View source for 17946952. Topic Identification Based on Virtual Space Machine Learning Models simplified abstract (Salesforce, Inc.)

=Topic Identification Based on Virtual Space Machine Learning Models=


==Organization Name==

[[:Category:Salesforce, Inc.|Salesforce, Inc.]]


[[Category:Salesforce, Inc.]]

==Inventor(s)==

[[:Category:Mitchell Mcneill of Dallas TX (US)|Mitchell Mcneill of Dallas TX (US)]][[Category:Mitchell Mcneill of Dallas TX (US)]]

[[:Category:Neil Brady of Indianapolis IN (US)|Neil Brady of Indianapolis IN (US)]][[Category:Neil Brady of Indianapolis IN (US)]]

[[:Category:Nathan Smith of Denver CO (US)|Nathan Smith of Denver CO (US)]][[Category:Nathan Smith of Denver CO (US)]]

==Topic Identification Based on Virtual Space Machine Learning Models - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17946952 titled 'Topic Identification Based on Virtual Space Machine Learning Models

==Simplified Explanation==

The patent application discusses techniques for displaying workflow responses based on determining topics associated with user requests. Here is a simplified explanation of the abstract:

* User requests are input into a machine learning model to identify topics and confidence levels.
* Topics are associated with user requests based on confidence levels.
* Graphical identifiers, such as emojis, are used to represent topics.
* Workflow responses are displayed in response to the graphical identifiers.

== Potential Applications ==
This technology could be applied in customer service platforms to automate responses based on user requests and topics identified.

== Problems Solved ==
This technology streamlines the process of responding to user requests by automatically identifying topics and providing relevant responses.

== Benefits ==
The benefits of this technology include improved efficiency in responding to user requests, increased accuracy in topic identification, and enhanced user experience.

== Potential Commercial Applications ==
A potential commercial application of this technology could be in chatbots for customer service, where automated responses can be generated based on identified topics.

== Possible Prior Art ==
One possible prior art could be the use of natural language processing in chatbots to understand user requests and provide relevant responses.

=== Unanswered Questions ===

=== How does this technology handle user requests in multiple languages? ===
This article does not address how the technology deals with user requests in languages other than English. It would be important to understand if the machine learning model can accurately identify topics and provide responses in various languages.

=== What measures are in place to ensure the accuracy of topic identification? ===
The article does not detail the specific techniques used to ensure the confidence levels associated with topics are accurate. It would be beneficial to know the validation processes in place to maintain the reliability of the identified topics.


==Original Abstract Submitted==

Techniques for displaying workflow responses based on determining topics associated with user requests are discussed herein. In some examples, a user may post a request (e.g., question) to a virtual space (e.g., a channel, thread, board, etc.) of a communication platform. The communication platform may input the request into a machine learning model trained to identify topics associated with the request and confidence levels associated with topics. In such examples, the communication platform may associate a topic with the user request based on the confidence level of the topic. In some examples, the communication platform may determine that the topic is associated with a graphical identifier (e.g., emoji). The communication platform may cause the graphical identifier to be displayed to the virtual space within which the user request was posted. In response to displaying the graphical identifier, the communication platform may display a workflow response to the virtual space.



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