System

Abstract

An object of a system according to an embodiment is to generate an optimal location area based on statistical data and to perform objective location selection.SOLUTION: A system includes a data collection part, a data analysis part, a location generation part, and a comparison part. The data collection unit collects statistical data of at least one of a floating population, a behavior distribution, a route price, and a unit price. The data analysis unit analyzes the statistical data collected by the data collection unit. The location generation part generates an optimum location area on the basis of a result analyzed by the data analysis part. A comparison part compares the location area generated by the location generation part with location selection based on the experience value or feeling of a person.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] With conventional technology, location selection relies on human experience and intuition, and there was a problem in that optimal location selection was not based on objective data.

[0005] The system according to the embodiment aims to generate an optimal location area based on statistical data and to perform objective location selection. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, a location generation unit, and a comparison unit. The data collection unit collects at least one statistical data item from among floating population, behavioral distribution, land price, and price per tsubo. The data analysis unit analyzes the statistical data collected by the data collection unit. The location generation unit generates an optimal location area based on the results of the analysis by the data analysis unit. The comparison unit compares the location area generated by the location generation unit with location selections based on human experience and intuition. [Effects of the Invention]

[0007] The system according to the embodiment generates an optimal location area based on statistical data, and can perform objective location selection. [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) The location selection system according to the embodiment of the present invention is a system that generates an optimal location area based on statistical data such as the flow of population, behavioral distribution, land price, and price per tsubo (square meter), etc. This allows the location selection system to perform scientific location selection based on statistical data.

[0029] A location selection system according to an embodiment includes a data collection unit, a data analysis unit, a location generation unit, and a comparison unit. The data collection unit collects at least one statistical data item among floating population, behavioral distribution, land price, and price per tsubo. For example, the data collection unit acquires floating population data from a government statistical database. The data collection unit can also collect data on the number of visitors to commercial facilities. The data collection unit can also acquire land price data from real estate agents. The data analysis unit analyzes the statistical data collected by the data collection unit. For example, the data analysis unit analyzes floating population data using a generation AI to understand demographic trends in a specific area. The data analysis unit can also analyze behavioral distribution data to identify people's behavior patterns in a specific area. The data analysis unit can also analyze land price data to evaluate the value of land. The location generation unit generates an optimal location area based on the results of the analysis by the data analysis unit. For example, the location generation unit identifies an area with a large floating population, active behavioral distribution, and appropriate land price and price per tsubo. The location generation unit can also use the generation AI to identify an optimal location area for opening a commercial facility. The location generation unit can also use the generation AI to identify an optimal location area for developing a residential area. The comparison unit compares the location area generated by the location generation unit with location selections based on human experience and intuition. For example, the comparison unit compares the location area generated by the generation AI with past successes and failures to predict the probability of success. The comparison unit can also collect expert opinions on the location area generated by the generation AI to complement the proposal. The comparison unit can also collect users' emotional reactions to the location area generated by the generation AI to evaluate the effectiveness of the proposal. This allows the location selection system according to the embodiment to perform scientific location selection based on statistical data. For example, the location selection system can achieve more accurate location selection for opening a new store or investing in real estate, thereby increasing the probability of business success. The location selection system can also improve the proposal content based on user feedback and propose more appropriate location areas.Furthermore, the location selection system continuously updates statistical data, allowing optimal location areas to always be generated based on the latest information.

[0030] The data analysis unit performs anomaly detection on statistical data, thereby improving the reliability of the data. The data analysis unit, for example, uses generative AI to perform anomaly detection on floating population data. For example, if a sudden increase in population is observed during a specific time period, the data is detected as an anomaly, improving the reliability of the data. The data analysis unit also performs anomaly detection on behavior distribution data to identify abnormal behavior patterns. For example, if unusual behavior is observed in a specific area, the data is detected as an anomaly. The data analysis unit also performs anomaly detection on land prices and unit price per tsubo data to identify abnormal price fluctuations. For example, if a sudden price increase is observed in a specific area, the data is detected as an anomaly, improving the reliability of the data. This improves the reliability of the data.

[0031] The data analysis unit can analyze statistical data in real time and select a location taking into account fluctuations by time of day and season. The data analysis unit, for example, uses generative AI to analyze population mobility data in real time and select a location taking into account fluctuations by time of day. For example, it identifies areas where the population fluctuates greatly between daytime and nighttime. The data analysis unit also analyzes behavior distribution data in real time and selects a location taking into account fluctuations by season. For example, it identifies areas where behavior patterns differ between summer and winter. The data analysis unit also analyzes land prices and price per tsubo data in real time and selects a location taking into account price fluctuations by time of day and season. For example, it identifies areas where prices rise in certain seasons. This makes it possible to select a location taking into account fluctuations by time of day and season.

[0032] The data collection unit can obtain more detailed data by utilizing drones or IoT devices. For example, the data collection unit uses drones to collect data on the flow of people in a specific area. For example, it can grasp population density in real time through aerial video analysis. The data collection unit also installs IoT devices to collect behavioral distribution data in a specific area. For example, it can use sensors to measure people's movement patterns and length of stay. The data collection unit also uses drones or IoT devices to collect land prices and unit prices per square meter in a specific area. For example, it can analyze the condition of a building and the surrounding environment in real time to obtain price data. This allows for the acquisition of more detailed data.

[0033] The data collection unit collects data from different cities or countries and can analyze the optimal location area from a global perspective. The data collection unit, for example, collects floating population data from different cities and analyzes the optimal location area from a global perspective. For example, it compares the demographic trends of major cities and identifies the optimal area. The data collection unit also collects behavioral distribution data from different countries and analyzes the optimal location area from a global perspective. For example, it compares behavioral patterns in each country and identifies the optimal area. The data collection unit also collects land prices and unit prices per square meter data from different cities or countries and analyzes the optimal location area from a global perspective. For example, it compares price trends in each country and identifies the optimal area. This makes it possible to analyze the optimal location area from a global perspective.

[0034] The location generation unit performs simulations on the location area it generates, and can predict future changes in demographics and economic conditions. For example, the location generation unit simulates future demographics for the location area generated by the generation AI. For example, it predicts trends in population growth and decline and identifies the optimal area. The location generation unit also simulates changes in economic conditions for the location area generated by the generation AI. For example, it predicts the impact of economic growth and recession and identifies the optimal area. The location generation unit also simulates future infrastructure development for the location area generated by the generation AI. For example, it takes into account development plans for new transportation infrastructure and identifies the optimal area. This makes it possible to predict future changes in demographics and economic conditions.

[0035] The location generation unit performs an environmental impact assessment on the location area to be generated, enabling the selection of a sustainable location. The location generation unit, for example, performs an environmental impact assessment on the location area generated by the generation AI. For example, it evaluates the risk of air pollution and water pollution within the area and selects a sustainable area. The location generation unit also evaluates energy consumption for the location area generated by the generation AI. For example, it evaluates energy efficiency within the area and selects a sustainable area. The location generation unit also evaluates the impact on the ecosystem for the location area generated by the generation AI. For example, it evaluates biodiversity within the area and selects a sustainable area. This enables the selection of a sustainable location.

[0036] The location generation unit can simultaneously propose areas suitable for different industries or business types for the location area it generates. For example, the location generation unit simultaneously proposes areas suitable for commercial facilities and areas suitable for residential areas for the location area generated by the generation AI. For example, it distinguishes between areas with a concentration of commercial facilities and quiet residential areas when making its proposals. The location generation unit also simultaneously proposes areas suitable for office buildings and areas suitable for leisure facilities for the location area generated by the generation AI. For example, it distinguishes between business districts and tourist destinations when making its proposals. The location generation unit also simultaneously proposes areas suitable for educational facilities and areas suitable for medical facilities for the location area generated by the generation AI. For example, it distinguishes between areas with many schools and areas with many hospitals when making its proposals. This makes it possible to simultaneously propose areas suitable for different industries or business types.

[0037] The location generation unit can make proposals for the location area it generates that take into account transportation access or the state of infrastructure development. For example, the location generation unit makes proposals for the location area generated by the generation AI that take into account good transportation access. For example, it selects an area with good access to major transportation facilities. The location generation unit also makes proposals for the location area generated by the generation AI that take into account the state of infrastructure development. For example, it selects an area where the power supply and water and sewerage systems are well developed. The location generation unit also makes proposals for the location area generated by the generation AI that take into account future infrastructure development plans. For example, it selects an area where there are plans to develop new transportation infrastructure. This makes it possible to make proposals that take into account transportation access and the state of infrastructure development.

[0038] The comparison unit can compare the location area generated by the generation AI with past successes and failures to predict the probability of success. For example, the comparison unit compares the location area generated by the generation AI with past successes and predicts the probability of success. For example, it compares it with the location conditions of commercial facilities that have been successful in the past and finds similarities. The comparison unit also compares the location area generated by the generation AI with past failures to evaluate risks. For example, it compares it with location conditions that have failed in the past and identifies risk factors. The comparison unit also comprehensively compares the location area generated by the generation AI with past successes and failures to predict the probability of success. For example, it analyzes the factors that lead to success and failure and selects the optimal area. This makes it possible to predict the probability of success.

[0039] The comparison unit can collect expert opinions for the location area generated by the generation AI and complement the proposals of the generation AI. For example, the comparison unit collects opinions from urban planning experts for the location area generated by the generation AI and complements the proposals. For example, it selects the optimal area from the perspective of urban planning. The comparison unit also collects opinions from real estate experts for the location area generated by the generation AI and complements the proposals. For example, it selects the optimal area taking into account trends in the real estate market. The comparison unit also collects opinions from marketing experts for the location area generated by the generation AI and complements the proposals. For example, it selects the optimal area from the perspective of consumer behavior. This makes it possible to collect expert opinions and complement the proposals.

[0040] The comparison unit collects opinions from experts in different industries or business types for the location area generated by the generation AI and can evaluate the proposal from multiple angles. For example, the comparison unit collects opinions from commercial facility experts for the location area generated by the generation AI and evaluates the proposal from multiple angles. For example, it selects the optimal area based on the operating experience of commercial facilities. The comparison unit also collects opinions from residential area experts for the location area generated by the generation AI and evaluates the proposal from multiple angles. For example, it selects the optimal area taking into account demand for residential areas. The comparison unit also collects opinions from public facility experts for the location area generated by the generation AI and evaluates the proposal from multiple angles. For example, it selects the optimal area taking into account the usage status of public facilities. This allows the proposal to be evaluated from multiple angles.

[0041] The comparison unit can compare the location area generated by the generation AI with past data to identify trends and patterns. For example, the comparison unit compares the location area generated by the generation AI with past data on population mobility to identify trends and patterns. For example, it analyzes trends in population growth and selects the optimal area. The comparison unit also compares the location area generated by the generation AI with past behavioral distribution data to identify trends and patterns. For example, it analyzes changes in behavioral patterns and selects the optimal area. The comparison unit also compares the location area generated by the generation AI with past land prices and price per tsubo data to identify trends and patterns. For example, it analyzes trends in price fluctuations and selects the optimal area. This makes it possible to identify trends and patterns.

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

[0043] The location selection system can further include a weather data collection unit. The weather data collection unit can collect weather data for a specific area and use it for location selection. For example, the weather data collection unit can collect annual precipitation and temperature data for a specific area and evaluate the impact of climatic conditions on business. The weather data collection unit can also collect wind speed and sunshine hours for a specific area and select a location that takes energy efficiency and comfort into consideration. Furthermore, the weather data collection unit can analyze weather data in real time and select a location that responds to sudden weather fluctuations. This enables more accurate location selection that takes weather conditions into consideration.

[0044] The location selection system can further include an energy consumption analysis unit. The energy consumption analysis unit can collect energy consumption data for a specific area and select a location that takes energy efficiency into consideration. For example, the energy consumption analysis unit can analyze electricity consumption and gas consumption in a specific area and identify areas with high energy efficiency. The energy consumption analysis unit can also evaluate the usage status of renewable energy and select sustainable areas. Furthermore, the energy consumption analysis unit can analyze energy consumption data in real time and select a location that takes fluctuations in energy efficiency into consideration. This makes it possible to select a sustainable location that takes energy efficiency into consideration.

[0045] The location selection system can further include a crime data analysis unit. The crime data analysis unit can collect crime data for a specific area and select a location that takes safety into consideration. For example, the crime data analysis unit can analyze the number and types of crimes in a specific area and identify safe areas. The crime data analysis unit can also analyze crime data in real time to identify crime trends. Furthermore, the crime data analysis unit can combine crime data with other statistical data and perform an overall safety evaluation. This enables location selection that takes safety into consideration.

[0046] The location selection system can further include a cultural data analysis unit. The cultural data analysis unit can collect cultural data on a specific area and select a location taking into account its cultural appeal. For example, the cultural data analysis unit can analyze data on historical buildings and cultural events in the specific area to identify culturally attractive areas. The cultural data analysis unit can also analyze the cultural background and preferences of residents in the specific area to select a culturally suitable area. Furthermore, the cultural data analysis unit can combine and analyze the cultural data with other statistical data to perform a comprehensive cultural evaluation. This makes it possible to select a location taking into account its cultural appeal.

[0047] The location selection system can further include an educational data analysis unit. The educational data analysis unit can collect educational data for a specific area and select a location that takes the educational environment into consideration. For example, the educational data analysis unit can analyze the number of schools and educational level in a specific area to identify an area with a good educational environment. The educational data analysis unit can also analyze evaluation data for educational institutions in a specific area to select an area that offers an excellent educational environment. Furthermore, the educational data analysis unit can combine and analyze the educational data with other statistical data to perform a comprehensive evaluation of the educational environment. This enables location selection that takes the educational environment into consideration.

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

[0049] Step 1: The data collection unit collects at least one statistical data item from the following: floating population, behavioral distribution, land price, and price per tsubo. For example, the data collection unit obtains floating population data from a government statistical database. It can also collect data on the number of visitors to commercial facilities and land price data from real estate agents. Step 2: The data analysis unit analyzes the statistical data collected by the data collection unit. For example, it uses generation AI to analyze the floating population data to understand the demographics of a specific area. It also analyzes behavioral distribution data to identify people's behavior patterns in a specific area, and analyzes roadside land price data to evaluate the value of the land. Step 3: The location generation unit generates the optimal location area based on the results of the analysis by the data analysis unit. For example, it identifies areas with a large mobile population, active behavioral distribution, and appropriate land prices and unit prices per square meter. It also uses generation AI to identify the optimal location area for developing commercial facilities and residential areas. Step 4: The comparison unit compares the location area generated by the location generation unit with location selections based on human experience and intuition. For example, it compares the location area generated by the AI ​​generation unit with past successes and failures to predict the probability of success. It also collects expert opinions and users' emotional reactions to evaluate the effectiveness of the proposal.

[0050] (Example 2) The location selection system according to the embodiment of the present invention is a system that generates an optimal location area based on statistical data such as the flow of population, behavioral distribution, land price, and price per tsubo (square meter), etc. This allows the location selection system to perform scientific location selection based on statistical data.

[0051] A location selection system according to an embodiment includes a data collection unit, a data analysis unit, a location generation unit, and a comparison unit. The data collection unit collects at least one statistical data item among floating population, behavioral distribution, land price, and price per tsubo. For example, the data collection unit acquires floating population data from a government statistical database. The data collection unit can also collect data on the number of visitors to commercial facilities. The data collection unit can also acquire land price data from real estate agents. The data analysis unit analyzes the statistical data collected by the data collection unit. For example, the data analysis unit analyzes floating population data using a generation AI to understand demographic trends in a specific area. The data analysis unit can also analyze behavioral distribution data to identify people's behavior patterns in a specific area. The data analysis unit can also analyze land price data to evaluate the value of land. The location generation unit generates an optimal location area based on the results of the analysis by the data analysis unit. For example, the location generation unit identifies an area with a large floating population, active behavioral distribution, and appropriate land price and price per tsubo. The location generation unit can also use the generation AI to identify an optimal location area for opening a commercial facility. The location generation unit can also use the generation AI to identify an optimal location area for developing a residential area. The comparison unit compares the location area generated by the location generation unit with location selections based on human experience and intuition. For example, the comparison unit compares the location area generated by the generation AI with past successes and failures to predict the probability of success. The comparison unit can also collect expert opinions on the location area generated by the generation AI to complement the proposal. The comparison unit can also collect users' emotional reactions to the location area generated by the generation AI to evaluate the effectiveness of the proposal. This allows the location selection system according to the embodiment to perform scientific location selection based on statistical data. For example, the location selection system can achieve more accurate location selection for opening a new store or investing in real estate, thereby increasing the probability of business success. The location selection system can also improve the proposal content based on user feedback and propose more appropriate location areas.Furthermore, the location selection system continuously updates statistical data, allowing optimal location areas to always be generated based on the latest information.

[0052] The data analysis unit performs anomaly detection on statistical data, thereby improving the reliability of the data. The data analysis unit, for example, uses generative AI to perform anomaly detection on floating population data. For example, if a sudden increase in population is observed during a specific time period, the data is detected as an anomaly, improving the reliability of the data. The data analysis unit also performs anomaly detection on behavior distribution data to identify abnormal behavior patterns. For example, if unusual behavior is observed in a specific area, the data is detected as an anomaly. The data analysis unit also performs anomaly detection on land prices and unit price per tsubo data to identify abnormal price fluctuations. For example, if a sudden price increase is observed in a specific area, the data is detected as an anomaly, improving the reliability of the data. This improves the reliability of the data.

[0053] The data analysis unit can analyze statistical data in real time and select a location taking into account fluctuations by time of day and season. The data analysis unit, for example, uses generative AI to analyze population mobility data in real time and select a location taking into account fluctuations by time of day. For example, it identifies areas where the population fluctuates greatly between daytime and nighttime. The data analysis unit also analyzes behavior distribution data in real time and selects a location taking into account fluctuations by season. For example, it identifies areas where behavior patterns differ between summer and winter. The data analysis unit also analyzes land prices and price per tsubo data in real time and selects a location taking into account price fluctuations by time of day and season. For example, it identifies areas where prices rise in certain seasons. This makes it possible to select a location taking into account fluctuations by time of day and season.

[0054] The data collection unit can obtain more detailed data by utilizing drones or IoT devices. For example, the data collection unit uses drones to collect data on the flow of people in a specific area. For example, it can grasp population density in real time through aerial video analysis. The data collection unit also installs IoT devices to collect behavioral distribution data in a specific area. For example, it can use sensors to measure people's movement patterns and length of stay. The data collection unit also uses drones or IoT devices to collect land prices and unit prices per square meter in a specific area. For example, it can analyze the condition of a building and the surrounding environment in real time to obtain price data. This allows for the acquisition of more detailed data.

[0055] The data collection unit collects data from different cities or countries and can analyze the optimal location area from a global perspective. The data collection unit, for example, collects floating population data from different cities and analyzes the optimal location area from a global perspective. For example, it compares the demographic trends of major cities and identifies the optimal area. The data collection unit also collects behavioral distribution data from different countries and analyzes the optimal location area from a global perspective. For example, it compares behavioral patterns in each country and identifies the optimal area. The data collection unit also collects land prices and unit prices per square meter data from different cities or countries and analyzes the optimal location area from a global perspective. For example, it compares price trends in each country and identifies the optimal area. This makes it possible to analyze the optimal location area from a global perspective.

[0056] The data analysis unit can use the emotion estimation function to collect emotion data of people in a specific area and identify areas with positive emotions. For example, the data analysis unit uses the emotion estimation function to collect emotion data of people in a specific area and identify areas with a high level of positive emotions. For example, it prioritizes selecting areas with high levels of joy and satisfaction. The data analysis unit also collects emotion data regarding events and happenings in the specific area and identifies areas with a high level of positive emotions. For example, it selects areas where positive emotions increase after an event. The data analysis unit also uses the emotion estimation function to eliminate negative emotion data in the specific area and identify areas with a high level of positive emotions. For example, it prioritizes selecting areas with little anger or dissatisfaction. This makes it possible to identify areas with a positive level of emotions.

[0057] The location generation unit performs simulations on the location area it generates, and can predict future changes in demographics and economic conditions. For example, the location generation unit simulates future demographics for the location area generated by the generation AI. For example, it predicts trends in population growth and decline and identifies the optimal area. The location generation unit also simulates changes in economic conditions for the location area generated by the generation AI. For example, it predicts the impact of economic growth and recession and identifies the optimal area. The location generation unit also simulates future infrastructure development for the location area generated by the generation AI. For example, it takes into account development plans for new transportation infrastructure and identifies the optimal area. This makes it possible to predict future changes in demographics and economic conditions.

[0058] The location generation unit performs an environmental impact assessment on the location area to be generated, enabling the selection of a sustainable location. The location generation unit, for example, performs an environmental impact assessment on the location area generated by the generation AI. For example, it evaluates the risk of air pollution and water pollution within the area and selects a sustainable area. The location generation unit also evaluates energy consumption for the location area generated by the generation AI. For example, it evaluates energy efficiency within the area and selects a sustainable area. The location generation unit also evaluates the impact on the ecosystem for the location area generated by the generation AI. For example, it evaluates biodiversity within the area and selects a sustainable area. This enables the selection of a sustainable location.

[0059] The location generation unit can use the emotion estimation function to preferentially select areas with positive emotions based on emotion data of people in the specific area. The location generation unit, for example, uses the emotion estimation function to collect emotion data of people in the specific area and preferentially select areas with a high level of positive emotions. For example, it selects areas with high levels of joy and satisfaction. The location generation unit also collects emotion data regarding events and happenings in the specific area and preferentially selects areas with positive emotions. For example, it selects areas where positive emotions increase after an event. The location generation unit also uses the emotion estimation function to eliminate negative emotion data in the specific area and preferentially select areas with a high level of positive emotions. For example, it selects areas with little anger or dissatisfaction. This allows preferential selection of areas with positive emotions.

[0060] The location generation unit can simultaneously propose areas suitable for different industries or business types for the location area it generates. For example, the location generation unit simultaneously proposes areas suitable for commercial facilities and areas suitable for residential areas for the location area generated by the generation AI. For example, it distinguishes between areas with a concentration of commercial facilities and quiet residential areas when making its proposals. The location generation unit also simultaneously proposes areas suitable for office buildings and areas suitable for leisure facilities for the location area generated by the generation AI. For example, it distinguishes between business districts and tourist destinations when making its proposals. The location generation unit also simultaneously proposes areas suitable for educational facilities and areas suitable for medical facilities for the location area generated by the generation AI. For example, it distinguishes between areas with many schools and areas with many hospitals when making its proposals. This makes it possible to simultaneously propose areas suitable for different industries or business types.

[0061] The location generation unit can make proposals for the location area it generates that take into account transportation access or the state of infrastructure development. For example, the location generation unit makes proposals for the location area generated by the generation AI that take into account good transportation access. For example, it selects an area with good access to major transportation facilities. The location generation unit also makes proposals for the location area generated by the generation AI that take into account the state of infrastructure development. For example, it selects an area where the power supply and water and sewerage systems are well developed. The location generation unit also makes proposals for the location area generated by the generation AI that take into account future infrastructure development plans. For example, it selects an area where there are plans to develop new transportation infrastructure. This makes it possible to make proposals that take into account transportation access and the state of infrastructure development.

[0062] The location generation unit can use the emotion estimation function to propose a branding strategy for an area based on emotional data of people in a specific area. For example, the location generation unit uses the emotion estimation function to collect emotional data of people in a specific area and propose a branding strategy based on areas with a high level of positive emotions. For example, it brands areas with a high level of joy and satisfaction. The location generation unit also collects emotional data about events and happenings in the specific area and proposes a branding strategy based on emotionally positive areas. For example, it brands areas where positive emotions increase after an event. The location generation unit also uses the emotion estimation function to eliminate negative emotional data in the specific area and propose a branding strategy based on areas with a high level of positive emotions. For example, it brands areas where anger and dissatisfaction are low. This makes it possible to propose a branding strategy for the area.

[0063] The comparison unit can compare the location area generated by the generation AI with past successes and failures to predict the probability of success. For example, the comparison unit compares the location area generated by the generation AI with past successes and predicts the probability of success. For example, it compares it with the location conditions of commercial facilities that have been successful in the past and finds similarities. The comparison unit also compares the location area generated by the generation AI with past failures to evaluate risks. For example, it compares it with location conditions that have failed in the past and identifies risk factors. The comparison unit also comprehensively compares the location area generated by the generation AI with past successes and failures to predict the probability of success. For example, it analyzes the factors that lead to success and failure and selects the optimal area. This makes it possible to predict the probability of success.

[0064] The comparison unit can collect expert opinions for the location area generated by the generation AI and complement the proposals of the generation AI. For example, the comparison unit collects opinions from urban planning experts for the location area generated by the generation AI and complements the proposals. For example, it selects the optimal area from the perspective of urban planning. The comparison unit also collects opinions from real estate experts for the location area generated by the generation AI and complements the proposals. For example, it selects the optimal area taking into account trends in the real estate market. The comparison unit also collects opinions from marketing experts for the location area generated by the generation AI and complements the proposals. For example, it selects the optimal area from the perspective of consumer behavior. This makes it possible to collect expert opinions and complement the proposals.

[0065] The comparison unit can use the emotion estimation function to collect users' emotional reactions to the location areas generated by the generation AI and evaluate the effectiveness of the proposal. For example, the comparison unit can use the emotion estimation function to collect users' emotional reactions to the location areas generated by the generation AI and evaluate areas with a high level of positive emotions. For example, it selects areas with a high level of joy and satisfaction. The comparison unit also collects users' emotional reactions to the location areas generated by the generation AI in real time and evaluates areas with a low level of negative emotions. For example, it selects areas with a low level of anger and dissatisfaction. The comparison unit also uses the emotion estimation function to analyze users' emotional reactions to the location areas generated by the generation AI and evaluate the effectiveness of the proposal. For example, it selects the optimal area based on the emotion score. This allows the effectiveness of the proposal to be evaluated.

[0066] The comparison unit collects opinions from experts in different industries or business types for the location area generated by the generation AI and can evaluate the proposal from multiple angles. For example, the comparison unit collects opinions from commercial facility experts for the location area generated by the generation AI and evaluates the proposal from multiple angles. For example, it selects the optimal area based on the operating experience of commercial facilities. The comparison unit also collects opinions from residential area experts for the location area generated by the generation AI and evaluates the proposal from multiple angles. For example, it selects the optimal area taking into account demand for residential areas. The comparison unit also collects opinions from public facility experts for the location area generated by the generation AI and evaluates the proposal from multiple angles. For example, it selects the optimal area taking into account the usage status of public facilities. This allows the proposal to be evaluated from multiple angles.

[0067] The comparison unit can compare the location area generated by the generation AI with past data to identify trends and patterns. For example, the comparison unit compares the location area generated by the generation AI with past data on population mobility to identify trends and patterns. For example, it analyzes trends in population growth and selects the optimal area. The comparison unit also compares the location area generated by the generation AI with past behavioral distribution data to identify trends and patterns. For example, it analyzes changes in behavioral patterns and selects the optimal area. The comparison unit also compares the location area generated by the generation AI with past land prices and price per tsubo data to identify trends and patterns. For example, it analyzes trends in price fluctuations and selects the optimal area. This makes it possible to identify trends and patterns.

[0068] The comparison unit can use the emotion estimation function to continuously improve the proposal content based on the user's emotional response to the location area generated by the generation AI. For example, the comparison unit uses the emotion estimation function to collect the user's emotional response to the location area generated by the generation AI in real time and continuously improve the proposal content. For example, it prioritizes proposing areas with a high level of positive emotion. The comparison unit also analyzes the user's emotional response to the location area generated by the generation AI and proposes areas with a low level of negative emotion. For example, it selects areas with a low level of anger and dissatisfaction. The comparison unit also uses the emotion estimation function to continuously improve the proposal content based on the user's emotional response to the location area generated by the generation AI. For example, it selects the optimal area based on the emotion score. This allows the proposal content to be continuously improved.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The location selection system can further include a weather data collection unit. The weather data collection unit can collect weather data for a specific area and use it for location selection. For example, the weather data collection unit can collect annual precipitation and temperature data for a specific area and evaluate the impact of climatic conditions on business. The weather data collection unit can also collect wind speed and sunshine hours for a specific area and select a location that takes energy efficiency and comfort into consideration. Furthermore, the weather data collection unit can analyze weather data in real time and select a location that responds to sudden weather fluctuations. This enables more accurate location selection that takes weather conditions into consideration.

[0071] The location selection system can further include an energy consumption analysis unit. The energy consumption analysis unit can collect energy consumption data for a specific area and select a location that takes energy efficiency into consideration. For example, the energy consumption analysis unit can analyze electricity consumption and gas consumption in a specific area and identify areas with high energy efficiency. The energy consumption analysis unit can also evaluate the usage status of renewable energy and select sustainable areas. Furthermore, the energy consumption analysis unit can analyze energy consumption data in real time and select a location that takes fluctuations in energy efficiency into consideration. This makes it possible to select a sustainable location that takes energy efficiency into consideration.

[0072] The location selection system can further include a crime data analysis unit. The crime data analysis unit can collect crime data for a specific area and select a location that takes safety into consideration. For example, the crime data analysis unit can analyze the number and types of crimes in a specific area and identify safe areas. The crime data analysis unit can also analyze crime data in real time to identify crime trends. Furthermore, the crime data analysis unit can combine crime data with other statistical data and perform an overall safety evaluation. This enables location selection that takes safety into consideration.

[0073] The location selection system can further include a cultural data analysis unit. The cultural data analysis unit can collect cultural data on a specific area and select a location taking into account its cultural appeal. For example, the cultural data analysis unit can analyze data on historical buildings and cultural events in the specific area to identify culturally attractive areas. The cultural data analysis unit can also analyze the cultural background and preferences of residents in the specific area to select a culturally suitable area. Furthermore, the cultural data analysis unit can combine and analyze the cultural data with other statistical data to perform a comprehensive cultural evaluation. This makes it possible to select a location taking into account its cultural appeal.

[0074] The location selection system can further include an educational data analysis unit. The educational data analysis unit can collect educational data for a specific area and select a location that takes the educational environment into consideration. For example, the educational data analysis unit can analyze the number of schools and educational level in a specific area to identify an area with a good educational environment. The educational data analysis unit can also analyze evaluation data for educational institutions in a specific area to select an area that offers an excellent educational environment. Furthermore, the educational data analysis unit can combine and analyze the educational data with other statistical data to perform a comprehensive evaluation of the educational environment. This enables location selection that takes the educational environment into consideration.

[0075] The location selection system can further use an emotion estimation function to evaluate the happiness level of an area based on emotional data of people in the specific area. For example, the emotion estimation function can be used to collect people's feelings of happiness and satisfaction in a specific area and identify areas with high happiness levels. The emotion estimation function can also be used to collect emotional data about events and happenings in a specific area and select areas with high happiness levels. Furthermore, the emotion estimation function can be used to eliminate negative emotional data in a specific area and identify areas with high happiness levels. This makes it possible to select a location that takes happiness levels into consideration.

[0076] The location selection system can further use an emotion estimation function to evaluate the stress level of an area based on emotional data of people in the specific area. For example, the emotion estimation function can be used to collect the stress and anxiety of people in the specific area and identify areas with low stress levels. The emotion estimation function can also be used to collect emotional data about events and happenings in the specific area and select areas with low stress levels. Furthermore, the emotion estimation function can be used to prioritize positive emotional data in the specific area and identify areas with low stress levels. This makes it possible to select locations that take stress levels into consideration.

[0077] The location selection system can further use an emotion estimation function to evaluate the social connections of an area based on emotional data of people in the area. For example, the emotion estimation function can be used to collect information on people's social connections and sense of community in a specific area, and identify areas with strong social connections. The emotion estimation function can also be used to collect emotional data on events and happenings in a specific area, and select areas with strong social connections. Furthermore, the emotion estimation function can be used to eliminate feelings of isolation and alienation in a specific area, and identify areas with strong social connections. This makes it possible to select a location that takes social connections into consideration.

[0078] The location selection system can further use an emotion estimation function to evaluate the health status of an area based on emotional data of people in the specific area. For example, the emotion estimation function can be used to collect information on people's feelings of health and vitality in the specific area, and identify areas with good health status. The emotion estimation function can also be used to collect emotional data on events and happenings in the specific area, and select areas with good health status. Furthermore, the emotion estimation function can be used to eliminate negative emotional data in the specific area, and identify areas with good health status. This makes it possible to select a location that takes health status into consideration.

[0079] The location selection system can further use an emotion estimation function to evaluate the creativity of an area based on emotional data of people in the specific area. For example, the emotion estimation function can be used to collect information on the creativity and sense of innovation of people in the specific area, and identify areas with high creativity. The emotion estimation function can also be used to collect emotional data regarding events and happenings in the specific area, and select areas with high creativity. Furthermore, the emotion estimation function can be used to eliminate negative emotional data in the specific area, and identify areas with high creativity. This makes it possible to select locations that take creativity into consideration.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The data collection unit collects at least one statistical data item from the following: floating population, behavioral distribution, land price, and price per tsubo. For example, the data collection unit obtains floating population data from a government statistical database. It can also collect data on the number of visitors to commercial facilities and land price data from real estate agents. Step 2: The data analysis unit analyzes the statistical data collected by the data collection unit. For example, it uses generation AI to analyze the floating population data to understand the demographics of a specific area. It also analyzes behavioral distribution data to identify people's behavior patterns in a specific area, and analyzes roadside land price data to evaluate the value of the land. Step 3: The location generation unit generates the optimal location area based on the results of the analysis by the data analysis unit. For example, it identifies areas with a large mobile population, active behavioral distribution, and appropriate land prices and unit prices per square meter. It also uses generation AI to identify the optimal location area for developing commercial facilities and residential areas. Step 4: The comparison unit compares the location area generated by the location generation unit with location selections based on human experience and intuition. For example, it compares the location area generated by the AI ​​generation unit with past successes and failures to predict the probability of success. It also collects expert opinions and users' emotional reactions to evaluate the effectiveness of the proposal.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] 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.

[0109] 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.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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, in order to avoid confusion and to 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.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects at least one statistical data item among floating population, behavioral distribution, land price, and price per tsubo; a data analysis unit that analyzes the statistical data collected by the data collection unit; a location generation unit that generates an optimal location area based on the results of the analysis by the data analysis unit; a comparison unit that compares the location area generated by the location generation unit with a location selection based on human experience and intuition; A system characterized by:

2. The data analysis unit Detect anomalies in the statistical data to improve the reliability of the data 2. The system of claim 1.

3. The data analysis unit The statistical data is analyzed in real time to select locations taking into account fluctuations by time of day and season.

2. The system of claim 1.

4. The data collection unit Use drones or IoT devices to capture more detailed data 2. The system of claim 1.

5. The data collection unit Collect data from different cities or countries to analyze optimal locations from a global perspective 2. The system of claim 1.

6. The data analysis unit Collecting people's emotional data in specific areas to identify areas with positive emotional profiles 2. The system of claim 1.

7. The location generation unit Run simulations on the generated location area to predict future changes in demographics and economic conditions 2. The system of claim 1.

8. The location generation unit Conduct an environmental impact assessment for the resulting location area to achieve sustainable location selection 2. The system of claim 1.

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