JUST RIGHT READER, INC. (20240282303). AUTOMATED CUSTOMIZATION ENGINE simplified abstract
Contents
AUTOMATED CUSTOMIZATION ENGINE
Organization Name
Inventor(s)
Sara Shenkan of Fort Worth TX (US)
AUTOMATED CUSTOMIZATION ENGINE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240282303 titled 'AUTOMATED CUSTOMIZATION ENGINE
The abstract describes a system and method for automatically generating and outputting reader feedback based on audio content provided by a user.
- Receiving audio content from a user that corresponds to textual content provided to the user.
- Comparing the received audio content to expected audio content using a machine learning algorithm.
- Determining deviations between the received and expected audio content based on the machine learning algorithm's output.
- Generating speech corresponding to the expected audio content based on user attributes.
- Outputting the generated speech to the user.
Potential Applications: - Educational platforms for providing feedback on pronunciation and reading skills. - Language learning apps for personalized feedback on speaking abilities. - Accessibility tools for individuals with visual impairments to receive audio feedback on text.
Problems Solved: - Automating the process of generating and providing feedback on audio content. - Personalizing feedback based on user attributes and preferences. - Improving user engagement and learning outcomes through tailored feedback.
Benefits: - Enhancing user experience by providing timely and relevant feedback. - Increasing user motivation and confidence through personalized feedback. - Streamlining the feedback generation process for content creators.
Commercial Applications: "Automated Reader Feedback System for Language Learning Apps"
Prior Art: Further research can be conducted in the fields of machine learning algorithms for audio analysis and feedback generation in educational technology.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms for audio processing and personalized feedback systems in educational technology.
Questions about Automated Reader Feedback System: 1. How does the system ensure accuracy in comparing received audio content to expected audio content? 2. What are the potential challenges in implementing personalized feedback based on user attributes in real-time scenarios?
Original Abstract Submitted
a system and method can be provided for automatically generating and outputting reader feedback. for example, the method can involve receiving audio content generated by a user and corresponding to textual content provided to the user. the method can further involve comparing the received audio content to expected audio content via a machine learning algorithm. additionally, the method can involve determining, based on an output of the machine learning algorithm, that a portion of the received audio content deviates from a portion of the expected audio content by greater than a threshold value. the method can also involve generating speech corresponding to the portion of the expected audio content. the speech corresponding to the portion of the expected audio content can be generated based on one or more attributes of the user. the method can further involve outputting the generated speech to the user.
