International business machines corporation (20240111794). SEARCHING A DATA SOURCE USING EMBEDDINGS OF A VECTOR SPACE simplified abstract
Contents
- 1 SEARCHING A DATA SOURCE USING EMBEDDINGS OF A VECTOR SPACE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SEARCHING A DATA SOURCE USING EMBEDDINGS OF A VECTOR SPACE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SEARCHING A DATA SOURCE USING EMBEDDINGS OF A VECTOR SPACE
Organization Name
international business machines corporation
Inventor(s)
Richard Obinna Osuala of Munich (DE)
Dominik Moritz Stein of Oberding (DE)
Andrea Giovannini of Zurich (CH)
SEARCHING A DATA SOURCE USING EMBEDDINGS OF A VECTOR SPACE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240111794 titled 'SEARCHING A DATA SOURCE USING EMBEDDINGS OF A VECTOR SPACE
Simplified Explanation
The abstract describes a method for querying a data source represented by data object embeddings in a vector space. A processor inputs a query and at least one token to a trained embedding generation model, which generates a set of embeddings including an embedding of the query and at least one token. These embeddings are used to search for matching data object embeddings, resulting in search result embeddings that represent the data objects.
- Explanation:
- Processor inputs query and token to trained model
- Model generates embeddings for query and token
- Search for matching data object embeddings
- Determine data objects represented by search result embeddings
Potential Applications
The technology can be applied in information retrieval systems, recommendation engines, and data analysis tools.
Problems Solved
This technology streamlines the process of querying and retrieving data objects based on embeddings, improving efficiency and accuracy in data search tasks.
Benefits
The method allows for more precise and targeted searches in large datasets, leading to faster retrieval of relevant information.
Potential Commercial Applications
- Optimizing search engines for better user experience
- Enhancing recommendation systems for personalized content delivery
Possible Prior Art
There may be prior art related to embedding generation models for data retrieval and search tasks, but specific examples are not provided in the abstract.
Unanswered Questions
How does this technology handle noisy or incomplete data in the embeddings?
The abstract does not mention how the method deals with noisy or incomplete data in the embeddings, which could affect the accuracy of search results.
What is the computational cost of generating and matching embeddings in this process?
The abstract does not address the computational resources required for generating and matching embeddings, which could impact the scalability of the technology.
Original Abstract Submitted
in several aspects for querying a data source represented by data object embeddings in a vector space, a processor inputs, to a trained embedding generation model, a received query and at least one token for receiving from the trained embedding generation model a set of embeddings of the vector space. the set of embeddings comprises an embedding of the received query and at least one embedding of the at least one token respectively, wherein the embedding of each token is a prediction of an embedding of a supplement of the query. the data object embeddings may be searched for data object embeddings that match the set of embeddings. this may result in search result embeddings of the set of embeddings. data objects that are represented by the search result embeddings may be determined. at least part of the determined data objects may be provided.
