system

Abstract

The system according to the embodiment aims to automate the process of evaluating the validity of estimates and enable flexible staffing. [Solution] A system according to an embodiment includes a reception unit, a collection unit, an evaluation unit, and a provision unit. The reception unit receives input of quotation data. The collection unit analyzes the quotation data received by the reception unit and collects data on similar past cases or market prices. The evaluation unit evaluates the validity of the quotation based on the data collected by the collection unit. The provision unit provides the evaluation results obtained by the evaluation 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] Conventional technology requires experience-based knowledge to evaluate the validity of estimates, making it difficult to allocate personnel flexibly.

[0005] The system according to the embodiment aims to automate the process of evaluating the validity of estimates and enable flexible staffing. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an evaluation unit, and a provision unit. The reception unit receives input of quotation data. The collection unit analyzes the quotation data received by the reception unit and collects data on similar past cases or market prices. The evaluation unit evaluates the validity of the quotation based on the data collected by the collection unit. The provision unit provides the evaluation results obtained by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment automates the process of assessing the validity of quotes and can enable flexible staffing. [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) An estimate assessment system according to an embodiment of the present invention utilizes a generation AI to assess the validity of estimates for various construction projects and system development projects. In this estimate assessment system, a user obtains an estimate from a contractor and inputs it into the system. The generation AI then analyzes the estimate and evaluates its validity based on data from similar past projects and market prices. Finally, the generation AI provides the assessment results to the user. The assessment results include whether the estimate is valid and a comparison with past data and market prices. This system enables assessment of the validity of estimates without prior experience, enabling flexible personnel allocation. Furthermore, the ability to quickly evaluate the validity of estimates improves work efficiency. For example, a system may be provided with a reception unit where a user obtains an estimate from a contractor and inputs it into the system. Next, a collection unit is provided that collects data from similar past projects and market prices as data referenced by the generation AI. Furthermore, an evaluation unit is provided that evaluates the validity of the estimate based on evaluation criteria and algorithms. Finally, a provision unit is provided that provides the evaluation results to the user. These elements are interrelated, and the entire system can evaluate the validity of estimates in a series of steps: the collection unit analyzes the estimate data received by the reception unit, the evaluation unit performs the evaluation, and the provision unit provides the evaluation results. This allows the estimate assessment system to efficiently evaluate the validity of estimate data and provide it to the user.

[0029] The estimate assessment system according to the embodiment includes a reception unit, a collection unit, an evaluation unit, and a provision unit. The reception unit inputs estimate data acquired by a user from a contractor. Examples of the estimate data include, but are not limited to, estimates for construction work and IT projects. The reception unit can manually input the estimate data acquired by a user from a contractor. The reception unit can also automatically accept estimate data via email or a file upload function. The reception unit can also digitize paper-based estimate data using OCR technology and input it into the system. For example, the reception unit can scan paper-based estimate data using a scanner and convert it into text data using OCR technology. The collection unit uses a generation AI to analyze the estimate data received by the reception unit and collect data on similar past projects and market prices. The collection unit can, for example, extract and analyze data on similar past projects from a database. The collection unit can also collect and analyze publicly available data and market research reports on the Internet. The collection unit can also automatically collect and analyze data on similar past projects and market prices using a generation AI. For example, the collection unit causes the generation AI to crawl public data on the Internet and collect relevant data. The evaluation unit evaluates the validity of the estimate based on the data collected by the collection unit. For example, the evaluation unit can compare the cost performance of the estimate and industry standards based on the collected data. The evaluation unit can also use the generation AI to analyze the collected data and evaluate the validity of the estimate. For example, the evaluation unit scores the validity of the estimate based on the data collected by the generation AI. The provision unit provides the evaluation results obtained by the evaluation unit to a user. For example, the provision unit can generate the evaluation results in report format and provide them to the user. The provision unit can also visually display the evaluation results using a dashboard. Furthermore, the provision unit can automatically generate the evaluation results using the generation AI and provide them to the user. For example, the provision unit causes the generation AI to provide specific advice or suggestions to the user based on the evaluation results.This allows the quotation assessment system to efficiently evaluate the validity of quotation data and provide it to the user.

[0030] The collection unit can collect data on similar past projects or market prices. For example, the collection unit extracts and analyzes data on similar past projects from a database. For example, the collection unit extracts past project reports from a database and collects related data. The collection unit can also collect and analyze public data on the Internet and market research reports. For example, the collection unit crawls public data on the Internet and collects related data. Furthermore, the collection unit can use the generation AI to automatically collect and analyze data on similar past projects and market prices. For example, the collection unit has the generation AI crawl public data on the Internet and collect related data. In this way, collecting data on similar past projects and market prices can provide basic data for evaluating the validity of estimates. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the collection unit has the generation AI crawl public data on the Internet and collect related data.

[0031] The evaluation unit can evaluate the validity of the estimate based on collected data of similar past cases or market prices. The evaluation unit, for example, compares the cost performance of the estimate and the industry standard based on the collected data. For example, the evaluation unit evaluates the cost performance of the estimate based on the collected data. The evaluation unit can also evaluate whether the estimate is valid compared to the industry standard based on the collected data. Furthermore, the evaluation unit can analyze the collected data and evaluate the validity of the estimate using a generation AI. For example, the evaluation unit scores the validity of the estimate based on the data collected by the generation AI. This makes it possible to provide an accurate evaluation result by evaluating the validity of the estimate based on the collected data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the evaluation unit scores the validity of the estimate based on the data collected by the generation AI.

[0032] The providing unit can provide the user with an evaluation result regarding the validity of the estimate obtained by the evaluation unit. The providing unit, for example, generates the evaluation result in a report format and provides it to the user. For example, the providing unit generates the evaluation result in a report format and provides it to the user. The providing unit can also visually display the evaluation result using a dashboard. For example, the providing unit visually displays the evaluation result using a dashboard. Furthermore, the providing unit can automatically generate the evaluation result using a generation AI and provide it to the user. For example, the generation AI provides specific advice or suggestions to the user based on the evaluation result. As a result, by providing the evaluation result to the user, the validity of the estimate can be quickly confirmed. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit provides specific advice or suggestions to the user based on the evaluation result by the generation AI.

[0033] The reception unit can input construction work estimate data obtained by a user from a contractor. For example, the reception unit manually inputs the estimate data obtained by a user from a contractor. For example, the reception unit manually inputs the estimate data obtained by a user from a contractor. The reception unit can also automatically accept the estimate data using email or a file upload function. For example, the reception unit automatically accepts the estimate data using email or a file upload function. The reception unit can also digitize paper-based estimate data using OCR technology and input it into the system. For example, the reception unit scans paper-based estimate data using a scanner and converts it into text data using OCR technology. This allows the system to accept the estimate data when the user inputs the estimate data obtained from the contractor. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit digitizes paper-based estimate data using OCR technology and inputs it into the system.

[0034] The reception unit can analyze the user's past estimate data input history and select an efficient input method. For example, the reception unit prioritizes and suggests input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception unit prioritizes and suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. For example, the reception unit predicts and suggests an input method to be used in a specific time period based on the user's past input history. Furthermore, the reception unit can analyze patterns of data previously input by the user and suggest an optimal input method. For example, the reception unit analyzes patterns of data previously input by the user and suggests an optimal input method. In this way, by analyzing the user's past input history, an optimal input method can be suggested and data entry efficiency can be improved. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit uses a generation AI to analyze the user's past input history and suggest an optimal input method.

[0035] The reception unit may filter the estimate data based on the user's current project or area of ​​interest when inputting the estimate data. For example, the reception unit may prioritize displaying estimate data related to a project currently in progress. For example, the reception unit may prioritize displaying estimate data related to a project currently in progress. The reception unit may also filter and display related estimate data based on the user's area of ​​interest. For example, the reception unit may filter and display related estimate data based on the user's area of ​​interest. Furthermore, the reception unit may suggest related estimate data based on projects in which the user has shown interest in the past. For example, the reception unit may suggest related estimate data based on projects in which the user has shown interest in the past. In this way, by filtering the estimate data based on the user's current project or area of ​​interest, highly relevant data can be preferentially input. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may filter the estimate data based on the user's current project or area of ​​interest.

[0036] When inputting estimate data, the reception unit can prioritize inputting estimate data for construction work that is highly relevant, taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes displaying estimate data related to that area. For example, when the user is in a specific area, the reception unit prioritizes displaying estimate data related to that area. The reception unit can also filter and display relevant estimate data based on the user's current location. For example, the reception unit can filter and display relevant estimate data based on the user's current location. Furthermore, the reception unit can also suggest relevant estimate data based on areas the user has visited in the past. For example, the reception unit suggests relevant estimate data based on areas the user has visited in the past. This allows highly relevant data to be preferentially input by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit allows the generation AI to preferentially input highly relevant data by taking into account the user's geographical location information.

[0037] When inputting quotation data, the reception unit can analyze the user's social media activity and input quotation data for related construction work. For example, the reception unit can prioritize displaying quotation data related to projects shared by the user on social media. For example, the reception unit can prioritize displaying quotation data related to projects shared by the user on social media. The reception unit can also filter and display quotation data related to areas of interest from the user's social media activity. For example, the reception unit can filter and display quotation data related to areas of interest from the user's social media activity. Furthermore, the reception unit can also suggest quotation data related to contractors the user follows on social media. For example, the reception unit can suggest quotation data related to contractors the user follows on social media. This allows related data to be input preferentially by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit uses a generation AI to analyze the user's social media activity and input related data.

[0038] During collection, the collection unit can evaluate the reliability of data from similar past projects and prioritize collecting reliable construction work estimate data. The collection unit, for example, analyzes data from similar past projects and prioritizes collecting reliable data. For example, the collection unit analyzes data from similar past projects and prioritizes collecting reliable data. The collection unit can also filter out unreliable data and collect only reliable data. For example, the collection unit filters out unreliable data and collects only reliable data. The collection unit can also evaluate the reliability of past data and prioritize collecting reliable data. For example, the collection unit evaluates the reliability of past data and prioritizes collecting reliable data. This prioritizes collecting reliable data, thereby improving the accuracy of the evaluation. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit uses a generation AI to evaluate the reliability of past data and prioritize collecting reliable data.

[0039] The collection unit can adjust the data update frequency at the time of collection to reflect market price fluctuations in real time. For example, when market prices fluctuate drastically, the collection unit increases the data update frequency. For example, when market prices fluctuate drastically, the collection unit increases the data update frequency. The collection unit can also decrease the data update frequency when market prices fluctuate little. For example, when market prices fluctuate little, the collection unit decreases the data update frequency. Furthermore, the collection unit can also adjust the data update frequency in accordance with market price fluctuations. For example, the collection unit adjusts the data update frequency in accordance with market price fluctuations. In this way, by adjusting the data update frequency in accordance with market price fluctuations, the latest information can be reflected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit adjusts the data update frequency by having the generation AI monitor market price fluctuations in real time.

[0040] During collection, the collection unit can prioritize collecting highly relevant construction work estimate data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also filter and collect relevant data based on the user's current location. For example, the collection unit filters and collects relevant data based on the user's current location. Furthermore, the collection unit can also collect relevant data based on areas the user has previously visited. For example, the collection unit collects relevant data based on areas the user has previously visited. In this way, highly relevant data can be preferentially collected by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information.

[0041] During collection, the collection unit collects construction work estimate data specific to the user's industry, allowing for more accurate evaluations. The collection unit, for example, prioritizes collecting data specific to the user's industry. For example, the collection unit prioritizes collecting data specific to the user's industry. The collection unit can also filter the industry-specific data to collect highly relevant data. For example, the collection unit filters the industry-specific data to collect highly relevant data. The collection unit can also collect data specific to the user's industry to improve the accuracy of the evaluation. For example, the collection unit collects data specific to the user's industry to improve the accuracy of the evaluation. In this way, by collecting data specific to the user's industry, the accuracy of the evaluation can be improved. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit uses a generation AI to collect data specific to the user's industry to improve the accuracy of the evaluation.

[0042] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation results during evaluation. The evaluation unit, for example, analyzes past evaluation results and optimizes the evaluation algorithm. For example, the evaluation unit analyzes past evaluation results and optimizes the evaluation algorithm. The evaluation unit can also identify adjustment points for the evaluation criteria from the past evaluation results and reflect them in the algorithm. For example, the evaluation unit identifies adjustment points for the evaluation criteria from the past evaluation results and reflects them in the algorithm. Furthermore, the evaluation unit can adjust parameters for improving the accuracy of the evaluation algorithm based on the past evaluation results. For example, the evaluation unit adjusts parameters for improving the accuracy of the evaluation algorithm based on the past evaluation results. In this way, the accuracy of the evaluation algorithm can be improved by referring to the past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evaluation unit optimizes the evaluation algorithm by using a generation AI to refer to past evaluation results.

[0043] During evaluation, the evaluation unit can apply different evaluation algorithms depending on the level of detail of the estimate data. For example, when the estimate data is detailed, the evaluation unit applies a detailed evaluation algorithm. For example, when the estimate data is detailed, the evaluation unit applies a detailed evaluation algorithm. Furthermore, when the estimate data is simple, the evaluation unit can apply a simple evaluation algorithm. For example, when the estimate data is simple, the evaluation unit applies a simple evaluation algorithm. Furthermore, the evaluation unit can select and apply an optimal evaluation algorithm depending on the level of detail of the estimate data. For example, the evaluation unit selects and applies an optimal evaluation algorithm depending on the level of detail of the estimate data. In this way, by applying an optimal evaluation algorithm depending on the level of detail of the estimate data, the accuracy of the evaluation can be improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evaluation unit applies different evaluation algorithms depending on the level of detail of the estimate data.

[0044] The evaluation unit can adjust the evaluation criteria during evaluation by taking into account the user's geographical location information. For example, when the user is in a specific area, the evaluation unit adjusts the evaluation criteria by taking into account the market price of the area. For example, when the user is in a specific area, the evaluation unit adjusts the evaluation criteria by taking into account the market price of the area. The evaluation unit can also apply region-specific evaluation criteria based on the user's current location. For example, the evaluation unit applies region-specific evaluation criteria based on the user's current location. Furthermore, the evaluation unit can also adjust the evaluation criteria based on areas the user has previously visited. For example, the evaluation unit adjusts the evaluation criteria based on areas the user has previously visited. In this way, region-specific evaluation criteria can be applied by taking into account the user's geographical location information. Some or all of the above-described processing in the evaluation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evaluation unit adjusts the evaluation criteria by taking into account the user's geographical location information.

[0045] During evaluation, the evaluation unit can apply evaluation criteria specific to the user's industry to perform a more accurate evaluation. The evaluation unit, for example, applies evaluation criteria specific to the industry to which the user belongs. For example, the evaluation unit applies evaluation criteria specific to the industry to which the user belongs. The evaluation unit can also filter the industry-specific evaluation criteria to apply more relevant criteria. For example, the evaluation unit filters the industry-specific evaluation criteria to apply more relevant criteria. The evaluation unit can also apply evaluation criteria specific to the user's industry to improve the accuracy of the evaluation. For example, the evaluation unit applies evaluation criteria specific to the user's industry to improve the accuracy of the evaluation. In this way, the accuracy of the evaluation can be improved by applying evaluation criteria specific to the user's industry. Some or all of the above-described processing in the evaluation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evaluation unit uses a generation AI to apply evaluation criteria specific to the user's industry to improve the accuracy of the evaluation.

[0046] The providing unit can select the optimal providing method by referring to the user's past feedback when providing the content. For example, the providing unit preferentially selects a providing method that the user has previously preferred. For example, the providing unit preferentially selects a providing method that the user has previously preferred. The providing unit can also select the optimal providing method from the user's past feedback. For example, the providing unit selects the optimal providing method from the user's past feedback. Furthermore, the providing unit can analyze methods that the user has previously been provided with and select the optimal providing method. For example, the providing unit analyzes methods that the user has previously been provided with and selects the optimal providing method. In this way, the optimal providing method can be selected and provided to the user by referring to the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit selects the optimal providing method by using a generation AI to refer to the user's past feedback.

[0047] The providing unit can apply different providing methods depending on the level of detail of the evaluation results when providing the evaluation results. For example, if the evaluation results are detailed, the providing unit applies the detailed providing method. For example, if the evaluation results are detailed, the providing unit applies the detailed providing method. Furthermore, if the evaluation results are brief, the providing unit can also apply the concise providing method. For example, if the evaluation results are brief, the providing unit applies the concise providing method. Furthermore, the providing unit can select and apply the optimal providing method depending on the level of detail of the evaluation results. For example, the providing unit selects and applies the optimal providing method depending on the level of detail of the evaluation results. This makes it possible to provide optimal information to the user by applying the optimal providing method depending on the level of detail of the evaluation results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit applies different providing methods depending on the level of detail of the evaluation results.

[0048] The providing unit can select the optimal delivery method by taking into account the user's geographical location information when providing the information. For example, when the user is in a specific area, the providing unit prioritizes selecting a delivery method related to that area. For example, when the user is in a specific area, the providing unit prioritizes selecting a delivery method related to that area. The providing unit can also select the optimal delivery method based on the user's current location. For example, the providing unit selects the optimal delivery method based on the user's current location. Furthermore, the providing unit can also select the optimal delivery method based on areas the user has previously visited. For example, the providing unit selects the optimal delivery method based on areas the user has previously visited. In this way, the optimal delivery method can be selected and provided to the user by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit selects the optimal delivery method by taking into account the user's geographical location information.

[0049] The providing unit can provide more useful evaluation results by providing information specific to the user's industry during the providing process. For example, the providing unit can prioritize providing information specific to the industry to which the user belongs. For example, the providing unit can prioritize providing information specific to the industry to which the user belongs. The providing unit can also filter the industry-specific information to provide highly relevant information. For example, the providing unit can filter the industry-specific information to provide highly relevant information. Furthermore, the providing unit can also provide information specific to the user's industry to increase the usefulness of the evaluation results. For example, the providing unit can provide information specific to the user's industry to increase the usefulness of the evaluation results. In this way, the usefulness of the evaluation results can be increased by providing information specific to the user's industry. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can cause the generation AI to provide information specific to the user's industry to increase the usefulness of the evaluation results.

[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] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also predict and suggest the input method to be used during a specific time period based on the user's past input history. It can also analyze patterns of data that the user has previously entered and suggest the optimal input method. In this way, by analyzing the user's past input history, it is possible to suggest the optimal input method and improve the efficiency of data entry.

[0052] The collection unit can prioritize collection of highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, data related to that area is prioritized for collection. In addition, related data can be filtered and collected based on the user's current location. Furthermore, related data can be collected based on areas the user has visited in the past. In this way, highly relevant data can be prioritized for collection by taking into account the user's geographical location information.

[0053] The providing unit can select the optimal providing method by referring to the user's past feedback. For example, the providing unit can preferentially select a providing method that the user has previously preferred. The providing unit can also select the optimal providing method from the user's past feedback. Furthermore, the providing unit can analyze the methods that the user has previously been provided with and select the optimal providing method. In this way, the optimal providing method can be selected and provided to the user by referring to the user's past feedback.

[0054] The reception unit can analyze the user's social media activity and input relevant quotation data. For example, quotation data related to projects shared by the user on social media can be preferentially displayed. The reception unit can also filter and display quotation data related to areas of interest from the user's social media activity. Furthermore, the reception unit can suggest quotation data related to contractors the user follows on social media. This allows relevant data to be preferentially input by analyzing the user's social media activity.

[0055] The collection unit collects data specific to the user's industry, enabling more accurate evaluations. For example, the collection unit prioritizes the collection of data specific to the industry to which the user belongs. The collection unit can also filter industry-specific data to collect highly relevant data. Furthermore, the collection unit can collect data specific to the user's industry to improve the accuracy of the evaluation. Thus, by collecting data specific to the user's industry, the accuracy of the evaluation can be improved.

[0056] During evaluation, the evaluation unit can optimize the evaluation algorithm by referring to past evaluation results. For example, it analyzes past evaluation results and optimizes the evaluation algorithm. It can also identify adjustment points for the evaluation criteria from past evaluation results and reflect them in the algorithm. Furthermore, it can adjust parameters to improve the accuracy of the evaluation algorithm based on past evaluation results. In this way, the accuracy of the evaluation algorithm can be improved by referring to past evaluation results.

[0057] When providing the evaluation results, the providing unit can apply different providing methods depending on the level of detail of the evaluation results. For example, if the evaluation results are detailed, a detailed providing method can be applied. Also, if the evaluation results are brief, a simple providing method can be applied. Furthermore, the optimal providing method can be selected and applied depending on the level of detail of the evaluation results. This makes it possible to provide optimal information to the user by applying the optimal providing method depending on the level of detail of the evaluation results.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit inputs quotation data obtained by the user from contractors. The quotation data can include estimates for construction work or IT projects. The reception unit can accept quotation data by manual input, email, file upload, or by digitizing paper-based quotation data using OCR technology. Step 2: The collection unit analyzes the quotation data received by the reception unit and collects data on similar past projects and market prices. The collection unit extracts data on similar past projects from the database and collects publicly available data on the Internet and market research reports. Furthermore, this data is automatically collected and analyzed using generative AI. Step 3: The evaluation department evaluates the validity of the estimate based on the data collected by the collection department. Based on the collected data, the evaluation department compares the cost performance of the estimate with industry standards and scores the validity of the estimate using generative AI. Step 4: The providing unit provides the evaluation results obtained by the evaluation unit to the user. The providing unit generates the evaluation results in report format and visually displays them using a dashboard. Furthermore, the generating AI is used to provide specific advice and suggestions based on the evaluation results.

[0060] (Example 2) An estimate assessment system according to an embodiment of the present invention utilizes a generation AI to assess the validity of estimates for various construction projects and system development projects. In this estimate assessment system, a user obtains an estimate from a contractor and inputs it into the system. The generation AI then analyzes the estimate and evaluates its validity based on data from similar past projects and market prices. Finally, the generation AI provides the assessment results to the user. The assessment results include whether the estimate is valid and a comparison with past data and market prices. This system enables assessment of the validity of estimates without prior experience, enabling flexible personnel allocation. Furthermore, the ability to quickly evaluate the validity of estimates improves work efficiency. For example, a system may be provided with a reception unit where a user obtains an estimate from a contractor and inputs it into the system. Next, a collection unit is provided that collects data from similar past projects and market prices as data referenced by the generation AI. Furthermore, an evaluation unit is provided that evaluates the validity of the estimate based on evaluation criteria and algorithms. Finally, a provision unit is provided that provides the evaluation results to the user. These elements are interrelated, and the entire system can evaluate the validity of estimates in a series of steps: the collection unit analyzes the estimate data received by the reception unit, the evaluation unit performs the evaluation, and the provision unit provides the evaluation results. This allows the estimate assessment system to efficiently evaluate the validity of estimate data and provide it to the user.

[0061] The estimate assessment system according to the embodiment includes a reception unit, a collection unit, an evaluation unit, and a provision unit. The reception unit inputs estimate data acquired by a user from a contractor. Examples of the estimate data include, but are not limited to, estimates for construction work and IT projects. The reception unit can manually input the estimate data acquired by a user from a contractor. The reception unit can also automatically accept estimate data via email or a file upload function. The reception unit can also digitize paper-based estimate data using OCR technology and input it into the system. For example, the reception unit can scan paper-based estimate data using a scanner and convert it into text data using OCR technology. The collection unit uses a generation AI to analyze the estimate data received by the reception unit and collect data on similar past projects and market prices. The collection unit can, for example, extract and analyze data on similar past projects from a database. The collection unit can also collect and analyze publicly available data and market research reports on the Internet. The collection unit can also automatically collect and analyze data on similar past projects and market prices using a generation AI. For example, the collection unit causes the generation AI to crawl public data on the Internet and collect relevant data. The evaluation unit evaluates the validity of the estimate based on the data collected by the collection unit. For example, the evaluation unit can compare the cost performance of the estimate and industry standards based on the collected data. The evaluation unit can also use the generation AI to analyze the collected data and evaluate the validity of the estimate. For example, the evaluation unit scores the validity of the estimate based on the data collected by the generation AI. The provision unit provides the evaluation results obtained by the evaluation unit to a user. For example, the provision unit can generate the evaluation results in report format and provide them to the user. The provision unit can also visually display the evaluation results using a dashboard. Furthermore, the provision unit can automatically generate the evaluation results using the generation AI and provide them to the user. For example, the provision unit causes the generation AI to provide specific advice or suggestions to the user based on the evaluation results.This allows the quotation assessment system to efficiently evaluate the validity of quotation data and provide it to the user.

[0062] The collection unit can collect data on similar past projects or market prices. For example, the collection unit extracts and analyzes data on similar past projects from a database. For example, the collection unit extracts past project reports from a database and collects related data. The collection unit can also collect and analyze public data on the Internet and market research reports. For example, the collection unit crawls public data on the Internet and collects related data. Furthermore, the collection unit can use the generation AI to automatically collect and analyze data on similar past projects and market prices. For example, the collection unit has the generation AI crawl public data on the Internet and collect related data. In this way, collecting data on similar past projects and market prices can provide basic data for evaluating the validity of estimates. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the collection unit has the generation AI crawl public data on the Internet and collect related data.

[0063] The evaluation unit can evaluate the validity of the estimate based on collected data of similar past cases or market prices. The evaluation unit, for example, compares the cost performance of the estimate and the industry standard based on the collected data. For example, the evaluation unit evaluates the cost performance of the estimate based on the collected data. The evaluation unit can also evaluate whether the estimate is valid compared to the industry standard based on the collected data. Furthermore, the evaluation unit can analyze the collected data and evaluate the validity of the estimate using a generation AI. For example, the evaluation unit scores the validity of the estimate based on the data collected by the generation AI. This makes it possible to provide an accurate evaluation result by evaluating the validity of the estimate based on the collected data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the evaluation unit scores the validity of the estimate based on the data collected by the generation AI.

[0064] The providing unit can provide the user with an evaluation result regarding the validity of the estimate obtained by the evaluation unit. The providing unit, for example, generates the evaluation result in a report format and provides it to the user. For example, the providing unit generates the evaluation result in a report format and provides it to the user. The providing unit can also visually display the evaluation result using a dashboard. For example, the providing unit visually displays the evaluation result using a dashboard. Furthermore, the providing unit can automatically generate the evaluation result using a generation AI and provide it to the user. For example, the generation AI provides specific advice or suggestions to the user based on the evaluation result. As a result, by providing the evaluation result to the user, the validity of the estimate can be quickly confirmed. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit provides specific advice or suggestions to the user based on the evaluation result by the generation AI.

[0065] The reception unit can input construction work estimate data obtained by a user from a contractor. For example, the reception unit manually inputs the estimate data obtained by a user from a contractor. For example, the reception unit manually inputs the estimate data obtained by a user from a contractor. The reception unit can also automatically accept the estimate data using email or a file upload function. For example, the reception unit automatically accepts the estimate data using email or a file upload function. The reception unit can also digitize paper-based estimate data using OCR technology and input it into the system. For example, the reception unit scans paper-based estimate data using a scanner and converts it into text data using OCR technology. This allows the system to accept the estimate data when the user inputs the estimate data obtained from the contractor. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit digitizes paper-based estimate data using OCR technology and inputs it into the system.

[0066] The reception unit can estimate the user's stress level and adjust the timing of inputting the quotation data based on the estimated stress level. For example, if the user is feeling stressed, the reception unit can delay the input timing to provide the user with time to relax. For example, if the user is feeling stressed, the reception unit can delay the input timing to provide the user with time to relax. Furthermore, if the user is in a hurry, the reception unit can advance the input timing to allow the user to input data quickly. For example, if the user is in a hurry, the reception unit can advance the input timing to allow the user to input data quickly. Furthermore, if the user is concentrating, the reception unit can send a notification prompting the user to input data at the optimal timing. For example, if the user is concentrating, the reception unit sends a notification prompting the user to input data at the optimal timing. This adjusts the input timing of the quotation data according to the user's emotions, thereby reducing the user's stress and enabling efficient data input. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit adjusts the input timing based on the estimated emotion by using the generation AI to estimate the user's emotion.

[0067] The reception unit can analyze the user's past estimate data input history and select an efficient input method. For example, the reception unit prioritizes and suggests input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception unit prioritizes and suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. For example, the reception unit predicts and suggests an input method to be used in a specific time period based on the user's past input history. Furthermore, the reception unit can analyze patterns of data previously input by the user and suggest an optimal input method. For example, the reception unit analyzes patterns of data previously input by the user and suggests an optimal input method. In this way, by analyzing the user's past input history, an optimal input method can be suggested and data entry efficiency can be improved. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit uses a generation AI to analyze the user's past input history and suggest an optimal input method.

[0068] The reception unit may filter the estimate data based on the user's current project or area of ​​interest when inputting the estimate data. For example, the reception unit may prioritize displaying estimate data related to a project currently in progress. For example, the reception unit may prioritize displaying estimate data related to a project currently in progress. The reception unit may also filter and display related estimate data based on the user's area of ​​interest. For example, the reception unit may filter and display related estimate data based on the user's area of ​​interest. Furthermore, the reception unit may suggest related estimate data based on projects in which the user has shown interest in the past. For example, the reception unit may suggest related estimate data based on projects in which the user has shown interest in the past. In this way, by filtering the estimate data based on the user's current project or area of ​​interest, highly relevant data can be preferentially input. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may filter the estimate data based on the user's current project or area of ​​interest.

[0069] The reception unit can estimate the user's stress level and determine the priority of the estimate data to be input based on the estimated stress level. For example, when the user is feeling stressed, the reception unit postpones data of low importance. For example, when the user is feeling stressed, the reception unit postpones data of low importance. Furthermore, when the user is relaxed, the reception unit can also prioritize input of data of high importance. For example, when the user is relaxed, the reception unit prioritizes input of data of high importance. Furthermore, when the user is in a hurry, the reception unit can also prioritize input of the most important data. For example, when the user is in a hurry, the reception unit prioritizes input of the most important data. In this way, by determining the priority of the estimate data according to the user's emotions, it is possible to prioritize input of important data. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit allows the generation AI to estimate the user's emotions and determine the priority of the quotation data to be entered based on the estimated emotions.

[0070] When inputting estimate data, the reception unit can prioritize inputting estimate data for construction work that is highly relevant, taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes displaying estimate data related to that area. For example, when the user is in a specific area, the reception unit prioritizes displaying estimate data related to that area. The reception unit can also filter and display relevant estimate data based on the user's current location. For example, the reception unit can filter and display relevant estimate data based on the user's current location. Furthermore, the reception unit can also suggest relevant estimate data based on areas the user has visited in the past. For example, the reception unit suggests relevant estimate data based on areas the user has visited in the past. This allows highly relevant data to be preferentially input by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit allows the generation AI to preferentially input highly relevant data by taking into account the user's geographical location information.

[0071] When inputting quotation data, the reception unit can analyze the user's social media activity and input quotation data for related construction work. For example, the reception unit can prioritize displaying quotation data related to projects shared by the user on social media. For example, the reception unit can prioritize displaying quotation data related to projects shared by the user on social media. The reception unit can also filter and display quotation data related to areas of interest from the user's social media activity. For example, the reception unit can filter and display quotation data related to areas of interest from the user's social media activity. Furthermore, the reception unit can also suggest quotation data related to contractors the user follows on social media. For example, the reception unit can suggest quotation data related to contractors the user follows on social media. This allows related data to be input preferentially by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit uses a generation AI to analyze the user's social media activity and input related data.

[0072] The collection unit can estimate the user's stress level and adjust the type of data to be collected based on the estimated stress level. For example, when the user is feeling stressed, the collection unit postpones collecting less important data. For example, when the user is feeling stressed, the collection unit postpones collecting less important data. The collection unit can also prioritize collecting detailed data when the user is relaxed. For example, when the user is relaxed, the collection unit prioritizes collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting the most important data. For example, when the user is in a hurry, the collection unit prioritizes collecting the most important data. This enables efficient data collection by adjusting the type of data to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, the generation AI, or without the generation AI. For example, the collection unit allows the generation AI to estimate the user's emotions and adjust the type of data to be collected based on the estimated emotions.

[0073] During collection, the collection unit can evaluate the reliability of data from similar past projects and prioritize collecting reliable construction work estimate data. The collection unit, for example, analyzes data from similar past projects and prioritizes collecting reliable data. For example, the collection unit analyzes data from similar past projects and prioritizes collecting reliable data. The collection unit can also filter out unreliable data and collect only reliable data. For example, the collection unit filters out unreliable data and collects only reliable data. The collection unit can also evaluate the reliability of past data and prioritize collecting reliable data. For example, the collection unit evaluates the reliability of past data and prioritizes collecting reliable data. This prioritizes collecting reliable data, thereby improving the accuracy of the evaluation. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit uses a generation AI to evaluate the reliability of past data and prioritize collecting reliable data.

[0074] The collection unit can adjust the data update frequency at the time of collection to reflect market price fluctuations in real time. For example, when market prices fluctuate drastically, the collection unit increases the data update frequency. For example, when market prices fluctuate drastically, the collection unit increases the data update frequency. The collection unit can also decrease the data update frequency when market prices fluctuate little. For example, when market prices fluctuate little, the collection unit decreases the data update frequency. Furthermore, the collection unit can also adjust the data update frequency in accordance with market price fluctuations. For example, the collection unit adjusts the data update frequency in accordance with market price fluctuations. In this way, by adjusting the data update frequency in accordance with market price fluctuations, the latest information can be reflected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit adjusts the data update frequency by having the generation AI monitor market price fluctuations in real time.

[0075] The collection unit can estimate the user's stress level and determine the priority of data to be collected based on the estimated stress level. For example, when the user is feeling stressed, the collection unit postpones data of low importance. For example, when the user is feeling stressed, the collection unit postpones data of low importance. The collection unit can also prioritize collecting detailed data when the user is relaxed. For example, when the user is relaxed, the collection unit prioritizes collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting the most important data. For example, when the user is in a hurry, the collection unit prioritizes collecting the most important data. In this way, by determining the priority of data to be collected according to the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the collection unit has the generation AI estimate the user's emotions and determine the priority of the data to be collected based on the estimated emotions.

[0076] During collection, the collection unit can prioritize collecting highly relevant construction work estimate data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also filter and collect relevant data based on the user's current location. For example, the collection unit filters and collects relevant data based on the user's current location. Furthermore, the collection unit can also collect relevant data based on areas the user has previously visited. For example, the collection unit collects relevant data based on areas the user has previously visited. In this way, highly relevant data can be preferentially collected by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information.

[0077] During collection, the collection unit collects construction work estimate data specific to the user's industry, allowing for more accurate evaluations. The collection unit, for example, prioritizes collecting data specific to the user's industry. For example, the collection unit prioritizes collecting data specific to the user's industry. The collection unit can also filter the industry-specific data to collect highly relevant data. For example, the collection unit filters the industry-specific data to collect highly relevant data. The collection unit can also collect data specific to the user's industry to improve the accuracy of the evaluation. For example, the collection unit collects data specific to the user's industry to improve the accuracy of the evaluation. In this way, by collecting data specific to the user's industry, the accuracy of the evaluation can be improved. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit uses a generation AI to collect data specific to the user's industry to improve the accuracy of the evaluation.

[0078] The evaluation unit can estimate the user's stress level and adjust the evaluation criteria based on the estimated stress level. For example, the evaluation unit relaxes the evaluation criteria when the user is feeling stressed. The evaluation unit can also apply detailed evaluation criteria when the user is relaxed. For example, the evaluation unit applies detailed evaluation criteria when the user is relaxed. The evaluation unit can also apply concise evaluation criteria when the user is in a hurry. For example, the evaluation unit applies concise evaluation criteria when the user is in a hurry. This allows for more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the evaluation unit estimates the user's emotions using the generation AI and adjusts the evaluation criteria based on the estimated emotions.

[0079] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation results during evaluation. The evaluation unit, for example, analyzes past evaluation results and optimizes the evaluation algorithm. For example, the evaluation unit analyzes past evaluation results and optimizes the evaluation algorithm. The evaluation unit can also identify adjustment points for the evaluation criteria from the past evaluation results and reflect them in the algorithm. For example, the evaluation unit identifies adjustment points for the evaluation criteria from the past evaluation results and reflects them in the algorithm. Furthermore, the evaluation unit can adjust parameters for improving the accuracy of the evaluation algorithm based on the past evaluation results. For example, the evaluation unit adjusts parameters for improving the accuracy of the evaluation algorithm based on the past evaluation results. In this way, the accuracy of the evaluation algorithm can be improved by referring to the past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evaluation unit optimizes the evaluation algorithm by using a generation AI to refer to past evaluation results.

[0080] During evaluation, the evaluation unit can apply different evaluation algorithms depending on the level of detail of the estimate data. For example, when the estimate data is detailed, the evaluation unit applies a detailed evaluation algorithm. For example, when the estimate data is detailed, the evaluation unit applies a detailed evaluation algorithm. Furthermore, when the estimate data is simple, the evaluation unit can apply a simple evaluation algorithm. For example, when the estimate data is simple, the evaluation unit applies a simple evaluation algorithm. Furthermore, the evaluation unit can select and apply an optimal evaluation algorithm depending on the level of detail of the estimate data. For example, the evaluation unit selects and applies an optimal evaluation algorithm depending on the level of detail of the estimate data. In this way, by applying an optimal evaluation algorithm depending on the level of detail of the estimate data, the accuracy of the evaluation can be improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evaluation unit applies different evaluation algorithms depending on the level of detail of the estimate data.

[0081] The evaluation unit can estimate the user's stress level and adjust the display method of the evaluation results based on the estimated stress level. For example, if the user is feeling stressed, the evaluation unit provides a simple, highly visible display method. For example, if the user is feeling stressed, the evaluation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the evaluation unit can provide a display method including detailed information. For example, if the user is relaxed, the evaluation unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the evaluation unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the evaluation unit provides a display method that focuses on the main points. This allows the display method of the evaluation results to be adjusted according to the user's emotions, thereby providing an optimal display method for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the evaluation unit uses the generation AI to estimate the user's emotions and adjusts the way the evaluation results are displayed based on the estimated emotions.

[0082] The evaluation unit can adjust the evaluation criteria during evaluation by taking into account the user's geographical location information. For example, when the user is in a specific area, the evaluation unit adjusts the evaluation criteria by taking into account the market price of the area. For example, when the user is in a specific area, the evaluation unit adjusts the evaluation criteria by taking into account the market price of the area. The evaluation unit can also apply region-specific evaluation criteria based on the user's current location. For example, the evaluation unit applies region-specific evaluation criteria based on the user's current location. Furthermore, the evaluation unit can also adjust the evaluation criteria based on areas the user has previously visited. For example, the evaluation unit adjusts the evaluation criteria based on areas the user has previously visited. In this way, region-specific evaluation criteria can be applied by taking into account the user's geographical location information. Some or all of the above-described processing in the evaluation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evaluation unit adjusts the evaluation criteria by taking into account the user's geographical location information.

[0083] During evaluation, the evaluation unit can apply evaluation criteria specific to the user's industry to perform a more accurate evaluation. The evaluation unit, for example, applies evaluation criteria specific to the industry to which the user belongs. For example, the evaluation unit applies evaluation criteria specific to the industry to which the user belongs. The evaluation unit can also filter the industry-specific evaluation criteria to apply more relevant criteria. For example, the evaluation unit filters the industry-specific evaluation criteria to apply more relevant criteria. The evaluation unit can also apply evaluation criteria specific to the user's industry to improve the accuracy of the evaluation. For example, the evaluation unit applies evaluation criteria specific to the user's industry to improve the accuracy of the evaluation. In this way, the accuracy of the evaluation can be improved by applying evaluation criteria specific to the user's industry. Some or all of the above-described processing in the evaluation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evaluation unit uses a generation AI to apply evaluation criteria specific to the user's industry to improve the accuracy of the evaluation.

[0084] The providing unit can estimate the user's stress level and adjust the method of providing the evaluation results based on the estimated stress level. For example, when the user is feeling stressed, the providing unit provides a simple, highly visible display method. For example, when the user is feeling stressed, the providing unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. For example, when the user is relaxed, the providing unit provides a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. For example, when the user is in a hurry, the providing unit provides a display method that focuses on the main points. This allows the method of providing the evaluation results to be adjusted according to the user's emotions, thereby providing an optimal display method for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit allows the generation AI to estimate the user's emotions and adjusts the method of providing the evaluation results based on the estimated emotions.

[0085] The providing unit can select the optimal providing method by referring to the user's past feedback when providing the content. For example, the providing unit preferentially selects a providing method that the user has previously preferred. For example, the providing unit preferentially selects a providing method that the user has previously preferred. The providing unit can also select the optimal providing method from the user's past feedback. For example, the providing unit selects the optimal providing method from the user's past feedback. Furthermore, the providing unit can analyze methods that the user has previously been provided with and select the optimal providing method. For example, the providing unit analyzes methods that the user has previously been provided with and selects the optimal providing method. In this way, the optimal providing method can be selected and provided to the user by referring to the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit selects the optimal providing method by using a generation AI to refer to the user's past feedback.

[0086] The providing unit can apply different providing methods depending on the level of detail of the evaluation results when providing the evaluation results. For example, if the evaluation results are detailed, the providing unit applies the detailed providing method. For example, if the evaluation results are detailed, the providing unit applies the detailed providing method. Furthermore, if the evaluation results are brief, the providing unit can also apply the concise providing method. For example, if the evaluation results are brief, the providing unit applies the concise providing method. Furthermore, the providing unit can select and apply the optimal providing method depending on the level of detail of the evaluation results. For example, the providing unit selects and applies the optimal providing method depending on the level of detail of the evaluation results. This makes it possible to provide optimal information to the user by applying the optimal providing method depending on the level of detail of the evaluation results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit applies different providing methods depending on the level of detail of the evaluation results.

[0087] The providing unit can estimate the user's stress level and determine the priority of the evaluation results based on the estimated stress level. For example, when the user is feeling stressed, the providing unit postpones results of lower importance. For example, when the user is feeling stressed, the providing unit postpones results of lower importance. Furthermore, when the user is relaxed, the providing unit can also prioritize providing results of higher importance. For example, when the user is relaxed, the providing unit prioritizes results of higher importance. Furthermore, when the user is in a hurry, the providing unit can also prioritize providing results of higher importance. For example, when the user is in a hurry, the providing unit prioritizes results of higher importance. In this way, by determining the priority of the evaluation results according to the user's emotions, it is possible to prioritize providing results of higher importance. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit has the generation AI estimate the user's emotions and determine the priority of the evaluation results based on the estimated emotions.

[0088] The providing unit can select the optimal delivery method by taking into account the user's geographical location information when providing the information. For example, when the user is in a specific area, the providing unit prioritizes selecting a delivery method related to that area. For example, when the user is in a specific area, the providing unit prioritizes selecting a delivery method related to that area. The providing unit can also select the optimal delivery method based on the user's current location. For example, the providing unit selects the optimal delivery method based on the user's current location. Furthermore, the providing unit can also select the optimal delivery method based on areas the user has previously visited. For example, the providing unit selects the optimal delivery method based on areas the user has previously visited. In this way, the optimal delivery method can be selected and provided to the user by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit selects the optimal delivery method by taking into account the user's geographical location information.

[0089] The providing unit can provide more useful evaluation results by providing information specific to the user's industry during the providing process. For example, the providing unit can prioritize providing information specific to the industry to which the user belongs. For example, the providing unit can prioritize providing information specific to the industry to which the user belongs. The providing unit can also filter the industry-specific information to provide highly relevant information. For example, the providing unit can filter the industry-specific information to provide highly relevant information. Furthermore, the providing unit can also provide information specific to the user's industry to increase the usefulness of the evaluation results. For example, the providing unit can provide information specific to the user's industry to increase the usefulness of the evaluation results. In this way, the usefulness of the evaluation results can be increased by providing information specific to the user's industry. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can cause the generation AI to provide information specific to the user's industry to increase the usefulness of the evaluation results. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, collection unit, evaluation unit, and provision 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 control unit 46A of the smart device 14, and inputs quotation data obtained by the user from a trader. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects data on similar past cases and market prices. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the validity of the quotation based on the collected data. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the evaluation result to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, evaluation unit, and provision unit, 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 control unit 46A of the smart glasses 214, and inputs quotation data obtained by the user from a vendor. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects data on past similar cases and market prices. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the validity of the quotation based on the collected data. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the evaluation result to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, collection unit, evaluation unit, and provision 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 control unit 46A of the headset type terminal 314, and inputs quotation data obtained by the user from a trader. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects data on similar past cases and market prices. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the validity of the quotation based on the collected data. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the evaluation results to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, collection unit, evaluation unit, and provision 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 control unit 46A of the robot 414, and inputs quotation data obtained by the user from a trader. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects data on similar past cases and market prices. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the validity of the quotation based on the collected data. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the evaluation results to the user.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also predict and suggest the input method to be used during a specific time period based on the user's past input history. It can also analyze patterns of data that the user has previously entered and suggest the optimal input method. In this way, by analyzing the user's past input history, it is possible to suggest the optimal input method and improve the efficiency of data entry.

[0092] The collection unit can prioritize collection of highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, data related to that area is prioritized for collection. In addition, related data can be filtered and collected based on the user's current location. Furthermore, related data can be collected based on areas the user has visited in the past. In this way, highly relevant data can be prioritized for collection by taking into account the user's geographical location information.

[0093] The evaluation unit can estimate the user's stress level and adjust the evaluation criteria based on the estimated stress level. For example, if the user is feeling stressed, the evaluation criteria can be relaxed. Also, if the user is relaxed, detailed evaluation criteria can be applied. Furthermore, if the user is in a hurry, simple evaluation criteria can be applied. In this way, by adjusting the evaluation criteria according to the user's emotions, more appropriate evaluation can be performed.

[0094] The providing unit can select the optimal providing method by referring to the user's past feedback. For example, the providing unit can preferentially select a providing method that the user has previously preferred. The providing unit can also select the optimal providing method from the user's past feedback. Furthermore, the providing unit can analyze the methods that the user has previously been provided with and select the optimal providing method. In this way, the optimal providing method can be selected and provided to the user by referring to the user's past feedback.

[0095] The reception unit can analyze the user's social media activity and input relevant quotation data. For example, quotation data related to projects shared by the user on social media can be preferentially displayed. The reception unit can also filter and display quotation data related to areas of interest from the user's social media activity. Furthermore, the reception unit can suggest quotation data related to contractors the user follows on social media. This allows relevant data to be preferentially input by analyzing the user's social media activity.

[0096] The collection unit collects data specific to the user's industry, enabling more accurate evaluations. For example, the collection unit prioritizes the collection of data specific to the industry to which the user belongs. The collection unit can also filter industry-specific data to collect highly relevant data. Furthermore, the collection unit can collect data specific to the user's industry to improve the accuracy of the evaluation. Thus, by collecting data specific to the user's industry, the accuracy of the evaluation can be improved.

[0097] During evaluation, the evaluation unit can optimize the evaluation algorithm by referring to past evaluation results. For example, it analyzes past evaluation results and optimizes the evaluation algorithm. It can also identify adjustment points for the evaluation criteria from past evaluation results and reflect them in the algorithm. Furthermore, it can adjust parameters to improve the accuracy of the evaluation algorithm based on past evaluation results. In this way, the accuracy of the evaluation algorithm can be improved by referring to past evaluation results.

[0098] The providing unit can estimate the user's stress level and adjust the display method of the evaluation results based on the estimated stress level. For example, if the user is feeling stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the evaluation results according to the user's emotions, it is possible to provide the optimal display method for the user.

[0099] When providing the evaluation results, the providing unit can apply different providing methods depending on the level of detail of the evaluation results. For example, if the evaluation results are detailed, a detailed providing method can be applied. Also, if the evaluation results are brief, a simple providing method can be applied. Furthermore, the optimal providing method can be selected and applied depending on the level of detail of the evaluation results. This makes it possible to provide optimal information to the user by applying the optimal providing method depending on the level of detail of the evaluation results.

[0100] The providing unit can estimate the stress level of the user and determine the priority of the evaluation results based on the estimated stress level. For example, if the user is feeling stressed, results of lower importance can be postponed. Also, if the user is relaxed, results of higher importance can be provided preferentially. Furthermore, if the user is in a hurry, the most important results can be provided preferentially. In this way, by determining the priority of the evaluation results according to the user's emotions, important results can be provided preferentially.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit inputs quotation data obtained by the user from contractors. The quotation data can include estimates for construction work or IT projects. The reception unit can accept quotation data by manual input, email, file upload, or by digitizing paper-based quotation data using OCR technology. Step 2: The collection unit analyzes the quotation data received by the reception unit and collects data on similar past projects and market prices. The collection unit extracts data on similar past projects from the database and collects publicly available data on the Internet and market research reports. Furthermore, this data is automatically collected and analyzed using generative AI. Step 3: The evaluation department evaluates the validity of the estimate based on the data collected by the collection department. Based on the collected data, the evaluation department compares the cost performance of the estimate with industry standards and scores the validity of the estimate using generative AI. Step 4: The providing unit provides the evaluation results obtained by the evaluation unit to the user. The providing unit generates the evaluation results in report format and visually displays them using a dashboard. Furthermore, the generating AI is used to provide specific advice and suggestions based on the evaluation results.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 that receives input of quotation data; a collection unit that analyzes the quotation data received by the reception unit and collects data on similar past cases or market prices; an evaluation unit that evaluates the validity of the estimate based on the data collected by the collection unit; a providing unit that provides the evaluation result obtained by the evaluation unit. A system characterized by:

2. The collecting unit Collect data on similar past transactions or market prices The system of claim 1 .

3. The evaluation unit Evaluate the validity of the estimate based on collected data on similar past transactions or market prices The system of claim 1 .

4. The providing unit The evaluation result regarding the validity of the estimate obtained by the evaluation unit is provided to the user. The system of claim 1 .

5. The reception unit The user inputs the construction work estimate data obtained from the contractor. The system of claim 1 .

6. The reception unit Estimate the stress level of the user and adjust the timing of inputting estimate data based on the estimated stress level. The system of claim 1 .

7. The reception unit Analyze the user's past quotation data entry history and select an efficient input method The system of claim 1 .

8. The reception unit Filter quote data entry based on the user's current project or area of ​​interest The system of claim 1 .

9. The reception unit Estimate the user's stress level and prioritize the input estimation data based on the estimated stress level. The system of claim 1 .

10. The reception unit When inputting quote data, the system takes into account the user's geographic location information and prioritizes input of relevant construction quote data. The system of claim 1 .

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