Program storage medium, apparatus and method provided with ruleset-selectable inference engine

An inference apparatus outputting a result of inferring inputted object data, the inference apparatus comprising:

• a processor programmed to:
• function as a machine learning engine that is a classifying-type engine configured to include adapted-to-category learning models each generated by using each adapted-to-category set of teacher data, the adapted-to-category set being obtained by classifying teacher data for each category, the processor, functioning as the machine learning engine, receives the inputted object data and uses use the learning models to output a category corresponding to the inputted object data;
• select, from a plurality of rulesets each prepared for each category and stored in a rule database, a ruleset corresponding to the category data outputted from the machine learning engine corresponding to the inputted object data;
• and
• function as a rule engine by executing inference to the inputted object data by using the selected ruleset as a ruleset of the category outputted from the machine learning engine that corresponds to the inputted object data, and outputting the inference result to execute processing based on the inference result.

An inference method executed by a processor of an inference apparatus outputting a result of inferring inputted object data, the processor of the inference apparatus functioning as a machine learning engine that includes adapted-to-category learning models each generated by using each adapted-to-category set of teacher data, the adapted-to-category set being obtained by classifying teacher data for each category, and the inference apparatus including a memory that stores a rule database that stores, in advance, a plurality of rulesets each prepared for each category, the inference method comprising:

• a first step, executed by the processor as the machine learning engine, of receiving the inputted object data and using the learning models to output a category data corresponding to the inputted object data;
• a second step, by the processor, of selecting, from the rulesets stored in the rule database in the memory, a ruleset corresponding to the category outputted from the machine learning engine corresponding to the inputted object data;
• and
• a third step, by the processor, of executing inference to the inputted object data by using the ruleset selected in the second step as a ruleset of the category outputted from the machine learning engine that corresponds to the inputted object data, and then outputting the inference result to execute processing based on the inference result.