Information processing system and program

The information processing system comprising:

• at least one processor,
• wherein the processor creates a child population of the next generation according to a genetic algorithm by regarding a combination of raw materials and the amounts of each of the raw materials as a parent population,
• calculates a fitness function value using the combination of raw materials and the amounts of each of the raw materials indicated by the created child population of the next generation,
• repeates the process of creating a child population of the next generation until a termination condition based on a physical property value range specified by the user is satisfied, and when the termination condition is satisfied, outputs information including the combination of raw materials indicated by each of the child populations of the next generation created last and the amounts of each of the raw materials.

The information processing system comprising:

• at least one storage device in which data relating to working examples of patent documents are stored;
• and
• at least one processor,
• wherein the processor extracts, from among the working examples stored in the storage device, a working example that use raw materials designated by a user and/or a working example that do not use the input raw materials among raw materials with similar physical property values realized by the combination of the raw materials, and outputs information for displaying a list of applicants or patent holders of patent documents in which the extracted working examples are described.

The information processing system comprising:

• at least one processor,
• wherein the at least one processor acquires target physical property values specified by a user,
• acquires a range of blend amounts specified by the user,
• determines multiple candidate blend amounts within the range of blend amounts specified by the user for each of the candidate raw materials, predicts physical property values for all combinations of a set of candidate raw materials and each of the candidate blend amounts of the candidate raw materials, and
• outputs the set of candidate raw materials and each of the candidate raw materials based on a comparison between the target physical property values and each of the predicted physical property values.

The information processing system comprising:

• at least one processor,
• wherein the at least one processor executes the steps of:
• determining a plurality of candidate values for the amount of other raw material in a range specified by a user when a portion of the raw materials constituting a target composition is replaced with other raw material specified by a user;
• determining predicted values of physical properties for all of the determined plurality of candidate values for the amount;
• and
• outputting information for presenting predicted values of physical properties in correspondence with each of the plurality of candidate values for the amount,
• in the step of determining the predicted value of the physical property, the at least one processor inputs at least one of a SMILES character string, a chemical graph structure data, a product name, or a substance name corresponding to each of the raw materials and an amount of each of the raw materials to a first machine learning model, or inputs a group of numerical values based on at least one of a chemical fingerprint, a SMILES character string, a chemical graph structure data, a product name, or a substance name corresponding to each of the raw materials and an amount of each of the raw materials to a second machine learning model, thereby obtaining a predicted value of the physical property,
• the first machine learning model is a model in which parameters are adjusted so that the output can be predicted from the input using a learning data set in which at least one of a SMILES character string, chemical graph structure data, product name, or substance name corresponding to each raw material and the amount of each raw material are input, and the physical property value of the prediction target physical property name is output,
• the second machine learning model is a model in which parameters are adjusted so that an output can be predicted from an input using a learning data set in which a group of numerical values obtained by converting at least one of a chemical fingerprint, a SMILES character string, or chemical graph structure data, or a product name or a substance name corresponding to each raw material and the amount of each raw material is input, and a physical property value of a prediction target physical property name is output.