PATENT CLAIM ANALYSIS

Application Number: 15895687
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2019-08
Patent Classification: ["345", "473000"]

Abstract:
Embodiments of the present invention are directed to facilitate creating cinemagraphs from virtual long-exposure images. In accordance with some embodiments of the present invention, virtual long-exposure image comprising a plurality of aligned frames is provided and a selection of a region of pixels in the virtual long-exposure image is received. Based on the selected region of pixels, a set of frames for animation is identified from the plurality of frames. The set of frames may be identified by automatically detecting a sequence of frames or by receiving a user selection of frames. The virtual LE image is combined with the set of frames to create a cinemagraph having a visible non-animated portion formed by the virtual LE image and a visible animated portion formed by the set of frames.

Claim (Index 17):
The system of  claim 16 , wherein providing the virtual long-exposure image comprises aggregating the plurality of short-exposure frames into a stack of frames and aligning frames based on similarity of background pixels at each pixel location within the stack, wherein the one or more blurred regions result from aggregating the plurality of short-exposure frames.

Metadata:
- Claim Count in Document: 21.0
- Percentile: 88.0
- Lexical Diversity: 2.15873
- Patent Class: 345.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['13656442', '15282533', '14377732', '15264789', '14896194']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6577109970458213
- 35 USC 102 Novelty (BERT): 0.4822117983790428
- Combined Prediction Score: 0.6401610771791435
- Mean Citation Score: 174.22106200000005
- Max Citation Score: 181.8266
- Similarity Product: 129.96345454461576

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

Dataset: test