System

Abstract

An object of a system according to an embodiment is to automate creation of business materials and improve the efficiency of sales activities.SOLUTION: A system according to an embodiment includes a reception unit, a selection unit, a generation unit, and a correction unit. An acceptance part inputs basic information of a business negotiation. The selection unit analyzes the information input by the reception unit and selects a template. The generation unit automatically generates negotiation materials based on the template selected by the selection unit. The correction unit performs a preview display of the material generated by the generation unit, and adds correction and additional information.SELECTED DRAWING: Figure 1

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 technologies have had the problem that creating sales documents requires a lot of time and effort, reducing the efficiency of sales activities.

[0005] The system according to the embodiment aims to automate the creation of business negotiation materials and improve the efficiency of sales activities. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a selection unit, a generation unit, and a correction unit. The reception unit inputs basic information about the business negotiation. The selection unit analyzes the information input by the reception unit and selects a template. The generation unit automatically generates business negotiation materials based on the template selected by the selection unit. The correction unit previews the materials generated by the generation unit and adds corrections and additional information. [Effects of the Invention]

[0007] The system according to the embodiment can automate the creation of sales negotiation materials and improve the efficiency of sales activities. [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) An automatic sales material generation system according to an embodiment of the present invention automatically generates sales negotiation materials to improve the efficiency of sales activities. The automatic sales material generation system inputs basic information about a sales negotiation, analyzes it using natural language processing technology, selects an appropriate template, and automatically generates sales negotiation materials. For example, the automatic sales material generation system inputs basic information such as the name of the store, the purpose of the sales negotiation, and details of the proposed product or service. The automatic sales material generation system then analyzes the input information and selects a template appropriate for the purpose of the sales negotiation and the content of the proposal. For example, if the sales negotiation is to introduce a new product, a template that emphasizes the features and benefits of the new product is selected. The automatic sales material generation system then automatically generates sales negotiation materials based on the selected template. For example, the slides reflect the features and benefits of the proposed product, pricing information, and implementation examples. Furthermore, the automatic sales material generation system provides the generated materials to sales representatives, who can preview them and add corrections or additional information using drag-and-drop and text editing functions. This significantly reduces the time required to create sales negotiation materials and improves the efficiency of sales activities. As a result, the automatic sales material generation system can reduce the workload of sales representatives and improve the efficiency of sales activities. For example, sales representatives can reduce the time they spend creating sales materials and devote more time to actual sales negotiations and customer service. In addition, the quality of sales materials can be maintained at a consistent level, improving the results of sales activities.

[0029] An automatic sales material generation system according to an embodiment includes a reception unit, a selection unit, a generation unit, and a correction unit. The reception unit inputs basic information about a business negotiation. The basic information about the business negotiation includes, for example, the name of a store operated by the reception unit, the purpose of the business negotiation, and details of a proposed product or service, but is not limited to these examples. The reception unit inputs, for example, the name of a store operated by the reception unit. The reception unit can also input the purpose of the business negotiation. The reception unit can also input details of a proposed product or service. The selection unit analyzes the input information and selects a template. The template includes, for example, a slide configuration and design according to the purpose of the business negotiation and the content of the proposal, but is not limited to these examples. The selection unit selects, for example, a template according to the purpose of the business negotiation. The selection unit can also select a template according to the content of the proposal. The selection unit can also analyze the input information using natural language processing technology and select an appropriate template. The generation unit automatically generates business negotiation materials based on the selected template. The business negotiation materials include, for example, features and advantages of the proposed product, pricing information, and implementation examples, but are not limited to these examples. The generation unit, for example, reflects the features of the proposed product in the slide. The generation unit can also reflect the advantages of the proposed product in the slide. The generation unit can also reflect price information in the slide. The generation unit can also reflect implementation examples in the slide. The correction unit previews the generated materials and adds corrections and additional information. The correction unit provides, for example, a drag-and-drop function. The correction unit can also provide a text editing function. The correction unit can also preview the generated materials and add corrections and additional information as necessary. As a result, the automatic sales material generation system according to the embodiment can significantly reduce the time required to create sales negotiation materials and improve the efficiency of sales activities.

[0030] The selection unit can select a template according to the purpose of the business negotiation and the content of the proposal. For example, the selection unit selects a template according to the purpose of the business negotiation. For example, in the case of proposing a new transaction, a template that emphasizes the benefits and procedures of the new transaction is selected. The selection unit can also select a template according to the content of the proposal. For example, a template that emphasizes the features of a product is selected. The selection unit can also analyze the input information using natural language processing technology and select an appropriate template. This makes it possible to improve the quality of materials by selecting a template that is optimal for the purpose of the business negotiation and the content of the proposal.

[0031] The generation unit can automatically fill in the content of each slide based on the input information. For example, the generation unit reflects the features of the proposed product in the slide. For example, the advantages and usage of the product are described in the slide. The generation unit can also reflect price information in the slide. For example, the price and discount information of the product are described in the slide. The generation unit can also reflect implementation cases in the slide. For example, success stories and feedback from other customers are described in the slide. In this way, the efficiency of document creation can be improved by automatically filling in the content of the slide based on the input information.

[0032] The correction unit may provide drag-and-drop and text editing functions. The correction unit may provide, for example, a drag-and-drop function. For example, text or images within a slide may be moved by drag-and-drop. The correction unit may also provide a text editing function. For example, the font or color of the text may be changed. The correction unit may also display a preview of the generated material and add corrections or additional information as necessary. This allows the user to easily correct the material.

[0033] The generation unit can generate materials based on pre-set quality standards. The generation unit can generate materials based on, for example, grammatical accuracy. For example, the generation unit can perform grammar checks to generate accurate sentences. The generation unit can also generate materials based on information comprehensiveness. For example, the generation unit can check whether all necessary information is included and generate comprehensive materials. The generation unit can also generate materials based on visual design. For example, the generation unit can generate materials taking into consideration easy-to-read layouts and color usage. This allows the quality of the materials to be maintained at a consistent level.

[0034] The reception unit can input basic information such as the name of the operating store, the purpose of the business negotiation, and details of the products and services to be proposed. The reception unit, for example, inputs the name of the operating store. For example, it inputs the name of the corporation or the store. The reception unit can also input the purpose of the business negotiation. For example, it inputs a proposal for a new transaction or a negotiation for contract renewal. The reception unit can also input details of the products and services to be proposed. For example, it inputs product specifications and service content. This allows the basic information of the business negotiation to be input accurately.

[0035] The reception unit can analyze past input data and suggest the optimal input method. For example, the reception unit automatically displays as candidates the names of managed stores and the purpose of business negotiations that the user has frequently input in the past. For example, the names of managed stores and the purpose of business negotiations are automatically displayed based on the past input history. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can suggest voice input or text input based on the past input history. The reception unit can also predict and suggest the names of managed stores and the purpose of business negotiations to be used in a specific time period based on the user's past input history. For example, the reception unit can automatically display the names of managed stores and the purpose of business negotiations to be used in a specific time period based on the past input history. In this way, an efficient input method can be provided by utilizing past input data.

[0036] The reception unit can automatically complete the input content based on the user's business situation and past business negotiation history when inputting information. For example, the reception unit automatically completes the name of the managed store and the proposed product based on the history of business negotiations conducted by the user in the past. For example, the reception unit automatically displays the name of the managed store and the proposed product based on the past business negotiation history. The reception unit can also automatically complete the purpose of related business negotiations and the contents of proposals based on the user's business situation (for example, the current project). For example, the purpose of business negotiations and the contents of proposals related to the current project are automatically displayed. The reception unit can also automatically complete similar business negotiation information based on the purpose of business negotiations and the contents of proposals previously input by the user. For example, similar business negotiation information is automatically displayed based on the past business negotiation history. This reduces the effort required for input by utilizing the user's business situation and past business negotiation history.

[0037] The reception unit can acquire information using voice input or image recognition when inputting information. For example, the reception unit allows the user to simply input the name of the operating store or the purpose of the business negotiation by voice, and the system automatically acquires the information. For example, the reception unit can use voice recognition technology to automatically convert information entered by voice into text. The reception unit can also allow the user to upload an image (e.g., a business card or product catalog), and the system can acquire information using image recognition. For example, an image analysis algorithm can be used to extract text information from the image. The reception unit can also allow the user to combine voice input and image recognition to input basic information more intuitively. For example, the user can enter the name of the operating store by voice and upload an image of a product catalog, and the system can automatically acquire the information. This allows information to be entered intuitively using voice input or image recognition.

[0038] When inputting the information, the reception unit can prioritize obtaining highly relevant information by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes obtaining information about operated stores related to that area. For example, information about operated stores related to that area is automatically displayed based on GPS data. Furthermore, when the user is negotiating in a specific area, the reception unit can also prioritize obtaining market information and competitive information for that area. For example, information about market trends and competitors for that area is automatically displayed. Furthermore, when the user is in a specific area, the reception unit can also prioritize obtaining related information based on the business negotiation history for that area. For example, information related to that area is automatically displayed based on past business negotiation history. In this way, highly relevant information can be efficiently obtained by taking into consideration the geographical location information.

[0039] The reception unit can analyze the user's social media activity at the time of input and automatically acquire related information. For example, the reception unit automatically acquires information about managed stores based on the location where the user checked in on social media. For example, it analyzes social media check-in data and automatically displays information about related managed stores. The reception unit can also analyze the content posted by the user on social media and automatically acquire the purpose of related business negotiations and proposals. For example, it analyzes the content posted and automatically displays the purpose of business negotiations and proposals. The reception unit can also automatically acquire related business negotiation information by referring to the activity of the user's friends on social media. For example, it analyzes the activity data of friends and automatically displays related business negotiation information. This makes it possible to efficiently acquire related information by utilizing social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback at the time of input. The reception unit customizes the input interface, for example, based on feedback provided by the user in the past. For example, the reception unit analyzes the past feedback and provides the user with an optimal input interface. The reception unit can also suggest the optimal input method (voice, text, etc.) based on the user's past feedback. For example, the reception unit can suggest voice input or text input based on the past feedback. The reception unit can also optimize the input procedure based on the user's past feedback and provide an efficient input method. For example, the reception unit can simplify the input procedure based on the past feedback and provide an efficient input method. In this way, the optimal input method can be provided to the user by reflecting the past feedback.

[0041] The selection unit can analyze past business negotiation data and suggest the optimal template. The selection unit can, for example, suggest the optimal template based on data of business negotiations that the user has previously succeeded. For example, the selection unit can analyze past business negotiation data and suggest a template that was used in a successful business negotiation. The selection unit can also suggest a template that is suitable for a specific business negotiation purpose based on the user's past business negotiation data. For example, the selection unit can suggest a template that is suitable for a specific business negotiation purpose based on the past business negotiation data. The selection unit can also analyze the user's past business negotiation data and suggest the most effective template. For example, the selection unit can suggest the most effective template based on the past business negotiation data. In this way, the optimal template can be suggested by utilizing past business negotiation data.

[0042] When selecting a template, the selection unit can apply different selection algorithms depending on the purpose of the business negotiation and the content of the proposal. For example, in a business negotiation to introduce a new product, the selection unit applies an algorithm to select a template that emphasizes the features and advantages of the new product. For example, a template that emphasizes the features and advantages of the new product is selected. In addition, in a business negotiation to negotiate price, the selection unit can also apply an algorithm to select a template that emphasizes price information and cost reduction effects. For example, a template that emphasizes price information and cost reduction effects is selected. In addition, in the introduction of implementation cases, the selection unit can also apply an algorithm to select a template that emphasizes success stories and customer testimonials. For example, a template that emphasizes success stories and customer testimonials is selected. This makes it possible to select the optimal template depending on the purpose of the business negotiation and the content of the proposal.

[0043] When selecting a template, the selection unit can improve the accuracy of the selection by referring to the user's past selection history. The selection unit, for example, suggests an optimal template based on templates selected by the user in the past. For example, it suggests an optimal template based on the past selection history. The selection unit can also suggest a template suitable for a specific business negotiation purpose based on the user's past selection history. For example, it suggests a template suitable for a specific business negotiation purpose based on the past selection history. The selection unit can also analyze the user's past selection history and suggest the most effective template. For example, it suggests the most effective template based on the past selection history. In this way, the accuracy of selection can be improved by utilizing the past selection history.

[0044] When selecting a template, the selection unit can determine the priority of templates based on the time of submission of the business negotiation. For example, if the deadline for submitting the business negotiation is approaching, the selection unit can prioritize displaying templates that can be created quickly. For example, a simple template that can be created quickly can be displayed first. The selection unit can also prioritize displaying templates that include detailed information if the deadline for submitting the business negotiation is far away. For example, a template that includes detailed information can be displayed first. The selection unit can also suggest the most appropriate template depending on the time of submission of the business negotiation. For example, a template that can be created quickly can be displayed if the deadline is near, and a template that includes detailed information can be displayed if the deadline is far away. This makes it possible to select the most appropriate template depending on the time of submission of the business negotiation.

[0045] When selecting a template, the selection unit can adjust the order of templates based on the relevance to the business negotiation. For example, the selection unit can prioritize displaying templates that are most relevant to the purpose of the business negotiation. For example, templates related to the purpose of the business negotiation can be displayed first. The selection unit can also prioritize displaying templates that are most relevant to the content of a proposal. For example, templates related to the content of the proposal can be displayed first. The selection unit can also suggest the most appropriate template based on the relevance to the business negotiation. For example, the selection unit can suggest the most appropriate template based on the purpose of the business negotiation or the content of the proposal. This makes it possible to select the most appropriate template based on the relevance to the business negotiation.

[0046] When selecting a template, the selection unit can adjust the content of the template according to the user's level of expertise. For example, if the user has specialized knowledge, the selection unit can suggest a template that includes detailed technical information. For example, templates that include technical details can be preferentially displayed. Furthermore, if the user does not have specialized knowledge, the selection unit can also suggest a template that includes easy-to-understand explanations. For example, easy-to-understand templates that avoid technical jargon can be preferentially displayed. Furthermore, the selection unit can suggest the optimal template according to the user's level of expertise. For example, the selection unit can evaluate the user's level of expertise based on survey results and past business negotiation history and suggest the optimal template. This makes it possible to select the optimal template according to the user's level of expertise.

[0047] When generating materials, the generation unit can adjust the level of detail of the generated materials based on the importance of the business negotiation. For example, in the case of an important business negotiation, the generation unit generates materials containing detailed information. For example, slides containing detailed information are generated. The generation unit can also generate materials that are concise and to the point for a less important business negotiation. For example, slides that are concise and to the point are generated. The generation unit can also generate materials with an optimal level of detail depending on the importance of the business negotiation. For example, materials with an optimal level of detail are generated based on the size and impact of the business negotiation. This makes it possible to generate materials with an optimal level of detail depending on the importance of the business negotiation.

[0048] When generating materials, the generation unit can apply different generation algorithms depending on the category of the business negotiation. For example, in a business negotiation to introduce a new product, the generation unit applies a generation algorithm that emphasizes the features and advantages of the new product. For example, it generates slides that emphasize the features and advantages of the new product. In addition, in a business negotiation to negotiate price, the generation unit can also apply a generation algorithm that emphasizes price information and cost reduction effects. For example, it generates slides that emphasize price information and cost reduction effects. In addition, in the introduction of implementation cases, the generation unit can also apply a generation algorithm that emphasizes success stories and customer testimonials. For example, it generates slides that emphasize success stories and customer testimonials. This makes it possible to generate optimal materials depending on the category of the business negotiation.

[0049] When generating materials, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, applies an optimal generation algorithm based on materials generated by the user in the past. For example, it applies an optimal generation algorithm based on past generation results. The generation unit can also suggest a generation method suitable for a specific business negotiation purpose based on the user's past generation results. For example, it suggests a generation method suitable for a specific business negotiation purpose based on past generation results. The generation unit can also analyze the user's past generation results and suggest the most effective generation method. For example, it suggests the most effective generation method based on past generation results. In this way, the accuracy of generation can be improved by utilizing past generation results.

[0050] When generating materials, the generation unit can determine the priority of generation based on the submission date of the business negotiation. For example, when the submission deadline of the business negotiation is approaching, the generation unit prioritizes generating materials that can be created quickly. For example, it generates concise materials that can be created quickly first. The generation unit can also prioritize generating materials that include detailed information when the submission deadline of the business negotiation is far away. For example, it generates materials that include detailed information first. The generation unit can also generate optimal materials depending on the submission date of the business negotiation. For example, it generates materials that can be created quickly when the submission deadline is near, and generates materials that include detailed information when the submission deadline is far away. This makes it possible to generate optimal materials depending on the submission date of the business negotiation.

[0051] When generating materials, the generation unit can adjust the order of generation based on the relevance of the business negotiation. For example, the generation unit prioritizes generating information that is most relevant to the purpose of the business negotiation. For example, information related to the purpose of the business negotiation is generated first. The generation unit can also prioritize generating information that is most relevant to the content of the proposal. For example, information related to the content of the proposal is generated first. The generation unit can also generate optimal information based on the relevance of the business negotiation. For example, optimal information is generated based on the purpose of the business negotiation and the content of the proposal. This makes it possible to generate optimal information based on the relevance of the business negotiation.

[0052] When generating materials, the generation unit can adjust the use of technical terminology in the materials to be generated according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates materials containing detailed technical information. For example, it generates materials containing technical details. Furthermore, if the user does not have technical expertise, the generation unit can also generate materials containing easy-to-understand explanations. For example, it generates easy-to-understand materials that avoid technical terminology. Furthermore, the generation unit can generate optimal materials according to the user's level of expertise. For example, it evaluates the user's level of expertise based on survey results and past business negotiation history and generates optimal materials. This makes it possible to generate optimal materials according to the user's level of expertise.

[0053] When making corrections, the correction unit can analyze past correction data and propose the optimal correction method. The correction unit can, for example, propose the optimal correction method based on corrections made by the user in the past. For example, the correction unit proposes the optimal correction method based on the past correction data. The correction unit can also propose a correction method suitable for a specific business negotiation purpose based on the user's past correction data. For example, the correction unit proposes a correction method suitable for a specific business negotiation purpose based on the past correction data. The correction unit can also analyze the user's past correction data and propose the most effective correction method. For example, the correction unit proposes the most effective correction method based on the past correction data. In this way, the optimal correction method can be proposed by utilizing the past correction data.

[0054] When making corrections, the correction unit can automatically complete the correction content based on the user's work situation and past business negotiation history. The correction unit automatically completes the correction content based on, for example, the history of business negotiations conducted by the user in the past. For example, the correction content can be automatically displayed based on the past business negotiation history. The correction unit can also automatically complete related correction content based on the user's work situation (for example, the current project). For example, the correction content related to the current project can be automatically displayed. The correction unit can also automatically complete similar correction information based on the purpose of business negotiations and proposal content previously entered by the user. For example, similar correction information can be automatically displayed based on the past business negotiation history. In this way, the effort required for corrections can be reduced by utilizing the user's work situation and past business negotiation history.

[0055] The correction unit can correct information using voice input or image recognition when making corrections. For example, the correction unit allows the user to simply input the corrections by voice, and the system automatically corrects the information. For example, using voice recognition technology, the corrections input by voice can be automatically converted into text. The correction unit can also allow the user to upload an image (for example, a screenshot showing the areas to be corrected), and the system can correct the information using image recognition. For example, an image analysis algorithm can be used to extract the corrections from the image. The correction unit can also combine voice input and image recognition to allow the user to input the corrections more intuitively. For example, the user can input the corrections by voice and indicate the areas to be corrected in an image, and the system automatically corrects the information. In this way, information can be corrected intuitively using voice input or image recognition.

[0056] When making corrections, the correction unit can prioritize correcting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific region, the correction unit prioritizes correcting business negotiation information related to that region. For example, business negotiation information related to that region is automatically displayed based on GPS data. Furthermore, when the user is conducting business negotiations in a specific region, the correction unit can also prioritize correcting market information and competitive information for that region. For example, market trends for that region and information about competitors are automatically displayed. Furthermore, when the user is in a specific region, the correction unit can also prioritize correcting related information based on the business negotiation history for that region. For example, information related to that region is automatically displayed based on past business negotiation history. In this way, highly relevant information can be efficiently corrected by taking into account the geographical location information.

[0057] When making corrections, the correction unit can analyze the user's social media activity and automatically correct related information. For example, the correction unit automatically corrects business negotiation information based on the location where the user checked in on social media. For example, the correction unit analyzes social media check-in data and automatically displays related business negotiation information. The correction unit can also analyze the content of the user's posts on social media and automatically correct the purpose of the related business negotiation and proposal content. For example, the correction unit analyzes the content of the posts and automatically displays the purpose of the business negotiation and proposal content. The correction unit can also automatically correct related business negotiation information by referring to the activities of the user's friends on social media. For example, the correction unit analyzes the activity data of friends and automatically displays related business negotiation information. This makes it possible to efficiently correct related information by utilizing social media activity.

[0058] The correction unit can customize the correction method by reflecting the user's past feedback when correcting. The correction unit, for example, customizes the correction interface based on feedback provided by the user in the past. For example, the correction unit analyzes the past feedback and provides the user with an optimal correction interface. The correction unit can also suggest an optimal correction method (voice, text, etc.) based on the user's past feedback. For example, the correction unit can suggest voice input or text input based on the past feedback. The correction unit can also optimize the correction procedure based on the user's past feedback and provide an efficient correction method. For example, the correction procedure can be simplified based on the past feedback and an efficient correction method can be provided. In this way, the optimal correction method can be provided to the user by reflecting the past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can automatically complete input content based on the user's past input history. For example, it can automatically display the name of a store or the purpose of a business deal that the user has previously entered as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. For example, it can suggest voice input or text input based on the user's past input history. This makes it possible to provide an efficient input method by utilizing past input data.

[0061] When generating materials, the generation unit can adjust the use of technical terms in the materials to be generated according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit can generate materials containing detailed technical information. Also, if the user does not have specialized knowledge, the generation unit can generate materials containing easy-to-understand explanations. Furthermore, the generation unit can generate optimal materials according to the user's level of expertise. This makes it possible to generate optimal materials according to the user's level of expertise.

[0062] The reception unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, information on operated stores related to that area can be acquired preferentially. Also, if the user is negotiating in a specific area, market information and competitive information for that area can be acquired preferentially. Furthermore, if the user is in a specific area, related information can be acquired preferentially based on the business negotiation history for that area. In this way, highly relevant information can be acquired efficiently by taking into account the geographical location information.

[0063] When selecting a template, the selection unit can determine the priority of templates based on the time of submission of the business negotiation. For example, if the deadline for submitting the business negotiation is approaching, templates that can be created quickly can be displayed preferentially. Also, if the deadline for submitting the business negotiation is far away, templates that include detailed information can be displayed preferentially. Furthermore, the selection unit can suggest the most suitable template depending on the time of submission of the business negotiation. This makes it possible to select the most suitable template depending on the time of submission of the business negotiation.

[0064] The correction unit can correct information using voice input or image recognition when making corrections. For example, the user can simply input the corrections by voice, and the system will automatically correct the information. Alternatively, the user can upload an image (for example, a screenshot showing the corrections) and the system can correct the information using image recognition. Furthermore, the user can combine voice input and image recognition to input the corrections more intuitively. This allows for intuitive correction of information using voice input and image recognition.

[0065] When selecting a template, the selection unit can improve the accuracy of the selection by referring to the user's past selection history. For example, the selection unit can suggest the most suitable template based on templates previously selected by the user. The selection unit can also suggest a template suitable for a specific business negotiation purpose based on the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history and suggest the most effective template. In this way, the selection accuracy can be improved by utilizing the past selection history.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit inputs basic information about the business negotiation. The basic information about the business negotiation includes the name of the operating store, the purpose of the business negotiation, details of the products and services to be proposed, etc. For example, the reception unit inputs the name of the operating store, the purpose of the business negotiation, and details of the products and services to be proposed. Step 2: The selection unit analyzes the information input by the reception unit and selects a template. The template includes slide configurations and designs that correspond to the purpose of the business negotiation and the content of the proposal. The selection unit can also select a template that corresponds to the purpose of the business negotiation and the content of the proposal, and analyze the input information using natural language processing technology, for example. Step 3: The generator automatically generates sales negotiation materials based on the template selected by the selector. The sales negotiation materials include the features and advantages of the proposed product, pricing information, and case studies. For example, the features and advantages of the proposed product, pricing information, and case studies are reflected in the slides. Step 4: The editing unit previews the data generated by the generating unit and adds corrections and additional information. The editing unit provides, for example, a drag-and-drop function or a text editing function, and can preview the generated data and add corrections and additional information as needed.

[0068] (Example 2) An automatic sales material generation system according to an embodiment of the present invention automatically generates sales negotiation materials to improve the efficiency of sales activities. The automatic sales material generation system inputs basic information about a sales negotiation, analyzes it using natural language processing technology, selects an appropriate template, and automatically generates sales negotiation materials. For example, the automatic sales material generation system inputs basic information such as the name of the store, the purpose of the sales negotiation, and details of the proposed product or service. The automatic sales material generation system then analyzes the input information and selects a template appropriate for the purpose of the sales negotiation and the content of the proposal. For example, if the sales negotiation is to introduce a new product, a template that emphasizes the features and benefits of the new product is selected. The automatic sales material generation system then automatically generates sales negotiation materials based on the selected template. For example, the slides reflect the features and benefits of the proposed product, pricing information, and implementation examples. Furthermore, the automatic sales material generation system provides the generated materials to sales representatives, who can preview them and add corrections or additional information using drag-and-drop and text editing functions. This significantly reduces the time required to create sales negotiation materials and improves the efficiency of sales activities. As a result, the automatic sales material generation system can reduce the workload of sales representatives and improve the efficiency of sales activities. For example, sales representatives can reduce the time they spend creating sales materials and devote more time to actual sales negotiations and customer service. In addition, the quality of sales materials can be maintained at a consistent level, improving the results of sales activities.

[0069] An automatic sales material generation system according to an embodiment includes a reception unit, a selection unit, a generation unit, and a correction unit. The reception unit inputs basic information about a business negotiation. The basic information about the business negotiation includes, for example, the name of a store operated by the reception unit, the purpose of the business negotiation, and details of a proposed product or service, but is not limited to these examples. The reception unit inputs, for example, the name of a store operated by the reception unit. The reception unit can also input the purpose of the business negotiation. The reception unit can also input details of a proposed product or service. The selection unit analyzes the input information and selects a template. The template includes, for example, a slide configuration and design according to the purpose of the business negotiation and the content of the proposal, but is not limited to these examples. The selection unit selects, for example, a template according to the purpose of the business negotiation. The selection unit can also select a template according to the content of the proposal. The selection unit can also analyze the input information using natural language processing technology and select an appropriate template. The generation unit automatically generates business negotiation materials based on the selected template. The business negotiation materials include, for example, features and advantages of the proposed product, pricing information, and implementation examples, but are not limited to these examples. The generation unit, for example, reflects the features of the proposed product in the slide. The generation unit can also reflect the advantages of the proposed product in the slide. The generation unit can also reflect price information in the slide. The generation unit can also reflect implementation examples in the slide. The correction unit previews the generated materials and adds corrections and additional information. The correction unit provides, for example, a drag-and-drop function. The correction unit can also provide a text editing function. The correction unit can also preview the generated materials and add corrections and additional information as necessary. As a result, the automatic sales material generation system according to the embodiment can significantly reduce the time required to create sales negotiation materials and improve the efficiency of sales activities.

[0070] The selection unit can select a template according to the purpose of the business negotiation and the content of the proposal. For example, the selection unit selects a template according to the purpose of the business negotiation. For example, in the case of proposing a new transaction, a template that emphasizes the benefits and procedures of the new transaction is selected. The selection unit can also select a template according to the content of the proposal. For example, a template that emphasizes the features of a product is selected. The selection unit can also analyze the input information using natural language processing technology and select an appropriate template. This makes it possible to improve the quality of materials by selecting a template that is optimal for the purpose of the business negotiation and the content of the proposal.

[0071] The generation unit can automatically fill in the content of each slide based on the input information. For example, the generation unit reflects the features of the proposed product in the slide. For example, the advantages and usage of the product are described in the slide. The generation unit can also reflect price information in the slide. For example, the price and discount information of the product are described in the slide. The generation unit can also reflect implementation cases in the slide. For example, success stories and feedback from other customers are described in the slide. In this way, the efficiency of document creation can be improved by automatically filling in the content of the slide based on the input information.

[0072] The correction unit may provide drag-and-drop and text editing functions. The correction unit may provide, for example, a drag-and-drop function. For example, text or images within a slide may be moved by drag-and-drop. The correction unit may also provide a text editing function. For example, the font or color of the text may be changed. The correction unit may also display a preview of the generated material and add corrections or additional information as necessary. This allows the user to easily correct the material.

[0073] The generation unit can generate materials based on pre-set quality standards. The generation unit can generate materials based on, for example, grammatical accuracy. For example, the generation unit can perform grammar checks to generate accurate sentences. The generation unit can also generate materials based on information comprehensiveness. For example, the generation unit can check whether all necessary information is included and generate comprehensive materials. The generation unit can also generate materials based on visual design. For example, the generation unit can generate materials taking into consideration easy-to-read layouts and color usage. This allows the quality of the materials to be maintained at a consistent level.

[0074] The reception unit can input basic information such as the name of the operating store, the purpose of the business negotiation, and details of the products and services to be proposed. The reception unit, for example, inputs the name of the operating store. For example, it inputs the name of the corporation or the store. The reception unit can also input the purpose of the business negotiation. For example, it inputs a proposal for a new transaction or a negotiation for contract renewal. The reception unit can also input details of the products and services to be proposed. For example, it inputs product specifications and service content. This allows the basic information of the business negotiation to be input accurately.

[0075] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. For example, it can reduce the number of input items to allow input with simple operations. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. For example, it can increase the number of input items to allow detailed information to be entered. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and allow basic information to be entered quickly. For example, it can automatically convert information entered by voice into text using voice recognition technology. This can reduce input stress by providing an input interface that corresponds to the user's emotions. Emotion estimation is realized 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.

[0076] The reception unit can analyze past input data and suggest the optimal input method. For example, the reception unit automatically displays as candidates the names of managed stores and the purpose of business negotiations that the user has frequently input in the past. For example, the names of managed stores and the purpose of business negotiations are automatically displayed based on the past input history. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can suggest voice input or text input based on the past input history. The reception unit can also predict and suggest the names of managed stores and the purpose of business negotiations to be used in a specific time period based on the user's past input history. For example, the reception unit can automatically display the names of managed stores and the purpose of business negotiations to be used in a specific time period based on the past input history. In this way, an efficient input method can be provided by utilizing past input data.

[0077] The reception unit can automatically complete the input content based on the user's business situation and past business negotiation history when inputting information. For example, the reception unit automatically completes the name of the managed store and the proposed product based on the history of business negotiations conducted by the user in the past. For example, the reception unit automatically displays the name of the managed store and the proposed product based on the past business negotiation history. The reception unit can also automatically complete the purpose of related business negotiations and the contents of proposals based on the user's business situation (for example, the current project). For example, the purpose of business negotiations and the contents of proposals related to the current project are automatically displayed. The reception unit can also automatically complete similar business negotiation information based on the purpose of business negotiations and the contents of proposals previously input by the user. For example, similar business negotiation information is automatically displayed based on the past business negotiation history. This reduces the effort required for input by utilizing the user's business situation and past business negotiation history.

[0078] The reception unit can acquire information using voice input or image recognition when inputting information. For example, the reception unit allows the user to simply input the name of the operating store or the purpose of the business negotiation by voice, and the system automatically acquires the information. For example, the reception unit can use voice recognition technology to automatically convert information entered by voice into text. The reception unit can also allow the user to upload an image (e.g., a business card or product catalog), and the system can acquire information using image recognition. For example, an image analysis algorithm can be used to extract text information from the image. The reception unit can also allow the user to combine voice input and image recognition to input basic information more intuitively. For example, the user can enter the name of the operating store by voice and upload an image of a product catalog, and the system can automatically acquire the information. This allows information to be entered intuitively using voice input or image recognition.

[0079] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize input of important information (such as the name of the store or the purpose of the business negotiation). For example, the reception unit can display important information first in the input interface to prompt input. Furthermore, if the user is relaxed, the reception unit can prioritize input of detailed information (such as the features and benefits of the proposed product). For example, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize input of minimal information (such as the name of the store or the purpose of the business negotiation). For example, voice input can be prioritized to allow the user to quickly enter basic information. This allows efficient input by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (such as LLM) or a multimodal generation AI.

[0080] When inputting the information, the reception unit can prioritize obtaining highly relevant information by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes obtaining information about operated stores related to that area. For example, information about operated stores related to that area is automatically displayed based on GPS data. Furthermore, when the user is negotiating in a specific area, the reception unit can also prioritize obtaining market information and competitive information for that area. For example, information about market trends and competitors for that area is automatically displayed. Furthermore, when the user is in a specific area, the reception unit can also prioritize obtaining related information based on the business negotiation history for that area. For example, information related to that area is automatically displayed based on past business negotiation history. In this way, highly relevant information can be efficiently obtained by taking into consideration the geographical location information.

[0081] The reception unit can analyze the user's social media activity at the time of input and automatically acquire related information. For example, the reception unit automatically acquires information about managed stores based on the location where the user checked in on social media. For example, it analyzes social media check-in data and automatically displays information about related managed stores. The reception unit can also analyze the content posted by the user on social media and automatically acquire the purpose of related business negotiations and proposals. For example, it analyzes the content posted and automatically displays the purpose of business negotiations and proposals. The reception unit can also automatically acquire related business negotiation information by referring to the activity of the user's friends on social media. For example, it analyzes the activity data of friends and automatically displays related business negotiation information. This makes it possible to efficiently acquire related information by utilizing social media activity.

[0082] The reception unit can customize the input method by reflecting the user's past feedback at the time of input. The reception unit customizes the input interface, for example, based on feedback provided by the user in the past. For example, the reception unit analyzes the past feedback and provides the user with an optimal input interface. The reception unit can also suggest the optimal input method (voice, text, etc.) based on the user's past feedback. For example, the reception unit can suggest voice input or text input based on the past feedback. The reception unit can also optimize the input procedure based on the user's past feedback and provide an efficient input method. For example, the reception unit can simplify the input procedure based on the past feedback and provide an efficient input method. In this way, the optimal input method can be provided to the user by reflecting the past feedback.

[0083] The selection unit can estimate the user's emotion and adjust template selection criteria based on the estimated user's emotion. For example, if the user is relaxed, the selection unit selects a template containing detailed information. For example, the selection unit prioritizes displaying templates containing detailed information. Furthermore, if the user is in a hurry, the selection unit can select a concise and to-the-point template. For example, the selection unit prioritizes displaying templates containing detailed information. Furthermore, if the user is stressed, the selection unit can select a visually simple and easy-to-understand template. For example, the selection unit prioritizes displaying templates containing simple and easy-to-understand information. In this way, the template selection criteria can be adjusted according to the user's emotion, thereby selecting an optimal template. 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.

[0084] The selection unit can analyze past business negotiation data and suggest the optimal template. The selection unit can, for example, suggest the optimal template based on data of business negotiations that the user has previously succeeded. For example, the selection unit can analyze past business negotiation data and suggest a template that was used in a successful business negotiation. The selection unit can also suggest a template that is suitable for a specific business negotiation purpose based on the user's past business negotiation data. For example, the selection unit can suggest a template that is suitable for a specific business negotiation purpose based on the past business negotiation data. The selection unit can also analyze the user's past business negotiation data and suggest the most effective template. For example, the selection unit can suggest the most effective template based on the past business negotiation data. In this way, the optimal template can be suggested by utilizing past business negotiation data.

[0085] When selecting a template, the selection unit can apply different selection algorithms depending on the purpose of the business negotiation and the content of the proposal. For example, in a business negotiation to introduce a new product, the selection unit applies an algorithm to select a template that emphasizes the features and advantages of the new product. For example, a template that emphasizes the features and advantages of the new product is selected. In addition, in a business negotiation to negotiate price, the selection unit can also apply an algorithm to select a template that emphasizes price information and cost reduction effects. For example, a template that emphasizes price information and cost reduction effects is selected. In addition, in the introduction of implementation cases, the selection unit can also apply an algorithm to select a template that emphasizes success stories and customer testimonials. For example, a template that emphasizes success stories and customer testimonials is selected. This makes it possible to select the optimal template depending on the purpose of the business negotiation and the content of the proposal.

[0086] When selecting a template, the selection unit can improve the accuracy of the selection by referring to the user's past selection history. The selection unit, for example, suggests an optimal template based on templates selected by the user in the past. For example, it suggests an optimal template based on the past selection history. The selection unit can also suggest a template suitable for a specific business negotiation purpose based on the user's past selection history. For example, it suggests a template suitable for a specific business negotiation purpose based on the past selection history. The selection unit can also analyze the user's past selection history and suggest the most effective template. For example, it suggests the most effective template based on the past selection history. In this way, the accuracy of selection can be improved by utilizing the past selection history.

[0087] The selection unit can estimate the user's emotion and adjust the display order of the templates based on the estimated user's emotion. For example, when the user is relaxed, the selection unit can prioritize displaying templates containing detailed information. For example, the selection unit can display templates containing detailed information first. Furthermore, when the user is in a hurry, the selection unit can prioritize displaying templates that are concise and to the point. For example, the selection unit can display templates that are concise and to the point first. Furthermore, when the user is stressed, the selection unit can prioritize displaying templates that are visually simple and easy to understand. For example, the selection unit can display templates that are visually simple and easy to understand first. In this way, by adjusting the display order of the templates according to the user's emotion, the optimal template can be quickly selected. 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.

[0088] When selecting a template, the selection unit can determine the priority of templates based on the time of submission of the business negotiation. For example, if the deadline for submitting the business negotiation is approaching, the selection unit can prioritize displaying templates that can be created quickly. For example, a simple template that can be created quickly can be displayed first. The selection unit can also prioritize displaying templates that include detailed information if the deadline for submitting the business negotiation is far away. For example, a template that includes detailed information can be displayed first. The selection unit can also suggest the most appropriate template depending on the time of submission of the business negotiation. For example, a template that can be created quickly can be displayed if the deadline is near, and a template that includes detailed information can be displayed if the deadline is far away. This makes it possible to select the most appropriate template depending on the time of submission of the business negotiation.

[0089] When selecting a template, the selection unit can adjust the order of templates based on the relevance to the business negotiation. For example, the selection unit can prioritize displaying templates that are most relevant to the purpose of the business negotiation. For example, templates related to the purpose of the business negotiation can be displayed first. The selection unit can also prioritize displaying templates that are most relevant to the content of a proposal. For example, templates related to the content of the proposal can be displayed first. The selection unit can also suggest the most appropriate template based on the relevance to the business negotiation. For example, the selection unit can suggest the most appropriate template based on the purpose of the business negotiation or the content of the proposal. This makes it possible to select the most appropriate template based on the relevance to the business negotiation.

[0090] When selecting a template, the selection unit can adjust the content of the template according to the user's level of expertise. For example, if the user has specialized knowledge, the selection unit can suggest a template that includes detailed technical information. For example, templates that include technical details can be preferentially displayed. Furthermore, if the user does not have specialized knowledge, the selection unit can also suggest a template that includes easy-to-understand explanations. For example, easy-to-understand templates that avoid technical jargon can be preferentially displayed. Furthermore, the selection unit can suggest the optimal template according to the user's level of expertise. For example, the selection unit can evaluate the user's level of expertise based on survey results and past business negotiation history and suggest the optimal template. This makes it possible to select the optimal template according to the user's level of expertise.

[0091] The generation unit can estimate the user's emotions and adjust the expression method of the generated materials based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates materials containing detailed information. For example, it generates slides containing detailed information. Furthermore, if the user is in a hurry, the generation unit can generate concise and to-the-point materials. For example, it generates concise and to-the-point slides. Furthermore, if the user is feeling stressed, the generation unit can generate visually simple and easy-to-understand materials. For example, it generates visually simple and easy-to-understand slides. This makes it possible to generate optimal materials according to the user's emotions. 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.

[0092] When generating materials, the generation unit can adjust the level of detail of the generated materials based on the importance of the business negotiation. For example, in the case of an important business negotiation, the generation unit generates materials containing detailed information. For example, slides containing detailed information are generated. The generation unit can also generate materials that are concise and to the point for a less important business negotiation. For example, slides that are concise and to the point are generated. The generation unit can also generate materials with an optimal level of detail depending on the importance of the business negotiation. For example, materials with an optimal level of detail are generated based on the size and impact of the business negotiation. This makes it possible to generate materials with an optimal level of detail depending on the importance of the business negotiation.

[0093] When generating materials, the generation unit can apply different generation algorithms depending on the category of the business negotiation. For example, in a business negotiation to introduce a new product, the generation unit applies a generation algorithm that emphasizes the features and advantages of the new product. For example, it generates slides that emphasize the features and advantages of the new product. In addition, in a business negotiation to negotiate price, the generation unit can also apply a generation algorithm that emphasizes price information and cost reduction effects. For example, it generates slides that emphasize price information and cost reduction effects. In addition, in the introduction of implementation cases, the generation unit can also apply a generation algorithm that emphasizes success stories and customer testimonials. For example, it generates slides that emphasize success stories and customer testimonials. This makes it possible to generate optimal materials depending on the category of the business negotiation.

[0094] When generating materials, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, applies an optimal generation algorithm based on materials generated by the user in the past. For example, it applies an optimal generation algorithm based on past generation results. The generation unit can also suggest a generation method suitable for a specific business negotiation purpose based on the user's past generation results. For example, it suggests a generation method suitable for a specific business negotiation purpose based on past generation results. The generation unit can also analyze the user's past generation results and suggest the most effective generation method. For example, it suggests the most effective generation method based on past generation results. In this way, the accuracy of generation can be improved by utilizing past generation results.

[0095] The generation unit can estimate the user's emotions and adjust the length of the materials to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates longer materials containing detailed information. For example, it generates longer slides containing detailed information. Furthermore, if the user is in a hurry, the generation unit can generate shorter materials that are concise and to the point. For example, it generates shorter slides that are concise and to the point. Furthermore, if the user is feeling stressed, the generation unit can generate visually simple and easy-to-understand materials. For example, it generates visually simple and easy-to-understand short slides. This makes it possible to generate materials of an optimal length according to the user's emotions. 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.

[0096] When generating materials, the generation unit can determine the priority of generation based on the submission date of the business negotiation. For example, when the submission deadline of the business negotiation is approaching, the generation unit prioritizes generating materials that can be created quickly. For example, it generates concise materials that can be created quickly first. The generation unit can also prioritize generating materials that include detailed information when the submission deadline of the business negotiation is far away. For example, it generates materials that include detailed information first. The generation unit can also generate optimal materials depending on the submission date of the business negotiation. For example, it generates materials that can be created quickly when the submission deadline is near, and generates materials that include detailed information when the submission deadline is far away. This makes it possible to generate optimal materials depending on the submission date of the business negotiation.

[0097] When generating materials, the generation unit can adjust the order of generation based on the relevance of the business negotiation. For example, the generation unit prioritizes generating information that is most relevant to the purpose of the business negotiation. For example, information related to the purpose of the business negotiation is generated first. The generation unit can also prioritize generating information that is most relevant to the content of the proposal. For example, information related to the content of the proposal is generated first. The generation unit can also generate optimal information based on the relevance of the business negotiation. For example, optimal information is generated based on the purpose of the business negotiation and the content of the proposal. This makes it possible to generate optimal information based on the relevance of the business negotiation.

[0098] When generating materials, the generation unit can adjust the use of technical terminology in the materials to be generated according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates materials containing detailed technical information. For example, it generates materials containing technical details. Furthermore, if the user does not have technical expertise, the generation unit can also generate materials containing easy-to-understand explanations. For example, it generates easy-to-understand materials that avoid technical terminology. Furthermore, the generation unit can generate optimal materials according to the user's level of expertise. For example, it evaluates the user's level of expertise based on survey results and past business negotiation history and generates optimal materials. This makes it possible to generate optimal materials according to the user's level of expertise.

[0099] The correction unit can estimate the user's emotions and adjust the display method of the correction interface based on the estimated user emotions. For example, when the user is feeling stressed, the correction unit provides a simple interface and minimizes the correction procedure. For example, it reduces the number of input items to allow corrections with simple operations. Furthermore, when the user is relaxed, the correction unit can provide detailed correction options and suggest customizable correction methods. For example, it increases the number of input items to allow detailed corrections. Furthermore, when the user is in a hurry, the correction unit can prioritize voice input and allow corrections to be entered quickly. For example, it can use voice recognition technology to automatically convert corrections entered by voice into text. This makes it possible to provide an optimal correction interface according to the user's emotions. Emotion estimation is realized 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.

[0100] When making corrections, the correction unit can analyze past correction data and propose the optimal correction method. The correction unit can, for example, propose the optimal correction method based on corrections made by the user in the past. For example, the correction unit proposes the optimal correction method based on the past correction data. The correction unit can also propose a correction method suitable for a specific business negotiation purpose based on the user's past correction data. For example, the correction unit proposes a correction method suitable for a specific business negotiation purpose based on the past correction data. The correction unit can also analyze the user's past correction data and propose the most effective correction method. For example, the correction unit proposes the most effective correction method based on the past correction data. In this way, the optimal correction method can be proposed by utilizing the past correction data.

[0101] When making corrections, the correction unit can automatically complete the correction content based on the user's work situation and past business negotiation history. The correction unit automatically completes the correction content based on, for example, the history of business negotiations conducted by the user in the past. For example, the correction content can be automatically displayed based on the past business negotiation history. The correction unit can also automatically complete related correction content based on the user's work situation (for example, the current project). For example, the correction content related to the current project can be automatically displayed. The correction unit can also automatically complete similar correction information based on the purpose of business negotiations and proposal content previously entered by the user. For example, similar correction information can be automatically displayed based on the past business negotiation history. In this way, the effort required for corrections can be reduced by utilizing the user's work situation and past business negotiation history.

[0102] The correction unit can correct information using voice input or image recognition when making corrections. For example, the correction unit allows the user to simply input the corrections by voice, and the system automatically corrects the information. For example, using voice recognition technology, the corrections input by voice can be automatically converted into text. The correction unit can also allow the user to upload an image (for example, a screenshot showing the areas to be corrected), and the system can correct the information using image recognition. For example, an image analysis algorithm can be used to extract the corrections from the image. The correction unit can also combine voice input and image recognition to allow the user to input the corrections more intuitively. For example, the user can input the corrections by voice and indicate the areas to be corrected in an image, and the system automatically corrects the information. In this way, information can be corrected intuitively using voice input or image recognition.

[0103] The correction unit can estimate the user's emotions and prioritize corrections based on the estimated user emotions. For example, if the user is feeling stressed, the correction unit can prioritize input of important corrections. For example, the input interface can display important corrections first and prompt the user to enter them. The correction unit can also prioritize input of detailed corrections if the user is relaxed. For example, the correction unit can provide detailed input options and suggest customizable correction methods. The correction unit can also prioritize input of minimal corrections if the user is in a hurry. For example, voice input can be prioritized to allow the user to quickly enter corrections. This allows efficient corrections to be achieved by prioritizing corrections according to the user's emotions. The emotion estimation is achieved 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.

[0104] When making corrections, the correction unit can prioritize correcting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific region, the correction unit prioritizes correcting business negotiation information related to that region. For example, business negotiation information related to that region is automatically displayed based on GPS data. Furthermore, when the user is conducting business negotiations in a specific region, the correction unit can also prioritize correcting market information and competitive information for that region. For example, market trends for that region and information about competitors are automatically displayed. Furthermore, when the user is in a specific region, the correction unit can also prioritize correcting related information based on the business negotiation history for that region. For example, information related to that region is automatically displayed based on past business negotiation history. In this way, highly relevant information can be efficiently corrected by taking into account the geographical location information.

[0105] When making corrections, the correction unit can analyze the user's social media activity and automatically correct related information. For example, the correction unit automatically corrects business negotiation information based on the location where the user checked in on social media. For example, the correction unit analyzes social media check-in data and automatically displays related business negotiation information. The correction unit can also analyze the content of the user's posts on social media and automatically correct the purpose of the related business negotiation and proposal content. For example, the correction unit analyzes the content of the posts and automatically displays the purpose of the business negotiation and proposal content. The correction unit can also automatically correct related business negotiation information by referring to the activities of the user's friends on social media. For example, the correction unit analyzes the activity data of friends and automatically displays related business negotiation information. This makes it possible to efficiently correct related information by utilizing social media activity.

[0106] The correction unit can customize the correction method by reflecting the user's past feedback when correcting. The correction unit, for example, customizes the correction interface based on feedback provided by the user in the past. For example, the correction unit analyzes the past feedback and provides the user with an optimal correction interface. The correction unit can also suggest an optimal correction method (voice, text, etc.) based on the user's past feedback. For example, the correction unit can suggest voice input or text input based on the past feedback. The correction unit can also optimize the correction procedure based on the user's past feedback and provide an efficient correction method. For example, the correction procedure can be simplified based on the past feedback and an efficient correction method can be provided. In this way, the optimal correction method can be provided to the user by reflecting the past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, selection unit, generation unit, and correction unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs basic information such as the name of the store, the purpose of the business negotiation, and details of the products and services to be proposed. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information and selects an appropriate template. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates business negotiation materials based on the selected template. The correction unit is realized, for example, by the control unit 46A of the smart device 14 and previews the generated materials, allowing corrections and additional information to be added using drag and drop or text editing functions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, selection unit, generation unit, and correction unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs basic information such as the name of the store, the purpose of the business negotiation, and details of the products and services to be proposed. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information and selects an appropriate template. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates business negotiation materials based on the selected template. The correction unit is realized, for example, by the control unit 46A of the smart glasses 214 and previews the generated materials, allowing corrections and additional information to be added using drag and drop or text editing functions. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, selection unit, generation unit, and correction unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314 and inputs basic information such as the name of the store, the purpose of the business negotiation, and details of the products and services to be proposed. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information and selects an appropriate template. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates business negotiation materials based on the selected template. The correction unit is realized, for example, by the control unit 46A of the headset terminal 314 and previews the generated materials, allowing corrections and additional information to be added using drag and drop or text editing functions. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, selection unit, generation unit, and correction unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs basic information such as the name of the store, the purpose of the business negotiation, and details of the products and services to be proposed. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information and selects an appropriate template. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates business negotiation materials based on the selected template. The correction unit is realized, for example, by the control unit 46A of the robot 414 and previews the generated materials, allowing corrections and additional information to be added using drag and drop or text editing functions.

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

[0108] The reception unit can automatically complete input content based on the user's past input history. For example, it can automatically display the name of a store or the purpose of a business deal that the user has previously entered as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. For example, it can suggest voice input or text input based on the user's past input history. This makes it possible to provide an efficient input method by utilizing past input data.

[0109] The selection unit can estimate the user's emotion and adjust the template selection criteria based on the estimated user's emotion. For example, if the user is relaxed, a template containing detailed information can be selected. If the user is in a hurry, a concise template that gets straight to the point can be selected. Furthermore, if the user is stressed, a visually simple and easy-to-understand template can be selected. In this way, the template selection criteria can be adjusted according to the user's emotion, allowing the optimal template to be selected.

[0110] When generating materials, the generation unit can adjust the use of technical terms in the materials to be generated according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit can generate materials containing detailed technical information. Also, if the user does not have specialized knowledge, the generation unit can generate materials containing easy-to-understand explanations. Furthermore, the generation unit can generate optimal materials according to the user's level of expertise. This makes it possible to generate optimal materials according to the user's level of expertise.

[0111] The correction unit can estimate the user's emotions and adjust the display method of the correction interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the correction steps. Alternatively, if the user is relaxed, detailed correction options can be provided and a customizable correction method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly enter corrections. In this way, it is possible to provide an optimal correction interface according to the user's emotions.

[0112] The reception unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, information on operated stores related to that area can be acquired preferentially. Also, if the user is negotiating in a specific area, market information and competitive information for that area can be acquired preferentially. Furthermore, if the user is in a specific area, related information can be acquired preferentially based on the business negotiation history for that area. In this way, highly relevant information can be acquired efficiently by taking into account the geographical location information.

[0113] When selecting a template, the selection unit can determine the priority of templates based on the time of submission of the business negotiation. For example, if the deadline for submitting the business negotiation is approaching, templates that can be created quickly can be displayed preferentially. Also, if the deadline for submitting the business negotiation is far away, templates that include detailed information can be displayed preferentially. Furthermore, the selection unit can suggest the most suitable template depending on the time of submission of the business negotiation. This makes it possible to select the most suitable template depending on the time of submission of the business negotiation.

[0114] The generation unit can estimate the user's emotions and adjust the presentation method of the generated materials based on the estimated user's emotions. For example, if the user is relaxed, materials containing detailed information can be generated. If the user is in a hurry, concise materials that focus on the main points can be generated. Furthermore, if the user is stressed, visually simple and easy-to-understand materials can be generated. In this way, optimal materials can be generated according to the user's emotions.

[0115] The correction unit can correct information using voice input or image recognition when making corrections. For example, the user can simply input the corrections by voice, and the system will automatically correct the information. Alternatively, the user can upload an image (for example, a screenshot showing the corrections) and the system can correct the information using image recognition. Furthermore, the user can combine voice input and image recognition to input the corrections more intuitively. This allows for intuitive correction of information using voice input and image recognition.

[0116] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow basic information to be entered quickly. In this way, input stress can be reduced by providing an input interface that corresponds to the user's emotions.

[0117] When selecting a template, the selection unit can improve the accuracy of the selection by referring to the user's past selection history. For example, the selection unit can suggest the most suitable template based on templates previously selected by the user. The selection unit can also suggest a template suitable for a specific business negotiation purpose based on the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history and suggest the most effective template. In this way, the selection accuracy can be improved by utilizing the past selection history.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit inputs basic information about the business negotiation. The basic information about the business negotiation includes the name of the operating store, the purpose of the business negotiation, details of the products and services to be proposed, etc. For example, the reception unit inputs the name of the operating store, the purpose of the business negotiation, and details of the products and services to be proposed. Step 2: The selection unit analyzes the information input by the reception unit and selects a template. The template includes slide configurations and designs that correspond to the purpose of the business negotiation and the content of the proposal. The selection unit can also select a template that corresponds to the purpose of the business negotiation and the content of the proposal, and analyze the input information using natural language processing technology, for example. Step 3: The generator automatically generates sales negotiation materials based on the template selected by the selector. The sales negotiation materials include the features and advantages of the proposed product, pricing information, and case studies. For example, the features and advantages of the proposed product, pricing information, and case studies are reflected in the slides. Step 4: The editing unit previews the data generated by the generating unit and adds corrections and additional information. The editing unit provides, for example, a drag-and-drop function or a text editing function, and can preview the generated data and add corrections and additional information as needed.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0121] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0137] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0170] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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. A reception section where basic information about business negotiations is entered; a selection unit that analyzes the information input by the reception unit and selects a template; a generation unit that automatically generates negotiation materials based on the template selected by the selection unit; a correction unit that previews the material generated by the generation unit and adds corrections and additional information. A system characterized by:

2. The selection unit Select a template based on the purpose of the negotiation and the proposal 2. The system of claim 1.

3. The generation unit Automatically fill in the content of each slide based on the information entered 2. The system of claim 1.

4. The correction unit Provides drag-and-drop and text editing functionality 2. The system of claim 1.

5. The generation unit Generate materials based on pre-defined quality standards 2. The system of claim 1.

6. The reception unit Enter the basic information of the store name, purpose of the business, and details of the products and services you are proposing.

2. The system of claim 1.

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

2. The system of claim 1.

8. The reception unit Analyzes past input data and suggests optimal input methods 2. The system of claim 1.

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