system

Abstract

The system according to this embodiment aims to enable clients to obtain quotes quickly and accurately. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a generation unit. The reception unit receives input of information necessary for the project from the client. The analysis unit analyzes the information received by the reception unit. The generation unit generates an estimate based on the information analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to obtain a quick and accurate estimate even when a client inputs information necessary for a project.

[0005] The system according to the embodiment aims to enable a client to obtain an estimate quickly and accurately.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives an input of information necessary for a project from a client. The analysis unit analyzes the information received by the reception unit. The generation unit generates an estimate based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment allows clients to obtain quotes quickly and accurately. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The estimation generation system according to an embodiment of the present invention is a system in which, when a client inputs information necessary for a project, an AI analyzes that data and provides an approximate estimate using relevant market data and statistical information. The estimation generation system, when a client inputs information necessary for a project, uses the AI ​​to analyze that data and calculate the necessary materials, labor costs, duration, etc., using relevant market data and statistical information. Next, the AI ​​creates an estimate based on the calculated results and provides it to the client. This mechanism significantly reduces the time required for estimation work and improves accuracy. For example, it can reduce the time required for conventional manual estimation work by an average of 70% and improve estimation accuracy to over 95%. Furthermore, accurate estimates can reduce unnecessary costs. In addition, the AI ​​can improve the accuracy of estimates by combining historical data with market analysis. This allows clients to quickly and accurately calculate costs, enabling smooth project planning and implementation. For example, companies in the construction, manufacturing, and IT industries can use this system to prevent project delays due to time and cost constraints and budget overruns due to inaccurate estimates. Also, even if there is a lack of expertise for creating estimates, the AI ​​automatically creates estimates, enabling efficient project management. Furthermore, a user-friendly interface improves usability and meets user needs. For example, project managers and business owners can use this system to reduce time and costs, enabling efficient project management. The use of AI and big data supports data-driven decision-making and enables sustainable business models. For example, it enables data analysis using natural language processing, learning from historical estimation data using machine learning, and real-time data updates and estimation adjustments. As a result, the estimation navigator agent can provide fast and accurate estimates, leading clients' businesses to the next level. This allows the estimation generation system to provide fast and accurate estimates for clients' projects.

[0029] The estimate generation system according to this embodiment comprises a reception unit, an analysis unit, and a generation unit. The reception unit receives information necessary for a project from a client. This information may include, but is not limited to, the project's objectives, budget, and schedule. The reception unit receives the information entered by the client in digital format, for example. The reception unit can also scan handwritten information entered by the client and convert it into digital data. Furthermore, the reception unit can convert dictated information from the client into text using voice input. For example, the reception unit stores the information entered by the client in a database and sends it to the analysis unit. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information entered by the client using, for example, data analysis techniques. Furthermore, the analysis unit can analyze the information entered by the client using machine learning algorithms. Furthermore, the analysis unit can analyze the information entered by the client using natural language processing techniques. For example, the analysis unit collects relevant market data and statistical information based on the information entered by the client and sends the analysis results to the generation unit. The generation unit generates an estimate based on the information analyzed by the analysis unit. The generation unit generates estimates using, for example, a cost estimation algorithm. The generation unit can also generate estimates using a time estimation algorithm. Furthermore, the generation unit can generate estimates using a risk assessment algorithm. For example, the generation unit creates an estimate based on information received from the analysis unit and provides it to the client. Thus, the estimate generation system according to this embodiment can, when a client inputs information necessary for a project, have the AI ​​analyze that data and provide a rough estimate using relevant market data and statistical information.

[0030] The reception department receives information from clients that is necessary for their projects. This information may include, but is not limited to, project objectives, budget, and schedule. The reception department accepts client-entered information in digital format, for example. It can also scan handwritten information entered by clients and convert it into digital data. Furthermore, it can convert dictated information from clients into text using voice input. For example, the reception department can save the information entered by clients to a database and send it to the analysis department. The reception department provides multiple input methods to flexibly accommodate the format of information entered by clients. For example, direct input via web forms, scanning and uploading handwritten documents, and converting dictated content into text using speech recognition technology. This allows clients to provide information in the most suitable way according to their circumstances and preferences. In addition, the reception department has functions to verify and correct the entered information to ensure its accuracy. For example, if there are errors or deficiencies in the entered information, it can notify the client and prompt them to make corrections. Furthermore, the reception department automatically categorizes the entered information, organizing it into categories such as project objectives, budget, and schedule. This allows the analysis department to process the information efficiently. The reception department not only receives information from clients but also provides interfaces to facilitate communication with them. For example, it allows clients to resolve questions and concerns about their input through chatbots and FAQ sections. This enables clients to provide information smoothly and expedite the quotation generation process.

[0031] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes client-entered information using data analysis techniques. It can also analyze client-entered information using machine learning algorithms. Furthermore, it can analyze client-entered information using natural language processing techniques. For example, the analysis unit collects relevant market data and statistics based on the client-entered information and sends the analysis results to the generation unit. The analysis unit uses a combination of multiple analysis techniques to comprehensively analyze the information provided by the client. For example, it extracts basic statistics using data analysis techniques and detects patterns and trends using machine learning algorithms. Furthermore, it analyzes text data using natural language processing techniques to extract important keywords and phrases. This allows the analysis unit to accurately understand client needs and requirements and provide the foundational data necessary to generate appropriate estimates. Based on the client-entered information, the analysis unit accesses databases and APIs to collect relevant market data and statistics. For example, it refers to industry standard prices and data from past projects to identify cases similar to the client's project. This allows the analysis unit to obtain reference information to provide realistic estimates for the client's project. Furthermore, the analysis unit can use machine learning algorithms to predict risks and challenges related to the client's project. For example, it can identify problems and risks that are likely to occur under specific conditions based on historical data and propose countermeasures. This allows the analysis unit to provide clients with crucial information for project success. Before sending the analysis results to the generation unit, the analysis unit conducts a verification process to confirm the accuracy and reliability of the results. For example, it compares results obtained using multiple analysis methods to confirm consistency and reliability. It can also undergo expert review to evaluate whether the estimates generated based on the analysis results are realistic. This allows the analysis unit to establish a foundation for providing clients with high-quality estimates.

[0032] The generation unit generates estimates based on information analyzed by the analysis unit. For example, the generation unit generates estimates using cost estimation algorithms. It can also generate estimates using time estimation algorithms. Furthermore, it can generate estimates using risk assessment algorithms. For example, the generation unit creates an estimate based on information received from the analysis unit and provides it to the client. Based on the data provided by the analysis unit, the generation unit comprehensively evaluates the project's cost, time, and risk to generate the optimal estimate. The cost estimation algorithm calculates the overall project cost by considering factors such as material costs, labor costs, and equipment costs. The time estimation algorithm creates a project schedule based on the duration and dependencies of each task. The risk assessment algorithm identifies potential risks associated with the project and proposes countermeasures. This allows the generation unit to provide clients with comprehensive and realistic estimates. To improve the accuracy of estimates, the generation unit refers to historical project data and industry benchmarks. For example, it evaluates the validity of estimates based on performance data from similar projects and makes adjustments as needed. The generation unit can also provide customized estimates, taking into account client requirements and constraints. For example, it proposes the optimal resource allocation and schedule to complete a project within a specific budget. Furthermore, to ensure transparency in estimates, the generation unit creates reports that explain in detail the basis and calculation method of the estimates. This allows clients to understand and accept the contents of the estimates. The generation unit utilizes the latest technologies to automate the estimate generation process. For example, it uses cloud computing to process large amounts of data quickly and generate estimates in real time. In addition, the generation unit implements a learning process using AI to continuously improve the accuracy of estimates. This allows the generation unit to always provide highly accurate estimates based on the latest information and technology.

[0033] The analysis unit includes a market data collection unit that collects relevant market data. The market data collection unit can, for example, collect competitor information. The market data collection unit can also, for example, collect demand forecast data. The market data collection unit can also, for example, collect price trend data. By collecting relevant market data, the accuracy of estimates is improved. Some or all of the above processing in the market data collection unit may be performed, for example, using a generative AI, or without using a generative AI. For example, the market data collection unit can input prompts to the generative AI to collect competitor information and analyze the data collected by the generative AI.

[0034] The analysis unit includes a statistical information collection unit that collects relevant statistical information. The statistical information collection unit can, for example, collect historical sales data. The statistical information collection unit can also, for example, collect demographic data. The statistical information collection unit can also, for example, collect economic indicator data. By collecting relevant statistical information, the accuracy of the estimate is improved. Some or all of the above processing in the statistical information collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the statistical information collection unit can input prompts to the generating AI in order to collect historical sales data, and can analyze the data collected by the generating AI.

[0035] The generation unit includes a delivery unit that creates an estimate based on the calculated results and provides it to the client. The delivery unit can, for example, provide the estimate to the client via email. The delivery unit can also, for example, display the estimate on a dashboard. The delivery unit can also, for example, generate the estimate in PDF format and provide it to the client. This allows for the provision of quick and accurate estimates by creating and providing estimates to the client based on the calculated results. Some or all of the above processing in the delivery unit may be performed using, for example, a generation AI, or without a generation AI. For example, the delivery unit can input prompts to the generation AI and provide the estimate generated by the generation AI to the client.

[0036] The reception area has a user-friendly interface. A user-friendly interface can, for example, provide intuitive operation. A user-friendly interface can also, for example, provide a visually appealing design. A user-friendly interface can also, for example, support voice input. This makes the interface easier for clients to use. Some or all of the above-described processes in the user-friendly interface may be performed, for example, using a generative AI, or not using a generative AI. For example, the user-friendly interface can input prompts to a generative AI and provide the client with the interface generated by the generative AI.

[0037] The analysis unit improves the accuracy of estimates by combining historical data and market analysis. For example, the analysis unit can collect and use historical project data for analysis. For example, the analysis unit can collect and use historical data for analysis. For example, the analysis unit can perform market analysis using SWOT analysis. By combining historical data and market analysis, the accuracy of estimates is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input historical project data into a generative AI and improve the accuracy of estimates based on the results of the generative AI's analysis.

[0038] The reception desk analyzes the client's past input history and proposes the optimal input method. For example, the reception desk automatically displays project information that the client has frequently entered in the past as a suggestion. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the client has used in the past. For example, the reception desk predicts and suggests project information to be used during a specific time period based on the client's past input history. In this way, the optimal input method can be suggested by analyzing the client's past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the client's past input history data into a generative AI and propose the optimal input method based on the results of the generative AI's analysis.

[0039] The reception desk dynamically changes input fields based on the client's current project status. For example, when a client enters information related to an ongoing project, the reception desk displays only the necessary fields. For example, when a client starts a new project, the reception desk suggests input fields by referring to past project information. For example, the reception desk automatically updates input fields according to the client's project progress. This enables efficient input by dynamically changing input fields according to the client's current project status. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the client's project status data into a generative AI and dynamically change the input fields based on the results of the generative AI's analysis.

[0040] The reception desk prioritizes displaying relevant input fields, taking into account the client's geographical location. For example, if the client is in a specific region, the reception desk prioritizes displaying project information relevant to that region. For example, if the client is on the move, the reception desk dynamically changes the required input fields based on the client's current location. For example, if the client is in a specific country or region, the reception desk displays input fields based on the regulations and requirements of that region. This allows for the priority display of relevant input fields by considering the client's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the client's geographical location information into a generative AI, and based on the results of the generative AI's analysis, it can prioritize displaying relevant input fields.

[0041] The reception desk analyzes the client's social media activity and suggests relevant input fields. For example, the reception desk suggests input fields based on project information shared by the client on social media. For example, the reception desk automatically extracts relevant project information from the client's social media activity and displays it as input fields. For example, the reception desk suggests input fields based on keywords mentioned by the client on social media. In this way, relevant input fields can be suggested by analyzing the client's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input the client's social media activity data into a generative AI and suggest relevant input fields based on the results of the generative AI's analysis.

[0042] The analysis unit adjusts the level of detail of the analysis based on the importance of the project during the analysis. For example, for important projects, the analysis unit performs a detailed analysis and provides highly accurate results. For example, for less important projects, the analysis unit performs a simplified analysis and provides results quickly. The analysis unit dynamically adjusts the level of detail of the analysis according to the importance of the project. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the project. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input project importance data into a generative AI and adjust the level of detail of the analysis based on the results of the generative AI's analysis.

[0043] The analysis unit applies different analysis algorithms depending on the project category during the analysis. For example, in the case of a construction project, the analysis unit applies an analysis algorithm specialized for the construction industry. For example, in the case of an IT project, the analysis unit applies an analysis algorithm specialized for the IT industry. For example, in the case of a manufacturing project, the analysis unit applies an analysis algorithm specialized for the manufacturing industry. By applying different analysis algorithms depending on the project category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input project category data into a generative AI and apply different analysis algorithms based on the results of the generative AI's analysis.

[0044] The generation unit adjusts the level of detail in the estimate based on the importance of the project when generating the estimate. For example, the generation unit provides a detailed estimate for important projects. For example, the generation unit provides a simplified estimate for less important projects. The generation unit dynamically adjusts the level of detail in the estimate according to the importance of the project. This enables efficient estimation by adjusting the level of detail in the estimate according to the importance of the project. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input project importance data into a generation AI and adjust the level of detail in the estimate based on the results of the analysis by the generation AI.

[0045] The generation unit applies different estimation algorithms depending on the project category when generating estimates. For example, in the case of a construction project, the generation unit applies an estimation algorithm specialized for the construction industry. For example, in the case of an IT project, the generation unit applies an estimation algorithm specialized for the IT industry. For example, in the case of a manufacturing project, the generation unit applies an estimation algorithm specialized for the manufacturing industry. By applying different estimation algorithms depending on the project category, a more appropriate estimate can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input project category data into a generation AI and apply different estimation algorithms based on the results of the analysis by the generation AI.

[0046] The market data collection unit adjusts the level of detail collected based on the importance of the project. For example, the market data collection unit collects detailed market data for important projects, and simplified market data for less important projects. The market data collection unit dynamically adjusts the level of detail collected according to the importance of the project. This enables efficient data collection by adjusting the level of detail of market data collection according to the importance of the project. Some or all of the above processing in the market data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the market data collection unit can input project importance data into a generative AI and adjust the level of detail collected based on the results of the generative AI's analysis.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The reception desk can analyze the information entered by the client in real time and provide immediate feedback based on that information. For example, when a client enters a budget, it can instantly display how the budget compares to the market average. When a client enters a schedule, it can evaluate the validity of the schedule by comparing it to past project data and suggest adjustments as needed. Furthermore, when a client enters the project objectives, it can present success and failure stories from similar projects, providing information useful for project planning. This allows clients to receive immediate feedback on their input and create more accurate and realistic project plans.

[0049] The market data collection unit can be equipped with the ability to visually display the collected market data. For example, it can display collected competitive information in graphs and charts, allowing clients to grasp the market situation at a glance. It can also display demand forecast data in a heatmap, visually showing which regions are experiencing high demand. Furthermore, it can display price trend data in a time-series graph, allowing for an intuitive understanding of price fluctuations from the past to the present. This allows clients to visually review market data and makes it easier for them to make data-driven decisions.

[0050] The statistical information collection department can be equipped with the functionality to perform simulations based on the collected statistical information. For example, it can simulate future sales forecasts based on past sales data and present them to clients. It can also simulate the size of target markets based on demographic data and use this to help formulate marketing strategies. Furthermore, it can simulate the impact of fluctuations in economic conditions on projects based on economic indicator data and use this for risk management. As a result, clients can refer to the simulation results based on statistical information and create more realistic project plans.

[0051] The service provider can incorporate interactive feedback features for the quotes they provide to clients. For example, they can offer an interface that allows clients to enter questions and comments about the quote and receive real-time responses. Furthermore, if a client wishes to modify part of the quote, they can enter the changes and receive an instantly recalculated quote. Additionally, the service can display detailed explanations and related data for each item if the client wishes to review the quote's details. This allows clients to quickly resolve any questions or requests for changes to the quote and obtain a more satisfactory estimate.

[0052] The reception desk can be equipped with a function to perform project risk assessments based on the client's input. For example, it can identify high-risk elements based on past data in relation to the budget and schedule entered by the client and present the risk assessment results. It can also identify potential risk factors in relation to the project's objectives and scope entered by the client and propose risk mitigation measures. Furthermore, it can predict the probability of project success based on the information entered by the client and provide advice for risk management. This allows the client to understand the project risks in advance and take appropriate measures.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The reception department receives information from the client regarding the project. This information includes the project's objectives, budget, and schedule. The reception department accepts the client's input in digital format, and can also scan handwritten information and convert it into digital data, as well as convert voice input into text. The reception department stores this information in a database and sends it to the analysis department. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information entered by the client using data analysis methods, machine learning algorithms, and natural language processing technologies. Based on the information entered by the client, the analysis unit collects relevant market data and statistical information and sends the analysis results to the generation unit. Step 3: The generation unit generates an estimate based on the information analyzed by the analysis unit. The generation unit generates an estimate using a cost estimation algorithm, a time estimation algorithm, and a risk assessment algorithm. The generation unit creates an estimate based on the information received from the analysis unit and provides it to the client.

[0055] (Example of form 2) The estimation generation system according to an embodiment of the present invention is a system in which, when a client inputs information necessary for a project, an AI analyzes that data and provides an approximate estimate using relevant market data and statistical information. The estimation generation system, when a client inputs information necessary for a project, uses the AI ​​to analyze that data and calculate the necessary materials, labor costs, duration, etc., using relevant market data and statistical information. Next, the AI ​​creates an estimate based on the calculated results and provides it to the client. This mechanism significantly reduces the time required for estimation work and improves accuracy. For example, it can reduce the time required for conventional manual estimation work by an average of 70% and improve estimation accuracy to over 95%. Furthermore, accurate estimates can reduce unnecessary costs. In addition, the AI ​​can improve the accuracy of estimates by combining historical data with market analysis. This allows clients to quickly and accurately calculate costs, enabling smooth project planning and implementation. For example, companies in the construction, manufacturing, and IT industries can use this system to prevent project delays due to time and cost constraints and budget overruns due to inaccurate estimates. Also, even if there is a lack of expertise for creating estimates, the AI ​​automatically creates estimates, enabling efficient project management. Furthermore, a user-friendly interface improves usability and meets user needs. For example, project managers and business owners can use this system to reduce time and costs, enabling efficient project management. The use of AI and big data supports data-driven decision-making and enables sustainable business models. For example, it enables data analysis using natural language processing, learning from historical estimation data using machine learning, and real-time data updates and estimation adjustments. As a result, the estimation navigator agent can provide fast and accurate estimates, leading clients' businesses to the next level. This allows the estimation generation system to provide fast and accurate estimates for clients' projects.

[0056] The estimate generation system according to this embodiment comprises a reception unit, an analysis unit, and a generation unit. The reception unit receives information necessary for a project from a client. This information may include, but is not limited to, the project's objectives, budget, and schedule. The reception unit receives the information entered by the client in digital format, for example. The reception unit can also scan handwritten information entered by the client and convert it into digital data. Furthermore, the reception unit can convert dictated information from the client into text using voice input. For example, the reception unit stores the information entered by the client in a database and sends it to the analysis unit. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information entered by the client using, for example, data analysis techniques. Furthermore, the analysis unit can analyze the information entered by the client using machine learning algorithms. Furthermore, the analysis unit can analyze the information entered by the client using natural language processing techniques. For example, the analysis unit collects relevant market data and statistical information based on the information entered by the client and sends the analysis results to the generation unit. The generation unit generates an estimate based on the information analyzed by the analysis unit. The generation unit generates estimates using, for example, a cost estimation algorithm. The generation unit can also generate estimates using a time estimation algorithm. Furthermore, the generation unit can generate estimates using a risk assessment algorithm. For example, the generation unit creates an estimate based on information received from the analysis unit and provides it to the client. Thus, the estimate generation system according to this embodiment can, when a client inputs information necessary for a project, have the AI ​​analyze that data and provide a rough estimate using relevant market data and statistical information.

[0057] The reception department receives information from clients that is necessary for their projects. This information may include, but is not limited to, project objectives, budget, and schedule. The reception department accepts client-entered information in digital format, for example. It can also scan handwritten information entered by clients and convert it into digital data. Furthermore, it can convert dictated information from clients into text using voice input. For example, the reception department can save the information entered by clients to a database and send it to the analysis department. The reception department provides multiple input methods to flexibly accommodate the format of information entered by clients. For example, direct input via web forms, scanning and uploading handwritten documents, and converting dictated content into text using speech recognition technology. This allows clients to provide information in the most suitable way according to their circumstances and preferences. In addition, the reception department has functions to verify and correct the entered information to ensure its accuracy. For example, if there are errors or deficiencies in the entered information, it can notify the client and prompt them to make corrections. Furthermore, the reception department automatically categorizes the entered information, organizing it into categories such as project objectives, budget, and schedule. This allows the analysis department to process the information efficiently. The reception department not only receives information from clients but also provides interfaces to facilitate communication with them. For example, it allows clients to resolve questions and concerns about their input through chatbots and FAQ sections. This enables clients to provide information smoothly and expedite the quotation generation process.

[0058] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes client-entered information using data analysis techniques. It can also analyze client-entered information using machine learning algorithms. Furthermore, it can analyze client-entered information using natural language processing techniques. For example, the analysis unit collects relevant market data and statistics based on the client-entered information and sends the analysis results to the generation unit. The analysis unit uses a combination of multiple analysis techniques to comprehensively analyze the information provided by the client. For example, it extracts basic statistics using data analysis techniques and detects patterns and trends using machine learning algorithms. Furthermore, it analyzes text data using natural language processing techniques to extract important keywords and phrases. This allows the analysis unit to accurately understand client needs and requirements and provide the foundational data necessary to generate appropriate estimates. Based on the client-entered information, the analysis unit accesses databases and APIs to collect relevant market data and statistics. For example, it refers to industry standard prices and data from past projects to identify cases similar to the client's project. This allows the analysis unit to obtain reference information to provide realistic estimates for the client's project. Furthermore, the analysis unit can use machine learning algorithms to predict risks and challenges related to the client's project. For example, it can identify problems and risks that are likely to occur under specific conditions based on historical data and propose countermeasures. This allows the analysis unit to provide clients with crucial information for project success. Before sending the analysis results to the generation unit, the analysis unit conducts a verification process to confirm the accuracy and reliability of the results. For example, it compares results obtained using multiple analysis methods to confirm consistency and reliability. It can also undergo expert review to evaluate whether the estimates generated based on the analysis results are realistic. This allows the analysis unit to establish a foundation for providing clients with high-quality estimates.

[0059] The generation unit generates estimates based on information analyzed by the analysis unit. For example, the generation unit generates estimates using cost estimation algorithms. It can also generate estimates using time estimation algorithms. Furthermore, it can generate estimates using risk assessment algorithms. For example, the generation unit creates an estimate based on information received from the analysis unit and provides it to the client. Based on the data provided by the analysis unit, the generation unit comprehensively evaluates the project's cost, time, and risk to generate the optimal estimate. The cost estimation algorithm calculates the overall project cost by considering factors such as material costs, labor costs, and equipment costs. The time estimation algorithm creates a project schedule based on the duration and dependencies of each task. The risk assessment algorithm identifies potential risks associated with the project and proposes countermeasures. This allows the generation unit to provide clients with comprehensive and realistic estimates. To improve the accuracy of estimates, the generation unit refers to historical project data and industry benchmarks. For example, it evaluates the validity of estimates based on performance data from similar projects and makes adjustments as needed. The generation unit can also provide customized estimates, taking into account client requirements and constraints. For example, it proposes the optimal resource allocation and schedule to complete a project within a specific budget. Furthermore, to ensure transparency in estimates, the generation unit creates reports that explain in detail the basis and calculation method of the estimates. This allows clients to understand and accept the contents of the estimates. The generation unit utilizes the latest technologies to automate the estimate generation process. For example, it uses cloud computing to process large amounts of data quickly and generate estimates in real time. In addition, the generation unit implements a learning process using AI to continuously improve the accuracy of estimates. This allows the generation unit to always provide highly accurate estimates based on the latest information and technology.

[0060] The analysis unit includes a market data collection unit that collects relevant market data. The market data collection unit can, for example, collect competitor information. The market data collection unit can also, for example, collect demand forecast data. The market data collection unit can also, for example, collect price trend data. By collecting relevant market data, the accuracy of estimates is improved. Some or all of the above processing in the market data collection unit may be performed, for example, using a generative AI, or without using a generative AI. For example, the market data collection unit can input prompts to the generative AI to collect competitor information and analyze the data collected by the generative AI.

[0061] The analysis unit includes a statistical information collection unit that collects relevant statistical information. The statistical information collection unit can, for example, collect historical sales data. The statistical information collection unit can also, for example, collect demographic data. The statistical information collection unit can also, for example, collect economic indicator data. By collecting relevant statistical information, the accuracy of the estimate is improved. Some or all of the above processing in the statistical information collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the statistical information collection unit can input prompts to the generating AI in order to collect historical sales data, and can analyze the data collected by the generating AI.

[0062] The generation unit includes a delivery unit that creates an estimate based on the calculated results and provides it to the client. The delivery unit can, for example, provide the estimate to the client via email. The delivery unit can also, for example, display the estimate on a dashboard. The delivery unit can also, for example, generate the estimate in PDF format and provide it to the client. This allows for the provision of quick and accurate estimates by creating and providing estimates to the client based on the calculated results. Some or all of the above processing in the delivery unit may be performed using, for example, a generation AI, or without a generation AI. For example, the delivery unit can input prompts to the generation AI and provide the estimate generated by the generation AI to the client.

[0063] The reception area has a user-friendly interface. A user-friendly interface can, for example, provide intuitive operation. A user-friendly interface can also, for example, provide a visually appealing design. A user-friendly interface can also, for example, support voice input. This makes the interface easier for clients to use. Some or all of the above-described processes in the user-friendly interface may be performed, for example, using a generative AI, or not using a generative AI. For example, the user-friendly interface can input prompts to a generative AI and provide the client with the interface generated by the generative AI.

[0064] The analysis unit improves the accuracy of estimates by combining historical data and market analysis. For example, the analysis unit can collect and use historical project data for analysis. For example, the analysis unit can collect and use historical data for analysis. For example, the analysis unit can perform market analysis using SWOT analysis. By combining historical data and market analysis, the accuracy of estimates is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input historical project data into a generative AI and improve the accuracy of estimates based on the results of the generative AI's analysis.

[0065] The reception desk estimates the client's emotions and adjusts the display of the input interface based on the estimated emotions. For example, if the client is stressed, the reception desk provides a simple interface and minimizes the input steps. If the client is relaxed, for example, the reception desk provides detailed input options and suggests a customizable input method. If the client is in a hurry, for example, the reception desk prioritizes voice input to allow for quick input of project information. This provides a more user-friendly interface by adjusting the display of the input interface according to the client's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the client's facial expression data into a generative AI and adjust the interface based on the emotions estimated by the generative AI.

[0066] The reception desk analyzes the client's past input history and proposes the optimal input method. For example, the reception desk automatically displays project information that the client has frequently entered in the past as a suggestion. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the client has used in the past. For example, the reception desk predicts and suggests project information to be used during a specific time period based on the client's past input history. In this way, the optimal input method can be suggested by analyzing the client's past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the client's past input history data into a generative AI and propose the optimal input method based on the results of the generative AI's analysis.

[0067] The reception desk dynamically changes input fields based on the client's current project status. For example, when a client enters information related to an ongoing project, the reception desk displays only the necessary fields. For example, when a client starts a new project, the reception desk suggests input fields by referring to past project information. For example, the reception desk automatically updates input fields according to the client's project progress. This enables efficient input by dynamically changing input fields according to the client's current project status. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the client's project status data into a generative AI and dynamically change the input fields based on the results of the generative AI's analysis.

[0068] The reception desk estimates the client's emotions and determines the priority of inputs based on the estimated emotions. For example, if the client is stressed, the reception desk will prioritize displaying important input items to allow for quick input. For example, if the client is relaxed, the reception desk will sequentially display detailed input items to allow for careful input. For example, if the client is in a hurry, the reception desk will display the most important input items first to allow for quick completion. This enables efficient input by determining the priority of inputs according to the client's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the client's facial expression data into a generative AI and determine the priority of inputs based on the emotions estimated by the generative AI.

[0069] The reception desk prioritizes displaying relevant input fields, taking into account the client's geographical location. For example, if the client is in a specific region, the reception desk prioritizes displaying project information relevant to that region. For example, if the client is on the move, the reception desk dynamically changes the required input fields based on the client's current location. For example, if the client is in a specific country or region, the reception desk displays input fields based on the regulations and requirements of that region. This allows for the priority display of relevant input fields by considering the client's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the client's geographical location information into a generative AI, and based on the results of the generative AI's analysis, it can prioritize displaying relevant input fields.

[0070] The reception desk analyzes the client's social media activity and suggests relevant input fields. For example, the reception desk suggests input fields based on project information shared by the client on social media. For example, the reception desk automatically extracts relevant project information from the client's social media activity and displays it as input fields. For example, the reception desk suggests input fields based on keywords mentioned by the client on social media. In this way, relevant input fields can be suggested by analyzing the client's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input the client's social media activity data into a generative AI and suggest relevant input fields based on the results of the generative AI's analysis.

[0071] The analysis unit estimates the client's emotions and adjusts the analysis method based on the estimated emotions. For example, if the client is stressed, the analysis unit selects a simple analysis method and provides results quickly. If the client is relaxed, for example, the analysis unit selects a detailed analysis method and provides results carefully. If the client is in a hurry, for example, the analysis unit prioritizes the analysis of the most important items and provides results quickly. This allows for more appropriate analysis results to be provided by adjusting the analysis method according to the client's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the client's facial expression data into a generative AI and adjust the analysis method based on the emotions estimated by the generative AI.

[0072] The analysis unit adjusts the level of detail of the analysis based on the importance of the project during the analysis. For example, for important projects, the analysis unit performs a detailed analysis and provides highly accurate results. For example, for less important projects, the analysis unit performs a simplified analysis and provides results quickly. The analysis unit dynamically adjusts the level of detail of the analysis according to the importance of the project. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the project. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input project importance data into a generative AI and adjust the level of detail of the analysis based on the results of the generative AI's analysis.

[0073] The analysis unit applies different analysis algorithms depending on the project category during the analysis. For example, in the case of a construction project, the analysis unit applies an analysis algorithm specialized for the construction industry. For example, in the case of an IT project, the analysis unit applies an analysis algorithm specialized for the IT industry. For example, in the case of a manufacturing project, the analysis unit applies an analysis algorithm specialized for the manufacturing industry. By applying different analysis algorithms depending on the project category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input project category data into a generative AI and apply different analysis algorithms based on the results of the generative AI's analysis.

[0074] The generation unit estimates the client's emotions and adjusts the way the estimate is presented based on the estimated emotions. For example, if the client is stressed, the generation unit provides a simple and easy-to-read estimate. If the client is relaxed, the generation unit provides a detailed estimate. If the client is in a hurry, the generation unit provides a concise and to-the-point estimate. By adjusting the way the estimate is presented according to the client's emotions, a more appropriate estimate can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the client's facial expression data into the generative AI and adjust the way the estimate is presented based on the emotions estimated by the generative AI.

[0075] The generation unit adjusts the level of detail in the estimate based on the importance of the project when generating the estimate. For example, the generation unit provides a detailed estimate for important projects. For example, the generation unit provides a simplified estimate for less important projects. The generation unit dynamically adjusts the level of detail in the estimate according to the importance of the project. This enables efficient estimation by adjusting the level of detail in the estimate according to the importance of the project. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input project importance data into a generation AI and adjust the level of detail in the estimate based on the results of the analysis by the generation AI.

[0076] The generation unit applies different estimation algorithms depending on the project category when generating estimates. For example, in the case of a construction project, the generation unit applies an estimation algorithm specialized for the construction industry. For example, in the case of an IT project, the generation unit applies an estimation algorithm specialized for the IT industry. For example, in the case of a manufacturing project, the generation unit applies an estimation algorithm specialized for the manufacturing industry. By applying different estimation algorithms depending on the project category, a more appropriate estimate can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input project category data into a generation AI and apply different estimation algorithms based on the results of the analysis by the generation AI.

[0077] The market data collection unit estimates the client's emotions and adjusts the market data collection method based on the estimated emotions. For example, if the client is stressed, the market data collection unit prioritizes market data that can be collected quickly. For example, if the client is relaxed, the market data collection unit collects detailed market data. For example, if the client is in a hurry, the market data collection unit prioritizes collecting the most important market data. This allows for the collection of more appropriate market data by adjusting the market data collection method according to the client's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the market data collection unit may be performed using AI or not. For example, the market data collection unit can input the client's facial expression data into a generative AI and adjust the market data collection method based on the emotions estimated by the generative AI.

[0078] The market data collection unit adjusts the level of detail collected based on the importance of the project. For example, the market data collection unit collects detailed market data for important projects, and simplified market data for less important projects. The market data collection unit dynamically adjusts the level of detail collected according to the importance of the project. This enables efficient data collection by adjusting the level of detail of market data collection according to the importance of the project. Some or all of the above processing in the market data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the market data collection unit can input project importance data into a generative AI and adjust the level of detail collected based on the results of the generative AI's analysis.

[0079] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0080] The reception desk can analyze the information entered by the client in real time and provide immediate feedback based on that information. For example, when a client enters a budget, it can instantly display how the budget compares to the market average. When a client enters a schedule, it can evaluate the validity of the schedule by comparing it to past project data and suggest adjustments as needed. Furthermore, when a client enters the project objectives, it can present success and failure stories from similar projects, providing information useful for project planning. This allows clients to receive immediate feedback on their input and create more accurate and realistic project plans.

[0081] The market data collection unit can be equipped with the ability to visually display the collected market data. For example, it can display collected competitive information in graphs and charts, allowing clients to grasp the market situation at a glance. It can also display demand forecast data in a heatmap, visually showing which regions are experiencing high demand. Furthermore, it can display price trend data in a time-series graph, allowing for an intuitive understanding of price fluctuations from the past to the present. This allows clients to visually review market data and makes it easier for them to make data-driven decisions.

[0082] The statistical information collection department can be equipped with the functionality to perform simulations based on the collected statistical information. For example, it can simulate future sales forecasts based on past sales data and present them to clients. It can also simulate the size of target markets based on demographic data and use this to help formulate marketing strategies. Furthermore, it can simulate the impact of fluctuations in economic conditions on projects based on economic indicator data and use this for risk management. As a result, clients can refer to the simulation results based on statistical information and create more realistic project plans.

[0083] The service provider can incorporate interactive feedback features for the quotes they provide to clients. For example, they can offer an interface that allows clients to enter questions and comments about the quote and receive real-time responses. Furthermore, if a client wishes to modify part of the quote, they can enter the changes and receive an instantly recalculated quote. Additionally, the service can display detailed explanations and related data for each item if the client wishes to review the quote's details. This allows clients to quickly resolve any questions or requests for changes to the quote and obtain a more satisfactory estimate.

[0084] The reception desk can be equipped with a function to perform project risk assessments based on the client's input. For example, it can identify high-risk elements based on past data in relation to the budget and schedule entered by the client and present the risk assessment results. It can also identify potential risk factors in relation to the project's objectives and scope entered by the client and propose risk mitigation measures. Furthermore, it can predict the probability of project success based on the information entered by the client and provide advice for risk management. This allows the client to understand the project risks in advance and take appropriate measures.

[0085] The analysis unit can estimate the client's emotions and adjust how the analysis results are presented based on those emotions. For example, if the client is stressed, a concise summary of the analysis results is provided for quick understanding. If the client is relaxed, detailed results are provided for deeper understanding. If the client is in a hurry, the most important points are highlighted to enable quick decision-making. By adjusting how the analysis results are presented according to the client's emotions, more appropriate information can be provided.

[0086] The reception desk can estimate the client's emotions and adjust the input interface design based on that estimation. For example, if the client is stressed, it can provide an interface with calming colors to reduce visual strain. If the client is relaxed, it can provide a bright and colorful interface to offer a pleasant input experience. If the client is in a hurry, it can provide a simple and intuitive interface to allow them to complete the input quickly. In this way, by adjusting the input interface design according to the client's emotions, a more comfortable input experience can be provided.

[0087] The generation unit can estimate the client's emotions and adjust the way the estimate is presented based on those emotions. For example, if the client is stressed, the estimate will be simplified and only the key points will be highlighted. If the client is relaxed, a detailed estimate will be provided, with careful explanations of each item. If the client is in a hurry, the estimate will be summarized concisely for quick understanding. By adjusting the way the estimate is presented according to the client's emotions, more appropriate information can be provided.

[0088] The market data collection department can estimate the client's emotions and adjust the market data collection method based on that estimation. For example, if the client is stressed, it prioritizes quickly collectible market data and provides only the essential information. If the client is relaxed, it collects detailed market data and provides comprehensive information. If the client is in a hurry, it prioritizes collecting the most important market data and provides it quickly. This allows for more appropriate information to be provided by adjusting the market data collection method according to the client's emotions.

[0089] The reception desk can estimate the client's emotions and prioritize input based on that estimation. For example, if the client is stressed, important input fields will be displayed first to allow for quick completion. If the client is relaxed, detailed input fields will be displayed sequentially to allow for careful completion. If the client is in a hurry, the most important input fields will be displayed first to allow for quick completion. This enables efficient input by prioritizing input according to the client's emotions.

[0090] The following briefly describes the processing flow for example form 2.

[0091] Step 1: The reception department receives information from the client regarding the project. This information includes the project's objectives, budget, and schedule. The reception department accepts the client's input in digital format, and can also scan handwritten information and convert it into digital data, as well as convert voice input into text. The reception department stores this information in a database and sends it to the analysis department. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information entered by the client using data analysis methods, machine learning algorithms, and natural language processing technologies. Based on the information entered by the client, the analysis unit collects relevant market data and statistical information and sends the analysis results to the generation unit. Step 3: The generation unit generates an estimate based on the information analyzed by the analysis unit. The generation unit generates an estimate using a cost estimation algorithm, a time estimation algorithm, and a risk assessment algorithm. The generation unit creates an estimate based on the information received from the analysis unit and provides it to the client.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0094] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives information necessary for the project from the client. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received from the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an estimate based on the information analyzed by the analysis unit. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated estimate to the client. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0096] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0097] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0102] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0103] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0104] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0105] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0106] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives information necessary for the project from the client. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the information received from the reception unit. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates an estimate based on the information analyzed by the analysis unit. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the generated estimate to the client. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0112] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0113] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives information necessary for the project from the client. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received from the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an estimate based on the information analyzed by the analysis unit. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated estimate to the client. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0129] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0136] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives information necessary for the project from the client. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received from the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an estimate based on the information analyzed by the analysis unit. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated estimate to the client. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0146] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0147] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0148] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0149] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0150] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0151] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0152] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0153] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0154] 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.

[0155] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0156] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0157] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0158] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0159] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0160] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0161] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0162] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0163] (Note 1) A reception desk that receives information from clients regarding the project, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates an estimate based on the information analyzed by the analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned analysis unit, It includes a market data collection unit that collects relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It includes a statistical information collection unit that collects relevant statistical information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The company has a department that creates estimates based on the calculated results and provides them to clients. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It features a user-friendly interface. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Combining historical data with market analysis improves the accuracy of estimates. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the client's emotions and adjusts how the input interface is displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the client's past input history and propose the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Dynamically change input fields based on the client's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the client's emotions and determines the priority of inputs based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system prioritizes displaying relevant input fields based on the client's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Analyze the client's social media activity and suggest relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the client's emotions and adjusts the analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is We estimate the client's emotions and adjust the way the estimate is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating an estimate, adjust the level of detail based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating estimates, different estimation algorithms are applied depending on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned market data collection unit, We estimate the client's emotions and adjust the market data collection method based on the estimated client emotions. The system described in Appendix 2, characterized by the features described herein. (Note 20) The aforementioned market data collection unit, When collecting market data, adjust the level of detail based on the importance of the project. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information from clients regarding the project, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates an estimate based on the information analyzed by the analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit, It includes a market data collection unit that collects relevant market data. The system according to feature 1.

3. The aforementioned analysis unit, It includes a statistical information collection unit that collects relevant statistical information. The system according to feature 1.

4. The generating unit is The company has a department that creates estimates based on the calculated results and provides them to clients. The system according to feature 1.

5. The aforementioned reception unit is It features a user-friendly interface. The system according to feature 1.

6. The aforementioned analysis unit, Combining historical data with market analysis improves the accuracy of estimates. The system according to feature 1.

7. The aforementioned reception unit is It estimates the client's emotions and adjusts how the input interface is displayed based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the client's past input history and propose the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is Dynamically change input fields based on the client's current project status. The system according to feature 1.

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