Abstract: The planned route creating unit 3 creates a planned route of the self-driving vehicle 10. The non-traveling area plan creating unit 4 creates a plan of the non-traveling area, which is an area where the self-driving vehicle 10 can travel and which is an area set as an area where the self-driving vehicle 10 does not travel. The non-traveling area plan creating unit 4 creates a plan of the non-traveling area at a frequency lower than the frequency at which the planned route creating unit 3 creates a planned route. The transmission unit 6 transmits the plan of the non-traveling area to the other vehicle each time the plan of the non-traveling area is created.

Abstract: Provided is a delivery assistance device enabling shortening of the time required for delivery in purchase of an item by online shopping. This delivery assistance device is provided with: management means for managing stock information about stock of an item held by each of a plurality of traveling vehicles; first reception means for receiving current position information indicating a current position of the traveling vehicle; first transmission means for transmitting the stock information to a customer terminal; second reception means for receiving order information and delivery address information; specification means for specifying a traveling vehicle arriving earliest at the delivery address; and second transmission means for transmitting the order information to the specified traveling vehicle, wherein the management means updates the stock information about stock of an item held by the specified traveling vehicle, based on the order information.

Abstract: Provided is for assisting work of an employee in a store efficiently. A work assistance system includes: an identification unit that identifies, based on each of a plurality of products to be arranged at a certain place, time information indicating a time associated with the product; and a change unit that changes arrangement of the plurality of products, based on rule information including at least a condition for arrangement according to a time indicated by the identified time information.

Abstract: A learning means 71 learns, based on learning data containing the meaning of a column in a table and the meaning of the table, a model indicating regularity between the meaning of the column in the table and the meaning of the table. An estimation means 72 estimates the meaning of the table based on the meaning of a column of a table to be input and the model.

Abstract: Provided is an area evaluation system comprising an area evaluation unit (601) that evaluates, when a first mission that uses a first area and a second mission that does not use the first area are provided, utility of the first area based on a difference between utility of the first mission and utility of the second mission.

Abstract: The non-traveling area plan creating unit 4 creates a plan of the non-traveling area, which is an area where the self-driving vehicle 10 can travel and which is an area set as an area where the self-driving vehicle 10 does not travel. The negotiation area information receiving unit 9 receives, from another vehicle, information on one or more negotiation areas each of which is an area other than the non-traveling area and is a subject of negotiation to be included in the non-traveling area. The permissible area determining unit 35 calculates, for each negotiation area, the first value which is the value of the negotiation area indicated by the information for the self-driving vehicle 10, and based on the first value calculated for each negotiation area, determines one negotiation area permissible to be included in the non-traveling area, or determines not to include any negotiation area in the non-traveling area.

Abstract: The target setting unit 7 sets a target state of the self-driving vehicle 10. The planned route creating unit 3 creates a planned route of the self-driving vehicle 10 for realizing the target state. The transmission unit 6 transmits the planned route to another vehicle. The response receiving unit 9 receives, from another vehicle, a notification indicating agreement with the planned route or disagreement with the planned route as a response to the planned route. The traveling control unit 8 controls the self-driving vehicle 10 so as to cause the self-driving vehicle 10 to travel along the planned route when the response receiving unit 9 has received the notification indicating agreement with the planned route.

Abstract: A flight management system (500) according to the present invention is provided with: an airspace information management unit (501) which manages airspace information, which is information related to an airspace represented by means of a three-dimensional spatial region and a temporal interval; an unapproved flight plan management unit (502) which, upon reception of a flight plan for a moving body, manages the flight plan in association with a combination of airspaces to be used in the flight plan, on the basis of the airspace information; and a flight plan approving unit (503) which, on the basis of the airspace information and the association described above, detects at least a conflict or an interference of airspaces between a plurality of flight plans, and approves or disapproves the flight plan on the basis of the result of detection.

Abstract: A negotiation device conducting automated negotiation by transmitting and receiving proposals to and from a counterpart device includes an estimating unit configured to estimate a negotiation characteristic of the counterpart device by using information corresponding to a proposal transmitted by the negotiation device.

Abstract: In a kernel learning apparatus, a data preprocessing circuitry preprocesses and represents each data example as a collection of feature representations that need to be interpreted. An explicit feature mapping circuit designs a kernel function with an explicit feature map to embed the feature representations of data into a nonlinear feature space and to produce the explicit feature map for the designed kernel function to train a predictive model. A convex problem formulating circuitry formulates a non-convex problem for training the predictive model into a convex optimization problem based on the explicit feature map. An optimal solution solving circuitry solves the convex optimization problem to obtain a globally optimal solution for training an interpretable predictive model.

Abstract: A negative example degree calculation apparatus 10 is provided with a purpose inference unit 11 that infers a purpose for which a user used an item in the past from relational data specifying the item used by the user in the past and purpose information indicating a use purpose of the item by the user, and a negative example degree computation unit 12 that computes a negative example degree indicating a possibility of the item not being selected by the user, based on the inferred purpose and the purpose information.

Abstract: A customer service assistance apparatus 10 is provided with a video image acquisition unit 11 that acquires a video image of the inside of a store, a specifying unit 12 that specifies a product that a customer in the store is interested in and attributes of the customer based on the acquired video image, an information acquisition unit 13 that searches a specific database using the specified product and attributes as queries, and acquires content corresponding to the product and attributes that serve as queries, and the transmission unit 14 that transmits the acquired content to a terminal apparatus that is used by a store salesperson of the store.

Abstract: A customer service assistance apparatus 10 is provided with a video image acquisition unit 11 that acquires a video image of the inside of a store, a movement path acquisition unit 12 that acquires a movement path of a customer in the store, based on the acquired video image, a purchase action inference unit 13 that applies the acquired movement path to a prediction model for predicting a purchase action result based on a customer's movement path, and a transmission unit 14 that transmits the inferred probability to a terminal apparatus that is used by a store salesperson of the store.

Abstract: A data acquiring section specifies individual tables to serve as reference destinations of designated respective foreign key columns and, for each combination of key values in primary key columns of the specified individual tables, acquires a total value of relation values in a designated relation value column. A grouping section groups the respective key values in the primary key columns of the individual tables on the basis of the total value acquired for each combination of the key values in the primary key columns of the individual tables and a statistical model of a designated type. An attribute value column adding section adds, to each individual table, an attribute value column of which attribute value is identification information on a group to which each key value in each primary key column of each individual table belongs.

Abstract: A prediction model generation system is provided that is capable of generating a prediction model for accurately predicting a relationship between an ID of a record in first master data and an ID of a record in second master data. Co-clustering means 71 performs co-clustering on first IDs and second IDs in accordance with first master data, second master data, and fact data indicating a relationship between each of the first IDs and each of the second IDs. Each of the first IDs serves as an ID of a record in the first master data. Each of the second IDs serves as an ID of a record in the second master data. Prediction model generation means 72 generates a prediction model for each combination of a first ID cluster and a second ID cluster. The prediction model uses the relationship between each of the first IDs and each of the second IDs as an objective variable. The first ID cluster serves as a cluster of the first IDs. The second ID cluster serves as a cluster of the second IDs.

Abstract: An area evaluation system includes a utility evaluation means 501 that, in a case where a route selection area in which a route is selected is changed, evaluates a utility of at least a partial area included in a pre-change or post-change route selection area based on operation routes for an operation plan of a mobile object in pre-change and post-change areas.

Abstract: An object is to provide a clustering system capable of performing clustering of a plurality of types of items to be able to recommend an item whose corresponding textual data exists but relational data with another type of item does not exist, to the other type of item. A first clustering means 3001 performs clustering of first IDs, based on the relational data. A second clustering means 3002 performs clustering of second IDs, based on the relational data and textual data associated with the second IDs. A topic assignment means 3003 assigns a topic for each word included in textual data corresponding to each second ID. A parameter decision means 3004 decides a parameter used for first clustering processing, a parameter used for second clustering processing, and a parameter used for topic assignment processing. The processing described above is repeated until it is determined that a predetermined condition is satisfied.

Abstract: The planned route creating unit 3 creates a planned route of the self-driving vehicle 10. The non-traveling area plan creating unit 4 creates a plan of the non-traveling area, which is an area where the self-driving vehicle 10 can travel and which is an area set as an area where the self-driving vehicle 10 does not travel. The non-traveling area plan creating unit 4 creates a plan of the non-traveling area at a frequency lower than the frequency at which the planned route creating unit 3 creates a planned route. The transmission unit 6 transmits the plan of the non-traveling area to the other vehicle each time the plan of the non-traveling area is created.

Abstract: The kernel function generation unit 81 defines a first kernel function by using two-dimensional feature representation that represents a combination of two features of data. The model learning unit 82 defines a linear model including an inner product of a mapping used in the first kernel function and a first weight and performs learning with the defined linear model. The component expanding unit 83 expands the learned linear model to define expanded component representation that is new component representation of the data. The expansion model generation unit 84 generates an expansion model including an inner product of data by the expanded component representation and a second weight.

Abstract: A prediction model generation system is provided that is capable of generating a prediction model for accurately predicting a relationship between an ID of a record in first master data and an ID of a record in second master data. Co-clustering means 71 performs co-clustering processing for performing co-clustering on first IDs and second IDs in accordance with first master data, second master data, and fact data indicating a relationship between each of the first IDs and each of the second IDs. Prediction model generation means 72 performs prediction model generation processing for generating a prediction model for each combination of a first ID cluster and a second ID cluster. The prediction model uses the relationship between each of the first IDs and each of the second IDs as an objective variable. The first ID cluster serves as a cluster of the first IDs. The second ID cluster serves as a cluster of the second IDs.