System

Abstract

An object of the system according to the embodiment is to efficiently generate a proposal, an advertisement, and a presentation material and to support sales activities.SOLUTION: A system according to an embodiment includes a proposal document generation unit, a data plan privilege unit, an advertisement generation unit, and a presentation material generation unit. The proposal document generation unit generates a proposal document. The data plan benefits section incorporates data plans or benefits into the proposal generated by the proposal generation section. The advertisement generation unit generates an advertisement. The presentation material generation unit generates a presentation material.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] With conventional technology, creating proposals, advertisements, and presentation materials required a lot of time and effort, making it difficult to conduct efficient sales activities.

[0005] The system according to the embodiment aims to efficiently generate proposals, advertisements, and presentation materials and to support sales activities. [Means for solving the problem]

[0006] The system according to the embodiment includes a proposal generation unit, a data plan benefit unit, an advertisement generation unit, and a presentation material generation unit. The proposal generation unit generates a proposal. The data plan benefit unit incorporates a data plan or benefit into the proposal generated by the proposal generation unit. The advertisement generation unit generates an advertisement. The presentation material generation unit generates presentation materials. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate proposals, advertisements, and presentation materials, and can support 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) The sales integration solution according to an embodiment of the present invention is a system that utilizes AI technology to enhance and streamline sales activities. This system leverages carriers' strengths to automatically create proposals and provide data plan benefits, strengthen targeting and advertising strategies by integrating AI with LINE, provide advanced proposals using generative AI, and create presentation materials in real time. This enables the sales integration solution to enhance and streamline sales activities and strengthen relationships between companies and their customers.

[0029] The sales integrated solution according to the embodiment includes a proposal generation unit, a data plan benefit unit, an advertisement generation unit, and a presentation material generation unit. The proposal generation unit generates a proposal. For example, the generation AI receives as input a prompt containing instructions from a user and generates a proposal based on the prompt. The data plan benefit unit incorporates a data plan and benefits into the proposal generated by the proposal generation unit. For example, the data plan and benefits unit generates a proposal that automatically incorporates data plans and benefits provided by a carrier. The advertisement generation unit generates an advertisement. For example, the generation AI analyzes user behavioral data and interests and generates advertisement content based on the analysis. The presentation material generation unit generates presentation materials. For example, the generation AI receives as input a prompt containing instructions from a user and generates presentation materials based on the prompt. This enables the sales integrated solution to enhance and streamline sales activities and strengthen relationships between companies and customers.

[0030] The proposal generation unit can simulate the effects of data plans and benefits included in the proposal and automatically select the optimal plan. The proposal generation unit, for example, uses generation AI to simulate the effects of data plans and benefits included in the proposal and automatically select the optimal plan. For example, it predicts the effects of each plan based on past data and incorporates the most effective plan into the proposal. In addition, when creating a proposal, the generation AI simulates data plans and benefits and automatically selects the plan that best suits the customer's needs. For example, it analyzes the customer's usage patterns and proposes the optimal plan based on that. In addition, the generation AI simulates the effects of data plans and benefits included in the proposal in real time and automatically selects the optimal plan. For example, it reflects the simulation results while creating the proposal. This makes it possible to maximize the effectiveness of the proposal.

[0031] The proposal generation unit can refer to past success stories and failure stories to generate optimal proposal content. For example, when creating a proposal, the generation AI of the proposal generation unit refers to past success stories and failure stories to generate optimal proposal content. For example, it automatically generates proposals with a high probability of success based on past data. In addition, the generation AI is used to analyze past success stories and failure stories when creating a proposal, and generates optimal proposal content based on that. For example, it analyzes factors for success and failure and reflects them in the proposal. In addition, when creating a proposal, the generation AI refers to past success stories and failure stories in real time to generate optimal proposal content. For example, it reflects past cases while creating a proposal. This increases the probability of the proposal's success.

[0032] The proposal generation unit can incorporate videos and interactive content into proposals to create visually appealing proposals. The proposal generation unit, for example, incorporates videos and interactive content into proposals to create visually appealing proposals. For example, product demo videos and interactive graphs are added to proposals. Furthermore, generative AI is used to automatically generate videos and interactive content into proposals to create visually appealing proposals. For example, content based on customer interests is incorporated into proposals. Furthermore, videos and interactive content are incorporated into proposals to create visually appealing proposals in real time. For example, content is automatically added while the proposal is being created. This makes it possible to create visually appealing proposals.

[0033] The proposal generation unit can combine data plans and benefits from different industries to generate proposals that meet new market needs. The proposal generation unit, for example, combines data plans and benefits from different industries to generate proposals that meet new market needs. For example, a proposal can be made that combines benefits from the communications industry and the entertainment industry. The generation AI can also be used to analyze data plans and benefits from different industries to generate proposals that meet new market needs. For example, a proposal can be made that references success stories from other industries. The proposal can also be generated in real time by combining data plans and benefits from different industries to meet new market needs. For example, data from other industries can be reflected while creating the proposal. This makes it possible to create proposals that meet new market needs.

[0034] The advertisement generation unit can analyze Line user data and generate targeted advertisements based on the user's life events. For example, the advertisement generation unit analyzes Line user data and generates targeted advertisements based on the user's life events (marriage, moving, etc.). For example, it delivers advertisements for wedding venues to users who are planning to get married. It also uses generation AI to analyze Line user data and automatically generate targeted advertisements based on the user's life events. For example, it delivers advertisements for moving services to users who are planning to move. It also analyzes Line user data in real time and generates targeted advertisements based on the user's life events. For example, it instantly delivers advertisements that correspond to the user's life events. This makes it possible to generate targeted advertisements that correspond to the user's life events.

[0035] The advertisement generation unit can analyze user responses after advertisement delivery in real time and dynamically optimize advertisement content. The advertisement generation unit, for example, analyzes user responses after advertisement delivery in real time and dynamically optimizes advertisement content. For example, it adjusts advertisement content based on click-through rates and engagement rates. It also uses generation AI to analyze user responses after advertisement delivery in real time and automatically optimize advertisement content. For example, it changes advertisement wording and images based on user response data. It also builds a system that analyzes user responses after advertisement delivery in real time and dynamically optimizes advertisement content. For example, it reflects user responses during advertisement delivery. This maximizes the effectiveness of advertisements.

[0036] The ad generation unit can also work with social media platforms other than Line to realize cross-platform targeted advertising. The ad generation unit can also work with social media platforms other than Line to realize cross-platform targeted advertising. For example, it can work with Facebook and Instagram to deliver ads. It can also use generation AI to analyze data from social media platforms other than Line and automatically generate cross-platform targeted ads. For example, it can deliver consistent ads across multiple platforms. It can also work with social media platforms other than Line to generate cross-platform targeted ads in real time. For example, it can reflect data from each platform while delivering ads. This makes it possible to deliver consistent ads across multiple platforms.

[0037] The advertisement generation unit can incorporate user-generated content into advertisement content to increase user engagement. The advertisement generation unit, for example, incorporates user-generated content into advertisement content to increase user engagement. For example, by incorporating user posts and reviews into advertisements. Furthermore, by using generation AI, user-generated content is automatically generated in advertisement content to increase user engagement. For example, user photos and videos are used in advertisements. Furthermore, a system is constructed that incorporates user-generated content into advertisement content to increase user engagement. For example, user content is reflected during advertisement delivery. This makes it possible to create advertisements that increase user engagement.

[0038] The presentation material generation unit can incorporate market data and competitive information that are updated in real time into presentation materials to provide the latest information. The presentation material generation unit, for example, incorporates market data and competitive information that are updated in real time into presentation materials to provide the latest information. For example, the latest market trends and information on competitors are reflected in the presentation materials. In addition, using a generation AI, market data and competitive information that are updated in real time into presentation materials are automatically generated to provide the latest information. For example, market data is updated during the presentation. In addition, a system is built that incorporates market data and competitive information that are updated in real time into presentation materials to provide the latest information. For example, market data is reflected while the presentation materials are being created. This makes it possible to provide presentation materials that provide the latest information.

[0039] The presentation material generation unit can analyze customer reactions during a presentation and instantly optimize the content of the materials. The presentation material generation unit, for example, analyzes customer reactions during a presentation and instantly optimizes the content of the materials. For example, it adjusts the presentation materials based on the customer's facial expressions and comments. It also uses generation AI to analyze customer reactions in real time during a presentation and automatically optimize the content of the materials. For example, it changes the presentation materials based on customer reaction data. It also builds a system that analyzes customer reactions during a presentation and instantly optimizes the content of the materials. For example, it reflects customer reactions during the presentation. This makes it possible to create optimal presentation materials based on customer reactions.

[0040] The presentation material generation unit can incorporate interactive elements into presentation materials to encourage customer participation. The presentation material generation unit, for example, incorporates interactive elements (such as quizzes or surveys) into presentation materials to encourage customer participation. For example, a quiz can be given during the presentation to attract customer attention. In addition, the generation AI can be used to automatically generate interactive elements into presentation materials to encourage customer participation. For example, a survey can be incorporated into the presentation materials. In addition, a system can be built that incorporates interactive elements into presentation materials to encourage customer participation. For example, a quiz or survey can be conducted during the presentation. This makes it possible to create presentation materials that encourage customer participation.

[0041] The presentation material generation unit automatically generates presentation materials in different languages, making it possible to accommodate international customers. The presentation material generation unit, for example, automatically generates presentation materials in different languages ​​to accommodate international customers. For example, it creates presentation materials in multiple languages, such as English and Chinese. It also uses generation AI to automatically generate presentation materials in different languages ​​to accommodate international customers. For example, it makes presentation materials multilingual. It also builds a system that automatically generates presentation materials in different languages ​​to accommodate international customers. For example, it reflects multilingual support while creating presentation materials. This makes it possible to create presentation materials that are suitable for international customers.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The proposal generation unit can simulate the effects of data plans and benefits included in the proposal and automatically select the optimal plan. For example, it can predict the effects of each plan based on past data and incorporate the most effective plan into the proposal. In addition, when creating a proposal, the generation AI simulates data plans and benefits and automatically selects the plan that best suits the customer's needs. For example, it can analyze the customer's usage patterns and propose the optimal plan based on that. In addition, the generation AI can simulate the effects of data plans and benefits included in the proposal in real time and automatically select the optimal plan. For example, it can reflect the simulation results while creating the proposal. This maximizes the effectiveness of the proposal.

[0044] The proposal generation unit can refer to past success stories and failure stories to generate optimal proposal content. For example, when creating a proposal, the generation AI refers to past success stories and failure stories to generate optimal proposal content. For example, proposals with a high probability of success are automatically generated based on past data. In addition, the generation AI is used to analyze past success stories and failure stories when creating a proposal, and generate optimal proposal content based on that. For example, factors for success and failure are analyzed and reflected in the proposal. In addition, when creating a proposal, the generation AI refers to past success stories and failure stories in real time to generate optimal proposal content. For example, past cases are reflected while creating the proposal. This increases the probability of the proposal's success.

[0045] The proposal generation unit can incorporate videos and interactive content into proposals to create visually appealing proposals. For example, videos and interactive content can be incorporated into proposals to create visually appealing proposals. For example, product demo videos and interactive graphs can be added to proposals. In addition, generative AI can be used to automatically generate videos and interactive content into proposals to create visually appealing proposals. For example, content based on customer interests can be incorporated into proposals. In addition, videos and interactive content can be incorporated into proposals to create visually appealing proposals in real time. For example, content can be automatically added while the proposal is being created. This makes it possible to create visually appealing proposals.

[0046] The proposal generation unit can combine data plans and benefits from different industries to generate proposals that meet new market needs. For example, it can combine data plans and benefits from different industries to generate proposals that meet new market needs. For example, it can make a proposal that combines benefits from the telecommunications and entertainment industries. It can also use generation AI to analyze data plans and benefits from different industries to generate proposals that meet new market needs. For example, it can make proposals that refer to successful cases from other industries. It can also combine data plans and benefits from different industries to generate proposals in real time that meet new market needs. For example, it can reflect data from other industries while creating a proposal. This makes it possible to create proposals that meet new market needs.

[0047] The advertisement generation unit can analyze Line user data and generate targeted advertisements based on the user's life events. For example, it analyzes Line user data and generates targeted advertisements based on the user's life events (marriage, moving, etc.). For example, it delivers advertisements for wedding venues to users who are planning to get married. It also uses generation AI to analyze Line user data and automatically generate targeted advertisements based on the user's life events. For example, it delivers advertisements for moving services to users who are planning to move. It also analyzes Line user data in real time and generates targeted advertisements based on the user's life events. For example, it delivers advertisements that correspond to the user's life events instantly. This makes it possible to target advertisements that correspond to the user's life events.

[0048] The ad generation unit can analyze user responses after ad delivery in real time and dynamically optimize ad content. For example, it analyzes user responses after ad delivery in real time and dynamically optimizes ad content. For example, it adjusts ad content based on click-through rates and engagement rates. It also uses generation AI to analyze user responses after ad delivery in real time and automatically optimize ad content. For example, it changes ad wording and images based on user response data. It also builds a system that analyzes user responses after ad delivery in real time and dynamically optimizes ad content. For example, it reflects user responses while ad delivery is in progress. This maximizes the effectiveness of advertising.

[0049] The ad generation unit can also work with social media platforms other than Line to realize cross-platform targeted advertising. For example, it can work with social media platforms other than Line to realize cross-platform targeted advertising. For example, it can work with Facebook and Instagram to deliver ads. It can also use generation AI to analyze data from social media platforms other than Line and automatically generate cross-platform targeted advertising. For example, it can deliver consistent ads across multiple platforms. It can also work with social media platforms other than Line to generate cross-platform targeted advertising in real time. For example, it can reflect data from each platform while delivering ads. This makes it possible to deliver consistent ads across multiple platforms.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The proposal generator generates a proposal. For example, the generation AI receives a prompt containing instructions from a user as input and generates a proposal based on the prompt. Step 2: The data plan benefits unit incorporates the data plans and benefits into the proposal generated by the proposal generation unit, for example, generating a proposal that automatically incorporates the data plans and benefits offered by the carrier. Step 3: The ad generation unit generates the ad. For example, the generation AI analyzes the user's behavioral data and interests and generates the ad content based on that. Step 4: The presentation material generation unit generates presentation materials. For example, the generation AI receives prompts containing instructions from the user as input and generates presentation materials based on the prompts.

[0052] (Example 2) The sales integration solution according to an embodiment of the present invention is a system that utilizes AI technology to enhance and streamline sales activities. This system leverages carriers' strengths to automatically create proposals and provide data plan benefits, strengthen targeting and advertising strategies by integrating AI with LINE, provide advanced proposals using generative AI, and create presentation materials in real time. This enables the sales integration solution to enhance and streamline sales activities and strengthen relationships between companies and their customers.

[0053] The sales integrated solution according to the embodiment includes a proposal generation unit, a data plan benefit unit, an advertisement generation unit, and a presentation material generation unit. The proposal generation unit generates a proposal. For example, the generation AI receives as input a prompt containing instructions from a user and generates a proposal based on the prompt. The data plan benefit unit incorporates a data plan and benefits into the proposal generated by the proposal generation unit. For example, the data plan and benefits unit generates a proposal that automatically incorporates data plans and benefits provided by a carrier. The advertisement generation unit generates an advertisement. For example, the generation AI analyzes user behavioral data and interests and generates advertisement content based on the analysis. The presentation material generation unit generates presentation materials. For example, the generation AI receives as input a prompt containing instructions from a user and generates presentation materials based on the prompt. This enables the sales integrated solution to enhance and streamline sales activities and strengthen relationships between companies and customers.

[0054] The proposal generation unit can simulate the effects of data plans and benefits included in the proposal and automatically select the optimal plan. The proposal generation unit, for example, uses generation AI to simulate the effects of data plans and benefits included in the proposal and automatically select the optimal plan. For example, it predicts the effects of each plan based on past data and incorporates the most effective plan into the proposal. In addition, when creating a proposal, the generation AI simulates data plans and benefits and automatically selects the plan that best suits the customer's needs. For example, it analyzes the customer's usage patterns and proposes the optimal plan based on that. In addition, the generation AI simulates the effects of data plans and benefits included in the proposal in real time and automatically selects the optimal plan. For example, it reflects the simulation results while creating the proposal. This makes it possible to maximize the effectiveness of the proposal.

[0055] The proposal generation unit can refer to past success stories and failure stories to generate optimal proposal content. For example, when creating a proposal, the generation AI of the proposal generation unit refers to past success stories and failure stories to generate optimal proposal content. For example, it automatically generates proposals with a high probability of success based on past data. In addition, the generation AI is used to analyze past success stories and failure stories when creating a proposal, and generates optimal proposal content based on that. For example, it analyzes factors for success and failure and reflects them in the proposal. In addition, when creating a proposal, the generation AI refers to past success stories and failure stories in real time to generate optimal proposal content. For example, it reflects past cases while creating a proposal. This increases the probability of the proposal's success.

[0056] The proposal generation unit can use the emotion estimation function to evaluate the emotional impact that the proposal content has on the customer and generate a proposal that will elicit a positive response. The proposal generation unit, for example, uses the emotion estimation function to evaluate the emotional impact that the proposal content has on the customer and generate a proposal that will elicit a positive response. For example, it adjusts the wording and structure of the proposal. The generation AI also uses the emotion estimation function to evaluate the emotional impact that the proposal content has on the customer in real time and generate a proposal that will elicit a positive response. For example, it reflects the emotional evaluation while creating the proposal. The emotion estimation function also analyzes the emotional impact that the proposal content has on the customer and generates a proposal that will elicit a positive response. For example, it adjusts the proposal content based on the customer's emotion data. This makes it possible to make proposals that take the customer's emotions into consideration.

[0057] The proposal generation unit can incorporate videos and interactive content into proposals to create visually appealing proposals. The proposal generation unit, for example, incorporates videos and interactive content into proposals to create visually appealing proposals. For example, product demo videos and interactive graphs are added to proposals. Furthermore, generative AI is used to automatically generate videos and interactive content into proposals to create visually appealing proposals. For example, content based on customer interests is incorporated into proposals. Furthermore, videos and interactive content are incorporated into proposals to create visually appealing proposals in real time. For example, content is automatically added while the proposal is being created. This makes it possible to create visually appealing proposals.

[0058] The proposal generation unit can combine data plans and benefits from different industries to generate proposals that meet new market needs. The proposal generation unit, for example, combines data plans and benefits from different industries to generate proposals that meet new market needs. For example, a proposal can be made that combines benefits from the communications industry and the entertainment industry. The generation AI can also be used to analyze data plans and benefits from different industries to generate proposals that meet new market needs. For example, a proposal can be made that references success stories from other industries. The proposal can also be generated in real time by combining data plans and benefits from different industries to meet new market needs. For example, data from other industries can be reflected while creating the proposal. This makes it possible to create proposals that meet new market needs.

[0059] The proposal generation unit uses the emotion estimation function to monitor the customer's emotional state in real time when creating a proposal, and can make a proposal at the optimal timing. The proposal generation unit, for example, uses the emotion estimation function to monitor the customer's emotional state in real time when creating a proposal, and makes a proposal at the optimal timing. For example, it sends a proposal when the customer's emotions are positive. In addition, the generation AI uses the emotion estimation function to analyze the customer's emotional state in real time when creating a proposal, and makes a proposal at the optimal timing. For example, it adjusts the timing of the proposal based on the customer's emotional data. In addition, a system is built that uses the emotion estimation function to monitor the customer's emotional state in real time when creating a proposal, and makes a proposal at the optimal timing. For example, it reflects the emotional evaluation while creating the proposal. This makes it possible to make a proposal at the optimal timing according to the customer's emotional state.

[0060] The advertisement generation unit can analyze Line user data and generate targeted advertisements based on the user's life events. For example, the advertisement generation unit analyzes Line user data and generates targeted advertisements based on the user's life events (marriage, moving, etc.). For example, it delivers advertisements for wedding venues to users who are planning to get married. It also uses generation AI to analyze Line user data and automatically generate targeted advertisements based on the user's life events. For example, it delivers advertisements for moving services to users who are planning to move. It also analyzes Line user data in real time and generates targeted advertisements based on the user's life events. For example, it instantly delivers advertisements that correspond to the user's life events. This makes it possible to generate targeted advertisements that correspond to the user's life events.

[0061] The advertisement generation unit can analyze user responses after advertisement delivery in real time and dynamically optimize advertisement content. The advertisement generation unit, for example, analyzes user responses after advertisement delivery in real time and dynamically optimizes advertisement content. For example, it adjusts advertisement content based on click-through rates and engagement rates. It also uses generation AI to analyze user responses after advertisement delivery in real time and automatically optimize advertisement content. For example, it changes advertisement wording and images based on user response data. It also builds a system that analyzes user responses after advertisement delivery in real time and dynamically optimizes advertisement content. For example, it reflects user responses during advertisement delivery. This maximizes the effectiveness of advertisements.

[0062] The advertisement generation unit can use the emotion estimation function to evaluate the emotional impact of an advertisement on a user and generate an advertisement that elicits positive emotions. The advertisement generation unit, for example, uses the emotion estimation function to evaluate the emotional impact of an advertisement on a user and generate an advertisement that elicits positive emotions. For example, it adjusts the advertisement wording and images. Furthermore, the generation AI uses the emotion estimation function to evaluate the emotional impact of an advertisement on a user in real time and generate an advertisement that elicits positive emotions. For example, it reflects the emotion evaluation during advertisement delivery. Furthermore, it uses the emotion estimation function to analyze the emotional impact of an advertisement on a user and generate an advertisement that elicits positive emotions. For example, it adjusts the advertisement content based on the user's emotion data. This makes it possible to generate advertisements that take user emotions into consideration.

[0063] The ad generation unit can also work with social media platforms other than Line to realize cross-platform targeted advertising. The ad generation unit can also work with social media platforms other than Line to realize cross-platform targeted advertising. For example, it can work with Facebook and Instagram to deliver ads. It can also use generation AI to analyze data from social media platforms other than Line and automatically generate cross-platform targeted ads. For example, it can deliver consistent ads across multiple platforms. It can also work with social media platforms other than Line to generate cross-platform targeted ads in real time. For example, it can reflect data from each platform while delivering ads. This makes it possible to deliver consistent ads across multiple platforms.

[0064] The advertisement generation unit can incorporate user-generated content into advertisement content to increase user engagement. The advertisement generation unit, for example, incorporates user-generated content into advertisement content to increase user engagement. For example, by incorporating user posts and reviews into advertisements. Furthermore, by using generation AI, user-generated content is automatically generated in advertisement content to increase user engagement. For example, user photos and videos are used in advertisements. Furthermore, a system is constructed that incorporates user-generated content into advertisement content to increase user engagement. For example, user content is reflected during advertisement delivery. This makes it possible to create advertisements that increase user engagement.

[0065] The advertisement generation unit can use the emotion estimation function to predict the user's emotional state before advertisement delivery and deliver the advertisement at the optimal timing. The advertisement generation unit, for example, uses the emotion estimation function to predict the user's emotional state before advertisement delivery and deliver the advertisement at the optimal timing. For example, the advertisement is delivered when the user's emotions are positive. Furthermore, the generation AI uses the emotion estimation function to predict the user's emotional state in real time before advertisement delivery and deliver the advertisement at the optimal timing. For example, the timing of advertisement delivery is adjusted based on the user's emotional data. Furthermore, the emotion estimation function is used to build a system that predicts the user's emotional state before advertisement delivery and delivers the advertisement at the optimal timing. For example, the emotion evaluation is reflected during advertisement delivery. This makes it possible to display advertisements at the optimal timing according to the user's emotional state.

[0066] The presentation material generation unit can incorporate market data and competitive information that are updated in real time into presentation materials to provide the latest information. The presentation material generation unit, for example, incorporates market data and competitive information that are updated in real time into presentation materials to provide the latest information. For example, the latest market trends and information on competitors are reflected in the presentation materials. In addition, using a generation AI, market data and competitive information that are updated in real time into presentation materials are automatically generated to provide the latest information. For example, market data is updated during the presentation. In addition, a system is built that incorporates market data and competitive information that are updated in real time into presentation materials to provide the latest information. For example, market data is reflected while the presentation materials are being created. This makes it possible to provide presentation materials that provide the latest information.

[0067] The presentation material generation unit can analyze customer reactions during a presentation and instantly optimize the content of the materials. The presentation material generation unit, for example, analyzes customer reactions during a presentation and instantly optimizes the content of the materials. For example, it adjusts the presentation materials based on the customer's facial expressions and comments. It also uses generation AI to analyze customer reactions in real time during a presentation and automatically optimize the content of the materials. For example, it changes the presentation materials based on customer reaction data. It also builds a system that analyzes customer reactions during a presentation and instantly optimizes the content of the materials. For example, it reflects customer reactions during the presentation. This makes it possible to create optimal presentation materials based on customer reactions.

[0068] The presentation material generation unit can use the emotion estimation function to evaluate the emotional impact that presentation materials have on customers and generate materials that will elicit a positive response. The presentation material generation unit, for example, uses the emotion estimation function to evaluate the emotional impact that presentation materials have on customers and generate materials that will elicit a positive response. For example, it adjusts the wording and structure of the presentation materials. The generation AI also uses the emotion estimation function to evaluate the emotional impact that presentation materials have on customers in real time and generate materials that will elicit a positive response. For example, it reflects the emotional evaluation during the presentation. The emotion estimation function also analyzes the emotional impact that presentation materials have on customers and generates materials that will elicit a positive response. For example, it adjusts the presentation materials based on customer emotion data. This makes it possible to create presentation materials that take customer emotions into consideration.

[0069] The presentation material generation unit can incorporate interactive elements into presentation materials to encourage customer participation. The presentation material generation unit, for example, incorporates interactive elements (such as quizzes or surveys) into presentation materials to encourage customer participation. For example, a quiz can be given during the presentation to attract customer attention. In addition, the generation AI can be used to automatically generate interactive elements into presentation materials to encourage customer participation. For example, a survey can be incorporated into the presentation materials. In addition, a system can be built that incorporates interactive elements into presentation materials to encourage customer participation. For example, a quiz or survey can be conducted during the presentation. This makes it possible to create presentation materials that encourage customer participation.

[0070] The presentation material generation unit automatically generates presentation materials in different languages, making it possible to accommodate international customers. The presentation material generation unit, for example, automatically generates presentation materials in different languages ​​to accommodate international customers. For example, it creates presentation materials in multiple languages, such as English and Chinese. It also uses generation AI to automatically generate presentation materials in different languages ​​to accommodate international customers. For example, it makes presentation materials multilingual. It also builds a system that automatically generates presentation materials in different languages ​​to accommodate international customers. For example, it reflects multilingual support while creating presentation materials. This makes it possible to create presentation materials that are suitable for international customers.

[0071] The presentation material generation unit can use the emotion estimation function to predict the emotional state of the customer before the presentation and make the presentation at the optimal timing. The presentation material generation unit, for example, uses the emotion estimation function to predict the emotional state of the customer before the presentation and make the presentation at the optimal timing. For example, the presentation starts when the customer's emotions are positive. The generation AI also uses the emotion estimation function to predict the emotional state of the customer in real time before the presentation and make the presentation at the optimal timing. For example, the timing of the presentation is adjusted based on the customer's emotion data. The emotion estimation function is also used to build a system that predicts the emotional state of the customer before the presentation and makes the presentation at the optimal timing. For example, the emotion evaluation is reflected before the presentation starts. This makes it possible to make a presentation at the optimal timing according to the customer's emotional state.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The proposal generation unit can simulate the effects of data plans and benefits included in the proposal and automatically select the optimal plan. For example, it can predict the effects of each plan based on past data and incorporate the most effective plan into the proposal. In addition, when creating a proposal, the generation AI simulates data plans and benefits and automatically selects the plan that best suits the customer's needs. For example, it can analyze the customer's usage patterns and propose the optimal plan based on that. In addition, the generation AI can simulate the effects of data plans and benefits included in the proposal in real time and automatically select the optimal plan. For example, it can reflect the simulation results while creating the proposal. This maximizes the effectiveness of the proposal.

[0074] The proposal generation unit can refer to past success stories and failure stories to generate optimal proposal content. For example, when creating a proposal, the generation AI refers to past success stories and failure stories to generate optimal proposal content. For example, proposals with a high probability of success are automatically generated based on past data. In addition, the generation AI is used to analyze past success stories and failure stories when creating a proposal, and generate optimal proposal content based on that. For example, factors for success and failure are analyzed and reflected in the proposal. In addition, when creating a proposal, the generation AI refers to past success stories and failure stories in real time to generate optimal proposal content. For example, past cases are reflected while creating the proposal. This increases the probability of the proposal's success.

[0075] The proposal generation unit can use the emotion estimation function to evaluate the emotional impact that the proposal content has on the customer and generate a proposal that will elicit a positive response. For example, the emotion estimation function can be used to evaluate the emotional impact that the proposal content has on the customer and generate a proposal that will elicit a positive response. For example, the wording and structure of the proposal can be adjusted. The generation AI can also use the emotion estimation function to evaluate the emotional impact that the proposal content has on the customer in real time and generate a proposal that will elicit a positive response. For example, the emotion evaluation can be reflected while the proposal is being created. The emotion estimation function can also be used to analyze the emotional impact that the proposal content has on the customer and generate a proposal that will elicit a positive response. For example, the proposal content can be adjusted based on the customer's emotion data. This makes it possible to make proposals that take the customer's emotions into consideration.

[0076] The proposal generation unit can incorporate videos and interactive content into proposals to create visually appealing proposals. For example, videos and interactive content can be incorporated into proposals to create visually appealing proposals. For example, product demo videos and interactive graphs can be added to proposals. In addition, generative AI can be used to automatically generate videos and interactive content into proposals to create visually appealing proposals. For example, content based on customer interests can be incorporated into proposals. In addition, videos and interactive content can be incorporated into proposals to create visually appealing proposals in real time. For example, content can be automatically added while the proposal is being created. This makes it possible to create visually appealing proposals.

[0077] The proposal generation unit can combine data plans and benefits from different industries to generate proposals that meet new market needs. For example, it can combine data plans and benefits from different industries to generate proposals that meet new market needs. For example, it can make a proposal that combines benefits from the telecommunications and entertainment industries. It can also use generation AI to analyze data plans and benefits from different industries to generate proposals that meet new market needs. For example, it can make proposals that refer to successful cases from other industries. It can also combine data plans and benefits from different industries to generate proposals in real time that meet new market needs. For example, it can reflect data from other industries while creating a proposal. This makes it possible to create proposals that meet new market needs.

[0078] The proposal generation unit uses the emotion estimation function to monitor the customer's emotional state in real time when creating a proposal, and can make a proposal at the optimal timing. For example, the emotion estimation function can be used to monitor the customer's emotional state in real time when creating a proposal, and make a proposal at the optimal timing. For example, the proposal can be sent when the customer's emotions are positive. The generation AI also uses the emotion estimation function to analyze the customer's emotional state in real time when creating a proposal, and make a proposal at the optimal timing. For example, the timing of the proposal can be adjusted based on the customer's emotional data. The emotion estimation function can also be used to build a system that monitors the customer's emotional state in real time when creating a proposal, and makes a proposal at the optimal timing. For example, the emotion evaluation can be reflected while creating the proposal. This makes it possible to make a proposal at the optimal timing according to the customer's emotional state.

[0079] The advertisement generation unit can analyze Line user data and generate targeted advertisements based on the user's life events. For example, it analyzes Line user data and generates targeted advertisements based on the user's life events (marriage, moving, etc.). For example, it delivers advertisements for wedding venues to users who are planning to get married. It also uses generation AI to analyze Line user data and automatically generate targeted advertisements based on the user's life events. For example, it delivers advertisements for moving services to users who are planning to move. It also analyzes Line user data in real time and generates targeted advertisements based on the user's life events. For example, it delivers advertisements that correspond to the user's life events instantly. This makes it possible to target advertisements that correspond to the user's life events.

[0080] The ad generation unit can analyze user responses after ad delivery in real time and dynamically optimize ad content. For example, it analyzes user responses after ad delivery in real time and dynamically optimizes ad content. For example, it adjusts ad content based on click-through rates and engagement rates. It also uses generation AI to analyze user responses after ad delivery in real time and automatically optimize ad content. For example, it changes ad wording and images based on user response data. It also builds a system that analyzes user responses after ad delivery in real time and dynamically optimizes ad content. For example, it reflects user responses while ad delivery is in progress. This maximizes the effectiveness of advertising.

[0081] The advertisement generation unit can use the emotion estimation function to evaluate the emotional impact of an advertisement on a user and generate an advertisement that elicits positive emotions. For example, the emotion estimation function is used to evaluate the emotional impact of an advertisement on a user and generate an advertisement that elicits positive emotions. For example, the advertisement wording and images are adjusted. Furthermore, the generation AI uses the emotion estimation function to evaluate the emotional impact of an advertisement on a user in real time and generate an advertisement that elicits positive emotions. For example, the emotion evaluation is reflected during advertisement delivery. Furthermore, the emotion estimation function is used to analyze the emotional impact of an advertisement on a user and generate an advertisement that elicits positive emotions. For example, the advertisement content is adjusted based on user emotion data. This makes it possible to generate advertisements that take user emotions into consideration.

[0082] The ad generation unit can also work with social media platforms other than Line to realize cross-platform targeted advertising. For example, it can work with social media platforms other than Line to realize cross-platform targeted advertising. For example, it can work with Facebook and Instagram to deliver ads. It can also use generation AI to analyze data from social media platforms other than Line and automatically generate cross-platform targeted advertising. For example, it can deliver consistent ads across multiple platforms. It can also work with social media platforms other than Line to generate cross-platform targeted advertising in real time. For example, it can reflect data from each platform while delivering ads. This makes it possible to deliver consistent ads across multiple platforms.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The proposal generator generates a proposal. For example, the generation AI receives a prompt containing instructions from a user as input and generates a proposal based on the prompt. Step 2: The data plan benefits unit incorporates the data plans and benefits into the proposal generated by the proposal generation unit, for example, generating a proposal that automatically incorporates the data plans and benefits offered by the carrier. Step 3: The ad generation unit generates the ad. For example, the generation AI analyzes the user's behavioral data and interests and generates the ad content based on that. Step 4: The presentation material generation unit generates presentation materials. For example, the generation AI receives prompts containing instructions from the user as input and generates presentation materials based on the prompts.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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. [Explanation of symbols]

[0152] 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 proposal generation unit that generates a proposal; a data plan and benefit unit that incorporates data plans or benefits into the proposal generated by the proposal generation unit; an advertisement generation unit that generates advertisements; a presentation material generation unit that generates presentation materials; A system characterized by:

2. The proposal generation unit The effects of the data plan or the benefits included in the proposal are simulated, and the optimal plan is automatically selected.

2. The system of claim 1.

3. The proposal generation unit Generate optimal proposals by referencing past successes and failures 2. The system of claim 1.

4. The proposal generation unit Evaluate the emotional impact of the proposal content on customers and generate proposals that elicit positive responses 2. The system of claim 1.

5. The proposal generation unit Incorporate video or interactive content into the proposal to make it visually appealing.

2. The system of claim 1.

6. The proposal generation unit Combining the data plans or offers from different industries to generate proposals that address new market needs 2. The system of claim 1.

7. The proposal generation unit Monitor the customer's emotional state in real time when creating a proposal and make the proposal at the optimal time 2. The system of claim 1.

8. The advertisement generation unit Analyze Line user data and generate targeted advertisements based on the user's life events 2. The system of claim 1.

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