Certain aspects of the disclosure are directed to context aggregation in a data communications network. According to a specific example, user-data communications between a client-specific endpoint device and the other participating endpoint device during a first time period can be retrieved from a plurality of interconnected data communications systems. The client entity can be configured and arranged to interface with a data communications server providing data communications services on a subscription basis. A context can be determined for each respective user-data communication between the endpoint devices during the first time period. A plurality of user-data communications between the client-specific endpoint device and the other participating endpoint device can be aggregated during a second time period, and a context can be determined for the aggregated user-data communications during the second time period based on a comparison of the aggregated user-data communications and the user-data communications during the first time period.

Certain aspects of the disclosure are directed to context aggregation in a data communications network. According to a specific example, user-data communications between a client-specific endpoint device and the other participating endpoint device during a first time period can be retrieved from a plurality of interconnected data communications systems. The client entity can be configured and arranged to interface with a data communications server providing data communications services on a subscription basis. A context can be determined for each respective user-data communication between the endpoint devices during the first time period. A plurality of user-data communications between the client-specific endpoint device and the other participating endpoint device can be aggregated during a second time period, and a context can be determined for the aggregated user-data communications during the second time period based on a comparison of the aggregated user-data communications and the user-data communications during the first time period.

A system and method for upgrading an executable chatbot is disclosed. The system may include a processor including a fallout utterance analyzer, a response identifier, a deviation identifier, a flow generator and enhancer. The fallout utterance analyzer may receive chats logs comprising a plurality of utterances and corresponding bot responses. The fallout utterance analyzer may classify the plurality of utterances into multiple buckets pertaining to at least one of an out-of-scope intent, a newly identified intent, and a new variation of an existing intent. The response identifier may generate auto-generated responses corresponding to new intents for upgrading the executable chatbot. The deviation identifier may overlay corresponding intent in the chat logs with the prestored flow dialog network to designate an extent of deviation with respect to flow prediction performance by the executable chatbot. The flow generator and enhancer may generate an auto-generated conversational dialog flow for upgrading the executable chatbot.

A system and method for upgrading an executable chatbot is disclosed. The system may include a processor including a fallout utterance analyzer, a response identifier, a deviation identifier, a flow generator and enhancer. The fallout utterance analyzer may receive chats logs comprising a plurality of utterances and corresponding bot responses. The fallout utterance analyzer may classify the plurality of utterances into multiple buckets pertaining to at least one of an out-of-scope intent, a newly identified intent, and a new variation of an existing intent. The response identifier may generate auto-generated responses corresponding to new intents for upgrading the executable chatbot. The deviation identifier may overlay corresponding intent in the chat logs with the prestored flow dialog network to designate an extent of deviation with respect to flow prediction performance by the executable chatbot. The flow generator and enhancer may generate an auto-generated conversational dialog flow for upgrading the executable chatbot.

A system for assessing text content of a product. The system includes a computing device having a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: provide text contents and confounding features of products; train a first regression model using the text content and the confounding features of the products; train the second regression model using the confounding features; operate the first regression model using the text contents and the confounding features to obtain a total loss; operate the second regression model using the confounding features of to obtain a partial loss; subtract the total loss from the partial loss to obtain a residual loss; use the residual loss to evaluate models and parameters for the regression models; and use the first regression model to obtain log odds of the words indicating importance of the words.

A system for assessing text content of a product. The system includes a computing device having a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: provide text contents and confounding features of products; train a first regression model using the text content and the confounding features of the products; train the second regression model using the confounding features; operate the first regression model using the text contents and the confounding features to obtain a total loss; operate the second regression model using the confounding features of to obtain a partial loss; subtract the total loss from the partial loss to obtain a residual loss; use the residual loss to evaluate models and parameters for the regression models; and use the first regression model to obtain log odds of the words indicating importance of the words.

A method of identifying text (e.g., a sentence or sentence portion) in a word processing text editor; automatically identifying a domain-specific deep-learning neural network that corresponds to an identified context, from among one or more domain-specific deep-learning neural networks; automatically identifying at least one suggested replacement word using the identified domain specific deep-learning neural network that corresponds to the identified context; and automatically controlling a display to display a user interface that includes functionality that presents prompt information that includes the at least one suggested replacement word. Changes for errors that are common in academic papers written by non-native speakers may be suggested.

A method of identifying text (e.g., a sentence or sentence portion) in a word processing text editor; automatically identifying a domain-specific deep-learning neural network that corresponds to an identified context, from among one or more domain-specific deep-learning neural networks; automatically identifying at least one suggested replacement word using the identified domain specific deep-learning neural network that corresponds to the identified context; and automatically controlling a display to display a user interface that includes functionality that presents prompt information that includes the at least one suggested replacement word. Changes for errors that are common in academic papers written by non-native speakers may be suggested.

A phrase that includes a trigger word that modifies a meaning within the phrase is received. The trigger word is identified. The words of the phrase that are modified by the trigger word are identified by analyzing features of the phrase that link the trigger word to other words. The phrase is interpreted by modifying the second subset of words according to the modification of the trigger word.

Embodiments are directed to organizing conversation information. Two or more machine learning (ML) models and a plurality of sentences provided from a conversation may be employed to generate insight scores for each sentence such that each insight score correlates to a probability that its sentence includes one or more of an action or a question. In response to one or more sentences having insight scores that exceed a threshold value an information score and a definiteness score may be determined for the one or more sentences. And one or more insights associated with the conversation may be generated based on the one or more sentences. A report may be generated that associates the one or more insights with one or more portions of the conversation that include the one or more sentences that are associated with the insights.