Patent Application
20200401908
This disclosure relates generally to generating context-aware recommendations.
Accurately identifying a given user of communal devices used concurrently by multiple users (e.g., a television) or shared by multiple users at different times (e.g., a household personal computer) at any given time is difficult. This difficulty may have several causes, such as for example, a possible reluctance of the user to sign in or various challenges that accompany particular user identification methods like facial recognition or voice identification. Content recommendation approaches are generally based on trends per geolocation, timeslot (time-band), or day of the week. Since conventional content recommendation systems are generally unable to distinguish between individual users of a communal device, an individual user's preferences may not be adequately captured.
Embodiments described herein relate to a knowledge-graph recommendation system for generating recommendations of media content based on personalized user contexts and personalized user viewing preferences. Rather than requiring identification of the particular users of a communal or shared device, the embodiments described below identify or classify the current user behavior generating the behavior captured by a set of user activity logs (e.g., automatic content recognition (ACR) events). As example and not by way of limitation, the knowledge-graph recommendation system may generate a prediction of media content that may be of interest to users of communal devices based on observed particular user viewing preferences, such as for example, for a television (TV) program of a particular genre, airing or “dropping” on or about a particular day, and at or about a particular time-band. As described below, knowledge graphs represent a range of user facts, items, and their relations. The interpretation of such knowledge may enable the employment of user behavioral information in prediction tasks, content recommendation, and persona modeling.
The knowledge-graph recommendation system is a personalized and context-aware (e.g., time- and location-aware) collaborative recommendation system. In particular embodiments, the knowledge-graph recommendation system provides personalized experiences to communal device users that convey relevant content, increase user engagement, and reduce the time to entertainment, which can be achieved by understanding, but not necessarily identifying, the people using the communal device. At times, accurately identifying the user behind the screen presents several challenges due to the possible reluctance of the user to log into an account and/or a lack of availability for user identification (e.g., facial recognition and/or voice identification can be lacking).
In particular embodiments, the knowledge-graph recommendation system includes a pipeline to aggregate events and metadata stored in one or more activity (ACR) logs and to build a graph schema (e.g., a user knowledge graph) to optimally search through this content. In particular embodiments the knowledge-graph recommendation system may apply machine learning (ML) to the graph to train an ML model to describe the behavior of the users and predicts the best program recommendation given the user's contextual information, such as geolocation, time of the query, and/or user preferences, etc. Further, in particular embodiments the knowledge-graph recommendation system provides highly personalized content recommendations that capture community collaborative recommendations as well as content metadata recommendations.
While the present embodiments may be discussed primarily with respect to television-content recommendation systems, it should be appreciated that the present techniques may be applied to any of a number of recommendation systems that may facilitate users in discovering particular items of interest (e.g., movies, TV series, documentaries, news programs, sporting telecasts, gameshows, video logs, video clips, etc. that the user may be interested in consuming; particular articles of clothing, shoes, fashion accessories, or other e-commerce items the user may be interested in purchasing; certain podcasts, audiobooks, or radio shows to which the particular user may be interested in listening; particular books, e-books, or e-articles the user may be interested in reading; certain restaurants, bars, concerts, hotels, groceries, or boutiques in which the particular user may be interested in patronizing; certain social media users in which the user may be interested in “friending”, or certain social media influencers or content creators in which the particular user may be interested in “following”; particular video-sharing platform publisher channels to which the particular user may be interested in subscribing; certain mobile applications (“apps”) the particular user may be interested in downloading; and so forth) at a particular instance in time.
Activity database 110 may store ACR data that includes recorded events containing an identification of the recently viewed media content (e.g., TV programs), the type of event, metadata associated with the recently viewed media content (e.g., TV programs), and the particular day and hour (e.g., starting-time timestamp or ending-time timestamp) the recently viewed media content (e.g., TV programs) was viewed. In particular embodiments, activity database 110 may further include user profile data, programming genre data, programming category data, programming clustering category group data, or other TV programming data or metadata. As an example and not by way of limitation, the ACR events stored in activity database 110 may include information about the program title, program type, program cast, program director as well as device geolocation, device model, device manufacturing year, cable operator, or internet operator. In particular embodiments, the time-band information also be enriched by other external sources of information that are not necessarily part of the ACR logs like census demographic information or statistics from data collection and measurement firms.
In particular embodiments, the ACR events may be expressed by content that is consumed (e.g., presented to a viewer) during a set of time-bands (e.g., 7 time-bands/day). This may be especially appropriate for broadcast programming where “dayparting” is the practice of dividing the broadcast day into several parts and in which different types of radio or television program typical for that time-band is aired. For example, television programs may be geared toward a particular demographic and what the target audience typically consumes at that time-band. Herein reference to a time-band may encompass the information associated with a part of a day and a day of the week, where appropriate. In particular embodiments, the maximum number of time-bands per device is 7 days in a week and 7 time-bands per day for a total of 49 time-bands. As an example and not by way of limitation, ACR events may denote “Monday at prime-time” as the name of a particular time-band and the information is the set of ACR logs recorded during that time-band.
In particular embodiments, graph module 115 may receive the ACR user observed viewing input of recently viewed by a particular user stored on activity database 110. As described in more detail, graph module 115 may transform the ACR event data stored on activity database 110 to a knowledge graph that represents the relations between concepts, data, events, and entities. In particular embodiments, graph processing module 120 may access the knowledge graph generated by graph module 115 to partition and process the knowledge graph into subgraphs and for use in training ML model 125. In particular embodiments, ML model 125 is configured to generate data (e.g., embeddings vector(s)) to represent all the entities present in the ACR logs (e.g., devices, programs, metadata, or location) stored on activity database 110 into an embedding space (e.g. n-dimensional Euclidean space), as described in more detail below. Embeddings extraction module 130 may take the output of ML model 125 and determine a representation of the behavior of devices across the entire knowledge graph. The representation of the behavior of devices from embeddings extraction module 130 may be stored in user graph database 135.
The use of sliding window 202 addresses two different issues. First, user behavior may change over time, and second, there may be insufficient ACR data associated with a particular time-band for the ML model to properly infer a pattern to best describe behavior associated with a particular communal device. As an example and not by way of limitation, if the data analysis is performed using the entire historical data, an introduction of noise to the dataset may result and the data analysis may consider behavioral patterns that might no longer be relevant to the users. As another example, the set of ACR events associated with a particular time-band is a signal that may be used to infer the preferences of users of a communal device and the strength of this signal may depend on the number of events and the duration of the events. If the data analysis only accounts for a relatively small sample (e.g., one week of ACR events), training the ML model may produce results that are unreliable or that inaccurately models the behavior associated with the communal device.
The resolution or granularity of the ACR data aggregation (e.g., 208) may depend on the aspects of the behavior of the communal device that should be considered. As an example and not by way of limitation, for TV content consumption behavior, the data provided to the graph module may include ACR data aggregations (e.g., 208) for programs and metadata for genre, cast and director and program type, where the ACR data will be grouped for all the available time-bands the communal device was active.
In particular embodiments, knowledge graph 300 may include aspect nodes 306 that may indicate different aspects or characteristics of particular media content. As an example and not by way of limitation, aspect nodes 306 for TV content may index aspects, such for as example, if the aspect is a program, program type, genre, cast members, or director. As another example, aspect nodes 306 for video or computer games may index aspects, such for as example, if the aspect is a game title, game genre, or game console. As another example, aspect nodes 306 for applications (“apps”) may index aspects, such for as example, if the aspect is an app type or app category. In particular embodiments, knowledge graph 300 may include nodes that index particular aspects associated with aspect nodes 306. As an example and not by way of limitation, aspect nodes 306 may correspond to a program may be connected to a show node 312A or 312B indicating a particular program (e.g., Drama Show X). As another example, aspect nodes 306 may correspond to a genre may be connected to a show node 330A or 330B indicating a particular genre (e.g., comedy). As another example, aspect nodes 306 may correspond to a director may be connected to a director node 340A or 340B indicating a particular director.
Edges 307 may be weighted with an associated value that quantifies the affinity between the two nodes it connects (e.g., show node 312A and genre node 330A). In particular embodiments, the weighting or affinity between nodes may be a function of the total duration the user was engaged with the corresponding content (e.g., media node 304). The weight of edge 307 may define how much influence the relationship between nodes has in the process of modeling the consumption behavior of a communal device. In particular embodiments, the relationship (edges 307) between nodes (e.g., 312A and 330A) may be treated as unidirectional because for practical purposes they are reciprocal. For example “programs” (e.g., show node 312A) that “belongs to” a “genre” (e.g., genre node 330A) may also be expressed as a “genre” (e.g., genre node 330A) that “groups/owns” many “programs” (e.g., show node 312A).
In particular embodiments, one or more meta-paths may be determined using a uniform random walk technique. The uniform random walk technique has a probability of traversing from a first node (e.g., v3) to jump from a second connected node (e.g., v4) that is equal for any other connected node (e.g., v2). In other words, it is equally probable that the uniform random walk would travel from node v3 to node v4 or node v2. In particular embodiments, one or more meta-paths may be determined using a weighted random walk technique. The weighted random walk has a probability of traversing from a first node (e.g., v3) to a second connected node (e.g., v4) that depends on the weight of the edge connecting the first node (e.g., v3) to the second node (e.g., v4). As an example and not by way of limitation, if the weight of the edge connecting node v3 to node v4 is higher than the weight of the edge connecting node v2 to node v4, then the meta-path is more likely to traverse from node v3 to node v4 than from node v2 to node v4. In particular embodiments, the weight of the edge connecting the nodes may be a function of the total duration the user was engaged with the corresponding media. In particular embodiments, the probability of traversing a particular step from a particular node may be proportional to the weight of the particular step divided by the sum of weights of all possible steps from that node.
In particular embodiments, one or more meta-paths may be determined using a guided or meta-path random walk technique. In other words, the meta-paths provide a blueprint of how to produce a random walk. The technique guided random walk is tailored for heterogeneous graphs where the knowledge graph includes different types of nodes (e.g., day, time-band, program type, program, or director for TV content). In particular embodiments, the traversed path may be guided by a semantic sub-graph that contains the conceptual structure of the graph (namely the relations between the different types of nodes). In other words, the random walk may traverse a node (e.g., v3) to a connected node (e.g., v4) based on a constraint of choosing a specific type of node in the next step of the walk. The sequence of the types of nodes may be based on the conceptual structure of the semantic sub-graph.
The ML model, described above, may be a two-layer neural network that attempts to model all the entities present in the ACR logs (e.g., devices, programs, metadata, location, etc.) into an embedding space, described below. In particular embodiments, ML may be applied on top of the knowledge graph or a portion of the knowledge to train an ML model that describes the consumption behavior of a communal device and predicts the next best-match program recommendation given contextual information like geolocation, time of the query, or user preferences. Training the ML model may be performed using the consolidated set of random walks which is the result of following a meta-path during the production of random walks. In particular embodiments, the ML model is trained by providing a context the ML model predicts is the most likely node that belongs to that context or by predicting the context based given a node. A context may be defined as nodes that are adjacent to a given node for a given meta-path. As an example and not by way of limitation, for the example of
Node embedding of the knowledge graph represents both the topology and semantics of the knowledge graph for all the concepts and relations in the knowledge graph while keeping track of the original context. Node embedding transforms nodes, edges, and their features from the higher dimensional time-band sub-graph 500 illustrated in the example of
where Em is the embedding of device's time-band information 702A-702C, wx is the weight of the xth aspect nodes (e.g., 330A-330C, and 312), Ex is the embedding vector of the xth aspect nodes (e.g., 330A-330C, and 312) and n is the number of nodes (e.g., 330A-330C, and 312) in embedding space 600. In particular embodiments, the value wx is a function of the distance in embedding space 600 between nodes (e.g., 312 and 330C). For unweighted graphs, where wx has a value of 1, centers of mass 702A-702C from equation (1) are equal to the average value of the embedding vectors Ex.
Embeddings or centers of mass 702A-702C for all time-bands logged across all device nodes 302A-302C may be used to identify patterns of user behavior. In particular embodiments, the user behavior may be identified by globally clustering embeddings or centers of mass 702A-702C of time-band embedding space 600 and each resulting cluster 704A-704B may be representative of the consumption behavior of one or more communal devices. In particular embodiments, each cluster 704A-704B or persona may be interpreted as identification by association, where devices (device nodes 302A-302C) having similar consumption behavior may share the same cluster 704A-704B. As an example and not by way of limitation, centers of mass 702A-702C may be clustered using any suitable clustering technique, such as for example, k-means or DBSCAN. For k-means clustering, determining a value for the number of clusters 704A-704B for the algorithm may be difficult when no previous knowledge of the data set is available. In particular embodiments, a value for the number of clusters may be estimated by visualizing the data points in 2-dimensions by using dimensional reduction and determine the number of clusters present when the data is plotted in a scatter-plot. As an example and not by way of limitation, T-distributed Stochastic Neighbor Embedding (T-SNE) may be used to perform this visualization and may be used in tandem with k-means clustering.
In particular embodiments, the devices, corresponding to device nodes 302A-302C, may be mapped to a particular persona 706A-706B that best represents the consumption behavior of a communal device for a particular time-band. As illustrated in the example of
In particular embodiments, both the embedding and the corresponding nodes are stored in a user knowledge graph (UKG) that may contain all aspects involved in the modeling of a persona such as for example genre nodes, program nodes 312, device nodes 302A-302C, time-band embedding vectors per device, and the embedding vectors for “personas” 706A-706B and program clusters, described above.
In particular embodiments, the knowledge-graph recommendation system may use a fuzzy query engine to generate personalized, context-aware recommendations. A query engine may be considered “fuzzy” since depending on where seed 802 is located in embedding space 800, different results may be obtained. Fuzzy query engines are able to mix several query terms into seed 802, thereby making it possible to trade-off the query results between relevance and personalization. The user knowledge graph embedding vectors allows the fuzzy query engine to query its data by using a seed 802 in the embedding vector space 800. In particular embodiments, seed 802 may be obtained as the result of linear operations (e.g. add, subtract, averaging, or translation) applied to one or more node embeddings. The returned set of recommendations may be extracted using the k-nearest neighbors (k-NN) to seed 802 sorted by similarity. In particular embodiments, the similarity may be computed using the Euclidean distance between seed and the nearest neighbors or by employing equivalent techniques that can operate over vectors like cosine similarity.
In particular embodiments, the knowledge-graph recommendation system may identify the “persona” 806A-806B that best represents the current context (e.g., the current day of the week and current time-band) to compose a time-band index. The knowledge-graph recommendation system may then access embedding vectors that are associated with the identified “persona” 806A-806B for that time-band from the data stored in the knowledge graph database. If more contextual information is available, the knowledge-graph recommendation system may access the embedding vectors for each of the terms in the “extended” context (e.g., genre or program embedding vectors). Once all the embedding vectors for the query terms are identified, seed 802 may be computed using the equation (1), described above, for the center of mass for node embeddings. Examples queries may take the form of:
X=embedding(persona),
X=w
1
×x embedding(persona)+w2×embedding(Y)
X=w
1×embedding(persona)+w2×embedding(genre)
X=w
1×embedding(persona)+w2×embedding(genre1)+w3×embedding(genre2)
X=w
1×embedding(persona)+w2×embedding(program1)+w3×embedding(program2)
In particular embodiments, the recommendations returned by the knowledge-graph recommendation system may be a set of media content sorted in ascending order by the distance between the persona and the content in the embedding space. Alternatively, the persona's embedding retrieving the recommendations can be offset by composing a seed that mixes the embeddings the persona with the embeddings of some other entities like genre, cast, director, etc. The example of
Particular embodiments may repeat one or more steps of the method of
This disclosure contemplates any suitable number of computer systems 1000. This disclosure contemplates computer system 1000 taking any suitable physical form. As example and not by way of limitation, computer system 1000 may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (e.g., a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, a tablet computer system, an augmented/virtual reality device, or a combination of two or more of these. Where appropriate, computer system 1000 may include one or more computer systems 1000; be unitary or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which may include one or more cloud components in one or more networks.
Where appropriate, one or more computer systems 1000 may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example, and not by way of limitation, one or more computer systems 1000 may perform in real-time or batch mode one or more steps of one or more methods described or illustrated herein. One or more computer systems 1000 may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.
In particular embodiments, computer system 1000 includes a processor 1002, memory 1004, storage 1006, an input/output (I/O) interface 1008, a communication interface 1010, and a bus 1012. Although this disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, this disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement.
In particular embodiments, processor 1002 includes hardware for executing instructions, such as those making up a computer program. As an example, and not by way of limitation, to execute instructions, processor 1002 may retrieve (or fetch) the instructions from an internal register, an internal cache, memory 1004, or storage 1006; decode and execute them; and then write one or more results to an internal register, an internal cache, memory 1004, or storage 1006. In particular embodiments, processor 1002 may include one or more internal caches for data, instructions, or addresses. This disclosure contemplates processor 1002 including any suitable number of any suitable internal caches, where appropriate. As an example, and not by way of limitation, processor 1002 may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). Instructions in the instruction caches may be copies of instructions in memory 1004 or storage 1006, and the instruction caches may speed up retrieval of those instructions by processor 1002.
Data in the data caches may be copies of data in memory 1004 or storage 1006 for instructions executing at processor 1002 to operate on; the results of previous instructions executed at processor 1002 for access by subsequent instructions executing at processor 1002 or for writing to memory 1004 or storage 1006; or other suitable data. The data caches may speed up read or write operations by processor 1002. The TLBs may speed up virtual-address translation for processor 1002. In particular embodiments, processor 1002 may include one or more internal registers for data, instructions, or addresses. This disclosure contemplates processor 1002 including any suitable number of any suitable internal registers, where appropriate. Where appropriate, processor 1002 may include one or more arithmetic logic units (ALUs); be a multi-core processor; or include one or more processors 1002. Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor.
In particular embodiments, memory 1004 includes main memory for storing instructions for processor 1002 to execute or data for processor 1002 to operate on. As an example, and not by way of limitation, computer system 1000 may load instructions from storage 1006 or another source (such as, for example, another computer system 1000) to memory 1004. Processor 1002 may then load the instructions from memory 1004 to an internal register or internal cache. To execute the instructions, processor 1002 may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, processor 1002 may write one or more results (which may be intermediate or final results) to the internal register or internal cache. Processor 1002 may then write one or more of those results to memory 1004. In particular embodiments, processor 1002 executes only instructions in one or more internal registers or internal caches or in memory 1004 (as opposed to storage 1006 or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory 1004 (as opposed to storage 1006 or elsewhere).
One or more memory buses (which may each include an address bus and a data bus) may couple processor 1002 to memory 1004. Bus 1012 may include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between processor 1002 and memory 1004 and facilitate accesses to memory 1004 requested by processor 1002. In particular embodiments, memory 1004 includes random access memory (RAM). This RAM may be volatile memory, where appropriate. Where appropriate, this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, where appropriate, this RAM may be single-ported or multi-ported RAM. This disclosure contemplates any suitable RAM. Memory 1004 may include one or more memories 1004, where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory.
In particular embodiments, storage 1006 includes mass storage for data or instructions. As an example, and not by way of limitation, storage 1006 may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. Storage 1006 may include removable or non-removable (or fixed) media, where appropriate. Storage 1006 may be internal or external to computer system 1000, where appropriate. In particular embodiments, storage 1006 is non-volatile, solid-state memory. In particular embodiments, storage 1006 includes read-only memory (ROM). Where appropriate, this ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these. This disclosure contemplates mass storage 1006 taking any suitable physical form. Storage 1006 may include one or more storage control units facilitating communication between processor 1002 and storage 1006, where appropriate. Where appropriate, storage 1006 may include one or more storages 1006. Although this disclosure describes and illustrates particular storage, this disclosure contemplates any suitable storage.
In particular embodiments, I/O interface 1008 includes hardware, software, or both, providing one or more interfaces for communication between computer system 1000 and one or more I/O devices. Computer system 1000 may include one or more of these I/O devices, where appropriate. One or more of these I/O devices may enable communication between a person and computer system 1000. As an example, and not by way of limitation, an I/O device may include a keyboard, keypad, microphone, monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet, touch screen, trackball, video camera, another suitable I/O device or a combination of two or more of these. An I/O device may include one or more sensors. This disclosure contemplates any suitable I/O devices and any suitable I/O interfaces 1006 for them. Where appropriate, I/O interface 1008 may include one or more device or software drivers enabling processor 1002 to drive one or more of these I/O devices. I/O interface 1008 may include one or more I/O interfaces 1006, where appropriate. Although this disclosure describes and illustrates a particular I/O interface, this disclosure contemplates any suitable I/O interface.
In particular embodiments, communication interface 1010 includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between computer system 1000 and one or more other computer systems 1000 or one or more networks. As an example, and not by way of limitation, communication interface 1010 may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI network. This disclosure contemplates any suitable network and any suitable communication interface 1010 for it.
As an example, and not by way of limitation, computer system 1000 may communicate with an ad hoc network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or one or more portions of the Internet or a combination of two or more of these. One or more portions of one or more of these networks may be wired or wireless. As an example, computer system 1000 may communicate with a wireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular telephone network (such as, for example, a Global System for Mobile Communications (GSM) network), or other suitable wireless network or a combination of two or more of these. Computer system 1000 may include any suitable communication interface 1010 for any of these networks, where appropriate. Communication interface 1010 may include one or more communication interfaces 1010, where appropriate. Although this disclosure describes and illustrates a particular communication interface, this disclosure contemplates any suitable communication interface.
In particular embodiments, bus 1012 includes hardware, software, or both coupling components of computer system 1000 to each other. As an example, and not by way of limitation, bus 1012 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serial advanced technology attachment (SATA) bus, a Video Electronics Standards Association local (VLB) bus, or another suitable bus or a combination of two or more of these. Bus 1012 may include one or more buses 1012, where appropriate. Although this disclosure describes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect.
Herein, a computer-readable non-transitory storage medium or media may include one or more semiconductor-based or other integrated circuits (ICs) (such, as for example, field-programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard drives (HHDs), optical discs, optical disc drives (ODDs), magneto-optical discs, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM-drives, SECURE DIGITAL cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more of these, where appropriate. A computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile, where appropriate.
Herein, “or” is inclusive and not exclusive, unless expressly indicated otherwise or indicated otherwise by context. Therefore, herein, “A or B” means “A, B, or both,” unless expressly indicated otherwise or indicated otherwise by context. Moreover, “and” is both joint and several, unless expressly indicated otherwise or indicated otherwise by context. Therefore, herein, “A and B” means “A and B, jointly or severally,” unless expressly indicated otherwise or indicated otherwise by context.
The embodiments disclosed herein are only examples, and the scope of this disclosure is not limited to them. Embodiments according to the invention are in particular disclosed in the attached claims directed to a method, a storage medium, a system and a computer program product, wherein any feature mentioned in one claim category, e.g. method, can be claimed in another claim category, e.g. system, as well. The dependencies or references back in the attached claims are chosen for formal reasons only. However, any subject matter resulting from a deliberate reference back to any previous claims (in particular multiple dependencies) can be claimed as well, so that any combination of claims and the features thereof are disclosed and can be claimed regardless of the dependencies chosen in the attached claims. The subject-matter which can be claimed comprises not only the combinations of features as set out in the attached claims but also any other combination of features in the claims, wherein each feature mentioned in the claims can be combined with any other feature or combination of other features in the claims. Furthermore, any of the embodiments and features described or depicted herein can be claimed in a separate claim and/or in any combination with any embodiment or feature described or depicted herein or with any of the features of the attached claims.
The scope of this disclosure encompasses all changes, substitutions, variations, alterations, and modifications to the example embodiments described or illustrated herein that a person having ordinary skill in the art would comprehend. The scope of this disclosure is not limited to the example embodiments described or illustrated herein. Moreover, although this disclosure describes and illustrates respective embodiments herein as including particular components, elements, feature, functions, operations, or steps, any of these embodiments may include any combination or permutation of any of the components, elements, features, functions, operations, or steps described or illustrated anywhere herein that a person having ordinary skill in the art would comprehend. Furthermore, reference in the appended claims to an apparatus or system or a component of an apparatus or system being adapted to, arranged to, capable of, configured to, enabled to, operable to, or operative to perform a particular function encompasses that apparatus, system, component, whether or not it or that particular function is activated, turned on, or unlocked, as long as that apparatus, system, or component is so adapted, arranged, capable, configured, enabled, operable, or operative. Additionally, although this disclosure describes or illustrates particular embodiments as providing particular advantages, particular embodiments may provide none, some, or all of these advantages.
This application claims the benefit, under 35 U.S.C. § 119(e), of U.S. Provisional Patent Application No. 62/863,825, filed on 19 Jun. 2019, which is incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 62863825 | Jun 2019 | US |
