Salesforce Com, Inc.

Pingping Xiu

Weiping Peng

Anjan Goswami

Scott Douglas White

Peter Francis White

Edgar Gerardo Velasco

Aditya Sakhuja

Jayesh Govindarajan

Na Cheng

Sitaram Asur

Michael Scott Micucci

Jacob Nathaniel Huffman

Zhenhua Xu

Pranav Suresh Shah

Ziwei Chen

Suhas Satish

Prakash Mallick

Parijat Mazumdar

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

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

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

Disclosed embodiments are related to feature hashing techniques. A processing device of a database system may identify a set of machine learning features; generate a first hash map of said set of machine learning features and a second different hash map of said set of machine learning features. The processing device may generate a memory compact model for an online machine learning system using the first and second hash maps, and store the memory compact model in the memory device.

Multiple feature hash map to enable feature selection and efficient memory usage

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Pingping Xiu