Google llc (20240220527). CLASSIFYING DATA OBJECTS simplified abstract

CLASSIFYING DATA OBJECTS

Organization Name

google llc

Inventor(s)

Gregory Sean Corrado of San Francisco CA (US)

Tomas Mikolov of Jersey City NJ (US)

Samuel Bengio of Los Altos CA (US)

Yoram Singer of Palo Alto CA (US)

Jonathon Shlens of San Francisco CA (US)

Andrea L. Frome of Oakland CA (US)

Jeffrey Adgate Dean of Palo Alto CA (US)

Mohammad Norouzi of Richmond Hill (CA)

CLASSIFYING DATA OBJECTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240220527 titled 'CLASSIFYING DATA OBJECTS

• Simplified Explanation:

The patent application describes methods, systems, and apparatus for classifying data objects based on high-dimensional representations of terms in a vocabulary and classification data for the objects.

• Key Features and Innovation:

- Obtaining high-dimensional representations of terms in a vocabulary. - Computing aggregate high-dimensional representations for data objects. - Selecting category labels for data objects based on closest term representations.

• Potential Applications:

- Data classification in various industries such as e-commerce, healthcare, and finance. - Content recommendation systems. - Sentiment analysis in social media monitoring.

• Problems Solved:

- Efficient and accurate data classification. - Handling large volumes of data with complex relationships. - Improving the performance of machine learning algorithms.

• Benefits:

- Enhanced data organization and retrieval. - Increased accuracy in categorizing data objects. - Improved decision-making based on classified data.

• Commercial Applications:

Title: High-Dimensional Data Classification Technology for Enhanced Data Organization Description: This technology can be used in various commercial applications such as targeted advertising, personalized recommendations, and fraud detection in financial transactions.

• Prior Art:

Readers can explore prior research on high-dimensional data classification, machine learning algorithms, and natural language processing techniques for related information.

• Frequently Updated Research:

Stay updated on advancements in high-dimensional data classification, deep learning models, and semantic analysis techniques for continuous improvements in data classification technology.

Questions about High-Dimensional Data Classification: 1. How does high-dimensional data classification differ from traditional data classification methods? High-dimensional data classification involves representing data objects in a high-dimensional space, allowing for more complex relationships to be captured compared to traditional methods.

2. What are the potential challenges in implementing high-dimensional data classification in real-world applications? Implementing high-dimensional data classification may require significant computational resources and expertise in handling large datasets efficiently to achieve accurate results.

Original Abstract Submitted

methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying data objects. one of the methods includes obtaining data that associates each term in a vocabulary of terms with a respective high-dimensional representation of the term; obtaining classification data for a data object, wherein the classification data includes a respective score for each of a plurality of categories, and wherein each of the categories is associated with a respective category label; computing an aggregate high-dimensional representation for the data object from high-dimensional representations for the category labels associated with the categories and the respective scores; identifying a first term in the vocabulary of terms having a high-dimensional representation that is closest to the aggregate high-dimensional representation; and selecting the first term as a category label for the data object.