International Business Machines Corporation

Yachiko Obara

Takayuki Osogami

Tetsuro Morimura

Shohei Ohsawa

Hiroki Yanagisawa

Takashi Imamichi

A training method is provided. The training method includes clustering, by a processor, a plurality of items that each have an item attribute value, according to the item attribute value. The training method further includes generating, by the processor, for each item, a cluster attribute value corresponding to a cluster associated with the item. The training method also includes training, by the processor, an estimation model for estimating selection behavior of a target with respect to a choice set including two or more items, based on the cluster attribute value associated with each item included in the choice set, by using training data that includes a group of a choice set of items presented to the target and an item selected by the target from among the choice set.

Training and estimation of selection behavior of target

A computer-implemented method for producing a formulation based on a prior distribution of a number of ingredients used in the formulation includes grouping a set of energy functions based on a number of ingredients used in a formulation, generating a probability distribution using the set of energy functions, obtaining at least one sample of the formulation by sampling from the probability distribution based on a previous sample, and triggering fabrication of the formulation in accordance with the at least one sample.

Producing a formulation based on prior distributions of a number of ingredients used in the formulation

Rating models may be generated by obtaining a plurality of consumption values, obtaining a plurality of rating values, training a model that estimates consumption values and rating values by utilizing a plurality of consumer attributes for each consumer, a plurality of item attributes for each item, and a plurality of weights for each attribute of each combination of a consumer and an item. Each estimated consumption value is a function of the plurality of weights for each attribute of each combination of each consumer and each item that corresponds with the estimated consumption value, and each estimated rating value is a function of the plurality of consumer attributes of a consumer that corresponds with the estimated rating value, the plurality of item attributes of an item that corresponds with the estimated rating value, and the plurality of weights that corresponds with the estimated rating value.

Rating model generation

A computer-implemented method is provided for generating a new formulation. The method includes dividing each of input formulations into constituent topics, based on analysis results for an analysis of the input formulations using a topic model algorithm. The method further incudes includes receiving an input query that specifies a set of fragrance. notes to he used to generate the new formulation, The method also includes choosing one of the input formulations which includes the set of fragrance notes to be used to generate the new formulation. The method additionally includes clustering the constituent topics of the chosen one of the input formulations based on a similarity metric. The method further includes generating the new formulation as a response to the input query by selecting, from the input formulations, materials for each of the clustered ones of the constituent topics.

Topic clustering to generate formulations

A computer implemented method is provided for generating a prediction of a next musical note by a computer having at least a processor and a memory. A computer processor system is also provided for generating a prediction of a next musical note. The method includes storing sequential musical notes in the memory. The method further includes dividing, by the processor, the sequential musical notes into sections of a given length based on a Generative Theory of Tonal Music. The method also includes generating, by the processor, the prediction of the next musical note based upon a music model, the sections, and the sequential musical notes stored in the memory. The given length is determined based on one or more conditions.

Music modeling

A computer implemented method is provided for generating a prediction of a next musical note by a computer having at least a processor and a memory. A computer processor system is also provided for generating a prediction of a next musical note. The method includes storing sequential musical notes in the memory. The method further includes generating, by the processor, the prediction of the next musical note based upon a music model and the sequential musical notes stored in the memory. The method also includes updating, by the processor, the music model based upon the prediction of the next musical note and an actual one of the next musical note. The method additionally includes resetting, by the processor, the memory at fixed time intervals.

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Tokyo, Japan

Yachiko Obara