system

Abstract

The system according to this embodiment aims to improve the housing market and promote the effective use of vacant houses by providing renovation and revenue-generating plans for vacant houses. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, and a revenue provision unit. The reception unit receives input for area, year of construction, number of rooms, and images or condition. The generation unit analyzes the information input by the reception unit and generates renovation and business plans according to rental market rates and budget. The provision unit provides the renovation and business plans generated by the generation unit with images and audio guides. The revenue provision unit provides revenue plans based on the renovation and business plans provided by the provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional technologies have problems such as an increase in vacant houses, an increase in new housing prices, and a shortage of suitable second-hand houses, and it is difficult to solve these problems simultaneously.

[0005] The system according to the embodiment aims to effectively utilize vacant houses and improve the housing market by providing renovation and profit plans for vacant houses.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a revenue provision unit. The reception unit receives input for area, year of construction, number of rooms, and images or condition. The generation unit analyzes the information input by the reception unit and generates renovation and business plans according to rental market rates and budget. The provision unit provides the renovation and business plans generated by the generation unit with images and audio guides. The revenue provision unit provides revenue plans based on the renovation and business plans provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve the housing market and promote the effective use of vacant houses by providing renovation and revenue-generating plans for vacant houses. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention utilizes generative AI to simultaneously solve three problems in urban areas of Japan: the rapidly increasing problem of vacant houses, the rising price of new housing due to soaring material costs and a shortage of skilled workers, and the lack of suitable used housing for young families and urban seniors to rent. This system provides two services as a business-to-business negotiation tool: renovation considerations and revenue plans. First, the user inputs basic information such as area, year of construction, and number of rooms, as well as images and condition, into an input format. Next, the generative AI provides multiple renovation and business plans tailored to rental market rates and budgets, with image and audio guidance. This system makes it easier for vacant house owners to consider renovations and revenue streams, and will lead to an increase in rental properties and a reduction in vacant houses, while also aiming to avoid increased tax burdens after legal revisions. Furthermore, young families will be able to obtain housing at an affordable price, urban seniors will have access to an environment for trial relocation for their second life, and the mobility of people will increase. In addition, the maintenance of local culture and economic revitalization will be promoted by people moving in from outside. For example, a user inputs basic information such as area, age of construction, and number of rooms, along with images and condition, into an input format. For instance, information on a 20-year-old 3LDK vacant house is entered. This information is then input into a generating AI. Next, the generating AI analyzes the input information and provides multiple renovation and business plans based on rental market rates and budget, using images and audio guidance. For example, based on rental market rates, renovation plans for the living room and kitchen are suggested. This allows vacant house owners to understand concrete renovation plans. Furthermore, the generating AI also provides a revenue plan. For example, rental income after renovation and the investment payback period are presented. This allows vacant house owners to consider the profitability of the renovation. This system makes it easier for vacant house owners to consider renovations and revenue streams, and with an eye on avoiding increased tax burdens after legal revisions, it will lead to an increase in rental properties and a reduction in vacant houses. In addition, young families will be able to obtain housing at an affordable price, urban seniors will have access to an environment for trial relocation to a second life, and the mobility of people will increase. Furthermore, the presence of newcomers from outside the area can help maintain local culture and revitalize the economy.This system will make it easier for vacant property owners to renovate and consider profitability, and will lead to an increase in rental properties and a reduction in vacant houses, while also aiming to avoid increased tax burdens after the legal reforms. In addition, young families will be able to obtain housing at an affordable price, and urban seniors will have access to an environment for trying out a second life through relocation, promoting the mobility of people. Furthermore, it will help maintain local culture and revitalize the economy through people moving in from outside.

[0029] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a revenue provision unit. The reception unit receives input for area, age of construction, number of rooms, and images or condition. The reception unit allows users to input basic information such as area, age of construction, and number of rooms, as well as images and condition, into an input format. For example, information on a 20-year-old 3LDK vacant house can be input. The generation unit analyzes the information input by the reception unit and generates renovation and business plans according to rental market rates and budget. The generation unit can generate multiple renovation and business plans according to rental market rates and budget, for example, using generation AI. For example, a living room renovation plan and a kitchen remodeling plan may be proposed based on rental market rates. The provision unit provides the renovation and business plans generated by the generation unit with images and audio guides. The provision unit can provide the generated renovation plans with images and audio guides. For example, a living room renovation plan and a kitchen remodeling plan may be provided with images and audio guides. The revenue provision unit provides a revenue plan based on the renovation and business plan provided by the provision unit. The revenue provision unit can, for example, provide rental income after renovation and the investment recovery period. For example, rental income after renovation and the investment recovery period are presented. As a result, the system according to the embodiment can input basic information such as area, year of construction, and number of rooms, as well as images and condition, generate multiple renovation and business plans according to rental market rates and budget, and provide them with images and audio guides to provide a revenue plan.

[0030] The reception desk inputs information such as area, year of construction, number of rooms, and images or condition. For example, the reception desk allows users to input basic information such as area, year of construction, and number of rooms, as well as images and condition, into an input format. Specifically, users input detailed property information through a dedicated interface. For example, they can input information about a vacant 3LDK house that is 20 years old. Users input the property area in square meters and the year of construction in years. They can also input the number of rooms, such as living room, dining room, kitchen, bedroom, and bathroom. Furthermore, it is possible to upload images and photos showing the condition of the property, allowing users to visually confirm the current state of the property. For example, uploading photos of the living room or images of the kitchen allows for a detailed understanding of the interior condition of the property. The reception desk centrally manages this information and stores it as data necessary for subsequent processing. The information entered by users is stored in a database and made accessible to the generation and provision departments. This allows the reception desk to provide an environment in which users can easily and quickly input property information, improving the overall efficiency of the system.

[0031] The generation unit analyzes the information entered by the reception unit and generates renovation and business plans that match rental market rates and budgets. For example, the generation unit can use generation AI to generate multiple renovation and business plans that match rental market rates and budgets. Specifically, the generation AI compares the entered property information with a local rental market rate database to calculate an appropriate rental market rate. Next, it generates renovation plans that match the user's budget. For example, living room renovation plans and kitchen remodeling plans may be proposed. The generation AI has learned from past renovation cases and market trends, and can propose the optimal plan. For example, a living room renovation plan may include changing the wallpaper, replacing the flooring, and installing lighting. A kitchen remodeling plan may include replacing the sink and stove, and adding storage space. The generation unit generates multiple such plans to provide the user with choices. Furthermore, in addition to renovation plans, the generation unit also generates business plans. For example, this includes setting rent when leasing a property or setting a sale price when selling a property. This allows the generation unit to provide the user with the optimal plan to maximize the value of the property.

[0032] The service provider provides the renovation and business plans generated by the generation unit through images and audio guides. For example, the service provider can provide the generated renovation plan through images and audio guides. Specifically, the service provider generates images using 3D models and computer graphics to display the generated renovation plan in an easy-to-understand visual way. For example, in a living room renovation plan, the new wallpaper, flooring, and lighting arrangements are displayed using 3D models, allowing the user to concretely grasp the image after the renovation. In a kitchen renovation plan, the new sink, stove, and storage space arrangements are displayed using computer graphics, allowing the user to visually confirm the image of the renovated kitchen. Furthermore, the service provider uses audio guides to explain the details of the renovation plan. For example, in a living room renovation plan, the reasons and effects of changing the wallpaper and replacing the flooring are explained in audio, making it easier for the user to understand the plan. In a kitchen renovation plan, the benefits of replacing the sink and stove, and the effects of increasing storage space are explained in audio. This allows the service provider to enable users to visually and aurally understand the contents of the renovation plan and assist them in selecting a plan.

[0033] The revenue provision department provides revenue plans based on the renovation and business plans provided by the provision department. For example, the revenue provision department can provide rental income after renovation and the investment recovery period. Specifically, the revenue provision department calculates rental income after renovation based on the renovation plans and business plans generated by the generation department. For example, it predicts rental income after renovating the living room based on the living room renovation plan. It also predicts rental income after renovating the kitchen based on the kitchen renovation plan. Furthermore, the revenue provision department compares the renovation costs with the rental income and calculates the investment recovery period. For example, if the cost of renovating the living room is 1 million yen and the rental income after renovation is 100,000 yen per month, the investment recovery period is 10 months. The revenue provision department provides this information to the user and provides indicators for evaluating the profitability of the renovation. Furthermore, the revenue provision department evaluates the value of the property after renovation and also sets the selling price and predicts the revenue at the time of sale. This allows the revenue-generating department to provide users with information that enables them to comprehensively evaluate the profitability of renovations and make optimal investment decisions.

[0034] The generation unit can analyze rental market trends using generation AI and generate renovation plans. For example, the generation unit can use generation AI to analyze rental market trends and generate renovation plans. For example, the generation AI can analyze local rental market data and propose the optimal renovation plan. The generation unit can also use generation AI to generate renovation plans based on rental market trends. For example, the generation AI can predict future rental market trends based on past rental data and generate renovation plans based on that. Furthermore, the generation unit can use generation AI to generate renovation plans that respond to fluctuations in rental market trends. For example, the generation AI can analyze data on fluctuations in rental market trends and propose the optimal renovation plan. In this way, the generation unit can provide renovation plans based on rental market trends by analyzing rental market trends using generation AI and generating renovation plans.

[0035] The generation unit can generate business plans that fit the budget using generation AI. For example, the generation unit can use generation AI to generate business plans that fit the budget. For example, the generation AI can analyze the user's budget data and propose the optimal business plan. The generation unit can also use generation AI to generate business plans based on the budget. For example, the generation AI can predict future budgets based on past budget data and generate a business plan based on that. Furthermore, the generation unit can use generation AI to generate business plans that respond to budget fluctuations. For example, the generation AI can analyze budget fluctuation data and propose the optimal business plan. In this way, the generation unit can provide budget-based business plans by generating business plans that fit the budget using generation AI.

[0036] The service provider can provide the generated renovation plan with images and audio guides. For example, a living room renovation plan or a kitchen remodeling plan can be provided with images and audio guides. The service provider can also provide the generated renovation plan visually and aurally. For example, the service provider can display a 3D model of the renovation plan and explain it with an audio guide. Furthermore, the service provider can provide the generated renovation plan interactively. For example, the service provider can provide an interface that allows the user to interact with and review the renovation plan. This allows the service provider to provide the generated renovation plan with images and audio guides, enabling users to understand the renovation plan visually and aurally.

[0037] The service provider can provide the generated business plan with images and audio guides. For example, the service provider can provide a detailed explanation of the business plan and revenue forecasts with images and audio guides. The service provider can also provide the generated business plan visually and aurally. For example, the service provider can display graphs and charts of the business plan and explain them with audio guides. Furthermore, the service provider can provide the generated business plan interactively. For example, the service provider can provide an interface that allows users to interact with and review the business plan. This enables users to understand the business plan visually and aurally by providing the generated business plan with images and audio guides.

[0038] The revenue-generating department can provide information on rental income and the payback period after renovation. For example, it can provide information on rental income and the payback period after renovation. The revenue-generating department can also evaluate the profitability after renovation. For example, it can compare the rental income and investment amount after renovation to evaluate profitability. Furthermore, the revenue-generating department can provide revenue forecasts after renovation. For example, it can provide forecast data on rental income after renovation. This allows the revenue-generating department to consider profitability by providing information on rental income and the payback period after renovation.

[0039] The reception desk can analyze a user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). Furthermore, the reception desk can predict and suggest input methods that the user will use during specific time periods based on their past input history. In addition, the reception desk can analyze a user's past input history and select the most efficient input method. This allows the reception desk to efficiently collect information by analyzing the user's past input history and selecting the optimal input method.

[0040] The reception unit can filter input information based on the user's current projects and areas of interest. For example, it can prioritize information related to the user's current projects. It can also filter and retrieve highly relevant information based on the user's areas of interest. Furthermore, it can appropriately filter necessary information according to the progress of the user's projects. This allows the reception unit to efficiently collect highly relevant information by filtering it based on the user's current projects and areas of interest.

[0041] The reception unit can prioritize retrieving highly relevant information by considering the user's geographical location when acquiring input information. For example, the reception unit can prioritize retrieving information related to the user's current location. Furthermore, the reception unit can filter and retrieve highly relevant information based on the user's geographical location. In addition, the reception unit can update the user's location information in real time to retrieve the most relevant information. As a result, the reception unit can efficiently retrieve highly relevant information by considering the user's geographical location.

[0042] The reception desk can analyze the user's social media activity when acquiring input information and obtain relevant information. For example, the reception desk can analyze the content of the user's social media posts and obtain relevant information. It can also analyze the activities of the user's social media followers and friends and obtain relevant information. Furthermore, the reception desk can analyze the user's social media trends and obtain optimal information. As a result, the reception desk can efficiently acquire highly relevant information by analyzing the user's social media activity.

[0043] The generation unit can adjust the level of detail in the generated plan based on the importance of rental market rates and budget during the generation process. For example, in areas with high rental market rates, the generation unit can generate a detailed renovation plan. Furthermore, if the budget is limited, the generation unit can generate a cost-effective plan. In addition, the generation unit can generate the optimal plan by considering the balance between rental market rates and budget. Thus, the generation unit can provide the best plan by adjusting the level of detail based on the importance of rental market rates and budget.

[0044] The generation unit can apply different generation algorithms depending on the renovation category during the generation process. For example, it can apply a kitchen-specific generation algorithm for kitchen renovations, a bathroom-specific generation algorithm for bathroom renovations, and a living room-specific generation algorithm for living room renovations. By applying different generation algorithms according to the renovation category, the generation unit can provide the optimal renovation plan.

[0045] The generation unit can prioritize generated plans based on rental market rates and budget submission deadlines. For example, in areas with high rental market rates, the generation unit can prioritize generating renovation plans. Furthermore, if the budget submission deadline is approaching, the generation unit can quickly generate plans. In addition, the generation unit can generate the optimal plan by considering rental market rates and budget submission deadlines. This allows the generation unit to provide the best possible plan by prioritizing plans based on rental market rates and budget submission deadlines.

[0046] The generation unit can adjust the order of generated plans based on the relevance of the renovations during the generation process. For example, if kitchen renovation is prioritized, the generation unit can generate a kitchen plan first. If bathroom renovation is highly relevant, the generation unit can generate a bathroom plan next. Furthermore, if living room renovation is highly relevant, the generation unit can generate a living room plan last. In this way, the generation unit can provide plans in the optimal order by adjusting the order of plans based on the relevance of the renovations.

[0047] The service provider can select the optimal display method by referring to the user's past operation history at the time of delivery. For example, the service provider can prioritize providing display methods that the user has used in the past. Furthermore, the service provider can select the optimal display method based on the user's past operation history. In addition, the service provider can analyze the user's past operation history and provide the most efficient display method. Thus, the service provider can provide the optimal display method by referring to the user's past operation history.

[0048] The service provider can customize the displayed content based on the user's current projects and areas of interest at the time of delivery. For example, the service provider can provide content related to the user's current ongoing projects. Furthermore, the service provider can provide highly relevant content based on the user's areas of interest. In addition, the service provider can customize the displayed content according to the progress of the user's projects. This allows the service provider to provide highly relevant information by customizing the displayed content based on the user's current projects and areas of interest.

[0049] The service provider can select the optimal display method at the time of delivery, taking into account the user's geographical location information. For example, the service provider can provide a display method related to the user's current location. Furthermore, the service provider can provide a highly relevant display method based on the user's geographical location information. In addition, the service provider can update the user's location information in real time and provide the optimal display method. This allows the service provider to provide a highly relevant display method by considering the user's geographical location information.

[0050] The service provider can analyze the user's social media activity at the time of delivery and provide relevant plans. For example, the service provider can analyze the content of the user's social media posts and provide relevant plans. Furthermore, the service provider can analyze the activity of the user's social media followers and friends and provide relevant plans. In addition, the service provider can analyze the user's social media trends and provide the optimal plan. In this way, the service provider can provide highly relevant plans by analyzing the user's social media activity.

[0051] The revenue generation department can select the optimal revenue plan by referring to the user's past revenue history when providing a revenue plan. For example, the revenue generation department can propose the optimal revenue plan based on the user's past revenue history. Furthermore, the revenue generation department can prioritize providing highly profitable plans based on the user's past revenue history. In addition, the revenue generation department can analyze the user's past revenue history and select the most efficient revenue plan. In this way, the revenue generation department can provide the optimal revenue plan by referring to the user's past revenue history.

[0052] The revenue generation department can customize revenue plans based on the user's current projects and areas of interest when providing them. For example, the revenue generation department can provide revenue plans related to projects the user is currently working on. Furthermore, the revenue generation department can provide highly relevant revenue plans based on the user's areas of interest. In addition, the revenue generation department can customize revenue plans according to the progress of the user's projects. This allows the revenue generation department to provide highly relevant revenue plans by customizing them based on the user's current projects and areas of interest.

[0053] The revenue generation department can select the optimal revenue plan when providing one, taking into account the user's geographical location. For example, the revenue generation department can provide a revenue plan related to the user's current location. Furthermore, the revenue generation department can provide highly relevant revenue plans based on the user's geographical location. In addition, the revenue generation department can update the user's location information in real time and provide the optimal revenue plan. This allows the revenue generation department to provide highly relevant revenue plans by considering the user's geographical location.

[0054] The revenue generation department can analyze users' social media activity and provide relevant revenue plans when offering them. For example, it can analyze the content of users' social media posts and provide relevant revenue plans. It can also analyze the activity of users' social media followers and friends and provide relevant revenue plans. Furthermore, it can analyze users' social media trends and provide optimal revenue plans. In this way, the revenue generation department can provide highly relevant revenue plans by analyzing users' social media activity.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The generation unit can analyze rental market trends using generation AI and generate renovation plans. For example, the generation AI can analyze local rental market data and propose the optimal renovation plan. Furthermore, the generation AI can predict future rental market trends based on past rental data and generate renovation plans accordingly. In addition, the generation AI can analyze fluctuations in rental market trends and propose the optimal renovation plan. Thus, by analyzing rental market trends using generation AI and generating renovation plans, the generation unit can provide renovation plans based on rental market trends.

[0057] The generation unit can generate business plans tailored to the budget using generation AI. For example, the generation AI can analyze the user's budget data and propose the optimal business plan. Furthermore, the generation AI can predict future budgets based on past budget data and generate business plans accordingly. It can also analyze budget fluctuation data and propose the optimal business plan. As a result, the generation unit can provide budget-based business plans by generating them using generation AI.

[0058] The service provider can provide the generated renovation plan with images and audio guides. For example, a living room renovation plan or a kitchen remodeling plan can be provided with images and audio guides. The service provider can also provide the generated renovation plan visually and aurally. For example, the service provider can display a 3D model of the renovation plan and explain it with an audio guide. Furthermore, the service provider can provide the generated renovation plan interactively. For example, the service provider can provide an interface that allows the user to interact with and review the renovation plan. This allows the service provider to provide the generated renovation plan with images and audio guides, enabling users to understand the renovation plan visually and aurally.

[0059] The service provider can provide the generated business plan with images and audio guides. For example, detailed explanations of the business plan and revenue forecasts can be provided with images and audio guides. The service provider can also provide the generated business plan visually and aurally. For example, the service provider can display graphs and charts of the business plan and explain them with audio guides. Furthermore, the service provider can provide the generated business plan interactively. For example, the service provider can provide an interface that allows users to interact with and review the business plan. This allows the service provider to provide the generated business plan visually and aurally, by providing it with images and audio guides.

[0060] The revenue-generating department can provide information on rental income and the payback period after renovation. For example, it can present information on rental income and the payback period after renovation. The revenue-generating department can also evaluate the profitability after renovation. For example, it can compare rental income and investment amount after renovation to evaluate profitability. Furthermore, the revenue-generating department can also provide revenue forecasts after renovation. For example, it can provide forecast data on rental income after renovation. This allows the revenue-generating department to consider profitability by providing information on rental income and the payback period after renovation.

[0061] The reception desk can analyze a user's past input history and select the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). Furthermore, the reception desk can predict and suggest input methods that a user will use during specific time periods based on their past input history. In addition, the reception desk can analyze a user's past input history and select the most efficient input method. This allows the reception desk to efficiently collect information by analyzing a user's past input history and selecting the optimal input method.

[0062] The reception unit can filter input information based on the user's current projects and areas of interest. For example, it can prioritize information related to the user's current projects. Furthermore, the reception unit can filter and retrieve highly relevant information based on the user's areas of interest. In addition, the reception unit can appropriately filter necessary information according to the progress of the user's projects. This allows the reception unit to efficiently collect highly relevant information by filtering it based on the user's current projects and areas of interest.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk inputs the area, year of construction, number of rooms, and images or condition. For example, the user can input basic information such as area, year of construction, and number of rooms, as well as images and condition, into the input format. Step 2: The generation unit analyzes the information entered by the reception unit and generates renovation and business plans that match the rental market and budget. For example, the generation AI can be used to generate multiple renovation and business plans that match the rental market and budget. Step 3: The provisioning unit provides the renovation and business plans generated by the generation unit with images and audio guides. For example, living room renovation plans and kitchen remodeling plans are provided with images and audio guides. Step 4: The revenue-generating department provides a revenue plan based on the renovation and business plan provided by the revenue-generating department. For example, they can provide rental income after renovation and the payback period.

[0065] (Example of form 2) The system according to an embodiment of the present invention utilizes generative AI to simultaneously solve three problems in urban areas of Japan: the rapidly increasing problem of vacant houses, the rising price of new housing due to soaring material costs and a shortage of skilled workers, and the lack of suitable used housing for young families and urban seniors to rent. This system provides two services as a business-to-business negotiation tool: renovation considerations and revenue plans. First, the user inputs basic information such as area, year of construction, and number of rooms, as well as images and condition, into an input format. Next, the generative AI provides multiple renovation and business plans tailored to rental market rates and budgets, with image and audio guidance. This system makes it easier for vacant house owners to consider renovations and revenue streams, and will lead to an increase in rental properties and a reduction in vacant houses, while also aiming to avoid increased tax burdens after legal revisions. Furthermore, young families will be able to obtain housing at an affordable price, urban seniors will have access to an environment for trial relocation for their second life, and the mobility of people will increase. In addition, the maintenance of local culture and economic revitalization will be promoted by people moving in from outside. For example, a user inputs basic information such as area, age of construction, and number of rooms, along with images and condition, into an input format. For instance, information on a 20-year-old 3LDK vacant house is entered. This information is then input into a generating AI. Next, the generating AI analyzes the input information and provides multiple renovation and business plans based on rental market rates and budget, using images and audio guidance. For example, based on rental market rates, renovation plans for the living room and kitchen are suggested. This allows vacant house owners to understand concrete renovation plans. Furthermore, the generating AI also provides a revenue plan. For example, rental income after renovation and the investment payback period are presented. This allows vacant house owners to consider the profitability of the renovation. This system makes it easier for vacant house owners to consider renovations and revenue streams, and with an eye on avoiding increased tax burdens after legal revisions, it will lead to an increase in rental properties and a reduction in vacant houses. In addition, young families will be able to obtain housing at an affordable price, urban seniors will have access to an environment for trial relocation to a second life, and the mobility of people will increase. Furthermore, the presence of newcomers from outside the area can help maintain local culture and revitalize the economy.This system will make it easier for vacant property owners to renovate and consider profitability, and will lead to an increase in rental properties and a reduction in vacant houses, while also aiming to avoid increased tax burdens after the legal reforms. In addition, young families will be able to obtain housing at an affordable price, and urban seniors will have access to an environment for trying out a second life through relocation, promoting the mobility of people. Furthermore, it will help maintain local culture and revitalize the economy through people moving in from outside.

[0066] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a revenue provision unit. The reception unit receives input for area, age of construction, number of rooms, and images or condition. The reception unit allows users to input basic information such as area, age of construction, and number of rooms, as well as images and condition, into an input format. For example, information on a 20-year-old 3LDK vacant house can be input. The generation unit analyzes the information input by the reception unit and generates renovation and business plans according to rental market rates and budget. The generation unit can generate multiple renovation and business plans according to rental market rates and budget, for example, using generation AI. For example, a living room renovation plan and a kitchen remodeling plan may be proposed based on rental market rates. The provision unit provides the renovation and business plans generated by the generation unit with images and audio guides. The provision unit can provide the generated renovation plans with images and audio guides. For example, a living room renovation plan and a kitchen remodeling plan may be provided with images and audio guides. The revenue provision unit provides a revenue plan based on the renovation and business plan provided by the provision unit. The revenue provision unit can, for example, provide rental income after renovation and the investment recovery period. For example, rental income after renovation and the investment recovery period are presented. As a result, the system according to the embodiment can input basic information such as area, year of construction, and number of rooms, as well as images and condition, generate multiple renovation and business plans according to rental market rates and budget, and provide them with images and audio guides to provide a revenue plan.

[0067] The reception desk inputs information such as area, year of construction, number of rooms, and images or condition. For example, the reception desk allows users to input basic information such as area, year of construction, and number of rooms, as well as images and condition, into an input format. Specifically, users input detailed property information through a dedicated interface. For example, they can input information about a vacant 3LDK house that is 20 years old. Users input the property area in square meters and the year of construction in years. They can also input the number of rooms, such as living room, dining room, kitchen, bedroom, and bathroom. Furthermore, it is possible to upload images and photos showing the condition of the property, allowing users to visually confirm the current state of the property. For example, uploading photos of the living room or images of the kitchen allows for a detailed understanding of the interior condition of the property. The reception desk centrally manages this information and stores it as data necessary for subsequent processing. The information entered by users is stored in a database and made accessible to the generation and provision departments. This allows the reception desk to provide an environment in which users can easily and quickly input property information, improving the overall efficiency of the system.

[0068] The generation unit analyzes the information entered by the reception unit and generates renovation and business plans that match rental market rates and budgets. For example, the generation unit can use generation AI to generate multiple renovation and business plans that match rental market rates and budgets. Specifically, the generation AI compares the entered property information with a local rental market rate database to calculate an appropriate rental market rate. Next, it generates renovation plans that match the user's budget. For example, living room renovation plans and kitchen remodeling plans may be proposed. The generation AI has learned from past renovation cases and market trends, and can propose the optimal plan. For example, a living room renovation plan may include changing the wallpaper, replacing the flooring, and installing lighting. A kitchen remodeling plan may include replacing the sink and stove, and adding storage space. The generation unit generates multiple such plans to provide the user with choices. Furthermore, in addition to renovation plans, the generation unit also generates business plans. For example, this includes setting rent when leasing a property or setting a sale price when selling a property. This allows the generation unit to provide the user with the optimal plan to maximize the value of the property.

[0069] The service provider provides the renovation and business plans generated by the generation unit through images and audio guides. For example, the service provider can provide the generated renovation plan through images and audio guides. Specifically, the service provider generates images using 3D models and computer graphics to display the generated renovation plan in an easy-to-understand visual way. For example, in a living room renovation plan, the new wallpaper, flooring, and lighting arrangements are displayed using 3D models, allowing the user to concretely grasp the image after the renovation. In a kitchen renovation plan, the new sink, stove, and storage space arrangements are displayed using computer graphics, allowing the user to visually confirm the image of the renovated kitchen. Furthermore, the service provider uses audio guides to explain the details of the renovation plan. For example, in a living room renovation plan, the reasons and effects of changing the wallpaper and replacing the flooring are explained in audio, making it easier for the user to understand the plan. In a kitchen renovation plan, the benefits of replacing the sink and stove, and the effects of increasing storage space are explained in audio. This allows the service provider to enable users to visually and aurally understand the contents of the renovation plan and assist them in selecting a plan.

[0070] The revenue provision department provides revenue plans based on the renovation and business plans provided by the provision department. For example, the revenue provision department can provide rental income after renovation and the investment recovery period. Specifically, the revenue provision department calculates rental income after renovation based on the renovation plans and business plans generated by the generation department. For example, it predicts rental income after renovating the living room based on the living room renovation plan. It also predicts rental income after renovating the kitchen based on the kitchen renovation plan. Furthermore, the revenue provision department compares the renovation costs with the rental income and calculates the investment recovery period. For example, if the cost of renovating the living room is 1 million yen and the rental income after renovation is 100,000 yen per month, the investment recovery period is 10 months. The revenue provision department provides this information to the user and provides indicators for evaluating the profitability of the renovation. Furthermore, the revenue provision department evaluates the value of the property after renovation and also sets the selling price and predicts the revenue at the time of sale. This allows the revenue-generating department to provide users with information that enables them to comprehensively evaluate the profitability of renovations and make optimal investment decisions.

[0071] The generation unit can analyze rental market trends using generation AI and generate renovation plans. For example, the generation unit can use generation AI to analyze rental market trends and generate renovation plans. For example, the generation AI can analyze local rental market data and propose the optimal renovation plan. The generation unit can also use generation AI to generate renovation plans based on rental market trends. For example, the generation AI can predict future rental market trends based on past rental data and generate renovation plans based on that. Furthermore, the generation unit can use generation AI to generate renovation plans that respond to fluctuations in rental market trends. For example, the generation AI can analyze data on fluctuations in rental market trends and propose the optimal renovation plan. In this way, the generation unit can provide renovation plans based on rental market trends by analyzing rental market trends using generation AI and generating renovation plans.

[0072] The generation unit can generate business plans that fit the budget using generation AI. For example, the generation unit can use generation AI to generate business plans that fit the budget. For example, the generation AI can analyze the user's budget data and propose the optimal business plan. The generation unit can also use generation AI to generate business plans based on the budget. For example, the generation AI can predict future budgets based on past budget data and generate a business plan based on that. Furthermore, the generation unit can use generation AI to generate business plans that respond to budget fluctuations. For example, the generation AI can analyze budget fluctuation data and propose the optimal business plan. In this way, the generation unit can provide budget-based business plans by generating business plans that fit the budget using generation AI.

[0073] The service provider can provide the generated renovation plan with images and audio guides. For example, a living room renovation plan or a kitchen remodeling plan can be provided with images and audio guides. The service provider can also provide the generated renovation plan visually and aurally. For example, the service provider can display a 3D model of the renovation plan and explain it with an audio guide. Furthermore, the service provider can provide the generated renovation plan interactively. For example, the service provider can provide an interface that allows the user to interact with and review the renovation plan. This allows the service provider to provide the generated renovation plan with images and audio guides, enabling users to understand the renovation plan visually and aurally.

[0074] The service provider can provide the generated business plan with images and audio guides. For example, the service provider can provide a detailed explanation of the business plan and revenue forecasts with images and audio guides. The service provider can also provide the generated business plan visually and aurally. For example, the service provider can display graphs and charts of the business plan and explain them with audio guides. Furthermore, the service provider can provide the generated business plan interactively. For example, the service provider can provide an interface that allows users to interact with and review the business plan. This enables users to understand the business plan visually and aurally by providing the generated business plan with images and audio guides.

[0075] The revenue-generating department can provide information on rental income and the payback period after renovation. For example, it can provide information on rental income and the payback period after renovation. The revenue-generating department can also evaluate the profitability after renovation. For example, it can compare the rental income and investment amount after renovation to evaluate profitability. Furthermore, the revenue-generating department can provide revenue forecasts after renovation. For example, it can provide forecast data on rental income after renovation. This allows the revenue-generating department to consider profitability by providing information on rental income and the payback period after renovation.

[0076] The reception system can estimate the user's emotions and adjust the timing of acquiring input information based on the estimated emotions. For example, if the user is stressed, the reception system can delay acquiring input information and wait until the user is relaxed. Conversely, if the user is relaxed, the reception system can accelerate the acquisition of input information to collect it smoothly. Furthermore, if the user is in a hurry, the reception system can optimize the timing of acquiring input information to collect it quickly. In this way, by adjusting the timing of acquiring input information based on the user's emotions, the reception system can reduce user stress and collect information smoothly. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The reception desk can analyze a user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). Furthermore, the reception desk can predict and suggest input methods that the user will use during specific time periods based on their past input history. In addition, the reception desk can analyze a user's past input history and select the most efficient input method. This allows the reception desk to efficiently collect information by analyzing the user's past input history and selecting the optimal input method.

[0078] The reception unit can filter input information based on the user's current projects and areas of interest. For example, it can prioritize information related to the user's current projects. It can also filter and retrieve highly relevant information based on the user's areas of interest. Furthermore, it can appropriately filter necessary information according to the progress of the user's projects. This allows the reception unit to efficiently collect highly relevant information by filtering it based on the user's current projects and areas of interest.

[0079] The reception desk can estimate the user's emotions and prioritize the information to retrieve based on those emotions. For example, if the user is stressed, the reception desk can prioritize retrieving high-priority information and postpone less important information. If the user is relaxed, the reception desk can retrieve all information equally. Furthermore, if the user is in a hurry, the reception desk can prioritize retrieving the most important information. In this way, the reception desk can prioritize the retrieval of important information by prioritizing information based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The reception unit can prioritize retrieving highly relevant information by considering the user's geographical location when acquiring input information. For example, the reception unit can prioritize retrieving information related to the user's current location. Furthermore, the reception unit can filter and retrieve highly relevant information based on the user's geographical location. In addition, the reception unit can update the user's location information in real time to retrieve the most relevant information. As a result, the reception unit can efficiently retrieve highly relevant information by considering the user's geographical location.

[0081] The reception desk can analyze the user's social media activity when acquiring input information and obtain relevant information. For example, the reception desk can analyze the content of the user's social media posts and obtain relevant information. It can also analyze the activities of the user's social media followers and friends and obtain relevant information. Furthermore, the reception desk can analyze the user's social media trends and obtain optimal information. As a result, the reception desk can efficiently acquire highly relevant information by analyzing the user's social media activity.

[0082] The generation unit can estimate the user's emotions and adjust the presentation of the generated plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a plan with detailed explanations. If the user is in a hurry, the generation unit can generate a concise plan that gets straight to the point. Furthermore, if the user is excited, the generation unit can generate a plan with visually stimulating effects. In this way, the generation unit can provide a plan that is easy for the user to understand by adjusting the presentation of the plan based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The generation unit can adjust the level of detail in the generated plan based on the importance of rental market rates and budget during the generation process. For example, in areas with high rental market rates, the generation unit can generate a detailed renovation plan. Furthermore, if the budget is limited, the generation unit can generate a cost-effective plan. In addition, the generation unit can generate the optimal plan by considering the balance between rental market rates and budget. Thus, the generation unit can provide the best plan by adjusting the level of detail based on the importance of rental market rates and budget.

[0084] The generation unit can apply different generation algorithms depending on the renovation category during the generation process. For example, it can apply a kitchen-specific generation algorithm for kitchen renovations, a bathroom-specific generation algorithm for bathroom renovations, and a living room-specific generation algorithm for living room renovations. By applying different generation algorithms according to the renovation category, the generation unit can provide the optimal renovation plan.

[0085] The generation unit can estimate the user's emotions and adjust the length of the generated plan based on those emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise plan. If the user is relaxed, the generation unit can generate a longer plan with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a plan with visually stimulating effects. In this way, the generation unit can provide the user with the optimal plan by adjusting the length of the plan based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. Generation AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The generation unit can prioritize generated plans based on rental market rates and budget submission deadlines. For example, in areas with high rental market rates, the generation unit can prioritize generating renovation plans. Furthermore, if the budget submission deadline is approaching, the generation unit can quickly generate plans. In addition, the generation unit can generate the optimal plan by considering rental market rates and budget submission deadlines. This allows the generation unit to provide the best possible plan by prioritizing plans based on rental market rates and budget submission deadlines.

[0087] The generation unit can adjust the order of generated plans based on the relevance of the renovations during the generation process. For example, if kitchen renovation is prioritized, the generation unit can generate a kitchen plan first. If bathroom renovation is highly relevant, the generation unit can generate a bathroom plan next. Furthermore, if living room renovation is highly relevant, the generation unit can generate a living room plan last. In this way, the generation unit can provide plans in the optimal order by adjusting the order of plans based on the relevance of the renovations.

[0088] The service provider can estimate the user's emotions and adjust how the plan is displayed based on those emotions. For example, if the user is stressed, the service provider can provide a simple and easy-to-read display. If the user is relaxed, the service provider can provide a display that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display. In this way, by adjusting how the plan is displayed based on the user's emotions, the service provider can provide a highly visual display for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The service provider can select the optimal display method by referring to the user's past operation history at the time of delivery. For example, the service provider can prioritize providing display methods that the user has used in the past. Furthermore, the service provider can select the optimal display method based on the user's past operation history. In addition, the service provider can analyze the user's past operation history and provide the most efficient display method. Thus, the service provider can provide the optimal display method by referring to the user's past operation history.

[0090] The service provider can customize the displayed content based on the user's current projects and areas of interest at the time of delivery. For example, the service provider can provide content related to the user's current ongoing projects. Furthermore, the service provider can provide highly relevant content based on the user's areas of interest. In addition, the service provider can customize the displayed content according to the progress of the user's projects. This allows the service provider to provide highly relevant information by customizing the displayed content based on the user's current projects and areas of interest.

[0091] The service provider can estimate the user's emotions and prioritize the plans offered based on those emotions. For example, if the user is stressed, the service provider can postpone less important plans and prioritize more important ones. If the user is relaxed, the service provider can offer all plans equally. Furthermore, if the user is in a hurry, the service provider can prioritize the most important plans. In this way, the service provider can prioritize important plans by prioritizing them based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The service provider can select the optimal display method at the time of delivery, taking into account the user's geographical location information. For example, the service provider can provide a display method related to the user's current location. Furthermore, the service provider can provide a highly relevant display method based on the user's geographical location information. In addition, the service provider can update the user's location information in real time and provide the optimal display method. This allows the service provider to provide a highly relevant display method by considering the user's geographical location information.

[0093] The service provider can analyze the user's social media activity at the time of delivery and provide relevant plans. For example, the service provider can analyze the content of the user's social media posts and provide relevant plans. Furthermore, the service provider can analyze the activity of the user's social media followers and friends and provide relevant plans. In addition, the service provider can analyze the user's social media trends and provide the optimal plan. In this way, the service provider can provide highly relevant plans by analyzing the user's social media activity.

[0094] The revenue generation unit can estimate the user's emotions and adjust how the revenue plan is displayed based on those emotions. For example, if the user is stressed, the revenue generation unit can provide a simple and easy-to-read display. If the user is relaxed, the revenue generation unit can provide a display that includes detailed information. Furthermore, if the user is in a hurry, the revenue generation unit can provide a concise display. In this way, the revenue generation unit can provide a highly visual display for the user by adjusting how the revenue plan is displayed based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The revenue generation department can select the optimal revenue plan by referring to the user's past revenue history when providing a revenue plan. For example, the revenue generation department can propose the optimal revenue plan based on the user's past revenue history. Furthermore, the revenue generation department can prioritize providing highly profitable plans based on the user's past revenue history. In addition, the revenue generation department can analyze the user's past revenue history and select the most efficient revenue plan. In this way, the revenue generation department can provide the optimal revenue plan by referring to the user's past revenue history.

[0096] The revenue generation department can customize revenue plans based on the user's current projects and areas of interest when providing them. For example, the revenue generation department can provide revenue plans related to projects the user is currently working on. Furthermore, the revenue generation department can provide highly relevant revenue plans based on the user's areas of interest. In addition, the revenue generation department can customize revenue plans according to the progress of the user's projects. This allows the revenue generation department to provide highly relevant revenue plans by customizing them based on the user's current projects and areas of interest.

[0097] The revenue delivery unit can estimate the user's emotions and prioritize revenue plans based on those emotions. For example, if a user is stressed, the revenue delivery unit can postpone less important revenue plans and prioritize more important ones. If a user is relaxed, the revenue delivery unit can provide all revenue plans equally. Furthermore, if a user is in a hurry, the revenue delivery unit can prioritize the most important revenue plans. In this way, the revenue delivery unit can prioritize important revenue plans by prioritizing them based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The revenue generation department can select the optimal revenue plan when providing one, taking into account the user's geographical location. For example, the revenue generation department can provide a revenue plan related to the user's current location. Furthermore, the revenue generation department can provide highly relevant revenue plans based on the user's geographical location. In addition, the revenue generation department can update the user's location information in real time and provide the optimal revenue plan. This allows the revenue generation department to provide highly relevant revenue plans by considering the user's geographical location.

[0099] The revenue generation department can analyze users' social media activity and provide relevant revenue plans when offering them. For example, it can analyze the content of users' social media posts and provide relevant revenue plans. It can also analyze the activity of users' social media followers and friends and provide relevant revenue plans. Furthermore, it can analyze users' social media trends and provide optimal revenue plans. In this way, the revenue generation department can provide highly relevant revenue plans by analyzing users' social media activity.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception desk can estimate the user's emotions and adjust the timing of information acquisition based on those emotions. For example, if the user is stressed, the timing of information acquisition can be delayed, waiting until the user is relaxed. If the user is relaxed, the timing of information acquisition can be sped up, allowing for smoother information gathering. Furthermore, if the user is in a hurry, the timing of information acquisition can be optimized to quickly gather information. In this way, the reception desk can reduce user stress and gather information smoothly by adjusting the timing of information acquisition based on the user's emotions.

[0102] The generation unit can analyze rental market trends using generation AI and generate renovation plans. For example, the generation AI can analyze local rental market data and propose the optimal renovation plan. Furthermore, the generation AI can predict future rental market trends based on past rental data and generate renovation plans accordingly. In addition, the generation AI can analyze fluctuations in rental market trends and propose the optimal renovation plan. Thus, by analyzing rental market trends using generation AI and generating renovation plans, the generation unit can provide renovation plans based on rental market trends.

[0103] The generation unit can generate business plans tailored to the budget using generation AI. For example, the generation AI can analyze the user's budget data and propose the optimal business plan. Furthermore, the generation AI can predict future budgets based on past budget data and generate business plans accordingly. It can also analyze budget fluctuation data and propose the optimal business plan. As a result, the generation unit can provide budget-based business plans by generating them using generation AI.

[0104] The service provider can provide the generated renovation plan with images and audio guides. For example, a living room renovation plan or a kitchen remodeling plan can be provided with images and audio guides. The service provider can also provide the generated renovation plan visually and aurally. For example, the service provider can display a 3D model of the renovation plan and explain it with an audio guide. Furthermore, the service provider can provide the generated renovation plan interactively. For example, the service provider can provide an interface that allows the user to interact with and review the renovation plan. This allows the service provider to provide the generated renovation plan with images and audio guides, enabling users to understand the renovation plan visually and aurally.

[0105] The service provider can provide the generated business plan with images and audio guides. For example, detailed explanations of the business plan and revenue forecasts can be provided with images and audio guides. The service provider can also provide the generated business plan visually and aurally. For example, the service provider can display graphs and charts of the business plan and explain them with audio guides. Furthermore, the service provider can provide the generated business plan interactively. For example, the service provider can provide an interface that allows users to interact with and review the business plan. This allows the service provider to provide the generated business plan visually and aurally, by providing it with images and audio guides.

[0106] The revenue-generating department can provide information on rental income and the payback period after renovation. For example, it can present information on rental income and the payback period after renovation. The revenue-generating department can also evaluate the profitability after renovation. For example, it can compare rental income and investment amount after renovation to evaluate profitability. Furthermore, the revenue-generating department can also provide revenue forecasts after renovation. For example, it can provide forecast data on rental income after renovation. This allows the revenue-generating department to consider profitability by providing information on rental income and the payback period after renovation.

[0107] The reception desk can estimate the user's emotions and adjust the timing of information acquisition based on those emotions. For example, if the user is stressed, the timing of information acquisition can be delayed, waiting until the user is relaxed. If the user is relaxed, the timing of information acquisition can be sped up, allowing for smoother information gathering. Furthermore, if the user is in a hurry, the timing of information acquisition can be optimized to quickly gather information. In this way, the reception desk can reduce user stress and gather information smoothly by adjusting the timing of information acquisition based on the user's emotions.

[0108] The reception desk can analyze a user's past input history and select the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). Furthermore, the reception desk can predict and suggest input methods that a user will use during specific time periods based on their past input history. In addition, the reception desk can analyze a user's past input history and select the most efficient input method. This allows the reception desk to efficiently collect information by analyzing a user's past input history and selecting the optimal input method.

[0109] The reception unit can filter input information based on the user's current projects and areas of interest. For example, it can prioritize information related to the user's current projects. Furthermore, the reception unit can filter and retrieve highly relevant information based on the user's areas of interest. In addition, the reception unit can appropriately filter necessary information according to the progress of the user's projects. This allows the reception unit to efficiently collect highly relevant information by filtering it based on the user's current projects and areas of interest.

[0110] The reception desk can estimate the user's emotions and prioritize the information to retrieve based on those emotions. For example, if the user is stressed, less important information can be postponed, and more important information can be prioritized. If the user is relaxed, all information can be retrieved equally. Furthermore, if the user is in a hurry, the most important information can be prioritized. In this way, the reception desk can prioritize important information by determining the priority of information based on the user's emotions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk inputs the area, year of construction, number of rooms, and images or condition. For example, the user can input basic information such as area, year of construction, and number of rooms, as well as images and condition, into the input format. Step 2: The generation unit analyzes the information entered by the reception unit and generates renovation and business plans that match the rental market and budget. For example, the generation AI can be used to generate multiple renovation and business plans that match the rental market and budget. Step 3: The provisioning unit provides the renovation and business plans generated by the generation unit with images and audio guides. For example, living room renovation plans and kitchen remodeling plans are provided with images and audio guides. Step 4: The revenue-generating department provides a revenue plan based on the renovation and business plan provided by the revenue-generating department. For example, they can provide rental income after renovation and the payback period.

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0116] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and revenue provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user can input basic information such as area, year of construction, and number of rooms, as well as images and conditions, into an input format. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses generation AI to generate multiple renovation and business plans according to rental market rates and budget. The provision unit is implemented by, for example, the output device 40 of the smart device 14, which provides the generated renovation plan with images and audio guidance. The revenue provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides rental income and investment recovery period after renovation. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and revenue provision unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input basic information such as area, year of construction, and number of rooms, as well as images and conditions, by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses generation AI to generate multiple renovation and business plans according to rental market rates and budget. The provision unit is implemented by, for example, the speaker 240 of the smart glasses 214, which provides the generated renovation plans with voice guidance. The revenue provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides rental income and investment recovery period after renovation. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and revenue provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input basic information such as area, year of construction, and number of rooms, as well as images and conditions, by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses generation AI to generate multiple renovation and business plans according to rental market rates and budget. The provision unit is implemented by, for example, the display 343 of the headset terminal 314, which provides the generated renovation plans as images. The revenue provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides rental income and investment recovery period after renovation. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and revenue provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input basic information such as area, year of construction, and number of rooms, as well as images and conditions, by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates multiple renovation and business plans according to rental market rates and budget using generation AI. The provision unit is implemented by, for example, the speaker 240 of the robot 414, which provides the generated renovation plans with voice guidance. The revenue provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides rental income and investment recovery period after renovation. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0166] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A reception desk where you input area, year of construction, number of rooms, and images and condition, The generation unit analyzes the information entered by the reception unit and generates renovation and business plans that correspond to rental market rates and budgets. A providing unit that provides the renovation and business plans generated by the generation unit as images and audio guides, The system comprises a revenue provision unit that provides a revenue plan based on the renovation and business plan provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates rental market data and creates renovation plans. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​generates business plans that fit the budget. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The generated renovation plan is provided with images and audio guides. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The generated business plan is provided with images and audio guides. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned revenue-generating unit is, Provides rental income and investment recovery period after renovation. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of acquiring input information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When retrieving input information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When retrieving input information, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When acquiring input information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts how the generated plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the level of detail in the generation plan is adjusted based on rental market rates and the importance of the budget. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, different generation algorithms are applied depending on the renovation category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the generation plan based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the priority of generation plans is determined based on rental market rates and the timing of budget submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the order of the generation plan is adjusted based on the relevance of the renovations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the user's emotions and adjust how the plans we offer are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When delivered, the displayed content is customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the plans to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the optimal display method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and offer a relevant plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned revenue-generating unit is, It estimates user sentiment and adjusts how revenue plans are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned revenue-generating unit is, When providing revenue plans, the system selects the most suitable plan by referring to the user's past revenue history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned revenue-generating unit is, When providing a revenue plan, customize it based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned revenue-generating unit is, It estimates user sentiment and prioritizes revenue plans based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned revenue-generating unit is, When providing revenue plans, the optimal plan is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned revenue-generating unit is, When providing revenue plans, we analyze users' social media activity and provide relevant revenue plans. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where you input area, year of construction, number of rooms, and images and condition, The generation unit analyzes the information entered by the reception unit and generates renovation and business plans that correspond to rental market rates and budgets. A providing unit that provides the renovation and business plans generated by the generation unit as images and audio guides, The system comprises a revenue provision unit that provides a revenue plan based on the renovation and business plan provided by the aforementioned provision unit. A system characterized by the following features.

2. The generating unit is The AI ​​generates rental market data and creates renovation plans. The system according to feature 1.

3. The generating unit is The AI ​​generates business plans that fit the budget. The system according to feature 1.

4. The aforementioned supply unit is, The generated renovation plan is provided with images and audio guides. The system according to feature 1.

5. The aforementioned supply unit is, The generated business plan is provided with images and audio guides. The system according to feature 1.

6. The aforementioned revenue-generating unit is, Provides rental income and investment recovery period after renovation. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of acquiring input information based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

Images