Abstract: Systems and methods for managing supply chain of products and services are disclosed herein. A system generates supply chain data based on historical data received from data sources corresponding to supply chain of product or service. Further, system extracts data entity and set of attributes from supply chain data, to determine semantically related data entities. Furthermore, system determines use case corresponding to management of supply chain, based on semantically related data entities. Additionally, system predicts, risk or priority associated with product or service in the supply chain, to generate risks and alerts, based on prediction. Further, system assigns critical and high-priority use case to one or more agents based on a performance score of the one or more agents. Furthermore, system provides insights and suggestions for managing the supply chain of product or service at regional level and global level of supply chain.

Abstract: Systems and methods for evaluating attributes in supply chain management is disclosed. The system may receive data from a set of data sources corresponding to a supply chain associated with at least a product, pre-process the data based on integration of the data from each of the set of data sources, generate supply chain data based on the integrated data, analyze, via an orchestration engine, the supply chain data to assess an impact of the supply chain data on the supply chain, predict, via the orchestration engine, a state associated with a purchase event of the product in the supply chain, and generate a resolution flow to be executed in the supply chain for managing the predicted state associated with the purchase event of the product.

Abstract: A device may receive audio data identifying a plurality of speakers and may process the audio data, with a plurality of clustering models, to identify a plurality of speaker segments. The device may determine a plurality of diarization error rates for the plurality of speaker segments and may identify a plurality of errors in the plurality of speaker segments. The device may select rectification models to rectify the plurality of errors and may segment and/or re-segment the audio data with the rectification models to generate re-segmented audio data. The device may determine a plurality of modified diarization error rates for the plurality of speaker segments based on the re-segmented audio data and may select one of the plurality of speaker segments based on the plurality of modified diarization error rates. The device may calculate an empathy score based on the selected speaker segment and may perform actions based on the empathy score.

Abstract: A system for solution architecture prediction may identify a previous solution from a data source and may create a historical solution evaluation matrix by mapping a plurality of concern categories with the previous solution. The system may identify a plurality of solution components preponderant to deriving a solution associated with the solution architecture prediction and create a potential solution evaluation matrix therefrom. The system may evaluate the historical solution evaluation matrix and the potential solution evaluation matrix to determine a credibility score for each solution comprised therein. Based on the evaluation, a solution prediction data may be generated including a previous solution, a potential solution, and the associated credibility score. A service solution may be selected from the solution prediction data to resolve the solution prediction requirement.

Abstract: A device may receive audio data identifying a plurality of speakers and may process the audio data, with a plurality of clustering models, to identify a plurality of speaker segments. The device may determine a plurality of diarization error rates for the plurality of speaker segments and may identify a plurality of errors in the plurality of speaker segments. The device may select rectification models to rectify the plurality of errors and may segment and/or re-segment the audio data with the rectification models to generate re-segmented audio data. The device may determine a plurality of modified diarization error rates for the plurality of speaker segments based on the re-segmented audio data and may select one of the plurality of speaker segments based on the plurality of modified diarization error rates. The device may calculate an empathy score based on the selected speaker segment and may perform actions based on the empathy score.

Abstract: A system for obtaining optimized regular expression may convert a received input data into a plurality of embeddings. The system may receive a generated regular expression corresponding to the plurality of embeddings, wherein the generated regular expression is at least one of an existing regular expression from a database and a newly generated regular expression. The system may parse the generated regular expression into a plurality of sub-blocks. The system may classify the plurality of sub-blocks to obtain a plurality of classified sub-blocks. The system may evaluate a quantifier class for each classified sub-block to identify a corresponding computationally expensive class. The system may perform an iterative analysis to obtain a plurality of optimized sub-blocks associated with a minimum computation time. The system may combine the plurality of optimized sub-blocks to obtain the optimized regular expression.

Abstract: A device may receive input data identifying a technical architecture diagram, a design document, an interface specification document, and technical architecture icons, and may process the input data identifying the technical architecture diagram, with a model, to determine hierarchical objects from the technical architecture diagram. The device may perform OCR and NLP of the hierarchical objects to determine blocks of data, and may compare the blocks of data and the input data identifying the design document to identify functionalities of applications. The device may compare the blocks of data and the input data identifying the interface specification document to identify attributes, and may compare the blocks of data and the input data identifying the technical architecture icons to identify icons. The device may consolidate the blocks of data, the functionalities, the attributes, and the icons into a final document, and may perform actions based on the final document.

Abstract: A system for solution architecture prediction may identify a previous solution from a data source and may create a historical solution evaluation matrix by mapping a plurality of concern categories with the previous solution. The system may identify a plurality of solution components preponderant to deriving a solution associated with the solution architecture prediction and create a potential solution evaluation matrix therefrom. The system may evaluate the historical solution evaluation matrix and the potential solution evaluation matrix to determine a credibility score for each solution comprised therein. Based on the evaluation, a solution prediction data may be generated including a previous solution, a potential solution, and the associated credibility score. A service solution may be selected from the solution prediction data to resolve the solution prediction requirement.

Abstract: A device may receive input data identifying a technical architecture diagram, a design document, an interface specification document, and technical architecture icons, and may process the input data identifying the technical architecture diagram, with a model, to determine hierarchical objects from the technical architecture diagram. The device may perform OCR and NLP of the hierarchical objects to determine blocks of data, and may compare the blocks of data and the input data identifying the design document to identify functionalities of applications. The device may compare the blocks of data and the input data identifying the interface specification document to identify attributes, and may compare the blocks of data and the input data identifying the technical architecture icons to identify icons. The device may consolidate the blocks of data, the functionalities, the attributes, and the icons into a final document, and may perform actions based on the final document.

Abstract: An example chatbot generation platform may receive a request to generate a chatbot; determine a chatbot template for the chatbot based on the request; obtain custom chatbot information according to the chatbot template; generate a chatbot corpus for the chatbot using the custom chatbot information and the chatbot template; generate a set of question and answer (QnA) pairs based on the chatbot corpus; configure a language analysis model for the chatbot; build the chatbot according to the set of QnA pairs and the language analysis model; and deploy the chatbot to a chatbot host platform for operation. The chatbot may be built to engage in an interaction with a user via the chatbot host platform, use the language analysis model to select one or more QnA pairs from the set of QnA pairs during the interaction, and train the language analysis model based on the interaction.

Abstract: Examples of a cognitive forecasting system are defined. In an example, the system receives a forecasting requirement from a user. The system obtains parameter data from a plurality of data sources associated with the forecasting requirement and identify a parameter set therein. The system implements an artificial intelligence component to sort the parameter data into a plurality of data domains and identify a set of preponderant data domains therein. The system may update the preponderant data domains based on a modification in the plurality of data domains. The system may establish a forecasting model corresponding to the forecasting requirement by performing a cognitive learning. The system may update the forecasting model corresponding to the update in the parameter data. The system may generate a forecasting result corresponding to the forecasting requirement. The system may generate the cognitive forecasting model that may account for real time fluctuations in the data.

Abstract: An example chatbot generation platform may receive a request to generate a chatbot; determine a chatbot template for the chatbot based on the request; obtain custom chatbot information according to the chatbot template; generate a chatbot corpus for the chatbot using the custom chatbot information and the chatbot template; generate a set of question and answer (QnA) pairs based on the chatbot corpus; configure a language analysis model for the chatbot; build the chatbot according to the set of QnA pairs and the language analysis model; and deploy the chatbot to a chatbot host platform for operation. The chatbot may be built to engage in an interaction with a user via the chatbot host platform, use the language analysis model to select one or more QnA pairs from the set of QnA pairs during the interaction, and train the language analysis model based on the interaction.

Abstract: A test object can be selectively included in a test run based on predicting the behavior of the test object. In one embodiment, the present invention includes predicting how likely the test object is to produce a failure in a test run and deciding whether to include the test object in the test run based on the predicted likelihood. This likelihood of producing a failure may be based on any number of circumstances. For example, these circumstances may include the history of prior failures and/or the length of time since the test object was last included in a test run.

Abstract: A test object can be selectively included in a test run based on predicting the behavior of the test object. In one embodiment, the present invention includes predicting how likely the test object is to produce a failure in a test run and deciding whether to include the test object in the test run based on the predicted likelihood. This likelihood of producing a failure may be based on any number of circumstances. For example, these circumstances may include the history of prior failures and/or the length of time since the test object was last included in a test run.