Salesforce Com, Inc.

Kla Tencor Corporation

Peter Francis White

Jayesh Govindarajan

Na Cheng

Weiping Peng

Edgar Gerardo Velasco

Xin Ru Wang

Zachary Alexander

Adrian Dieter Rapp

Jacob Nathaniel Huffman

Anjan Goswami

Pingping Xiu

Sheila Bernadette Christian

Ajay Gupta

Sitaram Asur

Anuprit Kale

Michael Scott Micucci

Sankar Venkataraman

Michael John Van Riet

Hedong Yang

Chien-Huei Adam Chen

Scott Douglas White

Zhenhua Xu

Aditya Sakhuja

Pranav Suresh Shah

Prakash Mallick

Colleen Smith

Vishal Shah

Alejandro Gabriel Perez Rodriguez

Hai Jiang

Ziwei Chen

Suhas Satish

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

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 non-transitory tangible computer readable medium containing instructions configured to cause one or more processors to execute a process. The process comprises monitoring user input, preferences, and navigation patterns of a website or computer application on a user computer device, such as a computer, phone, or tablet. Administrator-defined rules are applied to the user input, preferences, or navigation patterns to generate a suggestion for a customer service application, such as a self-service FAQ page, video chat assistance, live chat assistance, or a case ticket system. The customer service application suggestion is displayed on the user computer device and may be transferred to a different device.

Intelligent support recommendations for snap-ins

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

Display screen or portion thereof with animated graphical user interface

One embodiment relates to a method of inspecting a site location on a target substrate. Contours are obtained, the contours having been generated from a reference image using a design clip. A target image of the site location is acquired. The contours are aligned to the target image, and contrast values are computed for pixels on the contours. A threshold is applied to the contrast values to determine contour-based defect blobs. Another embodiment relates to a method of generating contours for use in inspecting a site location for defects. Other embodiments, aspects and features are also disclosed.

Contour-based defect detection using an inspection apparatus

Growing community of inventors

San Francisco, CA, United States of America

Peter Francis White