17768597. LEARNING DEVICE, LEARNING METHOD, INFERENCE DEVICE, INFERENCE METHOD, AND RECORDING MEDIUM simplified abstract (NEC Corporation)
Contents
- 1 LEARNING DEVICE, LEARNING METHOD, INFERENCE DEVICE, INFERENCE METHOD, AND RECORDING MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 LEARNING DEVICE, LEARNING METHOD, INFERENCE DEVICE, INFERENCE METHOD, AND RECORDING MEDIUM - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
LEARNING DEVICE, LEARNING METHOD, INFERENCE DEVICE, INFERENCE METHOD, AND RECORDING MEDIUM
Organization Name
Inventor(s)
LEARNING DEVICE, LEARNING METHOD, INFERENCE DEVICE, INFERENCE METHOD, AND RECORDING MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 17768597 titled 'LEARNING DEVICE, LEARNING METHOD, INFERENCE DEVICE, INFERENCE METHOD, AND RECORDING MEDIUM
Simplified Explanation
The learning device described in the patent application includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space with feature vectors extracted from attributed image data, while the case example storage unit stores computed feature vectors from image data as case examples associated with the metric space.
- Metric space learning unit: Learns metric space with feature vectors from attributed image data.
- Case example storage unit: Stores computed feature vectors from image data as case examples associated with the metric space.
Potential Applications
The technology described in the patent application could be applied in various fields such as image recognition, pattern recognition, and data analysis.
Problems Solved
This technology helps in organizing and storing feature vectors from image data efficiently, making it easier to retrieve and analyze information.
Benefits
The benefits of this technology include improved data organization, faster retrieval of information, and enhanced data analysis capabilities.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of advanced image recognition systems for industries such as healthcare, security, and manufacturing.
Possible Prior Art
One possible prior art for this technology could be existing image recognition systems that use feature vectors for data analysis and pattern recognition.
Unanswered Questions
How does the metric space learning unit handle different types of attributes in the image data?
The abstract does not provide specific details on how the metric space learning unit deals with various attributes in the image data.
What additional information is stored with the case examples in the case example storage unit?
The abstract mentions storing additional information associated with the case examples, but it does not specify what kind of information is stored.
Original Abstract Submitted
The learning device includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from attributed image data, for each combination of different attributes, using the attributed image data to which attribute information is assigned. The case example storage unit computes the feature vector from the image data for case example to store the computed feature vector as a case example associated with the metric space, and stores additional information associated with the case example.
