Machine Learning Model Training Using Randomized Solutions to Find Global Minimum
Summary
The USPTO published patent application US20260099758A1, filed October 4, 2024, for a machine learning technique that identifies global minimums across local minimums. Inventors Bikramaditya Padhi and Ramprasadh Kothandaraman disclosed an application server method using randomized solutions and threshold-based evaluation to optimize model training.
What changed
The USPTO published a patent application (US20260099758A1) for a machine learning method that generates randomized solutions to train models and identify global minimums across local minimums. The technique involves receiving training requests, generating initial randomized solutions based on model parameters, selecting solutions, generating additional randomized solutions, and determining whether solutions satisfy threshold criteria indicating a global minimum.
For technology companies and software developers, this patent represents potential IP considerations for machine learning optimization techniques. While patent publications do not create immediate compliance obligations, entities developing similar ML training methods should monitor for eventual patent issuance and potential licensing implications.
What to do next
- Monitor for patent issuance updates
Archived snapshot
Apr 14, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
ITERATIVE TECHNIQUE TO IDENTIFY GLOBAL MINIMUM IN A DATASET
Application US20260099758A1 Kind: A1 Apr 09, 2026
Inventors
Bikramaditya Padhi, Ramprasadh Kothandaraman
Abstract
An application server may receive a request to train the machine learning model on a dataset, and may generate a first set of randomized solutions based on inputting one or more of a set of model parameters into the machine learning model, where the first set of randomized solutions correspond to a set of outputs generated by the machine learning model and spans at least a subset of a set of local minimums. The application server may then select a first solution from the first set of randomized solutions and generate a second set of randomized solutions based on the first solution and inputting one or more of the set of model parameters into the machine learning model. The application server may then determine that the second set of randomized solutions includes a global minimum of the dataset based on the second set of randomized solutions satisfying a threshold.
CPC Classifications
G06N 20/00
Filing Date
2024-10-04
Application No.
18906974
Named provisions
Related changes
Get daily alerts for USPTO Patent Applications - AI & Computing (G06N)
Daily digest delivered to your inbox.
Free. Unsubscribe anytime.
Source
About this page
Every important government, regulator, and court update from around the world. One place. Real-time. Free. Our mission
Source document text, dates, docket IDs, and authority are extracted directly from USPTO.
The summary, classification, recommended actions, deadlines, and penalty information are AI-generated from the original text and may contain errors. Always verify against the source document.
Classification
Who this affects
Taxonomy
Browse Categories
Get alerts for this source
We'll email you when USPTO Patent Applications - AI & Computing (G06N) publishes new changes.
Subscribed!
Optional. Filters your digest to exactly the updates that matter to you.