WO2025022163 - MACHINE LEARNING POWERED SEARCH FOR AN OBJECT STORE DATABASE
National phase entry is expected:
Publication Number
WO/2025/022163
Publication Date
30.01.2025
International Application No.
PCT/IB2023/057610
International Filing Date
27.07.2023
Title **
[English]
MACHINE LEARNING POWERED SEARCH FOR AN OBJECT STORE DATABASE
[French]
RECHERCHE ALIMENTÉE PAR APPRENTISSAGE AUTOMATIQUE POUR UNE BASE DE DONNÉES DE MAGASIN D'OBJETS
Applicants **
SHARMA, Pratik
Inventors
SHARMA, Pratik
Application details
| Total Number of Claims/PCT | * |
| Number of Independent Claims | * |
| Number of Priorities | * |
| Number of Multi-Dependent Claims | * |
| Number of Drawings | * |
| Pages for Publication | * |
| Number of Pages with Drawings | * |
| Pages of Specification | * |
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| Number of Office Actions | * |
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International Searching Authority |
IP India
* |
| Recordal of a Change of the Applicant's Name/Address |
Change of Applicant's Name and Address
* |
| Type of Assignment |
The Standard Agent's Assignment
* |
| Applicant's Legal Status |
Natural Person
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| * | |
| * | |
| * | |
| * | |
| * | |
| Entry into National Phase under |
Chapter I
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| Patent Delivery |
Send the Letters Patent by Courier
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| Translation |
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Quotation for National Phase entry
| Country | Stages | Total | |
|---|---|---|---|
| China | Filing, Examination, Granting | 1881 | |
| EPO | Filing, Examination, Granting | 8722 | |
| Japan | Filing, Examination, Granting | 1869 | |
| South Korea | Filing, Examination, Granting | 1355 | |
| USA | Filing, Examination, Granting | 5940 |
Total:
19,767
The term for entry into the National Phase has expired. This quotation is for informational purposes only
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Abstract[English]
Vector Embedding refers to the process of transforming objects such as text, images, video, or audio into numerical representations that reside in a high-dimensional vector space. This technique is achieved through the use of Machine Learning (ML) algorithms that enable the understanding of the meaning and context of data (semantic relationships), learning of complex relationships and patterns within the data (syntactic relationships). Further we can use the resulting vector representations for a wide range of applications such as information retrieval, image classification, natural language processing, and many others. Vector embeddings have become increasingly popular due to their ability to capture semantic meaning and similarities between objects in a way that is easily computable and scalable. Here in this invention, we use a distributed object store system to store the above mentioned vector embeddings into different shards such that vector embeddings of similar objects are co-located in the same physical shard. Further globally unique key is a combination object identifier and vector embedding identifier, and in one of the illustrative embodiments the object identifier is the "prefix" followed by vector embedding identifier forming together the primary key for the distributed object store. Further when a similarity search has to be performed for Artificial Intelligence (Al) generated vector embeddings then first a "similarity index" or "degree of similarity" with a shard of the distributed object store is first quickly computed in a broad vector space consisting of all vector representations of all shards. Note for the above similarity measurement or computation we support Euclidean, cosine similarity, dot product, etc. and can accommodate more than 20,000 dimensions, making it well-suited to support a wide range of foundational and other AI/ML models. Finally in the shard with the highest similarity index, we perform search of related vector embeddings using k-Nearest Neighbours (kNN) algorithm to deliver reliable and precise results.