Artifact-to-Schema Mapping for Faster Authorization Evaluation

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Solution Overview

Problem

Conventional methods for manually vetting development service artifacts for eligibility of tax credits and asset amortization are inefficient, time-consuming, and prone to errors, leading to inaccurate identification of eligible resources and reduced benefits.

Innovation Solution

A hybrid system using AI models to automatically evaluate development services by identifying and mapping digital artifacts to authorization schemas, providing a visual interface for analysis, and generating documentation for submission.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual vetting methods are used to evaluate development services for tax credits and asset amortization, then detailed human review can be performed, but the process becomes time-consuming and inefficient

Engineering Contradiction:

Improveevaluation accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical review processes with an AI-based automated evaluation system. The system uses machine learning models to analyze digital artifacts, extract relevant information, and determine eligibility for tax credits and asset amortization, thereby eliminating time-consuming manual vetting while maintaining evaluation accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an AI-based intermediary system that acts as a mediator between development services and authorization schemas. This intermediary automatically evaluates digital artifacts, maps them to relevant authorization schemas, and generates preliminary determinations, reducing the time burden on human reviewers while preserving evaluation precision.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If manual evaluation processes are used to identify eligible resources, then thorough assessment can be conducted, but resource inefficiencies occur

Engineering Contradiction:

Improveidentification accuracyVSAvoidresource efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent substitutes manual resource-intensive evaluation processes with automated AI systems. The system efficiently processes digital artifacts, extracts key information, and determines eligibility for tax credits and asset amortization, thereby improving resource efficiency while maintaining reliable identification accuracy through consistent algorithmic evaluation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent enables the evaluation system to perform self-service operations by automatically analyzing digital artifacts, mapping them to authorization schemas, and generating eligibility determinations without requiring extensive manual intervention. This self-automating approach improves productivity and resource efficiency while maintaining reliable identification through built-in validation mechanisms.

Inventive Principle:

Principle #25Self-service

3Ease of operation

If conventional manual methods are used for authorization evaluation, then human judgment can be applied, but errors and inaccuracies increase

Engineering Contradiction:

Improveoperational simplicityVSAvoidevaluation accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent replaces manual operational processes with automated AI systems that consistently apply evaluation criteria. The system automatically analyzes digital artifacts, extracts relevant information, and determines eligibility with high precision, eliminating human errors while maintaining ease of operation through user-friendly interfaces and automated workflows.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent implements feedback mechanisms within the automated evaluation system, where the AI model continuously learns from evaluation results and refines its accuracy. The system provides feedback on evaluation confidence levels and can flag uncertain cases for human review, thereby improving measurement precision while maintaining operational simplicity through iterative learning and validation.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260105467A1Robust artifacts mapping and authorization systems and methods for operating the same

Publication Date: 2026.04.16 CITIBANK N A

AI summary

Systems and methods are disclosed comprising instructions to receive a request to evaluate authorization of a development service that comprises a digital artifact set, each digital artifact in the digital artifact set, access an authorization schema set available for the development service, identify an applicable authorization schema from the authorization schema set via comparing the content embeddings of the digital artifacts and the reference embeddings of the authorization schemas, retrieve a historical artifact attribute set representing tracked development actions for prior development services authorized via the applicable authorization schema, predict an authorization status for the development service using the historical artifact attribute set and the artifact attribute set, configure for display a visual representation of the applicable authorization schema and the mapped at least one digital artifact of the development service.