PATENT CLAIM ANALYSIS

Application Number: 16308353
Application Type: Utility
Filing Date: 2018-12
Publication Date: 2019-08
Patent Classification: ["701", "027000"]

Abstract:
A speed planning method and apparatus and a calculating apparatus for automatic driving of a vehicle. The method comprises: using a training sample set to perform machine learning to obtain a machine learning model (S 110 ); partitioning an input space, and obtaining a decision result corresponding to a determined partition based on the obtained machine learning model to form a partition decision table of each partition corresponding to the corresponding decision result (S 120 ); and obtaining each dimensional feature vector of a vehicle while driving in real time as an input feature, determining an input partition to which the input feature belongs, and querying the partition decision table based on the determined partition to obtain the corresponding decision result (S 130 ). The present disclosure effectively solves the problem that a model trained by means of machine learning cannot be locally adjusted and easily modifies the decision of a certain partition without affecting the decision results of other partitions at all. The intuitive nature of a partition decision table can effectively help to find and solve problems in the machine learning process. The partition decision table can speed up the decision process.

Claim (Index 18):
The speed planning apparatus according to  claim 17 , wherein the partition decision table obtaining unit is configured to:\n calculate a size of a space corresponding to a discrete coding result obtained by the discrete coding method; when the size of the space is greater than a determined threshold, use a dynamic storage method to store the partition decision table, traverse only an input of a training space, store an output result of the corresponding decision model, and store the trained decision model for backup; and when the space is smaller than the determined threshold, use a static storage method to store the partition decision table, traverse all code spaces, and store the output result of the decision model.

Metadata:
- Claim Count in Document: 42.0
- Percentile: 98.0
- Lexical Diversity: 2.17708
- Patent Class: 701.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['16134134', '13990146', '14680892', '16016691', '15484282']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3949996109042374
- 35 USC 102 Novelty (BERT): 0.5014280756992022
- Combined Prediction Score: 0.4056424573837339
- Mean Citation Score: 174.39188399999995
- Max Citation Score: 186.18304
- Similarity Product: 113.23345349449156

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

Dataset: test