Abstract: There is provided is a computer system that outputs a predicted value of data to be evaluated using a predictor generated using learning data. The computer system includes the predictor; an index calculation unit that calculates an interpretation index of the data to be evaluated; and an extraction unit that selects the learning data useful for a user to interpret the predicted value of the data to be evaluated, wherein index management information for managing an interpretation index of the learning data is stored, the index calculation unit calculates the interpretation index of the data to be evaluated, and the extraction unit calculates a selection index based on the interpretation index of the data to be evaluated and the interpretation index of the learning data, selects the learning data based on the selection index, and outputs display information for presenting information indicating a processing result.

Abstract: Provided is a computer system to present information useful for achieving purposes related to an object by utilizing AI prediction. The computer system manages a prediction model for predicting an object event based on evaluation data and feature profiling database that defines a change rule of each of the plurality of feature values included in the evaluation data, generates change policy data by changing the plurality of feature values included in the evaluation data based on the feature profiling database, calculates an evaluation value indicating effectiveness of the change policy data, and generates display data for presenting the change policy data and the evaluation value as information useful for achieving purposes related to the object.

Abstract: An information processing system includes a predictor, a contribution calculator, and a supplemental base generator. The system accesses databases that store relevance between feature variables in case data and a contribution of a feature variable in the case data to a result of prediction. The contribution calculator calculates the contribution of each of the feature variables in the evaluation target data to the output of the predictor, and outputs the calculated contributions and the acquired evaluation target data. The supplemental reason generator extracts a group of data proximate to the value and the contribution of a first feature variable, identifies a second feature variable relevant to the first feature variable, generates supplemental reason data based on a distribution of the proximate data group within a distribution of the second feature variable by use of the case data, and outputs the generated supplemental reason data.

Abstract: A computer system includes a calculation unit for extracting specific reference data from a plurality of reference data, configured to calculate a contribution of the each feature amount of explanatory data regarding a predicted value using the specific piece of reference data, the explanatory data, and a predictor, and stores the contribution that has been calculated as a pair contribution in association with the specific piece of the reference data and the explanatory data, the pair contribution being a contribution that has been calculated with the one piece of the reference data and the explanatory data being a pair, for all pairs including each reference data and the explanatory data; and an aggregation unit for reading the pair contribution that has been calculated for the each feature amount of the explanatory data, and configured to calculate by aggregating the contribution of the each feature amount of the explanatory data.

Abstract: Provided is a computer system to present information useful for achieving purposes related to an object by utilizing AI prediction. The computer system manages a prediction model for predicting an object event based on evaluation data and feature profiling database that defines a change rule of each of the plurality of feature values included in the evaluation data, generates change policy data by changing the plurality of feature values included in the evaluation data based on the feature profiling database, calculates an evaluation value indicating effectiveness of the change policy data, and generates display data for presenting the change policy data and the evaluation value as information useful for achieving purposes related to the object.

Abstract: There is provided is a computer system that outputs a predicted value of data to be evaluated using a predictor generated using learning data. The computer system includes the predictor; an index calculation unit that calculates an interpretation index of the data to be evaluated; and an extraction unit that selects the learning data useful for a user to interpret the predicted value of the data to be evaluated, wherein index management information for managing an interpretation index of the learning data is stored, the index calculation unit calculates the interpretation index of the data to be evaluated, and the extraction unit calculates a selection index based on the interpretation index of the data to be evaluated and the interpretation index of the learning data, selects the learning data based on the selection index, and outputs display information for presenting information indicating a processing result.

Abstract: To design parameters for building a system quickly, it is provided a design assisting system, comprising: a processor configured to execute a program; and a memory configured to store the program to be executed by the processor. The memory is configured to store a plurality of parameter sets for defining usage of resources of a computer system on which a business operation is run. The processor is configured to: calculate a resource value score of each resource for each of the plurality of parameter sets, based on information for defining items to be taken into account in system designing; sum up the calculated resource value scores for each of the plurality of parameter sets separately; and determine a priority level of each of the plurality of parameter sets, based on the resource value scores summed up for each of the plurality of parameter sets separately.