system

Abstract

The system according to the embodiment aims to efficiently generate sales proposal materials. [Solution] A system according to an embodiment includes an input unit, an analysis unit, a configuration determination unit, and a generation unit. The input unit inputs elements necessary for a proposal material. The analysis unit analyzes the information input by the input unit. The configuration determination unit determines the configuration of the proposal material based on the information analyzed by the analysis unit. The generation unit generates the proposal material in a presentation format based on the configuration determined by the configuration determination unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that creating sales proposal materials requires a lot of time and effort.

[0005] The system according to the embodiment aims to efficiently generate sales proposal materials. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a configuration determination unit, and a generation unit. The input unit inputs elements necessary for the proposal material. The analysis unit analyzes the information input by the input unit. The configuration determination unit determines the configuration of the proposal material based on the information analyzed by the analysis unit. The generation unit generates the proposal material in a presentation format based on the configuration determined by the configuration determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate sales proposal materials. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) In an embodiment of the present invention, a sales proposal automatic generation system allows a user to input elements necessary for a proposal, and a generation AI analyzes the elements to determine the structure of the proposal, generating the proposal in a presentation format (e.g., PowerPoint® format, Keynote® format, etc.). This system begins when a user inputs the elements necessary for the proposal. For example, the user inputs information such as the purpose of the proposal, target customers, proposal content, competitive analysis, budget, and schedule. This information is then input into the generation AI, which analyzes the input information and determines the structure of the proposal. For example, the AI ​​determines the overall tone and style of the proposal based on the purpose of the proposal and customizes the content of the proposal based on the target customers. The generation AI then generates the proposal in presentation format based on the determined structure. For example, the AI ​​automatically creates a cover page tailored to the purpose of the proposal, content tailored to the target customers, graphs and tables showing the results of the competitive analysis, slides showing the budget and schedule, and so on. This system allows users to create high-quality proposals without much effort. For example, when a sales representative makes a proposal to a new client, they can prepare effective proposals in a short amount of time. Furthermore, maintaining a consistent quality of the proposal materials improves the efficiency of sales activities across the entire company. This allows the sales proposal material automatic generation system to save the user time and effort and create high-quality proposal materials.

[0029] A sales proposal material automatic generation system according to an embodiment includes an input unit, an analysis unit, a configuration determination unit, and a generation unit. The input unit allows a user to input elements necessary for a proposal material. For example, the user can input information such as the purpose of the proposal, target customers, proposal content, competitive analysis, budget, and schedule. The analysis unit uses a generation AI to analyze the information input by the input unit. For example, the generation AI determines the overall tone and style of the proposal material based on the purpose of the proposal. The generation AI can also customize the content of the proposal material based on the target customers. The configuration determination unit determines the configuration of the proposal material based on the information analyzed by the analysis unit. For example, the configuration determines a cover page according to the purpose of the proposal, content tailored to the target customers, graphs and tables showing the results of the competitive analysis, slides showing the budget and schedule, and the like. The generation unit generates the proposal material in a presentation format based on the configuration determined by the configuration determination unit. For example, the generation AI can automatically adjust the design and layout of the proposal material to generate a visually appealing proposal material. This allows the sales proposal material automatic generation system according to an embodiment to reduce user effort and create high-quality proposal materials.

[0030] The analysis unit can base its analysis on past proposal materials or industry best practices. For example, the analysis unit refers to past proposal materials and optimizes the structure and content of the proposal materials. The analysis unit can also refer to industry best practices to improve the quality of the proposal materials. For example, it can extract success stories from specific projects or specific periods from past proposal materials and create proposal materials based on those. It can also refer to industry standards and success stories as industry best practices and reflect them in the proposal materials. In this way, the analysis unit can improve the accuracy of its analysis by referring to past proposal materials and industry best practices.

[0031] The generation unit can automatically adjust the design and layout of the proposal materials. The generation unit, for example, automatically adjusts the design and layout of the proposal materials. For example, the generation unit can change the design and layout depending on the purpose of the proposal. The generation unit can also automatically generate designs and layouts tailored to target customers. For example, the generation unit selects optimal designs and layouts based on the content of the proposal materials and reflects them in the proposal materials. This allows the generation unit to generate visually appealing proposal materials. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can automatically adjust the design and layout of the proposal materials using a generation AI to generate visually appealing proposal materials.

[0032] Furthermore, the sales proposal material automatic generation system includes an additional information section including success stories, customer testimonials, and risk management items. The additional information section, for example, can include success stories, customer testimonials, and risk management items in the proposal materials. For example, the additional information section creates proposal materials based on past success stories. The additional information section can also collect customer testimonials and reflect them in the proposal materials. For example, the additional information section can collect customer testimonials based on survey results and feedback and reflect them in the proposal materials. The additional information section can also include a risk management item. For example, the additional information section can clarify the types of risks and risk assessment methods and reflect them in the proposal materials. In this way, the additional information section can improve the reliability and persuasiveness of the proposal materials.

[0033] The input unit can analyze the user's past input history and suggest an appropriate input method. The input unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, the input unit automatically displays elements of the proposal document that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest elements of the proposal document to be used in a specific time period from the user's past input history. In this way, the input unit can suggest the optimal input method by analyzing the user's past input history, thereby improving input efficiency.

[0034] The input unit can customize input items based on the user's industry and job title at the time of input. For example, the input unit customizes input items based on the user's industry and job title at the time of input. For example, if the user belongs to the IT industry, the input unit can prioritize input of technical proposal content. Furthermore, if the user works in sales, the input unit can emphasize input items for customer information and competitive analysis. Furthermore, if the user works in marketing, the input unit can add input items for campaign information and market analysis. In this way, the input unit can customize input items based on the user's industry and job title, and create more appropriate proposal materials.

[0035] The input unit can prioritize displaying highly relevant input items by taking into account the user's geographical location information during input. For example, the input unit prioritizes displaying highly relevant input items by taking into account the user's geographical location information during input. For example, when the user is in a specific area, suggestions related to that area are prioritized. Also, when the user is on a business trip, the input unit can highlight input items related to the business trip destination. Furthermore, when the user is at home, the input unit can prioritize displaying input items related to remote work. In this way, the input unit can prioritize displaying highly relevant input items by taking into account the user's geographical location information, thereby supporting efficient input.

[0036] The input unit can analyze the user's social media activity at the time of input and suggest related input items. For example, the input unit can analyze the user's social media activity at the time of input and suggest related input items. For example, the input unit can automatically suggest elements of proposal materials based on information shared by the user on social media. The input unit can also display input items related to companies or industries that the user follows on social media. Furthermore, the input unit can analyze topics of interest from the user's social media activity and suggest related input items. In this way, the input unit can suggest related input items by analyzing the user's social media activity and support efficient input.

[0037] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the input information during analysis. For example, the analysis unit performs a detailed analysis of important proposal content to improve accuracy. The analysis unit can also perform a simplified analysis of information with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance, taking into account the balance of the entire proposal materials. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information and perform an efficient analysis.

[0038] The analysis unit can perform the analysis based on the latest trends or best practices in the industry during the analysis. For example, the analysis unit performs the analysis by referring to the latest trends and best practices in the industry during the analysis. For example, the analysis unit customizes the content of the proposal materials based on the latest trends in the industry. The analysis unit can also optimize the structure of the proposal materials by referring to best practices. Furthermore, the analysis unit can incorporate the latest data in the industry into the analysis to improve the accuracy of the proposal materials. In this way, the analysis unit can improve the accuracy of the analysis by referring to the latest trends and best practices in the industry.

[0039] The analysis unit can determine the priority of analysis based on the submission time of the input information during analysis. The analysis unit, for example, determines the priority of analysis based on the submission time of the input information during analysis. For example, the analysis unit prioritizes analysis of urgent proposal documents. The analysis unit can also set a high priority for proposal documents whose submission deadline is approaching. Furthermore, the analysis unit can adjust the analysis schedule according to the submission time. In this way, the analysis unit can determine the priority of analysis based on the submission time of the input information and perform efficient analysis.

[0040] The analysis unit can improve the accuracy of the analysis by referring to a related external database during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to a related external database during analysis. For example, the analysis unit can refer to an industry database to enrich the content of proposal materials. The analysis unit can also use an external database for competitive analysis. Furthermore, the analysis unit can incorporate market data to improve the accuracy of proposal materials. In this way, the analysis unit can improve the accuracy of the analysis by referring to a related external database.

[0041] The configuration determination unit can select the optimal configuration by referring to past success cases when determining the configuration. For example, the configuration determination unit selects the optimal configuration by referring to past success cases when determining the configuration. For example, the configuration determination unit optimizes the configuration of the proposal materials based on past success cases. The configuration determination unit can also enhance the content of the proposal materials by referring to data on success cases. Furthermore, the configuration determination unit can analyze past success cases and select the most effective configuration. In this way, the configuration determination unit can select the optimal configuration by referring to past success cases and improve the accuracy of the proposal materials.

[0042] The composition determination unit can customize the composition based on the purpose and target customers of the proposal materials when determining the composition. For example, the composition determination unit customizes the composition based on the purpose and target customers of the proposal materials when determining the composition. For example, the composition determination unit determines the overall tone and style of the proposal materials according to the purpose of the proposal. The composition determination unit can also customize the content of the proposal materials based on the target customers. Furthermore, the composition determination unit can select the optimal composition according to the purpose and target customers of the proposal materials. In this way, the composition determination unit can customize the composition based on the purpose and target customers of the proposal materials and create more appropriate proposal materials.

[0043] The configuration determination unit can adjust the priority of the configurations based on the submission time of the proposal materials when determining the configurations. The configuration determination unit, for example, adjusts the priority of the configurations based on the submission time of the proposal materials when determining the configurations. For example, the configuration determination unit determines the configuration with priority for urgent proposal materials. In addition, the configuration determination unit can also set a high priority for proposal materials whose submission deadline is approaching. Furthermore, the configuration determination unit can adjust the schedule of the configurations according to the submission time. In this way, the configuration determination unit can adjust the priority of the configurations based on the submission time of the proposal materials and support efficient configuration determination.

[0044] The configuration determination unit can improve the accuracy of the configuration by referring to related industry data when determining the configuration. For example, the configuration determination unit can improve the accuracy of the configuration by referring to related industry data when determining the configuration. For example, the configuration determination unit can refer to industry data to enhance the configuration of the proposal materials. The configuration determination unit can also use industry data for competitive analysis. Furthermore, the configuration determination unit can incorporate market data to improve the accuracy of the configuration of the proposal materials. In this way, the configuration determination unit can improve the accuracy of the configuration by referring to related industry data.

[0045] The generation unit can automatically adjust the design and layout based on the content of the proposal material when generating the proposal. For example, the generation unit automatically adjusts the design and layout based on the content of the proposal material when generating the proposal. For example, the generation unit automatically adjusts the design and layout according to the purpose of the proposal. The generation unit can also automatically generate a design and layout that is tailored to the target customer. Furthermore, the generation unit can automatically adjust the optimal design and layout based on the content of the proposal material. In this way, the generation unit can automatically adjust the design and layout based on the content of the proposal material and generate visually attractive proposal materials.

[0046] The generation unit can select the optimal design by referring to past designs of proposal materials when generating the proposal materials. For example, the generation unit selects the optimal design by referring to past designs of proposal materials when generating the proposal materials. For example, the generation unit generates proposal materials based on designs of past successful cases. The generation unit can also select the optimal design by referring to past designs of proposal materials. Furthermore, the generation unit can analyze the designs of past proposal materials and select the most effective design. In this way, the generation unit can select the optimal design by referring to the designs of past proposal materials, thereby improving the accuracy of the proposal materials.

[0047] The generation unit can adjust the generation priority based on the submission time of the proposal materials during generation. The generation unit, for example, adjusts the generation priority based on the submission time of the proposal materials during generation. For example, the generation unit gives priority to generation of urgent proposal materials. In addition, the generation unit can also set a high priority for proposal materials whose submission deadline is approaching. Furthermore, the generation unit can adjust the generation schedule according to the submission time. In this way, the generation unit can adjust the generation priority based on the submission time of the proposal materials, thereby supporting efficient material generation.

[0048] The generation unit can improve the accuracy of generation by referring to related design templates during generation. The generation unit can improve the accuracy of generation by referring to related design templates during generation, for example. For example, the generation unit automatically selects a design template according to the content of the proposal material. The generation unit can also generate the proposal material by referring to design templates of past success stories. Furthermore, the generation unit can improve the accuracy of the proposal material by using design templates based on industry best practices. In this way, the generation unit can improve the accuracy of generation by referring to related design templates.

[0049] When providing additional information, the additional information unit can select optimal information by referring to past success cases and customer feedback. For example, when providing additional information, the additional information unit selects optimal information by referring to past success cases and customer feedback. For example, the additional information unit provides additional information based on past success cases. The additional information unit can also select additional information related to the proposal materials by referring to customer feedback. Furthermore, the additional information unit can analyze past success cases and customer feedback and select the most effective additional information. In this way, the additional information unit can select optimal additional information by referring to past success cases and customer feedback, thereby improving the accuracy of the proposal materials.

[0050] The additional information unit can adjust the priority of information based on the submission time of the proposal materials when providing the additional information. For example, the additional information unit adjusts the priority of information based on the submission time of the proposal materials when providing the additional information. For example, the additional information unit provides additional information preferentially for urgent proposal materials. Also, the additional information unit can set a high priority for proposal materials whose submission deadline is approaching. Furthermore, the additional information unit can adjust the schedule for providing the additional information according to the submission time. In this way, the additional information unit can adjust the priority of information based on the submission time of the proposal materials and support efficient information provision.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The sales proposal material automatic generation system can further include a success rate analysis unit that analyzes the success rate of a user's past proposal materials. The success rate analysis unit optimizes the structure and content of the proposal materials based on the success rate of the past proposal materials. For example, the success rate analysis unit can extract elements that have shown a high success rate from past proposal materials and reflect them in new proposal materials. The success rate analysis unit can also analyze patterns of proposal materials that have been successful for specific customers or industries and customize proposal materials based on that analysis. Furthermore, the success rate analysis unit can monitor the success rate of proposal materials in real time and adjust the content of the proposal materials as necessary. This allows the sales proposal material automatic generation system to improve the accuracy of proposal materials based on past success rates.

[0053] The sales proposal automatic generation system can further include an industry terminology insertion unit that automatically inserts terms and phrases specific to the user's industry. For example, if the user belongs to the IT industry, the industry terminology insertion unit can automatically insert technical terms and phrases into the proposal materials. Alternatively, if the user belongs to the medical industry, the industry terminology insertion unit can also reflect medical terms and phrases in the proposal materials. Furthermore, the industry terminology insertion unit can incorporate the latest trends and best practices related to the user's industry into the proposal materials. In this way, the industry terminology insertion unit can automatically insert terms and phrases specific to the user's industry, thereby improving the expertise and reliability of the proposal materials.

[0054] The sales proposal material automatic generation system can further include a feedback collection unit that collects feedback on users' past proposal materials and suggests improvements to the proposal materials. The feedback collection unit, for example, collects customer feedback on past proposal materials and suggests improvements to the proposal materials based on that feedback. The feedback collection unit can also analyze evaluations of each element of the proposal materials and identify areas that need improvement. Furthermore, the feedback collection unit can suggest improvements to the proposal materials in real time, which the user can use as a reference when creating proposal materials. In this way, the feedback collection unit can improve the quality of the proposal materials based on past feedback.

[0055] The sales proposal material automatic generation system can further include a template suggestion unit that analyzes the user's past browsing history of proposal materials and suggests an optimal proposal material template. The template suggestion unit, for example, analyzes proposal material templates used by the user in the past and suggests an optimal template. The template suggestion unit can also customize proposal materials based on templates that have been successful for specific customers or industries. Furthermore, the template suggestion unit can monitor the browsing history of proposal materials in real time and suggest an optimal template as needed. This allows the template suggestion unit to improve the accuracy of proposal materials based on past browsing history.

[0056] The sales proposal material automatic generation system can further include a storage method suggestion unit that analyzes the storage locations of the user's past proposal materials and suggests the optimal storage method. The storage method suggestion unit, for example, analyzes storage locations used by the user in the past and suggests the optimal storage method. The storage method suggestion unit can also store proposal materials based on storage methods that have been successful for specific customers or industries. Furthermore, the storage method suggestion unit can monitor the storage locations of proposal materials in real time and suggest the optimal storage method as needed. In this way, the storage method suggestion unit can optimize the storage method of proposal materials based on past storage locations.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: In the input section, the user inputs the elements necessary for the proposal document. For example, information such as the purpose of the proposal, target customers, proposal content, competitive analysis, budget, schedule, etc. can be input. Step 2: The analysis unit uses the generation AI to analyze the information entered by the input unit. For example, the generation AI determines the overall tone and style of the proposal document based on the purpose of the proposal. The generation AI can also customize the content of the proposal document based on the target customer. Step 3: The structure determination section determines the structure of the proposal materials based on the information analyzed by the analysis section. For example, it determines the cover page according to the purpose of the proposal, the content tailored to the target customers, the graphs and tables showing the results of the competitive analysis, and the slides showing the budget and schedule. Step 4: The generator generates the proposal materials in a presentation format based on the structure determined by the structure determiner. For example, the generator AI can automatically adjust the design and layout of the proposal materials to generate visually appealing proposal materials.

[0059] (Example 2) In an embodiment of the present invention, a sales proposal automatic generation system allows a user to input elements necessary for a proposal, and a generation AI analyzes the elements to determine the structure of the proposal, generating the proposal in a presentation format (e.g., PowerPoint, Keynote, etc.). This system begins when a user inputs the elements necessary for the proposal. For example, the user inputs information such as the purpose of the proposal, target customers, proposal content, competitive analysis, budget, and schedule. This information is then input into the generation AI, which analyzes the input information and determines the structure of the proposal. For example, the AI ​​determines the overall tone and style of the proposal based on the purpose of the proposal and customizes the content of the proposal based on the target customers. The generation AI then generates the proposal in presentation format based on the determined structure. For example, the AI ​​automatically creates a cover page tailored to the purpose of the proposal, content tailored to the target customers, graphs and tables showing the results of the competitive analysis, slides showing the budget and schedule, and so on. This system allows users to create high-quality proposals without much effort. For example, when a sales representative makes a proposal to a new client, they can prepare effective proposals in a short amount of time. Furthermore, maintaining a consistent quality of the proposal materials improves the efficiency of sales activities across the entire company. This allows the sales proposal material automatic generation system to save the user time and effort and create high-quality proposal materials.

[0060] A sales proposal material automatic generation system according to an embodiment includes an input unit, an analysis unit, a configuration determination unit, and a generation unit. The input unit allows a user to input elements necessary for a proposal material. For example, the user can input information such as the purpose of the proposal, target customers, proposal content, competitive analysis, budget, and schedule. The analysis unit uses a generation AI to analyze the information input by the input unit. For example, the generation AI determines the overall tone and style of the proposal material based on the purpose of the proposal. The generation AI can also customize the content of the proposal material based on the target customers. The configuration determination unit determines the configuration of the proposal material based on the information analyzed by the analysis unit. For example, the configuration determines a cover page according to the purpose of the proposal, content tailored to the target customers, graphs and tables showing the results of the competitive analysis, slides showing the budget and schedule, and the like. The generation unit generates the proposal material in a presentation format based on the configuration determined by the configuration determination unit. For example, the generation AI can automatically adjust the design and layout of the proposal material to generate a visually appealing proposal material. This allows the sales proposal material automatic generation system according to an embodiment to reduce user effort and create high-quality proposal materials.

[0061] The analysis unit can base its analysis on past proposal materials or industry best practices. For example, the analysis unit refers to past proposal materials and optimizes the structure and content of the proposal materials. The analysis unit can also refer to industry best practices to improve the quality of the proposal materials. For example, it can extract success stories from specific projects or specific periods from past proposal materials and create proposal materials based on those. It can also refer to industry standards and success stories as industry best practices and reflect them in the proposal materials. In this way, the analysis unit can improve the accuracy of its analysis by referring to past proposal materials and industry best practices.

[0062] The generation unit can automatically adjust the design and layout of the proposal materials. The generation unit, for example, automatically adjusts the design and layout of the proposal materials. For example, the generation unit can change the design and layout depending on the purpose of the proposal. The generation unit can also automatically generate designs and layouts tailored to target customers. For example, the generation unit selects optimal designs and layouts based on the content of the proposal materials and reflects them in the proposal materials. This allows the generation unit to generate visually appealing proposal materials. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can automatically adjust the design and layout of the proposal materials using a generation AI to generate visually appealing proposal materials.

[0063] Furthermore, the sales proposal material automatic generation system includes an additional information section including success stories, customer testimonials, and risk management items. The additional information section, for example, can include success stories, customer testimonials, and risk management items in the proposal materials. For example, the additional information section creates proposal materials based on past success stories. The additional information section can also collect customer testimonials and reflect them in the proposal materials. For example, the additional information section can collect customer testimonials based on survey results and feedback and reflect them in the proposal materials. The additional information section can also include a risk management item. For example, the additional information section can clarify the types of risks and risk assessment methods and reflect them in the proposal materials. In this way, the additional information section can improve the reliability and persuasiveness of the proposal materials.

[0064] The sales proposal material automatic generation system further includes an input unit that estimates a user's emotions and adjusts the display method of the input interface based on the estimated user emotions. The input unit, for example, estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions. For example, if the user is stressed, the input unit provides a simple interface and minimizes input steps. Also, if the user is relaxed, the input unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the input unit can prioritize voice input to enable quick input of proposal material elements. This allows the input unit to adjust the display method of the input interface according to the user's emotions, reducing user stress and supporting efficient input. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0065] The input unit can analyze the user's past input history and suggest an appropriate input method. The input unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, the input unit automatically displays elements of the proposal document that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest elements of the proposal document to be used in a specific time period from the user's past input history. In this way, the input unit can suggest the optimal input method by analyzing the user's past input history, thereby improving input efficiency.

[0066] The input unit can customize input items based on the user's industry and job title at the time of input. For example, the input unit customizes input items based on the user's industry and job title at the time of input. For example, if the user belongs to the IT industry, the input unit can prioritize input of technical proposal content. Furthermore, if the user works in sales, the input unit can emphasize input items for customer information and competitive analysis. Furthermore, if the user works in marketing, the input unit can add input items for campaign information and market analysis. In this way, the input unit can customize input items based on the user's industry and job title, and create more appropriate proposal materials.

[0067] The sales proposal material automatic generation system further includes an input unit that estimates a user's emotions and prioritizes input items based on the estimated user emotions. The input unit, for example, estimates the user's emotions and prioritizes the input items based on the estimated user emotions. For example, if the user is nervous, the input unit displays the most important input items first and postpones the other items. Also, if the user is relaxed, the input unit can display all input items evenly. Furthermore, if the user is in a hurry, the input unit can display only the most important input items to enable quick input. This allows the input unit to prioritize input items according to the user's emotions and support efficient input. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] The input unit can prioritize displaying highly relevant input items by taking into account the user's geographical location information during input. For example, the input unit prioritizes displaying highly relevant input items by taking into account the user's geographical location information during input. For example, when the user is in a specific area, suggestions related to that area are prioritized. Also, when the user is on a business trip, the input unit can highlight input items related to the business trip destination. Furthermore, when the user is at home, the input unit can prioritize displaying input items related to remote work. In this way, the input unit can prioritize displaying highly relevant input items by taking into account the user's geographical location information, thereby supporting efficient input.

[0069] The input unit can analyze the user's social media activity at the time of input and suggest related input items. For example, the input unit can analyze the user's social media activity at the time of input and suggest related input items. For example, the input unit can automatically suggest elements of proposal materials based on information shared by the user on social media. The input unit can also display input items related to companies or industries that the user follows on social media. Furthermore, the input unit can analyze topics of interest from the user's social media activity and suggest related input items. In this way, the input unit can suggest related input items by analyzing the user's social media activity and support efficient input.

[0070] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis to improve the accuracy of the proposal materials. Alternatively, if the user is in a hurry, the analysis unit can perform a simplified analysis to quickly generate proposal materials. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. This allows the analysis unit to adjust the analysis algorithm according to the user's emotions and provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the input information during analysis. For example, the analysis unit performs a detailed analysis of important proposal content to improve accuracy. The analysis unit can also perform a simplified analysis of information with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the importance, taking into account the balance of the entire proposal materials. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information and perform an efficient analysis.

[0072] The analysis unit can perform the analysis based on the latest trends or best practices in the industry during the analysis. For example, the analysis unit performs the analysis by referring to the latest trends and best practices in the industry during the analysis. For example, the analysis unit customizes the content of the proposal materials based on the latest trends in the industry. The analysis unit can also optimize the structure of the proposal materials by referring to best practices. Furthermore, the analysis unit can incorporate the latest data in the industry into the analysis to improve the accuracy of the proposal materials. In this way, the analysis unit can improve the accuracy of the analysis by referring to the latest trends and best practices in the industry.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions and provide a highly visible display. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The analysis unit can determine the priority of analysis based on the submission time of the input information during analysis. The analysis unit, for example, determines the priority of analysis based on the submission time of the input information during analysis. For example, the analysis unit prioritizes analysis of urgent proposal documents. The analysis unit can also set a high priority for proposal documents whose submission deadline is approaching. Furthermore, the analysis unit can adjust the analysis schedule according to the submission time. In this way, the analysis unit can determine the priority of analysis based on the submission time of the input information and perform efficient analysis.

[0075] The analysis unit can improve the accuracy of the analysis by referring to a related external database during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to a related external database during analysis. For example, the analysis unit can refer to an industry database to enrich the content of proposal materials. The analysis unit can also use an external database for competitive analysis. Furthermore, the analysis unit can incorporate market data to improve the accuracy of proposal materials. In this way, the analysis unit can improve the accuracy of the analysis by referring to a related external database.

[0076] The composition determination unit can estimate the user's emotions and adjust the composition determination method based on the estimated user's emotions. The composition determination unit, for example, estimates the user's emotions and adjusts the composition determination method based on the estimated user's emotions. For example, if the user is relaxed, the composition determination unit can propose a detailed composition. Also, if the user is in a hurry, the composition determination unit can propose a simplified composition. Furthermore, if the user is excited, the composition determination unit can propose a visually appealing composition. This allows the composition determination unit to adjust the composition determination method according to the user's emotions and create more appropriate proposal materials. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The configuration determination unit can select the optimal configuration by referring to past success cases when determining the configuration. For example, the configuration determination unit selects the optimal configuration by referring to past success cases when determining the configuration. For example, the configuration determination unit optimizes the configuration of the proposal materials based on past success cases. The configuration determination unit can also enhance the content of the proposal materials by referring to data on success cases. Furthermore, the configuration determination unit can analyze past success cases and select the most effective configuration. In this way, the configuration determination unit can select the optimal configuration by referring to past success cases and improve the accuracy of the proposal materials.

[0078] The composition determination unit can customize the composition based on the purpose and target customers of the proposal materials when determining the composition. For example, the composition determination unit customizes the composition based on the purpose and target customers of the proposal materials when determining the composition. For example, the composition determination unit determines the overall tone and style of the proposal materials according to the purpose of the proposal. The composition determination unit can also customize the content of the proposal materials based on the target customers. Furthermore, the composition determination unit can select the optimal composition according to the purpose and target customers of the proposal materials. In this way, the composition determination unit can customize the composition based on the purpose and target customers of the proposal materials and create more appropriate proposal materials.

[0079] The configuration determination unit can estimate the user's emotions and determine the priority of the configurations based on the estimated user emotions. For example, the configuration determination unit estimates the user's emotions and determines the priority of the configurations based on the estimated user emotions. For example, if the user is nervous, the configuration determination unit determines the most important component first. Alternatively, if the user is relaxed, the configuration determination unit can determine all components equally. Furthermore, if the user is in a hurry, the configuration determination unit can prioritize only the most important component. This allows the configuration determination unit to determine the priority of the configurations according to the user's emotions and support efficient configuration determination. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The configuration determination unit can adjust the priority of the configurations based on the submission time of the proposal materials when determining the configurations. The configuration determination unit, for example, adjusts the priority of the configurations based on the submission time of the proposal materials when determining the configurations. For example, the configuration determination unit determines the configuration with priority for urgent proposal materials. In addition, the configuration determination unit can also set a high priority for proposal materials whose submission deadline is approaching. Furthermore, the configuration determination unit can adjust the schedule of the configurations according to the submission time. In this way, the configuration determination unit can adjust the priority of the configurations based on the submission time of the proposal materials and support efficient configuration determination.

[0081] The configuration determination unit can improve the accuracy of the configuration by referring to related industry data when determining the configuration. For example, the configuration determination unit can improve the accuracy of the configuration by referring to related industry data when determining the configuration. For example, the configuration determination unit can refer to industry data to enhance the configuration of the proposal materials. The configuration determination unit can also use industry data for competitive analysis. Furthermore, the configuration determination unit can incorporate market data to improve the accuracy of the configuration of the proposal materials. In this way, the configuration determination unit can improve the accuracy of the configuration by referring to related industry data.

[0082] The generation unit can estimate the user's emotions and adjust the design of the generated materials based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the design of the generated materials based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates proposal materials with a calm design. If the user is in a hurry, the generation unit can generate proposal materials with a simple, highly visible design. Furthermore, if the user is excited, the generation unit can generate proposal materials with a visually appealing design. This allows the generation unit to adjust the design of the materials according to the user's emotions and generate visually appealing proposal materials. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The generation unit can automatically adjust the design and layout based on the content of the proposal material when generating the proposal. For example, the generation unit automatically adjusts the design and layout based on the content of the proposal material when generating the proposal. For example, the generation unit automatically adjusts the design and layout according to the purpose of the proposal. The generation unit can also automatically generate a design and layout that is tailored to the target customer. Furthermore, the generation unit can automatically adjust the optimal design and layout based on the content of the proposal material. In this way, the generation unit can automatically adjust the design and layout based on the content of the proposal material and generate visually attractive proposal materials.

[0084] The generation unit can select the optimal design by referring to past designs of proposal materials when generating the proposal materials. For example, the generation unit selects the optimal design by referring to past designs of proposal materials when generating the proposal materials. For example, the generation unit generates proposal materials based on designs of past successful cases. The generation unit can also select the optimal design by referring to past designs of proposal materials. Furthermore, the generation unit can analyze the designs of past proposal materials and select the most effective design. In this way, the generation unit can select the optimal design by referring to the designs of past proposal materials, thereby improving the accuracy of the proposal materials.

[0085] The generation unit can estimate the user's emotions and determine the priority of materials to be generated based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and determines the priority of materials to be generated based on the estimated user emotions. For example, if the user is nervous, the generation unit generates the most important materials first. Alternatively, if the user is relaxed, the generation unit can generate all materials equally. Furthermore, if the user is in a hurry, the generation unit can prioritize generating only the most important materials. This allows the generation unit to determine the priority of materials according to the user's emotions and support efficient material generation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The generation unit can adjust the generation priority based on the submission time of the proposal materials during generation. The generation unit, for example, adjusts the generation priority based on the submission time of the proposal materials during generation. For example, the generation unit gives priority to generation of urgent proposal materials. In addition, the generation unit can also set a high priority for proposal materials whose submission deadline is approaching. Furthermore, the generation unit can adjust the generation schedule according to the submission time. In this way, the generation unit can adjust the generation priority based on the submission time of the proposal materials, thereby supporting efficient material generation.

[0087] The generation unit can improve the accuracy of generation by referring to related design templates during generation. The generation unit can improve the accuracy of generation by referring to related design templates during generation, for example. For example, the generation unit automatically selects a design template according to the content of the proposal material. The generation unit can also generate the proposal material by referring to design templates of past success stories. Furthermore, the generation unit can improve the accuracy of the proposal material by using design templates based on industry best practices. In this way, the generation unit can improve the accuracy of generation by referring to related design templates.

[0088] The additional information unit can estimate the user's emotion and adjust the display method of the additional information based on the estimated user emotion. For example, the additional information unit can estimate the user's emotion and adjust the display method of the additional information based on the estimated user emotion. For example, if the user is nervous, the additional information unit can provide a simple, highly visible display method. If the user is relaxed, the additional information unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the additional information unit can also provide a display method that focuses on the main points. In this way, the additional information unit can adjust the display method of the additional information according to the user's emotion and provide a highly visible display. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] When providing additional information, the additional information unit can select optimal information by referring to past success cases and customer feedback. For example, when providing additional information, the additional information unit selects optimal information by referring to past success cases and customer feedback. For example, the additional information unit provides additional information based on past success cases. The additional information unit can also select additional information related to the proposal materials by referring to customer feedback. Furthermore, the additional information unit can analyze past success cases and customer feedback and select the most effective additional information. In this way, the additional information unit can select optimal additional information by referring to past success cases and customer feedback, thereby improving the accuracy of the proposal materials.

[0090] The additional information unit can estimate the user's emotions and prioritize the additional information based on the estimated user emotions. For example, the additional information unit can estimate the user's emotions and prioritize the additional information based on the estimated user emotions. For example, if the user is nervous, the additional information unit can display the most important additional information first. Also, if the user is relaxed, the additional information unit can display all the additional information evenly. Furthermore, if the user is in a hurry, the additional information unit can prioritize only the most important additional information. This allows the additional information unit to prioritize the additional information according to the user's emotions and support efficient information provision. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The additional information unit can adjust the priority of information based on the submission time of the proposal materials when providing the additional information. For example, the additional information unit adjusts the priority of information based on the submission time of the proposal materials when providing the additional information. For example, the additional information unit provides additional information preferentially for urgent proposal materials. Also, the additional information unit can set a high priority for proposal materials whose submission deadline is approaching. Furthermore, the additional information unit can adjust the schedule for providing the additional information according to the submission time. In this way, the additional information unit can adjust the priority of information based on the submission time of the proposal materials and support efficient information provision. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, configuration determination unit, generation unit, additional information unit, and emotion estimation function, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is implemented by the control unit 46A of the smart device 14, allowing the user to input elements necessary for the proposal materials. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The configuration determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and determines the configuration of the proposal materials based on the analyzed information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates the proposal materials in a presentation format based on the determined configuration. The additional information unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and includes success stories, customer testimonials, and risk management items in the proposal materials. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the display method of the input interface and the priority of input items. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, configuration determination unit, generation unit, additional information unit, and emotion estimation function, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is implemented by the control unit 46A of the smart glasses 214, allowing a user to input elements necessary for the proposal materials. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The configuration determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and determines the configuration of the proposal materials based on the analyzed information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates the proposal materials in a presentation format based on the determined configuration. The additional information unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and includes success stories, customer testimonials, and risk management items in the proposal materials. The emotion estimation function is realized by, for example, the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the display method of the input interface and the priority of input items. === Hard Collateral 1-3 === Each of the multiple elements, including the input unit, analysis unit, configuration determination unit, generation unit, additional information unit, and emotion estimation function, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is implemented by the control unit 46A of the headset-type terminal 314, allowing a user to input elements necessary for the proposal materials. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The configuration determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and determines the configuration of the proposal materials based on the analyzed information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates the proposal materials in a presentation format based on the determined configuration. The additional information unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and includes success stories, customer testimonials, and risk management items in the proposal materials. The emotion estimation function is realized by, for example, the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the display method of the input interface and the priority of input items. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, analysis unit, configuration determination unit, generation unit, additional information unit, and emotion estimation function, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is implemented by the control unit 46A of the robot 414, allowing the user to input elements necessary for the proposal materials. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The configuration determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and determines the configuration of the proposal materials based on the analyzed information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates the proposal materials in a presentation format based on the determined configuration. The additional information unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and includes success stories, customer testimonials, and risk management items in the proposal materials. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the display method of the input interface and the priority of input items.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The sales proposal material automatic generation system can further include a success rate analysis unit that analyzes the success rate of a user's past proposal materials. The success rate analysis unit optimizes the structure and content of the proposal materials based on the success rate of the past proposal materials. For example, the success rate analysis unit can extract elements that have shown a high success rate from past proposal materials and reflect them in new proposal materials. The success rate analysis unit can also analyze patterns of proposal materials that have been successful for specific customers or industries and customize proposal materials based on that analysis. Furthermore, the success rate analysis unit can monitor the success rate of proposal materials in real time and adjust the content of the proposal materials as necessary. This allows the sales proposal material automatic generation system to improve the accuracy of proposal materials based on past success rates.

[0094] The sales proposal material automatic generation system can further include a tone adjustment unit that estimates the user's emotions and adjusts the tone and style of the proposal material based on the estimated user emotions. For example, the tone adjustment unit can generate proposal materials with a soft tone when the user is relaxed. Also, if the user is nervous, it can generate proposal materials with a simple and clear tone. Furthermore, if the user is excited, it can generate proposal materials with an energetic tone. In this way, the tone adjustment unit can adjust the tone and style of the proposal material according to the user's emotions and create more effective proposal materials.

[0095] The sales proposal automatic generation system can further include an industry terminology insertion unit that automatically inserts terms and phrases specific to the user's industry. For example, if the user belongs to the IT industry, the industry terminology insertion unit can automatically insert technical terms and phrases into the proposal materials. Alternatively, if the user belongs to the medical industry, the industry terminology insertion unit can also reflect medical terms and phrases in the proposal materials. Furthermore, the industry terminology insertion unit can incorporate the latest trends and best practices related to the user's industry into the proposal materials. In this way, the industry terminology insertion unit can automatically insert terms and phrases specific to the user's industry, thereby improving the expertise and reliability of the proposal materials.

[0096] The sales proposal material automatic generation system can further include a color adjustment unit that estimates the user's emotions and adjusts the colors of the proposal material based on the estimated user emotions. For example, the color adjustment unit generates proposal materials with calming colors when the user is relaxed. Also, if the user is nervous, it can generate proposal materials with simple, highly visible colors. Furthermore, if the user is excited, it can generate proposal materials with vivid colors. In this way, the color adjustment unit can adjust the colors of the proposal material according to the user's emotions and create visually appealing proposal materials.

[0097] The sales proposal material automatic generation system can further include a feedback collection unit that collects feedback on users' past proposal materials and suggests improvements to the proposal materials. The feedback collection unit, for example, collects customer feedback on past proposal materials and suggests improvements to the proposal materials based on that feedback. The feedback collection unit can also analyze evaluations of each element of the proposal materials and identify areas that need improvement. Furthermore, the feedback collection unit can suggest improvements to the proposal materials in real time, which the user can use as a reference when creating proposal materials. In this way, the feedback collection unit can improve the quality of the proposal materials based on past feedback.

[0098] The sales proposal material automatic generation system may further include a font adjustment unit that estimates the user's emotions and adjusts the font of the proposal material based on the estimated user emotions. For example, the font adjustment unit may use a soft font when the user is relaxed. Alternatively, it may use a simple, easy-to-read font when the user is nervous. Furthermore, it may use an energetic font when the user is excited. In this way, the font adjustment unit adjusts the font of the proposal material according to the user's emotions, making it possible to create visually appealing proposal materials.

[0099] The sales proposal material automatic generation system can further include a template suggestion unit that analyzes the user's past browsing history of proposal materials and suggests an optimal proposal material template. The template suggestion unit, for example, analyzes proposal material templates used by the user in the past and suggests an optimal template. The template suggestion unit can also customize proposal materials based on templates that have been successful for specific customers or industries. Furthermore, the template suggestion unit can monitor the browsing history of proposal materials in real time and suggest an optimal template as needed. This allows the template suggestion unit to improve the accuracy of proposal materials based on past browsing history.

[0100] The sales proposal material automatic generation system can further include an animation adjustment unit that estimates the user's emotions and adjusts the animation effects of the proposal material based on the estimated user emotions. For example, the animation adjustment unit can use a gentle animation effect when the user is relaxed. Alternatively, it can use a simple, highly visible animation effect when the user is nervous. Furthermore, it can use a dynamic animation effect when the user is excited. In this way, the animation adjustment unit can adjust the animation effects of the proposal material according to the user's emotions and create visually appealing proposal materials.

[0101] The sales proposal material automatic generation system can further include a storage method suggestion unit that analyzes the storage locations of the user's past proposal materials and suggests the optimal storage method. The storage method suggestion unit, for example, analyzes storage locations used by the user in the past and suggests the optimal storage method. The storage method suggestion unit can also store proposal materials based on storage methods that have been successful for specific customers or industries. Furthermore, the storage method suggestion unit can monitor the storage locations of proposal materials in real time and suggest the optimal storage method as needed. In this way, the storage method suggestion unit can optimize the storage method of proposal materials based on past storage locations.

[0102] The sales proposal material automatic generation system may further include an audio guide adjustment unit that estimates the user's emotions and adjusts the audio guide of the proposal material based on the estimated user emotions. For example, the audio guide adjustment unit may provide a gentle audio guide when the user is relaxed. Alternatively, the audio guide adjustment unit may provide a simple and clear audio guide when the user is nervous. Furthermore, the audio guide adjustment unit may provide an energetic audio guide when the user is excited. In this way, the audio guide adjustment unit can adjust the audio guide of the proposal material according to the user's emotions and create more effective proposal materials.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: In the input section, the user inputs the elements necessary for the proposal document. For example, information such as the purpose of the proposal, target customers, proposal content, competitive analysis, budget, schedule, etc. can be input. Step 2: The analysis unit uses the generation AI to analyze the information entered by the input unit. For example, the generation AI determines the overall tone and style of the proposal document based on the purpose of the proposal. The generation AI can also customize the content of the proposal document based on the target customer. Step 3: The structure determination section determines the structure of the proposal materials based on the information analyzed by the analysis section. For example, it determines the cover page according to the purpose of the proposal, the content tailored to the target customers, the graphs and tables showing the results of the competitive analysis, and the slides showing the budget and schedule. Step 4: The generator generates the proposal materials in a presentation format based on the structure determined by the structure determiner. For example, the generator AI can automatically adjust the design and layout of the proposal materials to generate visually appealing proposal materials.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting elements required for the proposal document; an analysis unit that analyzes the information input by the input unit; a composition determination unit that determines a composition of the proposal material based on the information analyzed by the analysis unit; a generation unit that generates proposal materials in a presentation format based on the configuration determined by the configuration determination unit; Equipped with A system characterized by:

2. The analysis unit Build on previous proposals or industry best practices The system of claim 1 .

3. The generation unit Automatically adjust the design and layout of proposal materials The system of claim 1 .

4. Include additional information sections, including success stories or testimonials, and risk management sections The system of claim 1 .

5. The input unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions. The system of claim 1 .

6. The input unit Analyzes the user's past input history and suggests appropriate input methods The system of claim 1 .

7. The input unit Customize input fields based on the user's industry or job role as they enter data The system of claim 1 .

8. The input unit Estimate the user's emotions and prioritize input items based on the estimated user emotions. The system of claim 1 .

9. The input unit As you type, it prioritizes relevant input fields based on your geographic location. The system of claim 1 .

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