system

Abstract

The system according to this embodiment aims to streamline the estimation process in system development. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects image information for the detailed specifications of the system. The analysis unit analyzes the data collected by the collection unit. The generation unit generates an estimate based on the analysis results obtained by the analysis unit. The provision unit provides the estimate 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 method for controlling a persona chatbot, which is 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the estimation work in system development requires time and effort and is difficult to perform efficiently.

[0005] The system according to the embodiment aims to improve the estimation work in system development.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects image information on the detailed specifications of the system. The analysis unit analyzes the data collected by the collection unit. The generation unit generates an estimate based on the analysis result obtained by the analysis unit. The provision unit provides the estimate generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the estimation process in system development. [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 linked 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 system according to an embodiment of the present invention is a mechanism that uses AI to promote the digitalization (DX) of estimates in system development. This system collects image information (image data visually displaying system specifications, flowcharts, etc.) for the detailed specifications of each system, and optimizes the estimation process by training the AI ​​with data from past successful and unsuccessful estimates (text data such as estimated cost, man-hours, and validity). Furthermore, it analyzes customer reactions (text data) to the proposed estimates and whether or not the development project was executed (text data) to generate information for better proposals. This mechanism can reduce the effort and time spent on the estimation stage and improve accuracy. Cost reduction and quality improvement can be expected through the efficiency and optimization of the system development estimation process. For example, the system collects image information for the detailed specifications of each system. This includes system specifications and flowcharts. Next, the system collects data from past successful and unsuccessful estimates. This includes text data such as estimated cost, man-hours, and validity. By training the AI ​​with this data, the estimation process is optimized. Furthermore, the system analyzes customer reactions to the proposed estimates and whether or not the development project was executed. This allows us to understand how customers reacted and whether proposals were actually adopted. Based on this data, the system generates information for better proposals. This mechanism reduces the time and effort required during the estimation phase and improves accuracy. We can expect cost reduction and quality improvement through the streamlining and optimization of the system development estimation process.

[0029] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects image information related to the detailed specifications of the system. The collection unit collects image information such as system specifications and flowcharts. By collecting image information such as system specifications and flowcharts, the collection unit can generate estimates based on the detailed specifications. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes data of past successful and unsuccessful estimates, for example. By analyzing data of past successful and unsuccessful estimates, the analysis unit can improve the accuracy of estimates. The generation unit generates estimates based on the analysis results obtained by the analysis unit. The generation unit generates estimates based on analysis results, for example. By generating estimates based on analysis results, the generation unit can improve the accuracy of estimates. The provision unit provides the estimates generated by the generation unit. The provision unit provides the generated estimates, for example. By providing the generated estimates, the provision unit can quickly share the results of the estimates. As a result, the system according to this embodiment can optimize the estimation process by collecting and analyzing image information related to the detailed specifications of the system, generating estimates, and providing them.

[0030] The data collection unit collects image information related to the detailed specifications of the system. Specifically, the unit collects image information such as system specifications and flowcharts. This includes scanning and digitizing paper specifications and directly importing existing digital files. The data collection unit can use OCR (Optical Character Recognition) technology to extract text information from images and store it in a database. Furthermore, the data collection unit simultaneously collects image metadata (creation date and time, creator, version information, etc.) and centrally manages this information. This allows the data collection unit to efficiently collect basic data for generating estimates based on the detailed specifications of the system. In addition, the data collection unit can use cloud storage to securely store the collected data and share it with other departments and systems as needed. This enables the data collection unit to ensure data consistency and reliability while achieving rapid data access.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, the analysis unit analyzes data from past successful and unsuccessful estimates. Using machine learning algorithms, the analysis unit extracts patterns and trends from past estimate data to gain insights for improving the accuracy of estimates. For example, the analysis unit clusters past estimate data and compares the characteristics of successful and unsuccessful estimates. This allows it to identify factors contributing to the success and failure of estimates and reflect them in future estimates. Furthermore, the analysis unit uses natural language processing technology to analyze text information from specifications and flowcharts and extract important keywords and phrases. This allows the analysis unit to improve the accuracy of estimates based on system specifications. In addition, the analysis unit uses data visualization tools to display the analysis results as graphs and charts, making them easily understandable to stakeholders. This allows the analysis unit to support data-driven decision-making and improve the efficiency of estimation work.

[0032] The generation unit generates estimates based on the analysis results obtained by the analysis unit. Specifically, the generation unit automatically calculates each item of the estimate based on the analysis results and generates an estimate document. The generation unit uses AI to select the optimal estimation model from the analysis results and improve the accuracy of the estimate. For example, the generation unit performs optimal resource allocation and cost calculations for a specific project based on past estimate data. In addition, the generation unit can customize the estimate by taking into account additional information and conditions entered by the user. This allows the generation unit to provide flexible estimates that meet the user's needs. Furthermore, the generation unit can automatically format the generated estimate and output it in formats such as PDF and Excel. This allows the generation unit to improve the accuracy and efficiency of estimates and significantly reduce the time required to create estimate documents.

[0033] The provisioning department provides the estimates generated by the generation department. Specifically, the provisioning department has functions for quickly sharing the generated estimates. The provisioning department stores the generated estimates in the cloud and makes them accessible to stakeholders. For example, the provisioning department can integrate estimates with project management tools and email systems and automatically send notifications to stakeholders. The provisioning department also has a version control function for estimates, allowing comparison with past estimates. This enables the provisioning department to quickly share the results of estimates and help stakeholders make decisions based on the latest information. Furthermore, the provisioning department has a feedback function for estimates, allowing it to receive comments and revision requests from stakeholders. This enables the provisioning department to improve the accuracy and reliability of estimates and streamline the estimation process.

[0034] The collection unit can collect image information such as system specifications and flowcharts. For example, the collection unit can collect image information such as system specifications and flowcharts. By collecting image information such as system specifications and flowcharts, the collection unit can generate estimates based on detailed specifications. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input image information such as system specifications and flowcharts into AI and have AI perform the image information collection.

[0035] The analysis unit can analyze data from past successful and unsuccessful estimates. For example, the analysis unit analyzes data from past successful and unsuccessful estimates. By analyzing data from past successful and unsuccessful estimates, the analysis unit can improve the accuracy of estimates. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data from past successful and unsuccessful estimates into an AI and have the AI ​​perform the data analysis.

[0036] The generation unit can generate estimates based on the analysis results. The generation unit can, for example, generate estimates based on the analysis results. By generating estimates based on the analysis results, the generation unit can improve the accuracy of the estimates. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into AI and have AI perform the generation of estimates.

[0037] The service provider can provide the generated estimate. The service provider can, for example, provide the generated estimate. By providing the generated estimate, the service provider can quickly share the results of the estimate. By providing the generated estimate, the results of the estimate can be quickly shared. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated estimate into AI and have AI perform the provision of the estimate.

[0038] The system includes a reaction analysis unit that analyzes customer responses. The reaction analysis unit can analyze customer responses. For example, the reaction analysis unit analyzes customer responses. By analyzing customer responses, the reaction analysis unit can generate information for better proposals. Some or all of the above processing in the reaction analysis unit may be performed using AI, for example, or without AI. For example, the reaction analysis unit can input customer response data into AI and have AI perform the response analysis.

[0039] The system includes an execution analysis unit that analyzes whether or not development projects are being executed. The execution analysis unit can analyze whether or not development projects are being executed. For example, the execution analysis unit analyzes whether or not development projects are being executed. By analyzing whether or not development projects are being executed, the execution analysis unit can determine whether or not a proposal has actually been adopted. In this way, by analyzing whether or not development projects are being executed, it is possible to determine whether or not a proposal has actually been adopted. Some or all of the above processing in the execution analysis unit may be performed using AI, for example, or without using AI. For example, the execution analysis unit can input development project execution data into AI and have AI perform the analysis of whether or not the projects are being executed.

[0040] The system includes a feedback unit for improving the accuracy of estimates. The feedback unit can provide feedback to improve the accuracy of estimates. For example, the feedback unit can provide feedback to improve the accuracy of estimates. By providing feedback to improve the accuracy of estimates, the feedback unit can improve the accuracy of estimates. In this way, by providing feedback to improve the accuracy of estimates, the accuracy of estimates can be improved. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input feedback data to improve the accuracy of estimates into the AI ​​and have the AI ​​perform the provision of feedback.

[0041] The data collection unit can select the optimal data collection method based on the resolution and format of the system specifications and flowcharts during data collection. For example, the data collection unit may prioritize the collection of high-resolution image data to ensure detailed information. For example, the data collection unit may uniformly collect image data of different formats to improve the efficiency of analysis. For example, the data collection unit may select an appropriate data collection tool according to the format of the image data to be collected. This allows for the selection of the optimal data collection method based on resolution and format, thereby ensuring detailed information and improving the efficiency of analysis. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the resolution and format of the system specifications and flowcharts into the AI ​​and have the AI ​​select the optimal data collection method.

[0042] The data collection unit can apply different collection algorithms depending on the development phase of the system during data collection. For example, in the initial phase, the collection unit collects broad information to grasp the overall picture. In the middle phase, for example, the collection unit focuses on collecting detailed specifications and flowcharts. In the final phase, for example, the collection unit collects final specifications and flowcharts to improve accuracy. In this way, the accuracy of data collection can be improved by applying different collection algorithms depending on the development phase. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the system development phase into AI and have AI execute the application of the collection algorithm.

[0043] The data collection unit can prioritize the collection of highly relevant information by considering the configuration information of the system's development team during the collection process. For example, the data collection unit prioritizes the collection of relevant specifications and flowcharts based on the development team's area of ​​expertise. For example, the data collection unit collects information referencing past success stories based on the development team's experience. For example, the data collection unit collects timely information based on the development team's schedule. This allows for efficient information collection by prioritizing the collection of highly relevant information while considering the configuration information of the development team. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the development team's configuration information into AI and have the AI ​​perform the collection of highly relevant information.

[0044] The data collection unit can improve the accuracy of data collection by referring to past version information of the system during the collection process. For example, the data collection unit can identify and collect changes based on past version information. For example, the data collection unit can prioritize the collection of important specifications and flowcharts by referring to past version information. For example, the data collection unit can analyze past version information to improve the accuracy of data collection. By improving the accuracy of data collection by referring to past version information, important information can be reliably collected. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past version information of the system into AI and have AI perform the task of improving the accuracy of data collection.

[0045] The analysis unit can adjust the level of detail of its analysis based on the importance of past successful and unsuccessful estimate data. For example, the analysis unit may prioritize data from successful estimates and perform a detailed analysis. For example, the analysis unit may prioritize data from unsuccessful estimates and perform an analysis to identify problems. For example, the analysis unit may analyze successful and unsuccessful data in a balanced manner to improve the overall accuracy of the estimate. In this way, the accuracy of the estimate can be improved by adjusting the level of detail of the analysis based on the importance of past estimate data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input past estimate data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis based on the importance of the data.

[0046] The analysis unit can apply different analysis algorithms depending on the category of the estimate during the analysis. For example, the analysis unit applies a detailed analysis algorithm for large-scale projects. For example, the analysis unit applies a simplified analysis algorithm for small-scale projects. For example, the analysis unit applies an industry-specific analysis algorithm for estimates specific to a particular industry. By applying different analysis algorithms depending on the category of the estimate, the accuracy of the estimate can be improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the estimate into the AI ​​and have the AI ​​perform the application of the analysis algorithm.

[0047] The analysis unit can determine the priority of analyses based on the submission dates of estimates during the analysis process. For example, the analysis unit may prioritize analyzing estimates with approaching deadlines. For example, it may postpone analyzing estimates with later submission dates. For example, the analysis unit may adjust the level of detail of the analysis according to the submission date. This allows for efficient analysis by determining the priority of analyses based on the submission dates of estimates. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the estimate submission dates into the AI ​​and have the AI ​​perform the analysis priority determination.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the estimates during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant estimates. For example, the analysis unit may postpone the analysis of less relevant estimates. For example, the analysis unit may adjust the level of detail of the analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the estimates. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the estimates into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0049] The generation unit can adjust the level of detail of the estimate based on the importance of the analysis results during generation. For example, the generation unit generates a detailed estimate based on important analysis results. For example, the generation unit generates a concise estimate based on less important analysis results. For example, the generation unit adjusts the level of detail of the estimate according to its importance. This improves the accuracy of the estimate by adjusting the level of detail based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the analysis results into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the estimate.

[0050] The generation unit can apply different generation algorithms depending on the category of the estimate during generation. For example, the generation unit applies a detailed generation algorithm for large-scale projects. For example, the generation unit applies a simplified generation algorithm for small-scale projects. For example, the generation unit applies an industry-specific generation algorithm for estimates specific to a particular industry. This improves the accuracy of estimates by applying different generation algorithms depending on the category of the estimate. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of the estimate into the AI ​​and have the AI ​​perform the application of the generation algorithm.

[0051] The generation unit can determine the priority of estimates based on the submission date during generation. For example, the generation unit may prioritize generating estimates with approaching deadlines. For example, it may postpone generating estimates with distant submission dates. For example, the generation unit may adjust the level of detail in the estimates according to the submission date. This allows for efficient generation of estimates by determining the priority of estimates based on the submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the estimate submission dates into the AI ​​and have the AI ​​perform the estimation priority determination.

[0052] The generation unit can adjust the order of estimates based on their relevance during generation. For example, the generation unit can prioritize generating highly relevant estimates. For example, the generation unit can postpone less relevant estimates. For example, the generation unit can adjust the level of detail of estimates according to their relevance. This allows for efficient generation of estimates by adjusting their order based on their relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the estimates into the AI ​​and have the AI ​​perform the estimation order adjustment.

[0053] The service provider can select the optimal service delivery method by referring to the user's past response history at the time of delivery. For example, the service provider may prioritize providing the service delivery method that the user has preferred in the past. For example, the service provider may select the optimal service delivery method based on the user's past response history. For example, the service provider may analyze the user's past response history and optimize the service delivery method. By doing so, by selecting the optimal service delivery method by referring to the user's past response history, the service provider can provide the user with the best possible estimate. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past response history into AI and have AI select the optimal service delivery method.

[0054] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit will provide a delivery method that matches the screen size. For example, if the user is using a tablet, the delivery unit will provide a delivery method optimized for a larger screen. For example, if the user is using a desktop, the delivery unit will provide a delivery method that includes detailed information. By selecting the optimal delivery method considering the user's device information, the delivery unit can provide the user with the best possible estimate. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into AI and have the AI ​​select the optimal delivery method.

[0055] The reaction analysis unit can optimize its analysis algorithm by referring to past reaction data during reaction analysis. For example, the reaction analysis unit selects the optimal analysis algorithm based on past reaction data. For example, the reaction analysis unit analyzes past reaction data to improve the accuracy of the reaction analysis. For example, the reaction analysis unit improves the efficiency of the reaction analysis by referring to past reaction data. In this way, the accuracy of the reaction analysis can be improved by optimizing the analysis algorithm by referring to past reaction data. Some or all of the above processes in the reaction analysis unit may be performed using AI, for example, or without using AI. For example, the reaction analysis unit can input past reaction data into AI and have AI perform the optimization of the analysis algorithm.

[0056] The reaction analysis unit can weight the analysis data based on the submission date of the reaction data during reaction analysis. For example, the reaction analysis unit prioritizes the analysis of reaction data that has been submitted recently. For example, the reaction analysis unit postpones the analysis of reaction data that has been submitted later. For example, the reaction analysis unit adjusts the weighting of the analysis data according to the submission date. This allows for efficient reaction analysis by weighting the analysis data based on the submission date of the reaction data. Some or all of the above processing in the reaction analysis unit may be performed using AI, for example, or without AI. For example, the reaction analysis unit can input the submission date of the reaction data into the AI ​​and have the AI ​​perform the weighting of the analysis data.

[0057] The execution analysis unit can optimize the analysis algorithm by referring to past execution data during execution analysis. For example, the execution analysis unit selects the optimal analysis algorithm based on past execution data. For example, the execution analysis unit analyzes past execution data to improve the accuracy of the execution analysis. For example, the execution analysis unit improves the efficiency of the execution analysis by referring to past execution data. In this way, the accuracy of the execution analysis can be improved by optimizing the analysis algorithm by referring to past execution data. Some or all of the above processes in the execution analysis unit may be performed using AI, for example, or without using AI. For example, the execution analysis unit can input past execution data into AI and have AI perform the optimization of the analysis algorithm.

[0058] The execution analysis unit can weight the analysis data based on the submission date of the execution data during execution analysis. For example, the execution analysis unit prioritizes analyzing execution data with a recent submission date. For example, the execution analysis unit postpones analyzing execution data with a later submission date. For example, the execution analysis unit adjusts the weighting of the analysis data according to the submission date. This allows for efficient execution analysis by weighting the analysis data based on the submission date of the execution data. Some or all of the above processing in the execution analysis unit may be performed using AI, for example, or without AI. For example, the execution analysis unit can input the submission date of the execution data into the AI ​​and have the AI ​​perform the weighting of the analysis data.

[0059] The feedback unit can optimize the feedback algorithm by referring to past feedback data during the feedback process. For example, the feedback unit can select the optimal feedback algorithm based on past feedback data. For example, the feedback unit can analyze past feedback data to improve the accuracy of the feedback. For example, the feedback unit can improve the efficiency of the feedback by referring to past feedback data. In this way, the accuracy of the feedback can be improved by optimizing the feedback algorithm by referring to past feedback data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input past feedback data into AI and have AI perform the optimization of the feedback algorithm.

[0060] The feedback unit can weight feedback data based on when it is submitted. For example, the feedback unit prioritizes analyzing feedback data that has been submitted recently. For example, the feedback unit postpones analyzing feedback data that has been submitted later. For example, the feedback unit adjusts the weighting of the feedback data according to the submission date. This allows for efficient feedback by weighting the feedback data based on when it was submitted. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the submission date of the feedback data into the AI ​​and have the AI ​​perform the weighting of the feedback data.

[0061] The feedback unit can provide scheduled suggestions by referring to the user's calendar information during the feedback process. For example, the feedback unit can refer to scheduled events registered in the user's calendar and adjust the timing of the feedback. For example, the feedback unit can provide feedback related to a specific event based on the user's calendar information. For example, the feedback unit can provide optimal feedback tailored to the scheduled events based on the user's calendar information. In this way, by referring to the user's calendar information and providing scheduled suggestions, the feedback unit can provide the most optimal feedback for the user. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's calendar information into AI and have the AI ​​execute scheduled suggestions.

[0062] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit may prioritize providing feedback methods that the user has preferred in the past. For example, the feedback unit may select the optimal feedback based on the user's past feedback history. For example, the feedback unit may analyze the user's past feedback history to improve the accuracy of the feedback. In this way, the accuracy of the feedback can be improved by providing optimal feedback by referring to the user's past feedback history. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit may input the user's past feedback history into AI and have the AI ​​perform the task of providing optimal feedback.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The system can collect user feedback on the proposed quote in real time and immediately improve the proposal. For example, if a user expresses dissatisfaction with the proposal, the system will revise the proposal based on that feedback and present it again. If a user reacts favorably to the proposal, the system will maintain the original content while adding more detailed information. If a user reacts indifferently to the proposal, the system will simplify the proposal and emphasize the key points. This allows the system to provide the best possible proposal by immediately improving the proposal based on user feedback. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input user feedback data into an AI and have the AI ​​perform improvements to the proposal.

[0065] The system can analyze user reactions to a proposal and automatically extract areas for improvement. For example, if a user reacts negatively to a proposal, it identifies the cause and extracts areas for improvement. If a user reacts positively, it analyzes the factors contributing to that reaction and applies them to other proposals. If a user reacts neutrally, it identifies areas for improvement and extracts areas for improvement. By analyzing user reactions and automatically extracting areas for improvement, the quality of proposals can be improved. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input user reaction data into an AI and have the AI ​​perform the reaction analysis and extraction of areas for improvement.

[0066] The system can optimize proposals by referring to the user's past response history to proposals. For example, it may prioritize proposals that the user has previously preferred. Based on the user's past response history, it selects the most suitable proposal. It analyzes the user's past response history to improve the accuracy of the proposals. In this way, by optimizing proposals by referring to the user's past response history, the system can provide the best possible proposal for the user. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input the user's past response history into AI and have the AI ​​perform the optimization of proposals.

[0067] The system can select the optimal proposal method by considering the user's device information in relation to the proposed estimate. For example, if the user is using a smartphone, it will provide a proposal method adapted to the screen size. If the user is using a tablet, it will provide a proposal method optimized for a larger screen. If the user is using a desktop, it will provide a proposal method that includes detailed information. By selecting the optimal proposal method considering the user's device information, the system can provide the best possible proposal for the user. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input the user's device information into an AI and have the AI ​​select the optimal proposal method.

[0068] The system can refer to the user's calendar information regarding the proposed estimate and make proposals based on their schedule. For example, it can refer to appointments registered in the user's calendar and adjust the timing of proposals. It can provide proposals related to specific events based on the user's calendar information. It can provide optimal proposals tailored to the user's schedule based on the user's calendar information. In this way, by referring to the user's calendar information and making proposals based on their schedule, the system can provide the best possible proposals for the user. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input the user's calendar information into AI and have the AI ​​execute proposals based on the schedule.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The collection unit collects image information related to the detailed specifications of the system. For example, it collects image information such as system specifications and flowcharts. This provides data for generating estimates based on the detailed specifications. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it improves the accuracy of estimates by analyzing data from past successful and unsuccessful estimates. Step 3: The generation unit generates an estimate based on the analysis results obtained by the analysis unit. This improves the accuracy of the estimate. Step 4: The providing unit provides the estimate generated by the generating unit. This allows for the quick sharing of the estimate results.

[0071] (Example of form 2) The system according to an embodiment of the present invention is a mechanism that uses AI to promote the digitalization (DX) of estimates in system development. This system collects image information (image data visually displaying system specifications, flowcharts, etc.) for the detailed specifications of each system, and optimizes the estimation process by training the AI ​​with data from past successful and unsuccessful estimates (text data such as estimated cost, man-hours, and validity). Furthermore, it analyzes customer reactions (text data) to the proposed estimates and whether or not the development project was executed (text data) to generate information for better proposals. This mechanism can reduce the effort and time spent on the estimation stage and improve accuracy. Cost reduction and quality improvement can be expected through the efficiency and optimization of the system development estimation process. For example, the system collects image information for the detailed specifications of each system. This includes system specifications and flowcharts. Next, the system collects data from past successful and unsuccessful estimates. This includes text data such as estimated cost, man-hours, and validity. By training the AI ​​with this data, the estimation process is optimized. Furthermore, the system analyzes customer reactions to the proposed estimates and whether or not the development project was executed. This allows us to understand how customers reacted and whether proposals were actually adopted. Based on this data, the system generates information for better proposals. This mechanism reduces the time and effort required during the estimation phase and improves accuracy. We can expect cost reduction and quality improvement through the streamlining and optimization of the system development estimation process.

[0072] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects image information related to the detailed specifications of the system. The collection unit collects image information such as system specifications and flowcharts. By collecting image information such as system specifications and flowcharts, the collection unit can generate estimates based on the detailed specifications. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes data of past successful and unsuccessful estimates, for example. By analyzing data of past successful and unsuccessful estimates, the analysis unit can improve the accuracy of estimates. The generation unit generates estimates based on the analysis results obtained by the analysis unit. The generation unit generates estimates based on analysis results, for example. By generating estimates based on analysis results, the generation unit can improve the accuracy of estimates. The provision unit provides the estimates generated by the generation unit. The provision unit provides the generated estimates, for example. By providing the generated estimates, the provision unit can quickly share the results of the estimates. As a result, the system according to this embodiment can optimize the estimation process by collecting and analyzing image information related to the detailed specifications of the system, generating estimates, and providing them.

[0073] The data collection unit collects image information related to the detailed specifications of the system. Specifically, the unit collects image information such as system specifications and flowcharts. This includes scanning and digitizing paper specifications and directly importing existing digital files. The data collection unit can use OCR (Optical Character Recognition) technology to extract text information from images and store it in a database. Furthermore, the data collection unit simultaneously collects image metadata (creation date and time, creator, version information, etc.) and centrally manages this information. This allows the data collection unit to efficiently collect basic data for generating estimates based on the detailed specifications of the system. In addition, the data collection unit can use cloud storage to securely store the collected data and share it with other departments and systems as needed. This enables the data collection unit to ensure data consistency and reliability while achieving rapid data access.

[0074] The analysis unit analyzes the data collected by the data collection unit. Specifically, the analysis unit analyzes data from past successful and unsuccessful estimates. Using machine learning algorithms, the analysis unit extracts patterns and trends from past estimate data to gain insights for improving the accuracy of estimates. For example, the analysis unit clusters past estimate data and compares the characteristics of successful and unsuccessful estimates. This allows it to identify factors contributing to the success and failure of estimates and reflect them in future estimates. Furthermore, the analysis unit uses natural language processing technology to analyze text information from specifications and flowcharts and extract important keywords and phrases. This allows the analysis unit to improve the accuracy of estimates based on system specifications. In addition, the analysis unit uses data visualization tools to display the analysis results as graphs and charts, making them easily understandable to stakeholders. This allows the analysis unit to support data-driven decision-making and improve the efficiency of estimation work.

[0075] The generation unit generates estimates based on the analysis results obtained by the analysis unit. Specifically, the generation unit automatically calculates each item of the estimate based on the analysis results and generates an estimate document. The generation unit uses AI to select the optimal estimation model from the analysis results and improve the accuracy of the estimate. For example, the generation unit performs optimal resource allocation and cost calculations for a specific project based on past estimate data. In addition, the generation unit can customize the estimate by taking into account additional information and conditions entered by the user. This allows the generation unit to provide flexible estimates that meet the user's needs. Furthermore, the generation unit can automatically format the generated estimate and output it in formats such as PDF and Excel. This allows the generation unit to improve the accuracy and efficiency of estimates and significantly reduce the time required to create estimate documents.

[0076] The provisioning department provides the estimates generated by the generation department. Specifically, the provisioning department has functions for quickly sharing the generated estimates. The provisioning department stores the generated estimates in the cloud and makes them accessible to stakeholders. For example, the provisioning department can integrate estimates with project management tools and email systems and automatically send notifications to stakeholders. The provisioning department also has a version control function for estimates, allowing comparison with past estimates. This enables the provisioning department to quickly share the results of estimates and help stakeholders make decisions based on the latest information. Furthermore, the provisioning department has a feedback function for estimates, allowing it to receive comments and revision requests from stakeholders. This enables the provisioning department to improve the accuracy and reliability of estimates and streamline the estimation process.

[0077] The collection unit can collect image information such as system specifications and flowcharts. For example, the collection unit can collect image information such as system specifications and flowcharts. By collecting image information such as system specifications and flowcharts, the collection unit can generate estimates based on detailed specifications. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input image information such as system specifications and flowcharts into AI and have AI perform the image information collection.

[0078] The analysis unit can analyze data from past successful and unsuccessful estimates. For example, the analysis unit analyzes data from past successful and unsuccessful estimates. By analyzing data from past successful and unsuccessful estimates, the analysis unit can improve the accuracy of estimates. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data from past successful and unsuccessful estimates into an AI and have the AI ​​perform the data analysis.

[0079] The generation unit can generate estimates based on the analysis results. The generation unit can, for example, generate estimates based on the analysis results. By generating estimates based on the analysis results, the generation unit can improve the accuracy of the estimates. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into AI and have AI perform the generation of estimates.

[0080] The service provider can provide the generated estimate. The service provider can, for example, provide the generated estimate. By providing the generated estimate, the service provider can quickly share the results of the estimate. By providing the generated estimate, the results of the estimate can be quickly shared. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated estimate into AI and have AI perform the provision of the estimate.

[0081] The system includes a reaction analysis unit that analyzes customer responses. The reaction analysis unit can analyze customer responses. For example, the reaction analysis unit analyzes customer responses. By analyzing customer responses, the reaction analysis unit can generate information for better proposals. Some or all of the above processing in the reaction analysis unit may be performed using AI, for example, or without AI. For example, the reaction analysis unit can input customer response data into AI and have AI perform the response analysis.

[0082] The system includes an execution analysis unit that analyzes whether or not development projects are being executed. The execution analysis unit can analyze whether or not development projects are being executed. For example, the execution analysis unit analyzes whether or not development projects are being executed. By analyzing whether or not development projects are being executed, the execution analysis unit can determine whether or not a proposal has actually been adopted. In this way, by analyzing whether or not development projects are being executed, it is possible to determine whether or not a proposal has actually been adopted. Some or all of the above processing in the execution analysis unit may be performed using AI, for example, or without using AI. For example, the execution analysis unit can input development project execution data into AI and have AI perform the analysis of whether or not the projects are being executed.

[0083] The system includes a feedback unit for improving the accuracy of estimates. The feedback unit can provide feedback to improve the accuracy of estimates. For example, the feedback unit can provide feedback to improve the accuracy of estimates. By providing feedback to improve the accuracy of estimates, the feedback unit can improve the accuracy of estimates. In this way, by providing feedback to improve the accuracy of estimates, the accuracy of estimates can be improved. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input feedback data to improve the accuracy of estimates into the AI ​​and have the AI ​​perform the provision of feedback.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of image information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. For example, if the user is relaxed, the data collection unit can advance the collection timing to efficiently collect information. For example, if the user is in a hurry, the data collection unit can optimize the collection timing to quickly collect information. In this way, by adjusting the collection timing according to the user's emotions, the user's burden can be reduced and information can be collected efficiently. 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0085] The data collection unit can select the optimal data collection method based on the resolution and format of the system specifications and flowcharts during data collection. For example, the data collection unit may prioritize the collection of high-resolution image data to ensure detailed information. For example, the data collection unit may uniformly collect image data of different formats to improve the efficiency of analysis. For example, the data collection unit may select an appropriate data collection tool according to the format of the image data to be collected. This allows for the selection of the optimal data collection method based on resolution and format, thereby ensuring detailed information and improving the efficiency of analysis. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the resolution and format of the system specifications and flowcharts into the AI ​​and have the AI ​​select the optimal data collection method.

[0086] The data collection unit can apply different collection algorithms depending on the development phase of the system during data collection. For example, in the initial phase, the collection unit collects broad information to grasp the overall picture. In the middle phase, for example, the collection unit focuses on collecting detailed specifications and flowcharts. In the final phase, for example, the collection unit collects final specifications and flowcharts to improve accuracy. In this way, the accuracy of data collection can be improved by applying different collection algorithms depending on the development phase. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the system development phase into AI and have AI execute the application of the collection algorithm.

[0087] The data collection unit can estimate the user's emotions and determine the priority of image information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information of high importance. If the user is relaxed, the data collection unit will collect information in a balanced manner. If the user is in a hurry, the data collection unit will prioritize information that can be collected quickly. In this way, by determining the priority of image information to collect according to the user's emotions, important information can be collected preferentially. 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0088] The data collection unit can prioritize the collection of highly relevant information by considering the configuration information of the system's development team during the collection process. For example, the data collection unit prioritizes the collection of relevant specifications and flowcharts based on the development team's area of ​​expertise. For example, the data collection unit collects information referencing past success stories based on the development team's experience. For example, the data collection unit collects timely information based on the development team's schedule. This allows for efficient information collection by prioritizing the collection of highly relevant information while considering the configuration information of the development team. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the development team's configuration information into AI and have the AI ​​perform the collection of highly relevant information.

[0089] The data collection unit can improve the accuracy of data collection by referring to past version information of the system during the collection process. For example, the data collection unit can identify and collect changes based on past version information. For example, the data collection unit can prioritize the collection of important specifications and flowcharts by referring to past version information. For example, the data collection unit can analyze past version information to improve the accuracy of data collection. By improving the accuracy of data collection by referring to past version information, important information can be reliably collected. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past version information of the system into AI and have AI perform the task of improving the accuracy of data collection.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is in a hurry, the analysis unit provides a concise analysis result. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​perform emotion estimation.

[0091] The analysis unit can adjust the level of detail of its analysis based on the importance of past successful and unsuccessful estimate data. For example, the analysis unit may prioritize data from successful estimates and perform a detailed analysis. For example, the analysis unit may prioritize data from unsuccessful estimates and perform an analysis to identify problems. For example, the analysis unit may analyze successful and unsuccessful data in a balanced manner to improve the overall accuracy of the estimate. In this way, the accuracy of the estimate can be improved by adjusting the level of detail of the analysis based on the importance of past estimate data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input past estimate data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis based on the importance of the data.

[0092] The analysis unit can apply different analysis algorithms depending on the category of the estimate during the analysis. For example, the analysis unit applies a detailed analysis algorithm for large-scale projects. For example, the analysis unit applies a simplified analysis algorithm for small-scale projects. For example, the analysis unit applies an industry-specific analysis algorithm for estimates specific to a particular industry. By applying different analysis algorithms depending on the category of the estimate, the accuracy of the estimate can be improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the estimate into the AI ​​and have the AI ​​perform the application of the analysis algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis result. Emotion estimation is achieved using an emotion estimation function, for example, with 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0094] The analysis unit can determine the priority of analyses based on the submission dates of estimates during the analysis process. For example, the analysis unit may prioritize analyzing estimates with approaching deadlines. For example, it may postpone analyzing estimates with later submission dates. For example, the analysis unit may adjust the level of detail of the analysis according to the submission date. This allows for efficient analysis by determining the priority of analyses based on the submission dates of estimates. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the estimate submission dates into the AI ​​and have the AI ​​perform the analysis priority determination.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the estimates during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant estimates. For example, the analysis unit may postpone the analysis of less relevant estimates. For example, the analysis unit may adjust the level of detail of the analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the estimates. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the estimates into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0096] The generation unit can estimate the user's emotions and adjust the estimation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a detailed estimation. If the user is in a hurry, the generation unit generates a concise estimation. If the user is excited, the generation unit generates a visually appealing estimation. By adjusting the estimation method according to the user's emotions, the system can provide the user with the most suitable estimation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0097] The generation unit can adjust the level of detail of the estimate based on the importance of the analysis results during generation. For example, the generation unit generates a detailed estimate based on important analysis results. For example, the generation unit generates a concise estimate based on less important analysis results. For example, the generation unit adjusts the level of detail of the estimate according to its importance. This improves the accuracy of the estimate by adjusting the level of detail based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the analysis results into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the estimate.

[0098] The generation unit can apply different generation algorithms depending on the category of the estimate during generation. For example, the generation unit applies a detailed generation algorithm for large-scale projects. For example, the generation unit applies a simplified generation algorithm for small-scale projects. For example, the generation unit applies an industry-specific generation algorithm for estimates specific to a particular industry. This improves the accuracy of estimates by applying different generation algorithms depending on the category of the estimate. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of the estimate into the AI ​​and have the AI ​​perform the application of the generation algorithm.

[0099] 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, concise estimate. If the user is relaxed, the generation unit will generate a detailed estimate. If the user is excited, the generation unit will generate a visually appealing estimate. By adjusting the length of the estimate according to the user's emotions, the system can provide the user with the most suitable estimate. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the generation unit may be performed using or without AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0100] The generation unit can determine the priority of estimates based on the submission date during generation. For example, the generation unit may prioritize generating estimates with approaching deadlines. For example, it may postpone generating estimates with distant submission dates. For example, the generation unit may adjust the level of detail in the estimates according to the submission date. This allows for efficient generation of estimates by determining the priority of estimates based on the submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the estimate submission dates into the AI ​​and have the AI ​​perform the estimation priority determination.

[0101] The generation unit can adjust the order of estimates based on their relevance during generation. For example, the generation unit can prioritize generating highly relevant estimates. For example, the generation unit can postpone less relevant estimates. For example, the generation unit can adjust the level of detail of estimates according to their relevance. This allows for efficient generation of estimates by adjusting their order based on their relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the estimates into the AI ​​and have the AI ​​perform the estimation order adjustment.

[0102] The service provider can estimate the user's emotions and adjust the way estimates are provided based on the estimated emotions. For example, if the user is nervous, the service provider may provide a simple and easy-to-understand method of delivery. If the user is relaxed, the service provider may provide a method that includes detailed information. If the user is in a hurry, the service provider may provide a concise method of delivery. By adjusting the method of delivery according to the user's emotions, the service provider can provide the user with the most suitable estimate. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, 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 can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0103] The service provider can select the optimal service delivery method by referring to the user's past response history at the time of delivery. For example, the service provider may prioritize providing the service delivery method that the user has preferred in the past. For example, the service provider may select the optimal service delivery method based on the user's past response history. For example, the service provider may analyze the user's past response history and optimize the service delivery method. By doing so, by selecting the optimal service delivery method by referring to the user's past response history, the service provider can provide the user with the best possible estimate. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past response history into AI and have AI select the optimal service delivery method.

[0104] The service provider can estimate the user's emotions and adjust the order in which estimates are provided based on the estimated emotions. For example, if the user is tense, the service provider will provide important estimates first. If the user is relaxed, the service provider will provide a balanced overall estimate. If the user is in a hurry, the service provider will prioritize estimates that can be provided quickly. By adjusting the order in which estimates are provided according to the user's emotions, the service provider can provide the best possible estimate for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0105] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit will provide a delivery method that matches the screen size. For example, if the user is using a tablet, the delivery unit will provide a delivery method optimized for a larger screen. For example, if the user is using a desktop, the delivery unit will provide a delivery method that includes detailed information. By selecting the optimal delivery method considering the user's device information, the delivery unit can provide the user with the best possible estimate. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into AI and have the AI ​​select the optimal delivery method.

[0106] The reaction analysis unit can estimate the user's emotions and adjust the reaction analysis method based on the estimated user emotions. For example, if the user is nervous, the reaction analysis unit performs a simple and highly visual reaction analysis. For example, if the user is relaxed, the reaction analysis unit performs a detailed reaction analysis. For example, if the user is in a hurry, the reaction analysis unit performs a concise reaction analysis. In this way, by adjusting the reaction analysis method according to the user's emotions, the system can provide the user with the most optimal reaction analysis. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reaction analysis unit may be performed using AI, for example, or without AI. For example, the reaction analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0107] The reaction analysis unit can optimize its analysis algorithm by referring to past reaction data during reaction analysis. For example, the reaction analysis unit selects the optimal analysis algorithm based on past reaction data. For example, the reaction analysis unit analyzes past reaction data to improve the accuracy of the reaction analysis. For example, the reaction analysis unit improves the efficiency of the reaction analysis by referring to past reaction data. In this way, the accuracy of the reaction analysis can be improved by optimizing the analysis algorithm by referring to past reaction data. Some or all of the above processes in the reaction analysis unit may be performed using AI, for example, or without using AI. For example, the reaction analysis unit can input past reaction data into AI and have AI perform the optimization of the analysis algorithm.

[0108] The reaction analysis unit can estimate the user's emotions and adjust the frequency of reaction analysis based on the estimated emotions. For example, if the user is tense, the reaction analysis unit can reduce the frequency of reaction analysis to alleviate the burden. For example, if the user is relaxed, the reaction analysis unit can increase the frequency of reaction analysis to collect more detailed data. For example, if the user is in a hurry, the reaction analysis unit can optimize the frequency of reaction analysis to quickly collect data. In this way, by adjusting the frequency of reaction analysis according to the user's emotions, the system can provide the user with the most optimal reaction analysis. 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-described processes in the reaction analysis unit may be performed using AI, for example, or without AI. For example, the reaction analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0109] The reaction analysis unit can weight the analysis data based on the submission date of the reaction data during reaction analysis. For example, the reaction analysis unit prioritizes the analysis of reaction data that has been submitted recently. For example, the reaction analysis unit postpones the analysis of reaction data that has been submitted later. For example, the reaction analysis unit adjusts the weighting of the analysis data according to the submission date. This allows for efficient reaction analysis by weighting the analysis data based on the submission date of the reaction data. Some or all of the above processing in the reaction analysis unit may be performed using AI, for example, or without AI. For example, the reaction analysis unit can input the submission date of the reaction data into the AI ​​and have the AI ​​perform the weighting of the analysis data.

[0110] The execution analysis unit can estimate the user's emotions and adjust the execution analysis method based on the estimated user emotions. For example, if the user is nervous, the execution analysis unit performs a simple and highly visual execution analysis. For example, if the user is relaxed, the execution analysis unit performs a detailed execution analysis. For example, if the user is in a hurry, the execution analysis unit performs a concise execution analysis. In this way, by adjusting the execution analysis method according to the user's emotions, the optimal execution analysis can be provided to the user. 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 execution analysis unit may be performed using AI, for example, or without AI. For example, the execution analysis unit can input user emotion data into AI and have the AI ​​perform emotion estimation.

[0111] The execution analysis unit can optimize the analysis algorithm by referring to past execution data during execution analysis. For example, the execution analysis unit selects the optimal analysis algorithm based on past execution data. For example, the execution analysis unit analyzes past execution data to improve the accuracy of the execution analysis. For example, the execution analysis unit improves the efficiency of the execution analysis by referring to past execution data. In this way, the accuracy of the execution analysis can be improved by optimizing the analysis algorithm by referring to past execution data. Some or all of the above processes in the execution analysis unit may be performed using AI, for example, or without using AI. For example, the execution analysis unit can input past execution data into AI and have AI perform the optimization of the analysis algorithm.

[0112] The execution analysis unit can estimate the user's emotions and adjust the frequency of execution analysis based on the estimated emotions. For example, if the user is tense, the execution analysis unit can reduce the frequency of execution analysis to alleviate the burden. For example, if the user is relaxed, the execution analysis unit can increase the frequency of execution analysis to collect more detailed data. For example, if the user is in a hurry, the execution analysis unit can optimize the frequency of execution analysis to quickly collect data. In this way, by adjusting the frequency of execution analysis according to the user's emotions, the system can provide the user with the most optimal execution analysis. 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-described processes in the execution analysis unit may be performed using AI, for example, or without AI. For example, the execution analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0113] The execution analysis unit can weight the analysis data based on the submission date of the execution data during execution analysis. For example, the execution analysis unit prioritizes analyzing execution data with a recent submission date. For example, the execution analysis unit postpones analyzing execution data with a later submission date. For example, the execution analysis unit adjusts the weighting of the analysis data according to the submission date. This allows for efficient execution analysis by weighting the analysis data based on the submission date of the execution data. Some or all of the above processing in the execution analysis unit may be performed using AI, for example, or without AI. For example, the execution analysis unit can input the submission date of the execution data into the AI ​​and have the AI ​​perform the weighting of the analysis data.

[0114] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is nervous, the feedback unit provides simple and easily understandable feedback. For example, if the user is relaxed, the feedback unit provides detailed feedback. For example, if the user is in a hurry, the feedback unit provides concise feedback. In this way, by adjusting the feedback method according to the user's emotions, the system can provide the user with the most optimal feedback. 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-described processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0115] The feedback unit can optimize the feedback algorithm by referring to past feedback data during the feedback process. For example, the feedback unit can select the optimal feedback algorithm based on past feedback data. For example, the feedback unit can analyze past feedback data to improve the accuracy of the feedback. For example, the feedback unit can improve the efficiency of the feedback by referring to past feedback data. In this way, the accuracy of the feedback can be improved by optimizing the feedback algorithm by referring to past feedback data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input past feedback data into AI and have AI perform the optimization of the feedback algorithm.

[0116] The feedback unit can estimate the user's emotions and adjust the frequency of feedback based on the estimated emotions. For example, if the user is tense, the feedback unit can reduce the frequency of feedback to alleviate the burden. For example, if the user is relaxed, the feedback unit can increase the frequency of feedback to collect more detailed data. For example, if the user is in a hurry, the feedback unit can optimize the frequency of feedback to quickly collect data. This allows the system to provide the user with the most appropriate feedback by adjusting the frequency of feedback according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processes in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0117] The feedback unit can weight feedback data based on when it is submitted. For example, the feedback unit prioritizes analyzing feedback data that has been submitted recently. For example, the feedback unit postpones analyzing feedback data that has been submitted later. For example, the feedback unit adjusts the weighting of the feedback data according to the submission date. This allows for efficient feedback by weighting the feedback data based on when it was submitted. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the submission date of the feedback data into the AI ​​and have the AI ​​perform the weighting of the feedback data.

[0118] The feedback unit can provide scheduled suggestions by referring to the user's calendar information during the feedback process. For example, the feedback unit can refer to scheduled events registered in the user's calendar and adjust the timing of the feedback. For example, the feedback unit can provide feedback related to a specific event based on the user's calendar information. For example, the feedback unit can provide optimal feedback tailored to the scheduled events based on the user's calendar information. In this way, by referring to the user's calendar information and providing scheduled suggestions, the feedback unit can provide the most optimal feedback for the user. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's calendar information into AI and have the AI ​​execute scheduled suggestions.

[0119] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit may prioritize providing feedback methods that the user has preferred in the past. For example, the feedback unit may select the optimal feedback based on the user's past feedback history. For example, the feedback unit may analyze the user's past feedback history to improve the accuracy of the feedback. In this way, the accuracy of the feedback can be improved by providing optimal feedback by referring to the user's past feedback history. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit may input the user's past feedback history into AI and have the AI ​​perform the task of providing optimal feedback.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The system can estimate the user's emotions and adjust the proposed estimate based on those emotions. For example, if the user is feeling anxious, the proposed estimate can be made more detailed to provide reassurance. If the user is excited, the proposal can be made concise to facilitate quick decision-making. If the user is relaxed, the proposal can be provided in a balanced manner to make it easier to grasp the overall picture. In this way, by adjusting the proposed estimate according to the user's emotions, the system can provide the optimal proposal for the user. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0122] The system can collect user feedback on the proposed quote in real time and immediately improve the proposal. For example, if a user expresses dissatisfaction with the proposal, the system will revise the proposal based on that feedback and present it again. If a user reacts favorably to the proposal, the system will maintain the original content while adding more detailed information. If a user reacts indifferently to the proposal, the system will simplify the proposal and emphasize the key points. This allows the system to provide the best possible proposal by immediately improving the proposal based on user feedback. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input user feedback data into an AI and have the AI ​​perform improvements to the proposal.

[0123] The system can estimate the user's emotions and adjust the timing of estimate presentation based on the estimated emotions. For example, if the user is stressed, the timing of estimate presentation can be delayed to reduce the user's burden. If the user is relaxed, the timing of estimate presentation can be accelerated to provide information efficiently. If the user is in a hurry, the timing of estimate presentation can be optimized to provide information quickly. In this way, by adjusting the timing of estimate presentation according to the user's emotions, the user's burden can be reduced and information can be provided efficiently. Emotion estimation is achieved 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 system may be performed using AI or not. For example, the system can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0124] The system can analyze user reactions to a proposal and automatically extract areas for improvement. For example, if a user reacts negatively to a proposal, it identifies the cause and extracts areas for improvement. If a user reacts positively, it analyzes the factors contributing to that reaction and applies them to other proposals. If a user reacts neutrally, it identifies areas for improvement and extracts areas for improvement. By analyzing user reactions and automatically extracting areas for improvement, the quality of proposals can be improved. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input user reaction data into an AI and have the AI ​​perform the reaction analysis and extraction of areas for improvement.

[0125] The system can estimate the user's emotions and adjust the format of the estimate based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible format. If the user is relaxed, it can provide a format with detailed information. If the user is in a hurry, it can provide a format that gets straight to the point. This allows the system to provide the user with the best possible estimate by adjusting the format of the estimate according to their emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. 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 system may be performed using AI or not. For example, the system can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0126] The system can optimize proposals by referring to the user's past response history to proposals. For example, it may prioritize proposals that the user has previously preferred. Based on the user's past response history, it selects the most suitable proposal. It analyzes the user's past response history to improve the accuracy of the proposals. In this way, by optimizing proposals by referring to the user's past response history, the system can provide the best possible proposal for the user. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input the user's past response history into AI and have the AI ​​perform the optimization of proposals.

[0127] The system can estimate the user's emotions and adjust the level of detail of the estimate based on the estimated emotions. For example, if the user is nervous, it provides a concise and to-the-point estimate. If the user is relaxed, it provides a detailed estimate. If the user is in a hurry, it provides an estimate that can be quickly understood. This allows the system to provide the best possible estimate for the user by adjusting the level of detail of the estimate according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. 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 system may be performed using AI or not. For example, the system can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0128] The system can select the optimal proposal method by considering the user's device information in relation to the proposed estimate. For example, if the user is using a smartphone, it will provide a proposal method adapted to the screen size. If the user is using a tablet, it will provide a proposal method optimized for a larger screen. If the user is using a desktop, it will provide a proposal method that includes detailed information. By selecting the optimal proposal method considering the user's device information, the system can provide the best possible proposal for the user. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input the user's device information into an AI and have the AI ​​select the optimal proposal method.

[0129] The system can estimate the user's emotions and prioritize estimates based on those estimates. For example, if the user is stressed, important estimates are provided first. If the user is relaxed, a balanced overall estimate is provided. If the user is in a hurry, estimates that can be provided quickly are prioritized. This allows the system to provide the best possible estimate for the user by prioritizing estimates according to their emotions. Emotion estimation is achieved using 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 processes described above in the system may be performed using AI or not. For example, the system can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0130] The system can refer to the user's calendar information regarding the proposed estimate and make proposals based on their schedule. For example, it can refer to appointments registered in the user's calendar and adjust the timing of proposals. It can provide proposals related to specific events based on the user's calendar information. It can provide optimal proposals tailored to the user's schedule based on the user's calendar information. In this way, by referring to the user's calendar information and making proposals based on their schedule, the system can provide the best possible proposals for the user. Some or all of the above processes in the system may be performed using AI or not. For example, the system can input the user's calendar information into AI and have the AI ​​execute proposals based on the schedule.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The collection unit collects image information related to the detailed specifications of the system. For example, it collects image information such as system specifications and flowcharts. This provides data for generating estimates based on the detailed specifications. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it improves the accuracy of estimates by analyzing data from past successful and unsuccessful estimates. Step 3: The generation unit generates an estimate based on the analysis results obtained by the analysis unit. This improves the accuracy of the estimate. Step 4: The providing unit provides the estimate generated by the generating unit. This allows for the quick sharing of the estimate results.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, reaction analysis unit, execution analysis unit, and feedback unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects image information such as system specifications and flowcharts using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit analyzes past estimate data, for example, using the specific processing unit 290 of the data processing unit 12. The generation unit generates estimates based on the analysis results, for example, using the specific processing unit 290 of the data processing unit 12. The provision unit provides the estimates generated, for example, using the control unit 46A of the smart device 14. The reaction analysis unit analyzes customer reactions, for example, using the specific processing unit 290 of the data processing unit 12. The execution analysis unit analyzes whether or not a development project is being executed, for example, using the specific processing unit 290 of the data processing unit 12. The feedback unit provides feedback to improve the accuracy of the estimates, for example, using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, reaction analysis unit, execution analysis unit, and feedback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects image information such as system specifications and flowcharts using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit analyzes past estimate data, for example, using the specific processing unit 290 of the data processing unit 12. The generation unit generates estimates based on the analysis results, for example, using the specific processing unit 290 of the data processing unit 12. The provision unit provides the estimates generated, for example, using the control unit 46A of the smart glasses 214. The reaction analysis unit analyzes customer reactions, for example, using the specific processing unit 290 of the data processing unit 12. The execution analysis unit analyzes whether or not a development project is being executed, for example, using the specific processing unit 290 of the data processing unit 12. The feedback unit provides feedback to improve the accuracy of the estimates, for example, using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, reaction analysis unit, execution analysis unit, and feedback unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects image information such as system specifications and flowcharts using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit analyzes past estimate data using, for example, the specific processing unit 290 of the data processing unit 12. The generation unit generates estimates based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the generated estimates using, for example, the control unit 46A of the headset terminal 314. The reaction analysis unit analyzes customer reactions using, for example, the specific processing unit 290 of the data processing unit 12. The execution analysis unit analyzes whether or not a development project is being executed using, for example, the specific processing unit 290 of the data processing unit 12. The feedback unit provides feedback to improve the accuracy of the estimates using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, reaction analysis unit, execution analysis unit, and feedback unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects image information such as system specifications and flowcharts using the camera 42 and communication I / F 44 of the robot 414. The analysis unit analyzes past estimate data, for example, by the specific processing unit 290 of the data processing unit 12. The generation unit generates estimates based on the analysis results, for example, by the specific processing unit 290 of the data processing unit 12. The provision unit provides estimates generated by, for example, the control unit 46A of the robot 414. The reaction analysis unit analyzes customer reactions, for example, by the specific processing unit 290 of the data processing unit 12. The execution analysis unit analyzes whether or not a development project is being executed, for example, by the specific processing unit 290 of the data processing unit 12. The feedback unit provides feedback to improve the accuracy of the estimates, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) A collection unit that collects image information for the detailed specifications of the system, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates an estimate based on the analysis results obtained by the analysis unit, The system comprises a providing unit that provides estimates generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect image information such as system specifications and flowcharts. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze data from past successful and unsuccessful estimates. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate an estimate based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the generated estimate The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes a reaction analysis unit to analyze customer reactions. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes an execution analysis unit that analyzes whether or not development projects are being executed. The system described in Appendix 1, characterized by the features described herein. (Note 8) It includes a feedback section to improve the accuracy of estimates. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of image data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the optimal collection method is selected based on the resolution and format of the system specifications and flowcharts. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, different collection algorithms are applied depending on the development phase of the system. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and determines the priority of image information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the configuration information of the development team. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system's past version information is referenced to improve the accuracy of the collection. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of past successful and unsuccessful estimate data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, different analysis algorithms are applied depending on the category of the estimate. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on the timing of the estimate submission. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analyses based on the relevance of the estimates. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is We estimate the user's emotions and adjust the estimation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, adjust the level of detail of the estimate based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, different generation algorithms are applied depending on the category of the estimate. The system described in Appendix 1, characterized by the features described herein. (Note 24) 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 25) The generating unit is During generation, the priority of estimates is determined based on when the estimates were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the order of estimates is adjusted based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 27) 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 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 29) 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 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The reaction analysis unit is The system estimates the user's emotions and adjusts the response analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The reaction analysis unit is During reaction analysis, the analysis algorithm is optimized by referring to past reaction data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The reaction analysis unit is It estimates the user's emotions and adjusts the frequency of response analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The reaction analysis unit is During reaction analysis, the analysis data is weighted based on when the reaction data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned execution analysis unit, We estimate the user's emotions and adjust the execution analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned execution analysis unit, During execution analysis, the analysis algorithm is optimized by referring to past execution data. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned execution analysis unit, The system estimates the user's emotions and adjusts the frequency of execution analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned execution analysis unit, During execution analysis, the analysis data is weighted based on when the execution data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned feedback unit is During the feedback process, the feedback algorithm is optimized by referring to past feedback data. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned feedback unit is It estimates the user's emotions and adjusts the frequency of feedback based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned feedback unit is When providing feedback, weight the feedback data based on when it was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned feedback unit is When providing feedback, we refer to the user's calendar information to make suggestions based on their schedule. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned feedback unit is When providing feedback, we refer to the user's past feedback history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects image information for the detailed specifications of the system, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates an estimate based on the analysis results obtained by the analysis unit, The system comprises a providing unit that provides estimates generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect image information such as system specifications and flowcharts. The system according to feature 1.

3. The aforementioned analysis unit, Analyze data from past successful and unsuccessful estimates. The system according to feature 1.

4. The generating unit is Generate an estimate based on the analysis results. The system according to feature 1.

5. The aforementioned supply unit is, Provide the generated estimate The system according to feature 1.

6. It includes a reaction analysis unit to analyze customer reactions. The system according to feature 1.

7. It includes an execution analysis unit that analyzes whether or not development projects are being executed. The system according to feature 1.

8. It includes a feedback section to improve the accuracy of estimates. The system according to feature 1.

9. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of image data collection based on the estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the optimal collection method is selected based on the resolution and format of the system specifications and flowcharts. The system according to feature 1.

Images