System

Abstract

An object of a system according to an embodiment is to optimize an environmental load and energy efficiency in city planning and building design.SOLUTION: A system includes an environmental load advice part, a traffic system efficiency improvement part, a carbon dioxide reduction part, a waste treatment optimization part, and a recycle rate improvement part. The environmental load advice unit provides advice related to an environmental load and energy efficiency. The transportation system efficiency improvement unit supports efficiency improvement of a transportation system and public facilities in a city. The carbon dioxide reduction unit reduces the amount of carbon dioxide emission. The waste processing optimization unit supports optimization of waste processing. The recycle rate improvement unit assists in improving the recycle rate.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 technologies have not adequately optimized environmental impact and energy efficiency in urban planning and building design, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize environmental load and energy efficiency in urban planning and building design. [Means for solving the problem]

[0006] The system according to the embodiment includes an environmental load advice unit, a transportation system efficiency improvement unit, a carbon dioxide reduction unit, a waste disposal optimization unit, and a recycling rate improvement unit. The environmental load advice unit provides advice on environmental load and energy efficiency. The transportation system efficiency unit supports the improvement of the efficiency of urban transportation systems and public facilities. The carbon dioxide reduction unit aims to reduce carbon dioxide emissions. The waste disposal optimization unit supports the optimization of waste disposal. The recycling rate improvement unit supports the improvement of the recycling rate. [Effects of the Invention]

[0007] The system according to the embodiment can optimize environmental load and energy efficiency in urban planning and building design. [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 sustainable urban design support system according to an embodiment of the present invention provides advice on environmental impact and energy efficiency in urban planning and building design, supports the efficiency of urban transportation systems and public facilities, and aims to reduce carbon dioxide emissions. As a result, the sustainable urban design support system can reduce the environmental impact of cities and improve energy efficiency.

[0029] A sustainable urban design support system according to an embodiment includes an environmental impact advice unit, a transportation system efficiency improvement unit, a carbon dioxide reduction unit, a waste disposal optimization unit, and a recycling rate improvement unit. The environmental impact advice unit provides advice on environmental impact and energy efficiency. For example, the generation AI makes suggestions on selecting materials to use in building design and introducing energy-efficient equipment. The generation AI generates advice based on data on urban planning and building design, and prompts including instructions on environmental impact and energy efficiency. The transportation system efficiency unit supports the improvement of the efficiency of urban transportation systems and public facilities. For example, the generation AI makes suggestions on road design to alleviate traffic congestion and optimizing public transportation schedules. The generation AI analyzes data on transportation systems and public facilities and makes suggestions for improving efficiency. The carbon dioxide reduction unit proposes specific measures to reduce carbon dioxide emissions throughout the city. For example, the generation AI proposes the introduction of renewable energy and the design of energy-efficient buildings. The generation AI analyzes carbon dioxide emissions throughout the city and proposes specific measures for reduction. The waste disposal optimization unit analyzes data on urban waste disposal and proposes optimal disposal methods. For example, the generation AI makes suggestions on how to separate waste and improve recycling rates. The generation AI also provides advice on how to operate waste treatment facilities and introduce equipment. The recycling rate improvement unit analyzes a city's recycling rate and proposes specific measures to improve it. For example, the generation AI makes suggestions on how to separate recyclable materials and how to efficiently operate recycling facilities. The generation AI also provides advice on implementing awareness-raising activities and educational programs related to recycling. As a result, the sustainable urban design support system according to the embodiment can reduce the environmental impact of a city and improve energy efficiency. For example, it can minimize environmental impact and maximize energy efficiency in urban planning and building design. Furthermore, by improving the efficiency of transportation systems and public facilities, carbon dioxide emissions can be reduced, and sustainable cities can be realized by optimizing waste treatment and improving recycling rates.

[0030] The environmental impact advice unit can perform a life cycle assessment of materials to be used in the design stage of a building and propose material selection that minimizes the environmental impact. For example, the generation AI can perform a life cycle assessment of materials to be used in the design stage of a building and propose material selection that minimizes the environmental impact. For example, the generation AI can recommend using renewable wood instead of concrete. The generation AI can also evaluate the environmental impact of the material manufacturing and disposal processes and select the optimal material. For example, the generation AI can evaluate carbon dioxide emissions and energy consumption in the manufacturing process and select materials with the least environmental impact. This can support the specific material selection that minimizes the environmental impact in building design.

[0031] The transportation system efficiency improvement unit can analyze traffic data in real time, predict traffic congestion, and propose detour routes in advance. In the transportation system efficiency improvement unit, for example, the generation AI analyzes traffic data in real time, predicts traffic congestion, and proposes detour routes in advance. For example, the generation AI provides the optimal detour route to avoid congestion on major roads. The generation AI also proposes the optimal detour route taking into account unexpected events such as traffic accidents and construction work. For example, the generation AI identifies the locations of traffic accidents and construction sites in real time and proposes detour routes. The generation AI also optimizes public transportation schedules and makes proposals to alleviate traffic congestion. For example, the generation AI analyzes bus and train schedules and proposes optimal schedules. This makes it possible to predict traffic congestion and propose detour routes in advance, thereby improving transportation efficiency.

[0032] The Carbon Dioxide Reduction Unit can analyze energy consumption data for the entire city and make proposals to maximize the effects of introducing renewable energy. For example, the Generation AI in the Carbon Dioxide Reduction Unit analyzes energy consumption data for the entire city and makes proposals to maximize the effects of introducing renewable energy. For example, the Generation AI proposes the optimal placement of solar power generation and wind power generation. The Generation AI also makes proposals to shift peak energy consumption. For example, the Generation AI analyzes peak energy consumption times and proposes measures to shift the peak. The Generation AI also makes proposals for the introduction of highly energy-efficient equipment. For example, the Generation AI proposes the introduction of highly efficient air conditioning equipment and lighting equipment. This makes it possible to make specific proposals to maximize the effects of introducing renewable energy.

[0033] The waste treatment optimization unit can propose the optimal treatment method for each type of waste, maximizing the efficiency of waste treatment. In the waste treatment optimization unit, for example, the generation AI proposes the optimal treatment method for each type of waste, maximizing the efficiency of waste treatment. For example, the generation AI proposes a method for recycling plastic waste. The generation AI also makes proposals on how to separate waste. For example, the generation AI analyzes waste separation standards and proposes the optimal separation method. The generation AI also makes proposals on how to operate waste treatment facilities. For example, the generation AI proposes measures to improve the operational efficiency of waste treatment facilities. This makes it possible to propose specific treatment methods to maximize the efficiency of waste treatment.

[0034] The recycling rate improvement unit can optimize the sorting method for recyclable materials and make proposals to maximize the recycling rate. In the recycling rate improvement unit, for example, the generation AI optimizes the sorting method for recyclable materials and makes proposals to maximize the recycling rate. For example, the generation AI proposes sorting methods for plastic, metal, paper, etc. The generation AI also makes proposals regarding how to operate recycling facilities. For example, the generation AI proposes measures to improve the operational efficiency of recycling facilities. The generation AI also makes proposals regarding the implementation of recycling awareness activities and educational programs. For example, the generation AI proposes the content and implementation methods of recycling awareness activities. This makes it possible to propose specific sorting methods to maximize the recycling rate.

[0035] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0036] The sustainable urban design support system can further include an urban greening promotion department. This department proposes specific measures to increase the green area of ​​the city. For example, the generation AI proposes greening plans that utilize vacant lots and rooftops in the city. The generation AI also provides advice on the design of urban parks and green spaces. Furthermore, the generation AI makes suggestions on how greening can help reduce urban temperatures and protect ecosystems. This will promote urban greening and reduce environmental impact.

[0037] The sustainable urban design support system can further include a resident participation promotion unit. The resident participation promotion unit proposes measures to reflect resident opinions in urban planning and environmental protection activities. For example, the generative AI proposes methods for conducting resident surveys and platforms for collecting opinions. The generative AI also provides advice on planning resident-participation workshops and events. Furthermore, the generative AI proposes specific design proposals for urban planning that reflect resident opinions. This makes it possible to realize sustainable urban designs that incorporate resident opinions.

[0038] The sustainable urban design support system can further include an energy consumption prediction unit. The energy consumption prediction unit predicts energy consumption throughout the city and supports efficient energy management. For example, the generation AI analyzes past energy consumption data and predicts future energy demand. The generation AI also identifies peak energy consumption times and proposes measures to shift the peaks. Furthermore, the generation AI provides advice on the introduction of energy-efficient equipment. This makes it possible to efficiently manage energy consumption throughout the city and achieve sustainable energy use.

[0039] The sustainable urban design support system can further include a water resources management unit. The water resources management unit supports the efficient use and management of a city's water resources. For example, the generation AI makes proposals for the design of a rainwater reuse system and the introduction of water-saving equipment. The generation AI also proposes specific measures for water quality management. Furthermore, the generation AI monitors the city's water resource usage in real time and supports efficient water resource management. This enables the city's water resources to be used sustainably and reduces environmental impact.

[0040] The sustainable urban design support system can further include a disaster prevention department. This department assesses the disaster risk of the city and proposes specific disaster prevention measures. For example, the generative AI analyzes disaster risks such as earthquakes and floods and proposes urban designs to minimize the risks. The generative AI also provides advice on the design of evacuation routes and shelters in the event of a disaster. Furthermore, the generative AI makes suggestions for implementing educational programs and training for disaster prevention. This reduces the disaster risk of the city and creates a safe urban environment.

[0041] The processing flow of the first embodiment will be briefly explained below.

[0042] Step 1: The environmental impact advice unit provides advice on environmental impact and energy efficiency. For example, the generative AI makes suggestions on the selection of materials to be used in the building design stage and the introduction of energy-efficient equipment. The generative AI generates advice based on data on urban planning and building design, as well as prompts containing instructions on environmental impact and energy efficiency. Step 2: The Transportation System Efficiency Unit will support the efficiency of urban transportation systems and public facilities. For example, the Generative AI will propose road designs to alleviate traffic congestion and optimize public transportation schedules. The Generative AI will analyze data on transportation systems and public facilities and make proposals for improving efficiency. Step 3: The Carbon Dioxide Reduction Department proposes specific measures to reduce carbon dioxide emissions across the city. For example, the Generative AI will make suggestions for introducing renewable energy and designing energy-efficient buildings. The Generative AI will analyze carbon dioxide emissions across the city and propose specific measures for reduction. Step 4: The waste treatment optimization unit analyzes data on the city's waste treatment and proposes optimal treatment methods. For example, the generation AI will make suggestions on how to separate waste and improve recycling rates. The generation AI will also provide advice on how to operate waste treatment facilities and introduce new equipment. Step 5: The Recycling Rate Improvement Unit analyzes the city's recycling rate and proposes specific measures to improve it. For example, the Generative AI will suggest ways to separate recyclable materials and how to operate recycling facilities efficiently. The Generative AI will also provide advice on implementing awareness-raising and educational programs on recycling.

[0043] (Example 2) The sustainable urban design support system according to an embodiment of the present invention provides advice on environmental impact and energy efficiency in urban planning and building design, supports the efficiency of urban transportation systems and public facilities, and aims to reduce carbon dioxide emissions. As a result, the sustainable urban design support system can reduce the environmental impact of cities and improve energy efficiency.

[0044] A sustainable urban design support system according to an embodiment includes an environmental impact advice unit, a transportation system efficiency improvement unit, a carbon dioxide reduction unit, a waste disposal optimization unit, and a recycling rate improvement unit. The environmental impact advice unit provides advice on environmental impact and energy efficiency. For example, the generation AI makes suggestions on selecting materials to use in building design and introducing energy-efficient equipment. The generation AI generates advice based on data on urban planning and building design, and prompts including instructions on environmental impact and energy efficiency. The transportation system efficiency unit supports the improvement of the efficiency of urban transportation systems and public facilities. For example, the generation AI makes suggestions on road design to alleviate traffic congestion and optimizing public transportation schedules. The generation AI analyzes data on transportation systems and public facilities and makes suggestions for improving efficiency. The carbon dioxide reduction unit proposes specific measures to reduce carbon dioxide emissions throughout the city. For example, the generation AI proposes the introduction of renewable energy and the design of energy-efficient buildings. The generation AI analyzes carbon dioxide emissions throughout the city and proposes specific measures for reduction. The waste disposal optimization unit analyzes data on urban waste disposal and proposes optimal disposal methods. For example, the generation AI makes suggestions on how to separate waste and improve recycling rates. The generation AI also provides advice on how to operate waste treatment facilities and introduce equipment. The recycling rate improvement unit analyzes a city's recycling rate and proposes specific measures to improve it. For example, the generation AI makes suggestions on how to separate recyclable materials and how to efficiently operate recycling facilities. The generation AI also provides advice on implementing awareness-raising activities and educational programs related to recycling. As a result, the sustainable urban design support system according to the embodiment can reduce the environmental impact of a city and improve energy efficiency. For example, it can minimize environmental impact and maximize energy efficiency in urban planning and building design. Furthermore, by improving the efficiency of transportation systems and public facilities, carbon dioxide emissions can be reduced, and sustainable cities can be realized by optimizing waste treatment and improving recycling rates.

[0045] The environmental impact advice unit can perform a life cycle assessment of materials to be used in the design stage of a building and propose material selection that minimizes the environmental impact. For example, the generation AI can perform a life cycle assessment of materials to be used in the design stage of a building and propose material selection that minimizes the environmental impact. For example, the generation AI can recommend using renewable wood instead of concrete. The generation AI can also evaluate the environmental impact of the material manufacturing and disposal processes and select the optimal material. For example, the generation AI can evaluate carbon dioxide emissions and energy consumption in the manufacturing process and select materials with the least environmental impact. This can support the specific material selection that minimizes the environmental impact in building design.

[0046] The transportation system efficiency improvement unit can analyze traffic data in real time, predict traffic congestion, and propose detour routes in advance. In the transportation system efficiency improvement unit, for example, the generation AI analyzes traffic data in real time, predicts traffic congestion, and proposes detour routes in advance. For example, the generation AI provides the optimal detour route to avoid congestion on major roads. The generation AI also proposes the optimal detour route taking into account unexpected events such as traffic accidents and construction work. For example, the generation AI identifies the locations of traffic accidents and construction sites in real time and proposes detour routes. The generation AI also optimizes public transportation schedules and makes proposals to alleviate traffic congestion. For example, the generation AI analyzes bus and train schedules and proposes optimal schedules. This makes it possible to predict traffic congestion and propose detour routes in advance, thereby improving transportation efficiency.

[0047] The Carbon Dioxide Reduction Unit can analyze energy consumption data for the entire city and make proposals to maximize the effects of introducing renewable energy. For example, the Generation AI in the Carbon Dioxide Reduction Unit analyzes energy consumption data for the entire city and makes proposals to maximize the effects of introducing renewable energy. For example, the Generation AI proposes the optimal placement of solar power generation and wind power generation. The Generation AI also makes proposals to shift peak energy consumption. For example, the Generation AI analyzes peak energy consumption times and proposes measures to shift the peak. The Generation AI also makes proposals for the introduction of highly energy-efficient equipment. For example, the Generation AI proposes the introduction of highly efficient air conditioning equipment and lighting equipment. This makes it possible to make specific proposals to maximize the effects of introducing renewable energy.

[0048] The waste treatment optimization unit can propose the optimal treatment method for each type of waste, maximizing the efficiency of waste treatment. In the waste treatment optimization unit, for example, the generation AI proposes the optimal treatment method for each type of waste, maximizing the efficiency of waste treatment. For example, the generation AI proposes a method for recycling plastic waste. The generation AI also makes proposals on how to separate waste. For example, the generation AI analyzes waste separation standards and proposes the optimal separation method. The generation AI also makes proposals on how to operate waste treatment facilities. For example, the generation AI proposes measures to improve the operational efficiency of waste treatment facilities. This makes it possible to propose specific treatment methods to maximize the efficiency of waste treatment.

[0049] The recycling rate improvement unit can optimize the sorting method for recyclable materials and make proposals to maximize the recycling rate. In the recycling rate improvement unit, for example, the generation AI optimizes the sorting method for recyclable materials and makes proposals to maximize the recycling rate. For example, the generation AI proposes sorting methods for plastic, metal, paper, etc. The generation AI also makes proposals regarding how to operate recycling facilities. For example, the generation AI proposes measures to improve the operational efficiency of recycling facilities. The generation AI also makes proposals regarding the implementation of recycling awareness activities and educational programs. For example, the generation AI proposes the content and implementation methods of recycling awareness activities. This makes it possible to propose specific sorting methods to maximize the recycling rate.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The sustainable urban design support system can further include an urban greening promotion department. This department proposes specific measures to increase the green area of ​​the city. For example, the generation AI proposes greening plans that utilize vacant lots and rooftops in the city. The generation AI also provides advice on the design of urban parks and green spaces. Furthermore, the generation AI makes suggestions on how greening can help reduce urban temperatures and protect ecosystems. This will promote urban greening and reduce environmental impact.

[0052] The sustainable urban design support system can further include a resident participation promotion unit. The resident participation promotion unit proposes measures to reflect resident opinions in urban planning and environmental protection activities. For example, the generative AI proposes methods for conducting resident surveys and platforms for collecting opinions. The generative AI also provides advice on planning resident-participation workshops and events. Furthermore, the generative AI proposes specific design proposals for urban planning that reflect resident opinions. This makes it possible to realize sustainable urban designs that incorporate resident opinions.

[0053] The sustainable urban design support system can further include an energy consumption prediction unit. The energy consumption prediction unit predicts energy consumption throughout the city and supports efficient energy management. For example, the generation AI analyzes past energy consumption data and predicts future energy demand. The generation AI also identifies peak energy consumption times and proposes measures to shift the peaks. Furthermore, the generation AI provides advice on the introduction of energy-efficient equipment. This makes it possible to efficiently manage energy consumption throughout the city and achieve sustainable energy use.

[0054] The sustainable urban design support system can further include a water resources management unit. The water resources management unit supports the efficient use and management of a city's water resources. For example, the generation AI makes proposals for the design of a rainwater reuse system and the introduction of water-saving equipment. The generation AI also proposes specific measures for water quality management. Furthermore, the generation AI monitors the city's water resource usage in real time and supports efficient water resource management. This enables the city's water resources to be used sustainably and reduces environmental impact.

[0055] The sustainable urban design support system can further include a disaster prevention department. This department assesses the disaster risk of the city and proposes specific disaster prevention measures. For example, the generative AI analyzes disaster risks such as earthquakes and floods and proposes urban designs to minimize the risks. The generative AI also provides advice on the design of evacuation routes and shelters in the event of a disaster. Furthermore, the generative AI makes suggestions for implementing educational programs and training for disaster prevention. This reduces the disaster risk of the city and creates a safe urban environment.

[0056] The sustainable urban design support system can further include a resident satisfaction assessment unit that uses an emotion estimation function to assess resident satisfaction. The resident satisfaction assessment unit estimates resident emotions and proposes improvements to urban planning and public facilities. For example, the generation AI analyzes resident emotion data and identifies areas where residents are satisfied or dissatisfied. Based on resident emotions, the generation AI also proposes improvements to urban planning and the design of new public facilities. Furthermore, the generation AI provides advice on planning events and activities that reflect resident emotions. This improves resident satisfaction and enables the realization of sustainable urban design.

[0057] The sustainable urban design support system can further include a traffic stress assessment unit that uses emotion estimation to assess traffic stress. The traffic stress assessment unit estimates residents' emotions regarding traffic conditions and proposes improvements to the transportation system. For example, the generation AI analyzes residents' emotion data and identifies stress caused by traffic congestion and public transportation delays. Based on residents' emotions, the generation AI also proposes improvements to the transportation system and the introduction of new transportation methods. Furthermore, the generation AI provides advice on how to provide traffic information that reflects residents' emotions. This reduces traffic stress and creates a comfortable transportation environment.

[0058] The sustainable urban design support system can further include a public facility usage evaluation unit that uses an emotion estimation function to evaluate the usage status of public facilities. The public facility usage evaluation unit estimates the emotions of users of public facilities and proposes improvements to the facilities. For example, the generation AI analyzes user emotion data to identify facility usage satisfaction and dissatisfaction. The generation AI also proposes improvements to the facility's layout and equipment based on the user's emotions. Furthermore, the generation AI provides advice on how to provide services that reflect the user's emotions. This can improve user satisfaction with public facilities and realize sustainable urban design.

[0059] The sustainable urban design support system can further include a health status assessment unit that uses emotion estimation to assess the health status of residents. The health status assessment unit estimates residents' emotions and suggests ways to improve their health status. For example, the generative AI analyzes residents' emotional data and identifies the causes of stress and anxiety. Based on the residents' emotions, the generative AI also suggests ways to improve their health status or introduce new health programs. Furthermore, the generative AI provides advice on how to provide health information that reflects the residents' emotions. This can improve residents' health status and realize sustainable urban design.

[0060] The sustainable urban design support system can further include a tourist satisfaction evaluation unit that uses an emotion estimation function to evaluate tourist satisfaction. The tourist satisfaction evaluation unit estimates tourist emotions and proposes improvements to tourist destinations. For example, the generation AI analyzes tourist emotion data and identifies satisfaction and dissatisfaction points at tourist destinations. The generation AI also proposes improvements to facilities and services at tourist destinations based on tourist emotions. Furthermore, the generation AI provides advice on how to provide tourist information that reflects tourist emotions. This improves tourist satisfaction and realizes sustainable urban design.

[0061] The processing flow of the second embodiment will be briefly explained below.

[0062] Step 1: The environmental impact advice unit provides advice on environmental impact and energy efficiency. For example, the generative AI makes suggestions on the selection of materials to be used in the building design stage and the introduction of energy-efficient equipment. The generative AI generates advice based on data on urban planning and building design, as well as prompts containing instructions on environmental impact and energy efficiency. Step 2: The Transportation System Efficiency Unit will support the efficiency of urban transportation systems and public facilities. For example, the Generative AI will propose road designs to alleviate traffic congestion and optimize public transportation schedules. The Generative AI will analyze data on transportation systems and public facilities and make proposals for improving efficiency. Step 3: The Carbon Dioxide Reduction Department proposes specific measures to reduce carbon dioxide emissions across the city. For example, the Generative AI will make suggestions for introducing renewable energy and designing energy-efficient buildings. The Generative AI will analyze carbon dioxide emissions across the city and propose specific measures for reduction. Step 4: The waste treatment optimization unit analyzes data on the city's waste treatment and proposes optimal treatment methods. For example, the generation AI will make suggestions on how to separate waste and improve recycling rates. The generation AI will also provide advice on how to operate waste treatment facilities and introduce new equipment. Step 5: The Recycling Rate Improvement Unit analyzes the city's recycling rate and proposes specific measures to improve it. For example, the Generative AI will suggest ways to separate recyclable materials and how to operate recycling facilities efficiently. The Generative AI will also provide advice on implementing awareness-raising and educational programs on recycling.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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).

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[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 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.

[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 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.

[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 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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."

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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]

[0130] 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 Environmental Impact Advice Department, which provides advice on environmental impact and energy efficiency; The Transportation System Efficiency Department supports the efficiency of urban transportation systems and public facilities, and Carbon Dioxide Reduction Department, which aims to reduce carbon dioxide emissions; A waste treatment optimization department that supports the optimization of waste treatment; A recycling rate improvement department that supports the improvement of the recycling rate. A system characterized by:

2. The environmental load advice unit We conduct life cycle assessments of materials used in building design and propose material selection that minimizes the environmental impact. The system of claim 1 .

3. The transportation system efficiency department Analyze traffic data in real time, predict traffic congestion, and suggest detour routes in advance The system of claim 1 .

4. The carbon dioxide reduction unit Analyzing energy consumption data for the entire city and making proposals to maximize the effectiveness of introducing renewable energy The system of claim 1 .

5. The waste treatment optimization unit Propose the most suitable treatment method for each type of waste and maximize the efficiency of said waste treatment. The system of claim 1 .

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