Microsoft technology licensing, llc (20240135098). INTERACTIVE CONCEPT EDITING IN COMPUTER-HUMAN INTERACTIVE LEARNING simplified abstract
Contents
- 1 INTERACTIVE CONCEPT EDITING IN COMPUTER-HUMAN INTERACTIVE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INTERACTIVE CONCEPT EDITING IN COMPUTER-HUMAN INTERACTIVE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
INTERACTIVE CONCEPT EDITING IN COMPUTER-HUMAN INTERACTIVE LEARNING
Organization Name
microsoft technology licensing, llc
Inventor(s)
Patrice Y. Simard of Bellevue WA (US)
David G. Grangier of Kirkland WA (US)
Leon Bottou of Kirkland WA (US)
Saleema A. Amershi of Seattle WA (US)
INTERACTIVE CONCEPT EDITING IN COMPUTER-HUMAN INTERACTIVE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135098 titled 'INTERACTIVE CONCEPT EDITING IN COMPUTER-HUMAN INTERACTIVE LEARNING
Simplified Explanation
The abstract describes a method for improving the relevance of search and analysis of large data sets by automatically classifying queries and web pages into useful categories. This involves building a large number of classifiers and schematizers to categorize different types of information, activities, and products, and applying them to millions of items to add valuable meta-data.
- Creation of classifiers and schematizers on large data sets
- Exercising classifiers and schematizers on hundreds of millions of items
- Adding usable meta-data to improve relevance
- Active labeling exploration, automatic regularization, and cold start
- Scaling with the number of items and classifiers
- Active featuring, segmentation, and schematization
Potential Applications
This technology can be applied in various fields such as e-commerce, data analysis, information retrieval, and content management systems.
Problems Solved
1. Difficulty in searching and analyzing extremely large data sets 2. Lack of relevant meta-data to improve search results
Benefits
1. Improved relevance and accuracy in search results 2. Efficient categorization and organization of data 3. Enhanced user experience in navigating large data sets
Potential Commercial Applications
"Enhancing Data Relevance and Analysis through Automated Classification and Categorization"
Possible Prior Art
There are existing technologies in the field of machine learning and data classification that may have similarities to this method.
Unanswered Questions
===How does this technology compare to existing data classification methods? This article does not provide a direct comparison to existing data classification methods, leaving the reader to wonder about the specific advantages and differences of this approach.
===What are the potential limitations or challenges of implementing this technology? The article does not address any potential limitations or challenges that may arise when implementing this technology, leaving room for further exploration into its practical applications and feasibility.
Original Abstract Submitted
a collection of data that is extremely large can be difficult to search and/or analyze. relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. a thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. creation of classifiers and schematizers is provided on large data sets. exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.
