PATENT CLAIM ANALYSIS

Application Number: 16232767
Application Type: Utility
Filing Date: 2018-12
Publication Date: 2019-05
Patent Classification: ["726", "026000"]

Abstract:
Embodiments of the invention relate to systems and methods for providing an anonymization engine. One embodiment of the present invention relates to a method comprising receiving a message directed at a recipient computer located outside a secure area by a privacy computer located within a secure area. The privacy computer may identify private information using a plurality of privacy rules and anonymize the message according to the plurality of privacy rules. Another embodiment may be directed to a method comprising receiving a request for sensitive data from a requesting computer. An anonymization computer may determine a sensitive data record associated with the request and may anonymize the sensitive data record by performing at least two of: removing unnecessary sensitive data entries from the sensitive data record, masking the sensitive data entries to maintain format, separating the sensitive data entries into associated data groupings, and de-contexting the data.

Claim (Index 34):
The recipient computer of  claim 30 , wherein at least the portion of the sensitive data is recreated by reversing anonymization of at least the portion of the sensitive data using the one or more privacy rules.

Metadata:
- Claim Count in Document: 44.0
- Percentile: 98.0
- Lexical Diversity: 2.16667
- Patent Class: 726.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['13964896', '15491455', '14947915', '13041918', '13927934']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3575451884426997
- 35 USC 102 Novelty (BERT): 0.5822643461330456
- Combined Prediction Score: 0.3800171042117343
- Mean Citation Score: 301.327838
- Max Citation Score: 437.386
- Similarity Product: 355.29124030780787

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

Dataset: test