Abstract: An information processing device according to the present application includes a generation unit and a providing unit. The generation unit uses a model that is trained to learn a relationship between a criterion for classifying users of a first company and a criterion for classifying users of a second company to generate a criterion (common key) for classifying the users of the second company into a first category, from the criterion for classifying the users of the first company into the first category. The providing unit provides a criterion generated by the generation unit.

Abstract: An information processing apparatus according to the application concerned includes an obtaining unit and a generating unit. The obtaining unit obtains a first-type graph including a plurality of node corresponding to a plurality of search target. The generating unit generates, based on placement of the plurality of nodes in the first-type graph, a second-type graph that includes a virtual node representing a node not associated to a search target by adding the virtual node to the first-type graph.

Abstract: A generation device according to the present application includes: an acquisition unit which acquires content targeted for disclosure (for example, an image which a user desires to disclose) and policy information which indicates a disclosure policy of the content (for example, a text which the user inputs as a caption of the image); and a generation unit which generates modified content in which a predetermined part of the content is modified based on the disclosure policy indicated by the policy information (for example, content in which an object identified based on the disclosure policy is emphasized).

Abstract: An output apparatus according to the present application includes a prediction unit and an output unit. The prediction unit predicts whether or not waveform information having a predetermined context is generated on the basis of detection information detected by a predetermined detection device. The output unit outputs waveform information having an opposite phase to the waveform information having the predetermined context in a case where it has been predicted that the waveform information having the predetermined context is generated.

Abstract: An estimation device according to the present application includes an acquiring unit and an estimation unit. The acquiring unit acquires user relationship information indicating a similarity relationship between a plurality of users. The estimation unit estimates, based on feeling information on a similar user who is similar to one of the users in the user relationship information acquired by the acquiring unit, a feeling of the one of the users.

Abstract: An output apparatus according to the present application includes an estimation unit, a decision unit, and an output unit. The estimation unit estimates an emotion of a user from detection information detected by a predetermined detection device. The decision unit decides information to be changed on the basis of the estimated emotion of the user. The output unit outputs information for changing the information to be changed.

Abstract: A boarding intention determination device according to the present application includes an acquisition unit and a determination unit. The acquisition unit acquires a positional relationship between boarding position information representing a boarding spot for boarding a mobile object and user position information representing a current position of a user. The determination unit determines whether the user has an intention to board the mobile object based on the positional relationship that is acquired by the acquisition unit and behavior information representing behaviors of the user.

Abstract: A training apparatus includes an acquiring unit that acquires a first model including an input layer to which input information is input; a plurality of intermediate layers that executes a calculation based on a feature of the input information that has been input; and an output layer that outputs output information that corresponds to output of the intermediate layer. The training apparatus includes a training unit that trains the first model such that, when predetermined input information is input to the first model, the first model outputs predetermined output information that corresponds to the predetermined input information and intermediate information output from a predetermined intermediate layer among the intermediate layers becomes close to feature information that corresponds to a feature of correspondence information that corresponds to the predetermined input information.

Abstract: An information processing device according to the present application includes an acquisition unit, a selection unit, and a search unit. The acquisition unit acquires a search query input by a user. The selection unit selects a type of target information to be searched on the basis of the search query acquired by the acquisition unit. The search unit searches for information corresponding to the search query from the type of target information to be searched selected by the selection unit.

Abstract: According to one aspect of an embodiment a learning apparatus includes a first acquiring unit that acquires first output information that is output by an output layer when predetermined input information is input to a model that includes an input layer, a plurality of intermediate layers, and the output layer. The learning apparatus includes a second acquiring unit that acquires intermediate output information that is based on pieces of intermediate information that are output by the plurality of intermediate layers when the input information is input to the model. The learning apparatus includes a learning unit that learns the model based on the first output information and the intermediate output information.

Abstract: An information processing apparatus according to the present application includes an acquiring unit and a deciding unit. The acquiring unit acquires attribute information on a user. The deciding unit decides, based on the attribute information acquired by the acquiring unit, a placement position in which a virtual riding point that corresponds to a riding point that is used for the user to ride in a moving vehicle and that is virtually set is placed.

Abstract: A determination device according to the present application has an acquisition unit, a categorization unit, and a determination unit. The acquisition unit acquires the search queries, which have been input by a plurality of input customers who have input a reference query. The categorization unit categorizes the search queries, which have been input in a predetermined period among search queries, into a plurality of categories. The determination unit determines whether a categorization result by the categorization unit satisfies a predetermined determination condition or not.

Abstract: A non-transitory computer readable storage medium having an arrangement program according to the present application thereon causes a computer to execute an acquiring process and an arranging process. The acquiring process acquires browsing mode information that indicates a mode of browsing content. The arranging process arranges, in a mode based on the browsing mode information acquired at the acquiring process, at least one element that is used to judge whether the content has actually been browsed by a user in a content area in which the content is provided.

Abstract: According to one aspect of an embodiment a learning apparatus includes a generating unit that generates a model. The model includes an encoder that encodes input information. The model includes a vector generating unit that generates a vector by applying a predetermined matrix to the information encoded by the encoder. The model includes a decoder that generates information corresponding to the information from the vector. The learning apparatus includes a learning unit that, when predetermined input information is input to the model, learns the model such that the model outputs output information corresponding to the input information and the predetermined matrix serves as a dictionary matrix of the input information.

Abstract: A data management device is provided, which includes an interpreting unit that interprets an input record, and convert the input record to abstract expression by which a correspondence relation between a data item and data itself is recognizable; and a converting unit that stores, in a storage unit, a data set, in which the data itself and index information capable of identifying the record are associated, as column data for each of the data items.

Abstract: An information processing apparatus according to the present application includes an acquiring unit and a selecting unit. The acquiring unit acquires a plurality of pieces of second triple information hierarchized based on a conceptual system in a plurality of pieces of first triple information indicating a relationship about three types of elements and statistical information indicating the number of pieces of the first triple information associated with each of the plurality of pieces of the second triple information. The selecting unit selects, based on the statistical information acquired by the acquiring unit and based on a predetermined standard related to the statistical information, from among the plurality of pieces of the second triple information, a plurality of pieces of target triple information to be used for a clustering process.

Abstract: The extraction device according to the present application includes an acquisition unit and an extraction unit. The acquisition unit acquires behavior histories of users being candidates to whom content is to be distributed. The extraction unit extracts target users expected to perform specified behavior, based on a behavior history designated by a content provider, of the behavior histories acquired by the acquisition unit. For example, the extraction unit uses a model for determining the degree of similarity between a user performing the specified behavior and a target user expected to perform specified behavior based on the behavior history designated by the content provider in order to extract the target users.

Abstract: A selection device includes an identification unit that identifies search histories related to element candidates that are candidates for a new element, and pieces of posted information on the element candidates. The selection device includes a selection unit that selects, from among the element candidates, an element candidate in order to generate learning data to cause a model to learn a feature of information on the new element based on the identified search histories and the identified pieces of posted information.

Abstract: An authentication device management device includes a generating unit, a registration unit, a transmission unit, and a responding unit. The generating unit generates a pair of a first key to attach a signature with respect to an authentication result obtained by an authentication device that performs personal authentication of a user, and a second key to verify the signature attached to the first key. The registration unit registers, in association with each other, the key identifier that identifies the generated key pair and user identification information. The transmission unit transmits the first key generated by the generating unit to the authentication device used by the user. When the responding unit accepts a transmission request for the second key related to the authentication device in which the first key transmitted by the transmission unit has been set, the responding unit responds by instructing the authentication server to transmit the second key.

Abstract: According to one aspect of an embodiment a provision device includes a generation unit that generates a distributed representation of each context on the basis of a relative connection that multiple contexts have. The provision device includes a provision unit that provides information representing a change between a distributed representation of a given context that is generated before distribution information about the given context is distributed and a distributed representation of the given context that is generated after the distribution information is distributed.