AI-assisted legal human resource allocation optimization system

The AI inter-corporation resource allocation system addresses the inefficiencies in cross-corporation resource allocation by normalizing constraints, generating and evaluating multiple candidates, and adjusting plans to meet deadlines and budgets, enhancing operational stability and auditability.

JP2026113625APending Publication Date: 2026-07-07池本 健介
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Patent Information

Authority / Receiving Office
JP · JP
Patent Type
Applications
Current Assignee / Owner
池本 健介
Filing Date
2026-04-03
Publication Date
2026-07-07

AI Technical Summary

Technical Problem

Existing systems fail to efficiently allocate resources across multiple corporations while satisfying diverse resource constraints, approval conditions, delegation conditions, and budget conditions, leading to potential deadline breaches, budget overruns, quality issues, and overload on specific entities.

Method used

An AI inter-corporation resource allocation optimization system that normalizes constraints, generates multiple allocation candidates, evaluates them based on various factors, and adjusts plans in response to deviations during execution, while maintaining audit records.

Benefits of technology

Enables effective utilization of processing capacity, ensures confidentiality, meets deadlines, and improves operational stability and auditability by optimizing resource allocation across multiple corporations.

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Abstract

When assigning projects or processing units to AI agents or AI processing modules belonging to multiple legal entities, select a resource allocation plan that is highly efficient overall while satisfying resource constraints, approval conditions, delegation conditions, budget conditions, and outcome conditions. [Solution] The AI-powered human resource allocation optimization system uses a case acquisition unit 210 and a constraint acquisition unit 220 to acquire case data and resource ledgers, approval policy ledgers, delegation condition ledgers, and budget ledgers for each corporation. An allocation candidate generation unit 230 generates multiple resource allocation candidates, including split allocation candidates, concentrated allocation candidates, hold candidates, and mixed processing candidates. An allocation evaluation unit 240 calculates evaluation values ​​based on the degree of deadline fulfillment, resource usage, budget suitability, processing load distribution, and expected fulfillment of outcome conditions. An allocation plan determination unit 250 selects and confirms the resource allocation plan. An execution control unit 260 issues a reallocation request in response to the detection of deviations from resource constraints, deadline delays, and expiration of approval conditions. A recording unit 270 maintains audit records in hash chain format.
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