Intuit, Inc.

Cisco Technology, Inc.

Blackberry Corporation

Palo Alto Research Center Inc.

Sun Microsystems, Inc.

Oracle America, Inc.

Interval Research Corporation

Centillium Communications, Inc.

Roger Charles Meike

Glenn Carter Scott

Michael R Gabriel

Amir R Eftekhari

Sricharan Kallur Palli Kumar

Cynthia Joann Osmon

Gregory Kenneth Coulombe

Priya Mahadevan

David Paul Yach

Richard John George

Michael F Plass

Tu Dien Do

Randall B Smith

Bruno Richard Preiss

Ian Maya Panchevre

Parikshit Lingampally

Todd Elliott

Molly Beth Davis

Pavlo Malynin

Cem Unsal

Rebecca Lynn Braynard Silberstein

Ashlee Beggs

Ian Pentland

Mike R Gabriel

Sricharan Kallur Kumar

Saikat Mukherjee

Radya Cherkaoui

Glenn T Edens

Amir Eftekhari

Per-Kristian G Halvorsen

Debby Hindus

Robert F Tow

Calum G Murray

Stephen Uhler

Conrad De Peuter

Sean Michael White

Andrew J Singer

Lisa J Stifelman

Wayne A Burdick

Jonathan Hong-Man Sau

Itai Jeczmien

Jason Hardiman

Fabio Dario Katz

Rachel Jack

Timothy Andrew Ryan

Donald Robert Hoover

Don Charnley

Luciano Sibio

Adel Amin Abdel Azim Youssef

Kaiduan Xie

Michael Richard Gabriel

Mike Gabriel

Adel Amin Youssef

Adel Youssef

A method including transcribing, automatically, an ongoing stream of voice data into text phrases. The method also includes receiving an indication of a selected text phrase in the text phrases. The method also includes converting the selected text phrase to a selected phrase vector. The method also includes generating a subsequent text phrase, after the selected text phrase, from the ongoing stream of voice data, and adding the subsequent text phrase to the text phrases. The method also includes converting the subsequent text phrase to a subsequent phrase vector. The method also includes generating a similarity confidence score from the selected phrase vector and the subsequent phrase vector, using a machine learning model. The method also includes highlighting, responsive to the similarity confidence score exceeding a threshold value, the subsequent text phrase in the text phrases.

Machine learning prediction of text to highlight during live automated text transcription

Certain aspects of the present disclosure provide techniques for executing a function in a software application through a conversational user interface based on a knowledge graph associated with the function. An example method generally includes receiving a request to execute a function in a software application through a conversational user interface. A graph definition of the function is retrieved from a knowledge engine. Input is iteratively requested through the conversational user interface for each parameter of the parameters identified in the graph definition of the function based on a traversal of the graph definition of the function. Based on a completeness graph associated with the function, it is determined that the requested inputs corresponding to the parameters identified in the graph definition of the function have been provided through the conversational user interface. The function is executed using the requested inputs as parameters for executing the function.

Conversational user interfaces based on knowledge graphs

Certain aspects of the present disclosure provide techniques for optimizing results generated by functions executed using a rule-based knowledge graph. The method generally includes generating a neural network based on a knowledge graph and inputs for performing a function using the knowledge graph. Inputs for the function are received and used to generate a result of the function. A request to optimize the generated result of the function is received. A loss function is generated for the neural network. Generally, the loss function identifies a desired optimization for the function. Values of parameters in the neural network are adjusted to optimize the generated result based on the generated loss function, and the adjusted values of the parameters in the neural network are output in response to the request to optimize the generated result of the function.

Method and system for optimizing results of a function in a knowledge graph using neural networks

A method including receiving, at a platform and from a first user using a first user device, selection of a uniform resource indicator (URI) unique to a second user using a second user device. The method also includes generating, automatically by the platform in response to receiving the URI, a conference session unique to the first user and the second user. The method also includes transmitting, automatically by the platform, a message to the second user, the message indicating that the conference session is initiated. The method also includes receiving, by the platform, an indication from the second user device that the second user joins the conference session. The method also includes joining, automatically by the platform, the first user device and the second user device in the conference session.

Instant conferencing system

Certain aspects of the present disclosure provide techniques for mapping natural language to stored information. The method generally includes receiving a long-tail query comprising a natural language utterance from a user of an application associated with a set of topics and providing the natural language utterance to a natural language model configured to identify nodes of a knowledge graph. The method further includes, based on output of the natural language model, identifying a node of a knowledge graph associated with the natural language utterance, wherein the output of the natural language model includes a node identifier for the node of the knowledge graph and providing the node identifier to the knowledge engine. The method further includes receiving a response associated with the node of the knowledge graph from the knowledge engine and transmitting the response to the user in response to the long-tail query.

Mapping natural language utterances to nodes in a knowledge graph

A method secures hash chains via hybrid consensus. A proximate payload for a proximate chain block for a proximate chain is obtained. A first identifier of the proximate chain and the proximate payload are hashed with a hash function to generate a second identifier of the proximate chain. The proximate chain block is added to the proximate chain. The proximate chain block includes the first identifier, the second identifier, and the proximate payload. A request to add the second identifier to a reporting chain is transmitted. A response indicating that the second identifier is incorporated into the reporting chain using the consensus mechanism is received.

Securing hash chains via hybrid consensus

After a host client establishes a multimedia stream with a guest client, host data is received from a host application. A state machine is updated using the host data. The host application executes on the host client. Guest data is received from a guest application. The state machine is updated using the guest data. The guest application executes on the guest client. Transaction data is propagated between the host application and the guest application. The transaction data is presented with the multimedia stream. The transaction data includes the host data and the guest data. Provider data is generated responsive to updating the state machine with the host data and the guest data. The provider data is sent to the guest client. The provider data is presented with the multimedia stream by the guest application on the guest client.

Transactions over live media

Certain aspects of the present disclosure provide techniques for processing natural language utterances in a knowledge graph. An example method generally includes receiving a long-tail query comprising a natural language utterance from a user of an application. Operands and operators are extracted from the natural language utterance using a natural language model. Operands may be mapped to nodes in a knowledge graph, the nodes representing values calculated from data input into the application, and operators may be mapped to operations to be performed on data extracted from the knowledge graph. The functions associated with the operators are executed using data extracted from the nodes in the knowledge graph associated with the operands to generate a query result. The query result is returned as a response to the received long-tail query.

Mapping natural language utterances to operations over a knowledge graph

The present disclosure relates to a trustworthy data exchange. Embodiments include receiving, from a device, a query, wherein the query comprises a question. Embodiments include identifying particular information related to the query. Embodiments include receiving credentials from a user for retrieving the particular information related to the query. Embodiments include retrieving, using the credentials, the particular information related to the query from one or more data repositories that are part of a distributed database comprising an immutable data store that maintains a verifiable history of changes to information stored in the distributed database. Embodiments include determining, based on the particular information related to the query, an answer to the query. Embodiments include providing the answer to the device.

Trustworthy data exchange using distributed databases

A method includes receiving a data stream comprising content generated by an application executing on a user device. The data stream is received from a guidance service that is separate from the application. The data stream is processed using a set of machine learning models to identify a first set of artifacts within the content. A first state of the application is identified based on the first set of artifacts. First transition data is identified in a logic flow of the application. The first transition data corresponds to transitioning from the first state to a second state of the application. Based on the first transition data, first guidance data is generated that describes user input for transitioning the application from the first state to the second state. The first guidance data is sent to the user device, where it is separately presented from the application by the guidance service.

Framework for machine guidance

Aspects of the present disclosure provide techniques for intent matching. Embodiments include receiving input of text by a user via a user interface. Embodiments include determining weights for portions of the text based on a plurality of keywords. Embodiment include generating an embedding of the text. Embodiments include determining an intent of the text by weighting, based on the weights, word mover's distances from the embedding of the text to a known embedding of known text associated with the intent in order to determine a similarity measure between the text and the known text. Embodiments include providing content to the user via the user interface based on the intent.

Enhanced intent matching using keyword-based word mover's distance

A method and system learn functions to be associated with data fields of forms to be incorporated into an electronic document preparation system. The functions are essentially sets of operations required to calculate the data field. The method and system receive form data related to a data field that expects data values resulting from performing specific operations. The method and system utilize machine learning and training set data to generate, test, and evaluate candidate functions to determine acceptable functions.

System and method for automatic learning of functions

Growing community of inventors

Redwood City, CA, United States of America

Roger Charles Meike