Techniques are described herein for enabling autonomous agents to generate conclusive answers. An example of a conclusive answer is text that addresses concerns of a user who is interacting with an autonomous agent. For example, an autonomous agent interacts with a user device, answering user utterances, for example questions or concerns. Based on the interactions, the autonomous agent determines that a conclusive answer is appropriate. The autonomous agent formulates the conclusive answer, which addresses multiple user utterances. The conclusive answer provided to the user device.

Methods and systems provide for extracting engaging questions from a communication session. In one embodiment, the system connects to a communication session with a number of participants; receives a transcript of a conversation between the participants produced during the communication session; extracts, from the transcript, utterances including one or more sentences spoken by the participants; identifies a subset of the utterances spoken by a subset of the participants associated with a prespecified organization; extracts engaging questions within the subset of utterances, the engaging questions each including a question asked by the participant associated with the organization that is immediately answered in the following utterance by a participant not associated with the organization; and presents, for display at one or more client devices, data corresponding to the extracted engaging questions.

Methods and systems provide for extracting engaging questions from a communication session. In one embodiment, the system connects to a communication session with a number of participants; receives a transcript of a conversation between the participants produced during the communication session; extracts, from the transcript, utterances including one or more sentences spoken by the participants; identifies a subset of the utterances spoken by a subset of the participants associated with a prespecified organization; extracts engaging questions within the subset of utterances, the engaging questions each including a question asked by the participant associated with the organization that is immediately answered in the following utterance by a participant not associated with the organization; and presents, for display at one or more client devices, data corresponding to the extracted engaging questions.

A security system that monitors requests to a protected resource is configured to determine that a syntactically-invalid language statement in a request is one that should be treated as a “security high risk” statement (SHRS) because it has a probability of containing sensitive data. A machine language that defines the structure and syntax of the language statements used by a client-server application may have multiple SHRSs. SHRSs are identified in advance by syntactical analysis of the language statements that comprise the machine language. The security system stores (or can otherwise obtain) a representation of each of the set of these high risk statements. In response to detecting that a request has a syntactically-invalid language statement, the system determines whether the invalid language statement has a measure of similarity sufficiently close to any of statement in the SHRS set. Upon a positive determination, an appropriate security action is taken to ensure sensitive data is not exposed.

A security system that monitors requests to a protected resource is configured to determine that a syntactically-invalid language statement in a request is one that should be treated as a “security high risk” statement (SHRS) because it has a probability of containing sensitive data. A machine language that defines the structure and syntax of the language statements used by a client-server application may have multiple SHRSs. SHRSs are identified in advance by syntactical analysis of the language statements that comprise the machine language. The security system stores (or can otherwise obtain) a representation of each of the set of these high risk statements. In response to detecting that a request has a syntactically-invalid language statement, the system determines whether the invalid language statement has a measure of similarity sufficiently close to any of statement in the SHRS set. Upon a positive determination, an appropriate security action is taken to ensure sensitive data is not exposed.

With the availability of huge amount of data, it has becoming difficult to identify and manage duplicate data, especially when the data is in a plurality of columns. A method and system for identifying duplicate columns using statistical, semantics and machine learning techniques have been provided. The system provides a design framework to compare huge datasets at column level and identify potential duplicate columns, not based on the column title, but based on all of its values. The disclosure has ability to compare values in multiple columns and identify potential duplicate columns wherein comparison of values is not only for the exact match, but for semantic match, smart match, fuzzy match, and match after UOM conversion etc. using Statistical, semantics and machine learning techniques.

With the availability of huge amount of data, it has becoming difficult to identify and manage duplicate data, especially when the data is in a plurality of columns. A method and system for identifying duplicate columns using statistical, semantics and machine learning techniques have been provided. The system provides a design framework to compare huge datasets at column level and identify potential duplicate columns, not based on the column title, but based on all of its values. The disclosure has ability to compare values in multiple columns and identify potential duplicate columns wherein comparison of values is not only for the exact match, but for semantic match, smart match, fuzzy match, and match after UOM conversion etc. using Statistical, semantics and machine learning techniques.

Systems, methods, and computer-readable storage media for responding to a query using a neural network and natural language processing. If necessary, the system can request disambiguation, then parse the query using a trained machine-learning classifier, resulting in at least one of an identified subject or an identified domain of the text query. The system can determine if the user is authorized to retrieve answers to the query and, if so, retrieve factual data associated with the query. The system can then retrieve a response template, and fill in the template with the retrieved facts. The system can then determine, by executing a machine comprehension model on the filled response template, a probable readability token, portion of text, of at least a portion of the filled response template and, upon identifying that the probable readability is above a threshold, reply to the text query with the at least a portion of the filled response template.

A vehicle includes a controller configured to select one of a group of topics for generating an answer to a question embedded within the group based on an operating parameter of the vehicle and a syntax of a phrase. The selection is responsive to input originating from utterances including a preceding topic and a following topic having a moniker therebetween that is associated with only one of the topics through the syntax. The vehicle may operate an interface to output the answer.

A vehicle includes a controller configured to select one of a group of topics for generating an answer to a question embedded within the group based on an operating parameter of the vehicle and a syntax of a phrase. The selection is responsive to input originating from utterances including a preceding topic and a following topic having a moniker therebetween that is associated with only one of the topics through the syntax. The vehicle may operate an interface to output the answer.