PATENT CLAIM ANALYSIS

Application Number: 15956101
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-08
Patent Classification: ["707", "748000"]

Abstract:
Users receive information which must be filtered, processed, analysed, reviewed, consolidated and distributed or acted upon. Prior art tools automatically processing content to assign sentiment to the content are ineffective as essential aspects such as context are not considered. Embodiments of the invention provide automatic contextual based sentiment classification of content in terms of both sentiments expressed and their intensity. Further a content set is analysed to rapidly establish an “at-a-glance” type assessment of the key topics/themes present within the content set and sentimentally annotate each. Importantly embodiments of the invention also provide for a user to establish the basis for the sentiment associated with an item of or set of content, i.e. make it explainable. Further embodiments of the invention provide for the establishment of psychological tone to sentiments where the sentiments and psychological tones to be tuned from the context or domain of the content.

Claim (Index 19):
A system comprising:\n an electronic device associated with a user executing a software application relating to establishing a sentiment count of an item of electronic content; wherein the sentiment of the item of electronic content is acquired from a remote server connected to the electronic device via a global communications network; and the remote server comprises a database stored within a non-volatile, non-transitory memory comprising a plurality of items of electronic content and computer software instructions stored within the non-volatile non-transitory memory for execution by the server, the computer software instructions when executed automatically generating an overall sentiment count for an item of electronic content of the plurality of items of electronic content via a process comprising the steps of:\n retrieving from the non-volatile non-transitory memory a plurality of predetermined portions of an item of electronic content of the plurality of items of electronic content; \n parsing each retrieved predetermined portion of the item of electronic content to establish a plurality of positive sentiment terms; \n counting occurrences of a positive sentiment term of the plurality of positive sentiment terms within that predetermined portion of the item of electronic content to establish a positive sentiment count for that predetermined portion of the item of electronic content; \n parsing each retrieved predetermined portion of the item of electronic content to establish a plurality of negative sentiment terms; \n counting occurrences of a negative sentiment term of the plurality of negative sentiment terms within the predetermined portion of the item of electronic content to establish a negative sentiment count for that predetermined portion of the item of electronic content; \n establishing a portion weighting for each retrieved predetermined portion of the item of electronic content; \n multiplying each of the positive sentiment count and the negative sentiment count for a predetermined portion of the item of electronic content by the portion weighting for that retrieved predetermined portion to establish a portion weighted positive sentiment for that predetermined portion of the item of electronic content and a portion weighted negative sentiment for that predetermined portion of the item of electronic content; \n determining a sentiment label to associate with the item of electronic content in dependence upon at least one of the occurrences of the positive sentiment term and occurrences of the negative sentiment term within each predetermined portion of the item of content; and \n summing all the portion weighted positive sentiments and the portion weighted negative sentiments for the item of electronic content of the plurality of items of electronic content to generate an overall sentiment count for an item of electronic content.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 91.0
- Lexical Diversity: 1.75
- Patent Class: 707.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['13753668', '13753645', '11479522', '14384834', '13052686']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1704190066242143
- 35 USC 102 Novelty (BERT): 0.5019798621637481
- Combined Prediction Score: 0.2035750921781677
- Mean Citation Score: 185.654656
- Max Citation Score: 233.09554
- Similarity Product: 156.7020966530895

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

Dataset: test