PATENT CLAIM ANALYSIS

Application Number: 16136778
Application Type: Utility
Filing Date: 2018-09
Publication Date: 2019-01
Patent Classification: ["726", "006000"]

Abstract:
In an embodiment, a password risk evaluator may receive a request including a user identifier (ID) and a password. The password risk evaluator may retrieve a password preference model associated with the user ID, and may determine a risk score indicating a likelihood that the password is associated with the user ID. For example, the password preference model may be based on previous passwords used by the user, and may identify one or more characteristics, formulas, rules, or other indicia typically employed by the user in creating passwords. If the password supplied in the request matches or is similar to one or more elements of the password preference model, it may be more likely that the password in the request is a password supplied by the user. That is, the risk score may be an authentication of the user, or part of the authentication of the user, in some embodiments.

Claim (Index 7):
The non-transitory computer-readable storage medium of  claim 6 , wherein the machine learning algorithm comprises:\n using at least one natural language processing technique to identify one or more authentication code elements; using at least one named entity recognition technique to further identify the user; translating the authentication code elements into one or more pattern sequences; updating elements of the authentication code preference model based on the pattern sequences; generating one or more model scores based on a distance between one or more pattern sequences from the first authentication code to one or more pattern sequences of the previous authentication codes for the user; using a Bayesian model to compute a first probability score of the first authentication code on the user and a second probability score on all users; using the second probability score as a priori to smooth the first probability score; and using one or more score fusion techniques to combine the one or more model scores, the first probability score, and the second probability scores to generate an overall score for the first authentication code.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 97.0
- Lexical Diversity: 2.47059
- Patent Class: 726.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15335711', '14665276', '13460378', '14672076', '15728249']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2519480083376476
- 35 USC 102 Novelty (BERT): 0.5178085398488208
- Combined Prediction Score: 0.2785340614887649
- Mean Citation Score: 199.407228
- Max Citation Score: 319.64227
- Similarity Product: 298.7703195487857

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

Dataset: test