System

Abstract

An object of a system according to an embodiment is to efficiently identify an unregistered empty house or land and propose a method of utilizing the same.SOLUTION: A system includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data of the drone flying in various places, opening and closing of rain doors and curtains, ON / OFF of electricity, an assumed number of years of construction obtained from appearance, comings and goings of people, a surrounding people flow, a damage situation, how weeds grow, an area of land, a shape, a sunshine environment, and a surrounding environment. The analysis unit analyzes the data collected by the collection unit and identifies an unregistered empty house or land. The proposal unit proposes a utilization method for the vacant house or land specified 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 has faced the challenge of making it difficult to efficiently identify unregistered vacant houses and land and propose ways to utilize them.

[0005] The system according to the embodiment aims to efficiently identify unregistered vacant houses and land and propose ways to utilize them. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit flies a drone over various locations and collects data on the opening and closing of shutters and curtains, the on / off status of lights, the estimated age of the building based on its exterior, people entering and exiting the building, the flow of people in the surrounding area, the state of damage, the growth of weeds, the land area, shape, sunlight conditions, and the surrounding environment. The analysis unit analyzes the data collected by the collection unit and identifies unregistered vacant houses and land. The proposal unit proposes ways to utilize the vacant houses and land identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently identify unregistered vacant houses and land and propose 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 non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A data collection and proposal system according to an embodiment of the present invention uses drones to fly across the country, collecting video, audio, and pedestrian flow data, identifying unregistered vacant homes and land, and proposing ways to utilize them. The system flies drones across various locations, collecting data such as the opening and closing of shutters and curtains, whether lights are on or off, the estimated age of the building based on its exterior, the number of people entering and exiting the building, the flow of people in the surrounding area, damage, weed growth, land area, shape, sunlight, and the surrounding environment. Next, AI analyzes the collected data to identify unregistered vacant homes and land. Furthermore, for identified vacant homes and land, proposals are made for the installation of solar panels, land sales, and laundromats in areas with good sunlight. Once a sufficient amount of land is secured, proposals are made for commercial development, factories, amusement parks, and other uses. For example, the data collection and proposal system uses drones to fly over various locations and collect data such as the opening and closing of shutters and curtains, whether lights are on or off, the estimated age of the building based on its exterior, people entering and exiting, the flow of people in the surrounding area, damage, weed growth, land area, shape, sunlight, and the surrounding environment. The drones are equipped with high-resolution cameras and sensors, allowing for detailed data collection. For example, if shutters or curtains remain closed for an extended period of time, or if the lights have not been turned on or off for an extended period of time, it is determined that the house is likely to be vacant. The estimated age of the house, damage, and weed growth based on the exterior are also useful for identifying vacant houses. Next, AI analyzes the collected data to identify unregistered vacant houses and land. The AI ​​analyzes the collected data and identifies properties that are likely to be vacant. For example, properties that have not been visited or visited for a long time or properties with little foot traffic in the surrounding area are determined to be vacant. The AI ​​also takes into account the land area, shape, sunlight, and surrounding environment to identify vacant houses and land. It then proposes ways to utilize the identified vacant houses and land. For example, in areas with good sunlight, we propose the installation of solar panels, and also suggest ways to utilize the land, such as for sale or as a laundromat. Furthermore, when a certain amount of land is secured, we propose commercial measures, factories, amusement parks, etc. This will enable the effective use of vacant houses and land, contributing to the revitalization of the local area.This allows the data collection and proposal system to promote the effective use of vacant houses and land, contributing to the revitalization of local communities. For example, it can quickly and accurately identify vacant houses and land and propose ways to use them, contributing to the development of local communities through collaboration with real estate-related companies and local governments.

[0029] A data collection and proposal system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit flies a drone over various locations to collect data such as the opening and closing of shutters and curtains, the on / off status of lights, the estimated age of a building based on its exterior, people entering and exiting the building, the flow of people in the surrounding area, damage conditions, weed growth, land area, shape, sunlight conditions, and the surrounding environment. The collection unit, for example, is equipped with a high-resolution camera or sensor to collect detailed data. The collection unit can also optimize the drone's flight route to efficiently collect data. For example, the collection unit can adjust the drone's flight route in real time to efficiently collect data. The analysis unit analyzes the data collected by the collection unit to identify unregistered vacant homes and land. The analysis unit, for example, uses AI to analyze the collected data and identify properties that are likely to be vacant. For example, the analysis unit can identify properties that have not been visited for a long time or properties with little foot traffic in the surrounding area. The analysis unit can also identify vacant houses and land by taking into account the land's area, shape, sunlight, and surrounding environment. The proposal unit proposes ways to utilize the vacant houses and land identified by the analysis unit. For example, the proposal unit can propose installing solar panels in areas with good sunlight. The proposal unit can also propose ways to utilize the land, such as selling it or building a laundromat. Furthermore, when a certain amount of land is secured, the proposal unit can propose commercial measures, factories, amusement parks, etc. As a result, the data collection and proposal system according to the embodiment can promote the effective use of vacant houses and land and contribute to regional revitalization. For example, the system can quickly and accurately identify vacant houses and land and propose ways to utilize them, thereby contributing to regional development through collaboration with real estate-related companies and local governments.

[0030] The collection unit can be equipped with a high-resolution camera or sensor to collect detailed data. The collection unit, for example, uses a high-resolution camera to collect detailed video data. For example, the collection unit uses a camera with high resolution and a high frame rate to collect detailed video data. The collection unit can also collect detailed environmental data using a sensor. For example, the collection unit can collect environmental data using a temperature sensor or a humidity sensor. Thus, detailed data can be collected by using a high-resolution camera or sensor. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input video data acquired by a high-resolution camera to a generation AI and have the generation AI analyze the video data.

[0031] The analysis unit can analyze the collected data and identify properties that are likely to be vacant. The analysis unit can, for example, analyze the collected data using AI to identify properties that are likely to be vacant. For example, the analysis unit can identify properties that have not been visited by people for a long period of time or properties with little foot traffic in the surrounding area. The analysis unit can also identify vacant houses and land by taking into account the land area and shape, sunlight environment, and surrounding environment. For example, the analysis unit can analyze the land area and shape to identify properties that are likely to be vacant. The analysis unit can also analyze sunlight environment and surrounding environment to identify properties that are likely to be vacant. In this way, by analyzing the collected data, properties that are likely to be vacant can be identified. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI identify vacant houses.

[0032] The proposal unit can propose the installation of solar panels in areas with suitable sunlight environments. For example, the proposal unit proposes the installation of solar panels in areas with suitable sunlight environments. For example, the proposal unit analyzes the hours of sunlight and the amount of solar radiation to identify areas where solar panels are suitable for installation. The proposal unit can also make specific proposals regarding the installation of solar panels. For example, the proposal unit makes proposals regarding the installation location and installation method of solar panels. This makes it possible to propose appropriate utilization methods for areas with good sunlight environments. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the sunlight environment into the generation AI and cause the generation AI to execute a proposal for installing solar panels.

[0033] The proposal unit can propose ways to utilize land for sale or coin laundries. The proposal unit, for example, analyzes the criteria and conditions for land suitable for sale and proposes land for sale. For example, the proposal unit analyzes location conditions and area to identify land suitable for sale. The proposal unit can also analyze the criteria and conditions for land suitable for coin laundries and propose coin laundries. For example, the proposal unit analyzes the surrounding population density and demand to identify land suitable for coin laundries. This makes it possible to propose various ways to utilize vacant houses and land. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input land data into a generation AI and have the generation AI execute proposals for land for sale or coin laundries.

[0034] The proposal unit can propose commercial measures, factories, or amusement parks when a certain amount of land has been secured. For example, the proposal unit proposes commercial measures when a certain amount of land has been secured. For example, the proposal unit proposes the establishment of a shopping mall or office building. The proposal unit can also propose the establishment of a factory. For example, the proposal unit analyzes the infrastructure development status and the surrounding environment and proposes the establishment of a factory. The proposal unit can also propose the establishment of an amusement park. For example, the proposal unit proposes the establishment of an amusement park taking into account ease of access and surrounding tourist attractions. This makes it possible to propose appropriate uses for large pieces of land. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input land data into a generation AI and have the generation AI execute proposals for commercial measures, factories, or amusement parks.

[0035] The collection unit can select an appropriate data collection method depending on the weather or time of day when flying the drone. For example, the collection unit selects the optimal data collection method depending on the weather and time of day when flying the drone. For example, the collection unit protects the drone's camera with a waterproof cover when it is raining and continues data collection. The collection unit can also use an infrared camera to ensure visibility and collect data at night. The collection unit can also set the drone's flight altitude low when there is strong wind to ensure stable data collection. This enables efficient data collection by selecting the optimal data collection method depending on the weather and time of day. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather data and time of day data into the generation AI and have the generation AI select the optimal data collection method.

[0036] The collection unit can adjust the frequency of data collection during drone flight according to specific events or seasons. For example, the collection unit can adjust the frequency of data collection during drone flight according to specific events or seasons. For example, the collection unit can increase the frequency of data collection in areas where festivals or events are held to collect detailed information. The collection unit can also increase the frequency of data collection in spring and autumn to capture seasonal changes. The collection unit can also increase the frequency of data collection in winter to check the snow accumulation situation. In this way, detailed information can be collected by adjusting the frequency of data collection according to specific events or seasons. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input event data and seasonal data into the generation AI and cause the generation AI to adjust the frequency of data collection.

[0037] The collection unit can perform real-time navigation to avoid surrounding buildings and obstacles during drone flight. For example, the collection unit can detect the shadow of a building while the drone is flying and automatically change the flight route. Furthermore, if the drone detects an obstacle, the collection unit can calculate an avoidance route in real time and continue flying. Furthermore, the collection unit can use sensors to ensure a safe flight route when the drone passes through a narrow passage. This enables safe data collection by performing real-time navigation to avoid surrounding buildings and obstacles. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on surrounding buildings and obstacles into the generation AI and cause the generation AI to perform real-time navigation.

[0038] The collection unit can adjust the data collection range based on geographical characteristics when flying the drone. For example, the collection unit adjusts the data collection range taking geographical characteristics into account when flying the drone. For example, the collection unit adjusts the drone's flight altitude in mountainous areas to collect data tailored to the terrain. The collection unit can also fly between buildings in urban areas to collect detailed data. The collection unit can also set a flight route along the coastline to take into account the effects of waves and collect data. This enables efficient data collection by adjusting the data collection range taking geographical characteristics into account. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical characteristic data to the generation AI and cause the generation AI to adjust the data collection range.

[0039] The collection unit can optimize the flight route based on surrounding traffic conditions when flying the drone. For example, the collection unit optimizes the flight route taking into account surrounding traffic conditions when flying the drone. For example, the collection unit changes the drone's flight route in areas where traffic congestion occurs to efficiently collect data. The collection unit can also set the flight route during times of low traffic volume to safely collect data. Furthermore, if a traffic accident occurs, the collection unit can change the drone's flight route in real time to continue data collection. This enables efficient data collection by optimizing the flight route taking into account surrounding traffic conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input traffic condition data into the generation AI and cause the generation AI to optimize the flight route.

[0040] The collection unit can collect data while flying the drone, taking into account the historical background of a specific region. For example, the collection unit collects data while flying the drone, taking into account the historical background of a specific region. For example, in an area with many historical buildings, the collection unit collects detailed data on the buildings and checks their state of preservation. The collection unit can also adjust the drone's flight route to collect important data in an area with many cultural assets. The collection unit can also record the details of historical events in an area where historical events are held. This makes it possible to collect detailed information by collecting data while taking into account the historical background of a specific region. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input historical background data into a generation AI and have the generation AI adjust the data collection.

[0041] The analysis unit can refer to past data and compare it with current data during analysis. For example, the analysis unit can refer to past data and compare it with current data during analysis. For example, the analysis unit can compare past data with current data to identify changes in vacant houses. The analysis unit can also evaluate the reliability of current data based on past data. The analysis unit can also integrate past data and current data to perform a more detailed analysis. In this way, by referring to past data and comparing it with current data, changes in vacant houses can be identified. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input past data and current data into the generation AI and have the generation AI compare the data.

[0042] The analysis unit can improve the accuracy of the analysis by integrating information from different data sources during analysis. For example, the analysis unit can improve the accuracy of the analysis by integrating information from different data sources during analysis. For example, the analysis unit can integrate video data and audio data to improve the accuracy of identifying vacant houses. The analysis unit can also integrate people flow data and environmental data to help identify vacant houses. The analysis unit can also integrate data from different sensors to improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by integrating information from different data sources. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information from different data sources into a generation AI and have the generation AI integrate the information.

[0043] The analysis unit can apply an algorithm for detecting specific patterns or trends during analysis. The analysis unit can, for example, apply an algorithm for detecting specific patterns or trends during analysis. For example, the analysis unit can apply an algorithm for identifying properties that have not been visited by people for a long period of time. The analysis unit can also apply an algorithm for detecting the possibility of a vacant house based on the growth of weeds or the state of damage. The analysis unit can also apply an algorithm for detecting trends in vacant houses based on surrounding pedestrian flow data. In this way, by applying an algorithm for detecting specific patterns or trends, the accuracy of identifying vacant houses can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data into a generation AI and cause the generation AI to detect patterns and trends.

[0044] The analysis unit can adjust the range of analysis based on geographical characteristics during analysis. For example, the analysis unit adjusts the range of analysis taking geographical characteristics into account during analysis. For example, in mountainous areas, the analysis unit performs an analysis tailored to the topography and identifies vacant houses. In urban areas, the analysis unit can also perform an analysis taking into account the density of buildings. In coastal areas, the analysis unit can also perform an analysis taking into account the influence of tides. In this way, adjusting the range of analysis taking into account geographical characteristics enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input geographical characteristic data into the generation AI and cause the generation AI to adjust the range of analysis.

[0045] The analysis unit can adjust the frequency of analysis during analysis according to specific events or seasons. For example, the analysis unit can adjust the frequency of analysis during analysis according to specific events or seasons. For example, the analysis unit can increase the frequency of analysis in areas where festivals or events are held, thereby providing detailed information. The analysis unit can also increase the frequency of analysis in spring and autumn to capture seasonal changes. The analysis unit can also increase the frequency of analysis in winter to check the snow accumulation situation. In this way, detailed information can be provided by adjusting the frequency of analysis according to specific events or seasons. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input event data and seasonal data into the generation AI and cause the generation AI to adjust the frequency of analysis.

[0046] The analysis unit can perform the analysis while taking into account the historical background of a specific region. For example, the analysis unit can perform the analysis while taking into account the historical background of a specific region. For example, in an area with many historical buildings, the analysis unit can analyze the preservation state of buildings. Furthermore, in an area with many cultural assets, the analysis unit can prioritize the analysis of important data. Furthermore, in an area where historical events are held, the analysis unit can analyze the impact of the events. In this way, detailed information can be provided by performing the analysis while taking into account the historical background of a specific region. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input historical background data into the generation AI and cause the generation AI to adjust the analysis.

[0047] The proposal unit can make an optimal proposal by taking into consideration the economic situation of a specific region when making a proposal. For example, the proposal unit can make an optimal proposal by taking into consideration the economic situation of a specific region when making a proposal. For example, the proposal unit can propose the establishment of a commercial facility in an economically active region. The proposal unit can also propose low-cost utilization methods in an economically stagnant region. The proposal unit can also make proposals that take into consideration future potential in an economically growing region. In this way, by making an optimal proposal by taking into consideration the economic situation of a specific region, it is possible to make a proposal that is suitable for the region. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input regional economic situation data into the generation AI and cause the generation AI to execute the optimal proposal.

[0048] The suggestion unit can improve the accuracy of a proposal by referring to past proposal results when making a proposal. The suggestion unit can improve the accuracy of a proposal by referring to past proposal results when making a proposal. For example, the suggestion unit makes a similar proposal based on past successful cases. The suggestion unit can also analyze past unsuccessful cases and make a proposal that reflects improvements. The suggestion unit can also statistically analyze past proposal results and make an optimal proposal. In this way, the accuracy of the proposal can be improved by referring to past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0049] The suggestion unit can apply an algorithm for detecting specific patterns or trends when making a proposal. The suggestion unit can, for example, apply an algorithm for detecting specific patterns or trends when making a proposal. For example, the suggestion unit can apply an algorithm that suggests installing solar panels in areas with good sunlight. The suggestion unit can also apply an algorithm that suggests ways to utilize land for sale, laundromats, etc. The suggestion unit can also apply an algorithm that makes suggestions for commercial measures, factories, amusement parks, etc. By applying an algorithm that detects specific patterns or trends, the accuracy of the proposal can be improved. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input data into a generation AI and cause the generation AI to detect patterns and trends.

[0050] The proposal unit can adjust the range of the proposal based on geographical characteristics when making a proposal. For example, the proposal unit adjusts the range of the proposal by taking geographical characteristics into account when making a proposal. For example, the proposal unit makes proposals that are tailored to the terrain in mountainous areas. The proposal unit can also make proposals by taking into account the density of buildings in urban areas. The proposal unit can also make proposals by taking into account the influence of tides along coastlines. In this way, adjusting the range of the proposal by taking geographical characteristics into account enables efficient proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input geographical characteristic data into the generation AI and cause the generation AI to adjust the range of the proposal.

[0051] The suggestion unit can adjust the content of the suggestion according to a specific event or season when making a suggestion. For example, the suggestion unit adjusts the content of the suggestion according to a specific event or season when making a suggestion. For example, in an area where festivals or events are held, the suggestion unit makes suggestions related to the events. Furthermore, the suggestion unit can make seasonal suggestions in spring or autumn to capture seasonal changes. Furthermore, the suggestion unit can make suggestions taking into account snow accumulation in winter. This enables detailed suggestions to be made by adjusting the content of the suggestion according to a specific event or season. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input event data and seasonal data into the generation AI and cause the generation AI to adjust the content of the suggestion.

[0052] The suggestion unit can make a suggestion based on the historical background of a specific region when making a suggestion. For example, the suggestion unit can make a suggestion taking into account the historical background of a specific region when making a suggestion. For example, in an area with many historical buildings, the suggestion unit can make a suggestion taking into account the preservation state of the buildings. Furthermore, in an area with many cultural assets, the suggestion unit can make a suggestion taking into account the protection of cultural assets. Furthermore, in an area where historical events are held, the suggestion unit can make a suggestion related to the event. In this way, making a suggestion taking into account the historical background of a specific region enables detailed suggestions. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input historical background data into a generation AI and cause the generation AI to adjust the suggestion.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can detect the movement of nearby animals while the drone is flying and adjust the flight route to ensure the safety of the animals. For example, if the collection unit detects a bird or small animal while the drone is flying, it will change the flight route to avoid a collision with the animal. The collection unit can also set a flight route taking into account the animal's habitat, allowing for data collection that takes into account the animal's living environment. Furthermore, the collection unit can analyze the animal's behavioral patterns and avoid flying during times when the animal is most active. This enables efficient data collection while ensuring the safety of the animals.

[0055] The analysis unit analyzes the collected data and can identify vacant homes as well as assess the crime risk in the area. For example, the analysis unit can identify properties that have not been visited for a long time or properties with little foot traffic in the surrounding area, and evaluate areas with a high crime risk. The analysis unit can also compare past crime data with current data and analyze fluctuations in crime risk. Furthermore, the analysis unit can propose installing security cameras and strengthening security in areas with a high crime risk. This makes it possible to propose specific measures to improve the safety of the area.

[0056] The proposal unit can propose ways to utilize the vacant houses and land identified by the analysis unit, making use of the local culture and traditions. For example, the proposal unit can propose the establishment of a workshop or exhibition space for local traditional crafts. The proposal unit can also propose the establishment of facilities related to local festivals and events. Furthermore, the proposal unit can also propose the establishment of educational facilities for learning about the local history and culture. In this way, by proposing ways to utilize the local culture and traditions, it is possible to increase the appeal of the region.

[0057] The collection unit can analyze the surrounding sound environment and evaluate noise levels while the drone is flying. For example, the collection unit can measure the surrounding noise level while the drone is flying and identify areas with high noise levels. The collection unit can also identify noise sources and evaluate areas where noise control measures are required. Furthermore, the collection unit can analyze fluctuations in noise levels and evaluate noise fluctuations by time of day or day of the week. This makes it possible to identify areas where noise control measures are required and propose specific measures.

[0058] The analysis unit analyzes the collected data and can identify vacant houses as well as evaluate local health risks. For example, the analysis unit analyzes surrounding environmental data and evaluates air and water quality. The analysis unit can also compare past health data with current data to analyze fluctuations in health risks. Furthermore, the analysis unit can make specific proposals for health improvement in areas with high health risks. This makes it possible to evaluate local health risks and propose specific countermeasures.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit flies drones around various locations to collect data such as the opening and closing of shutters and curtains, whether lights are turned on or off, the estimated age of the building based on its exterior, people coming and going, the flow of people in the surrounding area, the state of damage, the growth of weeds, the land area, shape, sunlight conditions, and the surrounding environment. The collection unit is equipped with high-resolution cameras and sensors to collect detailed data, and optimizes the drone's flight route to collect data efficiently. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies unregistered vacant houses and land. The analysis unit uses AI to analyze the collected data and identify properties that have not been visited by people for a long time or properties with little foot traffic in the surrounding area. The unit also takes into account the area and shape of the land, sunlight conditions, and the surrounding environment to identify vacant houses and land. Step 3: The proposal unit proposes ways to utilize the vacant houses and land identified by the analysis unit. The proposal unit can suggest installing solar panels in areas with good sunlight, and can also suggest uses such as land for sale or laundromats. Furthermore, if a certain amount of land area can be secured, proposals can be made for commercial facilities, factories, amusement parks, etc.

[0061] (Example 2) A data collection and proposal system according to an embodiment of the present invention uses drones to fly across the country, collecting video, audio, and pedestrian flow data, identifying unregistered vacant homes and land, and proposing ways to utilize them. The system flies drones across various locations, collecting data such as the opening and closing of shutters and curtains, whether lights are on or off, the estimated age of the building based on its exterior, the number of people entering and exiting the building, the flow of people in the surrounding area, damage, weed growth, land area, shape, sunlight, and the surrounding environment. Next, AI analyzes the collected data to identify unregistered vacant homes and land. Furthermore, for identified vacant homes and land, proposals are made for the installation of solar panels, land sales, and laundromats in areas with good sunlight. Once a sufficient amount of land is secured, proposals are made for commercial development, factories, amusement parks, and other uses. For example, the data collection and proposal system uses drones to fly over various locations and collect data such as the opening and closing of shutters and curtains, whether lights are on or off, the estimated age of the building based on its exterior, people entering and exiting, the flow of people in the surrounding area, damage, weed growth, land area, shape, sunlight, and the surrounding environment. The drones are equipped with high-resolution cameras and sensors, allowing for detailed data collection. For example, if shutters or curtains remain closed for an extended period of time, or if the lights have not been turned on or off for an extended period of time, it is determined that the house is likely to be vacant. The estimated age of the house, damage, and weed growth based on the exterior are also useful for identifying vacant houses. Next, AI analyzes the collected data to identify unregistered vacant houses and land. The AI ​​analyzes the collected data and identifies properties that are likely to be vacant. For example, properties that have not been visited or visited for a long time or properties with little foot traffic in the surrounding area are determined to be vacant. The AI ​​also takes into account the land area, shape, sunlight, and surrounding environment to identify vacant houses and land. It then proposes ways to utilize the identified vacant houses and land. For example, in areas with good sunlight, we propose the installation of solar panels, and also suggest ways to utilize the land, such as for sale or as a laundromat. Furthermore, when a certain amount of land is secured, we propose commercial measures, factories, amusement parks, etc. This will enable the effective use of vacant houses and land, contributing to the revitalization of the local area.This allows the data collection and proposal system to promote the effective use of vacant houses and land, contributing to the revitalization of local communities. For example, it can quickly and accurately identify vacant houses and land and propose ways to use them, contributing to the development of local communities through collaboration with real estate-related companies and local governments.

[0062] A data collection and proposal system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit flies a drone over various locations to collect data such as the opening and closing of shutters and curtains, the on / off status of lights, the estimated age of a building based on its exterior, people entering and exiting the building, the flow of people in the surrounding area, damage conditions, weed growth, land area, shape, sunlight conditions, and the surrounding environment. The collection unit, for example, is equipped with a high-resolution camera or sensor to collect detailed data. The collection unit can also optimize the drone's flight route to efficiently collect data. For example, the collection unit can adjust the drone's flight route in real time to efficiently collect data. The analysis unit analyzes the data collected by the collection unit to identify unregistered vacant homes and land. The analysis unit, for example, uses AI to analyze the collected data and identify properties that are likely to be vacant. For example, the analysis unit can identify properties that have not been visited for a long time or properties with little foot traffic in the surrounding area. The analysis unit can also identify vacant houses and land by taking into account the land's area, shape, sunlight, and surrounding environment. The proposal unit proposes ways to utilize the vacant houses and land identified by the analysis unit. For example, the proposal unit can propose installing solar panels in areas with good sunlight. The proposal unit can also propose ways to utilize the land, such as selling it or building a laundromat. Furthermore, when a certain amount of land is secured, the proposal unit can propose commercial measures, factories, amusement parks, etc. As a result, the data collection and proposal system according to the embodiment can promote the effective use of vacant houses and land and contribute to regional revitalization. For example, the system can quickly and accurately identify vacant houses and land and propose ways to utilize them, thereby contributing to regional development through collaboration with real estate-related companies and local governments.

[0063] The collection unit can be equipped with a high-resolution camera or sensor to collect detailed data. The collection unit, for example, uses a high-resolution camera to collect detailed video data. For example, the collection unit uses a camera with high resolution and a high frame rate to collect detailed video data. The collection unit can also collect detailed environmental data using a sensor. For example, the collection unit can collect environmental data using a temperature sensor or a humidity sensor. Thus, detailed data can be collected by using a high-resolution camera or sensor. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input video data acquired by a high-resolution camera to a generation AI and have the generation AI analyze the video data.

[0064] The analysis unit can analyze the collected data and identify properties that are likely to be vacant. The analysis unit can, for example, analyze the collected data using AI to identify properties that are likely to be vacant. For example, the analysis unit can identify properties that have not been visited by people for a long period of time or properties with little foot traffic in the surrounding area. The analysis unit can also identify vacant houses and land by taking into account the land area and shape, sunlight environment, and surrounding environment. For example, the analysis unit can analyze the land area and shape to identify properties that are likely to be vacant. The analysis unit can also analyze sunlight environment and surrounding environment to identify properties that are likely to be vacant. In this way, by analyzing the collected data, properties that are likely to be vacant can be identified. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI identify vacant houses.

[0065] The proposal unit can propose the installation of solar panels in areas with suitable sunlight environments. For example, the proposal unit proposes the installation of solar panels in areas with suitable sunlight environments. For example, the proposal unit analyzes the hours of sunlight and the amount of solar radiation to identify areas where solar panels are suitable for installation. The proposal unit can also make specific proposals regarding the installation of solar panels. For example, the proposal unit makes proposals regarding the installation location and installation method of solar panels. This makes it possible to propose appropriate utilization methods for areas with good sunlight environments. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the sunlight environment into the generation AI and cause the generation AI to execute a proposal for installing solar panels.

[0066] The proposal unit can propose ways to utilize land for sale or coin laundries. The proposal unit, for example, analyzes the criteria and conditions for land suitable for sale and proposes land for sale. For example, the proposal unit analyzes location conditions and area to identify land suitable for sale. The proposal unit can also analyze the criteria and conditions for land suitable for coin laundries and propose coin laundries. For example, the proposal unit analyzes the surrounding population density and demand to identify land suitable for coin laundries. This makes it possible to propose various ways to utilize vacant houses and land. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input land data into a generation AI and have the generation AI execute proposals for land for sale or coin laundries.

[0067] The proposal unit can propose commercial measures, factories, or amusement parks when a certain amount of land has been secured. For example, the proposal unit proposes commercial measures when a certain amount of land has been secured. For example, the proposal unit proposes the establishment of a shopping mall or office building. The proposal unit can also propose the establishment of a factory. For example, the proposal unit analyzes the infrastructure development status and the surrounding environment and proposes the establishment of a factory. The proposal unit can also propose the establishment of an amusement park. For example, the proposal unit proposes the establishment of an amusement park taking into account ease of access and surrounding tourist attractions. This makes it possible to propose appropriate uses for large pieces of land. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input land data into a generation AI and have the generation AI execute proposals for commercial measures, factories, or amusement parks.

[0068] The collection unit can estimate the user's emotions and adjust the drone's flight route based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the drone's flight route based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit shortens the drone's flight route to quickly complete data collection. Furthermore, if the user is relaxed, the collection unit can expand the drone's flight route to collect more detailed data. Furthermore, if the user is excited, the collection unit can change the drone's flight route to prioritize flying over interesting areas. This enables efficient data collection by adjusting the drone's flight route according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the flight route.

[0069] The collection unit can select an appropriate data collection method depending on the weather or time of day when flying the drone. For example, the collection unit selects the optimal data collection method depending on the weather and time of day when flying the drone. For example, the collection unit protects the drone's camera with a waterproof cover when it is raining and continues data collection. The collection unit can also use an infrared camera to ensure visibility and collect data at night. The collection unit can also set the drone's flight altitude low when there is strong wind to ensure stable data collection. This enables efficient data collection by selecting the optimal data collection method depending on the weather and time of day. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather data and time of day data into the generation AI and have the generation AI select the optimal data collection method.

[0070] The collection unit can adjust the frequency of data collection during drone flight according to specific events or seasons. For example, the collection unit can adjust the frequency of data collection during drone flight according to specific events or seasons. For example, the collection unit can increase the frequency of data collection in areas where festivals or events are held to collect detailed information. The collection unit can also increase the frequency of data collection in spring and autumn to capture seasonal changes. The collection unit can also increase the frequency of data collection in winter to check the snow accumulation situation. In this way, detailed information can be collected by adjusting the frequency of data collection according to specific events or seasons. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input event data and seasonal data into the generation AI and cause the generation AI to adjust the frequency of data collection.

[0071] The collection unit can perform real-time navigation to avoid surrounding buildings and obstacles during drone flight. For example, the collection unit can detect the shadow of a building while the drone is flying and automatically change the flight route. Furthermore, if the drone detects an obstacle, the collection unit can calculate an avoidance route in real time and continue flying. Furthermore, the collection unit can use sensors to ensure a safe flight route when the drone passes through a narrow passage. This enables safe data collection by performing real-time navigation to avoid surrounding buildings and obstacles. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on surrounding buildings and obstacles into the generation AI and cause the generation AI to perform real-time navigation.

[0072] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit can prioritize collecting important data and complete the task quickly. When the user is relaxed, the collection unit can collect detailed data and provide information for later analysis. When the user is excited, the collection unit can prioritize collecting interesting data to attract the user's attention. This enables efficient data collection by determining the priority of data to be collected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using AI, or without AI. For example, the collection unit can input the user's emotion data into a generation AI and have the generation AI determine the data priority.

[0073] The collection unit can adjust the data collection range based on geographical characteristics when flying the drone. For example, the collection unit adjusts the data collection range taking geographical characteristics into account when flying the drone. For example, the collection unit adjusts the drone's flight altitude in mountainous areas to collect data tailored to the terrain. The collection unit can also fly between buildings in urban areas to collect detailed data. The collection unit can also set a flight route along the coastline to take into account the effects of waves and collect data. This enables efficient data collection by adjusting the data collection range taking geographical characteristics into account. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical characteristic data to the generation AI and cause the generation AI to adjust the data collection range.

[0074] The collection unit can optimize the flight route based on surrounding traffic conditions when flying the drone. For example, the collection unit optimizes the flight route taking into account surrounding traffic conditions when flying the drone. For example, the collection unit changes the drone's flight route in areas where traffic congestion occurs to efficiently collect data. The collection unit can also set the flight route during times of low traffic volume to safely collect data. Furthermore, if a traffic accident occurs, the collection unit can change the drone's flight route in real time to continue data collection. This enables efficient data collection by optimizing the flight route taking into account surrounding traffic conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input traffic condition data into the generation AI and cause the generation AI to optimize the flight route.

[0075] The collection unit can collect data while flying the drone, taking into account the historical background of a specific region. For example, the collection unit collects data while flying the drone, taking into account the historical background of a specific region. For example, in an area with many historical buildings, the collection unit collects detailed data on the buildings and checks their state of preservation. The collection unit can also adjust the drone's flight route to collect important data in an area with many cultural assets. The collection unit can also record the details of historical events in an area where historical events are held. This makes it possible to collect detailed information by collecting data while taking into account the historical background of a specific region. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input historical background data into a generation AI and have the generation AI adjust the data collection.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for highly visible display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0077] The analysis unit can refer to past data and compare it with current data during analysis. For example, the analysis unit can refer to past data and compare it with current data during analysis. For example, the analysis unit can compare past data with current data to identify changes in vacant houses. The analysis unit can also evaluate the reliability of current data based on past data. The analysis unit can also integrate past data and current data to perform a more detailed analysis. In this way, by referring to past data and comparing it with current data, changes in vacant houses can be identified. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input past data and current data into the generation AI and have the generation AI compare the data.

[0078] The analysis unit can improve the accuracy of the analysis by integrating information from different data sources during analysis. For example, the analysis unit can improve the accuracy of the analysis by integrating information from different data sources during analysis. For example, the analysis unit can integrate video data and audio data to improve the accuracy of identifying vacant houses. The analysis unit can also integrate people flow data and environmental data to help identify vacant houses. The analysis unit can also integrate data from different sensors to improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by integrating information from different data sources. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information from different data sources into a generation AI and have the generation AI integrate the information.

[0079] The analysis unit can apply an algorithm for detecting specific patterns or trends during analysis. The analysis unit can, for example, apply an algorithm for detecting specific patterns or trends during analysis. For example, the analysis unit can apply an algorithm for identifying properties that have not been visited by people for a long period of time. The analysis unit can also apply an algorithm for detecting the possibility of a vacant house based on the growth of weeds or the state of damage. The analysis unit can also apply an algorithm for detecting trends in vacant houses based on surrounding pedestrian flow data. In this way, by applying an algorithm for detecting specific patterns or trends, the accuracy of identifying vacant houses can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data into a generation AI and cause the generation AI to detect patterns and trends.

[0080] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying important analysis results. Furthermore, if the user is relaxed, the analysis unit can also display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying analysis results that focus on the main points. Thus, by prioritizing the analysis results according to the user's emotions, important information can be displayed preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0081] The analysis unit can adjust the range of analysis based on geographical characteristics during analysis. For example, the analysis unit adjusts the range of analysis taking geographical characteristics into account during analysis. For example, in mountainous areas, the analysis unit performs an analysis tailored to the topography and identifies vacant houses. In urban areas, the analysis unit can also perform an analysis taking into account the density of buildings. In coastal areas, the analysis unit can also perform an analysis taking into account the influence of tides. In this way, adjusting the range of analysis taking into account geographical characteristics enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input geographical characteristic data into the generation AI and cause the generation AI to adjust the range of analysis.

[0082] The analysis unit can adjust the frequency of analysis during analysis according to specific events or seasons. For example, the analysis unit can adjust the frequency of analysis during analysis according to specific events or seasons. For example, the analysis unit can increase the frequency of analysis in areas where festivals or events are held, thereby providing detailed information. The analysis unit can also increase the frequency of analysis in spring and autumn to capture seasonal changes. The analysis unit can also increase the frequency of analysis in winter to check the snow accumulation situation. In this way, detailed information can be provided by adjusting the frequency of analysis according to specific events or seasons. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input event data and seasonal data into the generation AI and cause the generation AI to adjust the frequency of analysis.

[0083] The analysis unit can perform the analysis while taking into account the historical background of a specific region. For example, the analysis unit can perform the analysis while taking into account the historical background of a specific region. For example, in an area with many historical buildings, the analysis unit can analyze the preservation state of buildings. Furthermore, in an area with many cultural assets, the analysis unit can prioritize the analysis of important data. Furthermore, in an area where historical events are held, the analysis unit can analyze the impact of the events. In this way, detailed information can be provided by performing the analysis while taking into account the historical background of a specific region. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input historical background data into the generation AI and cause the generation AI to adjust the analysis.

[0084] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, the suggestion unit estimates the user's emotions and adjusts the way the suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide a simple, highly visible suggestion. If the user is relaxed, the suggestion unit can also provide a suggestion that includes detailed information. If the user is in a hurry, the suggestion unit can also provide a suggestion that focuses on the main points. This allows the suggestion to be highly visible by adjusting the way the suggestions are expressed based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0085] The proposal unit can make an optimal proposal by taking into consideration the economic situation of a specific region when making a proposal. For example, the proposal unit can make an optimal proposal by taking into consideration the economic situation of a specific region when making a proposal. For example, the proposal unit can propose the establishment of a commercial facility in an economically active region. The proposal unit can also propose low-cost utilization methods in an economically stagnant region. The proposal unit can also make proposals that take into consideration future potential in an economically growing region. In this way, by making an optimal proposal by taking into consideration the economic situation of a specific region, it is possible to make a proposal that is suitable for the region. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input regional economic situation data into the generation AI and cause the generation AI to execute the optimal proposal.

[0086] The suggestion unit can improve the accuracy of a proposal by referring to past proposal results when making a proposal. The suggestion unit can improve the accuracy of a proposal by referring to past proposal results when making a proposal. For example, the suggestion unit makes a similar proposal based on past successful cases. The suggestion unit can also analyze past unsuccessful cases and make a proposal that reflects improvements. The suggestion unit can also statistically analyze past proposal results and make an optimal proposal. In this way, the accuracy of the proposal can be improved by referring to past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0087] The suggestion unit can apply an algorithm for detecting specific patterns or trends when making a proposal. The suggestion unit can, for example, apply an algorithm for detecting specific patterns or trends when making a proposal. For example, the suggestion unit can apply an algorithm that suggests installing solar panels in areas with good sunlight. The suggestion unit can also apply an algorithm that suggests ways to utilize land for sale, laundromats, etc. The suggestion unit can also apply an algorithm that makes suggestions for commercial measures, factories, amusement parks, etc. By applying an algorithm that detects specific patterns or trends, the accuracy of the proposal can be improved. Some or all of the above-mentioned processing in the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input data into a generation AI and cause the generation AI to detect patterns and trends.

[0088] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. For example, when the user is feeling stressed, the suggestion unit can prioritize displaying important suggestions. When the user is relaxed, the suggestion unit can also display detailed suggestions. When the user is in a hurry, the suggestion unit can also prioritize displaying suggestions that focus on the main points. In this way, by determining the priority of suggestions according to the user's emotions, important suggestions can be displayed preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of suggestions.

[0089] The proposal unit can adjust the range of the proposal based on geographical characteristics when making a proposal. For example, the proposal unit adjusts the range of the proposal by taking geographical characteristics into account when making a proposal. For example, the proposal unit makes proposals that are tailored to the terrain in mountainous areas. The proposal unit can also make proposals by taking into account the density of buildings in urban areas. The proposal unit can also make proposals by taking into account the influence of tides along coastlines. In this way, adjusting the range of the proposal by taking geographical characteristics into account enables efficient proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input geographical characteristic data into the generation AI and cause the generation AI to adjust the range of the proposal.

[0090] The suggestion unit can adjust the content of the suggestion according to a specific event or season when making a suggestion. For example, the suggestion unit adjusts the content of the suggestion according to a specific event or season when making a suggestion. For example, in an area where festivals or events are held, the suggestion unit makes suggestions related to the events. Furthermore, the suggestion unit can make seasonal suggestions in spring or autumn to capture seasonal changes. Furthermore, the suggestion unit can make suggestions taking into account snow accumulation in winter. This enables detailed suggestions to be made by adjusting the content of the suggestion according to a specific event or season. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input event data and seasonal data into the generation AI and cause the generation AI to adjust the content of the suggestion.

[0091] The suggestion unit can make a suggestion based on the historical background of a specific region when making a suggestion. For example, the suggestion unit can make a suggestion taking into account the historical background of a specific region when making a suggestion. For example, in an area with many historical buildings, the suggestion unit can make a suggestion taking into account the preservation state of the buildings. Furthermore, in an area with many cultural assets, the suggestion unit can make a suggestion taking into account the protection of cultural assets. Furthermore, in an area where historical events are held, the suggestion unit can make a suggestion related to the event. In this way, making a suggestion taking into account the historical background of a specific region enables detailed suggestions. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input historical background data into a generation AI and cause the generation AI to adjust the suggestion. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 and sensors of the smart device 14 and optimizes the flight route using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data using AI to identify vacant houses and land. The proposal unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes ways to utilize the identified vacant houses and land. The proposal unit is also realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 or a sensor of the smart glasses 214 and optimizes the flight route using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data using AI to identify vacant houses and land. The proposal unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes ways to utilize the identified vacant houses and land. The proposal unit is also realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and proposal unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is realized by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 or sensors of the headset terminal 314 and optimizes the flight route using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data using AI to identify vacant houses and land. The proposal unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes ways to utilize the identified vacant houses and land. The proposal unit is also realized, for example, by the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and proposal unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 and sensors of the robot 414 and optimizes the flight route using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data using AI to identify vacant houses and land. The proposal unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes ways to utilize the identified vacant houses and land. The proposal unit is also realized, for example, by the control unit 46A of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The collection unit can detect the movement of nearby animals while the drone is flying and adjust the flight route to ensure the safety of the animals. For example, if the collection unit detects a bird or small animal while the drone is flying, it will change the flight route to avoid a collision with the animal. The collection unit can also set a flight route taking into account the animal's habitat, allowing for data collection that takes into account the animal's living environment. Furthermore, the collection unit can analyze the animal's behavioral patterns and avoid flying during times when the animal is most active. This enables efficient data collection while ensuring the safety of the animals.

[0094] The analysis unit analyzes the collected data and can identify vacant homes as well as assess the crime risk in the area. For example, the analysis unit can identify properties that have not been visited for a long time or properties with little foot traffic in the surrounding area, and evaluate areas with a high crime risk. The analysis unit can also compare past crime data with current data and analyze fluctuations in crime risk. Furthermore, the analysis unit can propose installing security cameras and strengthening security in areas with a high crime risk. This makes it possible to propose specific measures to improve the safety of the area.

[0095] The proposal unit can propose ways to utilize the vacant houses and land identified by the analysis unit, making use of the local culture and traditions. For example, the proposal unit can propose the establishment of a workshop or exhibition space for local traditional crafts. The proposal unit can also propose the establishment of facilities related to local festivals and events. Furthermore, the proposal unit can also propose the establishment of educational facilities for learning about the local history and culture. In this way, by proposing ways to utilize the local culture and traditions, it is possible to increase the appeal of the region.

[0096] The collection unit can analyze the surrounding sound environment and evaluate noise levels while the drone is flying. For example, the collection unit can measure the surrounding noise level while the drone is flying and identify areas with high noise levels. The collection unit can also identify noise sources and evaluate areas where noise control measures are required. Furthermore, the collection unit can analyze fluctuations in noise levels and evaluate noise fluctuations by time of day or day of the week. This makes it possible to identify areas where noise control measures are required and propose specific measures.

[0097] The analysis unit analyzes the collected data and can identify vacant houses as well as evaluate local health risks. For example, the analysis unit analyzes surrounding environmental data and evaluates air and water quality. The analysis unit can also compare past health data with current data to analyze fluctuations in health risks. Furthermore, the analysis unit can make specific proposals for health improvement in areas with high health risks. This makes it possible to evaluate local health risks and propose specific countermeasures.

[0098] The collection unit can estimate the user's emotions and adjust the drone's flight speed based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can increase the drone's flight speed to quickly complete data collection. Alternatively, if the user is relaxed, the collection unit can decrease the drone's flight speed to collect more detailed data. Furthermore, if the user is excited, the collection unit can change the drone's flight speed to prioritize flying over interesting areas. This allows for efficient data collection by adjusting the drone's flight speed according to the user's emotions.

[0099] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible notification method. If the user is relaxed, the analysis unit can also provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a notification method that focuses on the main points. In this way, by adjusting the notification method of the analysis results according to the user's emotions, highly visible notifications are possible.

[0100] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can delay the timing of suggestions to reduce the burden on the user. Also, if the user is relaxed, the suggestion unit can advance the timing of suggestions to provide more detailed information. Furthermore, if the user is excited, the suggestion unit can adjust the timing of suggestions to provide more interesting information preferentially. In this way, by adjusting the timing of suggestions according to the user's emotions, effective suggestions can be made.

[0101] The suggestion unit can estimate the user's emotions and customize the content of the suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can make simple and easy-to-implement suggestions. If the user is relaxed, the suggestion unit can also make suggestions including detailed information. Furthermore, if the user is excited, the suggestion unit can also make suggestions including interesting information. This allows the suggestion content to be customized according to the user's emotions, enabling effective suggestions.

[0102] The suggestion unit can estimate the user's emotions and adjust the format of the suggestions based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit can provide a simple, highly visible format. If the user is relaxed, the suggestion unit can also provide a format including detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also provide a format that focuses on the main points. In this way, by adjusting the format of the suggestions according to the user's emotions, highly visible suggestions can be made.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit flies drones around various locations to collect data such as the opening and closing of shutters and curtains, whether lights are turned on or off, the estimated age of the building based on its exterior, people coming and going, the flow of people in the surrounding area, the state of damage, the growth of weeds, the land area, shape, sunlight conditions, and the surrounding environment. The collection unit is equipped with high-resolution cameras and sensors to collect detailed data, and optimizes the drone's flight route to collect data efficiently. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies unregistered vacant houses and land. The analysis unit uses AI to analyze the collected data and identify properties that have not been visited by people for a long time or properties with little foot traffic in the surrounding area. The unit also takes into account the area and shape of the land, sunlight conditions, and the surrounding environment to identify vacant houses and land. Step 3: The proposal unit proposes ways to utilize the vacant houses and land identified by the analysis unit. The proposal unit can suggest installing solar panels in areas with good sunlight, and can also suggest uses such as land for sale or laundromats. Furthermore, if a certain amount of land area can be secured, proposals can be made for commercial facilities, factories, amusement parks, etc.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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 AI 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] 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.

[0134] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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 AI 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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 AI 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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. The drone flies around various locations and collects data on the opening and closing of shutters and curtains, on / off of lights, estimated age of the building from the exterior, people coming and going, traffic flow in the surrounding area, damage, weed growth, land area, shape, sunlight conditions, and surrounding environment. an analysis unit that analyzes the data collected by the collection unit and identifies unregistered vacant houses and land; a proposal unit that proposes ways to utilize the vacant houses and land identified by the analysis unit. A system characterized by:

2. The collecting unit Equipping it with a high-resolution camera or sensor to collect detailed data 2. The system of claim 1.

3. The analysis unit Analyze the collected data and identify properties that are likely to be vacant 2. The system of claim 1.

4. The proposal unit Propose the installation of solar panels in areas with suitable sunlight.

2. The system of claim 1.

5. The proposal unit Suggest ways to use land for sale or laundromats 2. The system of claim 1.

6. The proposal unit If a certain amount of land is secured, proposals will be made for commercial measures, factories, and amusement parks.

2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the drone's flight route based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit When flying a drone, choose the appropriate data collection method depending on the weather or time of day.

2. The system of claim 1.

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