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 6):
The computer-implemented method of  claim 4 , further comprising:\n obtaining second object scan data that corresponds to physical locations on a surface of a second human body, wherein the second human body is positioned in a second pose that is different from the first pose; and generating a second visual model of the second human body, wherein generating the second visual model comprises:\n aligning the template to the second object scan data to obtain second aligned object scan data; \n training the generic body model with the second aligned object scan data to obtain the trained body model including a second updated template; \n aligning the second updated template to the second object scan data using the second version of the trained body model as a constraint to obtain second constrained, aligned object data; and \n updating the trained body model with the second constrained, aligned object data to obtain the second visual model of the second human body in the second pose.

Metadata:
- Claim Count in Document: 51.0
- Percentile: 88.0
- Lexical Diversity: 2.33803
- Patent Class: 345.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['14433178', '15739658', '14604829', '11881172', '14602701']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6538428848575913
- 35 USC 102 Novelty (BERT): 0.5480003836649232
- Combined Prediction Score: 0.6432586347383245
- Mean Citation Score: 281.282038
- Max Citation Score: 426.38297
- Similarity Product: 305.3454407656312

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