View source for 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)

=RESOURCE CONSERVATION SYSTEM FOR SCALABLE IDENTIFICATION OF A SUBSET OF INPUTS FROM AMONG A GROUP OF INPUTS THAT CONTRIBUTED TO AN OUTPUT=


==Organization Name==

[[:Category:Bank of America Corporation|Bank of America Corporation]]


[[Category:Bank of America Corporation]]

==Inventor(s)==

[[:Category:Justin Horowitz of Fort Mill SC (US)|Justin Horowitz of Fort Mill SC (US)]][[Category:Justin Horowitz of Fort Mill SC (US)]]

[[:Category:Melissa Podrazka of Chicago IL (US)|Melissa Podrazka of Chicago IL (US)]][[Category:Melissa Podrazka of Chicago IL (US)]]

[[:Category:Sameer Sharma of Toronto (CA)|Sameer Sharma of Toronto (CA)]][[Category:Sameer Sharma of Toronto (CA)]]

==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.



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