Salesforce, Inc.

Aron Kale

Zachary Alexander

Sitaram Asur

Regunathan Radhakrishnan

Feifei Jiang

Yuanxin Wang

Na Cheng

Anuprit Kale

Yixin Mao

Victor Yee

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

A cloud platform establishes a communication session between an agent and a user. The communication session is over an electrical medium. The cloud platform generates an interface on a client device associated with the agent. A first portion of the interface is configured to exchange messages between the agent and the user for a conversation or otherwise transcribe a conversation between the agent and the user. The cloud platform obtains, at a first time, a set of utterances from a transcript of the conversation. The cloud platform accesses a database including a plurality of articles. The cloud platform generates relevance scores between the conversation and the plurality of articles. The cloud platform then selects a subset of articles having relevance scores above a threshold value or proportion. The identified articles are presented on a second portion of the interface.

Machine-learning based document recommendation for online real-time communication system

A text interaction record including interaction text from one or more messages between a client machine and a service provider is received at a database system. A search vector including a text embedding representing the interaction text in a multi-dimensional vector space may be determined based on the interaction text via a processor at the database system. A reference interaction record including reference interaction text may be retrieved from the database system based on the search vector. The reference interaction record may include a reference vector representing the reference interaction text in the multi-dimensional vector space. Recommended reply text is determined based on the interaction text and the reference interaction text by a large language model configured to generate the recommended reply text in response to a prompt that includes one or more natural language instructions.

Database system interaction embedding and indexing for text retrieval and generation

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Huntington Beach, CA, United States of America

Aron Kale