System

Abstract

An object of a system according to an embodiment is to analyze data of empty houses and propose an optimal utilization method.SOLUTION: A system includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data of empty houses. The analysis unit analyzes the empty house data collected by the data collection unit. The proposal unit proposes an optimal utilization method based on the result analyzed by the analysis unit.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 does not effectively utilize data on vacant houses and propose optimal ways to utilize them, so there is room for improvement.

[0005] The system of the embodiment aims to analyze data on vacant houses and propose optimal ways to utilize them. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data on vacant houses. The analysis unit analyzes the vacant house data collected by the data collection unit. The proposal unit proposes an optimal utilization method based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data on vacant houses and propose optimal ways to utilize them. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 ​​platform according to an embodiment of the present invention is a system that collects and analyzes data on vacant houses and proposes optimal ways to utilize them. This allows the AI ​​platform to grasp the current state of vacant houses and propose optimal ways to utilize them, such as renovating, renting, or selling.

[0029] An AI platform according to an embodiment includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data on vacant houses. For example, it collects data such as the location, age, structure, and current status of the vacant house. The data collection unit can also collect data on the exterior and interior of the vacant house using sensors or drones. For example, a drone can be used to photograph the exterior of the vacant house and collect the video data. A sensor can also be used to collect data such as the temperature, humidity, and vibration inside the vacant house. The analysis unit analyzes the vacant house data collected by the data collection unit. For example, an AI can evaluate the condition and market value of the vacant house based on the collected data. The AI ​​can analyze the data using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, an old house with a solid structure or a good location can be evaluated as having high market value. The proposal unit proposes optimal utilization methods based on the results of the analysis by the analysis unit. For example, it can propose renovating the house and using it as a rental property, selling it and handing it over to a new owner, or using it as a local community space. The AI ​​proposes utilization methods based on data on vacant houses and information on local needs and market trends. As a result, the AI ​​platform according to the embodiment can efficiently solve the problem of vacant houses by collecting and analyzing data on vacant houses and proposing optimal utilization methods.

[0030] The data collection unit can use a drone to capture images of the exterior of a vacant house and the surrounding environment, and then analyze the video data using AI. For example, the data collection unit can use a drone to capture 360-degree images of the exterior of a vacant house, and then have AI analyze the video data. For example, it can detect cracks in the building's exterior walls and damage to the roof, and identify areas that need repair. The data collection unit can also use a drone to capture images of the surrounding environment of a vacant house, and then have AI analyze the video data. For example, it can grasp the condition of surrounding roads and adjacent buildings. This makes it possible to use a drone to obtain a detailed understanding of the exterior of a vacant house and the surrounding environment.

[0031] The data collection unit installs sensors inside the vacant house and collects data such as temperature, humidity, and vibration in real time, which can then be analyzed by AI. For example, the data collection unit installs a temperature sensor inside the vacant house and collects temperature data in real time. For example, if the indoor temperature is too low in winter, it suggests a decrease in insulation performance. The data collection unit also installs a humidity sensor and collects humidity data in real time. For example, if the humidity is too high, it suggests the risk of mold growth. The data collection unit also installs a vibration sensor and collects vibration data in real time. For example, structural problems in the building can be detected. As a result, by using sensors, the internal conditions of the vacant house can be grasped in detail.

[0032] The data collection department can introduce a system that allows residents to easily provide information using a smartphone app. For example, the data collection department can develop a smartphone app that allows residents to easily provide information about vacant houses. For example, they can send photos and location information of vacant houses through the app. The data collection department can also collect feedback from residents through the app. For example, they can collect opinions on the current state of vacant houses and how to utilize them. This allows residents to easily provide information, improving the efficiency of data collection.

[0033] The data collection department can utilize local volunteers to build a system for collecting data from the entire community. For example, the data collection department could recruit local volunteers to collect data on vacant houses. For example, the volunteers could collect photos and location information of vacant houses and register them in a database. The data collection department could also collect local needs and opinions through the volunteers. For example, the volunteers could collect feedback from residents and reflect it in how vacant houses are utilized. In this way, utilizing local volunteers can improve the scope and accuracy of data collection.

[0034] The proposal unit can make more accurate proposals by having the AI ​​learn from past successes and failures. For example, the proposal unit can have the AI ​​learn from past successes and failures to improve the accuracy of proposals. For example, it can extract common points between successes and reflect them in proposals. The proposal unit can also make proposals to avoid risks based on failures. For example, it can learn from past failures to avoid repeating the same mistakes. In this way, the accuracy of proposals can be improved by learning from past cases.

[0035] When proposing ways to utilize vacant houses, the proposal department can take into account the local culture and history and make proposals that are rooted in the local area. For example, the proposal department uses AI to learn about the local culture and history and propose ways to utilize vacant houses based on that information. For example, it could propose using the house as a community space related to traditional local events. The proposal department can also propose ways to preserve local historical buildings. For example, it could use buildings with historical value as tourist resources. This makes it possible to make proposals that are rooted in the local area by taking into account the local culture and history.

[0036] The proposal unit can simulate the proposed utilization method using virtual reality (VR) to enable visual confirmation. The proposal unit, for example, can simulate the proposed utilization method using VR to enable residents to visually confirm it. For example, the interior of a vacant house after renovation can be experienced using VR. The proposal unit can also simulate the effects of the utilization method using VR. For example, it can simulate usage as a community space. In this way, the proposed utilization method can be visually confirmed using virtual reality.

[0037] The proposal department can introduce a system in which the proposed utilization methods are shared with local residents and experts and improved based on their feedback. The proposal department can, for example, introduce a system in which the proposed utilization methods are shared with local residents and improved based on their feedback. For example, it can hold a residents' meeting to collect opinions. The proposal department can also incorporate the opinions of experts. For example, it can reflect feedback from architectural experts and urban planning experts. This allows the proposal content to be improved based on feedback from residents and experts.

[0038] When analyzing local needs and market trends, the analysis unit collects social media data and uses AI to analyze it, allowing it to grasp the latest trends. For example, the analysis unit collects social media data and uses AI to analyze it, allowing it to grasp the latest local trends. For example, it analyzes the content posted by local residents and identifies popular activities and events. The analysis unit can also analyze market trends based on social media data. For example, it can grasp trends in the local real estate market and reflect this in how vacant houses are utilized. In this way, it is possible to grasp the latest trends by analyzing social media data.

[0039] The analysis unit can periodically conduct resident surveys to understand the needs of the community, and the AI ​​can analyze the results. For example, the analysis unit can periodically conduct resident surveys, and the AI ​​can analyze the results. For example, the analysis unit can collect residents' opinions and requests and reflect them in how vacant houses are utilized. The analysis unit can also understand the needs of the community based on the survey results. For example, it can create a renovation plan based on the needs of residents. In this way, the needs of the community can be understood by analyzing the resident surveys.

[0040] When analyzing regional needs and market trends, the analysis unit can compare data from different regions and refer to success stories from other regions. For example, the analysis unit collects data from different regions and uses AI to analyze it, thereby referring to success stories from other regions. For example, it can propose methods of utilizing data that have been successful in similar regions. The analysis unit can also compare market trends from different regions. For example, it can refer to real estate market trends in other regions. In this way, by comparing data from different regions, it can refer to success stories from other regions.

[0041] The analysis unit can hold local events and workshops to gather residents' opinions in order to understand local needs. For example, the analysis unit holds local events and workshops to gather residents' opinions. For example, the analysis unit can propose ways to utilize vacant houses based on the opinions of event participants. The analysis unit can also understand residents' needs through workshops. For example, the analysis unit can create a renovation plan based on residents' opinions. In this way, by gathering residents' opinions through events and workshops, local needs can be understood.

[0042] When formulating a renovation plan, the analysis unit has the AI ​​learn the knowledge of architectural experts, allowing it to make more specialized proposals. For example, the analysis unit has the AI ​​learn the knowledge of architectural experts to formulate a renovation plan. For example, it proposes the optimal renovation method based on the opinions of experts. The analysis unit can also make proposals to avoid risks based on expert knowledge. For example, it proposes a renovation plan based on the Building Standards Act. In this way, by learning the knowledge of architectural experts, it is possible to propose more specialized renovation plans.

[0043] The analysis unit can propose reusable materials to minimize renovation costs. The analysis unit, for example, proposes reusable materials to minimize renovation costs. For example, costs can be reduced by reusing existing building materials. The analysis unit can also propose the use of recycled materials. For example, recycled wood and reusable building materials can be used. In this way, by proposing reusable materials, renovation costs can be minimized.

[0044] The analysis unit can visualize renovation plans using 3D modeling, allowing residents and owners to visualize them more concretely. For example, the analysis unit can visualize renovation plans using 3D modeling, allowing residents and owners to visualize them more concretely. For example, it can display the interior of the building after renovation in 3D. The analysis unit can also use 3D modeling to simulate the effects of the renovation plan. For example, it can simulate how the space will be used after the renovation. In this way, by visualizing the renovation plan using 3D modeling, residents and owners can visualize it more concretely.

[0045] The analysis unit can introduce a system for raising funds for a renovation plan through crowdfunding. The analysis unit, for example, introduces a system for raising funds for a renovation plan through crowdfunding. For example, funds are raised through an online platform. The analysis unit can also propose a fundraising method based on successful cases of crowdfunding. For example, common points between the successful cases can be extracted and reflected in the proposal. This makes it possible to raise funds for a renovation plan by utilizing crowdfunding.

[0046] When renting or selling, the proposal unit uses AI to analyze market trends in real time and propose the optimal timing for renting or selling. For example, the proposal unit uses AI to analyze market trends in real time and propose the optimal timing for renting or selling. For example, it may propose selling when market prices are high. The proposal unit can also make proposals to avoid risks when renting or selling. For example, it may evaluate risks based on market trends and determine the optimal timing. In this way, by analyzing market trends in real time, it is possible to propose renting or selling at the optimal timing.

[0047] The proposal unit allows AI to suggest staging (arrangement of furniture and decorations) to maximize the appeal of a property when renting or selling it. For example, the proposal unit proposes staging to maximize the appeal of a property. For example, it arranges furniture and selects decorations to enhance the property's appeal. The proposal unit can also simulate the effects of staging. For example, it simulates how the property will look after staging. This allows AI to suggest staging to maximize the appeal of a property, thereby increasing the success rate of rentals and sales.

[0048] The proposal unit can create content that introduces the charms of the area when renting or selling, thereby increasing the value of the property. The proposal unit can create content that introduces the charms of the area when renting or selling, thereby increasing the value of the property. For example, it can create videos that introduce local tourist spots and facilities. The proposal unit can also provide content that introduces local events and culture. For example, it can introduce local festivals and traditional events. In this way, the value of the property can be increased by creating content that introduces the charms of the area.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The data collection unit collects data on the natural environment around the vacant house, and the analysis unit can use that data to propose environmentally friendly ways of using it. For example, it can collect data on surrounding green spaces and water sources and propose eco-friendly renovation methods. The data collection unit can also collect energy consumption data on the vacant house, and the analysis unit can use that data to propose energy-efficient ways of using it. For example, it can propose the installation of a solar power generation system. This can contribute to the creation of sustainable communities by proposing environmentally friendly ways of using the house.

[0051] The data collection unit collects transportation data around the vacant house, and the analysis unit can use that data to propose ways to utilize the property with good transportation access. For example, by collecting information on the location and operation status of the nearest public transportation, the unit can propose using the property as a rental property that is convenient for commuting to work or school. The data collection unit can also collect data on nearby parking lots, and the analysis unit can use that data to propose using the property as a property with parking. By proposing ways to utilize the property that take transportation access into consideration, the value of the property as a highly convenient property can be increased.

[0052] The data collection unit collects security data around vacant houses, and the analysis unit can use that data to propose ways to utilize the data while taking safety into consideration. For example, it can collect information on crime rates and the locations of police stations to promote the area as a safe place. The data collection unit can also collect data on security equipment installed in vacant houses, and the analysis unit can use that data to propose ways to strengthen security measures. For example, it can propose the installation of security cameras and security systems. By proposing ways to utilize the data while taking safety into consideration, it is possible to increase residents' sense of security.

[0053] When proposing ways to utilize vacant houses, the proposal department can take into account local climate data and propose methods that are suited to the climate. For example, in cold regions, it will propose renovations that improve insulation performance, and in warm regions, it will propose designs that emphasize ventilation. The proposal department can also propose energy-efficient methods of utilization based on climate data. For example, it will propose the introduction of solar power generation systems and designs that utilize natural ventilation. This makes it possible to provide a comfortable living environment by proposing methods of utilization that are suited to the local climate.

[0054] When proposing ways to utilize vacant homes, the proposal department can take into account data from local educational institutions and medical institutions and suggest ways to utilize them for families. For example, if there is a school or nursery school nearby, it can suggest using the home as a rental property for families with children. Also, if there is a hospital or clinic nearby, it can suggest using the home as housing for the elderly. This makes it possible to propose ways to utilize vacant homes for families and the elderly by taking into account data from local educational institutions and medical institutions.

[0055] When proposing ways to utilize vacant houses, the proposal department can take into account data on local commercial and public facilities to propose highly convenient uses. For example, if there is a shopping mall or supermarket nearby, it can suggest using the house as a rental property that is convenient for shopping. Also, if there is a park or library nearby, it can suggest using the house as a family home. In this way, by taking into account data on local commercial and public facilities, it is possible to propose highly convenient uses.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The data collection unit collects data on vacant houses. For example, it collects data such as the location, age, structure, and current state of the vacant house. The data collection unit can also use sensors or drones to collect data on the exterior and interior of the vacant house. For example, a drone can be used to photograph the exterior of the vacant house and collect the video data. Sensors can also be used to collect data such as the temperature, humidity, and vibration inside the vacant house. Step 2: The analysis unit analyzes the vacant house data collected by the data collection unit. For example, AI evaluates the condition and market value of vacant houses based on the collected data. AI can analyze the data using text generation AI (e.g., LLM) or multimodal generation AI. For example, even if a house is old, it may be evaluated as having a high market value if it has a sound structure or is in a good location. Step 3: The proposal unit proposes the optimal use of the property based on the results of the analysis by the analysis unit. For example, it may propose renovating the property and using it as a rental property, selling it and handing it over to a new owner, or using it as a local community space. The AI ​​proposes use methods based on data on vacant homes and information on local needs and market trends.

[0058] (Example 2) The AI ​​platform according to an embodiment of the present invention is a system that collects and analyzes data on vacant houses and proposes optimal ways to utilize them. This allows the AI ​​platform to grasp the current state of vacant houses and propose optimal ways to utilize them, such as renovating, renting, or selling.

[0059] An AI platform according to an embodiment includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data on vacant houses. For example, it collects data such as the location, age, structure, and current status of the vacant house. The data collection unit can also collect data on the exterior and interior of the vacant house using sensors or drones. For example, a drone can be used to photograph the exterior of the vacant house and collect the video data. A sensor can also be used to collect data such as the temperature, humidity, and vibration inside the vacant house. The analysis unit analyzes the vacant house data collected by the data collection unit. For example, an AI can evaluate the condition and market value of the vacant house based on the collected data. The AI ​​can analyze the data using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, an old house with a solid structure or a good location can be evaluated as having high market value. The proposal unit proposes optimal utilization methods based on the results of the analysis by the analysis unit. For example, it can propose renovating the house and using it as a rental property, selling it and handing it over to a new owner, or using it as a local community space. The AI ​​proposes utilization methods based on data on vacant houses and information on local needs and market trends. As a result, the AI ​​platform according to the embodiment can efficiently solve the problem of vacant houses by collecting and analyzing data on vacant houses and proposing optimal utilization methods.

[0060] The data collection unit can use a drone to capture images of the exterior of a vacant house and the surrounding environment, and then analyze the video data using AI. For example, the data collection unit can use a drone to capture 360-degree images of the exterior of a vacant house, and then have AI analyze the video data. For example, it can detect cracks in the building's exterior walls and damage to the roof, and identify areas that need repair. The data collection unit can also use a drone to capture images of the surrounding environment of a vacant house, and then have AI analyze the video data. For example, it can grasp the condition of surrounding roads and adjacent buildings. This makes it possible to use a drone to obtain a detailed understanding of the exterior of a vacant house and the surrounding environment.

[0061] The data collection unit installs sensors inside the vacant house and collects data such as temperature, humidity, and vibration in real time, which can then be analyzed by AI. For example, the data collection unit installs a temperature sensor inside the vacant house and collects temperature data in real time. For example, if the indoor temperature is too low in winter, it suggests a decrease in insulation performance. The data collection unit also installs a humidity sensor and collects humidity data in real time. For example, if the humidity is too high, it suggests the risk of mold growth. The data collection unit also installs a vibration sensor and collects vibration data in real time. For example, structural problems in the building can be detected. As a result, by using sensors, the internal conditions of the vacant house can be grasped in detail.

[0062] The data collection unit uses the emotion estimation function to collect the emotions of the owner of the vacant house and neighboring residents, and analyzes the emotion data to reflect it in how the vacant house is utilized. For example, the data collection unit uses the emotion estimation function to collect the emotions of the owner of the vacant house. For example, if the owner has negative emotions toward the vacant house, the data collection unit prioritizes proposing selling or renting the house. The data collection unit also uses the emotion estimation function to collect the emotions of neighboring residents. For example, if neighboring residents have positive emotions toward utilizing the vacant house, the data collection unit proposes using the house as a community space. In this way, by collecting emotion data, it is possible to propose utilization methods that reflect the wishes of the owner and neighboring residents.

[0063] The data collection department can introduce a system that allows residents to easily provide information using a smartphone app. For example, the data collection department can develop a smartphone app that allows residents to easily provide information about vacant houses. For example, they can send photos and location information of vacant houses through the app. The data collection department can also collect feedback from residents through the app. For example, they can collect opinions on the current state of vacant houses and how to utilize them. This allows residents to easily provide information, improving the efficiency of data collection.

[0064] The data collection department can utilize local volunteers to build a system for collecting data from the entire community. For example, the data collection department could recruit local volunteers to collect data on vacant houses. For example, the volunteers could collect photos and location information of vacant houses and register them in a database. The data collection department could also collect local needs and opinions through the volunteers. For example, the volunteers could collect feedback from residents and reflect it in how vacant houses are utilized. In this way, utilizing local volunteers can improve the scope and accuracy of data collection.

[0065] The data collection unit can use the emotion estimation function to analyze the emotions of residents in real time when collecting data on vacant houses, and provide feedback to elicit positive emotions. For example, the data collection unit uses the emotion estimation function to analyze the emotions of residents in real time when collecting data on vacant houses. For example, if a resident is feeling anxious, it provides feedback that gives them a sense of security. The data collection unit also uses the emotion estimation function to provide feedback to make residents' emotions positive. For example, it sends a message encouraging residents to have cooperative emotions. In this way, it is possible to analyze residents' emotions in real time and elicit positive emotions, thereby promoting cooperation in data collection.

[0066] The proposal unit can make more accurate proposals by having the AI ​​learn from past successes and failures. For example, the proposal unit can have the AI ​​learn from past successes and failures to improve the accuracy of proposals. For example, it can extract common points between successes and reflect them in proposals. The proposal unit can also make proposals to avoid risks based on failures. For example, it can learn from past failures to avoid repeating the same mistakes. In this way, the accuracy of proposals can be improved by learning from past cases.

[0067] When proposing ways to utilize vacant houses, the proposal department can take into account the local culture and history and make proposals that are rooted in the local area. For example, the proposal department uses AI to learn about the local culture and history and propose ways to utilize vacant houses based on that information. For example, it could propose using the house as a community space related to traditional local events. The proposal department can also propose ways to preserve local historical buildings. For example, it could use buildings with historical value as tourist resources. This makes it possible to make proposals that are rooted in the local area by taking into account the local culture and history.

[0068] The suggestion unit can use the emotion estimation function to analyze residents' emotional reactions to the proposed utilization methods and prioritize proposals that will garner the most positive reactions. The suggestion unit, for example, uses the emotion estimation function to analyze residents' emotional reactions to the proposed utilization methods. For example, it prioritizes the adoption of proposals that evoke a high proportion of positive emotions. The suggestion unit can also adjust the content of the proposal based on the emotional reactions. For example, if there are a high proportion of negative emotions, it can review the content of the proposal. In this way, by analyzing residents' emotional reactions, it becomes possible to make proposals that will garner the most positive reactions.

[0069] The proposal unit can simulate the proposed utilization method using virtual reality (VR) to enable visual confirmation. The proposal unit, for example, can simulate the proposed utilization method using VR to enable residents to visually confirm it. For example, the interior of a vacant house after renovation can be experienced using VR. The proposal unit can also simulate the effects of the utilization method using VR. For example, it can simulate usage as a community space. In this way, the proposed utilization method can be visually confirmed using virtual reality.

[0070] The proposal department can introduce a system in which the proposed utilization methods are shared with local residents and experts and improved based on their feedback. The proposal department can, for example, introduce a system in which the proposed utilization methods are shared with local residents and improved based on their feedback. For example, it can hold a residents' meeting to collect opinions. The proposal department can also incorporate the opinions of experts. For example, it can reflect feedback from architectural experts and urban planning experts. This allows the proposal content to be improved based on feedback from residents and experts.

[0071] When analyzing local needs and market trends, the analysis unit collects social media data and uses AI to analyze it, allowing it to grasp the latest trends. For example, the analysis unit collects social media data and uses AI to analyze it, allowing it to grasp the latest local trends. For example, it analyzes the content posted by local residents and identifies popular activities and events. The analysis unit can also analyze market trends based on social media data. For example, it can grasp trends in the local real estate market and reflect this in how vacant houses are utilized. In this way, it is possible to grasp the latest trends by analyzing social media data.

[0072] The analysis unit can periodically conduct resident surveys to understand the needs of the community, and the AI ​​can analyze the results. For example, the analysis unit can periodically conduct resident surveys, and the AI ​​can analyze the results. For example, the analysis unit can collect residents' opinions and requests and reflect them in how vacant houses are utilized. The analysis unit can also understand the needs of the community based on the survey results. For example, it can create a renovation plan based on the needs of residents. In this way, the needs of the community can be understood by analyzing the resident surveys.

[0073] The analysis unit can use the emotion estimation function to analyze the emotions of local residents and propose utilization methods that are likely to resonate with them emotionally. The analysis unit, for example, can use the emotion estimation function to analyze the emotions of local residents and propose utilization methods that are likely to resonate with them. For example, it can prioritize proposals that have a high emotion score among residents. The analysis unit can also adjust the content of the proposals based on the emotion data. For example, if there are a lot of negative emotions, it can review the content of the proposals. In this way, by analyzing the emotions of local residents, it can propose utilization methods that are likely to resonate with them.

[0074] When analyzing regional needs and market trends, the analysis unit can compare data from different regions and refer to success stories from other regions. For example, the analysis unit collects data from different regions and uses AI to analyze it, thereby referring to success stories from other regions. For example, it can propose methods of utilizing data that have been successful in similar regions. The analysis unit can also compare market trends from different regions. For example, it can refer to real estate market trends in other regions. In this way, by comparing data from different regions, it can refer to success stories from other regions.

[0075] The analysis unit can hold local events and workshops to gather residents' opinions in order to understand local needs. For example, the analysis unit holds local events and workshops to gather residents' opinions. For example, the analysis unit can propose ways to utilize vacant houses based on the opinions of event participants. The analysis unit can also understand residents' needs through workshops. For example, the analysis unit can create a renovation plan based on residents' opinions. In this way, by gathering residents' opinions through events and workshops, local needs can be understood.

[0076] The analysis unit uses the emotion estimation function to monitor the emotions of local residents in real time and can respond quickly to changes in needs. The analysis unit, for example, uses the emotion estimation function to monitor the emotions of local residents in real time. For example, it can respond quickly to changes in needs based on the residents' emotion scores. The analysis unit can also adjust the content of proposals based on emotion data. For example, if there are a lot of negative emotions, it can review the content of proposals. In this way, it is possible to monitor the emotions of local residents in real time and respond quickly to changes in needs, thereby responding immediately to local needs.

[0077] When formulating a renovation plan, the analysis unit has the AI ​​learn the knowledge of architectural experts, allowing it to make more specialized proposals. For example, the analysis unit has the AI ​​learn the knowledge of architectural experts to formulate a renovation plan. For example, it proposes the optimal renovation method based on the opinions of experts. The analysis unit can also make proposals to avoid risks based on expert knowledge. For example, it proposes a renovation plan based on the Building Standards Act. In this way, by learning the knowledge of architectural experts, it is possible to propose more specialized renovation plans.

[0078] The analysis unit can propose reusable materials to minimize renovation costs. The analysis unit, for example, proposes reusable materials to minimize renovation costs. For example, costs can be reduced by reusing existing building materials. The analysis unit can also propose the use of recycled materials. For example, recycled wood and reusable building materials can be used. In this way, by proposing reusable materials, renovation costs can be minimized.

[0079] The analysis unit can use the emotion estimation function to analyze residents' emotional reactions to renovation plans and prioritize plans that will garner the most positive reactions. The analysis unit, for example, uses the emotion estimation function to analyze residents' emotional reactions to renovation plans. For example, it can prioritize plans that garner a lot of positive emotions. The analysis unit can also adjust the content of the plan based on the emotional reactions. For example, if there are a lot of negative emotions, it can review the content of the plan. In this way, by analyzing residents' emotional reactions, it is possible to prioritize renovation plans that will garner the most positive reactions.

[0080] The analysis unit can visualize renovation plans using 3D modeling, allowing residents and owners to visualize them more concretely. For example, the analysis unit can visualize renovation plans using 3D modeling, allowing residents and owners to visualize them more concretely. For example, it can display the interior of the building after renovation in 3D. The analysis unit can also use 3D modeling to simulate the effects of the renovation plan. For example, it can simulate how the space will be used after the renovation. In this way, by visualizing the renovation plan using 3D modeling, residents and owners can visualize it more concretely.

[0081] The analysis unit can introduce a system for raising funds for a renovation plan through crowdfunding. The analysis unit, for example, introduces a system for raising funds for a renovation plan through crowdfunding. For example, funds are raised through an online platform. The analysis unit can also propose a fundraising method based on successful cases of crowdfunding. For example, common points between the successful cases can be extracted and reflected in the proposal. This makes it possible to raise funds for a renovation plan by utilizing crowdfunding.

[0082] The analysis unit uses the emotion estimation function to monitor residents' emotions regarding the renovation plan in real time, which can be useful for improving the plan. The analysis unit, for example, uses the emotion estimation function to monitor residents' emotions regarding the renovation plan in real time. For example, the plan content can be adjusted based on the residents' emotion scores. The analysis unit can also improve the plan based on the emotion data. For example, the analysis unit periodically collects data and reviews the plan content. In this way, monitoring residents' emotions in real time can be useful for improving the renovation plan.

[0083] When renting or selling, the proposal unit uses AI to analyze market trends in real time and propose the optimal timing for renting or selling. For example, the proposal unit uses AI to analyze market trends in real time and propose the optimal timing for renting or selling. For example, it may propose selling when market prices are high. The proposal unit can also make proposals to avoid risks when renting or selling. For example, it may evaluate risks based on market trends and determine the optimal timing. In this way, by analyzing market trends in real time, it is possible to propose renting or selling at the optimal timing.

[0084] The proposal unit allows AI to suggest staging (arrangement of furniture and decorations) to maximize the appeal of a property when renting or selling it. For example, the proposal unit proposes staging to maximize the appeal of a property. For example, it arranges furniture and selects decorations to enhance the property's appeal. The proposal unit can also simulate the effects of staging. For example, it simulates how the property will look after staging. This allows AI to suggest staging to maximize the appeal of a property, thereby increasing the success rate of rentals and sales.

[0085] The proposal unit can use the emotion estimation function to analyze the emotional reactions of residents and prospective buyers to proposals for renting or selling, and prioritize proposals that will garner the most positive reactions. The proposal unit, for example, uses the emotion estimation function to analyze the emotional reactions of residents and prospective buyers to proposals for renting or selling. For example, it can prioritize proposals that have a high proportion of positive emotions. The proposal unit can also adjust the content of the proposal based on the emotional reactions. For example, if there are a high proportion of negative emotions, it can review the content of the proposal. In this way, by analyzing the emotional reactions of residents and prospective buyers, it is possible to prioritize proposals that will garner the most positive reactions.

[0086] The proposal unit can create content that introduces the charms of the area when renting or selling, thereby increasing the value of the property. The proposal unit can create content that introduces the charms of the area when renting or selling, thereby increasing the value of the property. For example, it can create videos that introduce local tourist spots and facilities. The proposal unit can also provide content that introduces local events and culture. For example, it can introduce local festivals and traditional events. In this way, the value of the property can be increased by creating content that introduces the charms of the area.

[0087] The proposal unit can use the emotion estimation function to monitor the emotions of residents and prospective buyers regarding rental and sale proposals in real time and continuously update the optimal proposal. The proposal unit, for example, uses the emotion estimation function to monitor the emotions of residents and prospective buyers regarding rental and sale proposals in real time. For example, the proposal content is adjusted based on the emotion score. The proposal unit can also continuously update the proposal based on emotion data. For example, the proposal unit periodically collects data and reviews the proposal content. In this way, the emotions of residents and prospective buyers can be monitored in real time and the optimal proposals are continuously updated, thereby improving the accuracy of the proposals.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The data collection unit collects data on the natural environment around the vacant house, and the analysis unit can use that data to propose environmentally friendly ways of using it. For example, it can collect data on surrounding green spaces and water sources and propose eco-friendly renovation methods. The data collection unit can also collect energy consumption data on the vacant house, and the analysis unit can use that data to propose energy-efficient ways of using it. For example, it can propose the installation of a solar power generation system. This can contribute to the creation of sustainable communities by proposing environmentally friendly ways of using the house.

[0090] The data collection unit collects transportation data around the vacant house, and the analysis unit can use that data to propose ways to utilize the property with good transportation access. For example, by collecting information on the location and operation status of the nearest public transportation, the unit can propose using the property as a rental property that is convenient for commuting to work or school. The data collection unit can also collect data on nearby parking lots, and the analysis unit can use that data to propose using the property as a property with parking. By proposing ways to utilize the property that take transportation access into consideration, the value of the property as a highly convenient property can be increased.

[0091] The data collection unit collects security data around vacant houses, and the analysis unit can use that data to propose ways to utilize the data while taking safety into consideration. For example, it can collect information on crime rates and the locations of police stations to promote the area as a safe place. The data collection unit can also collect data on security equipment installed in vacant houses, and the analysis unit can use that data to propose ways to strengthen security measures. For example, it can propose the installation of security cameras and security systems. By proposing ways to utilize the data while taking safety into consideration, it is possible to increase residents' sense of security.

[0092] The data collection unit uses the emotion estimation function to collect the emotions of the owner of the vacant house, and by analyzing this emotion data, can propose utilization methods that are in line with the owner's wishes. For example, if the owner has an attachment to the vacant house, it will propose renovating it and reusing it as a home. On the other hand, if the owner places importance on the economic benefits of the vacant house, it will prioritize proposals for renting or selling it. This makes it possible to propose utilization methods that reflect the owner's emotions, thereby increasing the owner's satisfaction.

[0093] When residents provide information about vacant houses using a smartphone app, the data collection unit uses an emotion estimation function to collect the residents' emotions, and by analyzing this emotional data, it can propose utilization methods that are in line with the residents' wishes. For example, if residents are anxious about the current state of the vacant house, it can make proposals that will give them a sense of security. Also, if residents have positive feelings about utilizing the vacant house, it can suggest using it as a community space. This makes it easier to gain the residents' cooperation by proposing utilization methods that reflect their emotions.

[0094] The data collection department utilizes local volunteers, collects their emotions using emotion estimation, and can analyze the emotion data to propose data collection methods that are in line with the volunteers' intentions. For example, if a volunteer has positive emotions about data collection, the department can encourage them to actively collect data. On the other hand, if a volunteer has negative emotions about data collection, the department can propose ways to reduce the burden. In this way, by proposing data collection methods that reflect the volunteers' emotions, it becomes easier to obtain their cooperation.

[0095] The proposal unit trains the AI ​​to learn from past successes and failures, and uses emotion estimation to analyze residents' emotional reactions to proposed utilization methods, prioritizing proposals that will garner the most positive responses. For example, it can extract commonalities between past successes and adjust proposal content based on residents' emotional reactions. It can also learn from failures to avoid repeating the same mistakes. This makes it possible to analyze residents' emotional reactions and make proposals that will garner the most positive responses.

[0096] When proposing ways to utilize vacant houses, the proposal department can take into account local climate data and propose methods that are suited to the climate. For example, in cold regions, it will propose renovations that improve insulation performance, and in warm regions, it will propose designs that emphasize ventilation. The proposal department can also propose energy-efficient methods of utilization based on climate data. For example, it will propose the introduction of solar power generation systems and designs that utilize natural ventilation. This makes it possible to provide a comfortable living environment by proposing methods of utilization that are suited to the local climate.

[0097] When proposing ways to utilize vacant homes, the proposal department can take into account data from local educational institutions and medical institutions and suggest ways to utilize them for families. For example, if there is a school or nursery school nearby, it can suggest using the home as a rental property for families with children. Also, if there is a hospital or clinic nearby, it can suggest using the home as housing for the elderly. This makes it possible to propose ways to utilize vacant homes for families and the elderly by taking into account data from local educational institutions and medical institutions.

[0098] When proposing ways to utilize vacant houses, the proposal department can take into account data on local commercial and public facilities to propose highly convenient uses. For example, if there is a shopping mall or supermarket nearby, it can suggest using the house as a rental property that is convenient for shopping. Also, if there is a park or library nearby, it can suggest using the house as a family home. In this way, by taking into account data on local commercial and public facilities, it is possible to propose highly convenient uses.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The data collection unit collects data on vacant houses. For example, it collects data such as the location, age, structure, and current state of the vacant house. The data collection unit can also use sensors or drones to collect data on the exterior and interior of the vacant house. For example, a drone can be used to photograph the exterior of the vacant house and collect the video data. Sensors can also be used to collect data such as the temperature, humidity, and vibration inside the vacant house. Step 2: The analysis unit analyzes the vacant house data collected by the data collection unit. For example, AI evaluates the condition and market value of vacant houses based on the collected data. AI can analyze the data using text generation AI (e.g., LLM) or multimodal generation AI. For example, even if a house is old, it may be evaluated as having a high market value if it has a sound structure or is in a good location. Step 3: The proposal unit proposes the optimal use of the property based on the results of the analysis by the analysis unit. For example, it may propose renovating the property and using it as a rental property, selling it and handing it over to a new owner, or using it as a local community space. The AI ​​proposes use methods based on data on vacant homes and information on local needs and market trends.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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. a data collection department that collects data on vacant houses; An analysis unit that analyzes the vacant house data collected by the data collection unit; a proposal unit that proposes an optimal utilization method based on the results of the analysis by the analysis unit. A system characterized by:

2. The data collection unit A drone will be used to photograph the exterior of the vacant house and the surrounding environment, and the video data will be analyzed using the AI.

2. The system of claim 1.

3. The proposal unit The AI ​​learns from past successes and failures to make more accurate suggestions.

2. The system of claim 1.

4. The analysis unit When analyzing local needs and market trends, the data from social media is collected and analyzed by the AI ​​to grasp the latest trends.

2. The system of claim 1.

5. The data collection unit Using the emotion estimation function, the emotions of the owner of the vacant house and the neighboring residents are collected, and the emotional data is analyzed and reflected in how the vacant house is utilized.

2. The system of claim 1.

Images