system

Abstract

The system according to this embodiment aims to analyze the contents of an RFP and automatically generate and provide proposed deliverables. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, a provision unit, and a feedback receiving unit. The analysis unit analyzes the contents of the RFP. The generation unit generates proposed deliverables based on the requirements and conditions analyzed by the analysis unit. The provision unit provides the proposed deliverables generated by the generation unit to the user. The feedback receiving unit receives user feedback on the proposed deliverables provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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, the process of analyzing the content of an RFP and automatically generating proposed deliverables is not sufficiently efficient and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the content of an RFP and automatically generate and provide proposed deliverables.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, a provision unit, and a feedback receiving unit. The analysis unit analyzes the contents of the RFP. The generation unit generates proposed deliverables based on the requirements and conditions analyzed by the analysis unit. The provision unit provides the proposed deliverables generated by the generation unit to the user. The feedback receiving unit receives user feedback on the proposed deliverables provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the contents of an RFP and automatically generate and provide proposed deliverables. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 automated proposal deliverable creation system according to an embodiment of the present invention is a system that automatically creates proposal deliverables by reading an RFP (Request for Proposal). The automated proposal deliverable creation system reads the RFP into the system, an AI analyzes the contents of the RFP, and automatically generates proposal deliverables. This system significantly improves the efficiency of proposal creation and enables a reduction in human resources. For example, the automated proposal deliverable creation system reads the RFP into the system. At this time, the contents of the RFP are input as text data. For example, it includes information such as the project's objectives, requirements, schedule, and budget. This information is analyzed by the AI. Next, the automated proposal deliverable creation system's AI analyzes the contents of the RFP. The AI ​​understands the requirements and conditions described in the RFP and generates proposal deliverables based on them. For example, it automatically determines the structure and content of the proposal according to the project's objectives. It also creates specific proposal content based on the schedule and budget. The generated proposal deliverables are provided to the user. The user can review the generated proposal and make corrections or add information as needed. This significantly improves the efficiency of proposal creation and enables a reduction in human resources. This system automates the proposal creation process, improving proposal quality and enabling faster responses. For example, it allows for quicker responses to multiple requests for proposals, enhancing competitiveness. Furthermore, it reduces the time and cost associated with proposal creation, increasing corporate productivity. Additionally, AI analyzes proposal content and leverages past proposals and success stories to create more effective proposals. This improves the success rate of proposals and expands business opportunities. In short, this automated proposal creation system significantly improves the efficiency of proposal creation and reduces the need for human resources.

[0029] The automated proposal deliverable creation system according to this embodiment comprises an analysis unit, a generation unit, a delivery unit, and a feedback receiving unit. The analysis unit analyzes the contents of the RFP. The analysis unit, for example, analyzes the text data of the RFP and extracts requirements and conditions. The analysis unit can analyze the contents of the RFP using, for example, natural language processing technology. The analysis unit can also analyze the contents of the RFP using data mining technology. For example, the analysis unit can analyze the contents of the RFP using an AI model that takes the text data of the RFP as input and outputs requirements and conditions. The generation unit generates proposal deliverables based on the requirements and conditions analyzed by the analysis unit. The generation unit, for example, automatically determines the structure and content of the proposal based on the extracted requirements and conditions. The generation unit can generate the structure and content of the proposal using, for example, an algorithm. The generation unit can also generate the structure and content of the proposal using AI. For example, the generation unit can generate the proposal using an AI model that takes the extracted requirements and conditions as input and outputs the structure and content of the proposal. The delivery unit provides the proposed deliverables generated by the generation unit to the user. For example, the delivery unit provides the generated proposal document to the user. The delivery unit can provide the proposal document through a web application, for example. The delivery unit can also provide the proposal document through a mobile application. For example, the delivery unit can display the generated proposal document in a web application so that users can access it. The feedback receiving unit receives user feedback on the proposed deliverables provided by the delivery unit. For example, the feedback receiving unit receives user feedback and improves the quality of the proposed deliverables. The feedback receiving unit can receive user feedback through a feedback form, for example. The feedback receiving unit can also receive user feedback via email. For example, the feedback receiving unit can provide a feedback form on a web application so that users can input their feedback.As a result, the automated proposal deliverable creation system according to the embodiment can significantly improve the efficiency of proposal creation and reduce the amount of human resources required.

[0030] The analysis unit analyzes the content of the RFP. For example, the analysis unit analyzes the text data of the RFP and extracts requirements and conditions. Specifically, the analysis unit uses natural language processing technology to analyze the content of the RFP in detail. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis, and by combining these, it is possible to accurately extract important requirements and conditions from the text data of the RFP. For example, morphological analysis is used to divide the text into words, grammatical analysis is used to analyze the structure of sentences, and semantic analysis is used to understand the meaning of sentences. Furthermore, by using data mining technology, useful patterns and trends can be extracted from past RFP data and proposal data, which can be used to analyze new RFPs. The analysis unit uses an AI model that combines these technologies, taking the text data of the RFP as input and outputting requirements and conditions. For example, the AI ​​model receives the text data of the RFP as input, automatically extracts requirements and conditions, and outputs them as structured data. In this way, the analysis unit can quickly and accurately analyze the content of the RFP and provide data that forms the basis for proposal creation.

[0031] The generation unit generates proposal deliverables based on the requirements and conditions analyzed by the analysis unit. For example, the generation unit automatically determines the structure and content of the proposal based on the extracted requirements and conditions. Specifically, the generation unit uses an algorithm to determine the content and order of each section of the proposal and organize the overall structure. For example, it automatically generates sections such as the introduction, objectives, methodology, schedule, and budget of the proposal based on the requirements and conditions. The generation unit can also use AI to generate the specific content of the proposal. For example, the generation unit uses an AI model that takes the extracted requirements and conditions as input and outputs specific text for each section of the proposal. This AI model can learn from past proposal data and generate text that is optimal for the requirements and conditions. Furthermore, the generation unit can also automatically determine the format and design of the proposal. For example, it automatically sets the layout, font, and color scheme of the proposal to generate a visually appealing proposal. As a result, the generation unit can quickly and efficiently generate high-quality proposals based on the data provided by the analysis unit.

[0032] The delivery unit provides the user with the proposal deliverables generated by the generation unit. For example, the delivery unit provides the user with the generated proposal document. Specifically, the delivery unit can provide the proposal document through a web application or a mobile application. For example, the delivery unit can display the generated proposal document in a web application, making it accessible to users. Users can access the proposal document through a web browser and review its contents. The delivery unit can also provide the proposal document through a mobile application. Users can access the proposal document using a smartphone or tablet and review its contents. Furthermore, the delivery unit can also make the proposal document downloadable as a file in formats such as PDF or Word. This allows users to view or print the proposal document offline. It is important for the delivery unit to provide an intuitive and user-friendly interface to make it easy for users to access the proposal document. For example, providing a menu that allows easy navigation of each section of the proposal document and a search function will enable users to quickly find the information they need. This allows the delivery unit to efficiently provide the generated proposal document to users and improve the efficiency of the proposal creation process.

[0033] The Feedback Department receives user feedback on the proposed deliverables provided by the Delivery Department. For example, the Feedback Department uses user feedback to improve the quality of the proposed deliverables. Specifically, the Feedback Department can receive user feedback through a feedback form. For example, it can provide a feedback form on a web application, allowing users to input their opinions and suggestions for improvement regarding the proposal. The Feedback Department can also receive user feedback via email. Users send their feedback on the proposal via email, which the Feedback Department receives. Furthermore, the Feedback Department can analyze user feedback to improve the quality of the proposal. For example, it can categorize the feedback and identify common problems and areas for improvement. This allows for improvements to the structure and content of the proposal, which can then be reflected in future proposals. It is also crucial for the Feedback Department to collect user feedback in real time and respond quickly. For example, after receiving feedback, it can immediately share the feedback with the Analysis Department and the Generation Department to make necessary corrections and improvements. This allows the Feedback Department to continue providing high-quality proposals that reflect user feedback.

[0034] The analysis unit can analyze the text data of the RFP and extract requirements and conditions. For example, the analysis unit can analyze the text data of the RFP using natural language processing technology and extract requirements and conditions. The analysis unit can also analyze the text data of the RFP using data mining technology and extract requirements and conditions. For example, the analysis unit can perform analysis using an AI model that takes the text data of the RFP as input and outputs requirements and conditions. This streamlines the generation of proposals by analyzing the text data of the RFP and extracting requirements and conditions. Text data includes, but is not limited to, document format and data format. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the text data of the RFP into an AI and have the AI ​​perform the extraction of requirements and conditions.

[0035] The generation unit can automatically determine the structure and content of the proposal based on the extracted requirements and conditions. For example, the generation unit can determine the chapter structure of the proposal based on the extracted requirements and conditions. The generation unit can also determine the content of the proposal based on the extracted requirements and conditions. For example, the generation unit can generate a proposal using an AI model that takes the extracted requirements and conditions as input and outputs the structure and content of the proposal. This improves the quality of the proposal by automatically determining the structure and content of the proposal based on the extracted requirements and conditions. The structure and content of the proposal include, but are not limited to, the chapter structure and details of the content. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the extracted requirements and conditions into the AI ​​and have the AI ​​perform the determination of the structure and content of the proposal.

[0036] The service provider can provide the generated proposal to the user. For example, the service provider can provide the generated proposal to the user through a web application. The service provider can also provide the generated proposal to the user through a mobile application. For example, the service provider can display the generated proposal in a web application so that the user can access it. This makes it easier for users to review and revise the proposal by providing them with the generated proposal. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the generated proposal into an AI and have the AI ​​perform the task of providing it to the user.

[0037] The feedback receiving unit can receive user feedback and improve the quality of the proposed deliverables. The feedback receiving unit can receive user feedback, for example, through a feedback form. The feedback receiving unit can also receive user feedback, for example, through email. For example, the feedback receiving unit can provide a feedback form on a web application, allowing users to input feedback. This improves the quality of the proposed deliverables by receiving user feedback. Feedback includes, but is not limited to, the format of the feedback and the method of receiving it. Some or all of the above processing in the feedback receiving unit may be performed using AI or not. For example, the feedback receiving unit can input user feedback into AI and have the AI ​​perform the feedback receiving.

[0038] The analysis unit can select the optimal analysis algorithm by referring to the past analysis history of the RFP. For example, the analysis unit may prioritize the use of analysis algorithms from past successful proposals. The analysis unit may also analyze past failures and select algorithms to avoid the same mistakes. The analysis unit may also select algorithms suitable for a specific industry or project from the past analysis history. In this way, the optimal analysis algorithm can be selected by referring to the past analysis history. The optimal analysis algorithm includes, but is not limited to, past data and algorithm performance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past analysis history data into AI and have the AI ​​perform the selection of the optimal analysis algorithm.

[0039] The analysis unit can apply different analysis methods depending on the characteristics of the project when analyzing the contents of the RFP. For example, in the case of a large-scale project, the analysis unit can apply a detailed analysis method. For example, in the case of a short-term project, the analysis unit can also apply a rapid analysis method. For example, in the case of a project specialized in a particular industry, the analysis unit can also apply an analysis method suitable for that industry. This improves the accuracy of the analysis by applying an analysis method appropriate to the characteristics of the project. Project characteristics include, but are not limited to, the size of the project and industry characteristics. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input project characteristic data into AI and have the AI ​​perform the application of the analysis method.

[0040] The analysis unit can customize its analysis methods when analyzing an RFP, taking into account the user's industry characteristics. For example, in the case of an RFP in the IT industry, the analysis unit can apply an analysis method that emphasizes technical requirements. For example, in the case of an RFP in the medical industry, the analysis unit can also apply an analysis method that emphasizes regulations and legal requirements. For example, in the case of an RFP in the construction industry, the analysis unit can also apply an analysis method that emphasizes project schedule and budget. This improves the accuracy of the analysis by applying an analysis method that takes into account the user's industry characteristics. Industry characteristics include, but are not limited to, industry size and industry-specific requirements. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's industry characteristics data into AI and have the AI ​​perform the customization of the analysis method.

[0041] The analysis unit can improve the accuracy of its analysis of an RFP by referring to relevant external databases. For example, the analysis unit can refer to industry standard databases to verify the requirements of the RFP. The analysis unit can also refer to past project databases to extract success factors for similar projects. The analysis unit can also refer to public databases to verify legal requirements and regulations. This improves the accuracy of the analysis by referring to relevant external databases. External databases include, but are not limited to, the type of database and how to access them. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data from external databases into AI and have the AI ​​perform the improvement of analysis accuracy.

[0042] The generation unit can determine the optimal proposal content by referring to past successful cases when generating a proposal. For example, the generation unit can determine the optimal proposal content by referring to the content of past successful proposals. The generation unit can also determine the optimal proposal content by analyzing past failure cases and avoiding the same mistakes. The generation unit can also determine the optimal proposal content by referring to successful cases of similar projects. In this way, the optimal proposal content can be determined by referring to past successful cases. Successful cases include, but are not limited to, past projects and criteria for success. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input past successful case data into AI and have the AI ​​perform the determination of the optimal proposal content.

[0043] The generation unit can customize the specific proposal content based on the project schedule and budget when generating a proposal. For example, the generation unit can determine feasible proposal content based on the project schedule. The generation unit can also determine cost-effective proposal content based on the project budget. For example, the generation unit can determine the optimal proposal content by considering the balance between schedule and budget. This improves the feasibility of the proposal by customizing the proposal content based on the project schedule and budget. The schedule and budget include, but are not limited to, the project timeline and budget details. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input project schedule and budget data into AI and have the AI ​​perform the customization of the proposal content.

[0044] The generation unit can customize the proposal content when generating a proposal, taking into account the user's industry characteristics. For example, in the case of a proposal for the IT industry, the generation unit will generate content that emphasizes technical requirements. For example, in the case of a proposal for the medical industry, the generation unit can also generate content that emphasizes regulations and legal requirements. For example, in the case of a proposal for the construction industry, the generation unit can also generate content that emphasizes project schedules and budgets. This improves the effectiveness of the proposal by generating content that takes into account the user's industry characteristics. Industry characteristics include, but are not limited to, the size of the industry and industry-specific requirements. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's industry characteristics data into AI and have the AI ​​perform the customization of the proposal content.

[0045] The generation unit can improve the accuracy of the proposal content by referring to relevant external databases when generating the proposal. For example, the generation unit can refer to industry standard databases to verify the proposal content. The generation unit can also refer to past project databases to extract success factors of similar projects. The generation unit can also refer to public databases to check legal requirements and regulations. This improves the accuracy of the proposal content by referring to relevant external databases. External databases include, but are not limited to, the type of database and how to access it. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input data from external databases into AI and have the AI ​​perform the improvement of the accuracy of the proposal content.

[0046] The service provider can select the optimal service delivery method by referring to the user's past feedback history when providing a proposal. For example, the service provider may prioritize using service delivery methods that have been well-received in the past. For example, the service provider may select a service delivery method that reflects improvements based on past feedback. For example, the service provider may select the optimal service delivery method by referring to the feedback history of similar projects. This allows the service provider to select the optimal service delivery method by referring to past feedback history. Feedback history includes, but is not limited to, past feedback data and methods for saving the history. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider may input past feedback history data into AI and have the AI ​​select the optimal service delivery method.

[0047] The service provider can apply different delivery methods to proposals depending on the user's industry characteristics. For example, in the case of a proposal for the IT industry, the service provider may apply a delivery method that emphasizes technical requirements. For example, in the case of a proposal for the medical industry, the service provider may apply a delivery method that emphasizes regulations and legal requirements. For example, in the case of a proposal for the construction industry, the service provider may apply a delivery method that emphasizes project schedule and budget. By applying a delivery method that is appropriate to the user's industry characteristics, the effectiveness of the proposal is improved. Industry characteristics include, but are not limited to, industry size and industry-specific requirements. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's industry characteristics data into AI and have the AI ​​execute the application of the delivery method.

[0048] The service provider can select the optimal delivery method when providing a proposal, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a delivery method that matches the screen size. If the user is using a tablet, the service provider can also provide a delivery method optimized for a larger screen. If the user is using a desktop computer, the service provider can also provide a delivery method that includes detailed information. This allows the service provider to select the optimal delivery method by considering the user's device information. Device information includes, but is not limited to, the type of device and its characteristics. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into AI and have the AI ​​select the optimal delivery method.

[0049] The service provider can improve the accuracy of its proposals by referring to relevant external databases when submitting proposals. For example, the service provider can refer to industry standard databases to verify the proposal content. The service provider can also refer to past project databases to extract success factors for similar projects. The service provider can also refer to public databases to confirm legal requirements and regulations. This improves the accuracy of the proposals by referring to relevant external databases. External databases include, but are not limited to, the type of database and how to access them. Some or all of the above processes performed by the service provider may or may not be performed using AI. For example, the service provider can input data from external databases into AI and have the AI ​​perform the task of improving the accuracy of the proposals.

[0050] The feedback receiving unit can select the optimal feedback receiving method by referring to past feedback history when receiving feedback. For example, the feedback receiving unit may prioritize using feedback receiving methods that have been well-received in the past. For example, the feedback receiving unit may also select a receiving method that reflects improvements based on past feedback. For example, the feedback receiving unit may select the optimal receiving method by referring to the feedback history of similar projects. This allows the optimal receiving method to be selected by referring to past feedback history. Feedback history includes, but is not limited to, past feedback data and methods for saving history. Some or all of the above processing in the feedback receiving unit may be performed using AI or not. For example, the feedback receiving unit may input past feedback history data into AI and have the AI ​​select the optimal receiving method.

[0051] The feedback receiving unit can apply different receiving methods depending on the user's industry characteristics when receiving feedback. For example, when receiving feedback for the IT industry, the feedback receiving unit can apply a receiving method that emphasizes technical requirements. For example, when receiving feedback for the medical industry, the feedback receiving unit can also apply a receiving method that emphasizes regulations and legal requirements. For example, when receiving feedback for the construction industry, the feedback receiving unit can also apply a receiving method that emphasizes project schedules and budgets. By applying a receiving method that is appropriate to the user's industry characteristics, the effectiveness of the feedback is improved. Industry characteristics include, but are not limited to, the size of the industry and industry-specific requirements. Some or all of the above processing in the feedback receiving unit may be performed using AI or not. For example, the feedback receiving unit can input the user's industry characteristics data into AI and have the AI ​​perform the application of the receiving method.

[0052] The feedback receiving unit can select the optimal feedback method when receiving feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback receiving unit can provide a feedback form that matches the screen size. For example, if the user is using a tablet, the feedback receiving unit can also provide a feedback form optimized for a larger screen. For example, if the user is using a desktop, the feedback receiving unit can also provide a feedback form that includes detailed information. This allows the optimal feedback method to be selected by taking into account the user's device information. Device information includes, but is not limited to, the type of device and device characteristics. Some or all of the processing described above in the feedback receiving unit may be performed using AI or not. For example, the feedback receiving unit can input the user's device information into AI and have the AI ​​select the optimal feedback method.

[0053] The feedback receiving unit can improve the accuracy of the feedback received by referring to relevant external databases when receiving feedback. For example, the feedback receiving unit can refer to industry standard databases to verify the feedback content. The feedback receiving unit can also refer to past project databases to extract feedback from similar projects. The feedback receiving unit can also refer to public databases to check legal requirements and regulations. This improves the accuracy of the feedback content by referring to relevant external databases. External databases include, but are not limited to, the type of database and how to access it. Some or all of the above processing in the feedback receiving unit may be performed using AI or not. For example, the feedback receiving unit can input data from external databases into AI and have the AI ​​perform the task of improving the accuracy of the feedback content.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The analysis unit can select the optimal analysis algorithm by referring to the past analysis history of the RFP. For example, the analysis unit may prioritize the use of analysis algorithms from past successful proposals. The analysis unit may also analyze past failures and select algorithms to avoid the same mistakes. The analysis unit may also select algorithms suitable for a specific industry or project from the past analysis history. In this way, the optimal analysis algorithm can be selected by referring to the past analysis history. The optimal analysis algorithm includes, but is not limited to, past data and algorithm performance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past analysis history data into AI and have the AI ​​perform the selection of the optimal analysis algorithm.

[0056] The analysis unit can apply different analysis methods depending on the characteristics of the project when analyzing the contents of the RFP. For example, in the case of a large-scale project, the analysis unit can apply a detailed analysis method. For example, in the case of a short-term project, the analysis unit can also apply a rapid analysis method. For example, in the case of a project specialized in a particular industry, the analysis unit can also apply an analysis method suitable for that industry. This improves the accuracy of the analysis by applying an analysis method appropriate to the characteristics of the project. Project characteristics include, but are not limited to, the size of the project and industry characteristics. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input project characteristic data into AI and have the AI ​​perform the application of the analysis method.

[0057] The analysis unit can customize its analysis methods when analyzing an RFP, taking into account the user's industry characteristics. For example, in the case of an RFP in the IT industry, the analysis unit can apply an analysis method that emphasizes technical requirements. For example, in the case of an RFP in the medical industry, the analysis unit can also apply an analysis method that emphasizes regulations and legal requirements. For example, in the case of an RFP in the construction industry, the analysis unit can also apply an analysis method that emphasizes project schedule and budget. This improves the accuracy of the analysis by applying an analysis method that takes into account the user's industry characteristics. Industry characteristics include, but are not limited to, industry size and industry-specific requirements. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's industry characteristics data into AI and have the AI ​​perform the customization of the analysis method.

[0058] The generation unit can determine the optimal proposal content by referring to past successful cases when generating a proposal. For example, the generation unit can determine the optimal proposal content by referring to the content of past successful proposals. The generation unit can also determine the optimal proposal content by analyzing past failure cases and avoiding the same mistakes. The generation unit can also determine the optimal proposal content by referring to successful cases of similar projects. In this way, the optimal proposal content can be determined by referring to past successful cases. Successful cases include, but are not limited to, past projects and criteria for success. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input past successful case data into AI and have the AI ​​perform the determination of the optimal proposal content.

[0059] The generation unit can customize the specific proposal content based on the project schedule and budget when generating a proposal. For example, the generation unit can determine feasible proposal content based on the project schedule. The generation unit can also determine cost-effective proposal content based on the project budget. For example, the generation unit can determine the optimal proposal content by considering the balance between schedule and budget. This improves the feasibility of the proposal by customizing the proposal content based on the project schedule and budget. The schedule and budget include, but are not limited to, the project timeline and budget details. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input project schedule and budget data into AI and have the AI ​​perform the customization of the proposal content.

[0060] The generation unit can customize the proposal content when generating a proposal, taking into account the user's industry characteristics. For example, in the case of a proposal for the IT industry, the generation unit will generate content that emphasizes technical requirements. For example, in the case of a proposal for the medical industry, the generation unit can also generate content that emphasizes regulations and legal requirements. For example, in the case of a proposal for the construction industry, the generation unit can also generate content that emphasizes project schedules and budgets. This improves the effectiveness of the proposal by generating content that takes into account the user's industry characteristics. Industry characteristics include, but are not limited to, the size of the industry and industry-specific requirements. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's industry characteristics data into AI and have the AI ​​perform the customization of the proposal content.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The analysis unit analyzes the contents of the RFP. For example, the analysis unit analyzes the text data of the RFP and extracts requirements and conditions. The analysis unit can analyze the contents of the RFP using natural language processing technology and data mining technology. For example, the analysis unit analyzes the contents of the RFP using an AI model that takes the text data of the RFP as input and outputs requirements and conditions. Step 2: The generation unit generates the proposed deliverables based on the requirements and conditions analyzed by the analysis unit. The generation unit automatically determines the structure and content of the proposal based on the extracted requirements and conditions. The generation unit can use algorithms and AI to generate the structure and content of the proposal. For example, the generation unit generates the proposal using an AI model that takes the extracted requirements and conditions as input and outputs the structure and content of the proposal. Step 3: The delivery unit provides the proposed deliverables generated by the generation unit to the user. The delivery unit can provide the generated proposal to the user through a web application or mobile application. For example, the delivery unit can display the generated proposal in a web application so that the user can access it. Step 4: The feedback receiving department receives user feedback on the proposed deliverables provided by the delivery department. The feedback receiving department receives user feedback and improves the quality of the proposed deliverables. The feedback receiving department can receive user feedback through feedback forms or email. For example, the feedback receiving department can provide a feedback form on a web application, allowing users to enter their feedback.

[0063] (Example of form 2) The automated proposal deliverable creation system according to an embodiment of the present invention is a system that automatically creates proposal deliverables by reading an RFP (Request for Proposal). The automated proposal deliverable creation system reads the RFP into the system, an AI analyzes the contents of the RFP, and automatically generates proposal deliverables. This system significantly improves the efficiency of proposal creation and enables a reduction in human resources. For example, the automated proposal deliverable creation system reads the RFP into the system. At this time, the contents of the RFP are input as text data. For example, it includes information such as the project's objectives, requirements, schedule, and budget. This information is analyzed by the AI. Next, the automated proposal deliverable creation system's AI analyzes the contents of the RFP. The AI ​​understands the requirements and conditions described in the RFP and generates proposal deliverables based on them. For example, it automatically determines the structure and content of the proposal according to the project's objectives. It also creates specific proposal content based on the schedule and budget. The generated proposal deliverables are provided to the user. The user can review the generated proposal and make corrections or add information as needed. This significantly improves the efficiency of proposal creation and enables a reduction in human resources. This system automates the proposal creation process, improving proposal quality and enabling faster responses. For example, it allows for quicker responses to multiple requests for proposals, enhancing competitiveness. Furthermore, it reduces the time and cost associated with proposal creation, increasing corporate productivity. Additionally, AI analyzes proposal content and leverages past proposals and success stories to create more effective proposals. This improves the success rate of proposals and expands business opportunities. In short, this automated proposal creation system significantly improves the efficiency of proposal creation and reduces the need for human resources.

[0064] The automated proposal deliverable creation system according to this embodiment comprises an analysis unit, a generation unit, a delivery unit, and a feedback receiving unit. The analysis unit analyzes the contents of the RFP. The analysis unit, for example, analyzes the text data of the RFP and extracts requirements and conditions. The analysis unit can analyze the contents of the RFP using, for example, natural language processing technology. The analysis unit can also analyze the contents of the RFP using data mining technology. For example, the analysis unit can analyze the contents of the RFP using an AI model that takes the text data of the RFP as input and outputs requirements and conditions. The generation unit generates proposal deliverables based on the requirements and conditions analyzed by the analysis unit. The generation unit, for example, automatically determines the structure and content of the proposal based on the extracted requirements and conditions. The generation unit can generate the structure and content of the proposal using, for example, an algorithm. The generation unit can also generate the structure and content of the proposal using AI. For example, the generation unit can generate the proposal using an AI model that takes the extracted requirements and conditions as input and outputs the structure and content of the proposal. The delivery unit provides the proposed deliverables generated by the generation unit to the user. For example, the delivery unit provides the generated proposal document to the user. The delivery unit can provide the proposal document through a web application, for example. The delivery unit can also provide the proposal document through a mobile application. For example, the delivery unit can display the generated proposal document in a web application so that users can access it. The feedback receiving unit receives user feedback on the proposed deliverables provided by the delivery unit. For example, the feedback receiving unit receives user feedback and improves the quality of the proposed deliverables. The feedback receiving unit can receive user feedback through a feedback form, for example. The feedback receiving unit can also receive user feedback via email. For example, the feedback receiving unit can provide a feedback form on a web application so that users can input their feedback.As a result, the automated proposal deliverable creation system according to the embodiment can significantly improve the efficiency of proposal creation and reduce the amount of human resources required.

[0065] The analysis unit analyzes the content of the RFP. For example, the analysis unit analyzes the text data of the RFP and extracts requirements and conditions. Specifically, the analysis unit uses natural language processing technology to analyze the content of the RFP in detail. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis, and by combining these, it is possible to accurately extract important requirements and conditions from the text data of the RFP. For example, morphological analysis is used to divide the text into words, grammatical analysis is used to analyze the structure of sentences, and semantic analysis is used to understand the meaning of sentences. Furthermore, by using data mining technology, useful patterns and trends can be extracted from past RFP data and proposal data, which can be used to analyze new RFPs. The analysis unit uses an AI model that combines these technologies, taking the text data of the RFP as input and outputting requirements and conditions. For example, the AI ​​model receives the text data of the RFP as input, automatically extracts requirements and conditions, and outputs them as structured data. In this way, the analysis unit can quickly and accurately analyze the content of the RFP and provide data that forms the basis for proposal creation.

[0066] The generation unit generates proposal deliverables based on the requirements and conditions analyzed by the analysis unit. For example, the generation unit automatically determines the structure and content of the proposal based on the extracted requirements and conditions. Specifically, the generation unit uses an algorithm to determine the content and order of each section of the proposal and organize the overall structure. For example, it automatically generates sections such as the introduction, objectives, methodology, schedule, and budget of the proposal based on the requirements and conditions. The generation unit can also use AI to generate the specific content of the proposal. For example, the generation unit uses an AI model that takes the extracted requirements and conditions as input and outputs specific text for each section of the proposal. This AI model can learn from past proposal data and generate text that is optimal for the requirements and conditions. Furthermore, the generation unit can also automatically determine the format and design of the proposal. For example, it automatically sets the layout, font, and color scheme of the proposal to generate a visually appealing proposal. As a result, the generation unit can quickly and efficiently generate high-quality proposals based on the data provided by the analysis unit.

[0067] The delivery unit provides the user with the proposal deliverables generated by the generation unit. For example, the delivery unit provides the user with the generated proposal document. Specifically, the delivery unit can provide the proposal document through a web application or a mobile application. For example, the delivery unit can display the generated proposal document in a web application, making it accessible to users. Users can access the proposal document through a web browser and review its contents. The delivery unit can also provide the proposal document through a mobile application. Users can access the proposal document using a smartphone or tablet and review its contents. Furthermore, the delivery unit can also make the proposal document downloadable as a file in formats such as PDF or Word. This allows users to view or print the proposal document offline. It is important for the delivery unit to provide an intuitive and user-friendly interface to make it easy for users to access the proposal document. For example, providing a menu that allows easy navigation of each section of the proposal document and a search function will enable users to quickly find the information they need. This allows the delivery unit to efficiently provide the generated proposal document to users and improve the efficiency of the proposal creation process.

[0068] The Feedback Department receives user feedback on the proposed deliverables provided by the Delivery Department. For example, the Feedback Department uses user feedback to improve the quality of the proposed deliverables. Specifically, the Feedback Department can receive user feedback through a feedback form. For example, it can provide a feedback form on a web application, allowing users to input their opinions and suggestions for improvement regarding the proposal. The Feedback Department can also receive user feedback via email. Users send their feedback on the proposal via email, which the Feedback Department receives. Furthermore, the Feedback Department can analyze user feedback to improve the quality of the proposal. For example, it can categorize the feedback and identify common problems and areas for improvement. This allows for improvements to the structure and content of the proposal, which can then be reflected in future proposals. It is also crucial for the Feedback Department to collect user feedback in real time and respond quickly. For example, after receiving feedback, it can immediately share the feedback with the Analysis Department and the Generation Department to make necessary corrections and improvements. This allows the Feedback Department to continue providing high-quality proposals that reflect user feedback.

[0069] The analysis unit can analyze the text data of the RFP and extract requirements and conditions. For example, the analysis unit can analyze the text data of the RFP using natural language processing technology and extract requirements and conditions. The analysis unit can also analyze the text data of the RFP using data mining technology and extract requirements and conditions. For example, the analysis unit can perform analysis using an AI model that takes the text data of the RFP as input and outputs requirements and conditions. This streamlines the generation of proposals by analyzing the text data of the RFP and extracting requirements and conditions. Text data includes, but is not limited to, document format and data format. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the text data of the RFP into an AI and have the AI ​​perform the extraction of requirements and conditions.

[0070] The generation unit can automatically determine the structure and content of the proposal based on the extracted requirements and conditions. For example, the generation unit can determine the chapter structure of the proposal based on the extracted requirements and conditions. The generation unit can also determine the content of the proposal based on the extracted requirements and conditions. For example, the generation unit can generate a proposal using an AI model that takes the extracted requirements and conditions as input and outputs the structure and content of the proposal. This improves the quality of the proposal by automatically determining the structure and content of the proposal based on the extracted requirements and conditions. The structure and content of the proposal include, but are not limited to, the chapter structure and details of the content. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the extracted requirements and conditions into the AI ​​and have the AI ​​perform the determination of the structure and content of the proposal.

[0071] The service provider can provide the generated proposal to the user. For example, the service provider can provide the generated proposal to the user through a web application. The service provider can also provide the generated proposal to the user through a mobile application. For example, the service provider can display the generated proposal in a web application so that the user can access it. This makes it easier for users to review and revise the proposal by providing them with the generated proposal. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the generated proposal into an AI and have the AI ​​perform the task of providing it to the user.

[0072] The feedback receiving unit can receive user feedback and improve the quality of the proposed deliverables. The feedback receiving unit can receive user feedback, for example, through a feedback form. The feedback receiving unit can also receive user feedback, for example, through email. For example, the feedback receiving unit can provide a feedback form on a web application, allowing users to input feedback. This improves the quality of the proposed deliverables by receiving user feedback. Feedback includes, but is not limited to, the format of the feedback and the method of receiving it. Some or all of the above processing in the feedback receiving unit may be performed using AI or not. For example, the feedback receiving unit can input user feedback into AI and have the AI ​​perform the feedback receiving.

[0073] The analysis unit can estimate the user's emotions and adjust the RFP analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can speed up the analysis to provide results quickly. For example, if the user is relaxed, the analysis unit can perform a more detailed analysis to provide more information. For example, if the user is in a hurry, the analysis unit can focus on important requirements. This allows for more appropriate analysis results by adjusting the RFP analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0074] The analysis unit can select the optimal analysis algorithm by referring to the past analysis history of the RFP. For example, the analysis unit may prioritize the use of analysis algorithms from past successful proposals. The analysis unit may also analyze past failures and select algorithms to avoid the same mistakes. The analysis unit may also select algorithms suitable for a specific industry or project from the past analysis history. In this way, the optimal analysis algorithm can be selected by referring to the past analysis history. The optimal analysis algorithm includes, but is not limited to, past data and algorithm performance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past analysis history data into AI and have the AI ​​perform the selection of the optimal analysis algorithm.

[0075] The analysis unit can apply different analysis methods depending on the characteristics of the project when analyzing the contents of the RFP. For example, in the case of a large-scale project, the analysis unit can apply a detailed analysis method. For example, in the case of a short-term project, the analysis unit can also apply a rapid analysis method. For example, in the case of a project specialized in a particular industry, the analysis unit can also apply an analysis method suitable for that industry. This improves the accuracy of the analysis by applying an analysis method appropriate to the characteristics of the project. Project characteristics include, but are not limited to, the size of the project and industry characteristics. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input project characteristic data into AI and have the AI ​​perform the application of the analysis method.

[0076] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize displaying important requirements. If the user is relaxed, the analysis unit may also provide detailed analysis results. If the user is in a hurry, the analysis unit may also display the most important information first. This allows for the priority of important information to be provided by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​determine the priority of the analysis results.

[0077] The analysis unit can customize its analysis methods when analyzing an RFP, taking into account the user's industry characteristics. For example, in the case of an RFP in the IT industry, the analysis unit can apply an analysis method that emphasizes technical requirements. For example, in the case of an RFP in the medical industry, the analysis unit can also apply an analysis method that emphasizes regulations and legal requirements. For example, in the case of an RFP in the construction industry, the analysis unit can also apply an analysis method that emphasizes project schedule and budget. This improves the accuracy of the analysis by applying an analysis method that takes into account the user's industry characteristics. Industry characteristics include, but are not limited to, industry size and industry-specific requirements. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's industry characteristics data into AI and have the AI ​​perform the customization of the analysis method.

[0078] The analysis unit can improve the accuracy of its analysis of an RFP by referring to relevant external databases. For example, the analysis unit can refer to industry standard databases to verify the requirements of the RFP. The analysis unit can also refer to past project databases to extract success factors for similar projects. The analysis unit can also refer to public databases to verify legal requirements and regulations. This improves the accuracy of the analysis by referring to relevant external databases. External databases include, but are not limited to, the type of database and how to access them. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data from external databases into AI and have the AI ​​perform the improvement of analysis accuracy.

[0079] The generation unit can estimate the user's emotions and adjust the presentation of the proposal based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a proposal that includes detailed explanations. If the user is in a hurry, for example, the generation unit can also generate a concise proposal that gets straight to the point. If the user is excited, for example, the generation unit can also generate a visually appealing proposal. This allows for the generation of more effective proposals by adjusting the presentation according to the user's emotions. 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 processing 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 ​​adjust the presentation of the proposal.

[0080] The generation unit can determine the optimal proposal content by referring to past successful cases when generating a proposal. For example, the generation unit can determine the optimal proposal content by referring to the content of past successful proposals. The generation unit can also determine the optimal proposal content by analyzing past failure cases and avoiding the same mistakes. The generation unit can also determine the optimal proposal content by referring to successful cases of similar projects. In this way, the optimal proposal content can be determined by referring to past successful cases. Successful cases include, but are not limited to, past projects and criteria for success. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input past successful case data into AI and have the AI ​​perform the determination of the optimal proposal content.

[0081] The generation unit can customize the specific proposal content based on the project schedule and budget when generating a proposal. For example, the generation unit can determine feasible proposal content based on the project schedule. The generation unit can also determine cost-effective proposal content based on the project budget. For example, the generation unit can determine the optimal proposal content by considering the balance between schedule and budget. This improves the feasibility of the proposal by customizing the proposal content based on the project schedule and budget. The schedule and budget include, but are not limited to, the project timeline and budget details. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input project schedule and budget data into AI and have the AI ​​perform the customization of the proposal content.

[0082] The generation unit can estimate the user's emotions and adjust the length of the proposal based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise proposal. If the user is relaxed, the generation unit can also generate a longer proposal with detailed explanations. If the user is excited, the generation unit can also generate a proposal with visually stimulating effects. By adjusting the length of the proposal according to the user's emotions, a more appropriate proposal can be generated. 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 processing 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 ​​adjust the length of the proposal.

[0083] The generation unit can customize the proposal content when generating a proposal, taking into account the user's industry characteristics. For example, in the case of a proposal for the IT industry, the generation unit will generate content that emphasizes technical requirements. For example, in the case of a proposal for the medical industry, the generation unit can also generate content that emphasizes regulations and legal requirements. For example, in the case of a proposal for the construction industry, the generation unit can also generate content that emphasizes project schedules and budgets. This improves the effectiveness of the proposal by generating content that takes into account the user's industry characteristics. Industry characteristics include, but are not limited to, the size of the industry and industry-specific requirements. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's industry characteristics data into AI and have the AI ​​perform the customization of the proposal content.

[0084] The generation unit can improve the accuracy of the proposal content by referring to relevant external databases when generating the proposal. For example, the generation unit can refer to industry standard databases to verify the proposal content. The generation unit can also refer to past project databases to extract success factors of similar projects. The generation unit can also refer to public databases to check legal requirements and regulations. This improves the accuracy of the proposal content by referring to relevant external databases. External databases include, but are not limited to, the type of database and how to access it. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input data from external databases into AI and have the AI ​​perform the improvement of the accuracy of the proposal content.

[0085] The service provider can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. For example, if the user is relaxed, the service provider might provide a proposal with detailed explanations. If the user is in a hurry, the service provider might provide a concise proposal that gets straight to the point. If the user is excited, the service provider might provide a visually appealing proposal. By adjusting the presentation method according to the user's emotions, a more effective proposal can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI and have the AI ​​adjust the presentation method of the proposal.

[0086] The service provider can select the optimal service delivery method by referring to the user's past feedback history when providing a proposal. For example, the service provider may prioritize using service delivery methods that have been well-received in the past. For example, the service provider may select a service delivery method that reflects improvements based on past feedback. For example, the service provider may select the optimal service delivery method by referring to the feedback history of similar projects. This allows the service provider to select the optimal service delivery method by referring to past feedback history. Feedback history includes, but is not limited to, past feedback data and methods for saving the history. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider may input past feedback history data into AI and have the AI ​​select the optimal service delivery method.

[0087] The service provider can apply different delivery methods to proposals depending on the user's industry characteristics. For example, in the case of a proposal for the IT industry, the service provider may apply a delivery method that emphasizes technical requirements. For example, in the case of a proposal for the medical industry, the service provider may apply a delivery method that emphasizes regulations and legal requirements. For example, in the case of a proposal for the construction industry, the service provider may apply a delivery method that emphasizes project schedule and budget. By applying a delivery method that is appropriate to the user's industry characteristics, the effectiveness of the proposal is improved. Industry characteristics include, but are not limited to, industry size and industry-specific requirements. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's industry characteristics data into AI and have the AI ​​execute the application of the delivery method.

[0088] The service provider can estimate the user's emotions and determine the order in which proposals are presented based on those emotions. For example, if the user is in a hurry, the service provider might present the most important proposals first. If the user is relaxed, the service provider might present proposals with detailed explanations. If the user is excited, the service provider might present visually appealing proposals. This allows for the provision of more effective proposals by determining the order in which proposals are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI and have the AI ​​determine the order in which proposals are presented.

[0089] The service provider can select the optimal delivery method when providing a proposal, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a delivery method that matches the screen size. If the user is using a tablet, the service provider can also provide a delivery method optimized for a larger screen. If the user is using a desktop computer, the service provider can also provide a delivery method that includes detailed information. This allows the service provider to select the optimal delivery method by considering the user's device information. Device information includes, but is not limited to, the type of device and its characteristics. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into AI and have the AI ​​select the optimal delivery method.

[0090] The service provider can improve the accuracy of its proposals by referring to relevant external databases when submitting proposals. For example, the service provider can refer to industry standard databases to verify the proposal content. The service provider can also refer to past project databases to extract success factors for similar projects. The service provider can also refer to public databases to confirm legal requirements and regulations. This improves the accuracy of the proposals by referring to relevant external databases. External databases include, but are not limited to, the type of database and how to access them. Some or all of the above processes performed by the service provider may or may not be performed using AI. For example, the service provider can input data from external databases into AI and have the AI ​​perform the task of improving the accuracy of the proposals.

[0091] The feedback receiving unit can estimate the user's emotions and adjust the feedback receiving method based on the estimated emotions. For example, if the user is relaxed, the feedback receiving unit may request detailed feedback. If the user is in a hurry, for example, the feedback receiving unit may provide a concise feedback form. If the user is excited, for example, the feedback receiving unit may provide a visually appealing feedback form. This allows for more effective feedback to be received by adjusting the feedback receiving method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback receiving unit may be performed using AI or not. For example, the feedback receiving unit can input user emotion data into AI and have the AI ​​adjust the feedback receiving method.

[0092] The feedback receiving unit can select the optimal feedback receiving method by referring to past feedback history when receiving feedback. For example, the feedback receiving unit may prioritize using feedback receiving methods that have been well-received in the past. For example, the feedback receiving unit may also select a receiving method that reflects improvements based on past feedback. For example, the feedback receiving unit may select the optimal receiving method by referring to the feedback history of similar projects. This allows the optimal receiving method to be selected by referring to past feedback history. Feedback history includes, but is not limited to, past feedback data and methods for saving history. Some or all of the above processing in the feedback receiving unit may be performed using AI or not. For example, the feedback receiving unit may input past feedback history data into AI and have the AI ​​select the optimal receiving method.

[0093] The feedback receiving unit can apply different receiving methods depending on the user's industry characteristics when receiving feedback. For example, when receiving feedback for the IT industry, the feedback receiving unit can apply a receiving method that emphasizes technical requirements. For example, when receiving feedback for the medical industry, the feedback receiving unit can also apply a receiving method that emphasizes regulations and legal requirements. For example, when receiving feedback for the construction industry, the feedback receiving unit can also apply a receiving method that emphasizes project schedules and budgets. By applying a receiving method that is appropriate to the user's industry characteristics, the effectiveness of the feedback is improved. Industry characteristics include, but are not limited to, the size of the industry and industry-specific requirements. Some or all of the above processing in the feedback receiving unit may be performed using AI or not. For example, the feedback receiving unit can input the user's industry characteristics data into AI and have the AI ​​perform the application of the receiving method.

[0094] The feedback receiver can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is in a hurry, the feedback receiver will prioritize important feedback. For example, if the user is relaxed, the feedback receiver may also prioritize detailed feedback. For example, if the user is excited, the feedback receiver may also prioritize visually appealing feedback. This allows for prioritizing important feedback by determining the priority of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 feedback receiver may be performed using AI or not. For example, the feedback receiver can input user emotion data into an AI and have the AI ​​determine the priority of feedback.

[0095] The feedback receiving unit can select the optimal feedback method when receiving feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback receiving unit can provide a feedback form that matches the screen size. For example, if the user is using a tablet, the feedback receiving unit can also provide a feedback form optimized for a larger screen. For example, if the user is using a desktop, the feedback receiving unit can also provide a feedback form that includes detailed information. This allows the optimal feedback method to be selected by taking into account the user's device information. Device information includes, but is not limited to, the type of device and device characteristics. Some or all of the processing described above in the feedback receiving unit may be performed using AI or not. For example, the feedback receiving unit can input the user's device information into AI and have the AI ​​select the optimal feedback method.

[0096] The feedback receiving unit can improve the accuracy of the feedback received by referring to relevant external databases when receiving feedback. For example, the feedback receiving unit can refer to industry standard databases to verify the feedback content. The feedback receiving unit can also refer to past project databases to extract feedback from similar projects. The feedback receiving unit can also refer to public databases to check legal requirements and regulations. This improves the accuracy of the feedback content by referring to relevant external databases. External databases include, but are not limited to, the type of database and how to access it. Some or all of the above processing in the feedback receiving unit may be performed using AI or not. For example, the feedback receiving unit can input data from external databases into AI and have the AI ​​perform the task of improving the accuracy of the feedback content.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The analysis unit can estimate the user's emotions and adjust the RFP analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can speed up the analysis to provide results quickly. For example, if the user is relaxed, the analysis unit can perform a more detailed analysis to provide more information. For example, if the user is in a hurry, the analysis unit can focus on important requirements. This allows for more appropriate analysis results by adjusting the RFP analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0099] The analysis unit can select the optimal analysis algorithm by referring to the past analysis history of the RFP. For example, the analysis unit may prioritize the use of analysis algorithms from past successful proposals. The analysis unit may also analyze past failures and select algorithms to avoid the same mistakes. The analysis unit may also select algorithms suitable for a specific industry or project from the past analysis history. In this way, the optimal analysis algorithm can be selected by referring to the past analysis history. The optimal analysis algorithm includes, but is not limited to, past data and algorithm performance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past analysis history data into AI and have the AI ​​perform the selection of the optimal analysis algorithm.

[0100] The analysis unit can apply different analysis methods depending on the characteristics of the project when analyzing the contents of the RFP. For example, in the case of a large-scale project, the analysis unit can apply a detailed analysis method. For example, in the case of a short-term project, the analysis unit can also apply a rapid analysis method. For example, in the case of a project specialized in a particular industry, the analysis unit can also apply an analysis method suitable for that industry. This improves the accuracy of the analysis by applying an analysis method appropriate to the characteristics of the project. Project characteristics include, but are not limited to, the size of the project and industry characteristics. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input project characteristic data into AI and have the AI ​​perform the application of the analysis method.

[0101] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize displaying important requirements. If the user is relaxed, the analysis unit may also provide detailed analysis results. If the user is in a hurry, the analysis unit may also display the most important information first. This allows for the priority of important information to be provided by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​determine the priority of the analysis results.

[0102] The analysis unit can customize its analysis methods when analyzing an RFP, taking into account the user's industry characteristics. For example, in the case of an RFP in the IT industry, the analysis unit can apply an analysis method that emphasizes technical requirements. For example, in the case of an RFP in the medical industry, the analysis unit can also apply an analysis method that emphasizes regulations and legal requirements. For example, in the case of an RFP in the construction industry, the analysis unit can also apply an analysis method that emphasizes project schedule and budget. This improves the accuracy of the analysis by applying an analysis method that takes into account the user's industry characteristics. Industry characteristics include, but are not limited to, industry size and industry-specific requirements. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's industry characteristics data into AI and have the AI ​​perform the customization of the analysis method.

[0103] The generation unit can estimate the user's emotions and adjust the presentation of the proposal based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a proposal that includes detailed explanations. If the user is in a hurry, for example, the generation unit can also generate a concise proposal that gets straight to the point. If the user is excited, for example, the generation unit can also generate a visually appealing proposal. This allows for the generation of more effective proposals by adjusting the presentation according to the user's emotions. 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 processing 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 ​​adjust the presentation of the proposal.

[0104] The generation unit can determine the optimal proposal content by referring to past successful cases when generating a proposal. For example, the generation unit can determine the optimal proposal content by referring to the content of past successful proposals. The generation unit can also determine the optimal proposal content by analyzing past failure cases and avoiding the same mistakes. The generation unit can also determine the optimal proposal content by referring to successful cases of similar projects. In this way, the optimal proposal content can be determined by referring to past successful cases. Successful cases include, but are not limited to, past projects and criteria for success. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input past successful case data into AI and have the AI ​​perform the determination of the optimal proposal content.

[0105] The generation unit can customize the specific proposal content based on the project schedule and budget when generating a proposal. For example, the generation unit can determine feasible proposal content based on the project schedule. The generation unit can also determine cost-effective proposal content based on the project budget. For example, the generation unit can determine the optimal proposal content by considering the balance between schedule and budget. This improves the feasibility of the proposal by customizing the proposal content based on the project schedule and budget. The schedule and budget include, but are not limited to, the project timeline and budget details. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input project schedule and budget data into AI and have the AI ​​perform the customization of the proposal content.

[0106] The generation unit can estimate the user's emotions and adjust the length of the proposal based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise proposal. If the user is relaxed, the generation unit can also generate a longer proposal with detailed explanations. If the user is excited, the generation unit can also generate a proposal with visually stimulating effects. By adjusting the length of the proposal according to the user's emotions, a more appropriate proposal can be generated. 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 processing 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 ​​adjust the length of the proposal.

[0107] The generation unit can customize the proposal content when generating a proposal, taking into account the user's industry characteristics. For example, in the case of a proposal for the IT industry, the generation unit will generate content that emphasizes technical requirements. For example, in the case of a proposal for the medical industry, the generation unit can also generate content that emphasizes regulations and legal requirements. For example, in the case of a proposal for the construction industry, the generation unit can also generate content that emphasizes project schedules and budgets. This improves the effectiveness of the proposal by generating content that takes into account the user's industry characteristics. Industry characteristics include, but are not limited to, the size of the industry and industry-specific requirements. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's industry characteristics data into AI and have the AI ​​perform the customization of the proposal content.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The analysis unit analyzes the contents of the RFP. For example, the analysis unit analyzes the text data of the RFP and extracts requirements and conditions. The analysis unit can analyze the contents of the RFP using natural language processing technology and data mining technology. For example, the analysis unit analyzes the contents of the RFP using an AI model that takes the text data of the RFP as input and outputs requirements and conditions. Step 2: The generation unit generates the proposed deliverables based on the requirements and conditions analyzed by the analysis unit. The generation unit automatically determines the structure and content of the proposal based on the extracted requirements and conditions. The generation unit can use algorithms and AI to generate the structure and content of the proposal. For example, the generation unit generates the proposal using an AI model that takes the extracted requirements and conditions as input and outputs the structure and content of the proposal. Step 3: The delivery unit provides the proposed deliverables generated by the generation unit to the user. The delivery unit can provide the generated proposal to the user through a web application or mobile application. For example, the delivery unit can display the generated proposal in a web application so that the user can access it. Step 4: The feedback receiving department receives user feedback on the proposed deliverables provided by the delivery department. The feedback receiving department receives user feedback and improves the quality of the proposed deliverables. The feedback receiving department can receive user feedback through feedback forms or email. For example, the feedback receiving department can provide a feedback form on a web application, allowing users to enter their feedback.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the analysis unit, generation unit, provision unit, and feedback receiving unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and analyzes the contents of the RFP. The generation unit is implemented by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and generates proposed deliverables based on the analyzed requirements and conditions. The provision unit is implemented by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and provides the generated proposed deliverables to the user. The feedback receiving unit is implemented by the receiving device 38 of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and receives user feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the analysis unit, generation unit, provision unit, and feedback receiving unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and analyzes the contents of the RFP. The generation unit is implemented, for example, by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and generates proposed deliverables based on the analyzed requirements and conditions. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and provides the generated proposed deliverables to the user. The feedback receiving unit is implemented, for example, by the microphone 238 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and receives user feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the analysis unit, generation unit, provision unit, and feedback receiving unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and analyzes the contents of the RFP. The generation unit is implemented by the processor 46 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and generates proposed deliverables based on the analyzed requirements and conditions. The provision unit is implemented by the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and provides the generated proposed deliverables to the user. The feedback receiving unit is implemented by the microphone 238 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and receives user feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the analysis unit, generation unit, provision unit, and feedback receiving unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing unit 12, and analyzes the contents of the RFP. The generation unit is implemented by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing unit 12, and generates proposed deliverables based on the analyzed requirements and conditions. The provision unit is implemented by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing unit 12, and provides the generated proposed deliverables to the user. The feedback receiving unit is implemented by the microphone 238 of the robot 414 or the specific processing unit 290 of the data processing unit 12, and receives user feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) An analysis unit that analyzes the contents of the RFP, A generation unit that generates proposed deliverables based on the requirements and conditions analyzed by the aforementioned analysis unit, A provisioning unit that provides the proposed deliverables generated by the generation unit to the user, The system includes a feedback receiving unit that receives user feedback on the proposed deliverables provided by the aforementioned provisioning unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze the text data of the RFP and extract requirements and conditions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The structure and content of the proposal are automatically determined based on the extracted requirements and conditions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide the generated proposal to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback receiving unit is We accept user feedback and improve the quality of our proposed deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We estimate user sentiment and adjust the RFP analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, Refer to the past analysis history of RFPs and select the optimal analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing the contents of an RFP, different analytical methods are applied depending on the characteristics of the project. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing an RFP, customize the analysis methodology to take into account the user's industry characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing RFPs, refer to relevant external databases to improve analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is We estimate the user's emotions and adjust the presentation of the proposal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a proposal, we refer to past successful examples to determine the optimal proposal content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a proposal, customize the specific proposal content based on the project schedule and budget. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is Estimate the user's emotions and adjust the length of the proposal based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating a proposal, customize the proposal content to take into account the user's industry characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating proposals, we refer to relevant external databases to improve the accuracy of the proposal content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we deliver proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When submitting a proposal, we will refer to the user's past feedback history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing proposals, we apply different delivery methods depending on the user's industry characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which proposals are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When submitting a proposal, 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 23) The aforementioned supply unit is, When submitting proposals, we improve the accuracy of the content by referring to relevant external databases. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback receiving unit is It estimates the user's emotions and adjusts the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback receiving unit is When receiving feedback, the system will refer to past feedback history to select the most suitable method of receiving it. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback receiving unit is When receiving feedback, different methods of receiving feedback will be applied depending on the user's industry characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback receiving unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback receiving unit is When receiving feedback, the system selects the most suitable method of receiving it, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback receiving unit is When receiving feedback, we refer to relevant external databases to improve the accuracy of the feedback received. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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. An analysis unit that analyzes the contents of the RFP, A generation unit that generates proposed deliverables based on the requirements and conditions analyzed by the aforementioned analysis unit, A provisioning unit that provides the proposed deliverables generated by the generation unit to the user, The system includes a feedback receiving unit that receives user feedback on the proposed deliverables provided by the aforementioned provisioning unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the text data of the RFP and extract requirements and conditions. The system according to feature 1.

3. The generating unit is The structure and content of the proposal are automatically determined based on the extracted requirements and conditions. The system according to feature 1.

4. The aforementioned supply unit is, Provide the generated proposal to the user. The system according to feature 1.

5. The aforementioned feedback receiving unit is We accept user feedback and improve the quality of our proposed deliverables. The system according to feature 1.

6. The aforementioned analysis unit, We estimate user sentiment and adjust the RFP analysis method based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned analysis unit, Refer to the past analysis history of RFPs and select the optimal analysis algorithm. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing the contents of an RFP, different analytical methods are applied depending on the characteristics of the project. The system according to feature 1.

9. The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system according to feature 1.

10. The aforementioned analysis unit, When analyzing an RFP, customize the analysis methodology to take into account the user's industry characteristics. The system according to feature 1.

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