Digital Artifact Mapping for AI Authorization Review

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Solution Overview

Problem

Conventional methods for manually vetting development services for eligibility of tax credits and asset amortization are inefficient, resource-intensive, and prone to errors, leading to inaccurate identification of eligible artifacts 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 review, 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 credit eligibility, then detailed human review can be performed, but the process becomes resource-intensive and inefficient

Engineering Contradiction:

Improveaccuracy of eligibility determinationVSAvoidefficiency of evaluation process

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

An AI model acts as an intermediary between the development service data and the authorization schema evaluation. The model automatically maps digital artifacts to authorization schemas, performing the detailed review function that previously required human analysts, thereby improving efficiency while maintaining accuracy through sophisticated algorithmic analysis

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The manual mechanical process of human vetting is replaced with an automated AI-based system. The AI model processes development service data, identifies eligible artifacts, and evaluates authorization criteria without human intervention, substituting the mechanical human review process with an automated computational system that operates faster and more consistently

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual evaluation processes are used to identify eligible artifacts, then comprehensive review is possible, but errors increase and benefits are reduced

Engineering Contradiction:

Improveaccuracy of artifact identificationVSAvoidtime required for eligibility determination

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The AI model performs preliminary automated evaluation of development services before human review. By pre-identifying eligible artifacts and mapping them to authorization schemas, the system prepares comprehensive evaluation results in advance, reducing both errors and the time required for final determination while maintaining reliability

Inventive Principle:

Principle #10Preliminary action

3Productivity

If automated AI evaluation is implemented, then processing speed increases, but system complexity increases

Engineering Contradiction:

Improvespeed of eligibility determinationVSAvoidcomplexity of evaluation system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The AI model serves multiple functions within a single unified system: it processes development service data, maps digital artifacts to authorization schemas, evaluates eligibility criteria, and generates determination results. This multi-functional approach increases productivity while managing complexity by consolidating multiple operations into one versatile AI system rather than requiring separate specialized systems

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12499454B2Robust artifacts mapping and authorization systems and methods for operating the same

Publication Date: 2025.12.16 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.