View source for 17969111. INTELLIGENT ENTITY RELATION DETECTION simplified abstract (SAP SE)

=INTELLIGENT ENTITY RELATION DETECTION=


==Organization Name==

[[:Category:SAP SE|SAP SE]]


[[Category:SAP SE]]

==Inventor(s)==

[[:Category:Prajesh K. of Mattanur (IN)|Prajesh K. of Mattanur (IN)]][[Category:Prajesh K. of Mattanur (IN)]]

[[:Category:Prateek Bajaj of New Delhi (IN)|Prateek Bajaj of New Delhi (IN)]][[Category:Prateek Bajaj of New Delhi (IN)]]

==INTELLIGENT ENTITY RELATION DETECTION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17969111 titled 'INTELLIGENT ENTITY RELATION DETECTION

The patent application is focused on a method and system for determining topics of data objects using a machine learning model. Once topics are identified, data objects with similar topics can be automatically related, creating a semantic web approach that forms a metadata-driven network of analytical representation of business entities/objects.

* Machine learning model trained to determine topics of data objects
* Automatic relation of data objects with similar topics
* Semantic web approach using metadata for data objects and insights
* Continuous data stream pushed into a central tool for seamless analytical dashboards
* Metadata-driven network of analytical representation

### Potential Applications:
This technology can be applied in various industries such as marketing, e-commerce, and data analysis to efficiently organize and relate data objects based on topics.

### Problems Solved:
This technology addresses the challenge of manually organizing and relating data objects with similar topics, saving time and effort in data analysis processes.

### Benefits:
- Improved efficiency in data analysis
- Enhanced organization of data objects
- Seamless generation of analytical dashboards

### Commercial Applications:
The technology can be utilized by businesses for data analysis, market research, and customer segmentation to optimize decision-making processes and improve overall efficiency.

### Prior Art:
Researchers can explore prior art related to machine learning models for topic determination and semantic web approaches for data organization and analysis.

### Frequently Updated Research:
Stay updated on advancements in machine learning models for topic determination and semantic web technologies for data organization and analysis to enhance the efficiency and effectiveness of this technology.

#### Questions about the Technology:
1. How does the machine learning model determine topics of data objects?
2. What are the key benefits of using a semantic web approach for data organization and analysis?


==Original Abstract Submitted==

Example methods and systems are directed to determining topics of data objects. A machine learning model may be trained and used to determine topics of data objects. After topics for data objects are determined by the trained machine learning model, data objects having similar topics can be automatically related. A semantic web approach relies upon the metadata of the data objects being generated along with the metadata of the insights being generated (such as topic groups). Such a semantic association between various objects (using metadata) forms a metadata driven network of analytical representation of business entities/objects. A data-stream comprising the semantic web, indicating the relationships between the metadata of the data objects and the metadata for the topics, may be pushed continuously into a central tool or repository to allow users to generate seamless analytical dashboards with minimal efforts.



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