Soundhound, Inc.

Soundhound Ai Ip, LLC

Kamyar Mohajer

Keyvan Mohajer

Bernard Mont-Reynaud

Pranav Singh

Robert MacRAE

Aaron Master

Timothy P Stonehocker

Chris Wilson

The technology disclosed relates to natural language understanding-based search engines, ranking sponsored search results and simulated ranking of sponsored search results. Tools and methods describe how to simulate the ranking of sponsored search results. The tools further identify instances of user queries within the scope of trigger patterns, optionally providing examples both of user queries for which a sponsored search result is likely to be displayed and examples for which the sponsored search result will not rank highly enough to be displayed, at least on the first page of search results.

Virtual assistant domain functionality

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 platform provides for developers of applications, such as devices, with natural language interfaces to configure the availability of vertical domain modules in applications. Modules can include grammars for parsing natural language expressions and interfaces to data sources. Third party developers can create modules with pricing models for their usage or access to their data. Device developers can browse or search available modules and test their performance for specific queries. The platform provides for devices users to access the chosen modules as configured by device developers and for charging and payment between users, application developers, and module developers.

Modular virtual assistant platform

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

The present invention extends to methods, systems, and computer program products for a natural language module store. In general, the invention can be used to manage natural language modules offered through a natural language module store. Natural language module (NLM) developers can post NLMs at a NLM store to make the NLMs available for use by others. Developers can select NLMs for inclusion in natural language interpreters (NLIs) containing (and possibly integrating the functionality of) one or more NLMs. Prior to selecting a NLM, a developer can search or browse NLMs to identify an appropriate NLM. Optionally, a developer can test a NLM in the NLM store prior to inclusion in an NLI. For example, multiple NLMs purporting to provide the same specified natural language functionality can be tested relative to one another prior to selection of one of the NLMs for inclusion in an NLI.

Natural language module store

Technologies described relate to coordination between audio and tracking of the corresponding text in an audio recognition mode and an audio playback mode. Optionally, audio recognition includes receiving a signal corresponding to an audio track; determining an identity of the audio track and a current audio position within the audio track; displaying on a display a portion of a text that is linked and synchronized to the audio track identified and automatically scrolling the portion of the text displayed in pace with the audio track playing; and displaying a current text indicator that emphasizes current text, wherein the current text indicator is visually synchronized on the display to current audio playing from the audio track. Optionally include redetermining the current audio position in the audio track from the signal received and updating synchronization of the current text indicator with the current audio playing.

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San Jose, CA, United States of America

Kamyar Mohajer