the user while the user is browsing the rendered web page—without any subsequent renderings. For example, the user may wish to develop an application and may wish to associate the application with a web-object. Thus Process I and Process C may be re-executed. Or the user may wish to select, display, and execute other super-objects from various metadata lists that are associated with various web-objects. Thus, Process E, Process F, Process G, and Process H may be re-executed. The process 900 terminates when browsing of the visited web page is finalized, according to the embodiments.
According to the embodiments, as a result of the steps A→B→C, the system: i) controls web-objects to be interacted with by determining ‘what exactly the objects of web resources are’ at a contextual level and deciding ‘how super-objects may interact with them’ or equivalently ‘how users may interact with them with respect to the development and deployment process of super-objects’ according to system-defined interaction rules; and further ii) establishes associations between ‘prospective super-objects developed’ and ‘corresponding web-objects interacted’ via storing ‘respective analogue references extracted’ and ‘interaction procedures developed’ for each pair. As a result of the steps A→D, the system: i) re-establishes associations between super-objects and web-objects despite contextual and/or structural alterations—i.e., all kinds of alterations of objects in respective environments—via re-determining ‘what exactly the objects of web-resources are’ at a contextual level, and comparing ‘recently extracted analogue references’ with ‘analogue references stored’; and further ii) adapts the interaction procedures developed for the initial state to the most recent state via constructing and comparing ‘initial state representations’ with ‘most recent state representations’ of the associated web-objects. Thus, as a result of the steps A→B→C and A→D, the system ensures the stability and sustainability of interactions between i) users and web-objects with respect to development and deployment processes of super-objects; and ii) super-objects and web-objects with respect to association and execution processes of super-objects.
More comprehensively, as a result of the steps A→B→C, or A→I→B→C and A→D→E→F→H, or A→D→G→H, or A→D→E→D→G→H, the system i) fully-automatically controls the interactions between super-objects and web-objects—particularly—in the process of associating and executing super-objects; ii) semi-automatically controls the interactions between users and web-objects—particularly—in the process of development and deployment of super-objects; iii) semi-automatically controls the interactions between users and super-objects—particularly—in the process of users' interaction with deployed super-objects; and iv) semi-automatically controls the interactions between users—particularly—in the process of users' interaction with other users through super-objects that act as network links. Thus, the system ensures the consistency and persistency of interactions for all crucial processes in any web environment, within any scenario, in real-time, in a self-contained manner while enabling human and machine users to select, display, execute, and interact with super-objects including interacting with each other through the super-objects that act as network links, according to the embodiments.
S3) Methods and Systems for ‘Object-Aware Fuzzy Processing Based on Analogies’ Involving Physical and Digital Web-Objects—Interaction with Physical, Digital, and Abstract Web-Resources:
The process begins with an encounter of the system with one or more object/s in an environment—which may be an automation system, such as a robot, encountering physical objects, such as physical structures. The system may involve various sensors, transducers, actuators, and controllers designed to interact with the environment, i.e., objects in the environment, to be used in the process of i) analyzing, classifying, and clustering elements in order to identify each sub-object that belongs to the main object that is being processed and ii) manipulating those elements in order to execute existing or future—executable—procedures developed.
At 1202, the system analyzes, classifies, and clusters elements to identify each object (i.e., each element and each cluster of interrelated elements) according to distinctive attributes, functions, and interrelationship. Next, for each identified object, i.e., for each element and cluster of interrelated elements, the system generates an analogue reference based on the extracted attributes, functions, and interrelationship. Following the execution of process 1202, process 1204 and process 1210 may be initiated individually and simultaneously since they are independent of each other.
At 1204, based on the analogue references generated at 1202, the system identifies and filters objects suitable for manipulation, for example, based on system-defined interaction rules. Next, the system receives user's selection of an object as a root-object. Next, in coordination with the system, the user develops a set of instructions, i.e., execution procedures or application, to manipulate the root-object. The process also includes generating contents related with the execution procedures or the application.
At 1206, the system analyzes the application developed at 1204 and identifies attribute/s and/or function/s of the root-object—specially—related with the application. Next, the system generates a new analogue reference for the root-object that comprises the related attribute/s and/or function/s identified. Next, based on the analogue reference of the root-object, the system determines similarity threshold/s & adaptation rules.
At 1208, the system encapsulates: i) the set of instructions developed including related contents created; ii) the analogue reference of the root-object generated; and iii) the similarity threshold/s and adaptation rules determined; at 1206 as a super-object. Next, the system associates the super-object with the root-object by recording the super-object at a storage medium.
As a result of 1202, 1204, 1206 and 1208 the system: i) controls objects to be interacted with via determining ‘what exactly the encountered objects are’ at a contextual level, and deciding ‘how users may interact with them’ according to system-defined interaction rules with respect to the development and deployment process of super-objects; and further ii) establishes associations between ‘prospective super-objects developed’ and ‘corresponding objects interacted’ via storing ‘respective analogue references extracted’ and ‘interaction procedures developed’ for each pair.
At 1210, the system compares recently generated ‘analogue references of objects’ (OARs) with ‘recorded analogue references of root-objects’ (rOARs) to determine analogies and associates OARs with rOARs based on predefined similarity thresholds. Next, for each matching pair, the system associates the super-object with the corresponding object. Thus, the system re-associates super-objects with objects in the context of analogies.
At 1212, for each super-object, the system adapts the execution procedures developed for the root-object to the corresponding object based on i) the difference between the analogue reference of the root-object and the analogue reference of the corresponding object, and ii) adaptation rules identified at 1206. Next, the system executes the new execution procedures accordingly.
As a result of 1202, 1210, and 1212 the system: i) re-establishes associations between super-objects and objects despite contextual and/or structural alterations—i.e., all kinds of alterations of objects in respective environments—via re-determining ‘what exactly the objects are’ at a contextual level, and comparing ‘recently extracted analogue references’ with ‘analogue references stored’; and further ii) adapts the interaction procedures developed for the initial state to the most recent state via constructing and comparing ‘initial state representations’ with ‘most recent state representations’ of the associated objects.
Consequently, by the processes 1202→1204→1206→1208 and 1202→1210→1212, the system ensures the stability and sustainability of interactions with digital or physical objects in any environment, within any scenario, in real-time, in a self-contained manner.
According to another embodiment, the robots are equipped with advanced optic, ultrasonic, and x-ray imaging sensors, robotic arms and end-effectors designed to interact specially with lego structures. Within the environment, all sorts of lego structures and pieces exist, such as planes, trains, automobiles, snow mobiles, houses, cities, robots, space stations, etc., including sub-structures such as engines, wings, tracks, etc., and all sorts of lego pieces, wherein each lego piece corresponds to an element, i.e., elementary object. By the optical, ultrasonic and x-ray imaging a robot may perceive each element in detail including the enclosed ones within lego structures. While, by robotic arms and end-effectors a robot may manipulate lego structures and pieces precisely, such as assembling, disassembling, installing, etc. The task of the robots may be: i) assisting users to create prospective super-objects that include digital and physical contents—such as links for the Internet of things (IoT) and other lego pieces or lego structures for coupling—and instructions with respect to lego pieces, sub-structures or whole structures—such as the brick, engine, or whole structure of a lego car; ii) associating prospective super-objects with corresponding lego structures, i.e., storing user interactions with corresponding lego structures such as storing a set of instructions developed to modify a particular sub-structure of a particular lego structure when encountered; iii) re-establishing associations during subsequential encounters with lego structures; iv) adapting execution procedures developed for the initial state to the most recent state of lego structures; and v) executing corresponding execution procedures for each association—such as executing particular instructions to modify a particular sub-structure of a lego structure with various lego pieces or sub-structures or whole structures included in the corresponding super-object; according to the embodiment. In the process, although the children may try to confuse the system by manipulating lego structures with clever alterations, the system may cope with such adversary interventions by the methods and principles described herein.
Identification Phase: i) Analyzing a complex physical or digital object encountered in a physical or digital environment to identify each of the elements (elementary-objects) belonging to the object; generating, for each of the identified elements, uniform element identifiers (UEIs), each of the UEIs uniquely identifying a corresponding one of the identified elements; associating, with each of the UEIs, information regarding the affiliated object and the environment in which the affiliated object is located, distinctive information and/or properties of content of the corresponding one of the elements, and a relative position of the corresponding one of the elements in the main object; ii) Classifying, based on UEIs, each of the elements into one of a plurality of predetermined classes; iii) Clustering the elements in rank order to interrelate the elements within the affiliated object, wherein the cluster information identified for each of the elements—for example—based on respective relative position of the elements, or respective functional relations of the elements with other elements, or both; and v) Identifying, for each of the clusters of interrelated elements in rank order, function/s within the affiliated object, and generating, for each of the clusters of interrelated elements, uniform cluster function identifiers (UCFIs), each of the UCFIs including a corresponding one of the function/s of clusters of interrelated elements.
Development Phase: i) Deciding on the elements and clusters of interrelated elements—belonging to said complex object—to identify those of the elements and clusters of interrelated elements suitable for user interaction in respect of the development and deployment process of super-objects, the decision is based on—for example—a set of predefined interaction rules comprising the cluster information and the classification of elements and the uniform cluster function identifiers (UCFIs) of clusters of interrelated elements; ii) Receiving the user's selection of one of the elements or clusters of interrelated elements among the available ones for interaction; iii) Assisting the user to develop an application that dynamically process and/or manipulate the selected element or cluster of interrelated elements; setting adaptation rules in coordination with the user for the user generated application according to the corresponding element or cluster of interrelated elements that the user generated application is to be associated with; and iv) Receiving an input representative of a user interaction with one of the elements or clusters of interrelated elements available for user interaction in respect of the development and deployment process of super-objects.
Establishment Phase: Storing the user interaction—e.g., in a database—with the one of the elements—e.g., a script developed by the user in relation to the manipulation of that element—as a super-object in association with the one of the UEIs; or, storing the user interaction—e.g., in a database—with the one of the clusters of interrelated elements—e.g., a script developed by the user in relation to the manipulation of that cluster of interrelated elements—as a super-object in association with the corresponding set of the UEIs that belong to the cluster and recording the corresponding UCFI of the cluster and/or recording for each of the UEIs that belong to the cluster the corresponding one of the identified classes and cluster information.
Re-Identification Phase: i) Re-analyzing a previously interacted complex physical or digital object encountered in a physical or digital environment to identify each of the elements (elementary-objects) belonging to the object; generating, for each of the identified elements, new uniform element identifiers (nUEIs), each of the nUEIs uniquely identifying a corresponding one of the identified elements; associating, with each of the nUEIs, information regarding the affiliated object and the environment in which the affiliated object is located, distinctive information and/or properties of content of the corresponding one of the elements, and a relative position of the corresponding one of the elements in the main object; ii) Classifying, based on nUEIs, each of the elements into one of a plurality of predetermined classes; iii) Clustering the elements in rank order to interrelate the elements within the affiliated object, wherein the cluster information identified for each of the elements—for example—based on respective relative position of the elements, or respective functional relations of the elements with other elements, or both; and v) Identifying, for each of the clusters of interrelated elements in rank order, function/s within the affiliated object, and generating, for each of the clusters of interrelated elements, new uniform cluster function identifiers (nUCFIs), each of the nUCFIs including a corresponding one of the function/s of clusters of interrelated elements.
Association & Execution Phase: i) Comparing the nUEIs with the UEIs stored as recorded UEIs (rUEIs) and associating the nUEIs with the rUEIs based on a predefined threshold of similarity; and/or comparing the nUCFIs with UCFIs stored as recorded UCFIs (rUCFIs) and associating the nUCFIs with the rUCFIs based on a predefined threshold of similarity, thus re-establishing a previously established association—or establishing an analogue of that association—between a super-object and an element or a cluster of interrelated elements; ii) Constructing based on rUEIs the initial state representation and based on nUEIs the most recent state representation of each element or each cluster of interrelated elements that is associated with one or more super-objects; and comparing the initial state representation with the most recent state representation of those to identify contextual and structural differences—if any—between the states; and iii) identifying a procedure to adapt the respective execution procedures to the most recent state of each associated element or cluster of interrelated elements according to the identified contextual and structural differences—if any—and a predefined set of adaptation rules—if needed—defined for each corresponding user generated application and associated cluster pair; according to the embodiments.
In essence, an ontology involves representing knowledge in the form of a formal, explicit specification of concepts and relations between those concepts within a particular domain according to their properties. In other words, there are three main components to an ontology, which are i) concepts: the distinct types of things, i.e., classes or abstract objects, that exist in our data; ii) relations: properties that connect two concepts; and iii) attributes: properties that describe an individual concept. Within the context of ontologies, one of the approaches to represent knowledge is semantic networks or knowledge graphs. A semantic network involves representing knowledge through nodes and links, where nodes represent objects or concepts and links represent their relations.
In the example semantic network presented at
Semantically i) ‘Alberto Balsam is a nuclear chemist’ means that ‘the object named Alberto Balsalm possesses all of the properties (attributes) of the concept of nuclear chemist’, ii) ‘18-08-1971 is a date’ means that ‘the object named 18-08-1971 possesses all of the properties (attributes) of the concept of date’, and iii) ‘Berkeley is a place’ means that ‘the object named Berkeley possesses all of the properties (attributes) of the concept of place’. Further, i) ‘nuclear chemist is a subclass of person’ means that ‘the concept of nuclear chemist possesses all of the properties (attributes) of the concept of person’, ii) ‘birthplace is place’ means that ‘the concept of birthplace possesses all of the properties (attributes) of the concept of place’, and iii) ‘birth date is date’ means that ‘the concept of birth date possesses all of the properties (attributes) of the concept of date’, thus they are subclasses—or sub concepts—of their parent classes or parent concepts.
S4.1) A Novel Knowledge Representation and Reasoning Model ‘Dynamically-Fuzzy Semantic Networks and Inference based on Analogies’:
Transforming-objects or t-objects are instances of transforming-concepts, i.e., transforming-classes, or t-concepts in short. Concepts are distinct types of things, i.e., classes, that exist in a universe, where each individual concept has a unique set of attributes, i.e., a unique set of properties that describe that individual concept, in the universe to which it belongs. A transforming-concept is postulated to involve a unique set of conceptual states, wherein each state represents a unique set of properties that which is one of the possible concepts that can be manifested by the t-concept among the set of all possible concepts that can be manifested by the t-concept according to the rules dictated by the universe and the context that the t-concept is bounded within that universe. More precisely, if there are a total of N number of possible individual concepts that can be ever manifested by a t-concept within the context that the t-concept is bounded then there are N corresponding states, wherein each state represents a unique concept that can be manifested by the t-concept. Therefore, at the core, the determinant factors of a t-concept are the rules of the universe to which that t-concept belongs and the context by which the t-concept is bounded in that universe. In this context, a t-concept is identified to be related to another concept within a particular context if the similarity rate—with respect to the compared concept—is greater than or equal to a certain threshold of similarity, wherein the similarity threshold is dependent on the object being compared and the context that the similarity comparison is based on. N.B.: Described and formulated in detail in the following sections. Hence, the relationship of a t-concept with another concept—in the context of a particular concept—is neither binary, i.e., having the binary membership values 0 or 1, nor steady-fuzzy, i.e. having a certain fuzzy membership value in the unit interval [0, 1], but variably-fuzzy instead, i.e., having multiple fuzzy membership values in the unit interval [0, 1], each of which corresponds to a manifestation of each state of the t-concept. Consequently, a t-concept is a variable concept that may convey both known and unknown concepts in the context where it is bounded, thus the establishment of associations even with currently unknown concepts in a fuzzy manner is enabled. In this regard, a ‘concept’ is a special case of a t-concept possessing only one state in the context of the universe—as a whole—to which it belongs. Below are the core properties of the disclosed concept of ‘t-concept’.
In a universe wherein each concept comprises one or more elementary concepts, i.e., building block concepts that are not compositions of other concepts, (e.g., fundamental concepts, such as ‘presence’, ‘absence’, etc. or logical connectives such as ‘and’, ‘or’, ‘not’, ‘if’, ‘then’, etc.) and wherein each semantic relation comprises one or more elementary semantic relations (e.g., fundamental interactions such as basic interactions involving fundamental concepts), a t-concept possesses the following properties: i) Behavior: A t-concept is postulated to behave according to the rules dictated by the universe to which it belongs without the possibility to violate those rules; ii) States and Manifestations: A t-concept is postulated to involve multiple states, where each state represents a corresponding one of the possible conceptual manifestations of the t-concept, which may be any concept that is a member of the set of all possible concepts that the t-concept may involve according to the rules dictated by the universe and the context that the t-concept is bounded within that universe—to which the t-concept belongs; iii) Uncertainty: A t-concept is postulated to be transforming (or hopping) from one state to another with some degree of uncertainty, wherein that uncertainty is governed by the rules dictated by the universe and the context that the t-concept is bounded within that universe—to which the t-concept belongs, n.b., therefore, depending on the system, the spectrum of the degree of uncertainty may range from absolute predictability to absolute unpredictability; and iv) Relationship: A t-concept is defined to be related to another concept—or t-concept—if its similarity rate in the context of a reference concept is greater than or equal to a certain similarity threshold, i.e., 0<similarity threshold≤similarity rate≤1, wherein the similarity threshold is dependent on the object being compared and the context that the similarity comparison is based on. N.B.: Described and formulated in detail in the following sections.
Similarly, a transforming-object, which is a digital and/or physical instance of a transforming-concept, is an object that is postulated to transform between manifestations, appearances, or observations (accordingly nicknamed as transformer inspired by the transforming robot concept of Hasbro & Takara Tommy). Analogically, if a t-concept is a device that generates concepts within certain contextual boundaries then a t-object is a device that generates objects within certain contextual boundaries. More precisely, a t-object is an object that is postulated to involve multiple states within a certain context, where each state represents a corresponding one of the possible manifestations of the t-object—in accordance with the t-concept of which it is an instance—in a non-deterministic manner. Therefore, at the core, the determinant factors of a t-object are the rules of the universe to which that t-object belongs and the context by which the t-object is bounded in that universe. In this context, a t-object is identified to be related to another object within a particular context if the similarity rate—with respect to the compared object—is greater than or equal to a certain threshold of similarity, wherein the similarity threshold is dependent on the object being compared and the context that the similarity comparison is based on. N.B.: Described and formulated in detail in the following sections. Hence, the relationship of a t-object with another object—in the context of a particular concept—is neither binary, i.e., having the binary membership values 0 or 1, nor steady-fuzzy, i.e. having a certain fuzzy membership value in the unit interval [0, 1], but variably-fuzzy instead, i.e., having multiple fuzzy membership values in the unit interval [0, 1], each of which corresponds to a manifestation of each state of the t-object. Consequently, a t-object is a variable object that may convey both known and unknown objects in the context where it is bounded, thus the establishment of associations even with currently unknown objects in a fuzzy manner is enabled. In this regard, an ‘object’ is a special case of a t-object possessing only one state in the context of the universe—as a whole—to which it belongs. Below are the core properties of the disclosed concept of ‘t-object’.
In a universe wherein each object comprises one or more elementary objects, i.e., building block objects that are not compositions of other objects, and wherein each interaction comprises one or more elementary interactions e.g., elementary interactions between elementary objects, a transforming-object possesses the following properties: i) Behavior: A t-object is postulated to behave according to the rules dictated by the universe to which it belongs without the possibility to violate those rules; ii) States and Manifestations: A t-object is postulated to involve multiple states, wherein each state represents a corresponding one of the possible manifestations of the t-object, which may be any object that is a member of the set of all possible objects that the t-object may involve according to the rules dictated by the universe and the context that the t-object is bounded within that universe—to which the t-object belongs; iii) Uncertainty: A t-object is postulated to be transforming (or hopping) from one state to another with some degree of uncertainty, wherein that uncertainty is governed by the rules dictated by the universe and the context that the t-object is bounded within that universe—to which the t-object belongs, n.b., therefore, depending on the system, the spectrum of the degree of uncertainty may range from absolute predictability to absolute unpredictability; and iv) Relationship: A t-object is defined to be related to another object—or t-object—if its similarity rate in the context of a reference concept is greater than or equal to a certain similarity threshold, i.e., 0<similarity threshold≤similarity rate≤1, wherein the similarity threshold is dependent on the object being compared and the context that the similarity comparison is based on. N.B.: Described and formulated in detail in the following sections.
Regarding to methods for semantic similarity comparison, such as between a manifestation of a t-object and a reference concept that identifies—also—the context, a basic approach may be: For n, k∈Z+, ω∈R|0≤ω≤1, P=0 or 1, Similarity Rate=100%×(ω1P1+ω2P2+ω3P3+ . . . +ωnPn+ . . . +ωkPk)/k, where Pn is the membership value of the object or concept—that is being compared—with respect to nth property of the reference concept (class), while ωn is the weight of the same nth property. Further, property weightings ω, which may be based on priority, reliability, consistency, etc., may be altered dynamically, such as based on particular outputs of the network that the object being processed belongs. Further, binary membership values P may be fuzzified into a fuzzy membership function, thus binary truth values (i.e., 0 or 1) can be transformed into fuzzy truth values (i.e., real numbers between 0 and 1). Thus, for the manifestations involving uncertainty with respect to properties—for example, when encountered with an unprecedented manifestation of a t-object involving properties that are absolutely novel to mankind—similarity rates may still be identified reliably, according to the embodiments. According to other embodiments, said rules for similarity comparison and relationship status may be modified in accordance with the applicational needs, such as each membership value Pn of a t-object that is being compared with a reference concept, or object, or t-concept or t-object may also be required to individually exceed a minimum similarity threshold in order to consider said t-object related to the reference concept.
Note1: If a t-object or t-concept transforms within contextual boundaries, then it is said to be bounded. Similarly, if a t-object or t-concept transforms without any contextual boundaries, i.e., transforms within the context of the whole universe to which it belongs, then it is said to be unbounded. Note2: Each manifestation of a t-concept is assumed to be precisely perceivable, i.e., fuzziness or uncertainties caused by limitations with respect to perceivability is neglected, in order to ensure clarity in the descriptions. Note3: Each manifestation of a t-object is assumed to be precisely observable, i.e., fuzziness or uncertainties caused by limitations with respect to observability is neglected, in order to ensure clarity in the descriptions.
In
As presented on the knowledge graph, such analogue relations are indicated with a wavy arrow including ‘a statement of similarity threshold’ and whenever needed, also including ‘a statement of respective context’. According to the embodiments, unlike an analogue relationship between ‘an object and a concept’ or ‘a concept and a concept’ where the context is defined by the concept—in the direction of the corresponding arrow—itself, an analogue relationship between two objects must include the respective context that the similarities are measured since objects are comparable to each other in many different ways. In this respect, according to the knowledge graph, t-Alberto Balsalm's birthplace is at least G % similar ‘in the context of C1 to the City of Berkeley. For example, the context C1 may be a combination of topographic, demographic, architectural, and locational properties of a place. And t-Alberto Balsalm's birthplace may be transforming gradually between the City of Las Vegas (G %) and the City of Berkeley (100%) in the context of C1.
More precisely, the modified semantic network states that any manifestation of t-Alberto Balsalm's birthplace possesses at least one subset of properties such that the subset as a whole (i.e., irrespective of the individual properties belonging to the set, which may alter at each manifestation) is at least G % similar to the properties of the city of Berkeley in the context of C1, where C1 is a reference concept—or a set of concepts—that define the context that the similarity comparison is based on. To generalize this statement, if ‘t-object A is said to be at least S % similar to object B in the context of concept C’ then ‘any manifestation of t-object A possesses at least one subset of properties such that the subset as a whole (i.e., irrespective of the individual properties belonging to the set, which may alter at each manifestation) is at least S % similar to the properties of object B in the context of C’, where C is a reference concept (class) or a set of concepts (classes) that define the context that the similarity comparison is based on.
As indicated on the knowledge graph, t-Alberto Balsalm's birthplace is at least M % similar to t-Berkeley in the context of C1, n.b., previous threshold G % is now M % in the same context. More precisely, for any manifestation, each of the ‘t-Alberto Balsam's birthplace’ and ‘t-Berkeley’ simultaneously possess at least one subset of properties such that each of the subsets as a whole—irrespective of the individual properties belonging to the set, which may alter at each manifestation—is at least M % similar to the other in the context of C1, where C1 is a reference concept (class) or a set of concepts (classes), that defines the context that the similarity comparison is based on. Hence, ‘t-Berkeley’ is also at least M % similar to ‘t-Alberto Balsalm's birthplace’ in the context of C1, n.b., but not to t-Alberto Balsalm himself, thus the relationship cannot be represented with a double headed arrow. To generalize this statement, if ‘t-object A is said to be at least S % similar to t-object B in the context of concept C’ then ‘for any combination of manifestations of t-object A and t-object B, each of the t-object A and t-object B simultaneously possess at least one subset of properties such that each of the subsets as a whole (i.e., irrespective of the individual properties belonging to the set, which may alter at each manifestation) is at least S % similar to the other in the context of concept C, where C is a reference concept (class) or a set of concepts (classes) that defines the context that the similarity comparison is based on. Hence, ‘t-object B’ is also at least S % similar to ‘t-object A’ in the context of C, thus relationship may be represented with a double-headed arrow.
According to the knowledge graph, Nuclear Alchemist is a transforming-subclass of transforming-class Baba Yaga with a full membership, such that each conceptual manifestation of Nuclear Alchemist, with respect to attributes/properties, is at least 100% similar to each conceptual manifestation of Baba Yaga in the context of the concept of Baba Yaga. In other words, for each conceptual manifestation of Nuclear Alchemist, among all properties that the manifestation possesses, there exist a subset of properties that fulfills the ‘at least 100% similarity in the context of the concept of Baba Yaga’ condition when compared with the properties of the corresponding manifestation—which can be any among all possible manifestations—of the transforming-concept Baba Yaga. According to the knowledge graph, Nuclear Alchemist is a transforming-subclass of transforming-class Sorcerer with a fuzzy membership, such that each conceptual manifestation of Nuclear Alchemist, with respect to attributes/properties, is at least H % similar to each conceptual manifestation of Sorcerer in the context of the concept of Sorcerer (C4). In other words, for each conceptual manifestation of Nuclear Alchemist, among all properties that the manifestation possesses, there exist a subset of properties that fulfills the ‘at least H % similarity in the context of the concept of Sorcerer’ condition when compared with the properties of the corresponding manifestation—which can be any among all possible manifestations—of the transforming-concept of Sorcerer. Lastly, according to the knowledge graph, t-Alberto Balsalm is at least Q % similar to a Nuclear Alchemist in the context of the concept of Nuclear Alchemist (C5), i.e. in the context of being a Nuclear Alchemist. In other words, for each manifestation of t-Alberto Balsalm, among all properties that the manifestation possesses, there exist a subset of properties that fulfills the ‘at least Q % similarity in the context of the concept of Nuclear Alchemist’ condition when compared with the properties of the corresponding conceptual manifestation—which can be any among all possible manifestations—of the transforming-concept of Nuclear Alchemist. In other words, each manifestation of t-Alberto Balsalm, with respect to the attributes/properties of Nuclear Alchemist, such as knowledge and skills of a person with respect to nuclear alchemistry, is at least Q % similar to any conceptual manifestation of Nuclear Alchemist. N.B.: Nuclear Alchemist, Sorcerer and Baba Yaga are—obviously-‘bounded t-concepts’ or ‘bounded t-classes’.
Referring again to
Further, the rule STL≤STO
where TOk corresponds to any encountered object, STL corresponds to the similarity threshold, CB
where TCm corresponds to any encountered or envisioned concept, STJ corresponds to the similarity threshold, CB
Last but not least, the disclosed example—at
In conclusion, by utilizing the concept of t-objects and t-concepts, any object or concept that may exist within certain contextual boundaries, including those that does not yet exist, i.e., uninvented things, may be—albeit indirectly—processed in a fuzzy manner to a certain extend. Thus, for example, super-objects comprising event based strategic applications may also be developed proactively for future encounters, etc.
According to the various embodiments, the ontological model ‘STW≤STO
S4.3.1) Methods for Identifying the Context (CB), Root-Object (OB), and Similarity Threshold (ST) of a Super-Object in the Context of Dynamically-Fuzzy Semantic Relations Based on Analogies:
The process begins with an interaction of a system user—for example through a client of the system—with one or more object/s in an environment where the objects may be a wide variety of singular elements and/or clusters of interrelated elements. Further, the objects in the environment may be digital or physical or both, such as objects of the Internet of Things (IoT).
At 2702, the system receives a selection of the user that comprises a cluster of various objects that are selected purposefully or randomly among a plurality of objects in the environment. According to the embodiments, a selected cluster may be contextually and structurally complex and may involve collections of distant and even irrelevant objects. For example, in the case of the Internet, the cluster may comprise both ‘singular elementary objects with very basic functions’ and ‘subclusters of interrelated elementary objects with very complex functions’ and those elements/subclusters may be from irrelevant and contextually distant websites. Next, the system receives a set of instructions, i.e., procedures, developed by the user with respect to the selected cluster in order to process/manipulate the cluster. According to the embodiments, the instruction set, i.e., the procedures, may be developed by the user with the assistance of the system or completely independent of the system, while they may be basic or complex depending on the manipulated cluster and the procedures related with them.
As a result of the process 2702, the system receives a set of instructions developed by a user with respect to a cluster of objects that are selected by the user.
At 2704, the system analyzes, classifies, and clusters the elements of the selected cluster to identify each subcluster of interrelated elements in rank order—including each element as a 0th order cluster—according to distinctive attributes, functions, and interrelationship. Next, for each identified subcluster, i.e., each identified element or cluster of interrelated elements, the system generates an analogue reference based on the identified attributes, functions, and interrelationship. In one embodiment, the step 2704 is executed right after the sub-step of receiving the selection of the user at 2702, since the step 2704 does not involve processing the instruction set developed by the user with respect to the selected cluster.
As a result of the process 2704, the system semantically identifies each subcluster in rank order and assigns an analogue reference in this context.
At 2706, based on the analogue references generated at 2704, and the set of instructions received at 2702, the system analyzes the process developed by the user with respect to the selected cluster and conceptualizes the user generated process, i.e., extracts the context of the process in-depth, thus identifies the context (CB) of the prospective super-object. According to various embodiments, the context (CB) comprises the extracted function and/or distinctive attributes of each subcluster involved in the process including the interrelationship in terms of functions and/or distinctive attributes within the context of the user generated process. N.B.: But not within the context of the cluster itself, since it may be no longer valid in the context of the user generated process), while comprising the function and/or distinctive attributes of each object involved in the set of instructions that manipulate the cluster, such as functions/methods of a computer program and non-executable content with respect to the instruction set. In other words, the interaction between the ‘objects of the cluster’ and the ‘objects of the set of instructions’ constitutes the base of the context of the ‘user generated process’, i.e., ‘user generated application’. Further, depending on the complexity of the selected cluster and/or the complexity of the set of instructions with respect to the cluster, the user generated process may be represented by multiple subclusters and/or subsets of instructions to be processed in coordination. For example, the user generated process may be divided into sub-processes where each subset of instructions may be embedded with a super-object that is associated with the corresponding subcluster. In this process, each super-object to which the process is distributed operates in coordination with the others, for example, event-based rules—which may be embedded within the rule-base of each super-object—may define the order of execution of the instructions—whenever needed, according to the embodiments.
As a result of the process 2706, the system conceptualizes a user generated process (i.e., user generated application) with respect to an object cluster (thus the process becomes a concept, i.e., class) and identifies the context (CB) of the respective prospective super-object.
At 2708, based on the context (CB) identified at 2706, and the analogue references generated at 2704 the system constructs a model of the selected cluster. Next, the system associates any essential content—if any—with the constructed model and generates the root-object (OB) of the prospective super-object. According to an embodiment, first, the system eliminates subclusters that are not involved in the process thus the selected cluster becomes free of any redundant subclusters. Next, the system conceptualizes the remaining cluster in accordance with the context (CB) and extracts a model from said cluster while identifying any content of the remaining cluster that is essential to be included in/conveyed with the root-object. An essential content may be, for example, an image, a text, a link, etc. that should be partially or completely contained in the root-object. Next the system includes the essential content into the model and re-generates analogue references accordingly, thus, the system generates the root-object (OB) of the prospective super-object, according to the embodiment.
As a result of the process 2708, the system constructs a model of the selected cluster—which includes all essential contents with respect to the execution of the user generated set of instructions—and generates the root-object (OB) of the respective prospective super-object.
At 2710, based on the root-object (OB) generated at 2708 and the context (CB) identified at 2706, the system determines the similarity threshold (ST) of the prospective super-object in accordance with the operational needs and/or preferences with respect to the execution of the user generated set of instructions. The role of the similarity threshold (ST) within the ontological model ‘ST≤STO
As a result of the process 2710, the system determines the similarity threshold (ST) of the prospective super-object. N.B.: Further processing of partially processable cases is discussed as a part of the process 2800.
Referring again to Process 2710 with respect to Determinability of Critical Boundaries and the Optimal Value of Similarity Threshold in Finite Sets: In a universe where any interaction is precisely observable and perceivable, if the set of all possible outcomes of all permutations and/or all combinations of interactions between elements and/or clusters of interrelated elements is ‘finite’ then within the context of the disclosed ontological model ST≤STO
Referring again to Process 2710 with respect to Determinability of Critical Boundaries and the Optimal Value of Similarity Threshold in Infinite Sets: In a universe where any interaction is precisely observable and perceivable, if the set of all possible outcomes of all permutations and/or all combinations of interactions between elements and/or clusters of interrelated elements is ‘infinite’ then within the context of the disclosed ontological model ST≤STO
Referring again to Process 2710 with respect to Deciding on the Value of Similarity Threshold: As shown in the above processes, the lower critical boundary value LBD, the upper critical boundary value UBD and the optimal similarity threshold value STOP—which exists in the interval [LBD, UBD+ε]—are dependent variables, depending on the parameters ‘root-object OB’ and ‘context CB’, within the context of the ontological model ST≤STO
Referring again to Process 2710 with respect to the Recap of the Whole Process: Based on the root-object (OB) generated at 2708 and the context (CB) identified at 2706, the system determines the critical boundaries, i.e., lower critical boundary (LBD) and upper critical boundary (UBD), and the optimal similarity threshold (STOP) of the prospective super-object. In some embodiments, the system solely focuses on identifying the optimal similarity threshold STOP by utilizing the described methods with respect to identifying the critical boundaries and/or by utilizing other viable methods available. Next, based on the lower critical boundary (LBD), and/or upper critical boundary (UBD), and/or optimal similarity threshold (STOP) determined and—also—the assessment of operational needs, deciding on the similarity threshold (ST). Thus, the system identifies the association rule of the prospective super-object represented by the model ST≤STO
Following the execution of the step 2710 the system embeds the context (CB), the root-object (OB), and the similarity threshold (ST) with the prospective super-object—along with other essential information—and stores the prospective super-object in a storage medium—such as by recording it in a database. Thus, the prospective super-object becomes a super-object.
As a result of the process 2700 the system generates a super-object while identifying the association rule ‘ST≤STO
S4.3.5) Methods for Identifying Associations Between Objects and Super-Objects in the context of ‘Dynamically-Fuzzy Semantic Relations based on Analogies’:
At 2802, the system analyzes, classifies, and clusters elements to semantically identify each object, i.e., each singular element and each cluster of interrelated elements, according to distinctive attributes, functions, and interrelationship. Next, for each identified object—including each singular element—the system generates an analogue reference TOn based on the extracted attributes, functions, and interrelationship, where the integer variable ‘n’ represents a sequence of numbers in which each number is assigned respectively to the identified objects in the environment (2804).
Referring again to Process 2802 with respect to Specific Contents Conveyed: According to various embodiments, even specific, i.e., not generic, contents (such as the video of ‘Alien—1979 Theatrical Cut’) conveyed by objects (such as, a video player belonging to a streaming website) may be defined as components of a process, thus may be identified as a component in the context of the attributes and functions of the conveying objects themselves (e.g., the video player). For example, said object above may be identified as ‘a video player with certain attributes and certain functions including the function of conveying the full video of Alien—1979 Theatrical Cut, existing in the Nth page of the website M’. Furthermore, standalone specific contents in the form of singular elements, such as a contextually undefinable unique web image that solitarily exists in a web environment may also be defined as a component of a process. For example, said web image may be identified as ‘a contextually unidentified image with certain attributes and sole function of conveying itself as sampled and embedded herein for reference, existing in the Lth page of the website M’. Last but not least, similar to the specific contents, the same principle is also applicable to specific events. For example, an object similar to the video player above may be defined as ‘a video player with certain attributes and certain functions including the function of conveying the full video of Snowpiercer—2013 Theatrical Cut and making the video available for viewing in the US immediately after the first snowfall in the Sierra Nevada range until the first sky-pilot blooms on Mount Whitney, existing in the Kth page of the website J’. Furthermore, this example also sets a precedent with respect to the conceptualization of events. For example, the event of ‘the first snowfall in the Sierra Nevada in 2020’ can be conceptualized as ‘first snowfall’ in the context of the region ‘Sierra Nevada’ and the year ‘2020’. N.B.: Therefore, since events—including the most specific ones—can be conceptualized then the disclosed mechanics can be structured—solely—upon objects and classes excluding the events.
At 2806, based on the analogue references—represented by TOn—generated at 2804, the system compares each object with each super-object based on the association rule represented by the model ST≤STO
At 2808, for each association established at 2806, the system compares each associated pair in the context of executing the corresponding instruction set and extracts the difference of each object—represented by TOn within the model—with the corresponding root-object—represented by OB within the model—of the corresponding super-object in said context. According to the embodiments, the step 2808 also includes utilizing the previous comparisons executed at 2806 of TOn and OB pairs.
At 2810, based on the differentials identified at 2808 and auxiliary resources related to the adaptation process of user generated applications—such as predefined adaptation rules or multimodal LLMs that are included in the system—, the system adapts each instruction set—that is initially developed for the corresponding root-object of the corresponding super-object—for the corresponding analogous object (TOn) that is associated with the corresponding super-object at 2806—which may be either the most recent state or a viable analogue of the corresponding root-object—and executes the corresponding set of instructions for each analogous object (TOn) that is associated with the corresponding super-object in the environment.
S4.3.6) Strategies Related to Conditions that are Partially Satisfied Due to Missing, Incompatible, or Insufficient Components of Associated Objects and/or Loss of Components of Associated Objects During a Process:
Referring again to Process 2810 with respect to Strategies related to Conditions that are Partially Satisfied due to Missing, Incompatible, or Insufficient Components of Associated Objects and/or Loss of Components of Associated Objects during a Process: In principle, once an object or a cluster of objects is associated with a super-object, conditions for the execution of a task may be partially satisfied due to missing, incompatible, or insufficient components of the associated objects and/or loss of components of the associated objects during the process. In other words, the system may partially realize a given task—such as the execution of a user generated application—due to—for example—a missing content, and/or an incompatible attribute and/or an insufficient function of an associated object with respect to the execution of the related procedures/operations. In order to facilitate the description, referring all to as ‘missing components’; In the process of dealing with missing components, the system may attempt to i) dynamically create the missing components if possible, for example by creating an object that possess those missing properties, and/or ii) search accessible alternative sources in the environment in order to identify and associate the missing components, for example by identifying and associating an object that possess those missing properties. According to an embodiment, upon determination of the adaptation procedures—at 2810—with respect to the execution of a user generated application embedded with a super-object that is associated with an object or a cluster of objects, via utilizing disclosed methods and systems as well as state-of-the-art techniques: At 2810/1, the system identifies one or more missing components if any, such as a missing function or a certain content that is essential to fully perform the task; At 2810/2, the system dynamically generates components (objects) in order to compensate the missing components—if possible—and/or dynamically scans accessible environments for alternative objects that may compensate the missing components and/or dynamically creates composite objects from multiple objects collected from various environments and further modifies them according to the executional needs—if needed; At 2810/3, if succeeded, the system executes the task, else re-executes steps 2810/1, and 2810/2 in order to identify new ways or resources for the compensation of the missing components and the execution of the task; At 2810/4, during the process of execution of the user generated application if one or more components (objects) become unavailable or non-existing then the system re-executes the steps 2810/1, 2810/2, and 2810/3, according to the embodiment.
Referring again to Process 2810 with respect to Partial Execution of a User Generated Application: In essence, the system identifies each sub-process with respect to elements and/or clusters of interrelated elements, i.e., sub-clusters of the root-object, based on the context (CB) and root-object (OB). Next, the system identifies sub-process priority (i.e., order of importance of each independent sub-process) of the user-generated process based on the context (CB). For example, most essential sub-processes having the highest rank, while least essential (e.g., relatively negligible) sub-processes having the lowest rank. Or, for example, prerequisite sub-processes having higher ranks than the corresponding conditional processes while corequisite processes having the same rank. Next, based on the order of importance of the sub-processes, the system prioritizes the sub-clusters of the root-object (OB), i.e., identifies the priority of elements and clusters of interrelated elements. For example, the system may assign a weight coefficient for each element and each cluster of interrelated elements, with 1 being the most essential and with 0 being the least essential, e.g., relatively negligible. Based on weight coefficients assigned, the system and/or the developer of the respective user generated application may determine adaptation rules with respect to partial execution of the application. According to an embodiment, upon determination of the adaptation procedures—at 2810—with respect to the execution of a user generated application embedded with a super-object that is associated with an object or a cluster of objects, via utilizing disclosed methods and systems as well as the relevant state-of-the-art techniques: At 2810/B1, the system partially executes the user generated application according to the weight coefficients assigned.
As a result of the process 2800, the system identifies associations between super-objects and objects—among a set of encountered objects—and executes the each of the corresponding procedures, i.e., the each of the corresponding user generated applications that are embedded with the super-objects. In case of an alteration detected in one or more of the objects existing in the environment, all sub processes are executed in sequential order starting at 2802 and ending at 2810. According to various embodiments, in order to optimize the re-execution of the process 2800 in response to alterations, first the system preliminarily analyses the altered objects only and based on the previous information (e.g., previously extracted analogue references) determines the possible scope of the contextual effects of the alterations, i.e., identifies unaltered objects that may be affected contextually due to the altered objects. Thus, the system isolates the affected objects from the rest. Next, the system re-executes the process 2800 for the isolated objects—fully or partially depending on the needs—according to the embodiments.
S4.3.8) Revisiting Transforming-Objects with respect to Processes 2700 and 2800:
With respect to the execution of processes 2700 and 2800 for a transforming object, i.e., the embodiment of a transforming concept, the transition is straight forward and can be briefly described by an example. Consider the hypothetical example of a complex mechanical transforming system that transforms within the context of ‘internal combustion engines’ and a user generated process with respect to ‘integrating a turbo charger to any suitable internal combustion engine encountered’, wherein each manifestation of the transforming-object may vary from a gas turbine to a Wankel, from a Wankel to a piston engine, from a piston engine to a rocket engine including not yet invented—but possible—sub-classes in between. In this hypothetical example, i) the context of ‘internal combustion engine’ defines the boundaries of the transformations of the transforming-object (TO), ii) each possible manifestation of the transforming-object defines each possible encounter with the transforming-object that may be represented by an integer variable (TOn), iii) the process of ‘integrating a turbo charger’ defines the context (CB), and iv) the target object ‘suitable internal combustion engine’ with respect to the process defines the root-object (OB).
Revisiting the Ontological Model ‘ST≤STO
S5) Methods and Systems for ‘Object-Aware Fuzzy Control based on Analogies’—A PID Control System to Control a Transforming-Object Based on the Manipulation of Said Transforming-Object:
S5.1) Feedback Process: At 3202, for TOn relative to OB in the context of CB, identifying the similarity rate of TOn|Cy
S5.2) Comparator: At 3204, for r(t)=STW, identifying the error for cycle k, i.e., e|Cy
S5.3) Compensator: At 3206, for e|Cy
S5.4) Main Process (Plant): At 3208, for u|Cy
As a result of the process 3208 the system completes one cycle (Cyk) of the control process of the modified—or if k=1 about to be modified—t-object (TOn|Cy
Because objects are instances of concepts, the rules that apply to t-objects are also applicable to t-concepts. Therefore, virtually any method applicable to t-objects is also applicable to t-concepts since any t-concept may be defined to obey the laws of nature that apply to their physical counterparts or obey the rules that are defined for their digital t-object counterparts or obey the rules that are combinations thereof. For example, according to the embodiments, the process of ‘controlling the similarity of a t-concept relative to a reference-concept in the context of a base-concept via manipulating the t-concept’ is virtually the same process as the process of ‘controlling the similarity of a t-object relative to a reference-object in the context of a base-concept via manipulating the t-object’. More concretely, said process comprises the following subprocesses: a) identifying the similarity rate of TCn|Cy
It can be argued that if all the information is known about an object, then that object can be accurately conceptualized, however the opposite is not true—i.e., not all concepts can be physically realized. Similarly, not all methods involving t-concepts may be converted to methods involving t-objects. For example, a t-concept that does not obey the laws of nature may not be represented by a physical t-object. Or more interestingly, a t-concept may possess such extraordinary properties and behave according to such extraordinary rules that it may not be represent by a digital t-object, i.e., may not be simulated within the context of a digital universe based on digital objects/digital t-objects and their relations. Furthermore, there may be physical t-objects that may not be represented/simulated by digital objects/digital t-objects. For example, theoretical feasibility of digitally simulating a singularity covered by an event horizon—with 100% accuracy—may be debatable. Or in general, theoretical feasibility of digitally simulating any process involving infinitely or infinitesimally small physical quantities such as time intervals that are shorter than the Planck time—with 100% accuracy—may be debatable due to the absurdity of trying to simulate a process that inherently may not involve any events. For instance, consider the process of a particle crossing the event horizon of a blackhole resulting in an increase in mass in the nucleus. It may be argued that such an event is happening right at the event horizon not beyond the event horizon, thus it may be concluded that since there is no event occurring at the core, there is no process to be simulated at the core. However there still must be a transition phase that occurs at the event horizon that may involve smaller than Planck unit physical quantities relating to the process by which the particle is absorbed, i.e., a transitional process for a particle crossing the event horizon by which the particle becomes a part of singularity while increasing the mass of the core and the surface area of the event horizon. Furthermore, since the surface area of the event horizon increases by the absorption of that particle, it may be concluded that the shrinkage of that particle may not be infinite, i.e., a non-zero quantity that is smaller than the Planck length, therefore there may be also a fundamental unit of length in the singularity similar to that of the Planck length outside the event horizon of the singularity. The exact opposite of this example may be the initial singularity that is theorized to be existed before the Planck Epoch, which yields the contradiction that if no process may occur in singularity than how the universe managed to pass to the phase of Planck Epoch from the initial singularity. N.B.: Irregardless, the proof of any singularity may also be the refutation of the Simulation Hypothesis assuming that digital computation—therefore digital simulation—may only occur outside of the event horizon. Examples can be augmented to support the proposition that there may be physical processes that are theoretically impossible to digitally simulate. Nevertheless, digital-objects may be used to simulate physical t-objects or t-concepts in many useful ways. Indeed, digital-objects—and especially digital web-objects—may be considered as great resources for evaluating the concept of t-objects due to their programmability for dynamic uncertainty in various forms such as complex transformations that can occur gradually over time or complex alterations that can occur rapidly or abruptly during interactions. N.B.: Rules that determine the behavior of a certain t-concept does not have to be based on physical, mathematical or any other known principles including the logical principles as long as they are precisely defined.
A) According to various embodiments, the process of ‘associating—already existing—super-objects with other objects, including adapting and executing each of the corresponding set of instructions’ comprises the following subprocesses: A1) analyzing, classifying, and clustering digital and/or physical elementary objects that exist in an environment to identify each of the finitely many objects according to distinctive attributes, functions, and interrelationship, wherein each of said objects is either a singular elementary object or a cluster of interrelated elementary objects that presents on or connected to a data network as a resource of that data network; A2) generating, for each of the identified objects, an analogue reference TOn, each of the analogue references uniquely identifying a corresponding one of the identified objects according to the extracted attributes, functions, and interrelationship; A3) comparing each of the identified objects (TOn) with each of the super-objects based on the association rule represented by the ontological model ST≤STO
Note1: Regarding A1, an object may be digital or physical or any combination thereof, while being a resource of the data network that they are presented on or connected to, such as being a resource of the Internet of Things. Note2: Regarding A1 and A6, in the process of collecting data of objects and/or manipulating those that involve physical components, the system may utilize intermediary automated systems that comprise sensors, actuators, controllers, etc., such as robot agents working for the system to collect data and/or manipulate said type of objects. Note3: Regarding A2, the index n denotes any of the identified objects that exists in a particular environment as one of the consecutive numbers n=1, 2, 3, . . . N, wherein N is the total number of the objects that exist in that environment. Note4: Regarding A5, the conditional index n|assoc. denotes any of the associated objects that is a member of the set of identified objects that exist in an environment.
B) According to various embodiments, the process of ‘generating a super-object’ comprises the following subprocesses: B1) receiving a selection of a user that comprises a cluster of various objects that are selected purposefully or randomly among a plurality of digital and/or physical objects in an environment including an instruction set developed—by the user or a 3rd party user—with respect to the selected cluster in order to manipulate the cluster, wherein each of said objects is either a singular elementary object or a cluster of interrelated elementary objects that presents on or connected to a data network as a resource of that data network; B2) analyzing, classifying, and clustering elementary objects of the selected cluster to identify each of the objects according to distinctive attributes, functions, and interrelationship; B3) generating, for each of the identified objects, an analogue reference, each of the analogue references uniquely identifying a corresponding one of the identified objects according to the extracted attributes, functions, and interrelationship; B4) analyzing, based on the analogue references generated, the instruction set with respect to the selected cluster to extract the context of the process, i.e., conceptualizing the process, and identifying the context ‘CB of the prospective super-object; B5) constructing, based on the context ‘CB identified and the analogue references generated, a model of the selected cluster that includes any essential content and generating the root-object ‘OB of the prospective super-object; B6) determining, based on the root-object ‘OB generated and the context ‘CB identified, the lower critical boundary ‘LBD, and/or the upper critical boundary ‘UBD, and/or the optimal similarity threshold ‘STOP of the prospective super-object; B7) deciding on the similarity threshold ‘ST, based on the lower critical boundary ‘LBD, and/or upper critical boundary ‘UBD, and/or optimal similarity threshold ‘STOP determined and based on the assessment of operational needs; B8) identifying, based on the context ‘CB, root-object ‘OB, and similarity threshold ‘ST, the association rule of the prospective super-object as ‘ST≤STO
Note1: Regarding B1, a user maybe human or machine or combinations thereof, such as hybrid-intelligent agents. Note2: Regarding B2, according to the embodiments, any system can be conceptualized based on attributes, functions, and interrelationship, e.g., relations between sub-objects. Note3: Regarding B4, B5, B6, B7, B8, and B9, inverted comma on the left side of a parameter is to indicate the particularity of that parameter with respect to the super-object that is being developed.
C) According to various embodiments, the process of ‘controlling the similarity of a t-object relative to a reference-object in the context of a base-concept via manipulating the t-object’ comprises the following subprocesses: C1) identifying the similarity rate of TOn|Cy
N.B.: Regarding C, according to the embodiments, Process A or B may further comprise utilizing the process C partially or fully in the process of maintaining an association between a super-object and an object or establishing an association with an object that partially satisfies the condition of the corresponding association rule.
According to the embodiments, Process A further comprises: A7) executing the corresponding set of instructions of a super-object partially if executing the instruction set fully is not possible; A8) identifying a missing sub-object that is the cause of a partial execution with respect to an associated super-object; A9) identifying a partial association rule ST’≤STO
Note1: Regarding A7, alternatively the system may bring the process to a partially executable level and stay on hold without executing the instruction set until the missing sub-object is found. Note2: Regarding A9, derivative symbol is chosen to indicate—obviously not the rate of change but—the contextual relation of the parameters of a partial association rule of a super-object to the parameters of the association rule of said super-object. Note3: Regarding A10, alternative environments may be—for example—other resources hosting various other resources such as a website that hosts various other web pages—which host various other web-objects—, or other physical environments that host various other physical resources that are connected to the data network that the system is connected to, such as a fully-automated manufacturing plant, etc.
S8) Comparison of the Disclosed Ontological Model ‘Dynamically-Fuzzy Semantic Relations based on Analogies’ with the State-of-the-Art and Disclosure of Additional Methods and Principles:
According to the first examination report of the U.S. patent application Ser. No. 16/886,265—filed May 28, 2020, published Sep. 17, 2020, now U.S. Pat. No. 11,625,448—with respect to 35 U.S. Code § 103, the inventive step—which is an essential condition for patentability besides novelty and applicability—is determined to be the process of ‘establishing an association between a pair of objects by comparing similarities of those objects within a certain context and associating the pair if the similarity rate determined in that context exceeds a predefined threshold of similarity wherein the context is determined by the structure of the corresponding uniform element identifier (UVEI)’. Said process—which has been initially developed to overcome the problem of dynamic-uncertainty induced by web-resources—involves a fuzzy semantic relation based on analogies between an object and a family of objects wherein the family of objects are defined for a domain of similarities between a certain similarity threshold ST and 1, i.e., the relational-bandwidth [ST, 1] for 0<ST≤1, and determined based on the corresponding reference and context in accordance with the corresponding rules to which the objects are subject. N.B.: According to the disclosed ontological model, the ‘relational-bandwidth’ is the difference between the lower and upper similarity thresholds in a continuous band of similarity degrees, i.e., |STL−STH| for 0<STL≤STH≤1.
With respect to the developments in the relevant field—primarily in the field of uncertainty management in information processing—‘Similarity Fuzzy Semantic Networks’ is introduced by Castro et al.,—published Jun. 19, 2022—as a new knowledge representation model. Ref: Castro et al., 2022, ‘Similarity Fuzzy Semantic Networks and Inference’/Artificial Intelligence and Soft Computing, ICAISC 2022, Springer. According to Castro et al. (2022), ‘Similarity Fuzzy Semantic Networks’ consist of fuzzy semantic networks with a specific family of semantic relations between objects or concepts, which is named as ‘Similarity Semantic Relations’. According to Castro et al. (2022), similarity semantic relations are fuzzy semantic relations that represent that two objects or two concepts are similar in some sense or aspect in a certain degree: A is-similar-in-sense-D: αB, [Sec. 4.1, Eq. (8)] wherein D may be any topic or aspect, and it represents the assertation that objects or concepts A and B are similar in the sense D in α degree [Castro et al., 2022, Sec. 4.1]. Next, Castro et al., (2022) introduces the ‘Meta-Relations’ with respect to ‘similarity fuzzy semantic networks’. Because Castro et al., (2022) consider the ‘similarity fuzzy semantic networks’ as an extension of the conventional ‘fuzzy semantic networks’, they consider both analogical and non-analogical semantic relations (i.e., edges or links of the network) as second order concepts of a singular semantic network and define the semantic relations between them as second order semantic relations while naming them as meta-relations, i.e., relations about relations. In this context, Castro et al., (2022) introduces the meta-relations between ‘senses’ and ‘semantic relations’ of the network based on the fact that for every sense D, each concept must have a fuzzy neighborhood of similar concepts in sense D, wherein those semantic relations that are related to sense D may be transmitted by ‘similarity-in-sense-D’ [Castro et al., 2022, Sec. 4.2]. In this context, Castro et al., (2022) introduce a meta-relation to be used for ‘similarity inference’, i.e., a relation that goes from domain-specific semantic relations to is-similar-in-sense-D relations; ‘relationS is-related—to: y senseD’, [Sec. 4.2, Eq. (9)] representing the assertion that relationS is related to senseD and thus, it can be transmitted by is-similar-in-sense-D [Castro et al., 2022, Sec. 4.2]. Next, Castro et al. (2022) introduce the ‘Similarity Inference’ and the ‘Similarity Inference Rule’ as a new kind of reasoning based on similarity. In this context, Castro et al. (2022) propose that new knowledge may be extracted upon propagation of semantic relations through the is-similar-in-sense-D by means of the ‘Similarity Inference Rule’: ‘{A is-similar-in-sense-D: αB; B relationS: β C; relationS is-related-to: γ senseD}→A relationS: (γ *t(α,β)) C’ [Sec. 4.3, Eq. (10)] wherein t is a triangular norm, i.e., t-norm [Castro et al., 2022, Sec. 4.3].
S8.1) Comparison of ‘Similarity Fuzzy Semantic Relations’ [Castro et al., 2022] with the Disclosed Ontological Model ‘Dynamically-Fuzzy Semantic Relations Based on Analogies’:
The structure of semantic relations determines the structure of the ontological model. According to Castro et al., (2022), ‘Similarity Semantic Relations’, i.e., similarity fuzzy semantic relations, represent that two objects or two concepts are similar in some sense or aspect in a certain degree: A is-similar-in-sense-D: αB, wherein D may be any topic or aspect, and it represents the assertation that objects or concepts A and B are similar in the sense D in a degree [Castro et al., 2022]. The equivalent of the ‘similarity fuzzy semantic relations’ in the form with the notation that is used in this disclosure corresponds to the equality SA˜B/D=α, wherein A and B are objects or concepts, D is a concept that determines the context, and a is the degree of relation between A and B in the context of concept D on the basis of analogies. In contrast, a semantic relation in the context of the disclosed ontological model may be the inequality αL≤S(TA˜B)/D≤αH, wherein TA is an unbounded t-object or t-concept, B is an object or concept, D is a concept—or an object to be conceptualized to become a concept—that determines the context, αL is the lower similarity threshold, αH is the upper similarity threshold, and the similarity interval [αL, αH) is the domain of relation between TA and B in the context of concept D on the basis of analogies—which corresponds to a family of objects or concepts that are asserted to be the manifestations of TA. N.B.: In said example ontological model, in addition to TA, the reference object or concept B and/or the context D may further be defined as bounded t-objects or t-concepts while defining αL and/or αH—for example—as a function of time—although αH=1 is expected to be the most commonly used. Overall, the equality S(A˜B)/D=α is indeed only a special case of the inequality αL≤S(TA˜B)/D≤αH; wherein A is—inherently—a manifestation of—unbounded—TA while the lower and upper similarity thresholds are defined to be equal (αL=αH=α). In other words, ‘TA→A’ and ‘αL=αH=α’ yields the inequality to become the equality α≤S(A˜B)/D≤α≡S(A˜B)/D=α, and which shows that the ontological model ‘similarity fuzzy semantic networks’ is actually a special case of the disclosed ontological model.
Referring again to the above comparison in the context of bounded and unbounded t-objects and t-concepts: As stated before, unlike a bounded t-object or t-concept that transforms within contextual boundaries, such as boundaries of the quantum mechanics or logic, an unbounded t-object or t-concept that transforms without any contextual boundaries, i.e., transforms within the context of the whole universe to which it belongs, such as boundaries of the physical universe or the entire mathematics. In other words, an unbounded t-object or t-concept represents all possible objects or concepts that can be manifested in the corresponding universe respectively. Further, the general concept of ‘transforming-entities’ may be introduced as an entity that involve both t-object and t-concept properties, wherein unbounded t-entity may be defined as an entity that transforms within the context of all possible universes, thus involving both the existence and non-existence of everything. On the other hand, unlike an unbounded t-object or t-concept, the contextual boundaries of a bounded t-object or t-concept inherently and indirectly influences the context of the relation in the inequality αL≤S(TA˜B)/D≤αH. For example, consider the difference between a t-ball transforming in the context of all ball games and a t-ball that transforms in the context of only billiard games according to the rules set by the World Confederation of Billiards Sports—such as dimensional tolerances, colors, weight, etc. Such a difference with respect to self-induced contexts of t-objects or t-concepts inherently and indirectly influence the context of relations besides the context that the similarity comparison is executed. In conclusion, it may be deduced that the more self-induced context of a t-object or t-concept extends its boundaries the less influence it has on the context of a relation.
Referring again to the above comparison in the context of uncertainty representation: The former semantic relation further represents the degree of uncertainty between two objects or concepts based on their similarities within a certain context, wherein the degree of uncertainty is inversely correlated with α, i.e., degree of relation stated by the equality SA˜B/D=α. In short, as a increases the degree of uncertainty—which may be represented by μ—decreases, thus, α→1⇒μ→0. The latter semantic relation further represents the degree of ‘dynamic uncertainty’ between an object or concept and a t-object or t-concept based on their similarities within a certain context, wherein the dynamic uncertainty is induced by the t-object or t-concept, and the degree of dynamic uncertainty—which may be represented by Δμ—is inversely correlated with the length of the interval [αL, αH) or |αL−αH|, i.e., domain of the relation stated by the inequality αL≤S(TA˜B)/D≤αH,—which may be represented by Δα|α
S8.2) Similarity Inference in the Context of the Disclosed Ontological Model ‘Dynamically-Fuzzy Semantic Relations based on Analogies’—‘Generalized Similarity Inference Rule’:
Referring again to the ‘Similarity Fuzzy Semantic Networks and Inference’ [Castro et al., 2022] with respect to adaptation of the similarity inference formulated as the ‘Similarity Inference Rule’ to the disclosed ontological model: To recap, in the context of the disclosed ontological model, for each similarity relation there exists a context that defines the boundaries and the basis of the corresponding similarity relation. Said context may be any concept from the ‘uniform visual element identifier’ (UVEI) to the ‘rules of physics’—wherein the former conveys a conceptualization of a visual element based on a certain rule, while the latter conveys the physical properties of the universe. The concept of a context that provides a basis of comparison i.e., a reference-context, is a fundamental component of the disclosed ontological model—indeed it is logically impossible to compare the similarity of two things without any basis of comparison. Thus, a ‘reference-context’—or ‘sense’ as Castro et al., (2022) names it—constitutes the backbone of a relation based on analogies. Castro et al., (2022) proposes to include those to the inference mechanisms that are already being used for conventional semantic relations by considering analogical semantic relations—or ‘similarity semantic relations’ as they name it—also as second-order concepts—similar to that of the conventional semantic relations—and by defining the interrelationship among those relations—i.e., both analogical and conventional relations—as second order relations, i.e., meta-relations. In this context, according to Castro et al., (2022) the relation ‘relationS is-related-to: γ senseD’ is a meta-relation wherein ‘relationS’ is fuzzily related to ‘senseD’ in γ degree [Castro et al., 2022, Sec. 4.2]. Said meta-relation may be adapted to the disclosed ontological model as ‘relationS is-related-to: Δγ|γ
S8.3) Derivation of the ‘Generalized Similarity Inference Rule’ from the ‘Similarity Inference Rule’:
According to the Similarity Inference Rule, in the context of ‘Similarity Fuzzy Semantic Relations’, ‘{A is-similar-in-sense-D: αB; B relationS: βC; relationS is-related-to: γ senseD}→A relationS: (γ *t(α,β)) C’ wherein t is a t-norm [Castro et al., 2022]. Let {α, β, γ}≠0 or 1; α0=(α−ϵ), α1=(α+ϵ); β0=(β−ϵ), β1=(β+ϵ); and γ0=(γ−ϵ), γ1=(γ+ϵ). If the set of relational-degrees {α, β, γ}→(γ *t(α, β)) then {α0, β0, γ0}→(γ0*t(α0, β0)) and {α1, β1, γ1}→(γ1*t(α1, β1)) wherein each pair also corresponds to the intervals [α0, α1], [β0, β1], [γ0,γ1] respectively with width 2ϵ centered on the corresponding relational-degree in the domain [0, 1]; or equivalently corresponds to the relational-bandwidths Δα|α
S8.4) Conclusions with Respect to Comparison of the Disclosed Ontological Model with the State-of-the-Art and Disclosure of Additional Methods and Principles:
N.B.: In a conventional fuzzy semantic relation ‘A is-a: αB’, defining a as a variable such as a function of time does not automatically define A or B as a transforming entity. Thus, such modifications do not automatically convert the fuzzy semantic relation into a dynamically-fuzzy semantic relation. For example, A may be the cause of B in α0 degree initially but, as new evidence accumulates in time, α0 may become α1 and those changes may interpolate into a function of to time. Thus, what changes here is not the entities themselves, but the degree of uncertainty between them. However, defining α as a variable in an analogically fuzzy semantic relation ‘A is-similar-in-sense-D: αB’ automatically defines either A or B (or both) as a transforming entity, thus converts the ‘analogically fuzzy semantic relation’ into a ‘dynamically-fuzzy semantic relation based on analogies’. N.B.: A conventional fuzzy semantic relation involving dynamicity in the degree of relationship may be named as ‘variable-grade fuzzy semantic relation’—wherein the term ‘variable-grade’ denotes the variability or dynamicity in the degree of relationship—unless said fuzzy semantic relation does not involve any transforming entity.
Industrially implementing the envisioned Semantic Web Infrastructure for Superimposed Interactions (SWISI): The envisioned infrastructure (SWISI) promises to provide a steady and consistent interaction environment upon web-environments in exchange for the cost of the processing load on the system. Through the infrastructure both human and machine users may develop and deploy networks of intelligent modular sub-systems in the form of interoperable super-objects that dynamically manipulate web-objects to perform complex web-based tasks in complex and dynamic web environments in a stable and sustainable manner—irrespective of the complexity or dynamicity of alterations such as rapidly-developing radical transformations. In addition, users may further re-arrange those modular sub-systems in a multi-layered manner to enhance or customize the processes while interacting with already deployed sub-systems or interacting with each other through those sub-systems that act as network links. Consequently, providing such an infrastructure with such advanced capabilities may trigger an inevitable transformation of the entire Web—including its users—into an evolutionary system that inherently creates an infinite variety of constantly evolving fully-automated networks of modular sub-systems for multifarious industrial applications—which may be even considered as the informatical industrial revolution.
Comparison of the Superimposed Interaction Framework (SIF) with the current web-based interaction paradigms with respect to extracting semantics of web resources: The current paradigms are aiming on deciphering the semantics of the entire Web as much as possible by collecting data—such as by web crawling—and processing that data to extract the semantics of contents while mostly excluding the structural semantics of the interfaces that convey those contents—thus passing over the contextual semantics of the whole. In essence, this approach—although constitutes a proven strategy for search engines or similar processes—is not feasible in terms of establishing complex networks of interoperable automated agents that manipulate web resources especially due to the dynamic uncertainty induced by the chaotic nature of the resources—including the resources that are known to be absolutely static. However, Superimposed Interaction Framework (SIF) aims on deciphering the semantics of resources only during interactions iteratively in real-time in an object-aware manner, i.e., extracting the whole contextual semantics of a target web resource by dynamically revealing both the semantics of contents and the interfaces that convey those contents in real-time—such as identifying the role/function of each object including each functional interrelationship among those objects. In essence, this approach considers the Web not as an environment of documents but as an environment of complex and dynamic interfaces conveying complex and dynamic contents. This strategy inherently extents the scope of the framework from digital resources to physical resources therefore covering also the Internet of Things or the Internet of Everything.
Improving other web-based interaction paradigms by utilizing the envisioned Semantic Web Infrastructure for Superimposed Interactions (SWISI): An infrastructure provided to deliver on the promise of the SIF may further be utilized for the improvement of existing web-based interaction concepts. In essence, virtually any web-based interaction concept may benefit from the SIF especially in the context of dynamic uncertainty or problems induced in relation to dynamic uncertainty. Consider the concepts that have failed to reach their full potential from ‘web annotations’ to ‘user-script based augmented browsing’ or ‘decentralization’ to ‘API conformity’, SWISI may elevate those concepts to a super-dimension where they can reach their true potential, such as, in the form of ‘dynamic associations that establish adaptive task-oriented interactions between web-objects of uncoordinated remote servers’ or in the form of ‘interoperating super-objects that function as dynamic integration APIs to provide robust coalitions between web environments provided by unrelated—and even rival—servers’.
Expected impact of SWISI on the industry and society: Ensuring such an interaction capability may have an enormous impact on the industry resulting in shifts from the current interaction paradigms—therefore making the disclosure a disruptive technology. For example, in addition to the fully automatic deployment and maintenance facilities that it may provide, the envisioned infrastructure may also provide a low-code/no-code application development environment where layman users—besides expert users—may develop, arrange, and deploy—maintenance-free—intelligent modular sub-systems. Thus, the infrastructure may pave the way for the development of an enormous variety of unprecedented modular sub-systems created by masses. For example, any component of a website may be modified in order to improve the corresponding services. Or, components of even rival websites may be virtually integrated to facilitate incoordination, such as for real-time data extraction or mining. Or, a real-time evaluation and signalization mechanism may be formed, where existing or circulating contents are automatically identified, marked and flagged to indicate the presence of a remarkable condition—such as fake, suspicious or doctored news—wherever they appear. N.B.: For example, users may further involve into the discussion about a flagged content by either supporting or challenging the evidence provided by the automated agent or interact with each other about that content right on the spot—such as by posting or commenting their arguments—, regardless of technical and structural limitations or limitations imposed through administrative regulations—such as unfair moderation or censorship—of the websites where the suspicious or controversial content appears. Consequently, it may be realistic to assume that in the long run the envisioned infrastructure may even result on a shift from the current social interaction paradigms. For example, since the envisioned infrastructure (SWISI) inherently provides users with the ability to connect and interact with each other through any content of any web-resource—that exists or connected to the internet including physical resources of IoT—, the whole Web may function as a global platform where independent decentralized social networks may be ignited wherever and whenever needed. Therefore, the infrastructure may pave the way for web-wide social networking without borders and thus may even make the existing centralized monopolistic platforms, such as Facebook™, Twitter™, Instagram™ etc., redundant in the long run, by extending the borders of interaction far beyond their scope. All in all, a totally new industry may thus be formed that is capable of creating unprecedented radical solutions, while autonomous environments flourish within the initiative of users. Last but not least, employing such an infrastructure may not only pave the way for the emergence of radical solutions that may transform the industry, but also may pave the way for the emergence of autonomous environments that may transform the society. Through such environments, ordinary internet users may become publishers and broadcasters anywhere on the Web, or may become web developers and service providers throughout the Web, or may just become nodes of a web-wide social network without borders—and echo chambers.
Ethical, legal, and fair use issues of industrial implementations: In essence, an infrastructure constructed based on the methods and systems described herein may provide unparalleled capabilities to its users. In this context, some user generated applications may exceed the limitations dictated by the laws of the regions where they are applied. To eliminate any ethical and legal problem that may arise on behalf of the infrastructure provider, the envisioned infrastructure may administratively operate similarly to an ‘application infrastructure provider’ (AIP). In this context, the infrastructure acts as an intermediary only (similar to that of web hosting or cloud service providers), where all responsibility regarding services provided through the infrastructure may be on developers and/or service providers both legally and ethically. Accordingly, all kinds of violations and their consequences including legal sanctions may solely bind the developers and/or service providers. Nevertheless, the infrastructure provider may still impose restrictions in order to regulate the services to be provided by developers and/or service providers within the scope of regional laws and universal ethics including ethics in AI.
Note1: Within the scope of SIF, even a simple static web page has an enormously rich interactivity potential regardless of its popularity. Such a development may truly liberate social interactivity from the bounds of social media websites by providing an unbounded social interaction alternative webwide and thus may deeply affect websites/webpages that are currently idle, especially in terms of an increase in demand. Indeed, it may be questioned that ‘why someone should express himself or herself on a 3rd party intermediary platform about a subject, instead of expressing himself or herself right on the source of the subject matter when applicable’. Note2: Employing SWISI may further influence various fields of science and technology with also the idiosyncratic ‘big data’ that it may collect and accumulate. For example, SWISI may automatically collect complete data/information for each interaction process within the system, such as complete data/information about each software development and deployment process including the processes by which users interact with these applications, thus may pave the way for the accumulation of a big data of comprehensive processes. Note3: SWISI may further utilize deep learning and said collected big data of comprehensive processes for creating and conceptualizing content and application ideas by itself.
Example Cases with Respect to SWISI:
Introduction: The example cases below—which are presented through hypothetical developer users—may be realized through an infrastructure (SWISI) provided for both individual and corporate enterprises who may develop and deploy their own applications to provide services within the scope of the interaction options of SIF. As mentioned above, the infrastructure may be operated in a similar way to an application infrastructure provider (AIP) such as in the form of a cloud service that provides the computing and operational infrastructure for developing, deploying, and managing enterprise class applications, while the infrastructure provider is exempted from legal and ethical liability related to the activities of individual or corporate service providers. N.B.: The below cases must be considered mostly as simple, straight forward examples with respect to the implementation of SCI and SWISI.
Example Case for Evaluation of Information: The Wikipedia™ website provides an internal evaluation mechanism by which wiki users may evaluate any bit of information e.g., a word, a sentence, a paragraph, a picture, a video, an audio, etc., of a wiki article right on the spot, by marking, linking, reporting, annotating, opening up a discussion, offering an alternative etc. In this context, Wikipedia™ may develop and deploy its own ‘Superimposed Wikipedia Application’ through the envisioned infrastructure in order to extend this particular service webwide and enable wiki users to evaluate any information on any web page right on the spot. Similarly, fact checking organizations like BBC, AFP, Le Monde™, etc. may develop and deploy their own ‘Fact Checking Superimposed Applications’ through the envisioned infrastructure for automatically detecting suspicious news items anywhere on the web (e.g., detecting their source pages, circulating link previews, quotations, etc.) and automatically flagging wherever they appear, including highlighting the disputable fragments and opening them up for public discussion and expert evaluation right on the spot. Similarly, a superimposed application for criticism may be developed and system users may criticize all kinds of information existing/circulating at the web via superimposed critical posts whose scoring and ranking may be based on quality, e.g., critical thinking, rather than on quantity, e.g., popularity. In such a mechanism, ‘critical thinking’ may be defined as an ‘intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action.’ In this context, publishers may be expected to create posts conforming to the critical thinking criteria where they support analytical, positive, or negative criticism by concrete evidence. Such a mechanism may also contribute greatly to the dissemination of critical intelligence and skepticism. Uncensored analytical criticism directly at the very source may reduce social/massive reaction time and increase the quality feedback, thus enhancing overall intelligence.
Example Case for Social Networking: Social network platforms, such as Facebook™, enables users to create and publish their own interactive posts. A post appears on the author's Facebook page, while circulating on the news feed of other users' Facebook pages. In this context, Facebook™ may develop and deploy its own ‘Superimposed Facebook Application’ through the envisioned infrastructure in order to extend this particular service web wide and enable Facebook users to publish interactive posts in relation to any information on any web page right on the spot. For example, a Facebook user may initiate a discussion on an article of the constitution on the website of the Supreme Court of the United States or criticize a portion of a research paper published on the website of Harvard Medical School. Further, users may interact and socialize with each other through these posts thus they may build a web-wide social network without borders. In this context, the noticeability of users—who provide content on the Web—may be greatly enhanced. For example, an able critic may get noticed by millions—at the very source of the subject matter or information—by associating his/her own article on top of the article of a popular columnist in a global news website, e.g., New York Times™, Le Monde™, Die Welt™ etc. If liked, the user may gain followers and may be able to expand his/her presence worldwide through his/her own superimposed network. Similarly, any other social networking website like Twitter™, Instagram™, Reddit™, etc. or any online video platform like YouTube™, Vimeo™, Dailymotion™, etc., may develop and deploy its own ‘Superimposed Application’ through the envisioned infrastructure to extend its services radically, such as enabling its users to become publishers or broadcasters anywhere on the Web right on the spot.
Example Case for Improvement of Services: Enterprise service provider ESPA™ develops browser extensions, plugins, addons and various other applications for improvement of services and integration of websites. For some cases ESPA™ utilizes integration codes—such as APIs—provided by the websites. However, for most of the cases ESPA™ cannot utilize such codes since either they do not meet the needs, or they do not exist. In such cases experts of ESPA™ manually interpret each target web page to be processed/manipulated and develop tailored applications in order to provide the targeted services. In the ongoing process, whenever a serviced web page is altered contextually and/or structurally, a maintenance expert manually interprets the alterations of that page and adapts the corresponding application/s according to the alterations. Because of all these inadequacies, ESPA™ is able to offer only relatively simple applications with limited competence. However, ESPA™ may develop, deploy, and manage any application through the envisioned infrastructure in order to semi-automatically develop and automatically deploy said applications and automatically maintain the services that they provide, without the need of integration APIs. Further, by the competencies gained including the contextual adaptation ability with the automation provided by the envisioned infrastructure, ESPA™ may expand the scope of the services and develop much more competent applications. Besides, instead of tailored applications that are designed particularly for target web pages, ESPA™ may develop smart applications that are designed to fit similar websites contextually. For example, ESPA™ may develop a ‘one size fits all’ application package for online video platforms (e.g., YouTube™, Vimeo™, Dailymotion™, etc.) such that the pack comprises an application that manipulates video player interfaces for improvement of their features (e.g., by integrating an effects filter, a 2D to 3D convertor, a video editor for creating and sharing audio-visual commentaries, a star rating system for evaluating video content, etc.) including visually modifying the interfaces by integrating virtual controls (e.g., adding control buttons, displays, etc.), and another application that manipulates link previews of recommended videos (e.g., for displaying star ratings of corresponding videos). Moreover, ESPA™ may introduce non-generic dynamic interfaces such as lists, tables, forms, etc. to the system according to their roles/functions. Thus, ESPA™ may develop and deploy particular applications that manipulate and/or utilize system-defined unique interfaces for improvement of services and/or integration of websites. For example, ESPA™ may develop an application that automatically collects data from bus, railway, and airline websites to provide users with better route alternatives (e.g., the cheapest or the fastest route combination) without the need for any manual or semi-automatic maintenance against any kind of alterations made by said websites. Further, ESPA™ may share these alternatives directly on said websites instead of presenting them on an external website, and even may create a second-hand online ticket market upon them for users who want to sell/buy already purchased tickets.
Example Case for Industrial Processes: Boeing Corporation utilizes various automation tools for various processes such as tools for Supervisory Control and Data Acquisition (SCADA), Manufacturing Resource Planning (MRP-II), Enterprise Resource Planning (ERP), etc. Most of these tools are centralized and server-based computing systems, accordingly, some of them are web-based technologies. In this context, Boeing may deploy the system for its own intranet and make the system work on web-based automation systems, thus may enable employees to associate/integrate contents and features within certain limitations in accordance with these systems and corresponding processes. For example, both blue-collar and white-collar employees—such as manual workers, technicians, engineers, researchers, etc.—from various fields and units can be encouraged to review, criticize, and improve functions of the corresponding automation systems in accordance with their field of expertise—within certain limits. For example, a technician—such as an aircraft structures technician working in the fuselage construction process—may criticize a certain function of the automation system that operates in his/her field of duty—such as a poorly designed interface of an MRP-II or SCADA system. And in response to that criticism, a colleague of his/hers—such as an aerospace engineer working in the same process—may develop and deploy an application that improves the interface in the context of that criticism. Thus, employees may autonomously contribute to the improvement and optimization of the processes by developing and deploying sub-systems that are virtually integrated into the automation systems, without any direct interference and completely independent of software developers. In the long run, accepted and useful applications may be permanently embedded in the system by the software developers. In addition, for industrial processes, strict limits may be set accordingly, so that interactions do not put production processes at risk.
Example Case for E-commerce: Individual entrepreneurs and software developers Ayse and Levent may develop, deploy, and manage an application that provides a superimposed e-commerce network on web-resources. For example, via the application, posts that includes goods or services in accordance with the context of web pages and/or web-objects may be published, such as by e-commerce websites or local businesses. For example, a post may be published in relation with a service presented in corporate websites (e.g., IBM™, Microsoft™, Google™, etc.), or in relation with a product existing in major e-commerce websites (e.g., Amazon.com, Alibaba.com, Walmart.com, etc.). Thus, small, and medium scale service providers or e-commerce enterprises including local businesses may compete against large scale major/global corporations virtually through the superimposed e-commerce network provided by the infrastructure. Further, system users themselves may become sellers of goods and providers of services anywhere on the web. For example, a local micro business or an individual seller may offer the advantage of possessing a niche product or proposing a niche service or offering a fast & free delivery option—which can be unbeatable when compared to the base e-commerce website (e.g., Amazon.com), thus, the target user may prefer the local business or individual seller. In general, a superimposed e-commerce network may pave the way for fair competition of the small to medium scale enterprises against large scale e-commerce websites worldwide. Also, services regarding e-commerce websites that are provided by independent websites (e.g., fake review analyzing engines/services) may be integrated by the application to the target websites. Thus, Ayse and Levent may create a superimposed e-commerce platform serving to Web users all over the world.
Example Case for Entertainment: Individual entrepreneurs and software developers Lea and Deniz (whom the inventor of the disclosed technology loves more than anything in the universe) may develop, deploy, and manage an application that provides a superimposed entertainment platform, by which both an infrastructure for superimposed games may be provided and a superimposed network that is fully devoted to entertainment may be set up. For example, objects of web pages that contain images may be transformed into virtual graffiti fields through the application so that network users may process/manipulate images if they wish so. Or more interestingly, various intelligent superimposed video games may be designed and developed based on the system's interpretation of web-objects of web-resources, such as an animated character, e.g., Pac-Man™, may digest the ASCII characters of a governmental site within the context of a proposed law while strolling on the web pages and the player (e.g., a government officer, a government sympathizer, etc.) may try to recover the digested ASCII characters. Thus, Deniz (who had motivated the inventor in terms of theorizing the concept of t-objects and t-concepts) and Lea (who has provided the inspiration the inventor needed in his worst times to carry on) may create a superimposed gaming platform serving to Web users all over the world.
Comparison of Super-Objects with Web Annotations in the Context of Establishing an Association Between Two Objects:
Since SWISI may also provide an infrastructure for operations with respect to web-annotations, it may be useful to compare the current paradigm of web-annotations with the disclosed super-object model. According to the World Wide Web Consortium (W3C) standards, a web annotation is a set of connected resources—typically including a body and target—and conveys that the body is related to the target. In essence, a web annotation conveys the information that a particular object is related to another particular object in an object-based manner. Unlike a web annotation, a super-object conveys a model of a relationship—including the reason of that relationship—between a body and a root-object in an object-oriented manner—i.e., involving abstraction, inheritance, polymorphism and encapsulation—to be used as a reference to establish dynamic associations during encounters with similar objects. N.B.: Of course, along with other advanced features that are provided in the context of the disclosed ontological model ‘Dynamically-Fuzzy Semantic Relations based on Analogies’.
More concretely, a super-object conveys: i) a reference for an analogy—generated in the context of the task of said super-object—comprising one or more of the attributes, such as functions and other distinctive properties, of a root-object that is used to identify similar web-objects to be associated with—in the context of matching within a relational-bandwidth; and ii) a set of instructions—developed in relation to the root-object—for dynamically processing analogous web-objects—including predefined rules to be used in the process of determining the adaptation procedures. In practice, for example, unlike web annotations, since there is no target object to search for, there are no residual super-objects or—more importantly—false associations and executions. Such instabilities are inherent in the paradigm of web annotations, for example in the form of false annotations or orphan—i.e., residual—annotations due to the object-based searching, such as in between DOM states.
In conclusion, determination of conditions for maintaining or terminating an association between two objects is a multi-layered ontological problem. Web annotations are useful only within environments where the mutability does not exceed the capability of the object-based methodologies. In contrast, super-objects do not rely on object-based methodologies such as object-based links, adaptation and manipulation, rather, relies on object-aware analogue links and object-aware adaptation and processing—possibly a much more natural ‘human brain like’ linking and adaptation mechanism. When compared with the state-of-the-art, due to the adaptive nature of said methodology, embodiments do not require web crawling, scraping, or archiving web resources; keeping track of states and activities; recording and re-constructing intended previous versions of document object models to identify the correct representations, etc. Furthermore,—and more interestingly—because a model of a relationship—between a body and a root object—is used as a reference to establish associations during encounters with similar objects, any segment identifier, e.g., URIs of objects, may be totally excluded from the process of establishing and maintaining associations. Furthermore, in theory, even the URLs of web pages or websites may be eliminated and thus the conventional resource identifiers may be totally ignored.
Brief Summary of the Processes that are Involved in the Parent—Gen1—patent (U.S. Pat. No. 11,625,448) and the Following Child—Gen2—Continuation-in-Part patent application (application Ser. No. 18/178,382) that Constitute the Basics of the Grandchild—Gen3—Continuation-in-Part patent application (patent Ser. No. 18/583,521) and the Following Present—Gen4—Continuation-in-Part patent application:
In essence, the disclosed methods and systems—in Gen1&2—mainly focuses on controlling the interactions between super-objects and web-objects by identifying ‘what exactly the objects of web resources are’ at a contextual level and determining ‘how super-objects may interact with those objects in the context of a given task’ in real-time. The two critical phases in this process are i) the establishment of associations between super-objects and web-objects, and ii) the execution of instructions conveyed by super-objects with respect to said associations respectively. The former phase involves the challenge of establishing associations with dynamically uncertain alternating or transforming resources while the latter phase involves the challenge of adapting the initial executional procedures to the alterations or transformations of those dynamically uncertain resources.
According to the embodiments in Gen1&2, during encounters with web-resources in environments such as webpages, the system analyzes the resources contextually, e.g., according to roles/functions and their interrelationship, and identifies ‘each elementary object’ and ‘each cluster of interrelated elementary objects in rank order’ while assigning each singular elementary object and each cluster of interrelated elementary objects an analogue reference that is structured in accordance with the predefined contextual references, e.g., an UVEI for each singular elementary object and a UCFI for each cluster of interrelated elementary objects respectively. In order to initiate a new association between a super-object and a selected web-object, the system encapsulates the analogue reference of a selected web-object in the corresponding super-object and stores that super-object—for example—in a database.
In order to re-establish already existing initiated associations, once an interacted web resource, e.g., a webpage, is revisited and/or subsequently rendered and/or altered in a such way that that such activity is detected, the system re-executes the entire process of analyzing, clustering and referencing in order to compare previously recorded analogue references, e.g., rUVEIs and rUCFIs,—that are encapsulated within the super-objects—with recently extracted analogue references of web-objects, e.g., nUVEIs, to identify similarities within predefined similarity thresholds in the context of the predefined structure of said analogue references, e.g., UVEI/s or UCFI/s. Once a condition is satisfied then the system establishes the association for the corresponding super-object—web-object pair—wherein the web-object may be a version of the initially associated—original—object or an analogous object that fulfil the corresponding condition, according to the embodiments.
Following the establishment of an association or associations, the disclosed system adapts the each of the instruction sets that are developed for the initial state to the recent state of the associated web-objects by identifying and interpreting the differences between the conveyed analogue references and recently obtained analogue references in the context of the execution of the corresponding tasks and modifying, and executing each instruction set accordingly. N.B.: Both disclosures Gen1&2 are also included in the disclosure Gen3 and this present disclosure in full.
Terms in the Context of Web-Objects: i) An ‘elementary visual object’ corresponds to a ‘visual element’, in this context, a ‘meaningful cluster of elementary visual objects’ (i.e., a ‘cluster of interrelated elementary visual objects’) corresponds to a ‘cluster of interrelated visual elements’ and a ‘web-object’ corresponds to either an ‘elementary web-object’ or a ‘meaningful cluster of elementary web-objects’ (i.e., a ‘cluster of interrelated elementary web-objects’). An ‘elementary object’ corresponds to an ‘element’, in this context, a ‘meaningful cluster of elementary objects’ (i.e., a ‘cluster of interrelated elementary objects’) corresponds to a ‘cluster of interrelated elements’ and an ‘object’ corresponds to either an ‘elementary object’ or a ‘meaningful cluster of elementary objects’, i.e., a ‘cluster of interrelated elementary objects’. N.B.: Conceptually, a ‘meaningful cluster of elements’ is postulated to be identical to a ‘cluster of interrelated elements’ based on the assumption that interrelationship reveals meaningfulness. ii) An object may be any digital or physical object including their combinations. For example, an object may be a digital object that contains data and code, wherein data may be in the form of fields (i.e., attributes or properties), and code may be in the form of procedures (i.e., methods)—as in object-oriented programing. Or an object may be a physical object that contains only physical elements, such as a hammer, a table, a revolver, etc. Or an object may contain both mechanical elements and digital elements, such as an embodiment of a physical system comprising a digital control system (e.g., an electric vehicle, a jet engine, a nuclear fusion reactor, etc.). iii) An ‘interacted object’ or a ‘previously interacted object’ is an ‘object that is associated with one or more super-objects’. iv) Although digital web-objects are usually components of websites/web pages, they can also be digital components of physical web-objects, such as a program embedded in a robot that is connected to the IoT.
Terms in the Context of Super-Objects: i) ‘User generated contents’ and ‘user generated software applications’ are ‘user inputs into the system’ referred to as ‘user input’ or ‘super-object’. ‘User input’ and ‘super-object’ are terms that have the same meaning and used interchangeably in the specification, drawings, etc., unless indicated otherwise. User inputs, i.e., super-objects, are user generated contents or user generated software applications that are associated with singular elementary objects and/or meaningful clusters of elementary objects. ii) The interaction cycle of a super-object with a non-interactive web-object can be exemplified as follows: Consider a static and non-interactive web page that conveys a—static and non-interactive—content for web users. Assume that some of these users are reacting to said content via super-objects. Further, assume that the reacted web page itself is now reacting to those super-objects either via/through super-objects or by altering its content and so forth. iii) Super-objects may be developed by human users, or machine users, or by the system itself, or any combination thereof collectively. iv) A ‘user input’ should not be confused with web page activity such as detecting a pointing device input on a visual element field, clicking a button with the pointing device, operating a pull-down menu, and/or other operations by a user on a rendered web page and/or visual element that do not involve user generated input. However, said web page activity or any kind of user activity—including interpretations of physical activity, such as sentiment analysis of users—may also be collected, encapsulated, and used as data inputs with respect to a super-object. Furthermore, in principle, any kind of input, such as live camera streams or signal outputs of industrial sensors, may be used to generate user generated contents or used as data inputs and/or references for user generated software applications.
Terms in the Context of Superimposed Interactions: i) A ‘user interaction with a web-object’ is the user's input to the system as a result of the process of generating and associating a super-object with that web-object such as all executable and non-executable content developed, arranged, linked, enclosed, etc. by the user including all information created by the system with respect to that user input, i.e., super-object—unless indicated otherwise. ii) ‘Superimposing’ is defined herein as the combined process of (a) associating a super-object with an element or a cluster of elements of a web-resource; (b) identifying a previously interacted ‘element or a cluster of elements’ or a suitable analogue of that ‘element or cluster of elements’; (c) re-associating the corresponding super-object; and (d) executing that super-object according to the corresponding element or cluster of elements while adapting the executional procedures to the current state of the initially interacted ‘element or a cluster of elements’ or the suitable analog of that initial ‘element or cluster of elements’. iii) Superimposed Interactions or formerly named as Superimposed Communication is defined herein as the interactions with respect to superimposing, and ‘Superimposed Networking’ is defined herein as the networking based on superimposing. iv) A superimposed application is a cluster of one or more super-objects developed to execute a certain web-based task. Similarly, a superimposed content is a cluster of one or more ‘passive’ super-objects. N.B.: Because interaction with—only—digital web-objects exclude physical web-objects, the remaining interactable web-resources in this context are assumed to be websites/web pages or contents or components of websites/web pages of some sort even if they are not. v) The previously used term ‘Superimposed Communication’ is replaced with the term ‘Superimposed Interaction’ due to the fact that the disclosed methods and systems—including the parent patented application—are about interactions beyond communications. Nevertheless, both terms have the same meaning and can be used interchangeably. vi) A superimposed application—which is also a user generated software application associated with a web-object—may be a structure comprising one or more super-objects. Similarly, a superimposed content—which is a user generated content associated with a web-object—is a special case of a superimposed application that has a null set of instructions. Thus, a superimposed content is also a super-object or more precisely, a ‘passive super-object’.
Terms in the Context of General Concepts: i) The term ‘context’ is used in the meaning of ‘the interrelated conditions in which something exists or occurs’ (Ref: Merriam-Webster Dictionary) or ‘the circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood and assessed’ (Ref: Oxford Languages). In this regard, the term ‘contextual’ is used in the meaning of ‘about context’, but also in the meaning of ‘content wise’, ‘related to content’, etc. ii) The terms: ‘identifying’, ‘determining’, ‘extracting’, ‘detecting’, etc. have been used in a similar sense and may be used interchangeably unless indicated otherwise. iii) The term ‘analogy’ is used in the meaning of ‘a correspondence or partial similarity’ or ‘a comparison between one thing and another’. The term ‘analogue’ is used in the meaning of ‘a thing seen as comparable to another’ and depending on the context also used in the meaning of ‘representation by a continuously variable quantity instead of binary’. iv) The terms ‘property’ and ‘attribute’ are used synonymously, meaning ‘a quality or feature regarded as a characteristic or inherent part of someone or something’ v) The term ‘informatical’ is used in the meaning of ‘related to or based on representation and processing of information’ which is proposed herein as a more general alternative to the term ‘digital’ in the context of universes—or elements of those universes—wherein the information may not be represented in forms of binary numbers. In this context, for example, a digital-object is an informatical-object, however, an informatical-object may be or may not be a digital-object. vi) An information space is the set of concepts, and relations among them, held by an information system. vii) The previously used term ‘relationships’ is replaced with the term ‘relations’ due to grammatical preferences. Nevertheless, both terms have the same meaning and can be used interchangeably. viii) The term ‘alteration’ is used in the meaning of any kind of change such as adjustments, modifications, transformations, additions, deletions, etc., that may occur progressively or abruptly both contextually and structurally. Ix) The system may provide its own APIs developed to communicate with ‘machine users’ (i.e., other computer systems), thereby enabling machine users to have the capabilities available to ‘human users’ and beyond.
The methods, devices, processing, circuitry, and logic described above may be implemented in many different ways and in many different combinations of hardware and software. For example, all or parts of the implementations may be circuitry that includes an instruction processor, such as a Central Processing Unit (CPU), microcontroller, or a microprocessor; or as an Application Specific Integrated Circuit (ASIC), Programmable Logic Device (PLD), or Field Programmable Gate Array (FPGA); or as circuitry that includes discrete logic or other circuit components, including analog circuit components, digital circuit components or both; or any combination thereof. The circuitry may include discrete interconnected hardware components or may be combined on a single integrated circuit die, distributed among multiple integrated circuit dies, or implemented in a Multiple Chip Module (MCM) of multiple integrated circuit dies in a common package, as examples.
Accordingly, the circuitry may store or access instructions for execution, or may implement its functionality in hardware alone. The instructions may be stored in a tangible storage medium that is other than a transitory signal, such as a flash memory, a Random Access Memory (RAM), a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM); or on a magnetic or optical disc, such as a Compact Disc Read Only Memory (CDROM), Hard Disk Drive (HDD), or other magnetic or optical disk; or in or on another machine-readable medium. A product, such as a computer program product, may include a storage medium and instructions stored in or on the medium, and the instructions when executed by the circuitry in a device may cause the device to implement any of the processing described above or illustrated in the drawings.
The implementations may be distributed. For instance, the circuitry may include multiple distinct system components, such as multiple processors and memories, and may span multiple distributed processing systems. Parameters, databases, and other data structures may be separately stored and managed, may be incorporated into a single memory or database, may be logically and physically organized in many different ways, and may be implemented in many different ways. Example implementations include linked lists, program variables, hash tables, arrays, records (e.g., database records), objects (of any kind), and implicit storage mechanisms. Instructions may form parts (e.g., subroutines or other code sections) of a single program, may form multiple separate programs, may be distributed across multiple memories and processors, and may be implemented in many different ways. Example implementations include stand-alone programs, and as part of a library, such as a shared library like a Dynamic Link Library (DLL). The library, for example, may contain shared data and one or more shared programs that include instructions that perform any of the processing described above or illustrated in the drawings, when executed by the circuitry.
In some examples, each unit, subunit, and/or module of the system may include a logical component. Each logical component may be hardware or a combination of hardware and software. For example, each logical component may include an application specific integrated circuit (ASIC), a Field Programmable Gate Array (FPGA), a digital logic circuit, an analog circuit, a combination of discrete circuits, gates, or any other type of hardware or combination thereof. Alternatively, or in addition, each logical component may include memory hardware, such as a portion of the memory, for example, that comprises instructions executable with the processor or other processors to implement one or more of the features of the logical components. When any one of the logical components includes the portion of the memory that comprises instructions executable with the processor, the logical component may or may not include the processor. In some examples, each logical components may just be the portion of the memory or other physical memory that comprises instructions executable with the processor or other processor to implement the features of the corresponding logical component without the logical component including any other hardware. Because each logical component includes at least some hardware even when the included hardware comprises software, each logical component may be interchangeably referred to as a hardware logical component.
A second action may be said to be “in response to” a first action independent of whether the second action results directly or indirectly from the first action. The second action may occur at a substantially later time than the first action and still be in response to the first action. Similarly, the second action may be said to be in response to the first action even if intervening actions take place between the first action and the second action, and even if one or more of the intervening actions directly cause the second action to be performed. For example, a second action may be in response to a first action if the first action sets a flag and a third action later initiates the second action whenever the flag is set.
To clarify the use of and to hereby provide notice to the public, the phrases “at least one of <A>, <B>, . . . and <N>” or “at least one of <A>, <B>, . . . <N>, or combinations thereof” or “<A>, <B>, . . . and/or <N>” are defined by the Applicant in the broadest sense, superseding any other implied definitions hereinbefore or hereinafter unless expressly asserted by the Applicant to the contrary, to mean one or more elements selected from the group comprising A, B, . . . and N. In other words, the phrases mean any combination of one or more of the elements A, B, . . . or N including any one element alone or the one element in combination with one or more of the other elements which may also include, in combination, additional elements not listed.
While various embodiments have been described, it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible. Accordingly, the embodiments described herein are examples, not the only possible embodiments and implementations.
This application is a continuation-in-part of U.S. patent application Ser. No. 18/583,521 filed Feb. 21, 2024, which is a continuation-in-part of U.S. patent application Ser. No. 18/178,382 filed Mar. 3, 2023, which is a continuation-in-part of U.S. patent application Ser. No. 16/886,265 filed May 28, 2020, now U.S. Pat. No. 11,625,448, all of which are hereby incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 18583521 | Feb 2024 | US |
| Child | 19005625 | US | |
| Parent | 18178382 | Mar 2023 | US |
| Child | 18583521 | US | |
| Parent | 16886265 | May 2020 | US |
| Child | 18178382 | US |
