System

Abstract

An object of the system according to the embodiment is to enable even an inexperienced person to consistently perform processes from property selection to business plan creation.SOLUTION: A system according to an embodiment includes a property selection unit, a business plan creation unit, and an operation support unit. The property selection unit selects a property on the basis of the desired condition of the user. The business plan creation unit creates a business plan on the basis of the property selected by the property selection unit. The operation support unit supports operation based on the business plan created by the business plan creation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult for inexperienced people to handle the entire process from selecting a property to creating a business plan, making it difficult for people to enter the private lodging business.

[0005] The system according to the embodiment aims to enable even inexperienced people to carry out the entire process from property selection to business plan creation. [Means for solving the problem]

[0006] The system according to the embodiment includes a property selection unit, a business plan creation unit, and an operation support unit. The property selection unit selects a property based on a user's desired conditions. The business plan creation unit creates a business plan based on the property selected by the property selection unit. The operation support unit supports operation based on the business plan created by the business plan creation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows even inexperienced people to carry out the entire process from property selection to business plan creation. [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 private lodging business support system according to an embodiment of the present invention is a system that provides consistent support from property selection to business plan creation, even to inexperienced people. As a result, the private lodging business support system significantly lowers the barrier to entry into the private lodging business, even for inexperienced people.

[0029] A private lodging business support system according to an embodiment includes a property selection unit, a business plan creation unit, and an operations support unit. The property selection unit selects a property based on a user's desired conditions. For example, when a user inputs conditions such as "easily accessible, in the city center, and a monthly budget of less than 100,000 yen," the property selection unit analyzes a real estate database and lists properties that meet the conditions. The property selection unit also evaluates the convenience and future value of the property and proposes it to the user. For example, the property selection unit analyzes the property's location, surrounding environment, and competitive situation to develop a revenue forecast and an operations plan. The business plan creation unit creates a business plan based on the property selected by the property selection unit. For example, the business plan creation unit analyzes detailed property information and market data to create a business plan that includes a revenue forecast, marketing strategy, and operating cost estimate. The operations support unit supports operations based on the business plan created by the business plan creation unit. For example, the operations support unit automates reservation management, customer service, and cleaning schedule management. As a result, the private lodging business support system according to an embodiment can consistently assist even inexperienced users from property selection to business plan creation.

[0030] When selecting a property, the property selection unit takes into consideration at least one social factor, such as the crime rate in the surrounding area or the evaluation of schools, and can suggest properties that are safer and have a better educational environment. For example, when selecting a property, the property selection unit analyzes crime rate data in the surrounding area and prioritizes suggesting properties in safe areas. For example, it lists properties in areas with low crime rates. When selecting a property, the property selection unit also analyzes evaluation data of surrounding schools and suggests properties with a good educational environment. For example, it lists properties in areas with highly rated schools. When selecting a property, the property selection unit also analyzes access data for surrounding medical facilities and public transportation and suggests properties that are highly convenient. For example, it lists properties near hospitals and train stations. In this way, suggesting properties that are safe and have a good educational environment increases the user's sense of security.

[0031] The property selection unit takes into consideration future urban development plans or infrastructure development plans when selecting a property, and can propose properties that are likely to increase in value in the future. For example, the property selection unit analyzes future urban development plans when selecting a property, and proposes properties that are likely to increase in value in the future. For example, it lists properties in areas where new commercial facilities are planned to be built. Furthermore, the property selection unit analyzes infrastructure development plans when selecting a property, and proposes properties that are likely to increase in value in the future. For example, it lists properties in areas where new roads or railways are planned to be opened. Furthermore, the property selection unit analyzes future population growth forecast data when selecting a property, and proposes properties that are likely to increase in value in the future. For example, it lists properties in areas where population growth is expected. In this way, by proposing properties that are likely to increase in value in the future, the user's investment effect is increased.

[0032] When selecting a property, the property selection unit can make suggestions based on the user's lifestyle or hobbies, and list properties that allow pets or properties that are suitable for hobbies. For example, the property selection unit preferentially suggests properties that allow pets based on the user's lifestyle. For example, it lists properties that allow pets for a user who has a pet. The property selection unit also suggests properties that are suitable for the user's hobbies based on the user's hobbies. For example, it lists properties with gardens for a user whose hobby is gardening. The property selection unit also suggests properties with ample workspaces based on the user's lifestyle. For example, it lists properties with a large study for a user who often works from home. In this way, user satisfaction is improved by suggesting properties that suit the user's lifestyle and hobbies.

[0033] The property selection unit can compare properties in different regions or countries and propose the most suitable property from an international perspective. For example, the property selection unit compares properties in different regions and proposes the most suitable property from an international perspective. For example, it compares properties in urban areas and suburban areas and lists properties that meet the user's needs. The property selection unit also compares properties in different countries and proposes the most suitable property from an international perspective. For example, it compares properties in Japan and the United States and lists properties that meet the user's desired conditions. When comparing properties in different regions or countries, the property selection unit also takes into account cost of living and cultural factors and proposes the most suitable property. For example, it lists properties in areas with a low cost of living. This broadens the user's options by proposing the most suitable property from an international perspective.

[0034] When creating a business plan, the business plan creation unit can consider local tourism resources and event information to propose a business plan that maximizes tourist demand. For example, when creating a business plan, the AI ​​in the business plan creation unit analyzes local tourism resources and proposes a plan that maximizes tourist demand. For example, a plan that utilizes local famous places and tourist spots is created. Also, when creating a business plan, the AI ​​in the business plan creation unit analyzes local event information and proposes a plan that maximizes tourist demand. For example, a plan that matches local festivals and events is created. Also, when creating a business plan, the AI ​​in the business plan creation unit considers local tourism resources and event information to propose a plan that maximizes tourist demand. For example, a plan that utilizes local specialties and culture is created. This maximizes tourist demand, thereby increasing the probability of business success.

[0035] The business plan creation department can propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, the business plan creation department uses AI to propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, it may propose the use of renewable energy. Furthermore, the business plan creation department uses AI to propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, it may propose reducing waste and promoting recycling. Furthermore, the business plan creation department uses AI to propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, it may propose the introduction of ecotourism. In this way, an eco-friendly business is realized by proposing environmentally friendly and sustainable operating methods.

[0036] The business plan creation unit can refer to success stories from different industries when creating a business plan and propose a business plan that incorporates know-how from those industries. For example, the business plan creation unit refers to success stories from different industries when creating a business plan and proposes a plan that incorporates know-how from those industries. For example, a plan is created that refers to success stories from the IT industry. The business plan creation unit also refers to success stories from different industries when creating a business plan and proposes a plan that incorporates know-how from those industries. For example, a plan is created that refers to success stories from the food and beverage industry. The business plan creation unit also refers to success stories from different industries when creating a business plan and proposes a plan that incorporates know-how from those industries. For example, a plan is created that refers to success stories from the fashion industry. In this way, by incorporating know-how from different industries, the diversity and success probability of business plans are increased.

[0037] The business plan creation unit can propose a business plan that makes use of the user's past experience or skills when creating a business plan. For example, the business plan creation unit proposes a plan that makes use of the user's past experience and skills when creating a business plan. For example, it creates a plan that makes use of specific skills that the user possesses. Furthermore, the business plan creation unit proposes a plan that makes use of the user's past experience and skills when creating a business plan. For example, it creates a plan that makes use of projects that the user has previously been successful in. Furthermore, the business plan creation unit proposes a plan that makes use of the user's past experience and skills when creating a business plan. For example, it creates a plan that makes use of the user's network. In this way, by utilizing the user's past experience and skills, the feasibility and probability of success of the business plan are increased.

[0038] The operations support department can automatically suggest special promotions or services according to the season or event in the operations support. For example, AI automatically suggests special promotions according to the season in the operations support. For example, it suggests special beach resort plans in the summer. Furthermore, AI automatically suggests special services according to events in the operations support. For example, it suggests special decorations and events for Christmas. Furthermore, AI automatically suggests special promotions and services according to the season or event in the operations support. For example, it suggests special plans to coincide with local festivals. In this way, suggesting special promotions and services according to the season or event attracts customer interest and increases the probability of business success.

[0039] The operations support department can propose operating methods to optimize energy consumption or reduce costs in the operations support. For example, AI may propose optimization of energy consumption in the operations support. For example, power usage may be monitored in real time to perform optimal energy management. The operations support department may also propose operating methods to reduce costs in the operations support. For example, efficient inventory management or personnel allocation may be proposed. The operations support department may also propose operating methods to optimize energy consumption or reduce costs in the operations support. For example, the use of renewable energy or power-saving measures may be proposed. This may improve operational efficiency by optimizing energy consumption and reducing costs.

[0040] The Operations Support Department can incorporate operational methods from different industries and utilize customer service know-how from the hotel industry in its operations support. For example, the Operations Support Department can utilize customer service know-how from the hotel industry in its operations support. For example, it can aim to improve check-in and check-out efficiency and customer service. The Operations Support Department can also incorporate operational methods from the food and beverage industry in its operations support. For example, it can provide efficient kitchen management and menu development. The Operations Support Department can also incorporate operational methods from the logistics industry in its operations support. For example, it can provide efficient inventory management and optimize delivery schedules. In this way, by incorporating operational methods from different industries, operational efficiency and customer satisfaction can be improved.

[0041] The operation support unit can provide customizable support in the operation support according to the user's operation style or preferences. The operation support unit, for example, provides customizable support in the operation support according to the user's operation style. For example, it makes suggestions that match the operation method preferred by the user. The operation support unit also provides customizable support in the operation support according to the user's preferences. For example, it makes suggestions that match the interior and design that the user prefers. The operation support unit also provides customizable support in the operation support according to the user's operation style and preferences. For example, it makes suggestions that match the services and promotions that the user prefers. In this way, by providing customizable support according to the user's operation style and preferences, user satisfaction is improved.

[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] When selecting a property, the property selection unit takes into consideration at least one social factor, such as the crime rate in the surrounding area or the evaluation of schools, and can suggest properties that are safer and have a better educational environment. For example, when selecting a property, the property selection unit analyzes crime rate data in the surrounding area and prioritizes suggesting properties in safe areas. For example, it lists properties in areas with low crime rates. When selecting a property, the property selection unit also analyzes evaluation data of surrounding schools and suggests properties with a good educational environment. For example, it lists properties in areas with highly rated schools. When selecting a property, the property selection unit also analyzes access data for surrounding medical facilities and public transportation and suggests properties that are highly convenient. For example, it lists properties near hospitals and train stations. In this way, suggesting properties that are safe and have a good educational environment increases the user's sense of security.

[0044] The property selection unit takes into consideration future urban development plans or infrastructure development plans when selecting a property, and can propose properties that are likely to increase in value in the future. For example, the property selection unit analyzes future urban development plans when selecting a property, and proposes properties that are likely to increase in value in the future. For example, it lists properties in areas where new commercial facilities are planned to be built. Furthermore, the property selection unit analyzes infrastructure development plans when selecting a property, and proposes properties that are likely to increase in value in the future. For example, it lists properties in areas where new roads or railways are planned to be opened. Furthermore, the property selection unit analyzes future population growth forecast data when selecting a property, and proposes properties that are likely to increase in value in the future. For example, it lists properties in areas where population growth is expected. In this way, by proposing properties that are likely to increase in value in the future, the user's investment effect is increased.

[0045] When selecting a property, the property selection unit can make suggestions based on the user's lifestyle or hobbies, and list properties that allow pets or properties that are suitable for hobbies. For example, the property selection unit preferentially suggests properties that allow pets based on the user's lifestyle. For example, it lists properties that allow pets for a user who has a pet. The property selection unit also suggests properties that are suitable for the user's hobbies based on the user's hobbies. For example, it lists properties with gardens for a user whose hobby is gardening. The property selection unit also suggests properties with ample workspaces based on the user's lifestyle. For example, it lists properties with a large study for a user who often works from home. In this way, user satisfaction is improved by suggesting properties that suit the user's lifestyle and hobbies.

[0046] The property selection unit can compare properties in different regions or countries and propose the most suitable property from an international perspective. For example, the property selection unit compares properties in different regions and proposes the most suitable property from an international perspective. For example, it compares properties in urban areas and suburban areas and lists properties that meet the user's needs. The property selection unit also compares properties in different countries and proposes the most suitable property from an international perspective. For example, it compares properties in Japan and the United States and lists properties that meet the user's desired conditions. When comparing properties in different regions or countries, the property selection unit also takes into account cost of living and cultural factors and proposes the most suitable property. For example, it lists properties in areas with a low cost of living. This broadens the user's options by proposing the most suitable property from an international perspective.

[0047] When creating a business plan, the business plan creation unit can consider local tourism resources and event information to propose a business plan that maximizes tourist demand. For example, when creating a business plan, the AI ​​in the business plan creation unit analyzes local tourism resources and proposes a plan that maximizes tourist demand. For example, a plan that utilizes local famous places and tourist spots is created. Also, when creating a business plan, the AI ​​in the business plan creation unit analyzes local event information and proposes a plan that maximizes tourist demand. For example, a plan that matches local festivals and events is created. Also, when creating a business plan, the AI ​​in the business plan creation unit considers local tourism resources and event information to propose a plan that maximizes tourist demand. For example, a plan that utilizes local specialties and culture is created. This maximizes tourist demand, thereby increasing the probability of business success.

[0048] The business plan creation department can propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, the business plan creation department uses AI to propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, it may propose the use of renewable energy. Furthermore, the business plan creation department uses AI to propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, it may propose reducing waste and promoting recycling. Furthermore, the business plan creation department uses AI to propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, it may propose the introduction of ecotourism. In this way, an eco-friendly business is realized by proposing environmentally friendly and sustainable operating methods.

[0049] The business plan creation unit can refer to success stories from different industries when creating a business plan and propose a business plan that incorporates know-how from those industries. For example, the business plan creation unit refers to success stories from different industries when creating a business plan and proposes a plan that incorporates know-how from those industries. For example, a plan is created that refers to success stories from the IT industry. The business plan creation unit also refers to success stories from different industries when creating a business plan and proposes a plan that incorporates know-how from those industries. For example, a plan is created that refers to success stories from the food and beverage industry. The business plan creation unit also refers to success stories from different industries when creating a business plan and proposes a plan that incorporates know-how from those industries. For example, a plan is created that refers to success stories from the fashion industry. In this way, by incorporating know-how from different industries, the diversity and success probability of business plans are increased.

[0050] The business plan creation unit can propose a business plan that makes use of the user's past experience or skills when creating a business plan. For example, the business plan creation unit proposes a plan that makes use of the user's past experience and skills when creating a business plan. For example, it creates a plan that makes use of specific skills that the user possesses. Furthermore, the business plan creation unit proposes a plan that makes use of the user's past experience and skills when creating a business plan. For example, it creates a plan that makes use of projects that the user has previously been successful in. Furthermore, the business plan creation unit proposes a plan that makes use of the user's past experience and skills when creating a business plan. For example, it creates a plan that makes use of the user's network. In this way, by utilizing the user's past experience and skills, the feasibility and probability of success of the business plan are increased.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The property selection unit selects a property based on the user's desired conditions. For example, if the user inputs conditions such as "central city area with good access and a monthly budget of less than 100,000 yen," the property selection unit analyzes the real estate database and lists properties that meet the conditions. The property selection unit also evaluates the convenience and future value of the property and makes a proposal to the user. For example, it analyzes the property's location, surrounding environment, and competitive situation, and creates revenue forecasts and management plans. Step 2: The Business Plan Creation Department creates a business plan based on the properties selected by the Property Selection Department. For example, they analyze detailed property information and market data and create a business plan that includes revenue forecasts, marketing strategies, and operating cost estimates. Step 3: The Operations Support Department supports operations based on the business plan created by the Business Plan Creation Department, for example by automating reservation management, customer service, and cleaning schedule management.

[0053] (Example 2) The private lodging business support system according to an embodiment of the present invention is a system that provides consistent support from property selection to business plan creation, even to inexperienced people. As a result, the private lodging business support system significantly lowers the barrier to entry into the private lodging business, even for inexperienced people.

[0054] A private lodging business support system according to an embodiment includes a property selection unit, a business plan creation unit, and an operations support unit. The property selection unit selects a property based on a user's desired conditions. For example, when a user inputs conditions such as "easily accessible, in the city center, and within a monthly budget of 100,000 yen," the property selection unit analyzes a real estate database and lists properties that meet the conditions. The property selection unit also evaluates the convenience and future value of the property and proposes it to the user. For example, the property selection unit analyzes the property's location, surrounding environment, and competitive situation to develop a revenue forecast and an operations plan. The business plan creation unit creates a business plan based on the property selected by the property selection unit. For example, the business plan creation unit analyzes detailed property information and market data to create a business plan that includes a revenue forecast, marketing strategy, and operating cost estimate. The operations support unit supports operations based on the business plan created by the business plan creation unit. For example, the operations support unit automates reservation management, customer service, and cleaning schedule management. As a result, the private lodging business support system according to an embodiment can consistently assist even inexperienced users from property selection to business plan creation.

[0055] The property selection unit uses the emotion estimation function to analyze the emotions a user has when selecting a property, and prioritizes suggesting properties that elicit positive emotions. For example, when a user is selecting a property, the property selection unit uses the emotion estimation function to analyze the user's facial expressions and voice, and prioritizes suggesting properties that elicit positive emotions. For example, it lists properties that make the user smile. The property selection unit also uses the emotion estimation function to make relaxation suggestions to reduce the stress the user feels during the property selection process. For example, it prioritizes suggesting properties that have an environment where the user can relax. The property selection unit also uses the emotion estimation function to analyze the user's emotions in real time when the user is selecting a property, and provides advice to elicit positive emotions. For example, it suggests properties that excite the user. In this way, by suggesting properties based on the user's emotions, user satisfaction is improved.

[0056] When selecting a property, the property selection unit takes into consideration at least one social factor, such as the crime rate in the surrounding area or the evaluation of schools, and can suggest properties that are safer and have a better educational environment. For example, when selecting a property, the property selection unit analyzes crime rate data in the surrounding area and prioritizes suggesting properties in safe areas. For example, it lists properties in areas with low crime rates. When selecting a property, the property selection unit also analyzes evaluation data of surrounding schools and suggests properties with a good educational environment. For example, it lists properties in areas with highly rated schools. When selecting a property, the property selection unit also analyzes access data for surrounding medical facilities and public transportation and suggests properties that are highly convenient. For example, it lists properties near hospitals and train stations. In this way, suggesting properties that are safe and have a good educational environment increases the user's sense of security.

[0057] The property selection unit takes into consideration future urban development plans or infrastructure development plans when selecting a property, and can propose properties that are likely to increase in value in the future. For example, the property selection unit analyzes future urban development plans when selecting a property, and proposes properties that are likely to increase in value in the future. For example, it lists properties in areas where new commercial facilities are planned to be built. Furthermore, the property selection unit analyzes infrastructure development plans when selecting a property, and proposes properties that are likely to increase in value in the future. For example, it lists properties in areas where new roads or railways are planned to be opened. Furthermore, the property selection unit analyzes future population growth forecast data when selecting a property, and proposes properties that are likely to increase in value in the future. For example, it lists properties in areas where population growth is expected. In this way, by proposing properties that are likely to increase in value in the future, the user's investment effect is increased.

[0058] When selecting a property, the property selection unit can make suggestions based on the user's lifestyle or hobbies, and list properties that allow pets or properties that are suitable for hobbies. For example, the property selection unit preferentially suggests properties that allow pets based on the user's lifestyle. For example, it lists properties that allow pets for a user who has a pet. The property selection unit also suggests properties that are suitable for the user's hobbies based on the user's hobbies. For example, it lists properties with gardens for a user whose hobby is gardening. The property selection unit also suggests properties with ample workspaces based on the user's lifestyle. For example, it lists properties with a large study for a user who often works from home. In this way, user satisfaction is improved by suggesting properties that suit the user's lifestyle and hobbies.

[0059] The property selection unit can compare properties in different regions or countries and propose the most suitable property from an international perspective. For example, the property selection unit compares properties in different regions and proposes the most suitable property from an international perspective. For example, it compares properties in urban areas and suburban areas and lists properties that meet the user's needs. The property selection unit also compares properties in different countries and proposes the most suitable property from an international perspective. For example, it compares properties in Japan and the United States and lists properties that meet the user's desired conditions. When comparing properties in different regions or countries, the property selection unit also takes into account cost of living and cultural factors and proposes the most suitable property. For example, it lists properties in areas with a low cost of living. This broadens the user's options by proposing the most suitable property from an international perspective.

[0060] The property selection unit can use the emotion estimation function to make relaxation suggestions to reduce the stress the user feels during the property selection process. The property selection unit, for example, uses the emotion estimation function to make relaxation suggestions to reduce the stress the user feels during the property selection process. For example, it prioritizes suggesting properties with environments that allow the user to relax. The property selection unit also uses the emotion estimation function to make relaxation suggestions to reduce the stress the user feels during the property selection process. For example, it plays music that helps the user relax. The property selection unit also uses the emotion estimation function to make relaxation suggestions to reduce the stress the user feels during the property selection process. For example, it suggests an aroma that helps the user relax. This reduces the user's stress and allows the user to select a property in a relaxed state.

[0061] The business plan creation unit can use the emotion estimation function to analyze the anxieties and concerns the user has about the business plan and propose specific measures to resolve them. The business plan creation unit, for example, uses the emotion estimation function to analyze the anxieties and concerns the user has about the business plan and propose specific measures to resolve them. For example, it identifies points that the user feels anxious about and presents specific solutions. The business plan creation unit also uses the emotion estimation function to analyze the anxieties and concerns the user has about the business plan and proposes specific measures to resolve them. For example, it proposes measures to reduce risks that the user is concerned about. The business plan creation unit also uses the emotion estimation function to analyze the anxieties and concerns the user has about the business plan and proposes specific measures to resolve them. For example, it proposes resources to compensate for the parts that the user feels anxious about. This resolves the user's anxieties and concerns and allows the user to create a business plan with peace of mind.

[0062] When creating a business plan, the business plan creation unit can consider local tourism resources and event information to propose a business plan that maximizes tourist demand. For example, when creating a business plan, the AI ​​in the business plan creation unit analyzes local tourism resources and proposes a plan that maximizes tourist demand. For example, a plan that utilizes local famous places and tourist spots is created. Also, when creating a business plan, the AI ​​in the business plan creation unit analyzes local event information and proposes a plan that maximizes tourist demand. For example, a plan that matches local festivals and events is created. Also, when creating a business plan, the AI ​​in the business plan creation unit considers local tourism resources and event information to propose a plan that maximizes tourist demand. For example, a plan that utilizes local specialties and culture is created. This maximizes tourist demand, thereby increasing the probability of business success.

[0063] The business plan creation department can propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, the business plan creation department uses AI to propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, it may propose the use of renewable energy. Furthermore, the business plan creation department uses AI to propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, it may propose reducing waste and promoting recycling. Furthermore, the business plan creation department uses AI to propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, it may propose the introduction of ecotourism. In this way, an eco-friendly business is realized by proposing environmentally friendly and sustainable operating methods.

[0064] The business plan creation unit can refer to success stories from different industries when creating a business plan and propose a business plan that incorporates know-how from those industries. For example, the business plan creation unit refers to success stories from different industries when creating a business plan and proposes a plan that incorporates know-how from those industries. For example, a plan is created that refers to success stories from the IT industry. The business plan creation unit also refers to success stories from different industries when creating a business plan and proposes a plan that incorporates know-how from those industries. For example, a plan is created that refers to success stories from the food and beverage industry. The business plan creation unit also refers to success stories from different industries when creating a business plan and proposes a plan that incorporates know-how from those industries. For example, a plan is created that refers to success stories from the fashion industry. In this way, by incorporating know-how from different industries, the diversity and success probability of business plans are increased.

[0065] The business plan creation unit can propose a business plan that makes use of the user's past experience or skills when creating a business plan. For example, the business plan creation unit proposes a plan that makes use of the user's past experience and skills when creating a business plan. For example, it creates a plan that makes use of specific skills that the user possesses. Furthermore, the business plan creation unit proposes a plan that makes use of the user's past experience and skills when creating a business plan. For example, it creates a plan that makes use of projects that the user has previously been successful in. Furthermore, the business plan creation unit proposes a plan that makes use of the user's past experience and skills when creating a business plan. For example, it creates a plan that makes use of the user's network. In this way, by utilizing the user's past experience and skills, the feasibility and probability of success of the business plan are increased.

[0066] The business plan creation unit can use the emotion estimation function to provide feedback to increase the motivation felt by the user in the process of creating the business plan. The business plan creation unit, for example, uses the emotion estimation function to provide feedback to increase the motivation felt by the user in the process of creating the business plan. For example, the business plan creation unit identifies points that motivate the user and provides specific feedback. The business plan creation unit also uses the emotion estimation function to provide feedback to increase the motivation felt by the user in the process of creating the business plan. For example, the business plan creation unit highlights parts that motivate the user. The business plan creation unit also uses the emotion estimation function to provide feedback to increase the motivation felt by the user in the process of creating the business plan. For example, the business plan creation unit adds elements that motivate the user. This increases the user's motivation, thereby making it possible to smoothly proceed with the creation of the business plan.

[0067] The operations support department can use the emotion estimation function to analyze customer emotions in real time and propose responses to improve customer satisfaction. The operations support department, for example, uses the emotion estimation function to analyze customer emotions in real time and propose responses to improve customer satisfaction. For example, if a customer feels dissatisfied, it immediately proposes a countermeasure. The operations support department also uses the emotion estimation function to analyze customer emotions in real time and propose responses to improve customer satisfaction. For example, if a customer feels happy, it strengthens that element. The operations support department also uses the emotion estimation function to analyze customer emotions in real time and propose responses to improve customer satisfaction. For example, if a customer feels stressed, it makes suggestions to help the customer relax. In this way, customer satisfaction is improved by analyzing customer emotions in real time and taking appropriate responses.

[0068] The operations support department can automatically suggest special promotions or services according to the season or event in the operations support. For example, AI automatically suggests special promotions according to the season in the operations support. For example, it suggests special beach resort plans in the summer. Furthermore, AI automatically suggests special services according to events in the operations support. For example, it suggests special decorations and events for Christmas. Furthermore, AI automatically suggests special promotions and services according to the season or event in the operations support. For example, it suggests special plans to coincide with local festivals. In this way, suggesting special promotions and services according to the season or event attracts customer interest and increases the probability of business success.

[0069] The operations support department can propose operating methods to optimize energy consumption or reduce costs in the operations support. For example, AI may propose optimization of energy consumption in the operations support. For example, power usage may be monitored in real time to perform optimal energy management. The operations support department may also propose operating methods to reduce costs in the operations support. For example, efficient inventory management or personnel allocation may be proposed. The operations support department may also propose operating methods to optimize energy consumption or reduce costs in the operations support. For example, the use of renewable energy or power-saving measures may be proposed. This may improve operational efficiency by optimizing energy consumption and reducing costs.

[0070] The Operations Support Department can incorporate operational methods from different industries and utilize customer service know-how from the hotel industry in its operations support. For example, the Operations Support Department can utilize customer service know-how from the hotel industry in its operations support. For example, it can aim to improve check-in and check-out efficiency and customer service. The Operations Support Department can also incorporate operational methods from the food and beverage industry in its operations support. For example, it can provide efficient kitchen management and menu development. The Operations Support Department can also incorporate operational methods from the logistics industry in its operations support. For example, it can provide efficient inventory management and optimize delivery schedules. In this way, by incorporating operational methods from different industries, operational efficiency and customer satisfaction can be improved.

[0071] The operation support unit can provide customizable support in the operation support according to the user's operation style or preferences. The operation support unit, for example, provides customizable support in the operation support according to the user's operation style. For example, it makes suggestions that match the operation method preferred by the user. The operation support unit also provides customizable support in the operation support according to the user's preferences. For example, it makes suggestions that match the interior and design that the user prefers. The operation support unit also provides customizable support in the operation support according to the user's operation style and preferences. For example, it makes suggestions that match the services and promotions that the user prefers. In this way, by providing customizable support according to the user's operation style and preferences, user satisfaction is improved.

[0072] The operations support department can use the emotion estimation function to analyze the emotions of the operations staff and propose measures to increase their motivation. For example, the operations support department uses the emotion estimation function to analyze the emotions of the operations staff in real time and propose measures to increase their motivation. For example, if a staff member feels stressed, the operations support department makes suggestions to help them relax. The operations support department also uses the emotion estimation function to analyze the emotions of the operations staff and propose measures to increase their motivation. For example, the operations support department strengthens elements that make staff feel happy. The operations support department also uses the emotion estimation function to analyze the emotions of the operations staff and propose measures to increase their motivation. For example, if a staff member feels dissatisfied, the operations support department immediately presents countermeasures. In this way, by analyzing the emotions of the operations staff and proposing measures to increase their motivation, the efficiency of operations and staff satisfaction are improved.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] When selecting a property, the property selection unit takes into consideration at least one social factor, such as the crime rate in the surrounding area or the evaluation of schools, and can suggest properties that are safer and have a better educational environment. For example, when selecting a property, the property selection unit analyzes crime rate data in the surrounding area and prioritizes suggesting properties in safe areas. For example, it lists properties in areas with low crime rates. When selecting a property, the property selection unit also analyzes evaluation data of surrounding schools and suggests properties with a good educational environment. For example, it lists properties in areas with highly rated schools. When selecting a property, the property selection unit also analyzes access data for surrounding medical facilities and public transportation and suggests properties that are highly convenient. For example, it lists properties near hospitals and train stations. In this way, suggesting properties that are safe and have a good educational environment increases the user's sense of security.

[0075] The property selection unit takes into consideration future urban development plans or infrastructure development plans when selecting a property, and can propose properties that are likely to increase in value in the future. For example, the property selection unit analyzes future urban development plans when selecting a property, and proposes properties that are likely to increase in value in the future. For example, it lists properties in areas where new commercial facilities are planned to be built. Furthermore, the property selection unit analyzes infrastructure development plans when selecting a property, and proposes properties that are likely to increase in value in the future. For example, it lists properties in areas where new roads or railways are planned to be opened. Furthermore, the property selection unit analyzes future population growth forecast data when selecting a property, and proposes properties that are likely to increase in value in the future. For example, it lists properties in areas where population growth is expected. In this way, by proposing properties that are likely to increase in value in the future, the user's investment effect is increased.

[0076] When selecting a property, the property selection unit can make suggestions based on the user's lifestyle or hobbies, and list properties that allow pets or properties that are suitable for hobbies. For example, the property selection unit preferentially suggests properties that allow pets based on the user's lifestyle. For example, it lists properties that allow pets for a user who has a pet. The property selection unit also suggests properties that are suitable for the user's hobbies based on the user's hobbies. For example, it lists properties with gardens for a user whose hobby is gardening. The property selection unit also suggests properties with ample workspaces based on the user's lifestyle. For example, it lists properties with a large study for a user who often works from home. In this way, user satisfaction is improved by suggesting properties that suit the user's lifestyle and hobbies.

[0077] The property selection unit can compare properties in different regions or countries and propose the most suitable property from an international perspective. For example, the property selection unit compares properties in different regions and proposes the most suitable property from an international perspective. For example, it compares properties in urban areas and suburban areas and lists properties that meet the user's needs. The property selection unit also compares properties in different countries and proposes the most suitable property from an international perspective. For example, it compares properties in Japan and the United States and lists properties that meet the user's desired conditions. When comparing properties in different regions or countries, the property selection unit also takes into account cost of living and cultural factors and proposes the most suitable property. For example, it lists properties in areas with a low cost of living. This broadens the user's options by proposing the most suitable property from an international perspective.

[0078] When creating a business plan, the business plan creation unit can consider local tourism resources and event information to propose a business plan that maximizes tourist demand. For example, when creating a business plan, the AI ​​in the business plan creation unit analyzes local tourism resources and proposes a plan that maximizes tourist demand. For example, a plan that utilizes local famous places and tourist spots is created. Also, when creating a business plan, the AI ​​in the business plan creation unit analyzes local event information and proposes a plan that maximizes tourist demand. For example, a plan that matches local festivals and events is created. Also, when creating a business plan, the AI ​​in the business plan creation unit considers local tourism resources and event information to propose a plan that maximizes tourist demand. For example, a plan that utilizes local specialties and culture is created. This maximizes tourist demand, thereby increasing the probability of business success.

[0079] The business plan creation department can propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, the business plan creation department uses AI to propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, it may propose the use of renewable energy. Furthermore, the business plan creation department uses AI to propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, it may propose reducing waste and promoting recycling. Furthermore, the business plan creation department uses AI to propose environmentally friendly and sustainable operating methods when creating a business plan, thereby creating an eco-friendly business plan. For example, it may propose the introduction of ecotourism. In this way, an eco-friendly business is realized by proposing environmentally friendly and sustainable operating methods.

[0080] The business plan creation unit can refer to success stories from different industries when creating a business plan and propose a business plan that incorporates know-how from those industries. For example, the business plan creation unit refers to success stories from different industries when creating a business plan and proposes a plan that incorporates know-how from those industries. For example, a plan is created that refers to success stories from the IT industry. The business plan creation unit also refers to success stories from different industries when creating a business plan and proposes a plan that incorporates know-how from those industries. For example, a plan is created that refers to success stories from the food and beverage industry. The business plan creation unit also refers to success stories from different industries when creating a business plan and proposes a plan that incorporates know-how from those industries. For example, a plan is created that refers to success stories from the fashion industry. In this way, by incorporating know-how from different industries, the diversity and success probability of business plans are increased.

[0081] The business plan creation unit can propose a business plan that makes use of the user's past experience or skills when creating a business plan. For example, the business plan creation unit proposes a plan that makes use of the user's past experience and skills when creating a business plan. For example, it creates a plan that makes use of specific skills that the user possesses. Furthermore, the business plan creation unit proposes a plan that makes use of the user's past experience and skills when creating a business plan. For example, it creates a plan that makes use of projects that the user has previously been successful in. Furthermore, the business plan creation unit proposes a plan that makes use of the user's past experience and skills when creating a business plan. For example, it creates a plan that makes use of the user's network. In this way, by utilizing the user's past experience and skills, the feasibility and probability of success of the business plan are increased.

[0082] The business plan creation unit can use the emotion estimation function to analyze the anxieties and concerns the user has about the business plan and propose specific measures to resolve them. The business plan creation unit, for example, uses the emotion estimation function to analyze the anxieties and concerns the user has about the business plan and propose specific measures to resolve them. For example, it identifies points that the user feels anxious about and presents specific solutions. The business plan creation unit also uses the emotion estimation function to analyze the anxieties and concerns the user has about the business plan and proposes specific measures to resolve them. For example, it proposes measures to reduce risks that the user is concerned about. The business plan creation unit also uses the emotion estimation function to analyze the anxieties and concerns the user has about the business plan and proposes specific measures to resolve them. For example, it proposes resources to compensate for the parts that the user feels anxious about. This resolves the user's anxieties and concerns and allows the user to create a business plan with peace of mind.

[0083] The business plan creation unit can use the emotion estimation function to provide feedback to increase the motivation felt by the user in the process of creating the business plan. The business plan creation unit, for example, uses the emotion estimation function to provide feedback to increase the motivation felt by the user in the process of creating the business plan. For example, the business plan creation unit identifies points that motivate the user and provides specific feedback. The business plan creation unit also uses the emotion estimation function to provide feedback to increase the motivation felt by the user in the process of creating the business plan. For example, the business plan creation unit highlights parts that motivate the user. The business plan creation unit also uses the emotion estimation function to provide feedback to increase the motivation felt by the user in the process of creating the business plan. For example, the business plan creation unit adds elements that motivate the user. This increases the user's motivation, thereby making it possible to smoothly proceed with the creation of the business plan.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The property selection unit selects a property based on the user's desired conditions. For example, if the user inputs conditions such as "central city area with good access and a monthly budget of less than 100,000 yen," the property selection unit analyzes the real estate database and lists properties that meet the conditions. The property selection unit also evaluates the convenience and future value of the property and makes a proposal to the user. For example, it analyzes the property's location, surrounding environment, and competitive situation, and creates revenue forecasts and management plans. Step 2: The Business Plan Creation Department creates a business plan based on the properties selected by the Property Selection Department. For example, they analyze detailed property information and market data and create a business plan that includes revenue forecasts, marketing strategies, and operating cost estimates. Step 3: The Operations Support Department supports operations based on the business plan created by the Business Plan Creation Department, for example by automating reservation management, customer service, and cleaning schedule management.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The 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.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0114] 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.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The 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.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0152] 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]

[0153] 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 property selection unit that selects a property based on the user's desired conditions; a business plan creation unit that creates a business plan based on the property selected by the property selection unit; an operation support unit that supports operations based on the business plan created by the business plan creation unit; A system characterized by:

2. The property selection unit Analyzing the user's feelings about property selection and preferentially suggesting properties that evoke positive feelings 2. The system of claim 1.

3. The property selection unit When selecting properties, consider at least one social factor, such as the crime rate or school rating in the surrounding area, and propose properties with a safer and better educational environment.

2. The system of claim 1.

4. The property selection unit When selecting properties, we take into consideration future urban development plans or infrastructure development plans and propose properties that have the potential to increase in value in the future.

2. The system of claim 1.

5. The property selection unit When selecting a property, suggestions are made based on the user's lifestyle or hobbies, and properties that allow pets or are suitable for the hobbies are listed.

2. The system of claim 1.

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