Data Object Tagging for Similarity Search
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Solution Overview
Problem
Conventional data representation techniques using unique signatures for data objects are inadequate for searching and matching similar objects, as they fail to efficiently generate multiple signatures or organized representations from a single object, making it difficult to search for multiple objects with similar characteristics.
Innovation Solution
The technique generates multiple mapped tags for a data object by mapping its strings of characters to tags, allowing for the creation of cluster signatures that represent multiple objects and facilitate comparison and search across databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional unique signature representation is used for data objects, then each object can be uniquely identified, but it becomes difficult to search and match similar objects efficiently
Solution Approach 1:
The patent segments a data object into multiple tags by mapping its character strings to multiple semantic tags. Instead of treating each object as a single unique signature, the object is divided into multiple representational components (tags) that capture different aspects of its meaning, enabling efficient similarity search while preserving information.
Solution Approach 2:
The patent creates multiple signatures for each data object that can serve multiple functions: unique identification, similarity matching, and semantic search. These multi-functional signatures replace the conventional single-purpose unique signature, allowing the same representation to fulfill multiple operational needs.
2Productivity
If multiple signatures are generated for each data object, then search and matching of similar objects improves, but the complexity of data representation increases
Solution Approach 1:
The patent creates multiple simplified copies (signatures) of each data object, where each signature is a tagged representation of the object's characteristics. These copies enable efficient search operations without requiring complex processing of the original objects, thus improving productivity while managing complexity through replication rather than transformation.
Solution Approach 2:
The patent transforms data objects from their original format into tagged representations with specific parameters (tags). This parameter transformation allows the system to work with standardized, searchable representations rather than complex original data structures, improving search efficiency while the tagging system manages the complexity through structured parameterization.
3Adaptability or versatility
If conventional single signature representation is used, then data storage is efficient, but the ability to represent and search multiple aspects of data objects is limited
Solution Approach 1:
The patent extracts key semantic information from data objects and represents them as separate tags. Instead of storing and processing the entire original data, the system extracts essential characteristics into compact tagged representations, enabling versatile search capabilities while reducing the volume of data that needs to be processed.
Solution Approach 2:
The patent adds a new dimensional representation to data objects by creating tagged signatures that capture semantic meaning. This additional dimension allows the system to represent and search objects based on their meaning and characteristics rather than just their original format, increasing adaptability without proportionally increasing storage requirements.
Data Source
AI summary
A data object can be represented based on multiple “tags” (e.g., multiple signatures provided as a cluster of signatures based on multiple tags of a data model). Essentially, the representation of the data object need not necessarily reflect the entire data object but it can provide a useful indication (or a signal) (e.g., “s40={computer vision, image analysis, tracking, detection, 3d}”), In addition, a data representation provided (e.g., signature or cluster of signatures) can represent multiple data objects. However, a data object can be represented by multiple tags (e.g., signatures) as well. Also, multiple tags can be used to collectively represent a data object. The tags can provide information in an organized and logically structured manner. For example, a cluster signature can be provided with strings of one or more words (e.g., keywords) concatenated with logical operators (e.g., AND, OR, NOT).