Patent Application
20230058770
Many companies rely on data analytics systems to support discovering useful information, informing conclusions, and decision-making based on data. Data analytics systems can support forecasting—such as a forecasting system—that uses historical data as inputs to predict future trends. Forecasting systems can also be used for projecting demands for goods and services offered. Machine learning techniques implemented in forecasting systems allow for improved forecasting system operations including accelerating data processing speed, providing a more accurate forecast, automating forecasting updates based on recent data, analyzing more data, identifying hidden patterns in data, creating a more robust system, and increasing adaptability to changes. For example, when forecasting demand during a planning process, inference models can be employed to analyze available data that can be used to predict future outcomes.
Conventionally, data analytics systems are not equipped with a computing infrastructure and logic to systematically capture and operationalize commercial insights that support machine learning techniques. In particular, conventional forecasting systems in data analytics systems do not quantify intuitive personal skill levels or upskill potential in association with machine learning models. For example, forecasting systems may have insufficient data, non-representative data, poor quality data which limits the capacity to train machine learning models to provide accurate predictions. Moreover, forecasting systems operate based on traditional interfaces and data sources for accessing training data and lack a user interaction model that seamlessly integrates smart capture and analysis of relevant training data. As such, a more comprehensive data analytics systems—having an alternative basis for providing data analytics systems operations—can improve computing operations and interfaces in data analytics systems.
Various aspects of the technology described herein are generally directed to systems, methods, and computer storage media, for among other things, providing insight capturing interface of an insight capturing engine in a data analytics system. The insight capturing interface refers to an interaction mechanism for capturing and aggregating responses (e.g., answers to prompts) from users. The responses are used to generate, identify, and derive insights (e.g., relationship information) that correspond to items (e.g., products) associated with the responses. The insight capturing interface operates with the insight capturing engine to sequentially generate a combination of interface elements (e.g., insight capturing engine interface elements associated with product pairs) for retrieving the responses. In particular, the insight capturing engine operates to provide a survey (e.g., a perpetual survey) in which users (e.g., experts for a product) are continuously prompted with a pair of products to capture their insights, where the insights support generating inference models. For example, the survey may prompt users to select one out of two offered products that has a greater commercial potential or attractiveness and rank them against each other. Product pairs can be provided differently depending on corresponding goals of the survey (e.g., prioritizing sorting of the top of the product rank list or prioritizing sorting of the entire list of products).
In operation, the captured insight data (i.e., answers and other contextual data) can be saved in a transactional form as raw information. The captured insight data can then be processed to generate user scores, product scores and identify insights on expert skills and upskilling proposals (i.e., determining new and different skills to train employees on). The captured insight data can further be used to generate forecasting machine learning training data (e.g., training data comprising user scores, product scores, item features) that are then used to train forecasting machine learning models (e.g., inference models that embedded with the forecasting machine learning training data). The forecasting machine learning models—generated based on the captured insight data—can be employed for predictive analysis.
This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
The technology described herein is described in detail below with reference to the attached drawing figures, wherein:
By way of background, data analytics systems can support forecasting—such as a forecasting system—that uses historical data as inputs to predict future trends. Forecasting systems can be used for projecting demands for goods and services offered. Demand forecasting systems in combination with machine learning techniques can help forecasting future sales or other product predictions. Demand forecasting can include predicting demand for products such that manufactures can decide what products should be produced. Demand forecasting can improve different aspects of commerce from marketing campaigns to manufacturing management. Demand forecasting can include quantitative forecasting methods (e.g., regression analysis, adaptive and exponential smoothing) and qualitative forecasting methods (e.g., focus groups, historical analogy, and expert opinions).
Machine learning approaches to demand forecasting further allow learning from data and improving analysis. Operationally, developing a machine learning model for forecasting can be performed via a machine learning engine that supports gathering training data, defining goals and metrics associated with training data features or attributes (e.g., product features, time features etc.) Machine learning techniques can include Long Short-Term Memory (LSTM), Random Forest, and Linear Regression to develop forecasting models. For example, a linear regression approach can help predict future values from past values based on identifying underlying trends, so using historical data, additional information about the specific product, discounts, and average market cost, a prediction of a future value can be provided as a recommendation. The machine learning engine can further support training the forecasting models (i.e., using historical data and algorithms), validation (i.e., optimizing forecasting model parameters and hyper-parameters), and deployment (e.g., integration into production use).
By way of illustration, when forecasting demand during a planning process, inference models can be employed to analyze available data that can be used to predict future outcomes. For example, planning product demand can be part of a supply chain management process of forecasting or predicting demand for products to ensure they can be delivered and satisfy customers. The goal is to strike a balance between having sufficient inventory levels to meet customer needs without having a surplus. In some situations, a new product may be introduced into a business line, as such, historical data exists (e.g., past performance) for the demand for the product. In a scenario with new products, conventional forecasting techniques (e.g., machine learning models and time series data) are not available to make predictions on the demand of the new products. One way of addressing this problem is to look at similar products and past knowledge of similar products including implementing product master data systems. Product master data systems can refer to a system by which organizations organize and store information about all the key attributes (e.g., color, weight of fabrics, and other similar attributes to other products). While this additional data can be useful, even slightly more data can significant in improving forecast accuracy.
Conventionally, data analytics systems are not equipped with a computing infrastructure and logic to systematically capture and operationalize commercial insights that support machine learning techniques. For example, a data analytics system can include a forecasting system for projecting demands for goods and services offered. Forecasting systems may have insufficient data, non-representative data, poor quality data which limits the capacity to train machine learning models to provide accurate predictions. Forecasting systems operate based on traditional interfaces and data sources for accessing training data and lack a user interaction model that seamlessly integrates smart capture and analysis of relevant training data. At best, conventional data gathering for product planning may involve manually incorporating additional data into a forecasting engine to improve machine learning models that support making forecasting predictions. However, these manual processes can be cumbersome and tedious and lacking automated processing and integration with the data analytics system as a whole.
Moreover, conventional forecasting systems cannot quantify personnel skill level or upskill potential (e.g., new and relevant competencies). For example, a planning expert can have insights into a particular project or process; however forecasting systems lack a mechanism for capturing and quantifying this information in order to incorporate this information into forecasting system operations. Capturing the commercial insight and making the captured information usable in a forecasting system is missing in conventional forecasting systems, with such forecasting systems developed and implemented exclusively based on traditional data sources. As such, a more comprehensive data analytics systems—having an alternative basis for providing data analytics systems operations—can improve computing operations and interfaces in data analytics systems.
Embodiments of the present disclosure are directed to providing an insight capturing interface of an insight capturing engine in a data analytics system. The insight capturing interface refers to an interaction mechanism for capturing and aggregating responses (e.g., answers to prompts) from users. The responses are used to generate, identify, and derive insights (e.g., relationship information) that correspond to items (e.g., users and products) associated with the responses. The insight capturing interface operates with the insight capturing engine to sequentially generate a combination of interface elements (e.g., insight capturing engine interface elements associated with product pairs and product master data) for retrieving the responses. In particular, the insight capturing engine operates to provide a survey (e.g., a perpetual survey) in which users (e.g., experts for a product) are continuously prompted with a pair of products to capture their insights, where the insights support generating inference models. For example, the survey may prompt users to select one out of two offered products that has a greater commercial potential or attractiveness and rank them against each other. Product pairs can be provided differently depending on corresponding goals of the survey (e.g., prioritizing sorting of the top of the product rank list or prioritizing sorting of the entire list of products).
In operation, the captured insight data (i.e., answers and other contextual data) can be saved in a transactional form as raw information. The captured insight data can then be processed to generate user scores, product scores and identify insights on expert skills and upskilling proposals (i.e., determining new and different skills to train employees on). The captured insight data can further be used to generate forecasting machine learning training data (e.g., training data comprising user scores, product scores, item features) that are then used to train forecasting machine learning models (e.g., inference models that embedded with the forecasting machine learning training data). The forecasting machine learning models—generated based on the captured insight data—can be employed for predictive analysis via the forecasting engine. As such, the insight capturing engine is provided for addressing demand forecasting. Demand forecasting can be facilitated based on capturing personal commercial insight and quantifying skill level and upskilling potential using insight data from users (e.g., experts associated with certain products of an organization).
In one implementation, the insight capturing engine includes pair drawing, product scoring, ranking (products and users), skill analysis and updating. Pair drawing can include algorithms or procedures for serving product pairs during a survey. Product pairs can be served differently depending on goals associated with a survey. For example, a product pair can be identified for comparing similar attributes of a product, or a product pair may be identified to based previously received responses from a user. Moreover, product scoring can include algorithms or procedures on how to update the product score based on the survey outcome. For example, a first product that is selected in a survey—over a second product—receives an increased ranking score while the second product receives a decreased ranking score. Scoring increments and decrements may be weighted by an expert skill level. The insight capturing engine can also be implemented as a ranking tool. The score can be used to generate a ranked list of users and products associated with a survey. The insight capturing engine can generate scores for the captured insight data such that the scores are used as raw input data or training data for a forecasting engine and producing demand forecast. In this way, a forecasting system and forecasted outcomes can be improved compared to forecasting system without captured insight data.
Moreover, the skill update and upskilling analysis of the insight capturing engine includes algorithms or procedures that quantify the user (i.e., expert) skills level based on survey performance. The skill level is updated based on backtesting data in the survey, where aggregate data about survey pairs converge on a particular outcome. Backtesting involves applying a predictive model to historical data to determine the accuracy of the data. In this way, backtesting includes methods for determining how well a strategy or model would have done ex-post or assesses the viability of a strategy by discovering how it would play out using historical data. If backtesting works or fails, the insight capturing engine can employ the success data or failure data to update the skill levels of corresponding users. Additionally, the skill level can be incremented and decremented dependent on a difficult level associated with each question in the survey, and based on a product score distance.
Upskilling analysis is based on identifying or clustering a group of products for which a user has performed poorly or extremely well on, and performing cluster analysis to identify outlier experts that perform survey differently than common clusters. For example, the upskill analyzer can be used to perform clustering operations on the responses to find latent relationships in the captured insight data. The product scores and user scores—associated with user skills data—are then used as features in training forecasting machine learning models for predicting the demand for ranked products. It is contemplated that the insight capturing engine can operate independently of a forecasting system (including a planning system); however the inference models from the captured insight data can boost analysis traditionally performed via the forecasting system.
Operationally, the insight capturing engine can be built on a gamification framework, where the gamification framework provides interfaces, tools, and a library that include gaming techniques to motivate participation and long-term engagement from users. The gamification framework can include game-design interface elements and game principles (dynamics and mechanics) in an insight capturing process for products associated with demand planning. The gamification framework of the insight capturing engine can be configured to capture insights that may not be easily captured using traditional demand planning methods. For example, text associated with a product (e.g., text on packaging) may have a certain impact on a particular market—an insight that is not universally shared among experts associated with the product, or available in the product master data. In another example, an expert who understands that an upcoming trend for the next fashion season will drastically change normally has no automated or systematic way to communicate this information such that the information is adequately incorporated into a data analytics system. However, with the insight capturing engine, such insights can be captured and applied to other aspects of data analytics. Convergence models and data analytics can be used to cluster users, questions, answers, and score to derive additional insights. For example, as iterations questions are provided to users, the responses (e.g., for all users or for a subset of users) can get closer and closer to some specific value.
The insight capturing engine—via a perpetual survey—can provide a means for collecting expert insights independently of a planning process. The conventional forecasting data can also be complemented by commercial insights of experts. For example, experts commonly possess additional intuition that is not systematically captured in traditional forecasting data. Depending on the skill levels associated with an expert, the commercial insight could pose significant information gain for inference models, thus improving the predictive power of the inference model. An expert can access a survey through an application on a client device and answer constructed questions about two or more products at a time—without the distraction of a line of products. The aggregated captured insight data can be stored and transformed into variations of machine learning data that can be used to improve a forecasting system. In this way, the insight capturing engine is part of an end-to-end data analytics system that uses systematically captured insight data—to produce demand forecast training data for developing inference models—for demand forecasting associated with products or product families presented via a survey.
Crowdsource rating-ranking algorithms and models can be used to determine products (e.g., pair of products) to present in different questions. In one implementation, a set of priors (e.g., user scores indicating ranking)—that defines distributions of users—can be configured. A pre-sorted list of products can also be initially provided. The insight capture engine, as discussed, operates to increase information gain for each product and user, as such, questions are selected to increase the information gain. Different algorithms can be implemented for selecting pairs in order to achieve the goal of increasing information gain. For example, pairwise comparison questions can be generate based on the features (e.g., attributes) of the products being compared. The questions can be associated with a scale for rating preferences with respect to a pair of products and a corresponding criteria. The questions can be designed to include pairs of products that are presented to users. The pairs of products that are presented to a particular user can be based on the types of questions and answers previously presented to a particular user. The questions (and pairs of products associated with the questions) could also be based on answers provided by other users. The questions are not static, but rather are dynamically selected and presented to derive certain insights. For example, product pairs in a product ranking interface can be generated based on pairing algorithms for serving product pairs during a survey. Other variations and combinations of crowdsource rating-ranking algorithms and pairwise ranking aggregation are contemplated with embodiments described herein.
Product pairs can be served differently depending on the goal of the product pairs. For example, a goal for a particular survey can be to prioritize sorting a top portion of a product ranking list or prioritize sorting an entire product ranking list. In this way, the insight capture engine can include user profiles that include attributes (e.g., question and answer data for particular users) that are used in generating questions for the insight capturing engine interface. An insight capturing engine machine learning algorithms can be used to analyze the responses to identify patterns in the responses and make certain inferences. In this way, the insight capturing engine interface and insight capture engine machine learning algorithms can support efficient collection of embedded knowledge and relative performance between a plurality of users. Moreover, the insight capturing engine can include a user training feedback engine that includes insights that educate different users on their responses and the responses of other users.
By way of example, the insight capturing engine may pose a question asking whether product A will outperform product B. A user (e.g., client teams, experts, designers, sellers, anyone who can broaden the base of institutional knowledge)—running the insight capturing engine client on a device—can answer the question. The insight capturing engine can provide additional questions for different combinations and permutations of products. The insight capturing engine can the take the responses to the questions and create a ranking as well as score products, simultaneously as a plurality of users are responding to the different questions. The responses, scores, and ranks can be used along with historical information associated with existing products to support a forecasting engine. Advantageously, the responses, ranks, and score improve the forecast accuracy of a forecasting engine, if it had otherwise operated without the insight capturing engine. The insight capturing engine can deployed as a mobile application as part of a data analytics system and forecasting and planning ecosystem with captured insight data—associated with different types of users—as input to influence forecasts.
The insight capturing engine can generate scores associated captured insight data products. The insight capturing engine ranking output can be used to support backtesting performance of response sets associated with a user. Backtesting is used in modeling and can refer to testing a predictive model on historical data. Backtesting supports cross-validation applied to the previous time periods—or in this case previous response sets of users. In this way, user scores can be associated with a particular user, where the user score quantifies how good the user is at ranking products. For example, the user score can be used to indicate how much the user understands the commercial attractive or commercial appeal of different products. The user score can be used to weight up or down the user's input in the forecasting engine. The user score can also be used to inform and train user on where they are better or worse at ranking. A leaderboard can be available to quickly provide a ranking of users.
The insight capturing engine provides different types of interfaces that include interface elements that support systematically gathering insight data. For example, a product ranking interface, a product score interface, a user score interface, and a skill update interface with upskilling analysis. The insight capturing engine supports statistically rigorous computation that is associated with a gamification experience (via an insight capturing engine interface). The insight capturing engine interface provide a question and a response interface (e.g., swipe left and swipe right). The insight capturing engine interface along with the frequency of questions posed to a user allows capturing a significant amount of data that can be analyzed systematically to remove biases. For example, with the frequency and repetition is possible to identify conflicting information and can be statistically analyzed—for the user and the plurality users—and removed.
The insight capturing engine operates as a component of the data analytics engine where the data from the insight capturing engine is provided as input to a forecasting engine. The forecasting engine includes a forecasting algorithm that is designed to process the user score and product score to generate demand recommendations for products. The forecasting engine can also support backtesting based on historical data, where data from the insight capturing engine can be compared to current demand recommendation or past demand recommendations to provide feedback into the insight capture engine scoring and ranking. In one implementation, the insight capturing engine can include products that already exist with new products. The data from the existing products can be used to calibrate the operations for both the insight capturing engine and forecasting engine, as historical data is available for existing products. Several different approaches can be used for incorporating the insight data into the forecasting engine. For example, only ranking data can be incorporated, performance ratings and product attributes can be used as features in the forecasting engine and training forecasting models. Other variations and combinations are contemplated with embodiments described herein.
The insight capturing engine can be configured to maximize information gain from a plurality of users. The insight capturing engine can include machine learning models that operate such that the amount of information gained about a random variable (e.g., question or question attribute) can be maximized by observing other questions and answers. The insight capturing engine performs different types of comparisons to make decisions on what subsequent questions and interface elements should be presented. The insight capturing engine determines what questions to show to a user next based on the machine learning model. For example, product scores and user scores can be generated such that the scores are used to identify questions for a particular user. User scores can quantify a skill level of a user relative to other users. The questions and response data can be stored in a transactional database. The transactional database stores different attributes of each individual question instance. Products and users get scores that are also stored and periodically updated based on additional data that is stored in the transactional database.
Generally, scoring can include increasing score when a user is right and decreasing a score when a user is wrong, especially relative to other users for new products. Moreover, for old products, the score can be associated with past performance, where a user score is increased or decreased based on past performance data associated with a product. Scoring can also be associated with specific attributes of products. For example, if the only difference between a first product and a second product is the color and a user can accurately identify the first product as a product that is more commercially attractive, then the score and the attribute are identified as features that further support the forecasting engine. User scores can also be provided to automatically make recommendation for planning teams for current or future planning projects. For example, the user scores can be an indication of how good a particular user is at understanding the commercial attractiveness of different products. As such, those types of users could be identified for planning teams.
Aspects of the technical solution can be described by way of examples and with reference to
The data analytics system 100 provides an operating environment for aggregating, transforming, processing, and modeling data with the goal of discovering useful information, informing conclusions, and support decision-making. The data analytics system 100 includes the insight capturing engine 110, the forecasting engine 130, and the admin client 160 that support hardware and software operations in the data analytics system 100. For example, insight capturing engine 110, the forecasting engine 130, and the admin client can support generating questions for collection of insight captured data, presenting interfaces for collecting the insight captured data from a plurality of user and processing the data to generate forecasting machine learning training data and training forecasting machine learning models for making forecasting recommendations.
The forecasting engine 130 and forecasting machine learning models 150 can support demand forecasting using machine learning techniques can help forecasting future sales or other product predictions. Demand forecasting can include predicting demand for products such that manufactures can decide what products should be produced. Demand forecasting can specifically be associated with a planning process and inference models that analyze available data to predict further outcomes. As discussed, the captured insight data can be used to generate forecasting machine learning training data (e.g., training data comprising user scores, product scores, item features) to improve the forecasting machine learning models and their corresponding predictions or recommendations. Other variations and combinations of data analytics systems and forecasting engines are contemplated with embodiment described herein.
With reference to
In operation, insight capturing engine 110 captures and aggregates responses from users (e.g., via insight capturing engine client 110B). The insight capturing engine causes the insight capturing interface configuration engine 110A to generate insight capturing engine interface elements that are used to communicate questions (e.g., via a perpetual survey) to an insight capturing engine client. Responses (i.e., captured insight data) are received from the insight capturing engine client and used to generate, identify and derive insights that correspond to items associated with the questions and responses. Generating, identifying, and deriving insights can be based on different types of machine learning techniques that are applied to the captured insight data. The responses and insights can further be used to generate forecasting machine learning training data and forecasting machine learning models.
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As discussed, the conventional forecasting data can also be complemented by commercial insights of experts. For example, experts commonly possess additional intuition that is not systematically captured in traditional forecasting data. Depending on the skill levels associated with an expert, the commercial insight could pose significant information gain for inference models, thus improving the predictive power of the inference model. An expert can access a survey through an application on a client device and answer constructed questions about two or more products at a time—without the distraction of a line of products. The aggregated captured insight data can be stored and transformed into variations of machine learning data that can be used to improve a forecasting system.
Aspects of the technical solution can be described by way of examples and with reference to
With reference to
At block 12, a first product-pair question is generated. The first product-pair question can be identified. The first product-pair question can be a pairwise comparison question that can be generate based on the features (e.g., attributes) of the products being compared and user responses. At block 14, a response to the first product-pair question is received. At block 16, based on the response to the first product-pair question, a second product-pair question is generated. At block 18, a product score for a product is computed. The product score for the product is computed based on the plurality of responses of product-pair questions associated with the product. At block 20, a user score for a use is computed based on the plurality of product-pair questions associated with the user. At block 22, forecasting machine learning training data is generated. The forecasting machine learning training data is generated based on the captured insight data. At block 24, based on the forecasting machine learning training data, a forecasting machine learning training model is generated. At block 26, using the forecasting machine learning training model, a forecast recommendation is generated for a product. At block 26, using the forecasting machine learning model, a planning team member recommendation is generated.
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At block 304, generate a first product-pair question for two products. At block 306, receive a first response for the first product-pair question. At block 308, based on the response the first product-pair question, generate a second product-pair question. At block 310, receive a second response for the second product-pair question. At block 312, communicate the first response and the second response to cause generation of forecasting machine learning model training data associated with a forecasting system. Forecasting machine learning training data can be generated based on responses of corresponding product-pair questions. For example, a forecasting engine data preparation model processes the captured insight data and extracts or computes features associated with training the forecasting machine learning model, wherein the forecasting engine data preparation model retrieves additional forecasting machine learning model data not associated captured insight data. Forecasting machine learning models are generated using a forecasting engine that support training, validating, and deploying the forecasting machine learning model for product demand planning in a supply chain management process.
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Data centers can support distributed computing environment 600 that includes cloud computing platform 610, rack 620, and node 630 (e.g., computing devices, processing units, or blades) in rack 620. The technical solution environment can be implemented with cloud computing platform 610 that runs cloud services across different data centers and geographic regions. Cloud computing platform 610 can implement fabric controller 640 component for provisioning and managing resource allocation, deployment, upgrade, and management of cloud services. Typically, cloud computing platform 610 acts to store data or run service applications in a distributed manner. Cloud computing infrastructure 610 in a data center can be configured to host and support operation of endpoints of a particular service application. Cloud computing infrastructure 610 may be a public cloud, a private cloud, or a dedicated cloud.
Node 630 can be provisioned with host 650 (e.g., operating system or runtime environment) running a defined software stack on node 630. Node 630 can also be configured to perform specialized functionality (e.g., compute nodes or storage nodes) within cloud computing platform 610. Node 630 is allocated to run one or more portions of a service application of a tenant. A tenant can refer to a customer utilizing resources of cloud computing platform 610. Service application components of cloud computing platform 610 that support a particular tenant can be referred to as a tenant infrastructure or tenancy. The terms service application, application, or service are used interchangeably herein and broadly refer to any software, or portions of software, that run on top of, or access storage and compute device locations within, a datacenter.
When more than one separate service application is being supported by nodes 630, nodes 630 may be partitioned into virtual machines (e.g., virtual machine 652 and virtual machine 654). Physical machines can also concurrently run separate service applications. The virtual machines or physical machines can be configured as individualized computing environments that are supported by resources 660 (e.g., hardware resources and software resources) in cloud computing platform 610. It is contemplated that resources can be configured for specific service applications. Further, each service application may be divided into functional portions such that each functional portion is able to run on a separate virtual machine. In cloud computing platform 610, multiple servers may be used to run service applications and perform data storage operations in a cluster. In particular, the servers may perform data operations independently but exposed as a single device referred to as a cluster. Each server in the cluster can be implemented as a node.
Client device 680 may be linked to a service application in cloud computing platform 610. Client device 680 may be any type of computing device, which may correspond to computing device 600 described with reference to
Having briefly described an overview of embodiments of the present invention, an example operating environment in which embodiments of the present invention may be implemented is described below in order to provide a general context for various aspects of the present invention. Referring initially to
The invention may be described in the general context of computer code or machine-useable instructions, including computer-executable instructions such as program modules, being executed by a computer or other machine, such as a personal data assistant or other handheld device. Generally, program modules including routines, programs, objects, components, data structures, etc. refer to code that perform particular tasks or implement particular abstract data types. The invention may be practiced in a variety of system configurations, including hand-held devices, consumer electronics, general-purpose computers, more specialty computing devices, etc. The invention may also be practiced in distributed computing environments where tasks are performed by remote-processing devices that are linked through a communications network.
With reference to
Computing device 700 typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by computing device 700 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media.
Computer storage media include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computing device 700. Computer storage media excludes signals per se.
Communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.
Memory 712 includes computer storage media in the form of volatile and/or nonvolatile memory. The memory may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state memory, hard drives, optical-disc drives, etc. Computing device 700 includes one or more processors that read data from various entities such as memory 712 or I/O components 720. Presentation component(s) 716 present data indications to a user or other device. Exemplary presentation components include a display device, speaker, printing component, vibrating component, etc.
I/O ports 718 allow computing device 700 to be logically coupled to other devices including I/O components 720, some of which may be built in. Illustrative components include a microphone, joystick, game pad, satellite dish, scanner, printer, wireless device, etc.
Having identified various components utilized herein, it should be understood that any number of components and arrangements may be employed to achieve the desired functionality within the scope of the present disclosure. For example, the components in the embodiments depicted in the figures are shown with lines for the sake of conceptual clarity. Other arrangements of these and other components may also be implemented. For example, although some components are depicted as single components, many of the elements described herein may be implemented as discrete or distributed components or in conjunction with other components, and in any suitable combination and location. Some elements may be omitted altogether. Moreover, various functions described herein as being performed by one or more entities may be carried out by hardware, firmware, and/or software, as described below. For instance, various functions may be carried out by a processor executing instructions stored in memory. As such, other arrangements and elements (e.g., machines, interfaces, functions, orders, and groupings of functions) can be used in addition to or instead of those shown.
Embodiments described in the paragraphs below may be combined with one or more of the specifically described alternatives. In particular, an embodiment that is claimed may contain a reference, in the alternative, to more than one other embodiment. The embodiment that is claimed may specify a further limitation of the subject matter claimed.
The subject matter of embodiments of the invention is described with specificity herein to meet statutory requirements. However, the description itself is not intended to limit the scope of this patent. Rather, the inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different steps or combinations of steps similar to the ones described in this document, in conjunction with other present or future technologies. Moreover, although the terms “step” and/or “block” may be used herein to connote different elements of methods employed, the terms should not be interpreted as implying any particular order among or between various steps herein disclosed unless and except when the order of individual steps is explicitly described.
For purposes of this disclosure, the word “including” has the same broad meaning as the word “comprising,” and the word “accessing” comprises “receiving,” “referencing,” or “retrieving.” Further the word “communicating” has the same broad meaning as the word “receiving,” or “transmitting” facilitated by software or hardware-based buses, receivers, or transmitters using communication media described herein. In addition, words such as “a” and “an,” unless otherwise indicated to the contrary, include the plural as well as the singular. Thus, for example, the constraint of “a feature” is satisfied where one or more features are present. Also, the term “or” includes the conjunctive, the disjunctive, and both (a or b thus includes either a or b, as well as a and b).
For purposes of a detailed discussion above, embodiments of the present invention are described with reference to a distributed computing environment; however the distributed computing environment depicted herein is merely exemplary. Components can be configured for performing novel aspects of embodiments, where the term “configured for” can refer to “programmed to” perform particular tasks or implement particular abstract data types using code. Further, while embodiments of the present invention may generally refer to the technical solution environment and the schematics described herein, it is understood that the techniques described may be extended to other implementation contexts.
Embodiments of the present invention have been described in relation to particular embodiments which are intended in all respects to be illustrative rather than restrictive. Alternative embodiments will become apparent to those of ordinary skill in the art to which the present invention pertains without departing from its scope.
From the foregoing, it will be seen that this invention is one well adapted to attain all the ends and objects hereinabove set forth together with other advantages which are obvious and which are inherent to the structure.
It will be understood that certain features and sub-combinations are of utility and may be employed without reference to other features or sub-combinations. This is contemplated by and is within the scope of the claims.
The present application claims the benefit of U.S. Provisional Application No. 63/235,052, filed Aug. 19, 2021 and entitled “INSIGHT CAPTURING ENGINE IN A DATA ANALYTICS SYSTEM”, the entirety of which is incorporated by reference herein.
| Number | Date | Country | |
|---|---|---|---|
| 63235052 | Aug 2021 | US |
