View source for Verizon Patent and Licensing Inc. (20240321260). SYSTEMS AND METHODS FOR RECONSTRUCTING VIDEO DATA USING CONTEXTUALLY-AWARE MULTI-MODAL GENERATION DURING SIGNAL LOSS simplified abstract

=SYSTEMS AND METHODS FOR RECONSTRUCTING VIDEO DATA USING CONTEXTUALLY-AWARE MULTI-MODAL GENERATION DURING SIGNAL LOSS=


==Organization Name==

[[:Category:Verizon Patent and Licensing Inc.|Verizon Patent and Licensing Inc.]]


[[Category:Verizon Patent and Licensing Inc.]]

==Inventor(s)==

[[:Category:Subham Biswas of Thane (IN)|Subham Biswas of Thane (IN)]][[Category:Subham Biswas of Thane (IN)]]

[[:Category:Saurabh Tahiliani of Noida (IN)|Saurabh Tahiliani of Noida (IN)]][[Category:Saurabh Tahiliani of Noida (IN)]]

==SYSTEMS AND METHODS FOR RECONSTRUCTING VIDEO DATA USING CONTEXTUALLY-AWARE MULTI-MODAL GENERATION DURING SIGNAL LOSS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240321260 titled 'SYSTEMS AND METHODS FOR RECONSTRUCTING VIDEO DATA USING CONTEXTUALLY-AWARE MULTI-MODAL GENERATION DURING SIGNAL LOSS

'''Simplified Explanation:'''
The patent application describes a device that can process video data containing a text transcript, audio sequences, and image frames, detect network fluctuations, generate a new phrase from the text transcript, create a response phoneme based on the new phrase, and combine various embeddings to produce a final voice response and video output.

'''Key Features and Innovation:'''
* Device processes video data with text transcript, audio sequences, and image frames.
* Detects network fluctuations.
* Generates a new phrase and response phoneme.
* Creates text, audio, and image embeddings.
* Combines embeddings to produce final voice response and video output.

'''Potential Applications:'''
The technology could be used in video conferencing, virtual assistants, language translation, and content creation applications.

'''Problems Solved:'''
The device addresses issues related to network fluctuations, voice synthesis, and video generation in real-time communication scenarios.

'''Benefits:'''
* Improved voice synthesis and video generation.
* Enhanced user experience in video communication.
* Efficient processing of multimedia data.

'''Commercial Applications:'''
Potential commercial applications include video conferencing platforms, virtual assistant devices, language translation services, and content creation tools.

'''Prior Art:'''
Prior art related to this technology may include research on voice synthesis, video processing, and multimedia data integration.

'''Frequently Updated Research:'''
Researchers may be exploring advancements in voice synthesis algorithms, image processing techniques, and network optimization strategies relevant to this technology.

'''Questions about the Technology:'''
1. How does the device handle network fluctuations during video processing?
2. What are the potential limitations of the text, audio, and image embeddings in generating the final voice response and video output?


==Original Abstract Submitted==

a device may receive video data that includes a text transcript, audio sequences, and image frames, and may detect a network fluctuation. the device may process the text transcript to generate a new phrase, and may generate a response phoneme based on the new phrase. the device may generate a text embedding based on the response phoneme, and may process the audio sequences to generate a target voice sequence. the device may generate an audio embedding based on the target voice sequence, and may process the image frames to generate a target image sequence. the device may generate an image embedding based on the target image sequence, and may combine the embeddings to generate an embedding input vector. the device may generate a final voice response and a final video based on the embedding input vector, and may provide the video data, the final voice response, and the final video.



[[Category:G10L13/027]]
[[Category:G10L13/08]]
[[Category:H04N7/15]]
[[Category:CPC_G10L13/027]]