Salesforce, Inc.

Salesforce Com, Inc.

Zineb Laraki

Shashank Harinath

Qingqing Liu

Christian Posse

Roojuta Lalani

Jean-Marc Soumet

Sarah Aerni

Thomas Archie Cook, Jr

Karl Ryszard Skucha

Alex Edelstein

Manju Vijayakumar

Till Christian Bergmann

Michael Weil

Michael Machado

Penny Tselikis

Mike Rosenbaum

John Ball

Jason Teller

Mehmet Ezbiderli

Zachary Alexander

Na Cheng

Yixin Mao

Avanthika Ramesh

Shilpa Bhagavath

Shubham Mehrotra

Abhishek Sharma

Jacob Nathaniel Huffman

Nicholas Feinig

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

Methods, systems, apparatuses, devices, and computer program products are described. A system may identify, from an event log including log entries for a tenant of a multi-tenant database system, a pattern of log entries corresponding to main actions and satisfying a frequency threshold. The system may identify log entries associated with the pattern and corresponding to the main actions, detailed actions, or both. The system may retrieve data corresponding to a history field of a data object associated with the pattern and may determine at least a portion of a process flow for the data object according to the pattern and based on the log entries and the historical data. The process flow may include operations to perform using the data object. In some cases, the system may transmit, to a user device, an indication of the portion of the process flow for user review and implementation.

Automatic determination of alternative paths for a process flow using machine learning

A method that includes receiving an input at an interactive conversation service that uses an intent classification model. The method may further include generating, using an encoder model of the intent classification model, a set of output vectors corresponding to the input, where the encoder model is configured to determine a set of metrics corresponding to intent classifications. The method may further include determining, using an outlier detection model of the intent classification model, whether the input is in-domain or out-of-domain (OOD) based on a first vector of the set of output vectors satisfying a domain threshold relative to one or more of the intent classifications. The method may further include outputting, by the intent classification model, a second vector of the set of output vectors that indicates the set of metrics corresponding to the intent classifications or an indication that the input is OOD.

Multi-lingual intent model with out-of-domain detection

Methods, systems, apparatuses, devices, and computer program products are described. An intelligent routing system may route a data object to a path in a process flow using a model, such as a machine-learned model. The system may receive a first data object and may route the first data object along a path of the process flow using a random routing procedure, for example, for model training. The routing may involve performing operations based on the path and the features of the first data object. The system may update one or more models based on an outcome of the operations. Following training, the system may insert a model into the process flow at a decision point between paths. The system may receive a second data object and may route the second data object to a path using the model and based on features of the second data object.

Intelligent routing of data objects between paths using machine learning

Approaches to using unstructured input to update heterogeneous data stores include receiving unstructured text input, receiving a template for interpreting the unstructured text input, identifying, using an entity classifier, entities in the unstructured text input, identifying one or more potential parent entities from the identified entities based on the template, receiving a selection of a parent entity from the one or more potential parent entities, identifying one or more potential child entities from the identified entities based on the template and the selected parent entity, receiving a selection of a child entity from the one or more potential child entities, identifying an action item in the unstructured text input based on the identified entities and the template, determining, using an intent classifier, an intent of the action item, and updating a data store based on the determined intent, the identified entities, and the selected child entity.

Using unstructured input to update heterogeneous data stores

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Zineb Laraki