Abstract: The disclosure relates to a processing method for generating learning data, which may include: specifying requirement information for generating learning data, based on request information for making a request for learning; and transmitting the requirement information to a device that generates the learning data. The disclosure also relates to a system and a program that realize the method, and a storage medium that stores the program.

Abstract: An optical sensor according to one or more embodiments is an optical sensor including an optical member including a plurality of focusing units, each focusing unit focusing light from a subject, and a plurality of image capturing devices, each image capturing device including a plurality of light-receiving elements, each image capturing device being provided corresponding to one of the focusing units, and each image capturing device configured to receive light focused by the corresponding focusing unit and form a captured image of the subject. The light-receiving elements, of the plurality of light-receiving elements, that are to be used to form the captured image are set for each of the image capturing devices.

Abstract: A classification apparatus includes: a specifying unit integrated with a neural network that has been trained to classify a state of a space using information indicating a light projection pattern and information indicating a light reception pattern; a light projection information acquisition unit configured to acquire information indicating a light projection pattern of light projected into a predetermined space, and output the acquired information to the specifying unit; and a light receiving unit configured to acquire information indicating a light reception pattern of light received from the predetermined space, and output the acquired information to the specifying unit, wherein the specifying unit outputs a classification result of classifying a state of the predetermined space, based on the information indicating the light projection pattern acquired by the light projection information acquisition unit and on the information indicating the light reception pattern of the light received by the light recei

Abstract: A sensor metadata generation device includes: a class storage unit configured to store a sensor container class that includes, as components, one or a plurality of sensor classes each including definitions of attributes of one or a plurality of sensors, and a sensor composition class including a composition condition for compositing the one or a plurality of sensors as a single sensor entity, with a first interface that includes a declaration of a metadata acquisition method for acquiring metadata; an object generation unit configured, based on the sensor container class, to generate a sensor container object in which the configuration of the single sensor entity is defined; and a metadata acquisition unit configured to use the metadata acquisition method by the sensor container object to acquire metadata of the single sensor entity.

Abstract: An environment recognition system according to one aspect of the present invention includes: a sensor apparatus configured to detect brightness information pertaining to a brightness of the surrounding environment; and an information processing apparatus configured to calculate, by inputting the brightness information obtained by the sensor apparatus into a trained learning device that has been trained for identifying a brightness level of the surrounding environment and a factor determining the brightness, an environment information set including information of the brightness level of the surrounding environment and the factor determining the brightness.

Abstract: An evaluation device may include: a receiving unit that receives an image of the semiconductor substrate, the image captured by an imaging device provided on the semiconductor substrate manufacturing apparatus; a determination unit that determines, using a neural network, at least one value representative of a probability of a machine learning device outputting an erroneous output for the image, the machine learning device configured to: (i) receive the image of the semiconductor substrate, (ii) perform computation using the received image, and (iii) output information indicating the quality of the semiconductor substrate based on a result of the computation; and an output unit that outputs an output based on the at least one value representative of the probability. The neural network has been trained using: images of manufactured semiconductor substrates; and information indicating, for each one of the images, a level of erroneousness for an output from the machine learning device.

Abstract: An illumination device includes at least one light source configured to perform illumination with a plurality of illumination patterns, a detection unit for detecting state information on the state of an illumination target that is to be illuminated by the light source, an arithmetic unit configured to calculate, using a neural network, illumination pattern information for generating an illumination pattern appropriate for the illumination target from the state information, and an illumination control unit configured to control the light source in order to perform illumination with an illumination pattern based on the illumination pattern information.

Abstract: A coordinator (10) includes an accepting unit (11), a required resource information calculation unit (13a), an insufficient resource information calculation unit (13b), a combination determination unit (14), and a communication unit (18). The required resource information calculation unit (13a) calculates information regarding resources required to satisfy a request accepted by the accepting unit (11). The insufficient resource information calculation unit (13b) compares the required resources calculated by the required resource information calculation unit (13a) with resources included in each of a plurality of sensor nodes (30, 40, 50), and calculates information regarding an insufficient resource that each sensor node lacks. The combination determination unit (14) determines a combination of resources required to satisfy the request, based on the information regarding the insufficient resource calculated by the insufficient resource information calculation unit (13b).

Abstract: An image processing system according to an aspect of the invention includes: an optical sensor including: an optical member configured to refract light from a subject, the optical member including a plurality of transmissive units that have different optical properties, thus forming different refraction patterns of light that passes therethrough; and a plurality of image sensors configured to receive light that has passed through the transmissive units and to form captured images in which the subject appears; and an information processing apparatus configured to calculate attribute information representing an attribute of the subject by inputting the captured images obtained by the image sensors to a learning device that has learned the attribute of the subject.

Abstract: Monitored control system includes an action device and a monitoring device for monitoring the operation of the action device. The action device includes actuator modules, a sensor module and a control module to command the actuator modules to perform a predefined task using the sensor data. The monitoring device receive monitored data from the action device and compute an action device operation indicator indicative of a correct or abnormal operation of the action device on the basis of the monitored data. The action device operation indicator is computed on the basis of a learned value of an adjustable monitoring parameter. The learned value is determined from a set of labelled data generated from trial datasets recorded by the action device during an iterative learning procedure.

Abstract: A shape estimating apparatus includes an acquiring unit and an estimating unit. The acquiring unit is configured to acquire a two-dimensional image. The estimating unit has artificial intelligence, and is configured to provide the artificial intelligence with the two-dimensional image and cause the artificial intelligence to estimate a three-dimensional shape of a subject of the two-dimensional image. A learning result of machine learning performed using learning data containing supervisor data expressing a three-dimensional shape of a sample subject and a sample two-dimensional image obtained by capturing an image of the three-dimensional shape of the sample subject is set to the artificial intelligence.

Abstract: A sensor metadata generation device includes: a class storage unit configured to store a sensor container class that includes, as components, one or a plurality of sensor classes each including definitions of attributes of one or a plurality of sensors, and a sensor composition class including a composition condition for compositing the one or a plurality of sensors as a single sensor entity, with a first interface that includes a declaration of a metadata acquisition method for acquiring metadata; an object generation unit configured, based on the sensor container class, to generate a sensor container object in which the configuration of the single sensor entity is defined; and a metadata acquisition unit configured to use the metadata acquisition method by the sensor container object to acquire metadata of the single sensor entity.

Abstract: An identifying information assignment system includes: a generating portion configured to generate, for a learning result obtained by attaining a predetermined capability through a predetermined learning process by machine learning, identifying information for identifying the predetermined learning process; and an assignment portion configured to assign the generated identifying information to the learning result.

Abstract: A device with extensibility includes an architecture modeled as an ability acquisition model including an ability unit for implementing an ability, an data input unit that is an interface for an input from the ability unit, and a data output unit that is an interface for an output from the ability unit, as an architecture for additionally incorporating a new ability to a basic configuration of the device, and includes an ability setting unit for adding the new ability to the device by setting a function to each of the ability unit, the data input unit, and the data output unit, based on ability providing data including ability setting data, input setting data, and output setting data.

Abstract: In a network system, an own subsystem recognition unit is provided in each of subsystems, and holds a state and a function of the own subsystem. A shared information communication unit acquires states and functions of the subsystems. An objective acceptance unit accepts a setting of a target state. A shared storage unit stores knowledge regarding a combination structure of functions of the respective subsystems necessary for realizing the target state. A structure setting unit automatically sets a combination structure of functions of the respective subsystems, including determining whether the function of each of the subsystems is to be activated, based on the target state, states and functions of the subsystems, and the knowledge regarding a combination structure. A role determination unit determines functions to be executed by the respective subsystems based on the automatically set combination structure. A role notification unit notifies the subsystems of the determined respective functions.

Abstract: A factor estimation device is configured to receive information pertaining to objects, to extract state information from the information received, to identify a predetermined state pertaining to a first object from among the objects, to receive state information extracted that corresponds to the predetermined state and classify the predetermined state, to extract condition information from the information received, to identify the condition up until the predetermined state, and to receive condition information that is output by the condition-information extraction unit and corresponds to the condition identified and classify the condition identified. Subsequently, the factor estimation device is configured to estimate the condition that may result in the predetermined state on the basis of the result of classifying the predetermined state and the result of classifying the identified condition.

Abstract: A state determination device for determining a state of an object that is present in the vicinity of a plurality of electrodes includes: a selection unit configured to select a plurality of electrode pairs from the plurality of electrodes; a capacitance pattern measurement unit configured to measure a capacitance pattern for the plurality of electrode pairs; and a neural network that is trained to determine a state of the object based on the capacitance pattern.

Abstract: A shape estimating apparatus includes an acquiring unit and an estimating unit. The acquiring unit is configured to acquire a two-dimensional image. The estimating unit has artificial intelligence, and is configured to provide the artificial intelligence with the two-dimensional image and cause the artificial intelligence to estimate a three-dimensional shape of a subject of the two-dimensional image. A learning result of machine learning performed using learning data containing supervisor data expressing a three-dimensional shape of a sample subject and a sample two-dimensional image obtained by capturing an image of the three-dimensional shape of the sample subject is set to the artificial intelligence.

Abstract: An optical sensor according to one or more embodiments is an optical sensor including an optical member including a plurality of focusing units, each focusing unit focusing light from a subject, and a plurality of image capturing devices, each image capturing device including a plurality of light-receiving elements, each image capturing device being provided corresponding to one of the focusing units, and each image capturing device configured to receive light focused by the corresponding focusing unit and form a captured image of the subject. The light-receiving elements, of the plurality of light-receiving elements, that are to be used to form the captured image are set for each of the image capturing devices.

Abstract: The disclosure relates to a processing method for generating learning data, which may include: specifying requirement information for generating learning data, based on request information for making a request for learning; and transmitting the requirement information to a device that generates the learning data. The disclosure also relates to a system and a program that realize the method, and a storage medium that stores the program.