Intellectual Property India Publishes Patent Application for Ai-Based Employee Performance Evaluation And Predictive Career Development System And Method

MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641126219 A) filed by Dr. K. Vinayagam; Dr. Aarthi Suresh; Mr. Stefan Juan Weston; Dr. P. Subburaj; Dr. P. Murugan; Dr. S. Subbulakshmi; Dr. T. Shobana Thulasi; Dr. M. Prabhakar Christopher David; Dr. Famitha Begum; Mr. A. Senthilanand; A. Gokulakrishnan; and Dr. V. Vetrivel on October 22, 2026, for Ai-Based Employee Performance Evaluation And Predictive Career Development System And Method.

Inventors include Dr. K. Vinayagam; Dr. Aarthi Suresh; Mr. Stefan Juan Weston; Dr. P. Subburaj; Dr. P. Murugan; Dr. S. Subbulakshmi; Dr. T. Shobana Thulasi; Dr. M. Prabhakar Christopher David; Dr. Famitha Begum; Mr. A. Senthilanand; A. Gokulakrishnan; and Dr. V. Vetrivel.

The application for the patent was published on October 30, 2026, under issue no. 44/2026.

Abstract: An artificial-intelligence-based employee performance evaluation and predictive career development system receives authorized work-related data from multiple sources, validates the data for provenance, completeness, relevance, and access permissions, and maps validated records to a role-specific competency framework. A feature extraction module derives performance features, and an evaluation engine generates evidence-linked performance indicators with explanations of contributing evidence, applied criteria, data limitations, and uncertainty. A predictive engine estimates competency gaps, learning needs, and potential career pathways using validated skills, approved role requirements, skill trends, and permitted development preferences. A recommendation module prepares a development plan comprising suitable learning activities, mentoring, project exposure, milestones, and reassessment dates. A human review interface permits authorized reviewers to inspect evidence and modify recommendations, while an audit and feedback module records review actions and compares subsequent outcomes with earlier predictions. Privacy, access- control, and model-monitoring mechanisms support accountable use of the system. Accompanied Drawing [FIGS. 1-2].

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