Google llc (20240339217). Medical Condition Visual Search simplified abstract

From WikiPatents
Jump to navigation Jump to search

Medical Condition Visual Search

Organization Name

google llc

Inventor(s)

Peggy Yen Phuong Bui of San Francisco CA (US)

Bianca Madalina Buisman of Ruschlikon (CH)

Quang Anh Duong of San Francisco CA (US)

Anastasia Martynova of Alameda CA (US)

Ayush Jain of Los Altos CA (US)

Yuan Liu of Santa Clara CA (US)

Jonathan David Krause of Mountain View CA (US)

Amit Sanjay Talreja of Santa Clara CA (US)

Rajeev Vijay Rikhye of Fremont CA (US)

Mahvish A. Nagda of Palo Alto CA (US)

Pinal Bavishi of Sunnyvale CA (US)

Christopher James Eicher of Cupertino CA (US)

Abigail Ward of San Mateo CA (US)

Jieming Yu of Jersey City NJ (US)

Louis Wang of San Francisco CA (US)

Dounia Berrada of Saratoga CA (US)

Dale Richard Webster of Redwood City CA (US)

Harshit Kharbanda of Pleasanton CA (US)

Igor Bonaci of Wollerau (CH)

Kai Yu of San Francisco CA (US)

Ke Lan of San Jose CA (US)

[[:Category:Kaan Y�cer of San Francisco CA (US)|Kaan Y�cer of San Francisco CA (US)]][[Category:Kaan Y�cer of San Francisco CA (US)]]

Willa Angel Chen Miller of Sunnyvale CA (US)

Lars Thomas Hansen of Adliswil (CH)

Medical Condition Visual Search - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240339217 titled 'Medical Condition Visual Search

The patent application describes systems and methods for diagnostic visual search, which involve processing a search query with multiple classification models to determine search query intent and predict potential diagnoses. The search query may include an image that is analyzed to identify a body part and determine if it is descriptive of a diagnostic search query. Based on the intent determination, the image is then processed by a conditions classification model to predict one or more condition classifications. Condition information is then obtained and provided based on the predicted condition classifications.

  • Processing search queries with classification models
  • Determining search query intent and predicting potential diagnoses
  • Analyzing images to identify body parts and determine diagnostic relevance
  • Predicting condition classifications based on intent determination
  • Providing condition information based on predicted classifications

Potential Applications

The technology can be applied in medical imaging, telemedicine, healthcare diagnostics, and medical research.

Problems Solved

This technology streamlines the diagnostic process, improves accuracy in identifying medical conditions, and enhances the efficiency of healthcare services.

Benefits

The benefits of this technology include faster diagnosis, improved patient outcomes, reduced healthcare costs, and enhanced medical research capabilities.

Commercial Applications

Title: Advanced Diagnostic Visual Search Technology This technology can be used in medical imaging software, telemedicine platforms, healthcare AI systems, and research institutions to improve diagnostic accuracy and streamline medical processes.

Prior Art

Further research can be conducted in the field of medical imaging, AI in healthcare, and diagnostic visual search technologies to explore existing solutions and advancements.

Frequently Updated Research

Stay updated on the latest developments in medical imaging AI, telemedicine technologies, and healthcare diagnostics to leverage advancements in the field.

Questions about Diagnostic Visual Search

1. How does the technology differentiate between different types of medical conditions based on visual search queries? 2. What are the potential limitations of using visual search technology in medical diagnostics?


Original Abstract Submitted

systems and methods for diagnostic visual search can include processing a search query with a plurality of classification models to determine a search query intent and predict potential diagnosis. the search query can include an image that is processed to determine the presence of a body part and may be processed to determine if the search query is descriptive of a diagnostic search query. based on the intent determination, the image may then be processed by a conditions classification model to determine one or more predicted condition classifications. condition information can then be obtained and provided based on the one or more predicted condition classifications.