# US 12,762,464 B2 · Data searching system and method

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- Number: US 12,762,464 B2
- Document ID: US12762464B2
- Status: Granted
- Kind: B2
- Application number: 17/477,069
- Filed: 2021-09-16
- Published: 2022-01-06
- Granted: 2026-10-13
- Earliest claimed: 2017-03-31
- Assignees: Certara USA, Inc.
- Applicants: Certara USA, Inc.
- Inventors: Christopher M. L. Bouton
- Web page: https://patentgrep.com/patent/US12762464B2

## CPC

- G06N 20/00: Machine learning
- G06F 16/24568: {Data stream processing; Continuous queries}
- G06F 16/285: {Clustering or classification}
- G06F 16/951: Indexing; Web crawling techniques
- G06F 16/9532: Query formulation
- G06N 5/022: Knowledge engineering; Knowledge acquisition
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

The present invention relates to a deep learning system suitable for searching data sources for specific content. In particular, the present invention relates to an unconventional machine-implemented process, leveraging a machine learning algorithm, to provide a technology that searches data sources and recognizes feature data associated with one or more predetermined classifiers.

## Claims

### Claim 1

A method for use in searching data sources for specific criteria and automatically updating machine learning training sets and classifier, the method comprising:
storing a machine learning algorithm and machine learning training sets, wherein the machine learning algorithm is configured to reference the machine learning training sets;
a processor effectuating a recognition agent over one or more data sources;
a processor effectuating an extractor that collects feature data from the one or more data sources using one or more predetermined classifiers stored in a classifier dictionary of predetermined classifiers, wherein the predetermined classifiers comprise one or more hashtags;
an input device receiving specific criteria;
a processor effectuating an updater that dynamically updates the machine learning training sets and the classifier dictionary based on the collected feature data from the one or more data sources and the specific criteria, enabling the machine learning algorithm to identify at least one feature data including the specific criteria from the collected feature data; and
an output device transmitting the at least one feature data including the identified specific criteria;
wherein the machine learning algorithm, the machine learning training sets, and the classifier dictionary, are continuously updated, using the updater, based on a plurality of received specific criteria and corresponding feature data collected from the extractor to improve performance of the machine learning algorithm over time, and
wherein the machine learning algorithm is a deep learning algorithm.

### Claim 2 (depends on 1)

The method of claim 1, wherein the effectuating over the one or more data sources comprises the recognition agent denoting analytics in the data to aggregate feature data.

### Claim 3 (depends on 2)

The method of claim 2, further comprising utilizing the machine learning algorithm to search the aggregated feature data for the at least one feature data that is associated with the predetermined classifiers and the specific criteria.

### Claim 4 (depends on 1)

The method of claim 1, wherein the predetermined classifiers are associated with specific criteria types.

### Claim 5 (depends on 4)

The method of claim 4, wherein the specific criteria types are at least one of medical, scientific, legal or news related information, and at least one feature is at least one of an image, text, video, and audio.

### Claim 6 (depends on 4)

The method of claim 4, wherein the predetermined classifiers comprise named entities.

### Claim 7 (depends on 1)

The method of claim 1, wherein the one or more data sources are at least one of social media post feeds, text documents, tabular content or image repositories.

### Claim 8

A method for searching for information related to posts in a plurality of data sources, the method comprises:
extracting at least one feature associated with one or more classifiers from a plurality of data sources, the one or more classifiers being identified from a classifier dictionary of predetermined classifiers, wherein the one or more predetermined classifiers comprise one or more hashtags;
dynamically updating training sets for a machine learning algorithm and identifying specific criteria from the at least one feature utilizing the machine learning algorithm and the training sets;
aggregating information from data sources of the plurality of data sources containing the identified specific criteria, providing the aggregated information for display in a searchable format; and
updating, periodically, the training sets, the classifier dictionary, and the machine learning algorithm by repeatedly performing (i) the step of extracting to extract a plurality of features and (ii) the step of dynamically updating the training sets based on the plurality of features,
wherein the machine learning algorithm is a deep learning algorithm.

### Claim 9 (depends on 8)

The method of claim 8, wherein the plurality of data sources comprise posts comprising hashtags.