Salesforce Com, Inc.

Avaya Inc.

Oracle International Corporation

Salesforce, Inc.

Jayesh Govindarajan

Nicholas Beng Tek Geh

Edgar Gerardo Velasco

Ammar Haris

Zachary Alexander

Francisco Borges

Vyankatesh Balaji Deshpande

John F Buford

Na Cheng

Anuprit Kale

Peter Francis White

Anwar A Siddiqui

Alan Edward Baratz

Anthony Frank Bartolo

Naren M Chittar

Marc Brette

Weiping Peng

Scott Thurston Rickard, Jr

Guillaume Kempf

Jacob Nathaniel Huffman

Clifford Z Huang

Sitaram Asur

Anjan Goswami

Pingping Xiu

Aditya Sakhuja

Ravishankar Palaparthi

Colleen Smith

Vishal Shah

Alejandro Gabriel Perez Rodriguez

Satyen Hingorani

Ziwei Chen

Suhas Satish

An online system receives a search query from a user. In response to the request, the online system obtains search results matching the search query and identifies a set of attributes describing a context of the search query. The online system generates a data structure that includes a plurality of search coefficients. The search coefficients are selected based on the identified set of attributes. Some of the search coefficients have conflicting values. The online system traverses the data structure to identify the search coefficients having conflicting values. For each search coefficient having conflicting values, the online system resolves conflicts and determines a value of the search coefficient. The online system ranks search results based on the resolved values of the search coefficients.

Ranking search results using hierarchically organized coefficients for determining relevance

A method and system for recommending articles including: receiving a customer request from the customer during the session; generating case data for a case, by an article recommender app; configuring a training set based on the subject and description data of the customer request; identifying, by an artificial intelligence (AI) app, a first pool of articles from a knowledge database; identifying by at least one query, a second pool of articles from a case article database to into a merged pool of articles; assigning, by the AI app, an implicit label to one of the first pool and the second pool of the articles; applying a model derived by the AI app based on customer behavior and a set of features related to the case to classify each article of the merged pool of articles based at least in part on the predicted relevance of the article.

Method and system for service agent assistance of article recommendations to a customer in an app session

A system is provided for a machine learning engine using clustered case objects in a case management system. The system includes a multi-layer neural network. The system is configured to receive case object data comprising a case object and contextual objects in the case management system associated with the case object, the contextual objects comprising word vectors, generate a context embedding for the case object using the word vectors for the contextual objects, and cluster the case object with other case objects in the case management system based on the context embedding for the case object and other context embeddings for the other case objects.

Case object context embeddings for machine learning training of case context

A multi-tenant system stores a hierarchy of machine-learned models, wherein each machine-learned model is configured to receive as input a set of search results and generate as output scores for ranking the set of search results. Each machine-learned model is associated with a set of dimensions. The system evaluates search query performance. Performance below a threshold causes a new model to be generated and added to the hierarchy of models. Upon execution of a new search query associated with the same set of dimensions as the newly created model, the new model is used to rank that search query's search results.

Ranking search results using hierarchically organized machine learning based models

As part of providing the services to users, an online system stores multiple records that are accessible by users of the online system. When a user provides a search query, the online system extracts morphological and dictionary features from the query. The online system provides the extracted features to a machine learning model as an input. The machine learning model outputs a score for each potential entity type that indicates a likelihood that the search query is for a record associated with the entity type. The output from the machine learning model is used by the online system to select one or more entity types that the user is likely searching for. The online system searches the stored records based on the search query but limits the searching to records associated with at least one of the selected entity types.

Predicting a type of a record searched for by a user

A data processing system analyzes a corpus of conversation data received at an interactive conversation service to train a response recommendation model. The response recommendation model generates response vectors based on custom responses and using the trained model and generates a context vector based on received input at the interactive conversation service. The context vector is compared to the set of response vectors to identify a set of recommended responses, which are recommended to an agent conversing with a user using the interactive conversation service.

Response recommendation system

An online system stores documents for access by users. The online system also stores query independent information about the documents. Query independent features include data that can be used to score or rank a document independent of any terms entered as a search query. The online system periodically determines whether the values of query independent features have changed, such as by checking activity logs. The online system updates records of query independent features accordingly, and sends information about the updated records to an enterprise search platform for re-indexing. When a user sends a search query to the online system, the enterprise search platform determines whether documents are relevant to the query based on the document contents and the query independent features associated with the documents.

Re-indexing query-independent document features for processing search queries

A method is employed in association with a communication session over a peer-to-peer network between a first node associated with a first session participant and a second node associated with a second session participant. In the method, identification information identifying the second session participant is received. Profile information descriptive of the second session participant is gathered using the identification information. The communication session is conducted using the profile information descriptive of the second session participant.

Profile information based on participant identity for peer-to-peer sessions

An online system identifies and ranks records using multiple machine learning models in response to a search query. Therefore, the online system can provide selected records that are of the most relevance to a user of a client device that provided the search query. More specifically, the online system applies a first machine learning model that is of low complexity, such as a regression model. Therefore, the first machine learning model can quickly narrow down the large number of records of the online system to a first set of candidate records. The online system analyzes candidate records in the first set by applying a more complex, second machine learning model that more accurately determines records of interest for the user. In various embodiments, the online system can apply subsequent machine learning models of higher complexity for selecting and ranking records for provision to the client device.

Ranking search results using machine learning based models

A method of operating a communication system to establish communication sessions between an origination network and a peer-to-peer network comprises receiving session signaling to establish a session between an origination device in the origination network and a destination node in the peer-to-peer network, wherein the session signaling includes a participant identifier associated with the origination device. The method further comprises processing the participant identifier to determine if persona information that identifies an originating participant and an entity associated with the originating participant is available for display by a destination device registered as the destination node on the peer-to-peer network and, if the persona information is available, transferring the persona information for delivery to and display by the destination device to a destination participant. The method further comprises establishing the session over the origination network and the peer-to-peer network and exchanging user communications for the session between the origination device and the destination device.

Determination of persona information availability and delivery on peer-to-peer networks

Embodiments provide communication session analysis and intervention methods and systems. Exemplary methods include monitoring a communication session conducted by a first customer service agent, providing script items to the first customer service agent, receiving session information associated with the communication session, identifying an escalation event in the session information, during the communication session, using on one or more predetermined communication criteria, and inviting a second customer service agent to join the communication session, in response to the escalation event.

Identifying escalation events in communication sessions

A method of operating a communication system comprises registering a plurality of communication devices with a peer-to-peer network as a plurality of enterprise nodes using a plurality of network handles that identify positions in an enterprise hierarchy. The method further comprises receiving a request to establish a session with a participant node and an enterprise node of the plurality of enterprise nodes using a first network handle of the plurality of network handles that identifies a first position in the enterprise hierarchy. In response to the request, the method provides identifying a routing identifier associated with the enterprise node and exchanging user communications for the session with a communication device of the plurality of communication devices registered as the enterprise node using the routing identifier.

Hierarchal structuring of nodes in a peer-to-peer network

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Palo Alto, CA, United States of America

Jayesh Govindarajan