View source for 20240037673. VOICE ENABLED CONTENT TRACKER simplified abstract (INTUIT INC.)

=VOICE ENABLED CONTENT TRACKER=


==Organization Name==

[[:Category:INTUIT INC.|INTUIT INC.]]


[[Category:INTUIT INC.]]

==Inventor(s)==

[[:Category:Sangeetha Uthamalingam Santharam of San Diego CA (US)|Sangeetha Uthamalingam Santharam of San Diego CA (US)]][[Category:Sangeetha Uthamalingam Santharam of San Diego CA (US)]]

[[:Category:Bridget Diane Kimball of San Diego CA (US)|Bridget Diane Kimball of San Diego CA (US)]][[Category:Bridget Diane Kimball of San Diego CA (US)]]

==VOICE ENABLED CONTENT TRACKER - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240037673 titled 'VOICE ENABLED CONTENT TRACKER

==Simplified Explanation==

The present disclosure describes techniques and systems for automatically detecting, tracking, and processing information content based on voice input from a user. A voice-enabled content tracking system receives natural language content corresponding to audio input from a user. The system evaluates the natural language content using machine learning models to determine if it includes a specific type of information. If the content includes the specific type of information, the system determines the temporal association of that information and transmits a message to the user indicating the temporal association.

* Techniques and systems for automatically detecting, tracking, and processing information content based on voice input from a user are provided.
* A voice-enabled content tracking system receives natural language content corresponding to audio input from a user.
* The system evaluates the natural language content using a first machine learning model to determine if it includes a specific type of information.
* If the content includes the specific type of information, the system evaluates the natural language content using a second machine learning model to determine the temporal association of that information.
* A message including an indication of the temporal association of the first type of information is transmitted to the user.

Potential applications of this technology:

* Voice-enabled content tracking systems can be used in various applications such as personal assistants, voice-controlled devices, and smart home systems.
* This technology can be applied in customer service and support systems to automatically track and process customer requests and provide relevant information.
* Content tracking systems can be used in educational settings to track and analyze student responses and provide personalized feedback.

Problems solved by this technology:

* This technology automates the process of detecting and tracking specific information content based on voice input, reducing the need for manual intervention.
* It enables real-time analysis and processing of voice input, providing timely and accurate information to users.
* The use of machine learning models improves the accuracy and efficiency of detecting and tracking information content.

Benefits of this technology:

* Users can easily track and process specific information content through voice input, enhancing user experience and convenience.
* The automation of content tracking reduces the workload and time required for manual analysis and processing.
* Real-time transmission of messages indicating the temporal association of information allows users to stay updated and organized.


==Original Abstract Submitted==

certain aspects of the present disclosure provide techniques and systems for automatically detecting, tracking, and processing certain information content, based on voice input from a user. a voice enabled content tracking system receives natural language content corresponding to audio input from a user. a determination is made as to whether the natural language content includes a first type of information, based on evaluating the natural language content with a first machine learning model. in response to determining the natural language content comprises the first type of information, a temporal association of the first type of information is determined, based on evaluating the natural language content with a second machine learning model, and a message including an indication of the temporal association of the first type of information is transmitted to the user.



[[Category:G06Q40/12]]
[[Category:G10L15/22]]
[[Category:G06F3/0484]]
[[Category:G06Q40/10]]
[[Category:G10L15/18]]
[[Category:G06F3/16]]