Voicemonk Inc.

A9.com, Inc.

Jagadeshwar Nomula

Vinesh Gudla

Francois Huet

Erick Cantu-Paz

Durga Prasad Velamuri

David Mark Ciemiewicz

Priyank Singh

Vineel Yalamarthy

A system for generating search tokens for a user is provided. The system comprises a server, wherein the server comprises one or more processors. The server is operable to receive and store one or more user information in a user database. Further, the server identifies one or more of profiles or accounts of the user on one or more digital platforms. The server then collects, and stores one or more information related to one or more activities of the user on the digital platforms and in external systems, in the user database. The server then builds a user profile vector to characterize the user's behavior. Further, the server processes the user profile vector with the help of a learning module in order to derive one or more search tokens. Subsequently, the server may rank the search tokens to identify one or more content that is of interest to the user.

Advanced techniques to improve content presentation experiences for businesses and users

A system for recommending actions on a device includes at least one processor configured to record actions, which are performed by a user on the device, across a plurality of applications present on the device. The processor develops a personalized model, which is specific to a user of the device, for recommending actions, wherein the personalized model is at least based on the recorded actions. The processor recommends a follow on action to be carried out on a second application after a first action is carried out on a first application based on the personalized model.

System and method for recommending actions on a device

A system for executing actions based on user input is provided. The system comprises a virtual agent for a software application, wherein the virtual agent is configured to store a correlation between actions available in the software application. Further, the system associates one or more of the actions with one or more tags. The system receives at least an audio input from a user of the software application and uses the input to identify an action desired by the user to be performed among the actions. Further, the system executes one or more actions based on the desired action and the correlation between the actions available in the software application.

System for virtual agents to help customers and businesses

A system for executing actions based on user input is provided. The system comprises a virtual agent for a software application, wherein the virtual agent is configured to store a correlation between actions available in the software application. Further, the system associates one or more of the actions with one or more tags. The system receives at least an audio input from a user of the software application, and uses the input to identify an action desired by the user to be performed among the actions. Further, the system executes one or more actions based on the desired action and the correlation between the actions available in the software application.

A system for assisting a customer is provided. The system comprises a database comprising multimedia files, wherein one or more tags are associated with one or more of the multimedia files. The system further comprises a virtual agent serverconfigured to receive input from a first user (customer) and a second user (customer service representative). Further, the virtual agent serverprocesses the received input to understand a context of the input. Subsequently, the virtual agent serveridentifies one or more multimedia files based on the context and the tags associated with the multimedia files. Subsequently, the virtual agent servershares at least one of the identified multimedia files with at least one of the first user and the second user to enable better understanding between the first user and the second user.

System for assisting in marketing

Various approaches provide for determining the selection and/or ranking of items to display based at least in part upon the probabilities of a user being interested in those items. The probabilities can be based on profile information of a user. The profile information can include a three-dimensional virtual model of the user. When a search for content is received, the information for the three-dimensional virtual model can be used to determine a set of items. For each item, a matching score quantifying a visual appreciation can be determined based on how well an item virtually 'fits' the three-dimensional virtual model. Based on the matching score, the selection and/or ranking of items to display can be determined. In some situations, the items can be shown to appear to be worn by the three-dimensional virtual model. The user can purchase items, cause item information to be presented to other users or sent to other devices, and/or perform other such actions.

Displaying relevant content

Video content can be analyzed to identify particular sections of the video content. Speech to text or similar techniques can be used to obtain a transcription of the video content. The transcription can be indexed (e.g., timestamped) to the video content. Information describing how users are interacting with or consuming the video content (e.g., social media information, viewing history data, etc.) can be collected and used to identify the particular sections. Once the particular sections have been identified, other services can be provided. For example, custom trailers and summaries of the video content can be generated based on the identified sections. Additionally, the video content can be augmented to include additional information relevant to the particular sections, such as production information, actor information, or other information. The additional information can be added so as not to interfere with the important sections.

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Fremont, CA, United States of America

Jagadeshwar Nomula