18418502. RESOURCE CONSERVATION SYSTEM FOR SCALABLE IDENTIFICATION OF A SUBSET OF INPUTS FROM AMONG A GROUP OF INPUTS THAT CONTRIBUTED TO AN OUTPUT simplified abstract (Bank of America Corporation)
Contents
- 1 RESOURCE CONSERVATION SYSTEM FOR SCALABLE IDENTIFICATION OF A SUBSET OF INPUTS FROM AMONG A GROUP OF INPUTS THAT CONTRIBUTED TO AN OUTPUT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 RESOURCE CONSERVATION SYSTEM FOR SCALABLE IDENTIFICATION OF A SUBSET OF INPUTS FROM AMONG A GROUP OF INPUTS THAT CONTRIBUTED TO AN OUTPUT - 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
RESOURCE CONSERVATION SYSTEM FOR SCALABLE IDENTIFICATION OF A SUBSET OF INPUTS FROM AMONG A GROUP OF INPUTS THAT CONTRIBUTED TO AN OUTPUT
Organization Name
Inventor(s)
Justin Horowitz of Fort Mill SC (US)
Melissa Podrazka of Chicago IL (US)
RESOURCE CONSERVATION SYSTEM FOR SCALABLE IDENTIFICATION OF A SUBSET OF INPUTS FROM AMONG A GROUP OF INPUTS THAT CONTRIBUTED TO AN OUTPUT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18418502 titled 'RESOURCE CONSERVATION SYSTEM FOR SCALABLE IDENTIFICATION OF A SUBSET OF INPUTS FROM AMONG A GROUP OF INPUTS THAT CONTRIBUTED TO AN OUTPUT
Simplified Explanation
The patent application describes a resource conservation system with a determination processor that characterizes data structures based on inputs processed through artificial intelligence models. The processor identifies a numerical value for each data structure to determine the accuracy of the characterization and identifies the subset of inputs that contributed most to the output.
- Resource conservation system with determination processor
- Characterization of data structures based on inputs
- Processing inputs through a cascade of artificial intelligence models
- Numerical value assigned to each data structure for accuracy assessment
- Identification of subset of inputs contributing most to the output
Potential Applications
The technology could be applied in various fields such as data analysis, resource management, and optimization processes.
Problems Solved
The system helps in efficiently characterizing data structures and identifying key inputs contributing to the output, improving decision-making processes.
Benefits
- Enhanced accuracy in characterizing data structures - Efficient resource conservation and management - Improved understanding of data relationships
Potential Commercial Applications
"Optimizing Resource Conservation through Data Characterization and Analysis"
Possible Prior Art
There may be prior art related to data characterization systems and resource conservation methods, but specific examples are not provided in the abstract.
Unanswered Questions
How does the determination processor handle conflicting inputs from the artificial intelligence models?
The patent application does not specify how the system resolves conflicting inputs or outputs from the cascade of models.
What types of data structures are most suitable for characterization using this system?
The abstract does not mention the specific types of data structures that are ideal for analysis and characterization within this resource conservation system.
Original Abstract Submitted
A resource conservation system, including a determination processor may be provided. The determination processor may identify a characterization output that characterizes a plurality of data structures. The characterization output may be based on plurality of inputs. The inputs may be processed through a plurality, or cascade, of artificial intelligence models both in sequence and in parallel. A numerical value may be identified for each data structure. The value may identify a degree of certainty that the determination processor accurately characterized each data structure. When the degree is above a threshold, the determination processor may identify a subset of inputs that most contributed to the characterization output. The determination processor may execute an equation to identify a subset of inputs that most contributed to the output. The equation may involve inputs and/or outputs of each of the cascade of models. Identified inputs may be ranked based on contribution to the outcome.
