Salesforce Com, Inc.

Salesforce, Inc.

Anuprit Kale

Zachary Alexander

Naren M Chittar

Na Cheng

Victor Spivak

Marc Brette

Jayesh Govindarajan

Sitaram Asur

Yanik Grignon

Venkatesan Chandrasekaran

Edgar Gerardo Velasco

Guillaume Kempf

Weiping Peng

Francisco Borges

Jacob Nathaniel Huffman

Tiffany Deiandra McKenzie

Rick Lindstrom

Scott Rickard

Alampallam R Ramachandran

Scott Thurston Rickard, Jr

Peter Francis White

Feifei Jiang

Aron Kale

Victor Yee

Yasaman Mohsenin

Pranshu Sharma

Yuanxin Wang

Fermin Ordaz

Embodiments described herein provide systems and methods for document recommendation. A system receives a set of training data including a plurality of documents. The system determines whether the set of training data includes annotated contextual information corresponding to the plurality of documents. The system trains supervised and/or unsupervised models based on the availability of data. The models are used to generate vectors representing the documents. During a live text conversation, text from the conversation may be vectorized using the models and the vectors compared to those representing the documents in order to find the most relevant documents. The system may generate an indication of a recommended document.

Systems and methods for real-time document recommendation

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 some implementations of systems, apparatus, methods and computer program products for integrating question generation and answer retrieval in a question answer system. The system generates a question using a set of documents and determines whether it is semantically distinct from questions in a question-answer repository. After determining that the question is semantically distinct from questions in the question-answer repository, the system adds the question to the question-answer repository. Upon receipt of a user-submitted question, the system uses the question-answer repository to identify a semantically similar question. The system retrieves an answer corresponding to the identified question from the question-answer repository and provides the answer in response to the user-submitted question.

Question answering using dynamic question-answer database

A record management system stores records for an organization having a plurality of members and a plurality of groups, and manages accessibility of the records for the organization according to a specified record access policy. The record management system generates an accessibility database that indicates, for each member, records that are explicitly or implicitly accessible by each member such that the records accessible for each member can be quickly determined if needed. A member has explicit access to a record if there is an explicit indication of accessibility between the member and the record. A member has implicit access to a record through membership associations to other members or groups that have access to the record. The record management system also receives search queries from members and returns records that are relevant and accessible to the members based on the accessibility database.

Representing access permissions to documents

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

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 collected at an interactive conversation service to train an intent classification model. The intent classification model generates vectors based on the corpus of conversation data. A set of intents is selected and an intent seed input for each intent of the set of intents is input into the model to generate an intent vector corresponding to each intent. Vectors based on user inputs are generated and compared to the intent vectors to determine the intent.

Intent classification system

An online system stores objects that may be accessed by users. The online system also stores indexes of terms related to different entity types of objects. When a user provides a search query, the online system compares the search terms with terms stored in the indexes. Based on the comparisons, the online system determines term features for entity types associated with an index. The online system provides the term features as inputs to a machine learning model. The machine learning model outputs a score for each entity type indicating a likelihood that the search query is for an object associated with the entity type. The machine learning model output is used by the online system to select one or more entity types that the user is likely searching for. The online system offers objects of the likely entity types to the user as results of the search query.

Predicting user intent based on entity-type search indexes

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Oakland, CA, United States of America

Anuprit Kale