17959520. IDENTIFICATION OF FEATURE GROUPS IN FEATURE GRAPH DATABASES simplified abstract (AT&T Intellectual Property I, L.P.)
Contents
- 1 IDENTIFICATION OF FEATURE GROUPS IN FEATURE GRAPH DATABASES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 IDENTIFICATION OF FEATURE GROUPS IN FEATURE GRAPH DATABASES - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
IDENTIFICATION OF FEATURE GROUPS IN FEATURE GRAPH DATABASES
Organization Name
AT&T Intellectual Property I, L.P.
Inventor(s)
Edmond J. Abrahamian of Richmond Heights MO (US)
Ana Armenta of San Jose CA (US)
Andrew Campbell of Pleasant Hill CA (US)
Prince Paulraj of Coppell TX (US)
IDENTIFICATION OF FEATURE GROUPS IN FEATURE GRAPH DATABASES - A simplified explanation of the abstract
This abstract first appeared for US patent application 17959520 titled 'IDENTIFICATION OF FEATURE GROUPS IN FEATURE GRAPH DATABASES
Simplified Explanation
A processing system applies a community detection process to a feature graph database to identify communities of features associated with objects and relationships. Features are labeled with community labels and search results are provided based on these communities.
- Feature graph database with objects, features, and relationships
- Community detection process to identify feature communities
- Labeling features with community labels
- Providing search results based on feature communities
Potential Applications
This technology could be applied in various fields such as social network analysis, recommendation systems, and data mining to identify and analyze communities within a dataset.
Problems Solved
1. Efficiently identifying and analyzing communities of features within a large dataset 2. Improving search results by considering feature communities
Benefits
1. Enhanced understanding of relationships and patterns within a dataset 2. Improved search relevance and accuracy 3. Facilitates targeted analysis and decision-making based on feature communities
Potential Commercial Applications
Optimizing search engines, enhancing recommendation systems, improving targeted advertising, and streamlining data analysis processes in various industries could benefit from this technology.
Possible Prior Art
One possible prior art could be the use of clustering algorithms in data analysis to identify groups or communities within a dataset. Another could be the application of network analysis techniques to identify communities in social networks.
Unanswered Questions
How does the community detection process work in detail?
The abstract mentions a community detection process, but it does not provide specific information on the algorithms or methods used for this process.
What are the specific types of relationships considered in the feature graph database?
The abstract mentions relationships between objects, but it does not specify the nature or types of relationships considered in the database.
Original Abstract Submitted
A processing system may apply a community detection process to a feature graph database to identify a plurality of communities of features, the feature graph database comprising: a plurality of objects, each associated with one of a feature or a concept, and a plurality of relationships between the plurality of objects. Next, the processing system may label a first plurality of features of the feature graph database with at least a first community label, where the first plurality of features comprises features of at least a first community of the plurality of communities. The processing system may then obtain a search associated with at least one feature of the feature graph database, where the at least one feature is a part of the at least the first plurality of features of the at least the first community, and provide the first plurality of features in response to the search.
