MUMBAI, India, Sept. 7 -- Intellectual Property India has published a patent application (202641104335 A) filed by Dr. Y. V. Seshagiri Rao; Dr. Raviraj Sureshchandra Katare; Dr. Sucheta Sandeep Moharir; Dr. M. Maria Arockia Raj; Mrs. Savithri H; Dr. D. Surjith Jiji; Ms. K. Sivaranjani; Dr. V. Swarnalatha; and Dr. Nellore Manoj Kumar on August 30, 2026, for A Fuzzy Logic Mathematical Framework For Probabilistic Uncertainty Computation In Artificial Intelligence Decision Systems.
Inventors include Dr. Y. V. Seshagiri Rao; Dr. Raviraj Sureshchandra Katare; Dr. Sucheta Sandeep Moharir; Dr. M. Maria Arockia Raj; Mrs. Savithri H; Dr. D. Surjith Jiji; Ms. K. Sivaranjani; Dr. V. Swarnalatha; and Dr. Nellore Manoj Kumar.
The application for the patent was published on September 04, 2026, under issue no. 36/2026.
Abstract: A computer-implemented artificial intelligence decision system uniting a fuzzy logic framework with a probabilistic framework for uncertainty computation, and its application to controlling a technical system, is disclosed. An input relating to a technical system's state is attended by two kinds of uncertainty: the vagueness of its category, represented by fuzzy membership in fuzzy sets, and the randomness of its value, represented probabilistically. A joint representation unites both, in embodiments attending each fuzzy membership with a probability arising from propagating the value's randomness through the membership function. An inference module computes a decision by fuzzy inference over the fuzzy memberships according to fuzzy rules while propagating both the fuzzy and probabilistic uncertainty, so the decision inherits their combined, interacting uncertainty. An uncertainty quantification module computes a total uncertainty measure combining the fuzzy vagueness and probabilistic randomness. An application module applies the decision and its uncertainty to control the technical system, acting decisively when uncertainty is low and cautiously, gathering data, or deferring when high, producing a concrete technical effect of improved reliability and safety under real, multiform uncertainty.
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