Nvidia patents method to auto-label images for AI training
Nvidia describes a system that gathers content from multiple sources and filters it using specific conditions. It then matches the remaining content with text descriptions by calculating how similar they are, creating a labeled dataset ready for training machine learning models.
In the filing’s own words
In various examples, a technique for performing conditional data sourcing and curation includes retrieving, by a plurality of processing nodes, a plurality of content items from one or more data sources, wherein each processing node retrieves a different subset of the plurality of content items from the one or more data sources. The technique also includes applying a first set of filters to metadata associated with the content items to generate a plurality of filtered content items. The technique further includes generating, based on a subset of the metadata associated with the filtered content items and a second set of filters, mappings between the filtered content items and text descriptions for the filtered content items and storing, based on the mappings, the filtered content items in association with the text descriptions in one or more data stores.
What exactly they claim (claim 1)
1. A method comprising: retrieving, by a plurality of processing nodes, a plurality of content items from one or more data sources, wherein each processing node of the plurality of processing nodes retrieves a different subset of the plurality of content items from the one or more data sources; applying a first set of filters to metadata associated with the plurality of content items to generate a plurality of filtered content items, wherein the first set of filters comprises a set of conditions associated with the metadata; generating similarity scores respectively between content embeddings of content items in the plurality of filtered content items and label embeddings of respective text descriptions for the plurality of filtered content items; generating, based on the generated similarity scores and a second set of filters implementing at least a threshold similarity score, a plurality of mappings between the plurality of filtered content items and a plurality of text descriptions for the plurality of filtered content items; storing, based on the plurality of mappings, the plurality of filtered content items in respective association with the plurality of text descriptions, as a curated dataset in one or more data stores; and causing one or more parameters of a machine learning model to be updated using the curated dataset.
Facts
- Company
- Nvidia (NVIDIA Corporation)
- Official title
- Conditional data sourcing and curation for generating datasets in deep learning model training
- Patent number
- US12762189B2
- Application number
- 18905540
- Filed
- Oct 3, 2024
- Granted
- Oct 13, 2026
- Inventors
- 4
- Claims
- 19
Classified under
G06F 16/9035 Physics & computingG06F 16/9024 Physics & computingMore from Nvidia in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.