The data linkage system 90 includes a learning device 70 which generates a score function, which is a function that scores proximity between a user with an action history in a first domain and a user with an action history in a second domain, and an estimation device 80 which estimates a user matched in the first domain and the second domain. The score calculation means 82 calculates a score for a pair of users who have an action history in a first domain and an action history in a second domain using a score function. The matching means 83 estimates the same user as the user in the first domain from among the users in the second domain based on magnitude of the calculated score.

The data linkage system 90 includes a learning device 70 which generates a score function, which is a function that scores proximity between a user with an action history in a first domain and a user with an action history in a second domain, and an estimation device 80 which estimates a user matched in the first domain and the second domain. The score calculation means 82 calculates a score for a pair of users who have an action history in a first domain and an action history in a second domain using a score function. The matching means 83 estimates the same user as the user in the first domain from among the users in the second domain based on magnitude of the calculated score.

A recommendation system includes: a first acquisition unit configured to acquire user history information; a calculation unit configured to calculate a use maturity representing maturity of use of a service for each of a plurality of users on the basis of the user history information; a second acquisition unit configured to acquire recommendation history information including target algorithm information and recommendation success/non-success information; a selection unit configured to select a target algorithm on the basis of the recommendation history information of a user group having a use maturity corresponding to the use maturity of the target user among the plurality of users; and a recommendation unit configured to determine a content to be recommended to the target user using the selected target algorithm and output information representing the determined content.

A recommendation system includes: a first acquisition unit configured to acquire user history information; a calculation unit configured to calculate a use maturity representing maturity of use of a service for each of a plurality of users on the basis of the user history information; a second acquisition unit configured to acquire recommendation history information including target algorithm information and recommendation success/non-success information; a selection unit configured to select a target algorithm on the basis of the recommendation history information of a user group having a use maturity corresponding to the use maturity of the target user among the plurality of users; and a recommendation unit configured to determine a content to be recommended to the target user using the selected target algorithm and output information representing the determined content.

A task optimization method and a task optimization device in a mobile robot are provided. The task optimization method includes: obtaining at least one task type in a mobile robot and usage information when all users use a task corresponding to each task type; separately performing machine learning on the usage information of all the users corresponding to each task type to obtain at least one piece of user's usage habit information corresponding to each task type and usage probability thereof, thereby performing machine learning on usage information when all users use the task corresponding to the task type; based on the at least one piece of usage habit information corresponding to each task type, the usage probability thereof and the real-time usage information, optimizing the task corresponding to the task type used by the user in real time.

A task optimization method and a task optimization device in a mobile robot are provided. The task optimization method includes: obtaining at least one task type in a mobile robot and usage information when all users use a task corresponding to each task type; separately performing machine learning on the usage information of all the users corresponding to each task type to obtain at least one piece of user's usage habit information corresponding to each task type and usage probability thereof, thereby performing machine learning on usage information when all users use the task corresponding to the task type; based on the at least one piece of usage habit information corresponding to each task type, the usage probability thereof and the real-time usage information, optimizing the task corresponding to the task type used by the user in real time.

A search apparatus (1) coupled to a database, the apparatus (1) comprising: a processor configured to execute instructions; a memory storing instructions which, when executed by the processor, cause the processor to: search the database for items containing a search term, wherein items containing the search term are matched items; identify fields corresponding to attributes of the matched items; define a range of values for each field; divide the range of values for each field into a plurality of ranged field buckets; distribute the matched items between the ranged field buckets based on attributes of the matched items that are within the range of values for each ranged field bucket; calculate an effectiveness value for each field based on the number of matched items in each of the ranged field buckets; select one or more top fields, each top field having an effectiveness value that is greater than a predetermined effectiveness value; and provide an effectiveness indicator output which is indicative of the effectiveness of each top field such that a user can use the effectiveness indicator output to select a top field for use as a filter in a further search.

A search apparatus (1) coupled to a database, the apparatus (1) comprising: a processor configured to execute instructions; a memory storing instructions which, when executed by the processor, cause the processor to: search the database for items containing a search term, wherein items containing the search term are matched items; identify fields corresponding to attributes of the matched items; define a range of values for each field; divide the range of values for each field into a plurality of ranged field buckets; distribute the matched items between the ranged field buckets based on attributes of the matched items that are within the range of values for each ranged field bucket; calculate an effectiveness value for each field based on the number of matched items in each of the ranged field buckets; select one or more top fields, each top field having an effectiveness value that is greater than a predetermined effectiveness value; and provide an effectiveness indicator output which is indicative of the effectiveness of each top field such that a user can use the effectiveness indicator output to select a top field for use as a filter in a further search.

A system for operating a configuration platform. The system comprising a conversion pipeline, the conversion pipeline allowing converting a first set of data associated with a computer-aided design (CAD) system into polygon meshes suitable for rendering of the portion of a monument and configuration data. The system also comprising a content management system, the content management system storing the polygon meshes and the configuration data; and a 3D real-time engine, the 3D real-time engine allowing to determine, based on the configuration data, at least one of the positioning of the monument, the material surface associated with the monument, the material grain direction associated with the monument, the lighting associated with the monument, the annotations associated with the monument, the alternate states associated with the monument and the kinematic sequence associated with the monument; and to render the portion of the monument by the 3D real-time engine.

A system for operating a configuration platform. The system comprising a conversion pipeline, the conversion pipeline allowing converting a first set of data associated with a computer-aided design (CAD) system into polygon meshes suitable for rendering of the portion of a monument and configuration data. The system also comprising a content management system, the content management system storing the polygon meshes and the configuration data; and a 3D real-time engine, the 3D real-time engine allowing to determine, based on the configuration data, at least one of the positioning of the monument, the material surface associated with the monument, the material grain direction associated with the monument, the lighting associated with the monument, the annotations associated with the monument, the alternate states associated with the monument and the kinematic sequence associated with the monument; and to render the portion of the monument by the 3D real-time engine.