PATENT CLAIM ANALYSIS

Application Number: 15949775
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-10
Patent Classification: ["706", "012000"]

Abstract:
In some embodiments, the present invention provides for a computer system which includes a content database storing initial content data and a vocabulary data set; a processor configured to applying a machine learning model to transform the initial content data into a N-dimensional vector space; self-training the machine learning model based on the vocabulary data set; applying a clustering technique to the N-dimensional vector space to generate a cluster model of clusters, where each cluster includes a plurality of word representations; associating each cluster with a cluster identifier; obtaining subsequent content data; associating each data element of the subsequent content data with each cluster to generate a content data cluster mapping model; continuously tracking, for each user, each respective cluster identifier of each respective cluster associated with each action performed by each user with each data element to continuously self-adapt each user-specific, time-specific dynamic cluster mapping model.

Claim (Index 1):
A self-adaptive, self-train computer system, comprising:\n a plurality of electronic content feeds from a plurality of distinct electronic sources; at least one content database that is configured to store at least:\n i) initial content data derived from the plurality of electronic content feeds, and \n ii) vocabulary data that comprises at least one vocabulary data set that comprises \n at least one of:\n 1) a set of pre-determined words, phrases, or both, and \n 2) a set of user-specific words, phrases, or both that is associated with at least one particular user of the system; \n \n a non-transient memory, storing particular program code; and at least one computer processor that, when executing the particular program code, is configured to perform at least the following operations:\n obtaining at least one portion of the initial content data from the at least one content database; \n applying at least one machine learning model to transform the at least one portion of the initial content data into a N-dimensional vector space; \n self-training the at least one machine learning model based on the at least one vocabulary data set; \n applying at least one clustering technique to the N-dimensional vector space to generate a cluster model that defines a plurality of clusters, wherein each cluster comprises a plurality of word representations from the at least one portion of the initial content data; \n associating each cluster of the plurality of clusters of the cluster model with at least cluster identifier; \n obtaining at least one portion of first subsequent content data from the at least one content database; \n wherein the first subsequent content data is content data received from the plurality of electronic content feeds from the plurality of distinct electronic sources after the cluster model has been generated during at least one first time period; \n associating each data element of at least one portion of the first subsequent content data with each respective cluster of the plurality of clusters of the cluster model to generate a content data cluster mapping model; \n continuously tracking, for each user, in real-time, each respective cluster identifier of each respective cluster associated with at least one action performed by each respective user with at least one data element from at least one respective portion of the first subsequent content data during each respective time period to continuously self-adapt each respective user-specific, time-specific dynamic cluster mapping model based, at least in part, on the at least one action performed by each respective user during each respective time; \n wherein each respective user-specific, time-specific dynamic cluster mapping model comprises a plurality of user-specific, time-specific cluster identifiers; \n continuously updating, for each user, each respective user-specific, time-specific dynamic cluster mapping model in at least one user profile database; \n applying each respective user-specific, time-specific dynamic cluster mapping model to at least one portion of second content data obtained during at least one second time period to determine a user-specific output content data to be delivered to each respective user at a particular time period; and \n causing to display the user-specific output content data on a screen of at least one portable electronic device of each respective user.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 91.0
- Lexical Diversity: 2.31884
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15478363', '15924923', '10043714', '14180179', '11451217']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3774941342900575
- 35 USC 102 Novelty (BERT): 0.4720470425575374
- Combined Prediction Score: 0.3869494251168056
- Mean Citation Score: 148.445618
- Max Citation Score: 151.33942
- Similarity Product: 104.72733442945956

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