system

Abstract

The system according to this embodiment aims to efficiently handle everything from requesting a quote to signing a contract. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, and a contract unit. The reception unit receives input for the conditions for requesting a quote. The generation unit generates a quote based on the conditions entered by the reception unit. The contract unit directly enters into a contract based on the quote generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 prior art, there were problems that it took time and cost for quotation requests, selecting contractors, and presenting conditions.

[0005] The system according to the embodiment aims to efficiently perform from a quotation request to a contract.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a contract unit. The reception unit inputs conditions for requesting a quotation. The generation unit generates a quotation based on the conditions input by the reception unit. The contract unit directly conducts a contract based on the quotation generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently handle everything from requesting a quote to signing a contract. [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 estimate generation system according to an embodiment of the present invention is a system for reducing the time and cost of selecting a contractor and specifying conditions in situations where estimates are needed in daily life and business. This estimate generation system allows the user to input the conditions for requesting an estimate, and the AI ​​learns from existing estimate examples and market rates to generate an estimate that matches the user's conditions. Furthermore, it partners with companies, allowing for direct contracts based on the generated estimate. This mechanism saves both the user and the company the time and effort required to create estimates. First, the user inputs the conditions for requesting an estimate. For example, in situations requiring estimates such as moving, renovations, weddings, outsourcing, event hosting, or consulting, the user inputs specific conditions. This information is input into the AI. Next, the AI ​​analyzes the input conditions and learns from existing estimate examples and market rates. Based on publicly available estimate examples and market rate data, the AI ​​generates an estimate that matches the user's conditions. For example, if a moving estimate is requested, the AI ​​analyzes past moving estimate data and presents the most suitable estimate for the user's conditions. Furthermore, based on the generated estimate, direct contracts can be made through partnerships with companies. If the user is satisfied with the presented estimate, they can place an order directly. This allows users to avoid the awkwardness of declining a quote and saves them time and effort. Furthermore, it saves companies the trouble of creating quotes, resulting in a win-win business model. This system allows working professionals and sales representatives unfamiliar with quoting to efficiently obtain quotes by eliminating the need to select contractors and propose terms. Additionally, since the AI ​​provides optimal quotes even without prior knowledge, the risk of paying above market value is reduced. For example, when requesting a quote for renovations, the AI ​​will suggest the most suitable contractor and terms, allowing the user to place an order directly. In this way, the AI-powered quote generation system reduces the time and cost of quoting in daily life and business, enabling efficient quote acquisition. This allows the quote generation system to efficiently process user quote requests and quickly conclude contracts with companies.

[0029] The estimate generation system according to this embodiment comprises a reception unit, a generation unit, and a contract unit. The reception unit receives input from the user regarding the conditions for requesting an estimate. These conditions include, but are not limited to, budget, delivery date, and specifications. The reception unit, for example, stores the conditions entered by the user in a database and transmits them to the generation unit. The generation unit uses AI to generate an estimate based on the conditions entered by the reception unit. The generation unit generates an estimate based on, for example, publicly available estimate examples and market price data. The generation unit uses AI to learn from existing estimate examples and market prices and generates an estimate that matches the user's conditions. The generation unit, for example, analyzes past estimate data and presents the most suitable estimate for the user's conditions. The contract unit directly enters into a contract based on the estimate generated by the generation unit. The contract unit, for example, allows the user to place an order if they are satisfied with the presented estimate. The contract unit partners with companies and can directly enter into contracts based on the generated estimates. This enables the estimate generation system to efficiently process user estimate requests and quickly enter into contracts with companies. Some or all of the above-described processes in the reception, generation, and contract departments may be performed using AI or not. For example, the reception department inputs user-entered conditions into the AI, which analyzes the conditions and generates an estimate. The generation department provides the estimate generated by the AI ​​to the user, and the contract department enters into a contract based on the estimate generated by the AI.

[0030] The reception department receives the user's requirements for requesting a quote. These requirements may include, but are not limited to, budget, delivery date, and specifications. The reception department saves the user's entered requirements to a database and sends them to the generation department. Specifically, the reception department provides an interface for receiving user-entered information in real time and saving it to the database. Users can enter detailed requirements such as budget, delivery date, and specifications through web forms or mobile applications. These requirements undergo a validation process to check the consistency and validity of the input before being saved to the database. For example, it verifies whether the budget is within a realistic range, whether the delivery date is feasible, and whether the specifications are specific and clear. Once validation is complete, the reception department saves these requirements to the database and sends them to the generation department. Furthermore, the reception department can refer to the user's past quote request history and contract history to understand the user's trends and preferences. This enables quick and accurate responses based on past data when the user requests a quote again. The reception department provides a user-friendly interface to facilitate smooth interaction with users and allows users to easily enter requirements. For example, input forms can be equipped with auto-completion features and dropdown menus to allow users to quickly enter the necessary information. The reception desk can also provide relevant additional information and advice based on the conditions entered by the user. For instance, if the budget is limited, they can offer suggestions for cost reduction or alternative solutions. This allows the reception desk to respond flexibly to user needs and streamline the quotation request process.

[0031] The generation unit uses AI to generate estimates based on the conditions entered by the reception unit. For example, the generation unit generates estimates based on publicly available example estimates and market data. Specifically, the generation unit uses an AI model to generate the most suitable estimate for the user's conditions. The AI ​​model learns from past estimate data and market data, and executes algorithms to generate estimates that match the user's conditions. For example, it uses natural language processing technology to analyze the specifications entered by the user and identify the necessary materials and work processes. Furthermore, it uses machine learning algorithms to analyze past estimate data and generate estimates based on similar conditions. Based on this data, the generation unit presents the most suitable estimate for the user's conditions. Before providing the generated estimate to the user, the generation unit undergoes an internal quality check process. For example, it verifies whether the generated estimate is realistic and consistent with past data. Once the quality check is complete, the generation unit provides the estimate to the user. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy of the AI ​​model. For example, the user can rate their satisfaction with the presented estimate, and the AI ​​model can be retrained based on that evaluation. This allows the generation unit to continue providing highly accurate estimates that meet the user's needs. The generation unit utilizes high-performance computing resources to generate quotes in real time, enabling users to receive quotes quickly. This allows the generation unit to generate quotes efficiently and accurately, meeting user expectations.

[0032] The Contracts Department directly enters into contracts based on quotes generated by the Generation Department. For example, if a user is satisfied with the presented quote, the Contracts Department can place an order directly. Specifically, the Contracts Department provides an interface for users to review and approve quotes. Users can complete the ordering process by simply clicking a button to conclude the contract after reviewing the details of the quote and being satisfied. The Contracts Department partners with companies and can directly enter into contracts based on the generated quotes. For example, the Contracts Department integrates with the database of partner companies and automatically updates contract information based on the quotes. This allows users to skip cumbersome procedures and conclude contracts quickly. Furthermore, the Contracts Department has a function to track the progress of contracts in real time and notify users. For example, after a contract is concluded, it regularly notifies users of information regarding delivery dates and progress. This allows users to always be aware of the status of their contracts and proceed with transactions with peace of mind. The Contracts Department has a support system in place to respond quickly to any troubles or problems that may arise regarding contracts. For example, a dedicated support team responds to user inquiries and provides advice and support to resolve problems. This allows the Contracts Department to gain the trust of users and build long-term relationships. The contracts department provides a system to streamline the contract process and enable users to conclude contracts quickly and reliably. This allows the contracts department to improve user convenience and enhance the overall value of the quotation generation system.

[0033] The Learning Unit: The Learning Unit uses AI to learn about real-world estimate examples and market rates. For example, the Learning Unit collects publicly available estimate examples and market rate data, and the AI ​​learns from that data. The Learning Unit uses AI to analyze past estimate data and accumulate knowledge to generate estimates that are optimal for the user's conditions. For example, the Learning Unit uses industry standards and past transaction data to enable the AI ​​to learn estimate market rates. As a result, the Learning Unit can generate more accurate estimates by enabling the AI ​​to learn about estimate examples and market rates. Some or all of the above processes in the Learning Unit may be performed using AI or not. For example, the Learning Unit may use AI to collect publicly available estimate data, and the AI ​​may analyze that data to learn estimate market rates.

[0034] The service provider provides the generated quote to the user. For example, the service provider may send the generated quote to the user by email. The service provider may also provide the generated quote to the user through a website or app. For example, the service provider may display the contents of the quote in an easy-to-understand manner so that the user can easily review it. In this way, by providing the generated quote to the user, the service provider makes it easier for the user to review the quote. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider may provide the user with an AI-generated quote and provide an interface for the user to review the quote.

[0035] The generation unit can generate estimates based on publicly available estimate examples and market data. The generation unit generates estimates using data sources such as industry reports and government statistics. The generation unit uses AI to collect publicly available estimate data and generate estimates that match the user's conditions. The generation unit analyzes historical estimate data and presents the most suitable estimate for the user's conditions. This allows the generation unit to provide more realistic estimates by generating estimates based on publicly available data. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit may use AI to collect publicly available estimate data, and the AI ​​may analyze that data to generate estimates.

[0036] The generation unit can generate estimates that match the user's conditions. For example, the generation unit generates estimates based on conditions entered by the user. The generation unit uses AI to analyze the user's conditions and generate the optimal estimate. For example, the generation unit uses filtering conditions and matching algorithms to generate estimates that match the user's conditions. In this way, the generation unit can provide estimates that meet the user's needs by generating estimates that match the user's conditions. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit uses AI to analyze the user's conditions and the AI ​​generates the optimal estimate.

[0037] The contracts department can partner with companies and enter into direct contracts based on the generated quotes. For example, if a user is satisfied with the quoted price, the contracts department can place an order directly. The contracts department partners with companies and can enter into direct contracts based on the generated quotes. The contracts department partners with companies in ways such as partnership agreements or joint projects. This allows the contracts department to save time and effort for both users and companies by entering into direct contracts based on the generated quotes. Some or all of the above processes in the contracts department may be performed using AI or not. For example, the contracts department can use AI to generate quotes, and the AI ​​can then partner with companies and enter into contracts.

[0038] The reception department can analyze a user's past quotation request history and select the most suitable reception method. For example, the reception department can prioritize suggesting quotation request methods that the user has frequently used in the past. The reception department can also automatically suggest specific vendors or conditions based on the user's past request history. The reception department can also suggest the most suitable reception time based on the user's past request history. In this way, the reception department can provide the user with the most suitable reception method by analyzing past quotation request history. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department may use AI to analyze the user's past quotation request history and the AI ​​to select the most suitable reception method.

[0039] The reception desk can filter quotation requests based on the user's current projects and areas of interest. For example, the reception desk can prioritize receiving quotation requests related to projects the user is currently working on. The reception desk can also filter relevant quotation requests based on the user's areas of interest. The reception desk can also suggest appropriate quotation requests depending on the progress of the user's projects. This allows the reception desk to prioritize receiving highly relevant quotation requests by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may use AI to analyze the user's current projects and areas of interest, and the AI ​​may filter relevant quotation requests.

[0040] The reception desk can prioritize receiving quote requests by considering the user's geographical location. For example, if the user requests a quote from a nearby company, the reception desk will prioritize that request. The reception desk can also suggest the most suitable company based on the user's location. If the user is in a specific region, the reception desk can also prioritize receiving quote requests related to that region. In this way, the reception desk can prioritize receiving quote requests that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may use AI to analyze the user's geographical location and the AI ​​to prioritize receiving relevant quote requests.

[0041] The reception department can analyze a user's social media activity when receiving a request for a quote and accept relevant requests. For example, the reception department can accept relevant quote requests based on what the user has mentioned on social media. The reception department can also identify areas of interest from the user's social media activity and prioritize accepting relevant requests. The reception department can also suggest relevant quote requests by referring to the activities of the user's social media followers and friends. In this way, the reception department can prioritize accepting highly relevant quote requests by analyzing the user's social media activity. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department may use AI to analyze the user's social media activity and the AI ​​to accept relevant quote requests.

[0042] The generation unit can adjust the level of detail in the estimate based on the importance of the request. For example, the generation unit generates a detailed estimate for important requests. For general requests, the generation unit can also generate an estimate with a standard level of detail. For simple requests, the generation unit can also generate a concise estimate. This allows the generation unit to provide detailed estimates for important requests by adjusting the level of detail based on the importance of the request. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit may use AI to analyze the importance of the request and the AI ​​to adjust the level of detail in the estimate.

[0043] The generation unit can apply different estimation algorithms depending on the category of the request when generating an estimate. For example, in the case of a moving estimate, the generation unit applies an algorithm specifically for moving. In the case of a renovation estimate, the generation unit can also apply an algorithm specifically for renovations. In the case of a wedding estimate, the generation unit can also apply an algorithm specifically for weddings. This allows the generation unit to provide a more appropriate estimate by applying different algorithms depending on the category of the request. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit may use AI to analyze the category of the request, and the AI ​​may apply an appropriate estimation algorithm.

[0044] The generation unit can determine the priority of estimates based on the submission timing of the request when generating estimates. For example, the generation unit will prioritize estimates for urgent requests. For regular requests, the generation unit can also generate estimates with standard priority. For future requests, the generation unit can postpone the generation of estimates. This allows the generation unit to respond quickly to urgent requests by prioritizing estimates based on the submission timing of the request. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit may use AI to analyze the submission timing of the request and the AI ​​to determine the priority of estimates.

[0045] The generation unit can adjust the order of estimates based on the relevance of the requests when generating estimates. For example, the generation unit will prioritize generating estimates for requests that are highly relevant. The generation unit can also generate estimates in a standard order if the requests are general. The generation unit can also postpone generating estimates for requests that are less relevant. In this way, the generation unit can prioritize highly relevant requests by adjusting the order of estimates based on the relevance of the requests. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit may use AI to analyze the relevance of the requests, and the AI ​​may adjust the order of estimates.

[0046] The contracts department can analyze a user's past contract history to select the most suitable contract method at the time of contracting. For example, the contracts department can prioritize suggesting contract methods that the user has used in the past. The contracts department can also automatically suggest specific vendors and conditions based on the user's past contract history. The contracts department can also suggest the most suitable contract time slot based on the user's past contract history. In this way, the contracts department can provide the user with the most suitable contract method by analyzing past contract history. Some or all of the above processes in the contracts department may be performed using AI or not. For example, the contracts department may use AI to analyze the user's past contract history and the AI ​​to select the most suitable contract method.

[0047] The contracting department can customize the contracting method based on the user's current living situation at the time of contracting. For example, if the user is busy, the contracting department can suggest a method that allows for quick online contract completion. If the user is relaxed, the contracting department can also suggest an in-person contracting method. If the user is on the go, the contracting department can also suggest a contracting method using a mobile device. In this way, the contracting department can provide the user with the optimal contracting method by customizing it according to the user's living situation. Some or all of the above processing in the contracting department may be performed using AI or not. For example, the contracting department may use AI to analyze the user's living situation and the AI ​​to suggest the optimal contracting method.

[0048] The contracts department can select the optimal contract method at the time of contracting, taking into account the user's geographical location. For example, if the user is contracting with a nearby vendor, the contracts department may suggest an in-person contract method. The contracts department can also suggest the optimal contract method based on the user's location. If the user is in a specific region, the contracts department may also suggest a contract method relevant to that region. In this way, the contracts department can provide the optimal contract method by taking into account the user's geographical location. Some or all of the above processing in the contracts department may be performed using AI or not. For example, the contracts department may use AI to analyze the user's geographical location and the AI ​​to select the optimal contract method.

[0049] The contracts department can analyze a user's social media activity and propose contract options at the time of contracting. For example, the contracts department can propose relevant contract options based on what the user has mentioned on social media. The contracts department can also identify areas of interest from the user's social media activity and propose relevant contract options. The contracts department can also propose relevant contract options by referring to the activities of the user's social media followers and friends. In this way, the contracts department can provide highly relevant contract options by analyzing the user's social media activity. Some or all of the above processes in the contracts department may be performed using AI or not. For example, the contracts department may use AI to analyze the user's social media activity and the AI ​​to propose relevant contract options.

[0050] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal algorithm based on past learning data. The learning unit can also extract effective learning patterns from past learning data and optimize the algorithm. The learning unit can also analyze past learning data to improve the accuracy of the learning algorithm. Thus, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit may have AI analyze past learning data, and the AI ​​may optimize the learning algorithm.

[0051] The learning unit can select training data based on the user's current projects and areas of interest during training. For example, the learning unit may prioritize selecting training data related to the user's current project. The learning unit can also select relevant training data based on the user's areas of interest. The learning unit can also suggest appropriate training data according to the progress of the user's project. This enables highly relevant training by allowing the learning unit to select training data based on the user's current projects and areas of interest. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit may use AI to analyze the user's current projects and areas of interest, and the AI ​​may select relevant training data.

[0052] The learning unit can weight the training data based on the timing of the quotation requests during training. For example, the learning unit might assign a higher weight to the relevant training data for urgent requests. For regular requests, it can also assign a standard weight. For future requests, it can assign a lower weight. This allows the learning unit to perform more appropriate training by weighting the training data based on the timing of the quotation requests. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit could use AI to analyze the timing of the quotation requests and then use AI to weight the training data.

[0053] The learning unit can select learning data by referring to the user's past learning history during the learning process. For example, the learning unit can select relevant learning data based on what the user has learned in the past. The learning unit can also extract effective learning patterns from the user's past learning history and select learning data accordingly. The learning unit can also analyze the user's past learning history and suggest optimal learning data. This allows the learning unit to select more effective learning data by referring to the user's past learning history. Some or all of the above processes in the learning unit may be performed using AI, or they may not. For example, the learning unit may use AI to analyze the user's past learning history, and the AI ​​may select optimal learning data.

[0054] The service provider can select the optimal service delivery method by referring to the user's past quotation history when providing a quote. For example, the service provider can prioritize suggesting service delivery methods that the user has used in the past. The service provider can also automatically suggest specific vendors or conditions based on the user's past quotation history. The service provider can also suggest the optimal service delivery time based on the user's past quotation history. In this way, the service provider can provide the user with the most suitable service delivery method by referring to past quotation history. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider may use AI to analyze the user's past quotation history, and the AI ​​may select the optimal service delivery method.

[0055] The service provider can adjust its delivery method based on the user's current projects and areas of interest when providing estimates. For example, the service provider may prioritize providing estimates related to the user's current projects. The service provider may also provide relevant estimates based on the user's areas of interest. The service provider may also suggest appropriate estimates depending on the progress of the user's projects. In this way, the service provider can provide highly relevant estimates by adjusting its delivery method based on the user's current projects and areas of interest. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider may use AI to analyze the user's current projects and areas of interest, and the AI ​​may provide relevant estimates.

[0056] The service provider can select the optimal delivery method when providing a quote, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can select a delivery method that matches the screen size. If the user is using a tablet, the service provider can also select a delivery method optimized for a larger screen. If the user is using a desktop computer, the service provider can also select a delivery method that includes detailed information. In this way, the service provider can provide the optimal delivery method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider may use AI to analyze the user's device information, and the AI ​​may select the optimal delivery method.

[0057] The service provider can select a multilingual delivery method when providing a quote, depending on the user's language settings. For example, the service provider can automatically set the language of the quote based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. The service provider can also provide the quote in a specific language if the user selects one. In this way, the service provider can provide a user-friendly quote by offering a multilingual delivery method according to the user's language settings. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider may use AI to analyze the user's language settings and the AI ​​to provide the quote in the most suitable language.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The estimate generation system may also include a history analysis unit that analyzes the user's past estimate request history. The history analysis unit analyzes the content and conditions of estimates previously requested by the user to understand the user's trends and preferences. For example, if a user has frequently used a particular vendor in the past, that vendor can be prioritized in the proposal. It can also automatically set similar conditions based on the conditions of estimates previously requested by the user. In this way, the history analysis unit can further reduce the user's effort by proposing the optimal estimate based on the user's past behavior. Some or all of the above processing in the history analysis unit may be performed using AI or not. For example, the history analysis unit may use AI to analyze the user's past estimate request history, and the AI ​​may make the optimal proposal.

[0060] The estimate generation system may further include a geographic information unit that prioritizes providing highly relevant estimates by considering the user's geographic location. For example, the geographic information unit may prioritize providing estimates to nearby businesses if the user requests services from them. It can also suggest the most suitable businesses based on the user's location. If the user is in a specific region, it can also prioritize providing estimates relevant to that region. In this way, the geographic information unit can provide highly relevant estimates by considering the user's geographic location. Some or all of the above processing in the geographic information unit may be performed using AI or not. For example, the geographic information unit may use AI to analyze the user's geographic location and the AI ​​to provide relevant estimates.

[0061] The estimate generation system may further include a social analytics unit that analyzes the user's social media activity and proposes relevant estimates. The social analytics unit, for example, proposes relevant estimates based on what the user mentions on social media. It can also identify areas of interest from the user's social media activity and prioritize providing relevant estimates. It can also propose relevant estimates by referencing the activities of the user's social media followers and friends. This allows the social analytics unit to provide highly relevant estimates by analyzing the user's social media activity. Some or all of the above processing in the social analytics unit may be performed using AI or not. For example, the social analytics unit may use AI to analyze the user's social media activity and then use AI to propose relevant estimates.

[0062] The estimate generation system may further include a device adaptation unit that provides an optimal estimate by considering the user's device information. For example, if the user is using a smartphone, the device adaptation unit provides an estimate that matches the screen size. If the user is using a tablet, it may also provide an estimate optimized for a larger screen. If the user is using a desktop, it may also provide an estimate that includes detailed information. In this way, the device adaptation unit can provide an optimal estimate by considering the user's device information. Some or all of the above processing in the device adaptation unit may be performed using AI or not. For example, the device adaptation unit may use AI to analyze the user's device information, and the AI ​​may provide an optimal estimate.

[0063] The estimate generation system may further include a language adaptation unit that provides multilingual estimates according to the user's language settings. The language adaptation unit may, for example, automatically set the language of the estimate based on the language settings of the user's device. If the user uses multiple languages, a language switching function may also be provided. If the user selects a specific language, the estimate may also be provided in that language. In this way, the language adaptation unit can provide estimates that are easy for the user to understand by providing multilingual estimates according to the user's language settings. Some or all of the above processing in the language adaptation unit may be performed using AI or not. For example, the language adaptation unit may use AI to analyze the user's language settings and the AI ​​to provide the estimate in the most appropriate language.

[0064] The quotation generation system may further include a contract history section that analyzes the user's past contract history and proposes the optimal contract method. The contract history section may, for example, prioritize proposing contract methods previously used by the user. It can also automatically suggest specific vendors or conditions based on the user's past contract history. Furthermore, it can suggest the optimal contract time slot based on the user's past contract history. In this way, the contract history section can provide the user with the optimal contract method by analyzing past contract history. Some or all of the above processing in the contract history section may be performed using AI, or not. For example, the contract history section may use AI to analyze the user's past contract history, and the AI ​​may propose the optimal contract method.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception department inputs the conditions for which the user is requesting a quote. These conditions may include, for example, budget, delivery date, and specifications. The reception department saves the conditions entered by the user to the database and sends them to the generation department. Step 2: The generation unit uses AI to generate an estimate based on the conditions entered by the reception unit. The generation unit generates estimates based on publicly available estimate examples and market data, and analyzes past estimate data to present the estimate best suited to the user's conditions. Step 3: The contracting department enters into a direct contract based on the quote generated by the generation department. If the user is satisfied with the quote provided, the contracting department can place an order directly. Because they are partnered with companies, they can enter into a direct contract based on the generated quote.

[0067] (Example of form 2) The estimate generation system according to an embodiment of the present invention is a system for reducing the time and cost of selecting a contractor and specifying conditions in situations where estimates are needed in daily life and business. This estimate generation system allows the user to input the conditions for requesting an estimate, and the AI ​​learns from existing estimate examples and market rates to generate an estimate that matches the user's conditions. Furthermore, it partners with companies, allowing for direct contracts based on the generated estimate. This mechanism saves both the user and the company the time and effort required to create estimates. First, the user inputs the conditions for requesting an estimate. For example, in situations requiring estimates such as moving, renovations, weddings, outsourcing, event hosting, or consulting, the user inputs specific conditions. This information is input into the AI. Next, the AI ​​analyzes the input conditions and learns from existing estimate examples and market rates. Based on publicly available estimate examples and market rate data, the AI ​​generates an estimate that matches the user's conditions. For example, if a moving estimate is requested, the AI ​​analyzes past moving estimate data and presents the most suitable estimate for the user's conditions. Furthermore, based on the generated estimate, direct contracts can be made through partnerships with companies. If the user is satisfied with the presented estimate, they can place an order directly. This allows users to avoid the awkwardness of declining a quote and saves them time and effort. Furthermore, it saves companies the trouble of creating quotes, resulting in a win-win business model. This system allows working professionals and sales representatives unfamiliar with quoting to efficiently obtain quotes by eliminating the need to select contractors and propose terms. Additionally, since the AI ​​provides optimal quotes even without prior knowledge, the risk of paying above market value is reduced. For example, when requesting a quote for renovations, the AI ​​will suggest the most suitable contractor and terms, allowing the user to place an order directly. In this way, the AI-powered quote generation system reduces the time and cost of quoting in daily life and business, enabling efficient quote acquisition. This allows the quote generation system to efficiently process user quote requests and quickly conclude contracts with companies.

[0068] The estimate generation system according to this embodiment comprises a reception unit, a generation unit, and a contract unit. The reception unit receives input from the user regarding the conditions for requesting an estimate. These conditions include, but are not limited to, budget, delivery date, and specifications. The reception unit, for example, stores the conditions entered by the user in a database and transmits them to the generation unit. The generation unit uses AI to generate an estimate based on the conditions entered by the reception unit. The generation unit generates an estimate based on, for example, publicly available estimate examples and market price data. The generation unit uses AI to learn from existing estimate examples and market prices and generates an estimate that matches the user's conditions. The generation unit, for example, analyzes past estimate data and presents the most suitable estimate for the user's conditions. The contract unit directly enters into a contract based on the estimate generated by the generation unit. The contract unit, for example, allows the user to place an order if they are satisfied with the presented estimate. The contract unit partners with companies and can directly enter into contracts based on the generated estimates. This enables the estimate generation system to efficiently process user estimate requests and quickly enter into contracts with companies. Some or all of the above-described processes in the reception, generation, and contract departments may be performed using AI or not. For example, the reception department inputs user-entered conditions into the AI, which analyzes the conditions and generates an estimate. The generation department provides the estimate generated by the AI ​​to the user, and the contract department enters into a contract based on the estimate generated by the AI.

[0069] The reception department receives the user's requirements for requesting a quote. These requirements may include, but are not limited to, budget, delivery date, and specifications. The reception department saves the user's entered requirements to a database and sends them to the generation department. Specifically, the reception department provides an interface for receiving user-entered information in real time and saving it to the database. Users can enter detailed requirements such as budget, delivery date, and specifications through web forms or mobile applications. These requirements undergo a validation process to check the consistency and validity of the input before being saved to the database. For example, it verifies whether the budget is within a realistic range, whether the delivery date is feasible, and whether the specifications are specific and clear. Once validation is complete, the reception department saves these requirements to the database and sends them to the generation department. Furthermore, the reception department can refer to the user's past quote request history and contract history to understand the user's trends and preferences. This enables quick and accurate responses based on past data when the user requests a quote again. The reception department provides a user-friendly interface to facilitate smooth interaction with users and allows users to easily enter requirements. For example, input forms can be equipped with auto-completion features and dropdown menus to allow users to quickly enter the necessary information. The reception desk can also provide relevant additional information and advice based on the conditions entered by the user. For instance, if the budget is limited, they can offer suggestions for cost reduction or alternative solutions. This allows the reception desk to respond flexibly to user needs and streamline the quotation request process.

[0070] The generation unit uses AI to generate estimates based on the conditions entered by the reception unit. For example, the generation unit generates estimates based on publicly available example estimates and market data. Specifically, the generation unit uses an AI model to generate the most suitable estimate for the user's conditions. The AI ​​model learns from past estimate data and market data, and executes algorithms to generate estimates that match the user's conditions. For example, it uses natural language processing technology to analyze the specifications entered by the user and identify the necessary materials and work processes. Furthermore, it uses machine learning algorithms to analyze past estimate data and generate estimates based on similar conditions. Based on this data, the generation unit presents the most suitable estimate for the user's conditions. Before providing the generated estimate to the user, the generation unit undergoes an internal quality check process. For example, it verifies whether the generated estimate is realistic and consistent with past data. Once the quality check is complete, the generation unit provides the estimate to the user. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy of the AI ​​model. For example, the user can rate their satisfaction with the presented estimate, and the AI ​​model can be retrained based on that evaluation. This allows the generation unit to continue providing highly accurate estimates that meet the user's needs. The generation unit utilizes high-performance computing resources to generate quotes in real time, enabling users to receive quotes quickly. This allows the generation unit to generate quotes efficiently and accurately, meeting user expectations.

[0071] The Contracts Department directly enters into contracts based on quotes generated by the Generation Department. For example, if a user is satisfied with the presented quote, the Contracts Department can place an order directly. Specifically, the Contracts Department provides an interface for users to review and approve quotes. Users can complete the ordering process by simply clicking a button to conclude the contract after reviewing the details of the quote and being satisfied. The Contracts Department partners with companies and can directly enter into contracts based on the generated quotes. For example, the Contracts Department integrates with the database of partner companies and automatically updates contract information based on the quotes. This allows users to skip cumbersome procedures and conclude contracts quickly. Furthermore, the Contracts Department has a function to track the progress of contracts in real time and notify users. For example, after a contract is concluded, it regularly notifies users of information regarding delivery dates and progress. This allows users to always be aware of the status of their contracts and proceed with transactions with peace of mind. The Contracts Department has a support system in place to respond quickly to any troubles or problems that may arise regarding contracts. For example, a dedicated support team responds to user inquiries and provides advice and support to resolve problems. This allows the Contracts Department to gain the trust of users and build long-term relationships. The contracts department provides a system to streamline the contract process and enable users to conclude contracts quickly and reliably. This allows the contracts department to improve user convenience and enhance the overall value of the quotation generation system.

[0072] The Learning Unit: The Learning Unit uses AI to learn about real-world estimate examples and market rates. For example, the Learning Unit collects publicly available estimate examples and market rate data, and the AI ​​learns from that data. The Learning Unit uses AI to analyze past estimate data and accumulate knowledge to generate estimates that are optimal for the user's conditions. For example, the Learning Unit uses industry standards and past transaction data to enable the AI ​​to learn estimate market rates. As a result, the Learning Unit can generate more accurate estimates by enabling the AI ​​to learn about estimate examples and market rates. Some or all of the above processes in the Learning Unit may be performed using AI or not. For example, the Learning Unit may use AI to collect publicly available estimate data, and the AI ​​may analyze that data to learn estimate market rates.

[0073] The service provider provides the generated quote to the user. For example, the service provider may send the generated quote to the user by email. The service provider may also provide the generated quote to the user through a website or app. For example, the service provider may display the contents of the quote in an easy-to-understand manner so that the user can easily review it. In this way, by providing the generated quote to the user, the service provider makes it easier for the user to review the quote. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider may provide the user with an AI-generated quote and provide an interface for the user to review the quote.

[0074] The generation unit can generate estimates based on publicly available estimate examples and market data. The generation unit generates estimates using data sources such as industry reports and government statistics. The generation unit uses AI to collect publicly available estimate data and generate estimates that match the user's conditions. The generation unit analyzes historical estimate data and presents the most suitable estimate for the user's conditions. This allows the generation unit to provide more realistic estimates by generating estimates based on publicly available data. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit may use AI to collect publicly available estimate data, and the AI ​​may analyze that data to generate estimates.

[0075] The generation unit can generate estimates that match the user's conditions. For example, the generation unit generates estimates based on conditions entered by the user. The generation unit uses AI to analyze the user's conditions and generate the optimal estimate. For example, the generation unit uses filtering conditions and matching algorithms to generate estimates that match the user's conditions. In this way, the generation unit can provide estimates that meet the user's needs by generating estimates that match the user's conditions. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit uses AI to analyze the user's conditions and the AI ​​generates the optimal estimate.

[0076] The contracts department can partner with companies and enter into direct contracts based on the generated quotes. For example, if a user is satisfied with the quoted price, the contracts department can place an order directly. The contracts department partners with companies and can enter into direct contracts based on the generated quotes. The contracts department partners with companies in ways such as partnership agreements or joint projects. This allows the contracts department to save time and effort for both users and companies by entering into direct contracts based on the generated quotes. Some or all of the above processes in the contracts department may be performed using AI or not. For example, the contracts department can use AI to generate quotes, and the AI ​​can then partner with companies and enter into contracts.

[0077] The reception desk can estimate the user's emotions and adjust the timing of accepting quote requests based on those emotions. For example, if the user is feeling stressed, the reception desk will accept the quote request during a time when the user can relax. If the user is in a hurry, the reception desk can accept the quote request immediately and respond quickly. If the user is undecided, the reception desk can send a reminder at an appropriate time to encourage them to submit a quote request. In this way, the reception desk can reduce user stress by adjusting the timing of quote requests according to the user'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 reception desk may be performed using AI or not. For example, the reception desk may have AI estimate the user's emotions and adjust the timing of accepting quote requests based on the emotions estimated by the AI.

[0078] The reception department can analyze a user's past quotation request history and select the most suitable reception method. For example, the reception department can prioritize suggesting quotation request methods that the user has frequently used in the past. The reception department can also automatically suggest specific vendors or conditions based on the user's past request history. The reception department can also suggest the most suitable reception time based on the user's past request history. In this way, the reception department can provide the user with the most suitable reception method by analyzing past quotation request history. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department may use AI to analyze the user's past quotation request history and the AI ​​to select the most suitable reception method.

[0079] The reception desk can filter quotation requests based on the user's current projects and areas of interest. For example, the reception desk can prioritize receiving quotation requests related to projects the user is currently working on. The reception desk can also filter relevant quotation requests based on the user's areas of interest. The reception desk can also suggest appropriate quotation requests depending on the progress of the user's projects. This allows the reception desk to prioritize receiving highly relevant quotation requests by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may use AI to analyze the user's current projects and areas of interest, and the AI ​​may filter relevant quotation requests.

[0080] The reception desk can estimate the user's emotions and determine the priority of the quote requests to be received based on the estimated emotions. For example, if the user has an urgent request, the reception desk will prioritize it. If the user is relaxed, the reception desk may also process the request with the normal priority. If the user is feeling anxious, the reception desk may also raise the priority to respond quickly. This allows the reception desk to respond quickly to urgent requests by prioritizing quote requests according to the user'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 reception desk may be performed using AI or not. For example, the reception desk may have AI estimate the user's emotions and determine the priority of the quote requests based on the emotions estimated by the AI.

[0081] The reception desk can prioritize receiving quote requests by considering the user's geographical location. For example, if the user requests a quote from a nearby company, the reception desk will prioritize that request. The reception desk can also suggest the most suitable company based on the user's location. If the user is in a specific region, the reception desk can also prioritize receiving quote requests related to that region. In this way, the reception desk can prioritize receiving quote requests that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may use AI to analyze the user's geographical location and the AI ​​to prioritize receiving relevant quote requests.

[0082] The reception department can analyze a user's social media activity when receiving a request for a quote and accept relevant requests. For example, the reception department can accept relevant quote requests based on what the user has mentioned on social media. The reception department can also identify areas of interest from the user's social media activity and prioritize accepting relevant requests. The reception department can also suggest relevant quote requests by referring to the activities of the user's social media followers and friends. In this way, the reception department can prioritize accepting highly relevant quote requests by analyzing the user's social media activity. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department may use AI to analyze the user's social media activity and the AI ​​to accept relevant quote requests.

[0083] The generation unit can estimate the user's emotions and adjust the way the estimate is presented based on the estimated emotions. For example, if the user is stressed, the generation unit generates a simple and easy-to-understand estimate. If the user is relaxed, the generation unit can also generate an estimate that includes detailed information. If the user is in a hurry, the generation unit can generate a concise estimate that gets straight to the point. In this way, the generation unit can provide an estimate that is easy for the user to understand by adjusting the way the estimate is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 may have an AI estimate the user's emotions and adjust the way the estimate is presented based on the emotions estimated by the AI.

[0084] The generation unit can adjust the level of detail in the estimate based on the importance of the request. For example, the generation unit generates a detailed estimate for important requests. For general requests, the generation unit can also generate an estimate with a standard level of detail. For simple requests, the generation unit can also generate a concise estimate. This allows the generation unit to provide detailed estimates for important requests by adjusting the level of detail based on the importance of the request. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit may use AI to analyze the importance of the request and the AI ​​to adjust the level of detail in the estimate.

[0085] The generation unit can apply different estimation algorithms depending on the category of the request when generating an estimate. For example, in the case of a moving estimate, the generation unit applies an algorithm specifically for moving. In the case of a renovation estimate, the generation unit can also apply an algorithm specifically for renovations. In the case of a wedding estimate, the generation unit can also apply an algorithm specifically for weddings. This allows the generation unit to provide a more appropriate estimate by applying different algorithms depending on the category of the request. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit may use AI to analyze the category of the request, and the AI ​​may apply an appropriate estimation algorithm.

[0086] The generation unit can estimate the user's emotions and adjust the length of the estimate based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, to-the-point estimate. If the user is relaxed, the generation unit may also generate a longer estimate with detailed explanations. If the user is excited, the generation unit may also generate an estimate with visually stimulating effects. In this way, the generation unit can provide the user with the best possible estimate by adjusting the length of the estimate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 may have an AI estimate the user's emotions and adjust the length of the estimate based on the emotions estimated by the AI.

[0087] The generation unit can determine the priority of estimates based on the submission timing of the request when generating estimates. For example, the generation unit will prioritize estimates for urgent requests. For regular requests, the generation unit can also generate estimates with standard priority. For future requests, the generation unit can postpone the generation of estimates. This allows the generation unit to respond quickly to urgent requests by prioritizing estimates based on the submission timing of the request. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit may use AI to analyze the submission timing of the request and the AI ​​to determine the priority of estimates.

[0088] The generation unit can adjust the order of estimates based on the relevance of the requests when generating estimates. For example, the generation unit will prioritize generating estimates for requests that are highly relevant. The generation unit can also generate estimates in a standard order if the requests are general. The generation unit can also postpone generating estimates for requests that are less relevant. In this way, the generation unit can prioritize highly relevant requests by adjusting the order of estimates based on the relevance of the requests. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit may use AI to analyze the relevance of the requests, and the AI ​​may adjust the order of estimates.

[0089] The contracts department can estimate the user's emotions and adjust the contract method based on the estimated emotions. For example, if the user is stressed, the contracts department may propose a simple and easy-to-understand contract method. If the user is relaxed, the contracts department may also propose a contract method that includes detailed information. If the user is in a hurry, the contracts department may also propose a method that allows for quick contract completion. In this way, the contracts department can provide a contract method that is easy for the user to understand by adjusting the contract method according to the user'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 contracts department may be performed using AI or not. For example, the contracts department may have AI estimate the user's emotions and adjust the contract method based on the emotions estimated by the AI.

[0090] The contracts department can analyze a user's past contract history to select the most suitable contract method at the time of contracting. For example, the contracts department can prioritize suggesting contract methods that the user has used in the past. The contracts department can also automatically suggest specific vendors and conditions based on the user's past contract history. The contracts department can also suggest the most suitable contract time slot based on the user's past contract history. In this way, the contracts department can provide the user with the most suitable contract method by analyzing past contract history. Some or all of the above processes in the contracts department may be performed using AI or not. For example, the contracts department may use AI to analyze the user's past contract history and the AI ​​to select the most suitable contract method.

[0091] The contracting department can customize the contracting method based on the user's current living situation at the time of contracting. For example, if the user is busy, the contracting department can suggest a method that allows for quick online contract completion. If the user is relaxed, the contracting department can also suggest an in-person contracting method. If the user is on the go, the contracting department can also suggest a contracting method using a mobile device. In this way, the contracting department can provide the user with the optimal contracting method by customizing it according to the user's living situation. Some or all of the above processing in the contracting department may be performed using AI or not. For example, the contracting department may use AI to analyze the user's living situation and the AI ​​to suggest the optimal contracting method.

[0092] The contracts department can estimate the user's emotions and determine contract priorities based on those estimated emotions. For example, if the user desires an urgent contract, the contracts department will prioritize it. If the user is relaxed, the contracts department can proceed with the contract at the normal priority. If the user is feeling anxious, the contracts department can raise the priority to respond quickly. This allows the contracts department to respond quickly to urgent contracts by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the contracts department may be performed using AI or not. For example, the contracts department may have AI estimate the user's emotions and determine contract priorities based on the emotions estimated by the AI.

[0093] The contracts department can select the optimal contract method at the time of contracting, taking into account the user's geographical location. For example, if the user is contracting with a nearby vendor, the contracts department may suggest an in-person contract method. The contracts department can also suggest the optimal contract method based on the user's location. If the user is in a specific region, the contracts department may also suggest a contract method relevant to that region. In this way, the contracts department can provide the optimal contract method by taking into account the user's geographical location. Some or all of the above processing in the contracts department may be performed using AI or not. For example, the contracts department may use AI to analyze the user's geographical location and the AI ​​to select the optimal contract method.

[0094] The contracts department can analyze a user's social media activity and propose contract options at the time of contracting. For example, the contracts department can propose relevant contract options based on what the user has mentioned on social media. The contracts department can also identify areas of interest from the user's social media activity and propose relevant contract options. The contracts department can also propose relevant contract options by referring to the activities of the user's social media followers and friends. In this way, the contracts department can provide highly relevant contract options by analyzing the user's social media activity. Some or all of the above processes in the contracts department may be performed using AI or not. For example, the contracts department may use AI to analyze the user's social media activity and the AI ​​to propose relevant contract options.

[0095] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit can select training data that promotes relaxation. If the user is relaxed, the learning unit can also select training data containing detailed information. If the user is in a hurry, the learning unit can also select concise training data that gets straight to the point. This allows the learning unit to learn more effectively by selecting training data according to the user'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 learning unit may be performed using AI or not. For example, the learning unit may have AI estimate the user's emotions and select training data based on the emotions estimated by the AI.

[0096] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal algorithm based on past learning data. The learning unit can also extract effective learning patterns from past learning data and optimize the algorithm. The learning unit can also analyze past learning data to improve the accuracy of the learning algorithm. Thus, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit may have AI analyze past learning data, and the AI ​​may optimize the learning algorithm.

[0097] The learning unit can select training data based on the user's current projects and areas of interest during training. For example, the learning unit may prioritize selecting training data related to the user's current project. The learning unit can also select relevant training data based on the user's areas of interest. The learning unit can also suggest appropriate training data according to the progress of the user's project. This enables highly relevant training by allowing the learning unit to select training data based on the user's current projects and areas of interest. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit may use AI to analyze the user's current projects and areas of interest, and the AI ​​may select relevant training data.

[0098] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency. If the user is relaxed, the learning unit can also increase the learning frequency. If the user is in a hurry, the learning unit can adjust the learning frequency to proceed efficiently. In this way, the learning unit can enable more effective learning by adjusting the learning frequency according to the user'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 learning unit may be performed using AI or not using AI. For example, the learning unit may have AI estimate the user's emotions and adjust the learning frequency based on the emotions estimated by the AI.

[0099] The learning unit can weight the training data based on the timing of the quotation requests during training. For example, the learning unit might assign a higher weight to the relevant training data for urgent requests. For regular requests, it can also assign a standard weight. For future requests, it can assign a lower weight. This allows the learning unit to perform more appropriate training by weighting the training data based on the timing of the quotation requests. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit could use AI to analyze the timing of the quotation requests and then use AI to weight the training data.

[0100] The learning unit can select learning data by referring to the user's past learning history during the learning process. For example, the learning unit can select relevant learning data based on what the user has learned in the past. The learning unit can also extract effective learning patterns from the user's past learning history and select learning data accordingly. The learning unit can also analyze the user's past learning history and suggest optimal learning data. This allows the learning unit to select more effective learning data by referring to the user's past learning history. Some or all of the above processes in the learning unit may be performed using AI, or they may not. For example, the learning unit may use AI to analyze the user's past learning history, and the AI ​​may select optimal learning data.

[0101] The service provider can estimate the user's emotions and adjust the method of providing the estimate based on the estimated emotions. For example, if the user is stressed, the service provider may select a simple and easy-to-understand method of delivery. If the user is relaxed, the service provider may select a method that includes detailed information. If the user is in a hurry, the service provider may select a method that can be delivered quickly. In this way, the service provider can provide an estimate that is easy for the user to understand by adjusting the method of delivery according to the user'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 service provider may be performed using AI or not using AI. For example, the service provider may have AI estimate the user's emotions and adjust the method of providing the estimate based on the emotions estimated by the AI.

[0102] The service provider can select the optimal service delivery method by referring to the user's past quotation history when providing a quote. For example, the service provider can prioritize suggesting service delivery methods that the user has used in the past. The service provider can also automatically suggest specific vendors or conditions based on the user's past quotation history. The service provider can also suggest the optimal service delivery time based on the user's past quotation history. In this way, the service provider can provide the user with the most suitable service delivery method by referring to past quotation history. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider may use AI to analyze the user's past quotation history, and the AI ​​may select the optimal service delivery method.

[0103] The service provider can adjust its delivery method based on the user's current projects and areas of interest when providing estimates. For example, the service provider may prioritize providing estimates related to the user's current projects. The service provider may also provide relevant estimates based on the user's areas of interest. The service provider may also suggest appropriate estimates depending on the progress of the user's projects. In this way, the service provider can provide highly relevant estimates by adjusting its delivery method based on the user's current projects and areas of interest. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider may use AI to analyze the user's current projects and areas of interest, and the AI ​​may provide relevant estimates.

[0104] The service provider can estimate the user's emotions and adjust the order in which quotes are provided based on the estimated emotions. For example, if the user has an urgent request, the service provider will provide a priority quote. If the user is relaxed, the service provider can also provide quotes in the normal order. If the user is feeling anxious, the service provider can adjust the order to respond quickly. This allows the service provider to respond quickly to urgent requests by adjusting the order in which quotes are provided according to the user'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 service provider may be performed using AI or not. For example, the service provider may have AI estimate the user's emotions and adjust the order in which quotes are provided based on the emotions estimated by the AI.

[0105] The service provider can select the optimal delivery method when providing a quote, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can select a delivery method that matches the screen size. If the user is using a tablet, the service provider can also select a delivery method optimized for a larger screen. If the user is using a desktop computer, the service provider can also select a delivery method that includes detailed information. In this way, the service provider can provide the optimal delivery method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider may use AI to analyze the user's device information, and the AI ​​may select the optimal delivery method.

[0106] The service provider can select a multilingual delivery method when providing a quote, depending on the user's language settings. For example, the service provider can automatically set the language of the quote based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. The service provider can also provide the quote in a specific language if the user selects one. In this way, the service provider can provide a user-friendly quote by offering a multilingual delivery method according to the user's language settings. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider may use AI to analyze the user's language settings and the AI ​​to provide the quote in the most suitable language.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The estimate generation system may also include a history analysis unit that analyzes the user's past estimate request history. The history analysis unit analyzes the content and conditions of estimates previously requested by the user to understand the user's trends and preferences. For example, if a user has frequently used a particular vendor in the past, that vendor can be prioritized in the proposal. It can also automatically set similar conditions based on the conditions of estimates previously requested by the user. In this way, the history analysis unit can further reduce the user's effort by proposing the optimal estimate based on the user's past behavior. Some or all of the above processing in the history analysis unit may be performed using AI or not. For example, the history analysis unit may use AI to analyze the user's past estimate request history, and the AI ​​may make the optimal proposal.

[0109] The estimate generation system may further include an emotion adjustment unit that estimates the user's emotions and adjusts the way the estimate is presented based on the estimated emotions. For example, if the user is stressed, the emotion adjustment unit may generate a simple and easy-to-understand estimate. If the user is relaxed, it may also generate an estimate that includes detailed information. If the user is in a hurry, it may also generate a concise estimate that gets straight to the point. In this way, the emotion adjustment unit can provide an estimate that is easy for the user to understand by adjusting the way the estimate is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 emotion adjustment unit may be performed using AI or not using AI. For example, the emotion adjustment unit may have an AI estimate the user's emotions and adjust the way the estimate is presented based on the emotions estimated by the AI.

[0110] The estimate generation system may further include a geographic information unit that prioritizes providing highly relevant estimates by considering the user's geographic location. For example, the geographic information unit may prioritize providing estimates to nearby businesses if the user requests services from them. It can also suggest the most suitable businesses based on the user's location. If the user is in a specific region, it can also prioritize providing estimates relevant to that region. In this way, the geographic information unit can provide highly relevant estimates by considering the user's geographic location. Some or all of the above processing in the geographic information unit may be performed using AI or not. For example, the geographic information unit may use AI to analyze the user's geographic location and the AI ​​to provide relevant estimates.

[0111] The estimate generation system may further include a social analytics unit that analyzes the user's social media activity and proposes relevant estimates. The social analytics unit, for example, proposes relevant estimates based on what the user mentions on social media. It can also identify areas of interest from the user's social media activity and prioritize providing relevant estimates. It can also propose relevant estimates by referencing the activities of the user's social media followers and friends. This allows the social analytics unit to provide highly relevant estimates by analyzing the user's social media activity. Some or all of the above processing in the social analytics unit may be performed using AI or not. For example, the social analytics unit may use AI to analyze the user's social media activity and then use AI to propose relevant estimates.

[0112] The estimate generation system may further include a device adaptation unit that provides an optimal estimate by considering the user's device information. For example, if the user is using a smartphone, the device adaptation unit provides an estimate that matches the screen size. If the user is using a tablet, it may also provide an estimate optimized for a larger screen. If the user is using a desktop, it may also provide an estimate that includes detailed information. In this way, the device adaptation unit can provide an optimal estimate by considering the user's device information. Some or all of the above processing in the device adaptation unit may be performed using AI or not. For example, the device adaptation unit may use AI to analyze the user's device information, and the AI ​​may provide an optimal estimate.

[0113] The estimate generation system may further include an emotion ordering unit that estimates the user's emotions and adjusts the order in which estimates are provided based on the estimated emotions. For example, the emotion ordering unit may prioritize providing estimates if the user has an urgent request. If the user is relaxed, estimates may be provided in the normal order. If the user is feeling anxious, the order of provision may be adjusted to respond quickly. In this way, the emotion ordering unit can respond quickly to urgent requests by adjusting the order in which estimates are provided according to the user'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 emotion ordering unit may be performed using AI or not using AI. For example, the emotion ordering unit may have an AI estimate the user's emotions and adjust the order in which estimates are provided based on the emotions estimated by the AI.

[0114] The estimate generation system may further include a language adaptation unit that provides multilingual estimates according to the user's language settings. The language adaptation unit may, for example, automatically set the language of the estimate based on the language settings of the user's device. If the user uses multiple languages, a language switching function may also be provided. If the user selects a specific language, the estimate may also be provided in that language. In this way, the language adaptation unit can provide estimates that are easy for the user to understand by providing multilingual estimates according to the user's language settings. Some or all of the above processing in the language adaptation unit may be performed using AI or not. For example, the language adaptation unit may use AI to analyze the user's language settings and the AI ​​to provide the estimate in the most appropriate language.

[0115] The estimate generation system may further include an emotion length unit that estimates the user's emotions and adjusts the length of the estimate based on the estimated emotions. For example, if the user is in a hurry, the emotion length unit may generate a short, to-the-point estimate. If the user is relaxed, it may generate a longer estimate that includes detailed explanations. If the user is excited, it may generate an estimate with visually stimulating effects. In this way, the emotion length unit can provide the user with the best possible estimate by adjusting the length of the estimate according to the user'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 emotion length unit may be performed using AI or not. For example, the emotion length unit may have an AI estimate the user's emotions and adjust the length of the estimate based on the emotions estimated by the AI.

[0116] The quotation generation system may further include a contract history section that analyzes the user's past contract history and proposes the optimal contract method. The contract history section may, for example, prioritize proposing contract methods previously used by the user. It can also automatically suggest specific vendors or conditions based on the user's past contract history. Furthermore, it can suggest the optimal contract time slot based on the user's past contract history. In this way, the contract history section can provide the user with the optimal contract method by analyzing past contract history. Some or all of the above processing in the contract history section may be performed using AI, or not. For example, the contract history section may use AI to analyze the user's past contract history, and the AI ​​may propose the optimal contract method.

[0117] The estimate generation system may further include an emotion contracting unit that estimates the user's emotions and adjusts the contract method based on the estimated emotions. For example, if the user is stressed, the emotion contracting unit may propose a simple and easy-to-understand contract method. If the user is relaxed, it may also propose a contract method that includes detailed information. If the user is in a hurry, it may also propose a method that allows for quick contract completion. In this way, the emotion contracting unit can provide a contract method that is easy for the user to understand by adjusting the contract method according to the user'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 emotion contracting unit may be performed using AI or not using AI. For example, the emotion contracting unit may have AI estimate the user's emotions and adjust the contract method based on the emotions estimated by the AI.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The reception department inputs the conditions for which the user is requesting a quote. These conditions may include, for example, budget, delivery date, and specifications. The reception department saves the conditions entered by the user to the database and sends them to the generation department. Step 2: The generation unit uses AI to generate an estimate based on the conditions entered by the reception unit. The generation unit generates estimates based on publicly available estimate examples and market data, and analyzes past estimate data to present the estimate best suited to the user's conditions. Step 3: The contracting department enters into a direct contract based on the quote generated by the generation department. If the user is satisfied with the quote provided, the contracting department can place an order directly. Because they are partnered with companies, they can enter into a direct contract based on the generated quote.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the reception unit, generation unit, contract unit, learning unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit uses the reception device 38 of the smart device 14 to input the conditions for requesting a quote from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a quote using AI. The contract unit is implemented by the specific processing unit 290 of the data processing unit 12 and directly enters into a contract based on the generated quote. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and the AI ​​learns quote examples and market prices. The provision unit uses the output device 40 of the smart device 14 to provide the generated quote to the user. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the reception unit, generation unit, contract unit, learning 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 uses the microphone 238 of the smart glasses 214 to input the conditions for requesting a quote from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a quote using AI. The contract unit is implemented by the specific processing unit 290 of the data processing unit 12 and directly enters into a contract based on the generated quote. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and the AI ​​learns quote examples and market prices. The provision unit uses the speaker 240 of the smart glasses 214 to provide the generated quote to the user. 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.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, generation unit, contract unit, learning unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit uses the microphone 238 of the headset terminal 314 to input the conditions for requesting a quote from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a quote using AI. The contract unit is implemented by the specific processing unit 290 of the data processing unit 12 and directly enters into a contract based on the generated quote. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and the AI ​​learns quote examples and market prices. The provision unit provides the generated quote to the user using the display 343 of the headset terminal 314. 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.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the reception unit, generation unit, contract unit, learning unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit uses the microphone 238 of the robot 414 to input the conditions for requesting a quote from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a quote using AI. The contract unit is implemented by the specific processing unit 290 of the data processing unit 12 and directly enters into a contract based on the generated quote. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and the AI ​​learns quote examples and market prices. The provision unit uses the speaker 240 of the robot 414 to provide the generated quote to the user. 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) The reception desk where you enter the conditions for requesting a quote, A generation unit that generates an estimate based on the conditions entered by the reception unit, The system includes a contracting unit that directly enters into a contract based on the estimate generated by the generation unit. A system characterized by the following features. (Note 2) The AI ​​is equipped with a learning unit that learns from real-world examples of estimates and market prices. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a provisioning unit that provides the generated estimate to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate an estimate based on publicly available example estimates and market data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate quotes that match the user's requirements. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned contracts department, Based on the generated quotes, partner with companies and enter into direct contracts. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of accepting quote requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past quote request history and select the most suitable method of receiving requests. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving a request for a quote, the system filters the user based on their current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the quote requests to be accepted based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a request for a quote, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a request for a quote, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts how the estimate is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating an estimate, adjust the level of detail in the estimate based on the importance of the request. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating an estimate, different estimation algorithms are applied depending on the category of the request. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's sentiment and adjusts the length of the estimate based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating quotes, we prioritize quotes based on when the request details were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating quotes, the order of quotes is adjusted based on the relevance of the request. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned contracts department, It estimates the user's emotions and adjusts the contract method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned contracts department, At the time of contract signing, the system analyzes the user's past contract history to select the most suitable contract method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned contracts department, At the time of contract, the contract method will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned contracts department, It estimates user sentiment and determines contract priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned contracts department, When entering into a contract, the most suitable contract method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned contracts department, When a contract is made, we analyze the user's social media activity and propose a suitable contract method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned learning unit, During training, training data is selected based on the user's current projects and areas of interest. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned learning unit, During training, the training data is weighted based on when the request for quotation was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned learning unit, During training, training data is selected by referring to the user's past training history. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We estimate the user's emotions and adjust how we provide estimates based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing a quote, the system will refer to the user's past quote history to select the most suitable delivery method. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing a quote, we adjust the delivery method based on the user's current projects and areas of interest. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned supply unit is, It estimates the user's sentiment and adjusts the order in which estimates are provided based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing a quote, we select the optimal delivery method considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing a quote, the method of providing multilingual support will be selected according to the user's language settings. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0192] 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. The reception desk where you enter the conditions for requesting a quote, A generation unit that generates an estimate based on the conditions entered by the reception unit, The system includes a contracting unit that directly enters into a contract based on the estimate generated by the generation unit. A system characterized by the following features.

2. The AI ​​is equipped with a learning unit that learns from real-world examples of estimates and market prices. The system according to feature 1.

3. It includes a provisioning unit that provides the generated estimate to the user. The system according to feature 1.

4. The generating unit is Generate an estimate based on publicly available example estimates and market data. The system according to feature 1.

5. The generating unit is Generate quotes that match the user's requirements. The system according to feature 1.

6. The aforementioned contracts department, Based on the generated quotes, partner with companies and enter into direct contracts. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of accepting quote requests based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past quote request history and select the most suitable method of receiving requests. The system according to feature 1.

9. The aforementioned reception unit is When receiving a request for a quote, the system filters the user based on their current projects and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the quote requests to be accepted based on the estimated user emotions. The system according to feature 1.

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