View source for 18087976. AUTOMATED GROUND TRUTH GENERATION USING A NEURO-SYMBOLIC METAMODEL simplified abstract (Cisco Technology, Inc.)

=AUTOMATED GROUND TRUTH GENERATION USING A NEURO-SYMBOLIC METAMODEL=


==Organization Name==

[[:Category:Cisco Technology, Inc.|Cisco Technology, Inc.]]


[[Category:Cisco Technology, Inc.]]

==Inventor(s)==

[[:Category:Hugo Latapie of Long Beach CA (US)|Hugo Latapie of Long Beach CA (US)]][[Category:Hugo Latapie of Long Beach CA (US)]]

[[:Category:Gaowen Liu of Austin TX (US)|Gaowen Liu of Austin TX (US)]][[Category:Gaowen Liu of Austin TX (US)]]

[[:Category:Ozkan Kilic of Long Beach CA (US)|Ozkan Kilic of Long Beach CA (US)]][[Category:Ozkan Kilic of Long Beach CA (US)]]

[[:Category:Adam James Lawrence of Pasadena CA (US)|Adam James Lawrence of Pasadena CA (US)]][[Category:Adam James Lawrence of Pasadena CA (US)]]

[[:Category:Ramana Rao V. R. Kompella of Cupertino CA (US)|Ramana Rao V. R. Kompella of Cupertino CA (US)]][[Category:Ramana Rao V. R. Kompella of Cupertino CA (US)]]

==AUTOMATED GROUND TRUTH GENERATION USING A NEURO-SYMBOLIC METAMODEL - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18087976 titled 'AUTOMATED GROUND TRUTH GENERATION USING A NEURO-SYMBOLIC METAMODEL

The abstract describes a device that receives a request for ground truth examples to train a machine learning model, based on context data provided by the requestor. The device uses a metamodel with a semantic reasoner and a sub-symbolic layer to identify and label the ground truth examples, which are then provided to the requestor.

*Simplified Explanation:
- Device receives request for ground truth examples for machine learning model training.
- Context data about location provided by requestor.
- Metamodel with semantic reasoner and sub-symbolic layer used to identify and label examples.
- Ground truth examples provided to requestor.

*Key Features and Innovation:
- Use of metamodel with semantic reasoner and sub-symbolic layer.
- Context-based identification of ground truth examples.
- Provision of labeled examples for machine learning model training.

*Potential Applications:
- Training machine learning models for location-based analysis.
- Improving accuracy of machine learning algorithms with labeled ground truth examples.

*Problems Solved:
- Enhancing the quality of training data for machine learning models.
- Streamlining the process of obtaining and labeling ground truth examples.

*Benefits:
- Increased accuracy and efficiency in training machine learning models.
- Facilitation of location-specific analysis tasks.

*Commercial Applications:
- Optimizing location-based services and recommendations.
- Enhancing geospatial data analysis for various industries.

*Questions about the Technology:
1. How does the device differentiate between different types of ground truth examples?
2. What are the potential limitations of using a metamodel for identifying and labeling examples?

*Frequently Updated Research:
- Stay updated on advancements in machine learning model training techniques.
- Explore new developments in semantic reasoning and sub-symbolic layers for data analysis.


==Original Abstract Submitted==

In one embodiment, a device receives, from a requestor, a request for a set of ground truth examples of a particular type to be used to train a machine learning model. The request includes context data regarding a location to be analyzed by the machine learning model. The device identifies, based on the request, the set of ground truth examples using a metamodel comprising a semantic reasoner and a sub-symbolic layer. The device associates labels with the set of ground truth examples. The device provides, to the requestor, the set of ground truth examples and their labels.



[[Category:G06N3/08]]
[[Category:CPC_G06N3/08]]