Oath Inc.

Verizon Patent and Licensing Inc.

Other

Ankur Gupta

Timothy Daly

Tularam Ban

Timothy Patrick Daly, Jr

Chao Tan

Ahmad A Abdulkader

Sanaz Ahari

Girish Kumar

Farid Hosseini

Vijay Nair

Nazan Khan

Giridhar Kumaran

The present teaching relates to generating a conversational agent. In one example, a plurality of input utterances may be received from a developer. A paraphrase model is obtained. The paraphrase model is generated based on machine translation. For each of the plurality of input utterances, one or more paraphrases of the input utterance are generated based on the paraphrase model. For each of the plurality of input utterances, at least one of the one or more paraphrases is selected based on an instruction from the developer to generate selected paraphrases. The conversational agent is generated based on the plurality of input utterances and the selected paraphrases.

Method and system for generating a conversational agent by automatic paraphrase generation based on machine translation

One or more computing devices, systems, and/or methods for dynamically selecting a personality for a digital assistant are provided. For example, audio associated with a conversation with a digital assistant may be received from a user. The audio may be converted into a request comprising text. A task may be determined based upon the request. One or more sentences associated with information associated with the task may be determined. A context of the conversation may be determined based upon the request and a user profile of the user. A first personality may be selected for the digital assistant from one or more personalities based upon the context of the conversation. The first personality may be used to generate audio of the one or more sentences. The audio of the one or more sentences may be presented as part of the conversation to the user.

Digital assistant

Computer-readable media, computer systems, and computing methods are provided for classifying search results as either of good quality or of poor quality. Initially, a portion of the search results, such as the highest ranked documents, are selected for evaluation. A level of quality for each of the selected search results is determined using a classification process that includes the following steps: targeting features demonstrated by the selected search results to be evaluated; evaluating the selected features to generate a level-of-quality score for each of the selected search results; comparing the score against a predefined threshold value; and, based on the comparison, assigning each of the selected search results an absolute measurement. The absolute measurement indicates poor quality when the score is less than the threshold value. Upon recognizing that the selected search results are of poor quality, automatically executing a corrective action that reformulates the issued search query.

Classifying results of search queries

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Cupertino, CA, United States of America

Ankur Gupta