PATENT CLAIM ANALYSIS

Application Number: 16003049
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2018-12
Patent Classification: ["725", "014000"]

Abstract:
The present disclosure provides a computer-implemented method and system for progressive penalty and reward based ad scoring for real time supervised detection of televised video ads in televised media content. The method includes reception of the media content and selection of a set of frames per second from the media content. The method includes extraction of key points from each selected frame and derivation of binary descriptors from extracted key points. The method includes assignment of weight value to each binary descriptor and creation of a special pyramid of the binary descriptors. The method includes obtaining a first vocabulary of binary descriptors for each selected frame and accessing a second vocabulary of binary descriptors. The method includes comparison of each binary descriptor in the first vocabulary with binary descriptors in second vocabulary. The method includes progressively scoring each selected frame of the media content for detection of a first ad.

Claim (Index 10):
The computer-implemented method as recited in  claim 1 , wherein the second vocabulary of the binary descriptors is characterized by an n-ary tree data structure comprising of leaf nodes, the second vocabulary of the binary descriptors is created by:\n extracting the pre-defined number of key points and corresponding binary descriptors from each frame of the repository of ad frames; creating the special pyramid of the binary descriptors for each ad frame to obtain the pre-defined number of spatially identifiable binary descriptors; clustering the binary descriptors into a first set of clusters, the binary descriptors being clustered into the first set of clusters based on an evaluation of a minimum hamming distance between each binary descriptor; iteratively clustering the binary descriptors in each cluster of the first set of clusters and each subsequent cluster for a pre-determined iteration level to obtain the set of tree structured clusters; and assigning a weight value to each clustered binary descriptor based on a term frequency and an inverse document frequency and normalizing the weight values using L1 normalization, the weight value being normalized for an evaluated arithmetic sum of weight values as 1.

Metadata:
- Claim Count in Document: 47.0
- Percentile: 94.0
- Lexical Diversity: 2.53226
- Patent Class: 725.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['13132597', '12841078', '14414048', '13041457', '14903590']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3820158390912793
- 35 USC 102 Novelty (BERT): 0.5003110830143099
- Combined Prediction Score: 0.3938453634835824
- Mean Citation Score: 190.335102
- Max Citation Score: 210.71948
- Similarity Product: 142.45058839191913

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