System

Abstract

An object of a system according to an embodiment is to accurately specify a position and an amount of marine garbage, efficiently collect and analyze the marine garbage, and propose a garbage reduction measure.SOLUTION: A system includes an image analysis unit, a dust collection unit, a dust analysis unit, and a proposal unit. The image analysis unit analyzes the satellite image or the drone video to specify a position or an amount of the marine waste. The dust collection unit collects the dust based on the position information of the dust specified by the image analysis unit. The waste analyzing section analyzes the waste collected by the waste collecting section. The proposal section proposes a waste reduction measure on the basis of a result analyzed by the waste analysis section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has faced the challenge of making it difficult to accurately identify the location and amount of marine debris and to efficiently collect it.

[0005] The system of the embodiment aims to accurately identify the location and amount of marine debris, efficiently collect and analyze it, and propose measures to reduce the debris. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, a debris collection unit, a debris analysis unit, and a proposal unit. The image analysis unit analyzes satellite images or drone footage to identify the location or amount of marine debris. The debris collection unit collects debris based on the location information of the debris identified by the image analysis unit. The debris analysis unit analyzes the debris collected by the debris collection unit. The proposal unit proposes debris reduction measures based on the results of the analysis by the debris analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately identify the location and amount of marine debris, efficiently collect and analyze it, and propose measures to reduce the debris. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​ocean cleaning system according to an embodiment of the present invention is a system that automatically detects floating debris on the ocean, analyzes it using a generating AI, and efficiently collects and removes it. As a result, the AI ​​ocean cleaning system can consistently detect, collect, analyze, and propose reduction measures for marine debris.

[0029] An AI ocean cleaning system according to an embodiment includes an image analysis unit, a garbage collection unit, a garbage analysis unit, and a proposal unit. The image analysis unit analyzes satellite images or drone footage to identify the location or amount of marine debris. For example, the generation AI inputs satellite images or drone footage, detects features indicating the presence of marine debris, and outputs its location information. The generation AI uses a pre-fine-tuned model to analyze a wide range of image data to accurately grasp the distribution of debris. The garbage collection unit collects garbage based on the location information identified by the image analysis unit. For example, a drone flies over the ocean and guides a robot to collect the garbage. The robot efficiently collects the garbage and transports it to a designated location. The generation AI generates an operating program for the robot or drone and instructs it on the optimal route and method. The garbage analysis unit analyzes the garbage collected by the garbage collection unit. For example, the generation AI analyzes the collected garbage and identifies its type and amount. The generation AI identifies different materials, such as plastic, metal, and wood, and calculates their respective proportions. The proposal unit proposes waste reduction measures based on the results of the waste analysis unit. For example, the generation AI suggests reducing plastic use in specific areas or strengthening recycling programs. This allows the AI ​​ocean cleaning system to consistently detect, collect, analyze, and propose reduction measures for marine debris.

[0030] The image analysis unit can learn the different characteristics of each type of marine debris and output location information for each type. For example, using generative AI, the image analysis unit can learn the characteristics of different types of marine debris such as plastic, metal, and wood, and output location information for each type. For example, plastic can be identified based on its light reflection characteristics, metal on its magnetic properties, and wood on its shape characteristics. This allows for efficient collection by identifying the location information for each type of marine debris.

[0031] The image analysis unit can introduce an algorithm to correct for the effects of weather or time of day when analyzing satellite images or drone footage. For example, the image analysis unit introduces an algorithm to correct for the effects of weather or time of day when analyzing satellite images or drone footage. For example, the image analysis unit corrects images taken on cloudy or rainy days to improve the accuracy of garbage detection. In this way, the accuracy of garbage detection is improved by correcting for the effects of weather and time of day.

[0032] The image analysis unit can integrate data from marine sensors in addition to satellite images or drone footage to more accurately locate garbage. The image analysis unit can integrate data from marine sensors in addition to satellite images or drone footage to more accurately locate garbage. For example, it can analyze water quality data from marine sensors to detect features that indicate the presence of garbage. In this way, integrating data from marine sensors improves the accuracy of locating garbage.

[0033] The garbage collection unit can automatically generate the optimal collection method for each type of garbage and instruct the robot or drone to do so. For example, using generative AI, the garbage collection unit can automatically generate the optimal collection method for each type of garbage and instruct the robot or drone to do so. For example, suction is used for plastic, magnetic for metal, and a crane for wood. This allows for efficient garbage collection by automatically generating the optimal collection method for each type of garbage.

[0034] The garbage collection unit can monitor the operation of the autonomous robot or drone in real time and implement an algorithm that responds to obstacles or changes in weather. For example, the garbage collection unit can monitor the operation of the autonomous robot or drone in real time and implement an algorithm that responds to obstacles or changes in weather. For example, the garbage collection unit can change its route to avoid obstacles or adjust its operation in response to changes in weather. This improves the efficiency and safety of garbage collection by responding to obstacles and changes in weather.

[0035] The waste analysis unit can use generative AI to analyze the components of waste, identify recyclable materials, and propose recycling programs. The waste analysis unit can, for example, use generative AI to analyze the components of waste, identify recyclable materials, and propose recycling programs. For example, it can identify materials such as plastic, metal, and paper and propose recycling methods for each. This can contribute to environmental protection by identifying recyclable materials and proposing recycling programs.

[0036] The proposal unit can introduce an algorithm that identifies the source of waste and automatically generates reduction measures for each source. The proposal unit, for example, introduces an algorithm that identifies the source of waste and automatically generates reduction measures for each source. For example, the proposal unit identifies waste generated from a specific factory or region and proposes reduction measures accordingly. This makes it possible to effectively reduce waste by identifying the source of waste and proposing reduction measures for each source.

[0037] The proposal unit can integrate the garbage analysis data with other environmental data and propose comprehensive environmental protection measures. For example, the proposal unit can integrate the garbage analysis data with other environmental data (e.g., air pollution data or water quality data) and propose comprehensive environmental protection measures. For example, the proposal unit can analyze the relationship between garbage distribution and air pollution and propose comprehensive measures. In this way, by proposing comprehensive environmental protection measures, more extensive environmental protection can be achieved.

[0038] The proposal unit can quantitatively evaluate the effects of marine environmental protection activities and propose optimal action plans. The proposal unit can, for example, use generative AI to quantitatively evaluate the effects of marine environmental protection activities and propose optimal action plans. For example, the proposal unit can evaluate based on the amount of garbage collected and changes in biodiversity. In this way, the optimal action plans can be proposed by quantitatively evaluating the effects of marine environmental protection activities.

[0039] The proposal unit can automatically generate an educational program on marine environmental protection and provide it to local residents or businesses. The proposal unit can, for example, use a generation AI to automatically generate an educational program on marine environmental protection and provide it to local residents or businesses. For example, it can create content that explains the impact of garbage and the importance of recycling. By automatically generating an educational program and providing it to local residents and businesses, it is possible to raise awareness of marine environmental protection.

[0040] The proposal unit can apply the data of marine environmental protection activities to other environmental protection activities. For example, the proposal unit applies the data of marine environmental protection activities to other environmental protection activities (for example, forest protection or urban cleaning). For example, garbage distribution data is used to create plans for forest protection or urban cleaning. In this way, comprehensive environmental protection can be achieved by applying the data of marine environmental protection activities to other environmental protection activities.

[0041] The Proposal Division can share the results of marine environmental protection activities with other regions and countries, promoting conservation activities from a global perspective. The Proposal Division can, for example, share the results of marine environmental protection activities with other regions and countries, promoting conservation activities from a global perspective. For example, it can share success stories from each region and encourage implementation in other regions. In this way, sharing the results of marine environmental protection activities will promote conservation activities from a global perspective.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The AI ​​ocean cleaning system is also equipped with acoustic sensors, which can pinpoint the location of marine debris using sound waves. For example, acoustic sensors can detect sound waves reflected from debris in the ocean and identify its location. This improves the accuracy of locating debris by using acoustic data in addition to visual data. Acoustic sensors can also detect the movements of marine life and adjust the route of the garbage collection robot to avoid affecting the creatures. This enables efficient garbage collection while minimizing the impact on marine life.

[0044] The AI ​​ocean cleaning system can also equip trash collection robots with self-repair capabilities. For example, if a robot breaks down, it can self-diagnose and automatically carry out necessary repairs. This maximizes the robot's uptime and minimizes interruptions to trash collection operations. The self-repair capability also reduces robot maintenance costs and enables long-term operation.

[0045] The AI ​​ocean cleaning system can also equip trash collection robots with the ability to optimize energy efficiency. For example, the robots can monitor energy consumption in real time during collection operations and automatically select the optimal energy usage pattern. This allows for efficient trash collection while minimizing energy consumption. The robots can also reduce their environmental impact by using renewable energy.

[0046] The AI ​​ocean cleaning system can also equip garbage collection robots with collaborative operation capabilities. For example, multiple robots working together to collect garbage can be more efficient. Specifically, the robots communicate with each other and automatically determine the optimal division of labor. Furthermore, the robots complement each other, improving the accuracy and efficiency of the work. This allows multiple robots to work together to achieve efficient garbage collection.

[0047] The AI ​​ocean cleaning system can also equip trash collection robots with environmental monitoring capabilities. For example, the robots can collect water and air quality data during trash collection operations and monitor environmental conditions in real time. This allows them to assess the impact of trash collection operations and adjust their work methods as needed. Based on the environmental data, they can also propose long-term environmental protection measures. This makes it possible to balance trash collection operations with environmental protection.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The image analysis unit analyzes satellite images or drone footage to identify the location and amount of marine debris. For example, the generation AI inputs satellite images or drone footage, detects features that indicate the presence of marine debris, and outputs its location information. Using a pre-finished model, the generation AI analyzes a wide range of image data to accurately determine the distribution of debris. Step 2: The garbage collection unit collects the garbage based on the location information of the garbage identified by the image analysis unit. For example, a drone flies over the sea and guides a robot to collect the garbage. The robot efficiently collects the garbage and transports it to the designated location. The generation AI generates an operating program for the robot or drone, and instructs it on the optimal route and method. Step 3: The waste analysis unit analyzes the waste collected by the waste collection unit. For example, the generation AI analyzes the collected waste and identifies its type and amount. The generation AI identifies different materials such as plastic, metal, and wood and calculates the proportion of each. Step 4: The proposal unit proposes waste reduction measures based on the results of the waste analysis unit. For example, the generative AI might suggest reducing plastic use in a particular area or strengthening recycling programs.

[0050] (Example 2) The AI ​​ocean cleaning system according to an embodiment of the present invention is a system that automatically detects floating debris on the ocean, analyzes it using a generating AI, and efficiently collects and removes it. As a result, the AI ​​ocean cleaning system can consistently detect, collect, analyze, and propose reduction measures for marine debris.

[0051] An AI ocean cleaning system according to an embodiment includes an image analysis unit, a garbage collection unit, a garbage analysis unit, and a proposal unit. The image analysis unit analyzes satellite images or drone footage to identify the location or amount of marine debris. For example, the generation AI inputs satellite images or drone footage, detects features indicating the presence of marine debris, and outputs its location information. The generation AI uses a pre-fine-tuned model to analyze a wide range of image data to accurately grasp the distribution of debris. The garbage collection unit collects garbage based on the location information identified by the image analysis unit. For example, a drone flies over the ocean and guides a robot to collect the garbage. The robot efficiently collects the garbage and transports it to a designated location. The generation AI generates an operating program for the robot or drone and instructs it on the optimal route and method. The garbage analysis unit analyzes the garbage collected by the garbage collection unit. For example, the generation AI analyzes the collected garbage and identifies its type and amount. The generation AI identifies different materials, such as plastic, metal, and wood, and calculates their respective proportions. The proposal unit proposes waste reduction measures based on the results of the waste analysis unit. For example, the generation AI suggests reducing plastic use in specific areas or strengthening recycling programs. This allows the AI ​​ocean cleaning system to consistently detect, collect, analyze, and propose reduction measures for marine debris.

[0052] The image analysis unit can learn the different characteristics of each type of marine debris and output location information for each type. For example, using generative AI, the image analysis unit can learn the characteristics of different types of marine debris such as plastic, metal, and wood, and output location information for each type. For example, plastic can be identified based on its light reflection characteristics, metal on its magnetic properties, and wood on its shape characteristics. This allows for efficient collection by identifying the location information for each type of marine debris.

[0053] The image analysis unit can introduce an algorithm to correct for the effects of weather or time of day when analyzing satellite images or drone footage. For example, the image analysis unit introduces an algorithm to correct for the effects of weather or time of day when analyzing satellite images or drone footage. For example, the image analysis unit corrects images taken on cloudy or rainy days to improve the accuracy of garbage detection. In this way, the accuracy of garbage detection is improved by correcting for the effects of weather and time of day.

[0054] The image analysis unit can integrate data from marine sensors in addition to satellite images or drone footage to more accurately locate garbage. The image analysis unit can integrate data from marine sensors in addition to satellite images or drone footage to more accurately locate garbage. For example, it can analyze water quality data from marine sensors to detect features that indicate the presence of garbage. In this way, integrating data from marine sensors improves the accuracy of locating garbage.

[0055] The garbage collection unit can automatically generate the optimal collection method for each type of garbage and instruct the robot or drone to do so. For example, using generative AI, the garbage collection unit can automatically generate the optimal collection method for each type of garbage and instruct the robot or drone to do so. For example, suction is used for plastic, magnetic for metal, and a crane for wood. This allows for efficient garbage collection by automatically generating the optimal collection method for each type of garbage.

[0056] The garbage collection unit can monitor the operation of the autonomous robot or drone in real time and implement an algorithm that responds to obstacles or changes in weather. For example, the garbage collection unit can monitor the operation of the autonomous robot or drone in real time and implement an algorithm that responds to obstacles or changes in weather. For example, the garbage collection unit can change its route to avoid obstacles or adjust its operation in response to changes in weather. This improves the efficiency and safety of garbage collection by responding to obstacles and changes in weather.

[0057] The waste analysis unit can use generative AI to analyze the components of waste, identify recyclable materials, and propose recycling programs. The waste analysis unit can, for example, use generative AI to analyze the components of waste, identify recyclable materials, and propose recycling programs. For example, it can identify materials such as plastic, metal, and paper and propose recycling methods for each. This can contribute to environmental protection by identifying recyclable materials and proposing recycling programs.

[0058] The proposal unit can introduce an algorithm that identifies the source of waste and automatically generates reduction measures for each source. The proposal unit, for example, introduces an algorithm that identifies the source of waste and automatically generates reduction measures for each source. For example, the proposal unit identifies waste generated from a specific factory or region and proposes reduction measures accordingly. This makes it possible to effectively reduce waste by identifying the source of waste and proposing reduction measures for each source.

[0059] The suggestion unit can analyze the user's feelings toward the waste reduction measures and make suggestions to elicit positive feelings. The suggestion unit, for example, uses an emotion estimation function to analyze the user's feelings toward the waste reduction measures and make suggestions to elicit positive feelings. For example, the suggestion unit presents successful examples of the implementation of the reduction measures. This makes it possible to promote the implementation of waste reduction measures by making suggestions that take the user's feelings into consideration.

[0060] The proposal unit can integrate the garbage analysis data with other environmental data and propose comprehensive environmental protection measures. For example, the proposal unit can integrate the garbage analysis data with other environmental data (e.g., air pollution data or water quality data) and propose comprehensive environmental protection measures. For example, the proposal unit can analyze the relationship between garbage distribution and air pollution and propose comprehensive measures. In this way, by proposing comprehensive environmental protection measures, more extensive environmental protection can be achieved.

[0061] The proposal unit can quantitatively evaluate the effects of marine environmental protection activities and propose optimal action plans. The proposal unit can, for example, use generative AI to quantitatively evaluate the effects of marine environmental protection activities and propose optimal action plans. For example, the proposal unit can evaluate based on the amount of garbage collected and changes in biodiversity. In this way, the optimal action plans can be proposed by quantitatively evaluating the effects of marine environmental protection activities.

[0062] The proposal unit can automatically generate an educational program on marine environmental protection and provide it to local residents or businesses. The proposal unit can, for example, use a generation AI to automatically generate an educational program on marine environmental protection and provide it to local residents or businesses. For example, it can create content that explains the impact of garbage and the importance of recycling. By automatically generating an educational program and providing it to local residents and businesses, it is possible to raise awareness of marine environmental protection.

[0063] The suggestion unit can analyze the user's feelings toward marine environmental protection activities and conduct awareness-raising activities to elicit positive emotions. The suggestion unit can, for example, use an emotion estimation function to analyze the user's feelings toward marine environmental protection activities and conduct awareness-raising activities to elicit positive emotions. For example, it can present success stories to increase the user's motivation. In this way, awareness-raising activities that take the user's emotions into consideration can be conducted, thereby increasing the effectiveness of marine environmental protection activities.

[0064] The proposal unit can apply the data of marine environmental protection activities to other environmental protection activities. For example, the proposal unit applies the data of marine environmental protection activities to other environmental protection activities (for example, forest protection or urban cleaning). For example, garbage distribution data is used to create plans for forest protection or urban cleaning. In this way, comprehensive environmental protection can be achieved by applying the data of marine environmental protection activities to other environmental protection activities.

[0065] The Proposal Division can share the results of marine environmental protection activities with other regions and countries, promoting conservation activities from a global perspective. The Proposal Division can, for example, share the results of marine environmental protection activities with other regions and countries, promoting conservation activities from a global perspective. For example, it can share success stories from each region and encourage implementation in other regions. In this way, sharing the results of marine environmental protection activities will promote conservation activities from a global perspective.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The AI ​​ocean cleaning system is also equipped with acoustic sensors, which can pinpoint the location of marine debris using sound waves. For example, acoustic sensors can detect sound waves reflected from debris in the ocean and identify its location. This improves the accuracy of locating debris by using acoustic data in addition to visual data. Acoustic sensors can also detect the movements of marine life and adjust the route of the garbage collection robot to avoid affecting the creatures. This enables efficient garbage collection while minimizing the impact on marine life.

[0068] The AI ​​ocean cleaning system can also use emotion estimation to optimize the behavior of the garbage collection robot. For example, if the garbage collection robot estimates the user's emotions while working and senses stress or anxiety, it can adjust the robot's behavior to reduce the burden of the work. Also, if the user senses satisfaction, it can optimize the robot's behavior to maintain that emotion. This enables garbage collection work to take the user's emotions into consideration, improving work efficiency.

[0069] The AI ​​ocean cleaning system can also equip trash collection robots with self-repair capabilities. For example, if a robot breaks down, it can self-diagnose and automatically carry out necessary repairs. This maximizes the robot's uptime and minimizes interruptions to trash collection operations. The self-repair capability also reduces robot maintenance costs and enables long-term operation.

[0070] The AI ​​ocean cleaning system can also use emotion estimation to optimize the waste reduction measure suggestions. For example, the suggestion module estimates the user's emotions and suggests reduction measures that elicit positive emotions. Specifically, it presents information that is likely to interest the user and success stories to increase motivation. Furthermore, if the user is feeling negative emotions, it analyzes the cause and suggests improvement measures. This allows for effective waste reduction measures that take the user's emotions into consideration.

[0071] The AI ​​ocean cleaning system can also equip trash collection robots with the ability to optimize energy efficiency. For example, the robots can monitor energy consumption in real time during collection operations and automatically select the optimal energy usage pattern. This allows for efficient trash collection while minimizing energy consumption. The robots can also reduce their environmental impact by using renewable energy.

[0072] The AI ​​ocean cleaning system can also use emotion estimation to optimize the interface of the garbage collection robot. For example, if the user's emotions are estimated and they are feeling stressed or anxious, the interface design and operation method can be adjusted to make it easier to use. Also, if the user is feeling satisfied, the interface can be optimized to maintain that emotion. This makes it possible to create an easy-to-use interface that takes the user's emotions into consideration.

[0073] The AI ​​ocean cleaning system can also equip garbage collection robots with collaborative operation capabilities. For example, multiple robots working together to collect garbage can be more efficient. Specifically, the robots communicate with each other and automatically determine the optimal division of labor. Furthermore, the robots complement each other, improving the accuracy and efficiency of the work. This allows multiple robots to work together to achieve efficient garbage collection.

[0074] The AI ​​ocean cleaning system can also use emotion estimation to optimize the maintenance schedule of the garbage collection robot. For example, if the user's emotions are estimated and they are feeling stressed or anxious, the system can distribute maintenance tasks to reduce their burden. Also, if the user is feeling satisfied, the system can optimize the maintenance schedule to maintain that emotion. This allows for a maintenance schedule that takes the user's emotions into account.

[0075] The AI ​​ocean cleaning system can also equip trash collection robots with environmental monitoring capabilities. For example, the robots can collect water and air quality data during trash collection operations and monitor environmental conditions in real time. This allows them to assess the impact of trash collection operations and adjust their work methods as needed. Based on the environmental data, they can also propose long-term environmental protection measures. This makes it possible to balance trash collection operations with environmental protection.

[0076] The AI ​​ocean cleaning system also uses emotion estimation to analyze the garbage collection robot's operation log and propose improvement measures based on the user's emotions. For example, the system can analyze the operation log to identify situations in which the user felt stress or anxiety and analyze the causes. It then proposes improvement measures that take the user's emotions into account and optimizes the robot's operation. This allows for effective improvement measures that reflect the user's emotions.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The image analysis unit analyzes satellite images or drone footage to identify the location and amount of marine debris. For example, the generation AI inputs satellite images or drone footage, detects features that indicate the presence of marine debris, and outputs its location information. Using a pre-finished model, the generation AI analyzes a wide range of image data to accurately determine the distribution of debris. Step 2: The garbage collection unit collects the garbage based on the location information of the garbage identified by the image analysis unit. For example, a drone flies over the sea and guides a robot to collect the garbage. The robot efficiently collects the garbage and transports it to the designated location. The generation AI generates an operating program for the robot or drone, and instructs it on the optimal route and method. Step 3: The waste analysis unit analyzes the waste collected by the waste collection unit. For example, the generation AI analyzes the collected waste and identifies its type and amount. The generation AI identifies different materials such as plastic, metal, and wood and calculates the proportion of each. Step 4: The proposal unit proposes waste reduction measures based on the results of the waste analysis unit. For example, the generative AI might suggest reducing plastic use in a particular area or strengthening recycling programs.

[0079] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0081] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0084] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0085] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0086] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0087] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0088] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0089] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0090] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0091] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0092] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0093] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0120] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0129] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0130] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0131] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0132] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0133] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0134] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0135] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0136] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0137] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0138] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0139] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0140] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0141] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0142] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0143] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0144] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0145] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0146] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image analysis unit that analyzes satellite images or drone footage to identify the location or amount of marine debris; a dust collection unit that collects dust based on the position information of the dust identified by the image analysis unit; a dust analysis unit that analyzes the dust collected by the dust collection unit; a proposal unit that proposes waste reduction measures based on the results of the analysis by the waste analysis unit. A system characterized by:

2. The image analysis unit The different characteristics of each type of marine debris are learned, and the location information is output for each type.

2. The system of claim 1.

3. The garbage collection unit is Automatically generate the optimal collection method for each type of waste and instruct the robot or drone to do so.

2. The system of claim 1.

4. The waste analysis unit Generative AI is used to analyze the waste's composition, identify recyclable materials, and propose recycling programs.

2. The system of claim 1.

5. The proposal unit Analyzing the user's feelings toward the waste reduction measures and making the suggestions to elicit positive feelings 2. The system of claim 1.

Images