Salesforce, Inc.

Salesforce Com, Inc.

Yixin Mao

Zachary Alexander

Wenhao Liu

Na Cheng

Sitaram Asur

Caiming Xiong

Tian Xie

Nicholas Beng Tek Geh

Chien-Sheng Wu

Zineb Laraki

Regunathan Radhakrishnan

Feifei Jiang

Gary Brandeleer

Aron Kale

Avanthika Ramesh

Jacob Nathaniel Huffman

Yuanxin Wang

Kavya Murali

Victor Winslow Yee

Nicholas Feinig

Joseph R Zeimen

Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata. One method of assisting creation of an automation for conversational interactions involves providing a first graphical user interface (GUI) display including graphical indicia of a plurality of semantic groups associated with historical conversations, in response to selection of a semantic group, providing a second GUI display including second graphical indicia of a plurality of cluster groups of conversations associated with the selected semantic group, in response to second selection of a cluster group, providing a third GUI display including third graphical indicia of representative utterances associated with respective conversations of a subset of historical conversations assigned to the selected cluster group, and in response to third selection of a GUI element on the third GUI display, providing a fourth GUI display including GUI elements for defining the automation.

Database systems and methods of defining conversation automations

Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata. One method of assigning structural metadata to a group of conversation records involves receiving a user input modification pertaining to a group of semantically similar conversations, automatically reassigning a conversation to a different group of semantically similar conversations based on its representative utterance in a manner that is influenced by the user input modification, and automatically updating, at a database system, a record associated with the conversation to include metadata identifying the different group of semantically similar conversations.

Database systems with user-configurable automated metadata assignment

Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata. One method of assigning structural metadata to a record associated with a conversation involves obtaining a plurality of utterances associated with the conversation, identifying, from among the plurality of utterances, a representative utterance for semantic content of the conversation, assigning the conversation to a group of semantically similar conversations based on the representative utterance, and automatically updating the record associated with the conversation at a database system to include metadata identifying the group of semantically similar conversations.

Database systems with automated structural metadata assignment

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

Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata. One method of assigning structural metadata involves determining a candidate group of semantically similar conversations based on unassigned conversations, determining a clustering performance metric associated with the candidate group based on a relationship between the candidate group and a plurality of existing groups of semantically similar conversations, and when the clustering performance metric is greater than a threshold, automatically assigning one or more unassigned conversations to the candidate group based on the representative utterances associated therewith and automatically updating one or more associated records at a database system to include metadata identifying the candidate group.

Database systems with adaptive automated metadata assignment

A system uses conversation engines to process natural language requests and conduct automatic conversations with users. The system generates responses to users in an online conversation. The system ranks generated user responses for the online conversation. The system generates a context vector based on a sequence of utterances of the conversation and generates response vectors for generated user responses. The system ranks the user responses based on a comparison of the context vectors and user response vectors. The system uses a machine learning based model that uses a pretrained neural network that supports multiple languages. The system determines a context of an utterance based on utterances in the conversation. The system generates responses and ranks them based on the context. The ranked responses are used to respond to the user.

Machine learning based models for automatic conversations in online systems

A system and related processing methodologies for recommending a product based on a work order are described. The system receives an input case description, including a current repair item and a current work type. Historical work orders associating a plurality of products with repair items and work types are searched for a co-occurrence of the repair item matching the current repair item, and the work type matching the current work type. Upon finding a match, the product associated with the match is added to a set of candidate products for the current work order. A similarity measure between the candidate product and current work order description, a current work type category, and popularity of the candidate product is generated and then used in the generation of a probability score for the candidate product and current work order. If the probability score meets a threshold, the candidate product is recommended.

Techniques and architectures for recommending products based on work orders

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Yixin Mao