System

Abstract

An object of a system according to an embodiment is to optimize a budget in a remodeling project.SOLUTION: A system according to an embodiment includes an expense analysis unit, a cost proposal unit, and a plan providing unit. An expenditure analysis part analyzes expenditure necessary for the project. A cost proposal part proposes cost saving on the basis of a result analyzed by the expenditure analysis part. The plan providing unit provides an efficient plan based on the content proposed by the cost proposing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately optimize budgets for renovation projects, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize the budget for a renovation project. [Means for solving the problem]

[0006] The system according to the embodiment includes an expenditure analysis unit, a cost proposal unit, and a plan provision unit. The expenditure analysis unit analyzes the expenditures required for the project. The cost proposal unit proposes cost savings based on the results of the analysis by the expenditure analysis unit. The plan provision unit provides an efficient plan based on the content proposed by the cost proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the budget for a renovation project. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The renovation project budget optimization system according to an embodiment of the present invention uses a generation AI to analyze the expenses required for the project, propose cost-saving measures, and provide an efficient plan, thereby improving the success rate of the project.

[0029] A renovation project budget optimization system according to an embodiment includes an expenditure analysis unit, a cost proposal unit, and a plan provision unit. The expenditure analysis unit analyzes the expenditures required for the project. For example, the generation AI calculates optimal budgets for each item, such as material costs, labor costs, and equipment costs, based on data from past renovation projects. The generation AI also analyzes expenditures based on prompts containing detailed project information. For example, the prompt is input, "Please tell me the material and labor costs required for renovating a kitchen." The cost proposal unit makes cost-saving proposals based on the results of the analysis by the expenditure analysis unit. For example, the generation AI suggests cheaper alternatives with equivalent quality. The generation AI also suggests reducing labor costs by improving work efficiency and optimizing the schedule. The generation AI makes proposals based on prompts requesting cost-saving proposals. For example, the prompt is input, "Please tell me how to save costs on kitchen renovations." The plan provision unit provides an efficient plan based on the content proposed by the cost proposal unit. For example, the generation AI improves the overall efficiency of the project by optimizing the timing of material purchases and the order of work. The generation AI also proposes a plan that maximizes effectiveness within the budget. The generative AI provides plans based on prompts that request an efficient plan. For example, input the prompt, "Please tell me an efficient plan for a kitchen renovation." This allows the renovation project budget optimization system to improve the success rate of the project. For example, achieving high-quality renovations within budget increases customer satisfaction. It also reduces project risks by preventing schedule delays and budget overruns.

[0030] The expenditure analysis unit can analyze data from past renovation projects and predict spending patterns under specific conditions. For example, the generation AI analyzes data from past renovation projects and predicts spending patterns under specific conditions. For example, based on kitchen renovation data from the past 10 years, it analyzes fluctuations in material and labor costs and predicts future spending patterns. The generation AI also analyzes spending trends based on data from renovation projects in specific regions and seasons and optimizes the budget. Furthermore, the generation AI analyzes spending patterns based on data from specific renovation contractors and material manufacturers and proposes the optimal budget. This makes it possible to optimize the budget by predicting spending patterns based on past data.

[0031] The expenditure analysis unit can monitor market price fluctuations in real time and suggest the optimal timing for purchases. For example, the generation AI in the expenditure analysis unit monitors market price fluctuations in real time and suggests the optimal timing for purchases. For example, it tracks price fluctuations in material costs in real time and suggests purchasing at the cheapest time. The generation AI can also predict when the price of specific materials or equipment will drop and suggest purchasing at that time. Furthermore, the generation AI can analyze seasonal price fluctuations and suggest purchasing materials at the most cost-effective time. This allows costs to be reduced by monitoring market price fluctuations in real time and suggesting the optimal timing for purchases.

[0032] The expenditure analysis unit can analyze market data for different regions and propose the optimal expenditure plan for each region. For example, the generation AI analyzes market data for different regions and proposes the optimal expenditure plan for each region. For example, it compares data on renovation projects in urban and rural areas and proposes the optimal budget for each. The generation AI also takes into account differences in material and labor costs in specific regions and proposes the optimal expenditure plan for that region. Furthermore, the generation AI analyzes the economic situation and market trends for each region and proposes the optimal budget. In this way, by analyzing market data for each region, it is possible to propose the optimal expenditure plan for each region.

[0033] The expenditure analysis unit can provide the optimal expenditure plan for each type of renovation. In the expenditure analysis unit, for example, the generation AI provides the optimal expenditure plan for each type of renovation. For example, the generation AI analyzes material costs and labor costs for kitchen renovations and proposes the optimal budget. The generation AI also analyzes equipment costs and construction costs for bathroom renovations and proposes the optimal budget. Furthermore, the generation AI analyzes design costs and furniture costs for living room renovations and proposes the optimal budget. This makes it possible to optimize the budget by providing the optimal expenditure plan for each type of renovation.

[0034] The cost proposal department can analyze past project data and make proposals based on successful examples of cost savings. For example, the generation AI analyzes past project data and makes proposals based on successful examples of cost savings. For example, it can propose cheaper alternative materials that were used in past renovation projects. The generation AI can also propose efficient work schedules to reduce labor costs. Furthermore, the generation AI can review contract terms with specific contractors and suppliers to reduce costs. This makes it possible to make cost-saving proposals based on past success stories, enabling effective cost reductions.

[0035] The cost proposal unit can analyze the life cycle cost of materials and propose the most cost-effective material in the long term. For example, the generation AI analyzes the life cycle cost of materials and proposes the most cost-effective material in the long term. For example, it selects materials that are highly durable and have low maintenance costs. The generation AI also selects equipment that is highly energy efficient and has low operating costs. Furthermore, the generation AI selects materials that are environmentally friendly and recyclable. This makes it possible to reduce costs in the long term by selecting materials that take life cycle costs into consideration.

[0036] The cost proposal unit can apply cost-saving methods from different industries to renovation projects to propose new ways of saving. For example, the generation AI applies cost-saving methods from different industries to renovation projects to propose new ways of saving. For example, the generation AI incorporates efficiency methods from the manufacturing industry into renovation work. The generation AI also applies project management methods from the IT industry to renovation projects. Furthermore, the generation AI applies inventory management methods from the logistics industry to material procurement. In this way, new cost-saving methods can be proposed by applying methods from different industries.

[0037] The cost proposal unit can suggest eco-friendly materials and methods, achieving cost savings that are environmentally friendly. For example, the cost proposal unit can have the generative AI suggest eco-friendly materials and methods, achieving cost savings that are environmentally friendly. For example, by using renewable materials, costs are reduced while also reducing the environmental burden. The generative AI can also reduce operating costs by introducing energy-efficient equipment. Furthermore, the generative AI can reduce costs by promoting the recycling and reuse of waste. This makes sustainable renovations possible by achieving cost savings that are environmentally friendly.

[0038] The plan providing unit can monitor the progress of the project in real time and adjust the plan as necessary. In the plan providing unit, for example, the generation AI monitors the progress of the project in real time and adjusts the plan as necessary. For example, if a work delay occurs, the schedule will be readjusted. In addition, if a budget overrun is predicted, the generation AI will propose alternative plans to reduce costs. Furthermore, in the event of a material shortage, the generation AI will quickly arrange for substitute materials. In this way, project efficiency is improved by monitoring the progress of the project in real time and adjusting the plan as necessary.

[0039] The plan providing unit can simulate multiple scenarios and select the most efficient plan. In the plan providing unit, for example, the generation AI simulates multiple scenarios and selects the most efficient plan. For example, it simulates different work sequences and schedules and proposes the optimal plan. The generation AI also simulates different combinations of materials and equipment and proposes the most cost-effective plan. Furthermore, the generation AI simulates different personnel allocations and work methods and proposes the most efficient plan. In this way, the most efficient plan can be selected by simulating multiple scenarios.

[0040] The plan providing unit can apply different project management methods to a renovation project and propose the optimal method. For example, the generation AI can apply different project management methods to a renovation project and propose the optimal method. For example, the generation AI can use an agile method to perform flexible schedule management. The generation AI can also use a waterfall method to perform step-by-step progress management. Furthermore, the generation AI can use a hybrid method to combine the advantages of agile and waterfall. This allows the optimal method to be proposed by applying different project management methods.

[0041] The plan providing unit proposes optimal resource allocation for each phase of the project, enabling efficient progress. For example, the generation AI in the plan providing unit proposes optimal resource allocation for each phase of the project, enabling efficient progress. For example, it places emphasis on design work in the early stages, streamlining construction work in the later stages. The generation AI also prioritizes resource allocation for important work, ensuring smooth overall progress. Furthermore, the generation AI monitors resource surpluses and shortages in real time and adjusts as necessary. This allows the efficient progress of the project to be achieved by proposing optimal resource allocation for each phase.

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

[0043] The renovation project budget optimization system can also be equipped with a progress monitoring unit that monitors the project's progress in real time and adjusts the plan as necessary. For example, if a work delay occurs, the schedule is readjusted. Also, if the budget is predicted to exceed the budget, alternative plans for cost reduction are proposed. Furthermore, if a material shortage occurs, substitute materials are quickly arranged. In this way, project efficiency can be improved by monitoring the project's progress in real time and adjusting the plan as necessary.

[0044] The renovation project budget optimization system can also include a management method application unit that applies different project management methods to the renovation project and proposes the optimal method. For example, an agile method can be used to perform flexible schedule management, or a waterfall method can be used to perform step-by-step progress management. Furthermore, a hybrid method can be used to combine the advantages of agile and waterfall. This allows the optimal method to be proposed by applying different project management methods.

[0045] The renovation project budget optimization system can also be equipped with a resource allocation section that proposes optimal resource allocation for each phase of the project, ensuring efficient progress. For example, it can prioritize design work in the early stages and streamline construction work in the later stages. It can also prioritize resource allocation for important tasks, ensuring smooth overall progress. It can also monitor resource surpluses and shortages in real time and adjust as necessary. This allows it to propose optimal resource allocation for each phase, ensuring efficient progress of the project.

[0046] The renovation project budget optimization system can also be equipped with an inter-industry application section that applies cost-saving methods from other industries to renovation projects and proposes new ways to save money. For example, efficiency methods from the manufacturing industry can be incorporated into renovation work. Project management methods from the IT industry can also be applied to renovation projects. Furthermore, inventory management methods from the logistics industry can be applied to material procurement. In this way, new cost-saving methods can be proposed by applying methods from different industries.

[0047] The renovation project budget optimization system can also be equipped with an eco-suggestion section that suggests eco-friendly materials and methods to achieve cost savings that are environmentally friendly. For example, using renewable materials reduces costs while mitigating the environmental impact. Also, introducing energy-efficient equipment reduces operating costs. Furthermore, promoting the recycling and reuse of waste materials reduces costs. This allows for sustainable renovations by achieving cost savings that are environmentally friendly.

[0048] The renovation project budget optimization system can also be equipped with a scenario simulation unit that simulates multiple scenarios and selects the most efficient plan. For example, it can simulate different work sequences and schedules and propose the optimal plan. It can also simulate different combinations of materials and equipment and propose the most cost-effective plan. It can also simulate different staffing arrangements and work methods and propose the most efficient plan. In this way, it is possible to select the most efficient plan by simulating multiple scenarios.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The expenditure analysis unit analyzes the expenditures required for the project. For example, the generation AI calculates the optimal budget for each item, such as material costs, labor costs, and equipment costs, based on data from past renovation projects. The generation AI also analyzes expenditures based on prompts containing detailed information about the project. For example, the prompt is input as, "Please tell me the cost of materials and labor required for renovating a kitchen." Step 2: The cost proposal unit makes cost-saving proposals based on the results of the analysis by the expenditure analysis unit. For example, the generation AI suggests cheaper alternatives with the same quality. It also suggests reducing labor costs by streamlining work and optimizing schedules. The generation AI makes proposals based on a prompt for cost-saving proposals. For example, the prompt is "Please tell me how to save costs on kitchen renovations." Step 3: The plan provider provides an efficient plan based on the content proposed by the cost proposal provider. For example, the generation AI improves the efficiency of the entire project by optimizing the timing of material purchases and the order of work. It also proposes a plan that maximizes effectiveness within the budget. The generation AI provides a plan based on a prompt that requests the provision of an efficient plan. For example, the prompt is input as "Please tell me an efficient plan for remodeling a kitchen."

[0051] (Example 2) The renovation project budget optimization system according to an embodiment of the present invention uses a generation AI to analyze the expenses required for the project, propose cost-saving measures, and provide an efficient plan, thereby improving the success rate of the project.

[0052] A renovation project budget optimization system according to an embodiment includes an expenditure analysis unit, a cost proposal unit, and a plan provision unit. The expenditure analysis unit analyzes the expenditures required for the project. For example, the generation AI calculates optimal budgets for each item, such as material costs, labor costs, and equipment costs, based on data from past renovation projects. The generation AI also analyzes expenditures based on prompts containing detailed project information. For example, the prompt is input, "Please tell me the material and labor costs required for renovating a kitchen." The cost proposal unit makes cost-saving proposals based on the results of the analysis by the expenditure analysis unit. For example, the generation AI suggests cheaper alternatives with equivalent quality. The generation AI also suggests reducing labor costs by improving work efficiency and optimizing the schedule. The generation AI makes proposals based on prompts requesting cost-saving proposals. For example, the prompt is input, "Please tell me how to save costs on kitchen renovations." The plan provision unit provides an efficient plan based on the content proposed by the cost proposal unit. For example, the generation AI improves the overall efficiency of the project by optimizing the timing of material purchases and the order of work. The generation AI also proposes a plan that maximizes effectiveness within the budget. The generative AI provides plans based on prompts that request an efficient plan. For example, input the prompt, "Please tell me an efficient plan for a kitchen renovation." This allows the renovation project budget optimization system to improve the success rate of the project. For example, achieving high-quality renovations within budget increases customer satisfaction. It also reduces project risks by preventing schedule delays and budget overruns.

[0053] The expenditure analysis unit can analyze data from past renovation projects and predict spending patterns under specific conditions. For example, the generation AI analyzes data from past renovation projects and predicts spending patterns under specific conditions. For example, based on kitchen renovation data from the past 10 years, it analyzes fluctuations in material and labor costs and predicts future spending patterns. The generation AI also analyzes spending trends based on data from renovation projects in specific regions and seasons and optimizes the budget. Furthermore, the generation AI analyzes spending patterns based on data from specific renovation contractors and material manufacturers and proposes the optimal budget. This makes it possible to optimize the budget by predicting spending patterns based on past data.

[0054] The expenditure analysis unit can monitor market price fluctuations in real time and suggest the optimal timing for purchases. For example, the generation AI in the expenditure analysis unit monitors market price fluctuations in real time and suggests the optimal timing for purchases. For example, it tracks price fluctuations in material costs in real time and suggests purchasing at the cheapest time. The generation AI can also predict when the price of specific materials or equipment will drop and suggest purchasing at that time. Furthermore, the generation AI can analyze seasonal price fluctuations and suggest purchasing materials at the most cost-effective time. This allows costs to be reduced by monitoring market price fluctuations in real time and suggesting the optimal timing for purchases.

[0055] The expenditure analysis unit can use the emotion estimation function to analyze the user's emotions regarding the budget and propose an expenditure plan that takes budget flexibility into consideration. The expenditure analysis unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the budget and proposes an expenditure plan that takes budget flexibility into consideration. For example, if the user is feeling stressed about the budget, a proposal is made to loosen the budget a little. Also, if the user has positive emotions about the budget, a plan that will achieve maximum effect within the budget is proposed. Furthermore, if the user is feeling anxious about the budget, a proposal is made to review the budget and give the user a sense of security. In this way, by proposing an expenditure plan that takes the user's emotions into consideration, user satisfaction is improved.

[0056] The expenditure analysis unit can analyze market data for different regions and propose the optimal expenditure plan for each region. For example, the generation AI analyzes market data for different regions and proposes the optimal expenditure plan for each region. For example, it compares data on renovation projects in urban and rural areas and proposes the optimal budget for each. The generation AI also takes into account differences in material and labor costs in specific regions and proposes the optimal expenditure plan for that region. Furthermore, the generation AI analyzes the economic situation and market trends for each region and proposes the optimal budget. In this way, by analyzing market data for each region, it is possible to propose the optimal expenditure plan for each region.

[0057] The expenditure analysis unit can provide the optimal expenditure plan for each type of renovation. In the expenditure analysis unit, for example, the generation AI provides the optimal expenditure plan for each type of renovation. For example, the generation AI analyzes material costs and labor costs for kitchen renovations and proposes the optimal budget. The generation AI also analyzes equipment costs and construction costs for bathroom renovations and proposes the optimal budget. Furthermore, the generation AI analyzes design costs and furniture costs for living room renovations and proposes the optimal budget. This makes it possible to optimize the budget by providing the optimal expenditure plan for each type of renovation.

[0058] The expenditure analysis unit can use the emotion estimation function to identify expenditure items that the user is most satisfied with and propose an expenditure plan that focuses on those items. The expenditure analysis unit, for example, uses the emotion estimation function to identify expenditure items that the user is most satisfied with and proposes an expenditure plan that focuses on those items. For example, if the user is very interested in design, a budget that focuses on design expenses is proposed. Also, if the user is very interested in functionality, a budget that focuses on equipment expenses is proposed. Furthermore, if the user is very interested in eco-friendly materials, a budget that focuses on eco-material expenses is proposed. In this way, by proposing an expenditure plan that focuses on the expenditure items that the user is most satisfied with, user satisfaction is improved.

[0059] The cost proposal department can analyze past project data and make proposals based on successful examples of cost savings. For example, the generation AI analyzes past project data and makes proposals based on successful examples of cost savings. For example, it can propose cheaper alternative materials that were used in past renovation projects. The generation AI can also propose efficient work schedules to reduce labor costs. Furthermore, the generation AI can review contract terms with specific contractors and suppliers to reduce costs. This makes it possible to make cost-saving proposals based on past success stories, enabling effective cost reductions.

[0060] The cost proposal unit can analyze the life cycle cost of materials and propose the most cost-effective material in the long term. For example, the generation AI analyzes the life cycle cost of materials and proposes the most cost-effective material in the long term. For example, it selects materials that are highly durable and have low maintenance costs. The generation AI also selects equipment that is highly energy efficient and has low operating costs. Furthermore, the generation AI selects materials that are environmentally friendly and recyclable. This makes it possible to reduce costs in the long term by selecting materials that take life cycle costs into consideration.

[0061] The cost proposal unit can use the emotion estimation function to propose a cost-saving method that causes the least stress to the user. The cost proposal unit, for example, uses the emotion estimation function to propose a cost-saving method that causes the least stress to the user. For example, the cost proposal unit proposes budget cuts that do not cause stress to the user. It also proposes alternative materials and work methods that are easy for the user to accept. Furthermore, it explains the reasons and benefits of cost reductions in a way that gives the user a sense of security. In this way, the cost proposal unit improves user satisfaction by proposing a cost-saving method that minimizes stress to the user.

[0062] The cost proposal unit can apply cost-saving methods from different industries to renovation projects to propose new ways of saving. For example, the generation AI applies cost-saving methods from different industries to renovation projects to propose new ways of saving. For example, the generation AI incorporates efficiency methods from the manufacturing industry into renovation work. The generation AI also applies project management methods from the IT industry to renovation projects. Furthermore, the generation AI applies inventory management methods from the logistics industry to material procurement. In this way, new cost-saving methods can be proposed by applying methods from different industries.

[0063] The cost proposal unit can suggest eco-friendly materials and methods, achieving cost savings that are environmentally friendly. For example, the cost proposal unit can have the generative AI suggest eco-friendly materials and methods, achieving cost savings that are environmentally friendly. For example, by using renewable materials, costs are reduced while also reducing the environmental burden. The generative AI can also reduce operating costs by introducing energy-efficient equipment. Furthermore, the generative AI can reduce costs by promoting the recycling and reuse of waste. This makes sustainable renovations possible by achieving cost savings that are environmentally friendly.

[0064] The cost proposal unit can use the emotion estimation function to identify the cost-saving method that will evoke the most positive emotions in the user and propose that method. For example, the cost proposal unit uses the emotion estimation function to identify the cost-saving method that will evoke the most positive emotions in the user and proposes that method. For example, it proposes a cost-saving method that will give the user a sense of satisfaction. It also explains the reasons and benefits of the cost-saving method in a way that is easy for the user to accept. It also proposes a cost-saving method that will give the user a sense of security. In this way, by proposing a cost-saving method that will evoke positive emotions in the user, user satisfaction is improved.

[0065] The plan providing unit can monitor the progress of the project in real time and adjust the plan as necessary. In the plan providing unit, for example, the generation AI monitors the progress of the project in real time and adjusts the plan as necessary. For example, if a work delay occurs, the schedule will be readjusted. In addition, if a budget overrun is predicted, the generation AI will propose alternative plans to reduce costs. Furthermore, in the event of a material shortage, the generation AI will quickly arrange for substitute materials. In this way, project efficiency is improved by monitoring the progress of the project in real time and adjusting the plan as necessary.

[0066] The plan providing unit can simulate multiple scenarios and select the most efficient plan. In the plan providing unit, for example, the generation AI simulates multiple scenarios and selects the most efficient plan. For example, it simulates different work sequences and schedules and proposes the optimal plan. The generation AI also simulates different combinations of materials and equipment and proposes the most cost-effective plan. Furthermore, the generation AI simulates different personnel allocations and work methods and proposes the most efficient plan. In this way, the most efficient plan can be selected by simulating multiple scenarios.

[0067] The plan providing unit can use the emotion estimation function to analyze the user's stress level and propose a plan to minimize stress. The plan providing unit, for example, uses the emotion estimation function to analyze the user's stress level and propose a plan to minimize stress. For example, the plan providing unit adjusts the schedule to avoid tasks that are likely to cause the user stress. The generation AI also makes suggestions to create a work environment that allows the user to relax. Furthermore, the generation AI suggests a method of reporting progress that gives the user a sense of security. In this way, by proposing a plan that minimizes the user's stress, user satisfaction is improved.

[0068] The plan providing unit can apply different project management methods to a renovation project and propose the optimal method. For example, the generation AI can apply different project management methods to a renovation project and propose the optimal method. For example, the generation AI can use an agile method to perform flexible schedule management. The generation AI can also use a waterfall method to perform step-by-step progress management. Furthermore, the generation AI can use a hybrid method to combine the advantages of agile and waterfall. This allows the optimal method to be proposed by applying different project management methods.

[0069] The plan providing unit proposes optimal resource allocation for each phase of the project, enabling efficient progress. For example, the generation AI in the plan providing unit proposes optimal resource allocation for each phase of the project, enabling efficient progress. For example, it places emphasis on design work in the early stages, streamlining construction work in the later stages. The generation AI also prioritizes resource allocation for important work, ensuring smooth overall progress. Furthermore, the generation AI monitors resource surpluses and shortages in real time and adjusts as necessary. This allows the efficient progress of the project to be achieved by proposing optimal resource allocation for each phase.

[0070] The plan providing unit can use the emotion estimation function to identify the plan that will most satisfy the user and propose that plan. The plan providing unit, for example, can use the emotion estimation function to identify the plan that will most satisfy the user and propose that plan. For example, it can propose a schedule or work content that will give the user a sense of satisfaction. The generation AI can also propose a progress management method that will give the user a sense of security. Furthermore, the generation AI can suggest ways to create a work environment that will allow the user to relax. In this way, by proposing the plan that will most satisfy the user, user satisfaction is improved.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The renovation project budget optimization system can also be equipped with a progress monitoring unit that monitors the project's progress in real time and adjusts the plan as necessary. For example, if a work delay occurs, the schedule is readjusted. Also, if the budget is predicted to exceed the budget, alternative plans for cost reduction are proposed. Furthermore, if a material shortage occurs, substitute materials are quickly arranged. In this way, project efficiency can be improved by monitoring the project's progress in real time and adjusting the plan as necessary.

[0073] The renovation project budget optimization system can also include a stress analysis unit that uses an emotion estimation function to analyze the user's stress level and propose a plan to minimize stress. For example, the system adjusts the schedule to avoid tasks that tend to cause stress to the user. It also proposes ways to create a work environment that allows the user to relax. It also suggests a method of reporting progress that gives the user a sense of security. This allows the system to propose a plan that minimizes the user's stress, thereby improving user satisfaction.

[0074] The renovation project budget optimization system can also include a management method application unit that applies different project management methods to the renovation project and proposes the optimal method. For example, an agile method can be used to perform flexible schedule management, or a waterfall method can be used to perform step-by-step progress management. Furthermore, a hybrid method can be used to combine the advantages of agile and waterfall. This allows the optimal method to be proposed by applying different project management methods.

[0075] The renovation project budget optimization system may further include a satisfaction analysis unit that uses the emotion estimation function to identify expenditure items that the user is most satisfied with and proposes a spending plan that emphasizes those items. For example, if the user is very interested in design, a budget that emphasizes design costs may be proposed. If the user is very interested in functionality, a budget that emphasizes equipment costs may be proposed. Furthermore, if the user is very interested in eco-friendly materials, a budget that emphasizes eco-material costs may be proposed. In this way, by proposing a spending plan that emphasizes the expenditure items that the user is most satisfied with, it is possible to improve user satisfaction.

[0076] The renovation project budget optimization system can also be equipped with a resource allocation section that proposes optimal resource allocation for each phase of the project, ensuring efficient progress. For example, it can prioritize design work in the early stages and streamline construction work in the later stages. It can also prioritize resource allocation for important tasks, ensuring smooth overall progress. It can also monitor resource surpluses and shortages in real time and adjust as necessary. This allows it to propose optimal resource allocation for each phase, ensuring efficient progress of the project.

[0077] The renovation project budget optimization system can further include a positive emotion analysis unit that uses the emotion estimation function to identify cost-saving methods that evoke the most positive emotions in the user and proposes those methods. For example, it proposes cost-saving methods that give the user a sense of satisfaction. It also explains the reasons and benefits of cost-saving methods that are easy for the user to accept. It also proposes cost-saving methods that give the user a sense of security. In this way, it is possible to improve user satisfaction by proposing cost-saving methods that evoke positive emotions in the user.

[0078] The renovation project budget optimization system can also be equipped with an inter-industry application section that applies cost-saving methods from other industries to renovation projects and proposes new ways to save money. For example, efficiency methods from the manufacturing industry can be incorporated into renovation work. Project management methods from the IT industry can also be applied to renovation projects. Furthermore, inventory management methods from the logistics industry can be applied to material procurement. In this way, new cost-saving methods can be proposed by applying methods from different industries.

[0079] The renovation project budget optimization system can also be equipped with an eco-suggestion section that suggests eco-friendly materials and methods to achieve cost savings that are environmentally friendly. For example, using renewable materials reduces costs while mitigating the environmental impact. Also, introducing energy-efficient equipment reduces operating costs. Furthermore, promoting the recycling and reuse of waste materials reduces costs. This allows for sustainable renovations by achieving cost savings that are environmentally friendly.

[0080] The renovation project budget optimization system can further include a stress reduction suggestion unit that uses the emotion estimation function to suggest cost-saving methods that cause the least stress to the user. For example, the system can suggest budget cuts that do not cause the user stress. It can also suggest alternative materials and work methods that are easy for the user to accept. It can also explain the reasons and benefits of cost reductions in a way that gives the user a sense of security. This can improve user satisfaction by suggesting cost-saving methods that minimize stress to the user.

[0081] The renovation project budget optimization system can also be equipped with a scenario simulation unit that simulates multiple scenarios and selects the most efficient plan. For example, it can simulate different work sequences and schedules and propose the optimal plan. It can also simulate different combinations of materials and equipment and propose the most cost-effective plan. It can also simulate different staffing arrangements and work methods and propose the most efficient plan. In this way, it is possible to select the most efficient plan by simulating multiple scenarios.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The expenditure analysis unit analyzes the expenditures required for the project. For example, the generation AI calculates the optimal budget for each item, such as material costs, labor costs, and equipment costs, based on data from past renovation projects. The generation AI also analyzes expenditures based on prompts containing detailed information about the project. For example, the prompt is input as, "Please tell me the cost of materials and labor required for renovating a kitchen." Step 2: The cost proposal unit makes cost-saving proposals based on the results of the analysis by the expenditure analysis unit. For example, the generation AI suggests cheaper alternatives with the same quality. It also suggests reducing labor costs by streamlining work and optimizing schedules. The generation AI makes proposals based on a prompt for cost-saving proposals. For example, the prompt is "Please tell me how to save costs on kitchen renovations." Step 3: The plan provider provides an efficient plan based on the content proposed by the cost proposal provider. For example, the generation AI improves the efficiency of the entire project by optimizing the timing of material purchases and the order of work. It also proposes a plan that maximizes effectiveness within the budget. The generation AI provides a plan based on a prompt that requests the provision of an efficient plan. For example, the prompt is input as "Please tell me an efficient plan for remodeling a kitchen."

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an expenditure analysis unit that analyzes expenditures required for the project; a cost suggestion unit that makes cost saving suggestions based on the results of the analysis by the expenditure analysis unit; a plan providing unit that provides an efficient plan based on the content proposed by the cost proposal unit. A system characterized by:

2. The expenditure analysis unit Analyze data from past renovation projects to predict spending patterns under specific conditions 2. The system of claim 1.

3. The expenditure analysis unit Monitor market price fluctuations in real time and suggest optimal purchase timing 2. The system of claim 1.

4. The expenditure analysis unit Analyze users' feelings about their budgets and propose spending plans that take into account the flexibility of the budget.

2. The system of claim 1.

5. The expenditure analysis unit Analyze market data from different regions and propose optimal spending plans for each region 2. The system of claim 1.

6. The expenditure analysis unit Providing optimal spending plans for each type of renovation 2. The system of claim 1.

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