Soundhound, Inc.

Apple Inc.

Soundhound Ai Ip, LLC

Robert MacRAE

Keyvan Mohajer

Kamyar Mohajer

Patrick L Coffman

Bernard Mont-Reynaud

Christopher S Wilson

Scott Halstvedt

Andrew J Sinesio

Ankur Goyal

Kranti K Parisa

Sara E Kufeldt

Frank-Robert Kline, Iii

Jon Grossmann

A system and method of real-time feedback confirmation to solicit a virtual assistant response from an evolving semantic state of at least a portion of an utterance. A user accesses a virtual assistant on an electronic device having the system and/or method configured to capture a command, a question, and/or a fulfillment request from audio such as, the speech emitted from the speaking user. The speech may be intercepted by a speech engine configured to transcribe the speech into text that is matched with the fragment pattern's regular expression to generate a fragment and/or the speech may be processed with a machine learning model to identify fragments. The fragments are identified by a domain handler configured to update a data structure of the current semantic state of the utterance in real-time on an interface of an electronic device.

Real-time natural language processing and fulfillment

Systems and processes for operating an intelligent automated assistant are provided. In one example, a user request for a media item is received. Based on the user request, at least one media item and a description of the at least one media item are identified. A confidence level is obtained that an identified media item of the at least one media item corresponds to the requested media item. In accordance with a determination that the confidence level exceeds a first confidence threshold, a length of the identified description is reduced to obtain a modified description and the modified description of the identified media item is provided in a first spoken response.

Reducing description length based on confidence

A system that includes a stand-alone device or a server connected client device are in communication with a server and provide recommendations. The device includes an input component, a storage component, a processor and an output component. The server-connected client device includes an input component that receives the user's request, a communication component that communicates the request to the server and receives the recommendation from the server, and an output component that provides the recommendation to user.

System and method for providing natural language recommendations

A command-processing server provides natural language services to applications. More specifically, the command-processing server receives natural language inputs from users for use in applications such as virtual assistants. Some user inputs create user-defined rules that consist of trigger conditions and of corresponding actions that are executed when the triggers fire. The command-processing server stores the rules received from a user in association with the specific user. The command-processing server also identifies rules that can be generalized across users and promoted into generic rules applicable to many or all users. The generic rules may or may not have an associated context constraining their application.

User-defined extensions of the command input recognized by a virtual assistant

Systems parse natural language expressions to extract items and values of their attributes and store them in a database. Systems also parse natural language expressions to extract values of attributes of user preferences and store them in a database. Recommendation engines use the databases to make recommendations. Parsing is of speech or text and uses conversation state, discussion context, synonym recognition, and speaker profile. Database pointers represent relative attribute values. Recommendations use machine learning to crowdsource from databases of many user preferences and to overcome the cold start problem. Parsing and recommendations use current or stored values of environmental parameters. Databases store different values of the same user preference attributes for different activities. Systems add unrecognized attributes and legal values when encountered in natural language expressions.

Natural language recommendation feedback

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Mountain View, CA, United States of America

Robert MacRAE