Salesforce Com, Inc.

Salesforce, Inc.

Weiping Peng

Na Cheng

Steven Tamm

Orjan Nils Kjellberg

Arvind Krishnan

Andrew Waite

Scott D Beechuk

William Gradin

Shannon Hale

Luke A Ball

Anna Bonner Mieritz

Evan Michael Moses

Scott Peter Perket

Beril Guvendik Maples

Khanh Kim P Nguyen

Gregg Johnson

Zachary Alexander

Benjamin Carl Snyder

Jacob Nathaniel Huffman

Edgar Gerardo Velasco

Peter Francis White

Anjan Goswami

Pingping Xiu

Jayesh Govindarajan

Anuprit Kale

Aditya Sakhuja

Rick Lindstrom

Rasmus Mencke

Michael Scott Micucci

Sitaram Asur

Scott Douglas White

David Gary Mendelson

Zhenhua Xu

Khoa Le

Son Thanh Chang

Feifei Jiang

Paul Gene Byrne

Pranav Suresh Shah

Jennifer Yang

Nandini Suresh Kumar

Christopher Larry

Prakash Mallick

Colleen Smith

Vishal Shah

Alejandro Gabriel Perez Rodriguez

Ziwei Chen

Suhas Satish

Benjamin Snyder

HuaSheng Su

Described herein are systems, apparatus, methods and computer program products for machine learning intent classification. In various embodiments, historical utterances provided by users may be utilized for bot training. Context and personally identifiable information may be removed from the utterances. The utterances may be associated with vectors. The utterances and vectors may be used to determine recommendations.

Secure complete phrase utterance recommendation system

Disclosed are methods, apparatus, systems, and computer readable storage media for interacting with one or more data objects in an online social network. A user interface includes a publisher with a custom action. The custom action is defined in accordance with custom action instructions provided by a first entity via an application programming interface (API). Such custom action instructions may declaratively or programmatically define the data object, the interaction to be performed with the data object, and other attributes associated with the custom action. A user may request to interact with the data object via selection of the custom action by a second entity. Information may be submitted through the publisher to interact with the data object to update a database system associated with the data object. A feed item may be presented for inclusion in the information feed based on the update.

Systems and methods for creating custom actions

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

Computing systems, database systems, and related methods are provided for recommending values for fields of database objects and dynamically updating a recommended value for a field of a database record in response to updated auxiliary data associated with the database record. One method involves obtaining associated conversational data, segmenting the conversational data, converting each respective segment of conversational data into a numerical representation, generating a combined numerical representation of the conversational data based on the sequence of numerical representations using an aggregation model, generating the recommended value based on the combined numerical representation of the conversational data using a prediction model associated with the field, and autopopulating the field of the case database object with the recommended value.

Dynamic field value recommendation methods and systems

Methods, systems, and devices for processing and answering a natural language query at a database server are described. An end user may submit a question in natural language over a communication platform. An answer engine running on the database server may receive the question, and may process the content of the question using natural language processing (NLP) techniques. The answer engine may construct a search query based on the NLP, and may retrieve a set of documents from a database using the search query. The answer engine may rank the documents, prune the number of documents, modify the documents for the given communication platform, or perform any combination of these functions. In some cases, an intermediate user may review the documents, and may select one or more documents for publication. The answer engine may send the selected documents to the end user as answers in response to the question.

Omni-platform question answering system

Disclosed are examples of systems, apparatus, methods and computer program products for generating or updating cross-community streams. A plurality of communities can be maintained on behalf of a plurality of member organizations. Members of each community can have access to a corresponding set of records. One or more selections operable to assign one or more records to one or more cross-community streams can be displayed in a user interface on a display of a device of a first user. A first request from the first user to assign a first set of one or more records to a first cross-community stream can be processed. The first cross-community stream can be generated or updated.

Generating or updating cross-community streams

A method is provided for providing a final result set to a user. In some embodiments, the method includes receiving from the user an input question directed to an organization belonging to a particular category. The method includes applying a plurality of rules to the input question, at least one rule being assigned a weight dependent on the particular category to which the organization belongs. The method further includes extracting, based on applying the plurality of rules, multiple collections of keywords and generating a plurality of search queries. Each search query includes a different collection of keywords. The method also includes submitting the plurality of search queries to a database and in response, receiving multiple result sets from the database. The method further includes in response to the input question, providing a final result including a subset of documents included in the multiple result sets to the user.

Extraction of keywords for generating multiple search queries

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

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San Francisco, CA, United States of America

Weiping Peng