System

Abstract

An object of the system according to the embodiment is to efficiently create and propose sales phrases and moving images of services and products.SOLUTION: A system includes an input unit, an analysis unit, a generation unit, and a proposal unit. The input unit inputs features, advantages, and specifications of a service or a product. The analysis unit analyzes the information input by the input unit. The generation unit generates a sales phrase or a moving image based on the information analyzed by the analysis unit. The suggestion unit suggests the sales phrase or video generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of requiring a significant amount of man-hours to create sales pitches and videos for services and products.

[0005] The system according to the embodiment aims to efficiently create and propose sales pitches and videos for services and products. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a generation unit, and a proposal unit. The input unit inputs the features, advantages, and specifications of a service or product. The analysis unit analyzes the information input by the input unit. The generation unit generates sales pitches and videos based on the information analyzed by the analysis unit. The proposal unit proposes the sales pitches and videos generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create and propose sales slogans and videos for services and products. [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 proposal tool according to the embodiment of the present invention is a system that automatically creates and proposes sales pitches and videos tailored to the sales target by allowing the user to input the features, advantages, and specifications of the service or product they have developed, and then input the industry, position, and department of the target. This allows the proposal tool to reduce the man-hours required for the initial proposal hook.

[0029] A proposal tool according to an embodiment includes an input unit, an analysis unit, a generation unit, and a proposal unit. The input unit inputs features, advantages, and specifications of a service or product. For example, a user can input product specifications in text and upload a video explaining how to use the product. Images showing the product's design and appearance can also be attached. The analysis unit analyzes the information input by the input unit. For example, the analysis unit can analyze the input information using text analysis technology. It can also analyze the content of uploaded images using image analysis technology. It can also analyze the content of uploaded videos using audio analysis technology. The generation unit generates sales pitches and videos based on the information analyzed by the analysis unit. For example, the generation unit generates sales pitches based on the input information using a generation AI. It can also generate sales videos based on the input information using a generation AI. It can also generate presentation videos based on the input information using a generation AI. The proposal unit proposes the sales pitches and videos generated by the generation unit. For example, the proposal unit compiles the generated sales pitches into a proposal and provides it to the user. The proposal unit can also attach the generated sales video to a proposal and provide it to the user. The proposal unit can also attach the generated presentation video to a proposal and provide it to the user. In this way, the proposal tool according to the embodiment can automatically generate and propose optimal sales pitches and videos based on information input by the user.

[0030] The input unit can input information in multiple media formats (text, images, and videos). For example, the input unit uses a generation AI to automatically summarize the product features and benefits entered in text and make it consistent with the description content of the image or video. The input unit also uses a generation AI to analyze the content of images and videos uploaded by the user and automatically generate a summary that matches the information entered in text. For example, the generation AI summarizes the content of a video explaining how to use a product and integrates it with the text information. The input unit also uses a generation AI to analyze the content of each media and generate a unified summary to compile information entered in different media formats into a consistent summary. For example, the generation AI summarizes the content of product design images and explanatory videos and matches them with the text information. This allows users to input information in multiple media formats.

[0031] The analysis unit analyzes the information input by the input unit and can generate optimal sales pitches. For example, the analysis unit allows the generation AI to automatically add relevant supplemental information based on the basic product specifications input by the user, and generate a detailed spec sheet. The analysis unit also allows the generation AI to analyze the product's features and advantages input by the user, and automatically supplement related technical information and market data. For example, the generation AI adds information about the product's technical advantages and competitiveness in the market. The analysis unit also allows the generation AI to automatically collect supplemental information based on the information input by the user, and generate a detailed spec sheet. For example, the generation AI adds supplemental information such as product usage examples and customer testimonials. This allows the input information to be analyzed and optimal sales pitches to be generated.

[0032] The generation unit can generate a sales video tailored to the sales recipient based on the information input by the input unit. The generation unit, for example, uses an emotion estimation function to analyze the user's emotions regarding the input information and suggests additional information to elicit positive emotions. The generation unit also analyzes the user's emotions regarding the features and advantages of a product input by the user and suggests additional information to elicit positive emotions. For example, the generation unit adds information about success stories of the product and customer satisfaction. The generation unit also analyzes the user's emotional response to the information input by the user in real time and automatically suggests supplemental information to elicit positive emotions. For example, the generation unit adds information that highlights the unique features and advantages of the product. The generation unit also uses the emotion estimation function to analyze the user's emotions regarding the input information and makes specific suggestions to elicit positive emotions. For example, the generation unit elicits positive emotions by adding examples of product use and customer testimonials. In this way, a sales video tailored to the sales recipient can be generated based on the input information.

[0033] The proposal unit can automatically compile the sales pitches and videos generated by the generation unit into a proposal. For example, the proposal unit adds a voice input function so that the user can dictate the features and advantages of a product, and the generation AI automatically converts that content into text. The proposal unit also builds a system in which the generation AI analyzes information input by voice by the user and automatically converts it into text. For example, the proposal unit explains the product's specifications and advantages by voice and saves it as text. The proposal unit also uses voice input so that the user can dictate the features and advantages of a product, and the generation AI automatically converts that content into text and saves it in a database. For example, the proposal unit dictates the product's design and use examples and saves it as text. This allows the generated sales pitches and videos to be automatically compiled into a proposal.

[0034] The input unit adds voice input, allowing the user to orally explain the features and advantages, which the generation AI can then automatically convert into text. For example, the input unit allows the generation AI to automatically generate infographics based on the features and advantages of a product input by the user, and provide them in a format that is visually easy to understand. The input unit also builds a system in which the generation AI analyzes the information input by the user and automatically generates infographics that are visually easy to understand. For example, the input unit illustrates how to use a product and its advantages. The input unit also allows the generation AI to automatically generate infographics based on the information input by the user, and provide them in a format that is visually easy to understand. For example, the input unit generates infographics that include images showing the design and appearance of a product. This allows the generation AI to automatically convert into text when the user orally explains the features and advantages.

[0035] The generation unit allows the generation AI to automatically generate infographics based on the information input by the input unit and provide them in a format that is visually easy to understand. The generation unit, for example, uses an emotion estimation function to provide real-time feedback on the user's emotions regarding the input information and adjust the input content. The generation unit also analyzes the user's emotional response to the information input by the user in real time and adjusts the input content based on the results. For example, the generation unit adds information that emphasizes the advantages of a product when the emotion score is low. The generation unit also develops a system that uses the emotion estimation function to provide real-time feedback on the user's emotions regarding the input information and adjusts the input content. For example, the generation unit makes specific suggestions to elicit positive emotions. This allows the generation AI to automatically generate infographics based on the information input by the user and provide them in a format that is visually easy to understand.

[0036] The analysis unit can use the generation AI to provide relevant market trends and competitive information based on the industry and job title. The analysis unit builds a system in which the generation AI automatically provides relevant market trends and competitive information based on, for example, the industry and job title entered by the user. The analysis unit also develops a system in which the generation AI automatically provides relevant market trends and competitive information based on the industry and job title entered by the user. For example, the analysis unit provides market trends and competitive information for a specific department or job title. The analysis unit also develops a system in which the generation AI automatically provides relevant market trends and competitive information based on the industry and job title entered by the user. For example, the analysis unit provides market trends and competitive information for a specific industry and job title. This makes it possible to provide relevant market trends and competitive information based on the industry and job title entered by the user.

[0037] The analysis unit uses the generation AI to automatically generate a target list based on information and present multiple candidates. The analysis unit builds a system in which the generation AI automatically generates a target list based on information such as industry and job title entered by a user, and presents multiple candidates. The analysis unit also develops a system in which the generation AI automatically generates a target list based on information entered by a user, and presents multiple candidates. For example, the analysis unit generates an optimal target list for a specific department or job title. The analysis unit also develops a system in which the generation AI automatically generates a target list based on information entered by a user, and presents multiple candidates. For example, the analysis unit generates an optimal target list for a specific industry or job title. This allows the generation AI to automatically generate a target list based on information entered by a user, and present multiple candidates.

[0038] The analysis unit can try out different combinations of industries and job titles, and the generation AI can simulate the optimal target setting. The analysis unit, for example, builds a system in which the generation AI tries out different combinations of industries and job titles, and simulates the optimal target setting. The analysis unit also builds a system in which the generation AI tries out different combinations of industries and job titles, and simulates the optimal target setting. For example, the analysis unit proposes the optimal target based on a combination of multiple industries and job titles. The analysis unit also develops a system in which the generation AI tries out different combinations of industries and job titles, and simulates the optimal target setting. For example, the analysis unit proposes the optimal target based on a combination of multiple industries and job titles. This allows the generation AI to try out different combinations of industries and job titles, and simulate the optimal target setting.

[0039] The generation unit can analyze past success stories and extract and propose the most effective sales pitch patterns. For example, the generation unit builds a system in which a generation AI analyzes past success stories and extracts and proposes the most effective sales pitch patterns. The generation unit also analyzes past success stories and proposes the most effective sales pitch patterns based on information entered by the user. For example, the generation unit generates optimal sales pitches for a specific industry or position. The generation unit also develops a system in which a generation AI analyzes past success stories and proposes the most effective sales pitch patterns. For example, the generation unit generates optimal sales pitches for a specific target. This makes it possible to analyze past success stories and extract and propose the most effective sales pitch patterns.

[0040] The generation unit uses the generation AI to automatically generate multiple sales pitches based on information input by a user and can perform A / B testing. For example, the generation unit builds a system in which the generation AI automatically generates multiple sales pitches based on information input by a user and performs A / B testing. The generation unit also develops a system in which the generation AI automatically generates multiple sales pitches based on information input by a user and performs A / B testing. For example, the generation unit tests different sales pitches on a target and selects the optimal pitch. The generation unit also develops a system in which the generation AI automatically generates multiple sales pitches based on information input by a user and performs A / B testing. For example, the generation unit tests different sales pitches on a target and selects the optimal pitch. This allows the generation AI to automatically generate multiple sales pitches based on information input by a user and perform A / B testing.

[0041] The generation unit automatically generates sales pitches in different languages, making it possible to accommodate international targets. The generation unit, for example, uses a generation AI to build a system that automatically generates sales pitches in different languages ​​and accommodates international targets. The generation unit also uses a generation AI to automatically generate sales pitches in different languages ​​based on information input by a user, making it possible to accommodate international targets. For example, the generation unit generates sales pitches that correspond to a specific language. The generation unit also uses a generation AI to develop a system that automatically generates sales pitches in different languages ​​and accommodates international targets. For example, the generation unit generates sales pitches in multiple languages ​​and accommodates international targets. This makes it possible to automatically generate sales pitches in different languages ​​and accommodate international targets.

[0042] The generation unit can vocalize the generated sales pitch and send it to the target as a voice message. The generation unit, for example, uses a generation AI to build a system that vocalizes the generated sales pitch and sends it to the target as a voice message. The generation unit also uses a generation AI to automatically generate a sales pitch based on information input by a user, vocalize it, and send it to the target. For example, the generation unit sends the sales pitch as a voice message. The generation unit also uses a generation AI to develop a system that vocalizes the generated sales pitch and sends it to the target as a voice message. For example, the generation unit uses voice synthesis technology to vocalize the sales pitch and send it to the target. This allows the generated sales pitch to be vocalized and sent to the target as a voice message.

[0043] The generation unit can analyze past success stories and extract and propose the most effective video patterns. For example, the generation unit builds a system in which a generation AI analyzes past success stories and extracts and proposes the most effective video patterns. The generation unit also analyzes past success stories and proposes the most effective video patterns based on information entered by the user. For example, the generation unit generates the optimal video for a specific industry or position. The generation unit also develops a system in which a generation AI analyzes past success stories and proposes the most effective video patterns. For example, the generation unit generates the optimal video for a specific target. This makes it possible to analyze past success stories and extract and propose the most effective video patterns.

[0044] The generation unit uses a generation AI to automatically generate multiple videos based on information input by a user and can perform A / B testing. For example, the generation unit builds a system in which the generation AI automatically generates multiple videos based on information input by a user and performs A / B testing. The generation unit also develops a system in which the generation AI automatically generates multiple videos based on information input by a user and performs A / B testing. For example, the generation unit tests different videos on a target and selects the optimal video. The generation unit also develops a system in which the generation AI automatically generates multiple videos based on information input by a user and performs A / B testing. For example, the generation unit tests different videos on a target and selects the optimal video. This allows the generation AI to automatically generate multiple videos based on information input by a user and perform A / B testing.

[0045] The generation unit automatically generates sales videos in different languages, making it possible to accommodate international targets. The generation unit, for example, uses a generation AI to build a system that automatically generates sales videos in different languages, making it possible to accommodate international targets. The generation unit also uses a generation AI to automatically generate sales videos in different languages ​​based on information input by a user, making it possible to accommodate international targets. For example, the generation unit generates sales videos that support a specific language. The generation unit also uses a generation AI to develop a system that automatically generates sales videos in different languages, making it possible to accommodate international targets. For example, the generation unit generates sales videos in multiple languages, making it possible to accommodate international targets. This makes it possible to automatically generate sales videos in different languages, making it possible to accommodate international targets.

[0046] The generation unit divides the generated sales video into a shortened version and a long version, and can use them depending on the target's level of interest. For example, the generation unit uses a generation AI to build a system that divides the generated sales video into a shortened version and a long version, and can use them depending on the target's level of interest. Furthermore, the generation unit uses the generation AI to automatically divide the sales video into a shortened version and a long version based on information input by a user, and can use them depending on the target's level of interest. For example, the generation unit generates a shortened version as a video summarizing the main points, and a long version as a video containing detailed explanations. Furthermore, the generation unit uses a generation AI to develop a system that divides the generated sales video into a shortened version and a long version, and can use them depending on the target's level of interest. For example, the generation unit generates a shortened version as a concise introduction video, and a long version as a detailed presentation video. This allows the generation unit to divide the generated sales video into a shortened version and a long version, and can use them depending on the target's level of interest.

[0047] The proposal department can analyze past success stories and propose the most effective proposal format. For example, the proposal department builds a system in which a generation AI analyzes past success stories and proposes the most effective proposal format. The proposal department also analyzes past success stories and proposes the most effective proposal format based on information entered by a user. For example, the proposal department generates the optimal proposal for a specific industry or position. The proposal department also develops a system in which a generation AI analyzes past success stories and proposes the most effective proposal format. For example, the proposal department generates the optimal proposal for a specific target. This makes it possible to analyze past success stories and propose the most effective proposal format.

[0048] The proposal unit can use the generation AI to automatically generate multiple versions of a proposal based on information input by a user and perform A / B testing. For example, the proposal unit builds a system in which the generation AI automatically generates multiple versions of a proposal based on information input by a user and performs A / B testing. The proposal unit also develops a system in which the generation AI automatically generates multiple versions of a proposal based on information input by a user and performs A / B testing. For example, the proposal unit tests different proposals against a target and selects the optimal proposal. The proposal unit also develops a system in which the generation AI automatically generates multiple versions of a proposal based on information input by a user and performs A / B testing. For example, the proposal unit tests different proposals against a target and selects the optimal proposal. This allows the generation AI to automatically generate multiple versions of a proposal based on information input by a user and perform A / B testing.

[0049] The proposal unit can vocalize the generated proposal and send it to the target as a voice message. The proposal unit, for example, uses a generation AI to build a system that vocalizes the generated proposal and sends it to the target as a voice message. The proposal unit also uses a generation AI to automatically generate a proposal based on information input by a user, vocalize it, and send it to the target. For example, the proposal unit sends the proposal as a voice message. The proposal unit also uses a generation AI to develop a system that vocalizes the generated proposal and sends it to the target as a voice message. For example, the proposal unit uses voice synthesis technology to vocalize the proposal and send it to the target. This allows the generated proposal to be vocalized and sent to the target as a voice message.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The proposal tool can also analyze the user's past proposal history, extract successful proposal patterns, and reflect them in new proposals. For example, it can analyze the structure and wording of past successful proposals and apply similar patterns to new proposals. It can also extract effective approaches for specific industries or job positions from past proposal history and reflect them in the proposal content. Furthermore, it can predict the optimal timing for making a proposal to a specific customer based on past proposal history and adjust the timing of sending the proposal. In this way, the proposal tool can utilize the user's past success stories to make more effective proposals.

[0052] The input unit can further use natural language processing technology to understand the context of the information entered by the user and automatically suggest appropriate complementary information. For example, it can automatically add related technical details and market data to the product features entered by the user. The input unit can also analyze the context of the information entered by the user and automatically suggest related success stories and customer testimonials. Furthermore, the input unit can understand the context of the information entered by the user and automatically suggest appropriate images and videos. This allows the input unit to further enrich the information entered by the user and make effective suggestions.

[0053] The analysis unit can further automatically search for related patent information and technical literature based on the information input by the user and reflect this in the proposal. For example, related patent information is automatically added to the technical features of a product input by the user. The analysis unit can also automatically search for related technical literature based on the information input by the user and reflect this in the proposal. Furthermore, the analysis unit can automatically collect related market trends and competitive information based on the information input by the user and reflect this in the proposal. This allows the analysis unit to further enhance the information input by the user and make effective proposals.

[0054] The generation unit can further generate interactive suggested content based on the information input by the user. For example, the generation unit generates an interactive presentation based on the features and advantages of a product input by the user. The generation unit can also generate an interactive quiz or questionnaire based on the information input by the user and incorporate it into the suggested content. Furthermore, the generation unit can generate an interactive simulation based on the information input by the user and incorporate it into the suggested content. This allows the generation unit to generate more attractive and effective suggested content based on the information input by the user.

[0055] The suggestion unit can further provide the proposal content in multiple formats based on the information input by the user. For example, the suggestion unit can provide the generated proposal in PDF format or PowerPoint format. The suggestion unit can also provide the generated proposal in web page format so that it can be viewed online. The suggestion unit can also provide the generated proposal in mobile app format so that it can be viewed on a smartphone or tablet. In this way, the suggestion unit can provide the proposal content generated by the user in various formats and reach a wider range of targets.

[0056] The input unit can further automatically generate related social media posts based on the information input by the user. For example, the input unit automatically generates content to be posted on social media such as Twitter, Facebook, and LinkedIn based on the features and advantages of a product input by the user. The input unit can also automatically suggest related hashtags and keywords based on the information input by the user. Furthermore, the input unit can automatically set a posting schedule on social media based on the information input by the user. In this way, the input unit can support an effective social media strategy based on the information input by the user.

[0057] The generation unit can further generate a customized demonstration video based on the information input by the user. For example, the generation unit generates a demonstration video customized for a specific customer based on the features and advantages of a product input by the user. The generation unit can also generate a demonstration video tailored to a specific industry or job title based on the information input by the user. Furthermore, the generation unit can also generate a demonstration video tailored to a specific usage scenario based on the information input by the user. This allows the generation unit to generate a more effective demonstration video based on the information input by the user.

[0058] The analysis unit can further automatically search for relevant laws, regulations, and industry standards based on the information input by the user and reflect them in the proposal content. For example, relevant laws, regulations, and industry standards are automatically added to the technical features of a product input by the user. The analysis unit can also automatically search for relevant laws, regulations, and industry standards based on the information input by the user and reflect them in the proposal content. Furthermore, the analysis unit can automatically collect relevant laws, regulations, and industry standards based on the information input by the user and reflect them in the proposal content. This allows the analysis unit to further enhance the information input by the user and make effective proposals.

[0059] The generation unit can further generate customized training content based on the information input by the user. For example, the generation unit can generate a training video customized for a specific customer based on the features and benefits of a product input by the user. The generation unit can also generate a training manual tailored to a specific industry or job position based on the information input by the user. Furthermore, the generation unit can generate training materials tailored to a specific usage scenario based on the information input by the user. This allows the generation unit to generate more effective training content based on the information input by the user.

[0060] The suggestion unit can further provide the proposal content in multiple languages ​​based on the information input by the user. For example, the suggestion unit can provide the generated proposal in multiple languages, such as English, Chinese, and Spanish. The suggestion unit can also provide the generated proposal in a multilingual web page format so that it can be viewed online. The suggestion unit can also provide the generated proposal in a multilingual mobile app format so that it can be viewed on a smartphone or tablet. This allows the suggestion unit to provide the proposal content generated by the user in various languages ​​and reach a wider range of targets.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The input section allows users to enter the features, benefits, and specifications of a service or product. For example, users can enter product specifications in text and upload videos explaining how to use the product. They can also attach images showing the product's design and appearance. Step 2: The analysis unit analyzes the information input by the input unit. For example, the analysis unit analyzes the input information using text analysis technology. It can also analyze the content of uploaded images using image analysis technology. It can also analyze the content of uploaded videos using audio analysis technology. Step 3: The generation unit generates sales pitches and videos based on the information analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate sales pitches based on the input information. The generation unit can also use a generation AI to generate a sales video based on the input information. The generation unit can also use a generation AI to generate a presentation video based on the input information. Step 4: The suggestion unit suggests the sales pitches and videos generated by the generation unit. For example, the suggestion unit compiles the generated sales pitches into a proposal and provides it to the user. The suggestion unit can also attach the generated sales video to the proposal and provide it to the user. The suggestion unit can also attach the generated presentation video to the proposal and provide it to the user.

[0063] (Example 2) The proposal tool according to the embodiment of the present invention is a system that automatically creates and proposes sales pitches and videos tailored to the sales target by allowing the user to input the features, advantages, and specifications of the service or product they have developed, and then input the industry, position, and department of the target. This allows the proposal tool to reduce the man-hours required for the initial proposal hook.

[0064] A proposal tool according to an embodiment includes an input unit, an analysis unit, a generation unit, and a proposal unit. The input unit inputs features, advantages, and specifications of a service or product. For example, a user can input product specifications in text and upload a video explaining how to use the product. Images showing the product's design and appearance can also be attached. The analysis unit analyzes the information input by the input unit. For example, the analysis unit can analyze the input information using text analysis technology. It can also analyze the content of uploaded images using image analysis technology. It can also analyze the content of uploaded videos using audio analysis technology. The generation unit generates sales pitches and videos based on the information analyzed by the analysis unit. For example, the generation unit generates sales pitches based on the input information using a generation AI. It can also generate sales videos based on the input information using a generation AI. It can also generate presentation videos based on the input information using a generation AI. The proposal unit proposes the sales pitches and videos generated by the generation unit. For example, the proposal unit compiles the generated sales pitches into a proposal and provides it to the user. The proposal unit can also attach the generated sales video to a proposal and provide it to the user. The proposal unit can also attach the generated presentation video to a proposal and provide it to the user. In this way, the proposal tool according to the embodiment can automatically generate and propose optimal sales pitches and videos based on information input by the user.

[0065] The input unit can input information in multiple media formats (text, images, and videos). For example, the input unit uses a generation AI to automatically summarize the product features and benefits entered in text and make it consistent with the description content of the image or video. The input unit also uses a generation AI to analyze the content of images and videos uploaded by the user and automatically generate a summary that matches the information entered in text. For example, the generation AI summarizes the content of a video explaining how to use a product and integrates it with the text information. The input unit also uses a generation AI to analyze the content of each media and generate a unified summary to compile information entered in different media formats into a consistent summary. For example, the generation AI summarizes the content of product design images and explanatory videos and matches them with the text information. This allows users to input information in multiple media formats.

[0066] The analysis unit analyzes the information input by the input unit and can generate optimal sales pitches. For example, the analysis unit allows the generation AI to automatically add relevant supplemental information based on the basic product specifications input by the user, and generate a detailed spec sheet. The analysis unit also allows the generation AI to analyze the product's features and advantages input by the user, and automatically supplement related technical information and market data. For example, the generation AI adds information about the product's technical advantages and competitiveness in the market. The analysis unit also allows the generation AI to automatically collect supplemental information based on the information input by the user, and generate a detailed spec sheet. For example, the generation AI adds supplemental information such as product usage examples and customer testimonials. This allows the input information to be analyzed and optimal sales pitches to be generated.

[0067] The generation unit can generate a sales video tailored to the sales recipient based on the information input by the input unit. The generation unit, for example, uses an emotion estimation function to analyze the user's emotions regarding the input information and suggests additional information to elicit positive emotions. The generation unit also analyzes the user's emotions regarding the features and advantages of a product input by the user and suggests additional information to elicit positive emotions. For example, the generation unit adds information about success stories of the product and customer satisfaction. The generation unit also analyzes the user's emotional response to the information input by the user in real time and automatically suggests supplemental information to elicit positive emotions. For example, the generation unit adds information that highlights the unique features and advantages of the product. The generation unit also uses the emotion estimation function to analyze the user's emotions regarding the input information and makes specific suggestions to elicit positive emotions. For example, the generation unit elicits positive emotions by adding examples of product use and customer testimonials. In this way, a sales video tailored to the sales recipient can be generated based on the input information.

[0068] The proposal unit can automatically compile the sales pitches and videos generated by the generation unit into a proposal. For example, the proposal unit adds a voice input function so that the user can dictate the features and advantages of a product, and the generation AI automatically converts that content into text. The proposal unit also builds a system in which the generation AI analyzes information input by voice by the user and automatically converts it into text. For example, the proposal unit explains the product's specifications and advantages by voice and saves it as text. The proposal unit also uses voice input so that the user can dictate the features and advantages of a product, and the generation AI automatically converts that content into text and saves it in a database. For example, the proposal unit dictates the product's design and use examples and saves it as text. This allows the generated sales pitches and videos to be automatically compiled into a proposal.

[0069] The input unit adds voice input, allowing the user to orally explain the features and advantages, which the generation AI can then automatically convert into text. For example, the input unit allows the generation AI to automatically generate infographics based on the features and advantages of a product input by the user, and provide them in a format that is visually easy to understand. The input unit also builds a system in which the generation AI analyzes the information input by the user and automatically generates infographics that are visually easy to understand. For example, the input unit illustrates how to use a product and its advantages. The input unit also allows the generation AI to automatically generate infographics based on the information input by the user, and provide them in a format that is visually easy to understand. For example, the input unit generates infographics that include images showing the design and appearance of a product. This allows the generation AI to automatically convert into text when the user orally explains the features and advantages.

[0070] The generation unit allows the generation AI to automatically generate infographics based on the information input by the input unit and provide them in a format that is visually easy to understand. The generation unit, for example, uses an emotion estimation function to provide real-time feedback on the user's emotions regarding the input information and adjust the input content. The generation unit also analyzes the user's emotional response to the information input by the user in real time and adjusts the input content based on the results. For example, the generation unit adds information that emphasizes the advantages of a product when the emotion score is low. The generation unit also develops a system that uses the emotion estimation function to provide real-time feedback on the user's emotions regarding the input information and adjusts the input content. For example, the generation unit makes specific suggestions to elicit positive emotions. This allows the generation AI to automatically generate infographics based on the information input by the user and provide them in a format that is visually easy to understand.

[0071] The analysis unit can use the emotion estimation function to analyze the user's emotions regarding the input information and suggest additional information to elicit positive emotions. For example, the analysis unit can use the emotion estimation function to analyze the user's emotions regarding the features and advantages of a product input by the user and suggest additional information to elicit positive emotions. The analysis unit can also analyze the user's emotional response to the information input by the user in real time and automatically suggest complementary information to elicit positive emotions. For example, the analysis unit can add information that highlights the unique features and advantages of the product. The analysis unit can also use the emotion estimation function to analyze the user's emotions regarding the input information and make specific suggestions to elicit positive emotions. For example, the analysis unit can elicit positive emotions by adding examples of product use and customer testimonials. This allows the analysis of the user's emotions regarding the input information and suggest additional information to elicit positive emotions.

[0072] The analysis unit uses the emotion estimation function to provide feedback on the user's emotions regarding the input information in real time, and can adjust the input content. For example, the analysis unit uses the emotion estimation function to build a system that provides feedback on the user's emotions regarding the input information in real time and adjusts the input content. The analysis unit also analyzes the user's emotional response to the information input in real time and adjusts the input content based on the results. For example, the analysis unit adds information that emphasizes the advantages of a product when the emotion score is low. The analysis unit also uses the emotion estimation function to develop a system that provides feedback on the user's emotions regarding the input information in real time and adjusts the input content. For example, the analysis unit makes specific suggestions to elicit positive emotions. This allows feedback on the user's emotions regarding the input information in real time and adjustment of the input content.

[0073] The analysis unit can use the generation AI to provide relevant market trends and competitive information based on the industry and job title. The analysis unit builds a system in which the generation AI automatically provides relevant market trends and competitive information based on, for example, the industry and job title entered by the user. The analysis unit also develops a system in which the generation AI automatically provides relevant market trends and competitive information based on the industry and job title entered by the user. For example, the analysis unit provides market trends and competitive information for a specific department or job title. The analysis unit also develops a system in which the generation AI automatically provides relevant market trends and competitive information based on the industry and job title entered by the user. For example, the analysis unit provides market trends and competitive information for a specific industry and job title. This makes it possible to provide relevant market trends and competitive information based on the industry and job title entered by the user.

[0074] The analysis unit can use the emotion estimation function to analyze the user's emotions regarding the target profile and propose optimal target settings. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the target profile and build a system that proposes optimal target settings. The analysis unit also analyzes the emotional response to the target profile entered by the user in real time and proposes optimal target settings based on the results. For example, the analysis unit proposes a target profile with a high emotion score. The analysis unit also uses the emotion estimation function to analyze the user's emotions regarding the target profile and develop a system that proposes optimal target settings. For example, the analysis unit proposes a target profile that elicits positive emotions. This makes it possible to analyze the user's emotions regarding the target profile and propose optimal target settings.

[0075] The analysis unit uses the generation AI to automatically generate a target list based on information and present multiple candidates. The analysis unit builds a system in which the generation AI automatically generates a target list based on information such as industry and job title entered by a user, and presents multiple candidates. The analysis unit also develops a system in which the generation AI automatically generates a target list based on information entered by a user, and presents multiple candidates. For example, the analysis unit generates an optimal target list for a specific department or job title. The analysis unit also develops a system in which the generation AI automatically generates a target list based on information entered by a user, and presents multiple candidates. For example, the analysis unit generates an optimal target list for a specific industry or job title. This allows the generation AI to automatically generate a target list based on information entered by a user, and present multiple candidates.

[0076] The analysis unit can try out different combinations of industries and job titles, and the generation AI can simulate the optimal target setting. The analysis unit, for example, builds a system in which the generation AI tries out different combinations of industries and job titles, and simulates the optimal target setting. The analysis unit also builds a system in which the generation AI tries out different combinations of industries and job titles, and simulates the optimal target setting. For example, the analysis unit proposes the optimal target based on a combination of multiple industries and job titles. The analysis unit also develops a system in which the generation AI tries out different combinations of industries and job titles, and simulates the optimal target setting. For example, the analysis unit proposes the optimal target based on a combination of multiple industries and job titles. This allows the generation AI to try out different combinations of industries and job titles, and simulate the optimal target setting.

[0077] The analysis unit uses the emotion estimation function to provide feedback on the user's emotions regarding the target settings in real time and adjust the settings. For example, the analysis unit uses the emotion estimation function to build a system that provides feedback on the user's emotions regarding the target settings in real time and adjusts the settings. The analysis unit also analyzes the emotional response to the target settings entered by the user in real time and adjusts the settings based on the results. For example, the analysis unit suggests changing the target settings when the emotion score is low. The analysis unit also uses the emotion estimation function to develop a system that provides feedback on the user's emotions regarding the target settings in real time and adjusts the settings. For example, the analysis unit suggests target settings that elicit positive emotions. This allows feedback on the user's emotions regarding the target settings in real time and adjusts the settings.

[0078] The generation unit can analyze past success stories and extract and propose the most effective sales pitch patterns. For example, the generation unit builds a system in which a generation AI analyzes past success stories and extracts and proposes the most effective sales pitch patterns. The generation unit also analyzes past success stories and proposes the most effective sales pitch patterns based on information entered by the user. For example, the generation unit generates optimal sales pitches for a specific industry or position. The generation unit also develops a system in which a generation AI analyzes past success stories and proposes the most effective sales pitch patterns. For example, the generation unit generates optimal sales pitches for a specific target. This makes it possible to analyze past success stories and extract and propose the most effective sales pitch patterns.

[0079] The generation unit uses the generation AI to automatically generate multiple sales pitches based on information input by a user and can perform A / B testing. For example, the generation unit builds a system in which the generation AI automatically generates multiple sales pitches based on information input by a user and performs A / B testing. The generation unit also develops a system in which the generation AI automatically generates multiple sales pitches based on information input by a user and performs A / B testing. For example, the generation unit tests different sales pitches on a target and selects the optimal pitch. The generation unit also develops a system in which the generation AI automatically generates multiple sales pitches based on information input by a user and performs A / B testing. For example, the generation unit tests different sales pitches on a target and selects the optimal pitch. This allows the generation AI to automatically generate multiple sales pitches based on information input by a user and perform A / B testing.

[0080] The generation unit can use the emotion estimation function to predict the target's emotional response to the generated sales pitch and select the optimal pitch. For example, the generation unit uses the emotion estimation function to predict the target's emotional response to the generated sales pitch and build a system that selects the optimal pitch. The generation unit also uses the generation AI to predict the target's emotional response to the generated sales pitch using the emotion estimation function and selects the optimal pitch. For example, the generation unit preferentially selects pitches with a high emotion score. The generation unit also uses the emotion estimation function to develop a system that predicts the target's emotional response to the generated sales pitch and selects the optimal pitch. For example, the generation unit selects pitches that elicit positive emotions. This makes it possible to predict the target's emotional response to the generated sales pitch and select the optimal pitch.

[0081] The generation unit automatically generates sales pitches in different languages, making it possible to accommodate international targets. The generation unit, for example, uses a generation AI to build a system that automatically generates sales pitches in different languages ​​and accommodates international targets. The generation unit also uses a generation AI to automatically generate sales pitches in different languages ​​based on information input by a user, making it possible to accommodate international targets. For example, the generation unit generates sales pitches that correspond to a specific language. The generation unit also uses a generation AI to develop a system that automatically generates sales pitches in different languages ​​and accommodates international targets. For example, the generation unit generates sales pitches in multiple languages ​​and accommodates international targets. This makes it possible to automatically generate sales pitches in different languages ​​and accommodate international targets.

[0082] The generation unit can vocalize the generated sales pitch and send it to the target as a voice message. The generation unit, for example, uses a generation AI to build a system that vocalizes the generated sales pitch and sends it to the target as a voice message. The generation unit also uses a generation AI to automatically generate a sales pitch based on information input by a user, vocalize it, and send it to the target. For example, the generation unit sends the sales pitch as a voice message. The generation unit also uses a generation AI to develop a system that vocalizes the generated sales pitch and sends it to the target as a voice message. For example, the generation unit uses voice synthesis technology to vocalize the sales pitch and send it to the target. This allows the generated sales pitch to be vocalized and sent to the target as a voice message.

[0083] The generation unit can analyze past success stories and extract and propose the most effective video patterns. For example, the generation unit builds a system in which a generation AI analyzes past success stories and extracts and proposes the most effective video patterns. The generation unit also analyzes past success stories and proposes the most effective video patterns based on information entered by the user. For example, the generation unit generates the optimal video for a specific industry or position. The generation unit also develops a system in which a generation AI analyzes past success stories and proposes the most effective video patterns. For example, the generation unit generates the optimal video for a specific target. This makes it possible to analyze past success stories and extract and propose the most effective video patterns.

[0084] The generation unit uses a generation AI to automatically generate multiple videos based on information input by a user and can perform A / B testing. For example, the generation unit builds a system in which the generation AI automatically generates multiple videos based on information input by a user and performs A / B testing. The generation unit also develops a system in which the generation AI automatically generates multiple videos based on information input by a user and performs A / B testing. For example, the generation unit tests different videos on a target and selects the optimal video. The generation unit also develops a system in which the generation AI automatically generates multiple videos based on information input by a user and performs A / B testing. For example, the generation unit tests different videos on a target and selects the optimal video. This allows the generation AI to automatically generate multiple videos based on information input by a user and perform A / B testing.

[0085] The generation unit uses the emotion estimation function to predict the target's emotional response to the generated video and select the optimal video. For example, the generation unit uses the emotion estimation function to predict the target's emotional response to the generated video and builds a system that selects the optimal video. The generation unit also uses a generation AI to predict the target's emotional response to the video generated using the emotion estimation function and selects the optimal video. For example, the generation unit preferentially selects videos with high emotion scores. The generation unit also uses the emotion estimation function to predict the target's emotional response to the generated video and develops a system that selects the optimal video. For example, the generation unit selects videos that elicit positive emotions. This makes it possible to predict the target's emotional response to the generated video and select the optimal video.

[0086] The generation unit automatically generates sales videos in different languages, making it possible to accommodate international targets. The generation unit, for example, uses a generation AI to build a system that automatically generates sales videos in different languages, making it possible to accommodate international targets. The generation unit also uses a generation AI to automatically generate sales videos in different languages ​​based on information input by a user, making it possible to accommodate international targets. For example, the generation unit generates sales videos that support a specific language. The generation unit also uses a generation AI to develop a system that automatically generates sales videos in different languages, making it possible to accommodate international targets. For example, the generation unit generates sales videos in multiple languages, making it possible to accommodate international targets. This makes it possible to automatically generate sales videos in different languages, making it possible to accommodate international targets.

[0087] The generation unit divides the generated sales video into a shortened version and a long version, and can use them depending on the target's level of interest. For example, the generation unit uses a generation AI to build a system that divides the generated sales video into a shortened version and a long version, and can use them depending on the target's level of interest. Furthermore, the generation unit uses the generation AI to automatically divide the sales video into a shortened version and a long version based on information input by a user, and can use them depending on the target's level of interest. For example, the generation unit generates a shortened version as a video summarizing the main points, and a long version as a video containing detailed explanations. Furthermore, the generation unit uses a generation AI to develop a system that divides the generated sales video into a shortened version and a long version, and can use them depending on the target's level of interest. For example, the generation unit generates a shortened version as a concise introduction video, and a long version as a detailed presentation video. This allows the generation unit to divide the generated sales video into a shortened version and a long version, and can use them depending on the target's level of interest.

[0088] The generation unit uses the emotion estimation function to provide real-time feedback on the user's emotions regarding the generated video and adjust the video. For example, the generation unit uses the emotion estimation function to provide real-time feedback on the user's emotions regarding the generated sales video and build a system to adjust the video. The generation unit also uses a generation AI to analyze the user's emotions regarding the sales video generated using the emotion estimation function in real time and adjust the video based on the results. For example, the generation unit suggests regenerating the content of the video if the emotion score is low. The generation unit also uses the emotion estimation function to develop a system to provide real-time feedback on the user's emotions regarding the generated sales video and adjust the video. For example, the generation unit suggests a video that elicits positive emotions. This allows the user's emotions regarding the generated video to be provided in real-time and adjusted.

[0089] The proposal department can analyze past success stories and propose the most effective proposal format. For example, the proposal department builds a system in which a generation AI analyzes past success stories and proposes the most effective proposal format. The proposal department also analyzes past success stories and proposes the most effective proposal format based on information entered by a user. For example, the proposal department generates the optimal proposal for a specific industry or position. The proposal department also develops a system in which a generation AI analyzes past success stories and proposes the most effective proposal format. For example, the proposal department generates the optimal proposal for a specific target. This makes it possible to analyze past success stories and propose the most effective proposal format.

[0090] The proposal unit can use the generation AI to automatically generate multiple versions of a proposal based on information input by a user and perform A / B testing. For example, the proposal unit builds a system in which the generation AI automatically generates multiple versions of a proposal based on information input by a user and performs A / B testing. The proposal unit also develops a system in which the generation AI automatically generates multiple versions of a proposal based on information input by a user and performs A / B testing. For example, the proposal unit tests different proposals against a target and selects the optimal proposal. The proposal unit also develops a system in which the generation AI automatically generates multiple versions of a proposal based on information input by a user and performs A / B testing. For example, the proposal unit tests different proposals against a target and selects the optimal proposal. This allows the generation AI to automatically generate multiple versions of a proposal based on information input by a user and perform A / B testing.

[0091] The proposal unit uses the emotion estimation function to predict the target's emotional response to the generated proposal and can select the optimal proposal. For example, the proposal unit uses the emotion estimation function to predict the target's emotional response to the generated proposal and builds a system that selects the optimal proposal. The proposal unit also predicts the target's emotional response to the proposal generated by the generation AI using the emotion estimation function and selects the optimal proposal. For example, the proposal unit preferentially selects proposals with high emotion scores. The proposal unit also develops a system that uses the emotion estimation function to predict the target's emotional response to the generated proposal and selects the optimal proposal. For example, the proposal unit selects a proposal that elicits positive emotions. This makes it possible to predict the target's emotional response to the generated proposal and select the optimal proposal.

[0092] The proposal unit can vocalize the generated proposal and send it to the target as a voice message. The proposal unit, for example, uses a generation AI to build a system that vocalizes the generated proposal and sends it to the target as a voice message. The proposal unit also uses a generation AI to automatically generate a proposal based on information input by a user, vocalize it, and send it to the target. For example, the proposal unit sends the proposal as a voice message. The proposal unit also uses a generation AI to develop a system that vocalizes the generated proposal and sends it to the target as a voice message. For example, the proposal unit uses voice synthesis technology to vocalize the proposal and send it to the target. This allows the generated proposal to be vocalized and sent to the target as a voice message.

[0093] The suggestion unit uses the emotion estimation function to provide feedback on the user's emotions regarding the generated proposal in real time and adjust the proposal. For example, the suggestion unit uses the emotion estimation function to build a system that provides feedback on the user's emotions regarding the generated proposal in real time and adjusts the proposal. The suggestion unit also analyzes the user's emotions regarding the proposal generated by the generation AI using the emotion estimation function in real time and adjusts the proposal based on the results. For example, the suggestion unit suggests regenerating the contents of the proposal if the emotion score is low. The suggestion unit also uses the emotion estimation function to develop a system that provides feedback on the user's emotions regarding the generated proposal in real time and adjusts the proposal. For example, the suggestion unit proposes a proposal that elicits positive emotions. This allows the user's emotions regarding the generated proposal to be fed back in real time and the proposal to be adjusted.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The proposal tool can also analyze the user's past proposal history, extract successful proposal patterns, and reflect them in new proposals. For example, it can analyze the structure and wording of past successful proposals and apply similar patterns to new proposals. It can also extract effective approaches for specific industries or job positions from past proposal history and reflect them in the proposal content. Furthermore, it can predict the optimal timing for making a proposal to a specific customer based on past proposal history and adjust the timing of sending the proposal. In this way, the proposal tool can utilize the user's past success stories to make more effective proposals.

[0096] The input unit can further use natural language processing technology to understand the context of the information entered by the user and automatically suggest appropriate complementary information. For example, it can automatically add related technical details and market data to the product features entered by the user. The input unit can also analyze the context of the information entered by the user and automatically suggest related success stories and customer testimonials. Furthermore, the input unit can understand the context of the information entered by the user and automatically suggest appropriate images and videos. This allows the input unit to further enrich the information entered by the user and make effective suggestions.

[0097] The analysis unit can further automatically search for related patent information and technical literature based on the information input by the user and reflect this in the proposal. For example, related patent information is automatically added to the technical features of a product input by the user. The analysis unit can also automatically search for related technical literature based on the information input by the user and reflect this in the proposal. Furthermore, the analysis unit can automatically collect related market trends and competitive information based on the information input by the user and reflect this in the proposal. This allows the analysis unit to further enhance the information input by the user and make effective proposals.

[0098] The generation unit can further generate interactive suggested content based on the information input by the user. For example, the generation unit generates an interactive presentation based on the features and advantages of a product input by the user. The generation unit can also generate an interactive quiz or questionnaire based on the information input by the user and incorporate it into the suggested content. Furthermore, the generation unit can generate an interactive simulation based on the information input by the user and incorporate it into the suggested content. This allows the generation unit to generate more attractive and effective suggested content based on the information input by the user.

[0099] The suggestion unit can further provide the proposal content in multiple formats based on the information input by the user. For example, the suggestion unit can provide the generated proposal in PDF format or PowerPoint format. The suggestion unit can also provide the generated proposal in web page format so that it can be viewed online. The suggestion unit can also provide the generated proposal in mobile app format so that it can be viewed on a smartphone or tablet. In this way, the suggestion unit can provide the proposal content generated by the user in various formats and reach a wider range of targets.

[0100] The input unit can further automatically generate related social media posts based on the information input by the user. For example, the input unit automatically generates content to be posted on social media such as Twitter, Facebook, and LinkedIn based on the features and advantages of a product input by the user. The input unit can also automatically suggest related hashtags and keywords based on the information input by the user. Furthermore, the input unit can automatically set a posting schedule on social media based on the information input by the user. In this way, the input unit can support an effective social media strategy based on the information input by the user.

[0101] The generation unit can further generate a customized demonstration video based on the information input by the user. For example, the generation unit generates a demonstration video customized for a specific customer based on the features and advantages of a product input by the user. The generation unit can also generate a demonstration video tailored to a specific industry or job title based on the information input by the user. Furthermore, the generation unit can also generate a demonstration video tailored to a specific usage scenario based on the information input by the user. This allows the generation unit to generate a more effective demonstration video based on the information input by the user.

[0102] The analysis unit can further automatically search for relevant laws, regulations, and industry standards based on the information input by the user and reflect them in the proposal content. For example, relevant laws, regulations, and industry standards are automatically added to the technical features of a product input by the user. The analysis unit can also automatically search for relevant laws, regulations, and industry standards based on the information input by the user and reflect them in the proposal content. Furthermore, the analysis unit can automatically collect relevant laws, regulations, and industry standards based on the information input by the user and reflect them in the proposal content. This allows the analysis unit to further enhance the information input by the user and make effective proposals.

[0103] The generation unit can further generate customized training content based on the information input by the user. For example, the generation unit can generate a training video customized for a specific customer based on the features and benefits of a product input by the user. The generation unit can also generate a training manual tailored to a specific industry or job position based on the information input by the user. Furthermore, the generation unit can generate training materials tailored to a specific usage scenario based on the information input by the user. This allows the generation unit to generate more effective training content based on the information input by the user.

[0104] The suggestion unit can further provide the proposal content in multiple languages ​​based on the information input by the user. For example, the suggestion unit can provide the generated proposal in multiple languages, such as English, Chinese, and Spanish. The suggestion unit can also provide the generated proposal in a multilingual web page format so that it can be viewed online. The suggestion unit can also provide the generated proposal in a multilingual mobile app format so that it can be viewed on a smartphone or tablet. This allows the suggestion unit to provide the proposal content generated by the user in various languages ​​and reach a wider range of targets.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The input section allows users to enter the features, benefits, and specifications of a service or product. For example, users can enter product specifications in text and upload videos explaining how to use the product. They can also attach images showing the product's design and appearance. Step 2: The analysis unit analyzes the information input by the input unit. For example, the analysis unit analyzes the input information using text analysis technology. It can also analyze the content of uploaded images using image analysis technology. It can also analyze the content of uploaded videos using audio analysis technology. Step 3: The generation unit generates sales pitches and videos based on the information analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate sales pitches based on the input information. The generation unit can also use a generation AI to generate a sales video based on the input information. The generation unit can also use a generation AI to generate a presentation video based on the input information. Step 4: The suggestion unit suggests the sales pitches and videos generated by the generation unit. For example, the suggestion unit compiles the generated sales pitches into a proposal and provides it to the user. The suggestion unit can also attach the generated sales video to the proposal and provide it to the user. The suggestion unit can also attach the generated presentation video to the proposal and provide it to the user.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 AI 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.

[0124] 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.

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

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

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

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

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

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

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

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

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

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

[0135] In the headset type terminal 314, the 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.

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

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

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

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

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The 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.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 AI 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An input section for entering the features, advantages, and specifications of services and products; an analysis unit that analyzes the information input by the input unit; a generation unit that generates sales pitches and videos based on the information analyzed by the analysis unit; a suggestion unit that suggests the sales pitches and videos generated by the generation unit. A system characterized by:

2. The input unit Ability to input information in multiple media formats (text, images, video) 2. The system of claim 1.

3. The analysis unit Analyzing the information input by the input unit and generating optimal sales pitches 2. The system of claim 1.

4. The generation unit A sales video tailored to the sales partner is generated based on the information input by the input unit.

2. The system of claim 1.

5. The proposal unit The sales pitches and sales videos generated by the generation unit are automatically compiled into a proposal.

2. The system of claim 1.

6. The input unit By adding voice input, users can verbally describe features and benefits, which the AI ​​then automatically converts into text.

2. The system of claim 1.

Images