Seattle, WA, United States of America

Matias H Ganc


Average Co-Inventor Count = 4.0

ph-index = 2

Forward Citations = 15(Granted Patents)


Company Filing History:


Years Active: 2012-2014

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2 patents (USPTO):Explore Patents

Title: Matias H Ganc: Innovator in Data Feed Classification

Introduction

Matias H Ganc is a notable inventor based in Seattle, WA. He has made significant contributions to the field of data feed classification, holding 2 patents that enhance the efficiency of item classification in electronic commerce.

Latest Patents

His latest patents include a "System, method, and computer readable medium for item feed classification." This invention relates to the classification of items included in a data feed, which is generated for provision to a referral network site. The feed comprises items sold through an electronic commerce network site. The item classification information corresponding to an item sold is identified based on the taxonomy information of the referral network site and included in the generated feed of items. Another patent, "Data feed item classification," shares a similar focus on the classification of items in a data feed, emphasizing the importance of accurate item classification for effective data management.

Career Highlights

Matias is currently employed at Amazon Technologies, Inc., where he applies his expertise in data classification to improve the company's e-commerce operations. His work is instrumental in ensuring that items are accurately classified and presented to users, enhancing the overall shopping experience.

Collaborations

Throughout his career, Matias has collaborated with talented individuals such as Jeetendra G Mirchandani and Mohit Gupta. These collaborations have contributed to the development of innovative solutions in the field of data feed classification.

Conclusion

Matias H Ganc is a prominent inventor whose work in data feed classification has made a significant impact on the e-commerce industry. His patents reflect his commitment to innovation and efficiency in item classification.

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