Abstract: In order to suitably assist breeding, a selective breeding assistance apparatus (1) includes: a reception section (11) for receiving a request pertaining to breeding; a generation section (12) for generating response information based on the request using a learned model which has learned a relation between a property of an existing variety and a breeding process of the existing variety, the response information including information pertaining to an existing variety that serves as a crossbreeding candidate for developing a new variety; and an output section (13) for outputting the response information.

Abstract: In order to suitably assist the development of a new food product, a food development assistance apparatus (1) includes: an accepting means (11) for accepting a request regarding development of a new food product; an inferring means (12) for inferring, based on the request and a learned model which has learned a relation between a method for producing an existing product which is an existing food product and at least one selected from the group consisting of an ingredient used in the existing product, a commodity category of the existing product, a development concept of the existing product, and a property of the existing product, a method for producing a new product which matches the request; and an outputting means (13) for outputting information which indicates the method for producing the new product inferred by the inferring means.

Abstract: Received Signal Strength Indicator (RSSI) is measured accurately even in a case where a discovery signal is transmitted. A receiver receives a plurality of subframes, at least one of which includes a discovery signal, and a measurer measures Reference Signal Reception Power (RSRP) using a first resource in which the discovery signal is mapped, measures RSSI using a second resource different from the first resource for which the discovery signal is mapped, and calculates Reference Signal Reception Quality (RSRQ) using RSRP and RSSI.

Abstract: In order for the growth of a crop to be assisted, a crop growth assistance apparatus (1) includes an accepting section (11) for accepting a request containing any of a size, a taste, a harvest period, and a harvest yield of a crop which is a subject of growth; a generating section (12) for generating response information containing a method for growing the crop which is the subject of growth, in accordance with the request and a learned model having learned relations between methods for growing a plurality of crops and growth results including any of sizes, tastes, harvest periods, and harvest yields of the plurality of crops; and an outputting section (13) for outputting the method for growing the crop which is the subject of growth.

Abstract: In order to suitably carry out personnel assistance, a recruitment assistance apparatus (1) includes: a reception section (11) for receiving a request pertaining to a place of assignment of a subject staff; an inference section (12) for inferring a candidate place of assignment that is of the subject staff and that conforms to the request with use of a learned model which has learned a relation between (i) at least one selected from the group consisting of a skill and work experience of each of a plurality of persons and (ii) an affiliation of each of the plurality of persons; and an output section (13) for outputting information indicating the candidate place of assignment which has been inferred by the inference section.

Abstract: In order to suitably assist new cosmetic product development, a cosmetic production assistance apparatus (1) includes: a reception section (11) for receiving a request pertaining to new cosmetic product development; an inference section (12) for inferring a production method for producing a new product conforming to the request based on a learned model which has learned a relation between (i) a component and efficacy of an existing product and (ii) a method for producing the existing product; and an output section (13) for outputting information indicating the inferred production method.

Abstract: An object to generate, from a logical formula representing an inference result, a natural language sentence that is more diverse as compared with a document used to generate an inference rule is attained. In order to attain the object, an information processing apparatus (1) includes: an acquisition section (11) that acquires a logical formula representing an inference result which is based on an inference rule for an observation event; a generation section (12) that generates a natural language sentence from at least one text element with use of a language model generated on the basis of a corpus which does not contain a document used to generate the inference rule, the at least one text element being included in constituents of the logical formula and being text; and an output section (13) that outputs the natural language.

Abstract: A scenario generation apparatus includes: a keyword selection unit that extracts a hierarchical structure from documents that is a basis of a scenarios for use in chatbot and has the hierarchical structure, and selects keywords from the headings in each layer of the extracted hierarchical structure, a link candidate generation unit that generates a first scenario candidate in which a link is set between the keywords and a second scenario candidate in which a link is not set between the keywords, by using documents, an evaluation value calculation unit that calculates an evaluation value for the link for each of the generated first scenario candidate and the second scenario candidate according to a setting rule, and a scenario generation unit that adopts one of the scenario candidates based on the calculated evaluation value and generates the scenario using the adopted scenario candidate.

Abstract: A question responding apparatus is provided with a type determination unit that determines, on the basis of a question from a user, the type of answer to the question; a path selection unit that selects one path among paths from a current position node selected from among nodes in a hierarchical structure of information to matching nodes that match the type of answer; and a choices processing unit that presents information that indicates a node included in the selected path to the user as a choice.

Abstract: The information processing device generates headings from structured documents. The acquisition means acquires a structured document including headings and texts. The feature word extraction means extracts feature words from subordinate elements of the heading for the headings included in the structured document. The heading generation means generates a new heading corresponding to the subordinate elements based on the extracted feature words.

Abstract: The information processing device generates headings from structured documents. The acquisition means acquires a structured document including headings and texts. The training data generation means generates training data including the heading as a label and subordinate elements of the heading as input data. The training means trains a generation model using the training data, wherein the generation model generates a heading from the subordinate elements. The heading generation means generates headings included in an objective document using the trained generation model.

Abstract: A document analysis apparatus 10 includes: a candidate generation unit 11 that, for each line included in a document that is a target of structural analysis, specifies another line in a parallel relationship with the line by performing extraction of a marker indicating a hierarchy, and generates a candidate for a hierarchical structure of the document that is the target based on the result of the specification of each line; and a candidate evaluation unit 12 that, if two or more candidates have been generated, performs evaluation on each candidate for the hierarchical structure, and selects one candidate for the hierarchical structure as the hierarchical structure of the document that is the target based on the evaluation result.

Abstract: A scenario generation apparatus includes: a keyword selection unit that extracts a hierarchical structure from documents that is a basis of a scenarios for use in chatbot and has the hierarchical structure, and selects keywords from the headings in each layer of the extracted hierarchical structure, a link candidate generation unit that generates a first scenario candidate in which a link is set between the keywords and a second scenario candidate in which a link is not set between the keywords, by using documents, an evaluation value calculation unit that calculates an evaluation value for the link for each of the generated first scenario candidate and the second scenario candidate according to a setting rule, and a scenario generation unit that adopts one of the scenario candidates based on the calculated evaluation value and generates the scenario using the adopted scenario candidate.

Abstract: A data structuring device 1 includes an acquisition unit 2 and a modeling unit 3. The acquisition unit 2 acquires branch expression data. Branch expression data is data that includes a branch expression, which is an utterance expression in a conversation for which multiple mutually different replies can be assumed. The conversation data is converted to a graph structure in which the content of the conversation is divided into multiple nodes, and the multiple nodes are connected by means of edges. On the basis of the branch expression data obtained by the acquisition unit 2, the modeling unit 3 carries out a process on the multiple items of conversation data in the graph structure in which nodes that share a common branch expression are combined in one node, thereby modeling the multiple items of conversation data in one tree structure.

Abstract: An addition unit 11, with regard to data indicating a conversation history including one or more sets of sentences formed from a problem sentence being a sentence indicating one problem, a question sentence being a sentence indicating a question for the one problem, and an answer sentence being a sentence indicating an answer to the question, adds a label indicating a problem state to the problem sentence within the data, a label indicating a question state to the question sentence within the data, and a label indicating an answer state to the answer sentence within the data. An extraction unit 12 extracts, from the data, a set of sentences with which the states indicated by the labels have been associated according to a state transition model that is a model configured from the one problem state, question state, and answer state, and that represents a transition of the states.

Abstract: A document analysis apparatus 10 includes: a candidate generation unit 11 that, for each line included in a document that is a target of structural analysis, specifies another line in a parallel relationship with the line by performing extraction of a marker indicating a hierarchy, and generates a candidate for a hierarchical structure of the document that is the target based on the result of the specification of each line; and a candidate evaluation unit 12 that, if two or more candidates have been generated, performs evaluation on each candidate for the hierarchical structure, and selects one candidate for the hierarchical structure as the hierarchical structure of the document that is the target based on the evaluation result.

Abstract: A conversation support apparatus 10 includes: an utterance reception unit 11 that receives an utterance in an on-going conversation; an utterance position estimation unit 12 that, for each node in a conversation tree in which at least one of a label and a topic is provided to each node, collates the at least one of the label and the topic provided to the node and the received utterance, and estimates a node that is most related to the received utterance; and a display unit 13 that adds a visual characteristic representation to the estimated node and displays the conversation tree on a screen.

Abstract: A display format determination apparatus is provided with a display number prediction unit that calculates, for each of a plurality of display formats for displaying choices, a predicted value representing a prediction of the number of display items displayed until one of the choices is selected, on the basis of an index value representing, for each choice, a magnitude of a probability that each choice is selected; and a display format determination unit that selects one of the display formats on the basis of the calculated predicted value.

Abstract: A question responding apparatus is provided with a type determination unit that determines, on the basis of a question from a user, the type of answer to the question; a path selection unit that selects one path among paths from a current position node selected from among nodes in a hierarchical structure of information to matching nodes that match the type of answer; and a choices processing unit that presents information that indicates a node included in the selected path to the user as a choice.

Abstract: An addition unit 11, with regard to data indicating a conversation history including one or more sets of sentences formed from a problem sentence being a sentence indicating one problem, a question sentence being a sentence indicating a question for the one problem, and an answer sentence being a sentence indicating an answer to the question, adds a label indicating a problem state to the problem sentence within the data, a label indicating a question state to the question sentence within the data, and a label indicating an answer state to the answer sentence within the data. An extraction unit 12 extracts, from the data, a set of sentences with which the states indicated by the labels have been associated according to a state transition model that is a model configured from the one problem state, question state, and answer state, and that represents a transition of the states.