Abstract: In the education support apparatus of the present disclosure, an acquisition unit acquires information of the user and information of a target of the user, an attribute analysis unit analyzes an attribute of the user and a request attribute of the target of the user by using a language model, from the information of the user and the information of the target of the user, an attribute gap analysis unit analyzes a difference in an attribute between the attribute of the user and the request attribute of the target of the user, and an educational content proposal unit generates at least one educational content for the first target for causing the user to add an attribute, based on the difference in the attribute, and proposes the generated educational content to the user.

Abstract: An acquisition unit of a life plan proposal device acquires attribute information indicating a user and living condition information relevant to a living condition of the user. An expansion unit adds additional attribute information of the user by an information generation method using the acquired attribute information. An estimation unit estimates a life event that is likely to happen in the user's life and a time of the life event by using the attribute information including the added attribute information, and the living condition information of the user. An output unit outputs a life plan generated by using information of the estimated life event.

Abstract: A dialog ability enhancement assistance device 30 includes a reception unit 31 that receives information for selecting a scene 312 in which participants including a user and one or more machine learning models 311 have a dialog with each other, and the machine learning models 311 included in the participants, a construction unit 32 that constructs an environment 321 in which the participants have a dialog with each other in the selected scene 312, an acquisition unit 33 that acquires dialog content 331 between the user and the machine learning models 311 in the environment 321, and an evaluation unit 34 that evaluates, based on an evaluation criterion 341 for evaluating a dialog ability according to the dialog content 331, the dialog ability of the user from the acquired dialog content 331.

Abstract: In an information processing device, a prompt acquisition means acquires a prompt which is described in natural language and includes a designation of a task. An input variable acquisition means interprets the prompt using a language model, acquires related information corresponding to the task designated by the prompt, and acquires an input variable to be used for prediction corresponding to the task designated, based on the related information. An output means outputs the input variable acquired.

Abstract: A server apparatus includes a request acquiring means, an answer acquiring means, and an answer providing means. The request acquiring means acquires a request related to a specific theme from a user, which is a first request to at least two or more learning models. The answer acquiring means acquires answers from each of the at least two or more learning models by using at least a scene showing a situation related to the specific theme and the first request. The answer providing means provides the answers acquired from each of the at least two or more learning models to the user.

Abstract: Use of two types of machine-learned generative models is optimized. An information processing apparatus includes: an accepting section which accepts an input of a query; a determining section which determines a generative model that is among a first generative model included in the information processing apparatus and a second generative model included in another apparatus and that is to be caused to generate content in accordance with the query; and a presenting section which presents, to a user, the content generated according to determination by the determining section.

Abstract: In order to achieve that a mode in which information is provided is changed for each person, an information output apparatus includes a first acquisition unit, a second acquisition unit, a third acquisition unit, and an output unit. The first acquisition unit acquires subject information including a text. The second acquisition unit acquires attribute information of a user. The third acquisition unit inputs the attribute information and the subject information to a generative model, and acquires, from the generative model, provision information that is information based on the subject information and is to be provided to the user. The output unit outputs the provision information.

Abstract: High-quality content generated with use of machine-learned generative model is presented to a user. An information processing apparatus includes: a first generating section for causing a first generative model to generate first content; a second generating section for using the first content to cause a second generative model to generate second content; and a presenting section for presenting the second content to a user. This information processing apparatus even makes it possible to optimize the second content to be presented to a user.

Abstract: The acquisition means acquires reader information and content information. The prompt generation means generates a prompt based on the reader information and the content information. The content editing means inputs the prompt into a large language model and acquire an output from the large language model as edited content.

Abstract: Whether an object is acting under control of a human or via artificial intelligence is made identifiable. An information processing apparatus includes: a judging unit 101 for judging whether an object displayed as an agent of action is acting under control of a human or via artificial intelligence; and a display control unit 102 for causing a display manner of the object to be in accordance with the result of the judgment made by the judging unit 101.

Abstract: Provided is an information processing apparatus which allows a user to more efficiently obtain useful information from a plurality of services and to more easily carry out optimum selection for the user. An information processing apparatus includes: an input acquisition section for acquiring a first request from a user; an answer acquisition section for acquiring first answers respectively from a first machine learning model and a second machine learning model in response to the first request while causing the second machine learning model to refer to a first answer obtained from the first machine learning model; and a display control section for causing a display apparatus to display a first answer set which includes the first answers obtained respectively from the first machine learning model and the second machine learning model.

Abstract: An information processing device is configured to include an acquisition unit, a determination unit, an instruction unit, an instruction unit, and output unit. The acquisition unit is configured to acquire measurement target and measurement environment information, and measurement environment information that a measurer can measure with an odor sensor. The determination unit is configured to determine a measurement target that the measurer should be instructed to measure, based on the measurement target and measurement environment information and the measurement environment information that can be measured, the instruction is configured to instruct the measurer to measure the determined measurement target. The output unit configured to output a reward to the measurer after the acquisition means acquires odor data of the determined measurement target.

Abstract: A training data generation device includes a label candidate generation unit, a reception unit, and a training data generation uni. The acquisition unit is configured to acquire smell data and information pertaining to the smell data. The label candidate generation unit which generates label candidates on the basis of the information pertaining to the smell data; an output unit which outputs the generated label candidates. The reception unit is configured to receive selection of a label from the output label candidates. The training data generation unit which generates training data from the selected label and the smell data.