PATENT CLAIM ANALYSIS

Application Number: 15932370
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-08
Patent Classification: ["345", "420000"]

Abstract:
Present application refers to a method, a model generation unit and a computer program (product) for generating trained models (M) of moving persons, based on physically measured person scan data (S). The approach is based on a common template (T) for the respective person and on the measured person scan data (S) in different shapes and different poses. Scan data are measured with a 3D laser scanner. A generic personal model is used for co-registering a set of person scan data (S) aligning the template (T) to the set of person scans (S) while simultaneously training the generic personal model to become a trained person model (M) by constraining the generic person model to be scan-specific, person-specific and pose-specific and providing the trained model (M), based on the co-registering of the measured object scan data (S).

Claim (Index 18):
The system of  claim 12 , wherein the processor is further programmed by specific computer-executable instructions to at least:\n use a first coupling weight for aligning the template mesh to the object scan data; and use a second coupling weight for aligning the object scan data to the template mesh with the updated parameter applied to the template mesh, wherein the first coupling weight is less than the second coupling weight.

Metadata:
- Claim Count in Document: 51.0
- Percentile: 88.0
- Lexical Diversity: 2.33803
- Patent Class: 345.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['14433178', '15739658', '14604829', '11881172', '14602701']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6815540452588486
- 35 USC 102 Novelty (BERT): 0.5475785680377753
- Combined Prediction Score: 0.6681564975367412
- Mean Citation Score: 281.282038
- Max Citation Score: 426.38297
- Similarity Product: 277.9036519333356

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test