17970162. SYSTEM AND METHOD TO INFER THOUGHTS AND A PROCESS THROUGH WHICH A HUMAN GENERATED LABELS FOR A CODING SCHEME simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)

SYSTEM AND METHOD TO INFER THOUGHTS AND A PROCESS THROUGH WHICH A HUMAN GENERATED LABELS FOR A CODING SCHEME

Organization Name

TOYOTA JIDOSHA KABUSHIKI KAISHA

Inventor(s)

Yin-Ying Chen of San Jose CA (US)

Shabnam Hakimi of San Francisco CA (US)

Kenton Michael Lyons of Los Altos CA (US)

Yanxia Zhang of Foster City CA (US)

Matthew Kyung-Soo Hong of Mountain View CA (US)

Totte Harinen of San Francisco CA (US)

Monica PhuongThao Van of Palo Alto CA (US)

Charlene Wu of San Francisco CA (US)

SYSTEM AND METHOD TO INFER THOUGHTS AND A PROCESS THROUGH WHICH A HUMAN GENERATED LABELS FOR A CODING SCHEME - A simplified explanation of the abstract

This abstract first appeared for US patent application 17970162 titled 'SYSTEM AND METHOD TO INFER THOUGHTS AND A PROCESS THROUGH WHICH A HUMAN GENERATED LABELS FOR A CODING SCHEME

Simplified Explanation

The abstract describes a method for inferring intent and discrepancies in a label coding scheme by analyzing how individuals label unstructured content and detecting meaning discrepancies for specific labels.

• Compiling data on how individuals label content according to a coding scheme with multiple labels.
• Analyzing the context of content labeled in a specific way by individuals.
• Detecting discrepancies in meaning for a particular label used by individuals.
• Inferring the strategic thinking of individuals based on detected meaning discrepancies for the label.
• Displaying metadata related to the strategic thinking and meaning discrepancies for the label among individuals in a coded dataset.

Potential Applications

This technology could be applied in various fields such as data analysis, content management, and information retrieval systems.

Problems Solved

This method helps in identifying inconsistencies in labeling content, improving the accuracy and reliability of data categorization.

Benefits

Enhanced understanding of how individuals interpret and label content, leading to better decision-making processes based on coded data.

Potential Commercial Applications

"Improving Data Categorization and Analysis through Labeling Inconsistency Detection"

Possible Prior Art

There may be prior art related to data labeling and analysis methods, but specific examples are not provided in this abstract.

Unanswered Questions

How does this method handle large datasets with numerous labels and discrepancies?

The abstract does not mention the scalability of the method for handling extensive datasets and multiple discrepancies.

Are there any limitations to inferring intent solely based on discrepancies in label meanings?

The abstract does not address any potential limitations or challenges in accurately inferring intent from discrepancies in label meanings.

Original Abstract Submitted

A method for inferring intent and discrepancies in a label coding scheme is described. The method includes compiling data indicating how one or more individuals labeled unstructured content according to the label coding scheme comprising a plurality of labels. The method also includes analyzing a context associated with a content labeled in a particular manner by the one or more individuals. The method further includes detecting discrepancies of meaning for a particular label used by the one or more individuals. The method also includes inferring a strategic thinking of the one or more individuals associated with the discrepancies of meaning detected for the particular label. The method further includes displaying recorded metadata associated with the strategic thinking and the discrepancies of meaning detected for the particular label between the one or more individuals regarding a coded dataset.