system

Abstract

The system according to the embodiment aims to provide a consistent process for custom-built homes and home renovations, from selecting a contractor to obtaining estimates and evaluation. [Solution] A system according to an embodiment includes a reception unit, a selection unit, an acquisition unit, and an evaluation unit. The reception unit inputs a user's request. The selection unit analyzes the information input by the reception unit and selects a contractor. The acquisition unit acquires a quote from the contractor selected by the selection unit. The evaluation unit evaluates the quote acquired by the acquisition unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, when it comes to custom-built homes and home renovations, the process from selecting a contractor to obtaining estimates and evaluating them is not consistent, which leaves users feeling uneasy.

[0005] The system according to the embodiment aims to provide a consistent process for custom-built homes and home renovations, from selecting a contractor to obtaining estimates and evaluation. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a selection unit, an acquisition unit, and an evaluation unit. The reception unit inputs a user's request. The selection unit analyzes the information input by the reception unit and selects a contractor. The acquisition unit acquires a quote from the contractor selected by the selection unit. The evaluation unit evaluates the quote acquired by the acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can handle all aspects of custom-built homes and home renovations, from selecting a contractor to obtaining and evaluating estimates. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A home renovation support system according to an embodiment of the present invention utilizes a generative AI to eliminate concerns about custom-built homes and home renovations. This system allows users to input their requirements for custom-built homes and home renovations. The generative AI analyzes the information, selects appropriate contractors, obtains and evaluates quotes, and allows users to select and contract with contractors with peace of mind. For example, when a user inputs their budget, desired floor plan, and renovation details, the generative AI selects the most suitable contractor based on that information. The selected contractor then obtains quotes, which the generative AI then evaluates. Based on the evaluation results, the user can select a contractor and enter into a contract. This allows users to seamlessly select a contractor, obtain quotes, and evaluate them, eliminating concerns. For example, users can select a contractor who can perform high-quality work within their budget, allowing them to safely request work at a reasonable cost. This allows the home renovation support system to efficiently accept, analyze, select a contractor, obtain quotes, and evaluate user requests.

[0029] A home renovation support system according to an embodiment includes a reception unit, a selection unit, an acquisition unit, and an evaluation unit. The reception unit inputs a user's requests. The user's requests include, but are not limited to, a budget, a desired floor plan, and the details of the renovation. The reception unit allows the user to input, for example, the budget, the desired floor plan, and the details of the renovation. The selection unit analyzes the information input by the reception unit and selects an appropriate contractor. The selection unit selects the optimal contractor based on data such as past construction performance, evaluations, and costs. The selection unit can use a generation AI to select the contractor most suitable for the user's requests. The acquisition unit acquires an estimate from the contractor selected by the selection unit. The acquisition unit can, for example, request an estimate from the selected contractor and acquire the estimate. The evaluation unit evaluates the estimate acquired by the acquisition unit. The evaluation unit evaluates, for example, the details, cost, and construction quality of the estimate and provides it to the user. The evaluation unit can use a generation AI to evaluate whether the cost of the estimate is appropriate and whether the construction quality is high. As a result, the home renovation support system according to the embodiment can efficiently receive, analyze, select contractors, obtain estimates, and evaluate user requests.

[0030] The reception unit allows the user to input the user's budget, desired floor plan, and desired renovation details. The reception unit allows the user to input, for example, the user's budget, desired floor plan, desired renovation details, and other requests. The budget includes, for example, an upper limit and lower limit of the amount, a currency unit, and the like. The desired floor plan includes, for example, the number of rooms, the arrangement of the rooms, the area, and the like. The renovation details include, for example, kitchen renovation, bathroom renovation, and the like. This allows the user to input specific requests in detail.

[0031] The selection unit can select a contractor based on data on past construction performance, evaluations, and costs. The selection unit can select a contractor based on data on past construction performance, evaluations, and costs, for example. Construction performance includes, for example, the number of past projects and success rate. Evaluations include, for example, customer satisfaction and review content. Costs include, for example, total cost and cost performance. This makes it possible to select the optimal contractor based on past data.

[0032] The acquisition unit can acquire an estimate from the selected contractor. For example, the acquisition unit can request an estimate from the selected contractor and acquire the estimate. The estimate includes, for example, cost, construction details, delivery date, etc. This allows the estimate to be efficiently acquired from the selected contractor.

[0033] The evaluation unit can evaluate the contents of the estimate, the cost, the construction quality, etc. The evaluation unit can evaluate, for example, the contents of the estimate, the cost, the construction quality, etc. The contents of the estimate include, for example, a detailed cost breakdown and details of the construction content. The cost includes, for example, the total cost and cost performance. The construction quality includes, for example, the quality of the materials used and the accuracy of the construction. This makes it possible to evaluate the contents of the estimate, the cost, and the construction quality.

[0034] The reception unit can analyze the user's past request history and propose an optimal input format. The reception unit can, for example, analyze the user's past request history and propose an optimal input format. The past request history includes, for example, past input data, request trends, etc. The optimal input format includes, for example, customization based on the user's input history. For example, requests that the user has frequently input in the past can be automatically displayed as candidates. In addition, input methods (voice, text, etc.) that the user has used in the past can be preferentially proposed. Furthermore, requests to be used in a specific time period can be predicted and proposed based on the user's past request history. In this way, input efficiency can be improved by proposing an optimal input format based on the past request history.

[0035] The reception unit can customize input items based on the user's current living situation and areas of interest when inputting a request. The reception unit can customize input items based on the user's current living situation and areas of interest when inputting a request, for example. Living situations include, for example, family composition and living environment. Areas of interest include, for example, hobbies and topics of interest. For example, when a user inputs their family composition, it is possible to propose a floor plan based on the number of family members and their ages. Furthermore, when a user inputs their hobbies and areas of interest, it is possible to propose renovation ideas based on those. Furthermore, when a user inputs their current living situation (for example, working from home), it is possible to propose a renovation plan that is suitable for that. In this way, by providing input items based on the user's living situation and areas of interest, it is possible to input more appropriate requests.

[0036] The reception unit can prioritize displaying highly relevant input items in consideration of the user's geographical location information when inputting a request. For example, the reception unit can prioritize displaying highly relevant input items in consideration of the user's geographical location information when inputting a request. Geographical location information includes, for example, GPS data, address information, etc. Highly relevant input items include, for example, region-specific requests and local contractor information. For example, if the user lives in an urban area, a renovation plan suitable for the urban area can be proposed. Also, if the user lives in a suburban area, a renovation plan suitable for the suburban area can be proposed. Furthermore, if the user lives in a specific region, a renovation plan suitable for the climate and environment of that region can be proposed. In this way, by providing input items based on the user's geographical location information, more appropriate requests can be input.

[0037] The reception unit can analyze the user's social media activity when a request is input and suggest related requests. The reception unit can analyze the user's social media activity when a request is input and suggest related requests, for example. Social media activity includes, for example, the content of posts and reactions from followers. Related requests include, for example, suggestions based on the content of past posts. For example, related requests can be suggested based on renovation ideas shared by the user on social media. Related requests can also be suggested based on information about accounts the user follows on social media. Furthermore, related requests can be suggested based on posts the user has "liked" on social media. In this way, more appropriate requests can be input by suggesting requests based on the user's social media activity.

[0038] The selection unit can optimize the selection algorithm by referring to past construction performance data during selection. The selection unit can optimize the selection algorithm by referring to past construction performance data during selection, for example. The construction performance data includes, for example, past project data and success rates. The selection algorithm includes, for example, adjustment of machine learning algorithms. For example, a highly reliable contractor can be selected based on the past construction performance data. Furthermore, a contractor with high cost performance can be selected based on the past construction performance data. Furthermore, a contractor with high construction quality can be selected based on the past construction performance data. As a result, by optimizing the selection algorithm based on the past construction performance data, a more reliable contractor can be selected.

[0039] The selection unit can customize the selection of a contractor based on the user's budget and desired floor plan during selection. The selection unit can customize the selection of a contractor based on the user's budget and desired floor plan during selection, for example. The budget includes, for example, an upper limit, a lower limit, and a currency unit. The desired floor plan includes, for example, the number of rooms, the arrangement of the rooms, and the area. For example, it is possible to select an optimal contractor within the user's budget. It is also possible to select a contractor that can accommodate the user's desired floor plan. Furthermore, it is possible to select a contractor taking into consideration the balance between the user's budget and the desired floor plan. This makes it possible to select a more appropriate contractor by selecting a contractor that suits the user's budget and desired floor plan.

[0040] The selection unit can select the most suitable contractor by taking into consideration the geographical distribution of contractors during selection. The selection unit can, for example, select the most suitable contractor by taking into consideration the geographical distribution of contractors during selection. Geographical distribution includes, for example, the location of the contractor and transportation costs. For example, a contractor close to the area where the user lives can be selected. Furthermore, by taking into consideration the geographical distribution of contractors, a contractor that minimizes transportation costs and travel time can be selected. Furthermore, geographically close contractors can be selected preferentially, enabling a quick response. In this way, by taking into consideration the geographical distribution of contractors, a contractor that minimizes transportation costs and travel time can be selected.

[0041] The selection unit can improve the accuracy of the selection by referring to related literature and evaluations of the contractor during selection. The selection unit can improve the accuracy of the selection by referring to related literature and evaluations of the contractor during selection, for example. Related literature includes, for example, evaluation reports and research papers of the contractor. Evaluations include, for example, customer satisfaction and review content. For example, a reliable contractor can be selected by referring to the contractor's past evaluations. Also, a contractor with high construction quality can be selected by referring to related literature of the contractor. Furthermore, a contractor with high cost performance can be selected based on the contractor's evaluation data. In this way, by referring to related literature and evaluations of the contractor, a more reliable contractor can be selected.

[0042] The acquisition unit can analyze past estimate acquisition history and select the optimal acquisition method. The acquisition unit can, for example, analyze past estimate acquisition history and select the optimal acquisition method. The estimate acquisition history includes, for example, past acquisition data and acquisition method trends. The optimal acquisition method includes, for example, optimization based on past data. For example, a method for quickly acquiring an estimate can be selected based on the past estimate acquisition history. Also, a method for acquiring a detailed estimate can be selected based on the past estimate acquisition history. Furthermore, a method for acquiring an estimate with high cost performance can be selected based on the past estimate acquisition history. As a result, estimates can be acquired efficiently by selecting the optimal acquisition method based on the past estimate acquisition history.

[0043] The acquisition unit can perform filtering based on the user's current living situation and areas of interest when acquiring an estimate. For example, the acquisition unit can perform filtering based on the user's current living situation and areas of interest when acquiring an estimate. Living situations include, for example, family composition and living environment. Areas of interest include, for example, hobbies and topics of interest. For example, if the user inputs their family composition, an estimate can be obtained based on the number of family members and their ages. Also, if the user inputs their hobbies and areas of interest, an estimate can be obtained based on them. Furthermore, if the user inputs their current living situation (for example, working from home), an estimate appropriate for that can be obtained. In this way, an estimate can be obtained that is appropriate for the user's living situation and areas of interest, making it possible to provide a more appropriate estimate.

[0044] The acquisition unit can prioritize acquisition of highly relevant estimates by taking into consideration the geographical location information of the contractors when acquiring estimates. For example, the acquisition unit can prioritize acquisition of highly relevant estimates by taking into consideration the geographical location information of the contractors when acquiring estimates. Geographical location information includes, for example, GPS data, address information, etc. Highly relevant estimates include, for example, requests specific to a region and information on local contractors. For example, estimates can be acquired from contractors close to the user's area of ​​residence. Furthermore, estimates that minimize transportation costs and travel time can be acquired by taking into consideration the geographical distribution of contractors. Furthermore, estimates can be prioritized from geographically nearby contractors, enabling a quick response. As a result, estimates that minimize transportation costs and travel time can be acquired by taking into consideration the geographical location information of contractors.

[0045] The acquisition unit can analyze the contractor's social media activity when acquiring an estimate and acquire related estimates. The acquisition unit can, for example, analyze the contractor's social media activity when acquiring an estimate and acquire related estimates. Social media activity includes, for example, the content of posts and the reactions of followers. Related estimates can be acquired based on, for example, the content of past posts. For example, related estimates can be acquired based on construction examples shared by the contractor on social media. Related estimates can also be acquired based on information about accounts followed by the contractor on social media. Furthermore, related estimates can be acquired based on posts that the contractor has "liked" on social media. In this way, by acquiring estimates based on the contractor's social media activity, it is possible to provide more appropriate estimates.

[0046] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data during evaluation. The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data during evaluation, for example. The evaluation data includes, for example, past evaluation results, customer feedback, etc. The evaluation algorithm includes, for example, adjustment of a machine learning algorithm. For example, a highly reliable evaluation algorithm can be provided based on past evaluation data. Furthermore, a highly cost-effective evaluation algorithm can be provided based on past evaluation data. Furthermore, an evaluation algorithm with high construction quality can be provided based on past evaluation data. As a result, by optimizing the evaluation algorithm based on past evaluation data, a more reliable evaluation can be performed.

[0047] The evaluation unit can adjust the level of detail of the evaluation based on the content, cost, and construction quality of the estimate at the time of evaluation. The evaluation unit can adjust the level of detail of the evaluation based on the content, cost, and construction quality of the estimate at the time of evaluation, for example. The content of the estimate includes, for example, a detailed cost breakdown and details of the construction content. The cost includes, for example, the total cost and cost performance. The construction quality includes, for example, the quality of the materials used and the accuracy of the construction. For example, if the content of the estimate is detailed, a detailed evaluation can be provided. Furthermore, if the cost of the estimate is high, an evaluation that emphasizes cost performance can be provided. Furthermore, if the construction quality is high, an evaluation that emphasizes that quality can be provided. In this way, a more appropriate evaluation can be provided by performing an evaluation based on the content, cost, and construction quality of the estimate.

[0048] The evaluation unit can weight the evaluation based on the time of submission of the estimate during the evaluation. The evaluation unit can, for example, weight the evaluation based on the time of submission of the estimate during the evaluation. The time of submission of the estimate includes, for example, the submission deadline and the promptness of submission. The weighting of the evaluation includes, for example, evaluation of prompt response and weighting based on the time of submission. For example, if the estimate is submitted early, an evaluation can be provided that emphasizes the promptness. Also, if the estimate is submitted late, an evaluation can be provided that takes into account the reason for the delay. Furthermore, the weighting of the evaluation can be adjusted based on the time of submission of the estimate. In this way, by performing an evaluation based on the time of submission of the estimate, prompt response can be evaluated.

[0049] The evaluation unit can improve the accuracy of the evaluation by referring to literature related to the estimate and market data during the evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to literature related to the estimate and market data during the evaluation, for example. Relevant literature includes, for example, contractor evaluation reports and research papers. Market data includes, for example, industry trend data and information on competitors. For example, by referring to literature related to the estimate, a highly reliable evaluation can be provided. Furthermore, by referring to market data, an evaluation with high cost performance can be provided. Furthermore, an evaluation with high construction quality can be provided based on literature related to the estimate and market data. As a result, by performing the evaluation based on literature related to the estimate and market data, a more reliable evaluation can be provided.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When inputting a user's requests, the reception unit can refer to the user's past renovation history and compare the past requests with the current requests. For example, it can make optimal suggestions for the current requests based on information about rooms that have been renovated in the past and data on the materials used. It can also extract the user's particular satisfactions and dissatisfaction points from the past renovation history and reflect them in the current requests. Furthermore, it can take into account the evaluations of contractors previously selected by the user based on the past renovation history and use this information to help select a current contractor. This makes it possible to utilize the user's past renovation experience to input requests and select a contractor more appropriately.

[0052] The selection unit can take into account the current operating status of the contractor when selecting a contractor based on the user's request. For example, it can check the number of current projects and resource usage status of the contractor and prioritize the selection of a contractor with available resources. It can also refer to the completion dates and progress of the contractor's past projects to select a reliable contractor. Furthermore, it can propose a schedule that can meet the user's request based on the contractor's current operating status. This makes it possible to select a contractor taking into account their operating status, allowing for a prompt and appropriate response to the user's request.

[0053] When obtaining a quote from a selected contractor, the obtaining unit can evaluate the validity of the quote by referring to the contractor's past quote history. For example, it can determine whether the current quote is a fair price based on past quote data. It can also check the quality of the service provided by the contractor and compliance with delivery deadlines from the past quote history to obtain a highly reliable quote. Furthermore, it can take into account the user's evaluation of the contractor's quote based on the past quote history and use this to help obtain the current quote. In this way, it is possible to obtain a more reliable quote by utilizing the past quote history.

[0054] The evaluation unit can take into account the success rate of a contractor's past projects when evaluating the content, cost, construction quality, etc. of an estimate. For example, the reliability of a current estimate can be evaluated based on the success rate and customer satisfaction of the contractor's past projects. In addition, the evaluation unit can evaluate the contractor's construction quality and cost performance based on data from past projects and provide the evaluation results to the user. Furthermore, future risks can be predicted based on the contractor's past project success rate, and appropriate advice can be provided to the user. This allows for more reliable evaluations to be made by utilizing past project data.

[0055] The evaluation unit can take into account the environmental impact of materials used by contractors when evaluating the content, cost, construction quality, etc. of estimates. For example, eco-friendly contractors can be evaluated based on environmental impact data on materials used by contractors. The evaluation unit can also evaluate the environmental impact of contractors' construction methods and provide this information to users. Furthermore, based on data on the contractors' environmental impact, it can propose environmentally friendly renovation plans to users. In this way, by performing evaluations that take environmental impact into consideration, it is possible to provide users with more sustainable renovation plans.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit inputs the user's requests. The user's requests include, for example, the budget, the desired layout, the details of the renovation, etc. The reception unit allows the user to input these requests. Step 2: The selection unit analyzes the information entered by the reception unit and selects an appropriate contractor. The selection unit selects the most suitable contractor based on data such as past construction performance, evaluations, and costs. Using generation AI, it is possible to select the contractor that best suits the user's requests. Step 3: The acquisition unit acquires quotations from the contractors selected by the selection unit. The acquisition unit can request quotations from the selected contractors and acquire quotations. Step 4: The evaluation unit evaluates the estimate obtained by the acquisition unit. The evaluation unit evaluates the content, cost, construction quality, etc. of the estimate and provides it to the user. Using the generation AI, it is possible to evaluate whether the cost of the estimate is appropriate and whether the construction quality is high.

[0058] (Example 2) A home renovation support system according to an embodiment of the present invention utilizes a generative AI to eliminate concerns about custom-built homes and home renovations. This system allows users to input their requirements for custom-built homes and home renovations. The generative AI analyzes the information, selects appropriate contractors, obtains and evaluates quotes, and allows users to select and contract with contractors with peace of mind. For example, when a user inputs their budget, desired floor plan, and renovation details, the generative AI selects the most suitable contractor based on that information. The selected contractor then obtains quotes, which the generative AI then evaluates. Based on the evaluation results, the user can select a contractor and enter into a contract. This allows users to seamlessly select a contractor, obtain quotes, and evaluate them, eliminating concerns. For example, users can select a contractor who can perform high-quality work within their budget, allowing them to safely request work at a reasonable cost. This allows the home renovation support system to efficiently accept, analyze, select a contractor, obtain quotes, and evaluate user requests.

[0059] A home renovation support system according to an embodiment includes a reception unit, a selection unit, an acquisition unit, and an evaluation unit. The reception unit inputs a user's requests. The user's requests include, but are not limited to, a budget, a desired floor plan, and the details of the renovation. The reception unit allows the user to input, for example, the budget, the desired floor plan, and the details of the renovation. The selection unit analyzes the information input by the reception unit and selects an appropriate contractor. The selection unit selects the optimal contractor based on data such as past construction performance, evaluations, and costs. The selection unit can use a generation AI to select the contractor most suitable for the user's requests. The acquisition unit acquires an estimate from the contractor selected by the selection unit. The acquisition unit can, for example, request an estimate from the selected contractor and acquire the estimate. The evaluation unit evaluates the estimate acquired by the acquisition unit. The evaluation unit evaluates, for example, the details, cost, and construction quality of the estimate and provides it to the user. The evaluation unit can use a generation AI to evaluate whether the cost of the estimate is appropriate and whether the construction quality is high. As a result, the home renovation support system according to the embodiment can efficiently receive, analyze, select contractors, obtain estimates, and evaluate user requests.

[0060] The reception unit allows the user to input the user's budget, desired floor plan, and desired renovation details. The reception unit allows the user to input, for example, the user's budget, desired floor plan, desired renovation details, and other requests. The budget includes, for example, an upper limit and lower limit of the amount, a currency unit, and the like. The desired floor plan includes, for example, the number of rooms, the arrangement of the rooms, the area, and the like. The renovation details include, for example, kitchen renovation, bathroom renovation, and the like. This allows the user to input specific requests in detail.

[0061] The selection unit can select a contractor based on data on past construction performance, evaluations, and costs. The selection unit can select a contractor based on data on past construction performance, evaluations, and costs, for example. Construction performance includes, for example, the number of past projects and success rate. Evaluations include, for example, customer satisfaction and review content. Costs include, for example, total cost and cost performance. This makes it possible to select the optimal contractor based on past data.

[0062] The acquisition unit can acquire an estimate from the selected contractor. For example, the acquisition unit can request an estimate from the selected contractor and acquire the estimate. The estimate includes, for example, cost, construction details, delivery date, etc. This allows the estimate to be efficiently acquired from the selected contractor.

[0063] The evaluation unit can evaluate the contents of the estimate, the cost, the construction quality, etc. The evaluation unit can evaluate, for example, the contents of the estimate, the cost, the construction quality, etc. The contents of the estimate include, for example, a detailed cost breakdown and details of the construction content. The cost includes, for example, the total cost and cost performance. The construction quality includes, for example, the quality of the materials used and the accuracy of the construction. This makes it possible to evaluate the contents of the estimate, the cost, and the construction quality.

[0064] The reception unit can estimate the user's emotion and adjust the request input method based on the estimated user emotion. The reception unit can, for example, estimate the user's emotion and adjust the request input method based on the estimated user emotion. Emotion estimation includes, for example, facial expression recognition and voice analysis. Request input methods include, for example, changing the input form and introducing voice input. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Furthermore, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to input their request quickly. This makes it possible to reduce the user's stress by providing an input method that suits the user's emotion.

[0065] The reception unit can analyze the user's past request history and propose an optimal input format. The reception unit can, for example, analyze the user's past request history and propose an optimal input format. The past request history includes, for example, past input data, request trends, etc. The optimal input format includes, for example, customization based on the user's input history. For example, requests that the user has frequently input in the past can be automatically displayed as candidates. In addition, input methods (voice, text, etc.) that the user has used in the past can be preferentially proposed. Furthermore, requests to be used in a specific time period can be predicted and proposed based on the user's past request history. In this way, input efficiency can be improved by proposing an optimal input format based on the past request history.

[0066] The reception unit can customize input items based on the user's current living situation and areas of interest when inputting a request. The reception unit can customize input items based on the user's current living situation and areas of interest when inputting a request, for example. Living situations include, for example, family composition and living environment. Areas of interest include, for example, hobbies and topics of interest. For example, when a user inputs their family composition, it is possible to propose a floor plan based on the number of family members and their ages. Furthermore, when a user inputs their hobbies and areas of interest, it is possible to propose renovation ideas based on those. Furthermore, when a user inputs their current living situation (for example, working from home), it is possible to propose a renovation plan that is suitable for that. In this way, by providing input items based on the user's living situation and areas of interest, it is possible to input more appropriate requests.

[0067] The reception unit can estimate the user's emotions and determine the priority of requests to be input based on the estimated user emotions. The reception unit can, for example, estimate the user's emotions and determine the priority of requests to be input based on the estimated user emotions. Estimation of emotions includes, for example, facial expression recognition and voice analysis. The priority of requests includes, for example, importance and urgency. For example, if the user is feeling stressed, the user can be prompted to input important requests with priority. Furthermore, if the user is relaxed, the user can be prompted to input detailed requests. Furthermore, if the user is in a hurry, the user can be prompted to input only the most important requests. In this way, by determining the priority of requests according to the user's emotions, important requests can be input with priority.

[0068] The reception unit can prioritize displaying highly relevant input items in consideration of the user's geographical location information when inputting a request. For example, the reception unit can prioritize displaying highly relevant input items in consideration of the user's geographical location information when inputting a request. Geographical location information includes, for example, GPS data, address information, etc. Highly relevant input items include, for example, region-specific requests and local contractor information. For example, if the user lives in an urban area, a renovation plan suitable for the urban area can be proposed. Also, if the user lives in a suburban area, a renovation plan suitable for the suburban area can be proposed. Furthermore, if the user lives in a specific region, a renovation plan suitable for the climate and environment of that region can be proposed. In this way, by providing input items based on the user's geographical location information, more appropriate requests can be input.

[0069] The reception unit can analyze the user's social media activity when a request is input and suggest related requests. The reception unit can analyze the user's social media activity when a request is input and suggest related requests, for example. Social media activity includes, for example, the content of posts and reactions from followers. Related requests include, for example, suggestions based on the content of past posts. For example, related requests can be suggested based on renovation ideas shared by the user on social media. Related requests can also be suggested based on information about accounts the user follows on social media. Furthermore, related requests can be suggested based on posts the user has "liked" on social media. In this way, more appropriate requests can be input by suggesting requests based on the user's social media activity.

[0070] The selection unit can estimate the user's emotions and adjust the selection criteria for the service provider based on the estimated user emotions. The selection unit can, for example, estimate the user's emotions and adjust the selection criteria for the service provider based on the estimated user emotions. Estimation of emotions includes, for example, facial expression recognition and voice analysis. The selection criteria include, for example, changing the evaluation criteria and adjusting the selection algorithm. For example, if the user is stressed, a highly reliable service provider can be preferentially selected. Furthermore, if the user is relaxed, a service provider with high cost performance can be selected. Furthermore, if the user is in a hurry, a service provider that can respond quickly can be selected. In this way, by providing service provider selection criteria according to the user's emotions, a more appropriate service provider can be selected.

[0071] The selection unit can optimize the selection algorithm by referring to past construction performance data during selection. The selection unit can optimize the selection algorithm by referring to past construction performance data during selection, for example. The construction performance data includes, for example, past project data and success rates. The selection algorithm includes, for example, adjustment of machine learning algorithms. For example, a highly reliable contractor can be selected based on the past construction performance data. Furthermore, a contractor with high cost performance can be selected based on the past construction performance data. Furthermore, a contractor with high construction quality can be selected based on the past construction performance data. As a result, by optimizing the selection algorithm based on the past construction performance data, a more reliable contractor can be selected.

[0072] The selection unit can customize the selection of a contractor based on the user's budget and desired floor plan during selection. The selection unit can customize the selection of a contractor based on the user's budget and desired floor plan during selection, for example. The budget includes, for example, an upper limit, a lower limit, and a currency unit. The desired floor plan includes, for example, the number of rooms, the arrangement of the rooms, and the area. For example, it is possible to select an optimal contractor within the user's budget. It is also possible to select a contractor that can accommodate the user's desired floor plan. Furthermore, it is possible to select a contractor taking into consideration the balance between the user's budget and the desired floor plan. This makes it possible to select a more appropriate contractor by selecting a contractor that suits the user's budget and desired floor plan.

[0073] The selection unit can estimate the user's emotions and determine the priority of the selected vendors based on the estimated user emotions. The selection unit can, for example, estimate the user's emotions and determine the priority of the selected vendors based on the estimated user emotions. Estimation of emotions includes, for example, facial expression recognition and voice analysis. The prioritization of vendors includes, for example, evaluation criteria and urgency. For example, when the user is stressed, a reliable vendor can be preferentially selected. Furthermore, when the user is relaxed, a vendor with high cost performance can be preferentially selected. Furthermore, when the user is in a hurry, a vendor that can respond quickly can be preferentially selected. In this way, by prioritizing vendors according to the user's emotions, a more appropriate vendor can be selected.

[0074] The selection unit can select the most suitable contractor by taking into consideration the geographical distribution of contractors during selection. The selection unit can, for example, select the most suitable contractor by taking into consideration the geographical distribution of contractors during selection. Geographical distribution includes, for example, the location of the contractor and transportation costs. For example, a contractor close to the area where the user lives can be selected. Furthermore, by taking into consideration the geographical distribution of contractors, a contractor that minimizes transportation costs and travel time can be selected. Furthermore, geographically close contractors can be selected preferentially, enabling a quick response. In this way, by taking into consideration the geographical distribution of contractors, a contractor that minimizes transportation costs and travel time can be selected.

[0075] The selection unit can improve the accuracy of the selection by referring to related literature and evaluations of the contractor during selection. The selection unit can improve the accuracy of the selection by referring to related literature and evaluations of the contractor during selection, for example. Related literature includes, for example, evaluation reports and research papers of the contractor. Evaluations include, for example, customer satisfaction and review content. For example, a reliable contractor can be selected by referring to the contractor's past evaluations. Also, a contractor with high construction quality can be selected by referring to related literature of the contractor. Furthermore, a contractor with high cost performance can be selected based on the contractor's evaluation data. In this way, by referring to related literature and evaluations of the contractor, a more reliable contractor can be selected.

[0076] The acquisition unit can estimate the user's emotions and adjust the timing of estimate acquisition based on the estimated user emotions. The acquisition unit can, for example, estimate the user's emotions and adjust the timing of estimate acquisition based on the estimated user emotions. Emotion estimation includes, for example, facial expression recognition and voice analysis. The timing of estimate acquisition includes, for example, timing adjustment based on the user's emotional state. For example, if the user is stressed, an estimate can be acquired quickly. Also, if the user is relaxed, a detailed estimate can be acquired. Furthermore, if the user is in a hurry, an estimate including only the most important items can be acquired quickly. This allows for a more appropriate estimate to be acquired by providing the timing of estimate acquisition according to the user's emotions.

[0077] The acquisition unit can analyze past estimate acquisition history and select the optimal acquisition method. The acquisition unit can, for example, analyze past estimate acquisition history and select the optimal acquisition method. The estimate acquisition history includes, for example, past acquisition data and acquisition method trends. The optimal acquisition method includes, for example, optimization based on past data. For example, a method for quickly acquiring an estimate can be selected based on the past estimate acquisition history. Also, a method for acquiring a detailed estimate can be selected based on the past estimate acquisition history. Furthermore, a method for acquiring an estimate with high cost performance can be selected based on the past estimate acquisition history. As a result, estimates can be acquired efficiently by selecting the optimal acquisition method based on the past estimate acquisition history.

[0078] The acquisition unit can perform filtering based on the user's current living situation and areas of interest when acquiring an estimate. For example, the acquisition unit can perform filtering based on the user's current living situation and areas of interest when acquiring an estimate. Living situations include, for example, family composition and living environment. Areas of interest include, for example, hobbies and topics of interest. For example, if the user inputs their family composition, an estimate can be obtained based on the number of family members and their ages. Also, if the user inputs their hobbies and areas of interest, an estimate can be obtained based on them. Furthermore, if the user inputs their current living situation (for example, working from home), an estimate appropriate for that can be obtained. In this way, an estimate can be obtained that is appropriate for the user's living situation and areas of interest, making it possible to provide a more appropriate estimate.

[0079] The acquisition unit can estimate the user's emotions and determine the priority of estimates to be acquired based on the estimated user emotions. The acquisition unit can, for example, estimate the user's emotions and determine the priority of estimates to be acquired based on the estimated user emotions. Emotion estimation includes, for example, facial expression recognition and voice analysis. The priority of estimates includes, for example, importance and urgency. For example, if the user is stressed, important estimates can be acquired with priority. Furthermore, if the user is relaxed, detailed estimates can be acquired with priority. Furthermore, if the user is in a hurry, estimates including only the most important items can be acquired with priority. In this way, by determining the priority of estimates according to the user's emotions, more appropriate estimates can be acquired.

[0080] The acquisition unit can prioritize acquisition of highly relevant estimates by taking into consideration the geographical location information of the contractors when acquiring estimates. For example, the acquisition unit can prioritize acquisition of highly relevant estimates by taking into consideration the geographical location information of the contractors when acquiring estimates. Geographical location information includes, for example, GPS data, address information, etc. Highly relevant estimates include, for example, requests specific to a region and information on local contractors. For example, estimates can be acquired from contractors close to the user's area of ​​residence. Furthermore, estimates that minimize transportation costs and travel time can be acquired by taking into consideration the geographical distribution of contractors. Furthermore, estimates can be prioritized from geographically nearby contractors, enabling a quick response. As a result, estimates that minimize transportation costs and travel time can be acquired by taking into consideration the geographical location information of contractors.

[0081] The acquisition unit can analyze the contractor's social media activity when acquiring an estimate and acquire related estimates. The acquisition unit can, for example, analyze the contractor's social media activity when acquiring an estimate and acquire related estimates. Social media activity includes, for example, the content of posts and the reactions of followers. Related estimates can be acquired based on, for example, the content of past posts. For example, related estimates can be acquired based on construction examples shared by the contractor on social media. Related estimates can also be acquired based on information about accounts followed by the contractor on social media. Furthermore, related estimates can be acquired based on posts that the contractor has "liked" on social media. In this way, by acquiring estimates based on the contractor's social media activity, it is possible to provide more appropriate estimates.

[0082] The evaluation unit can estimate the user's emotions and adjust the evaluation method of the estimate based on the estimated user's emotions. The evaluation unit can, for example, estimate the user's emotions and adjust the evaluation method of the estimate based on the estimated user's emotions. Examples of emotion estimation include facial expression recognition and voice analysis. The evaluation method can include, for example, changing the evaluation criteria and adjusting the evaluation algorithm. For example, if the user is feeling stressed, a simple and easy-to-understand evaluation method can be provided. Also, if the user is relaxed, a detailed evaluation method can be provided. Furthermore, if the user is in a hurry, a key evaluation method can be provided. This allows for more appropriate evaluation by providing an evaluation method according to the user's emotions.

[0083] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data during evaluation. The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data during evaluation, for example. The evaluation data includes, for example, past evaluation results, customer feedback, etc. The evaluation algorithm includes, for example, adjustment of a machine learning algorithm. For example, a highly reliable evaluation algorithm can be provided based on past evaluation data. Furthermore, a highly cost-effective evaluation algorithm can be provided based on past evaluation data. Furthermore, an evaluation algorithm with high construction quality can be provided based on past evaluation data. As a result, by optimizing the evaluation algorithm based on past evaluation data, a more reliable evaluation can be performed.

[0084] The evaluation unit can adjust the level of detail of the evaluation based on the content, cost, and construction quality of the estimate at the time of evaluation. The evaluation unit can adjust the level of detail of the evaluation based on the content, cost, and construction quality of the estimate at the time of evaluation, for example. The content of the estimate includes, for example, a detailed cost breakdown and details of the construction content. The cost includes, for example, the total cost and cost performance. The construction quality includes, for example, the quality of the materials used and the accuracy of the construction. For example, if the content of the estimate is detailed, a detailed evaluation can be provided. Furthermore, if the cost of the estimate is high, an evaluation that emphasizes cost performance can be provided. Furthermore, if the construction quality is high, an evaluation that emphasizes that quality can be provided. In this way, a more appropriate evaluation can be provided by performing an evaluation based on the content, cost, and construction quality of the estimate.

[0085] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated user emotions. The evaluation unit can, for example, estimate the user's emotions and determine the priority of evaluations based on the estimated user emotions. Emotion estimation includes, for example, facial expression recognition and voice analysis. The priority of evaluations includes, for example, importance and urgency. For example, if the user is feeling stressed, important evaluation items can be provided preferentially. Furthermore, if the user is relaxed, detailed evaluation items can be provided. Furthermore, if the user is in a hurry, evaluation items that focus on the main points can be provided. In this way, by determining the priority of evaluations according to the user's emotions, more appropriate evaluations can be performed.

[0086] The evaluation unit can weight the evaluation based on the time of submission of the estimate during the evaluation. The evaluation unit can, for example, weight the evaluation based on the time of submission of the estimate during the evaluation. The time of submission of the estimate includes, for example, the submission deadline and the promptness of submission. The weighting of the evaluation includes, for example, evaluation of prompt response and weighting based on the time of submission. For example, if the estimate is submitted early, an evaluation can be provided that emphasizes the promptness. Also, if the estimate is submitted late, an evaluation can be provided that takes into account the reason for the delay. Furthermore, the weighting of the evaluation can be adjusted based on the time of submission of the estimate. In this way, by performing an evaluation based on the time of submission of the estimate, prompt response can be evaluated.

[0087] The evaluation unit can improve the accuracy of the evaluation by referring to literature related to the estimate and market data during the evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to literature related to the estimate and market data during the evaluation, for example. Relevant literature includes, for example, contractor evaluation reports and research papers. Market data includes, for example, industry trend data and information on competitors. For example, by referring to literature related to the estimate, a highly reliable evaluation can be provided. Furthermore, by referring to market data, an evaluation with high cost performance can be provided. Furthermore, an evaluation with high construction quality can be provided based on literature related to the estimate and market data. As a result, by performing the evaluation based on literature related to the estimate and market data, a more reliable evaluation can be provided. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, selection unit, acquisition unit, and evaluation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and the user inputs their requests, such as their budget, desired floor plan, and renovation details. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generation AI to select a contractor that best suits the user's requests. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and requests a quote from the selected contractor and acquires the quote. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the content, cost, construction quality, etc. of the quote and provides it to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, selection unit, acquisition unit, and evaluation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and the user vocally inputs their requests, such as their budget, desired floor plan, and renovation details. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generation AI to select a contractor that best suits the user's requests. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and requests a quote from the selected contractor and acquires the quote. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the content, cost, construction quality, etc. of the quote and provides it to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, selection unit, acquisition unit, and evaluation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, and the user vocally inputs their requests, such as their budget, desired floor plan, and renovation details. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generation AI to select a contractor that best suits the user's requests. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and requests a quote from the selected contractor and acquires the quote. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the content, cost, construction quality, etc. of the quote and provides it to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, selection unit, acquisition unit, and evaluation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and the user vocally inputs their requests, such as their budget, desired floor plan, and renovation details. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generation AI to select a contractor that best suits the user's requests. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and requests a quote from the selected contractor and acquires the quote. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the content, cost, construction quality, etc. of the quote and provides it to the user.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] When inputting a user's requests, the reception unit can refer to the user's past renovation history and compare the past requests with the current requests. For example, it can make optimal suggestions for the current requests based on information about rooms that have been renovated in the past and data on the materials used. It can also extract the user's particular satisfactions and dissatisfaction points from the past renovation history and reflect them in the current requests. Furthermore, it can take into account the evaluations of contractors previously selected by the user based on the past renovation history and use this information to help select a current contractor. This makes it possible to utilize the user's past renovation experience to input requests and select a contractor more appropriately.

[0090] The selection unit can take into account the current operating status of the contractor when selecting a contractor based on the user's request. For example, it can check the number of current projects and resource usage status of the contractor and prioritize the selection of a contractor with available resources. It can also refer to the completion dates and progress of the contractor's past projects to select a reliable contractor. Furthermore, it can propose a schedule that can meet the user's request based on the contractor's current operating status. This makes it possible to select a contractor taking into account their operating status, allowing for a prompt and appropriate response to the user's request.

[0091] When obtaining a quote from a selected contractor, the obtaining unit can evaluate the validity of the quote by referring to the contractor's past quote history. For example, it can determine whether the current quote is a fair price based on past quote data. It can also check the quality of the service provided by the contractor and compliance with delivery deadlines from the past quote history to obtain a highly reliable quote. Furthermore, it can take into account the user's evaluation of the contractor's quote based on the past quote history and use this to help obtain the current quote. In this way, it is possible to obtain a more reliable quote by utilizing the past quote history.

[0092] The evaluation unit can take into account the success rate of a contractor's past projects when evaluating the content, cost, construction quality, etc. of an estimate. For example, the reliability of a current estimate can be evaluated based on the success rate and customer satisfaction of the contractor's past projects. In addition, the evaluation unit can evaluate the contractor's construction quality and cost performance based on data from past projects and provide the evaluation results to the user. Furthermore, future risks can be predicted based on the contractor's past project success rate, and appropriate advice can be provided to the user. This allows for more reliable evaluations to be made by utilizing past project data.

[0093] The evaluation unit can take into account the environmental impact of materials used by contractors when evaluating the content, cost, construction quality, etc. of estimates. For example, eco-friendly contractors can be evaluated based on environmental impact data on materials used by contractors. The evaluation unit can also evaluate the environmental impact of contractors' construction methods and provide this information to users. Furthermore, based on data on the contractors' environmental impact, it can propose environmentally friendly renovation plans to users. In this way, by performing evaluations that take environmental impact into consideration, it is possible to provide users with more sustainable renovation plans.

[0094] The reception unit can estimate the user's emotions and adjust the request input method based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to input their request quickly. In this way, by providing an input method that corresponds to the user's emotions, it is possible to reduce the user's stress.

[0095] The selection unit can estimate the user's emotions and adjust the selection criteria for a service provider based on the estimated user's emotions. For example, if the user is feeling stressed, a highly reliable service provider can be selected with priority. Furthermore, if the user is relaxed, a service provider with high cost performance can be selected. Furthermore, if the user is in a hurry, a service provider that can respond quickly can be selected. In this way, by providing service provider selection criteria according to the user's emotions, a more appropriate service provider can be selected.

[0096] The acquisition unit can estimate the user's emotions and adjust the timing of obtaining an estimate based on the estimated user's emotions. For example, if the user is feeling stressed, an estimate can be obtained quickly. If the user is relaxed, a detailed estimate can be obtained. Furthermore, if the user is in a hurry, an estimate including only the most important items can be obtained quickly. This allows a more appropriate estimate to be obtained by providing the timing of obtaining an estimate according to the user's emotions.

[0097] The evaluation unit can estimate the user's emotions and adjust the estimation evaluation method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy-to-understand evaluation method can be provided. If the user is relaxed, a detailed evaluation method can be provided. Furthermore, if the user is in a hurry, a basic evaluation method can be provided. This allows for more appropriate evaluation by providing an evaluation method according to the user's emotions.

[0098] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated user's emotions. For example, if the user is feeling stressed, important evaluation items can be provided with priority. If the user is relaxed, detailed evaluation items can be provided. Furthermore, if the user is in a hurry, evaluation items that focus on the main points can be provided. In this way, by determining the priority of evaluations according to the user's emotions, more appropriate evaluations can be performed.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The reception unit inputs the user's requests. The user's requests include, for example, the budget, the desired layout, the details of the renovation, etc. The reception unit allows the user to input these requests. Step 2: The selection unit analyzes the information entered by the reception unit and selects an appropriate contractor. The selection unit selects the most suitable contractor based on data such as past construction performance, evaluations, and costs. Using generation AI, it is possible to select the contractor that best suits the user's requests. Step 3: The acquisition unit acquires quotations from the contractors selected by the selection unit. The acquisition unit can request quotations from the selected contractors and acquire quotations. Step 4: The evaluation unit evaluates the estimate obtained by the acquisition unit. The evaluation unit evaluates the content, cost, construction quality, etc. of the estimate and provides it to the user. Using the generation AI, it is possible to evaluate whether the cost of the estimate is appropriate and whether the construction quality is high.

[0101] 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.

[0102] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting a user's request; a selection unit that analyzes the information input by the reception unit and selects a trader; an acquisition unit that acquires quotations from the contractors selected by the selection unit; an evaluation unit that evaluates the estimate obtained by the acquisition unit; Equipped with A system characterized by:

2. The reception unit Enter the user's budget, desired layout, and renovation requirements 2. The system of claim 1.

3. The selection unit Select a contractor based on past construction performance, evaluations, and cost data 2. The system of claim 1.

4. The acquisition unit Get quotes from selected vendors 2. The system of claim 1.

5. The evaluation unit Evaluate the contents of the estimate, costs, construction quality, etc.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the request input method based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past request history and suggest the optimal input format 2. The system of claim 1.

8. The reception unit Customize your request based on your current life situation and interests 2. The system of claim 1.

Images