Company Filing History:
Years Active: 2020-2024
Title: Innovations of Matthew Valley
Introduction
Matthew Valley is an accomplished inventor based in American Fork, UT (US). He has made significant contributions to the field of predictive analysis through his innovative patents. With a total of four patents to his name, Valley continues to push the boundaries of technology and machine learning.
Latest Patents
One of Matthew Valley's latest patents focuses on predictive analysis systems and methods using machine learning. This system utilizes a predictive analysis model to analyze contracts or other documents. It parses a document and/or a repository of information associated with the document. The system identifies one or more terms in the document and corresponding terms in the repository. It determines a difference parameter between a first term extracted from the document and a second term extracted from the repository. The system assesses whether the difference between the first and second terms, represented by the difference parameter, is likely to be acceptable to the user using the predictive analysis model. Additionally, it reports a validation parameter indicating a level of acceptability associated with the difference. User feedback on the accuracy of the predictive analysis model is utilized to train, modify, and improve the model.
Career Highlights
Matthew Valley is currently employed at Deepsee.ai Inc., where he applies his expertise in machine learning and predictive analysis. His work has garnered attention for its innovative approach to analyzing complex documents and contracts.
Collaborations
Some of his notable coworkers include Joseph M Wood and Robert D Bailey, who contribute to the collaborative environment at Deepsee.ai Inc.
Conclusion
Matthew Valley's contributions to predictive analysis and machine learning demonstrate his commitment to innovation in technology. His patents reflect a deep understanding of complex systems and a dedication to improving user experience through advanced analytical methods.