System

Abstract

An object of a system according to an embodiment is to propose an optimal design to a user and enable customization.SOLUTION: A system includes a data collection unit, a learning unit, a proposal unit, and a customization unit. The data collection unit collects various design data and marketing data. The learning unit learns the data collected by the data collection unit. The proposal unit proposes an optimal design to the user on the basis of the data learned by the learning unit. The customization unit enables the user to customize the design proposed by the proposal 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 does not adequately optimize marketing and design, leaving room for improvement.

[0005] The system according to the embodiment aims to propose optimal designs to users and enable customization. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a learning unit, a proposal unit, and a customization unit. The data collection unit collects various design data and marketing data. The learning unit learns the data collected by the data collection unit. The proposal unit proposes an optimal design to the user based on the data learned by the learning unit. The customization unit allows the user to customize the design proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal design to the user and enable customization. [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 marketing support system according to an embodiment of the present invention collects various design data and marketing data, uses a generation AI to learn, proposes optimal designs to users, and allows users to customize them. This allows the marketing support system to reduce design man-hours and request costs, and also allows users to create designs that satisfy them.

[0029] A marketing support system according to an embodiment includes a data collection unit, a learning unit, a proposal unit, and a customization unit. The data collection unit collects various design data and marketing data. For example, the data collection unit collects design data such as graphic design, web design, and product design. The data collection unit also collects marketing data such as consumer behavior data, advertising effectiveness data, and sales data. The learning unit learns from the data collected by the data collection unit. For example, the learning unit analyzes the data using a machine learning algorithm to learn the effectiveness of the design. The learning unit can also learn from the data using deep learning technology. The proposal unit proposes an optimal design to a user based on the data learned by the learning unit. For example, the proposal unit analyzes prompts entered by the user and proposes an appropriate design. The proposal unit can also propose designs tailored to the user's needs and preferences. The customization unit allows the user to customize the design proposed by the proposal unit. For example, the customization unit allows the user to change the color or layout of the design. The customization unit can also allow the user to add or delete elements of the design. As a result, the marketing support system according to the embodiment can reduce the number of design steps and the cost of designing, and can also create designs that satisfy the user.

[0030] The data collection unit tracks market trends in real time and can reflect design trends based on market trends. For example, the data collection unit allows the generation AI to collect market trend data in real time and learn the latest design trends. For example, it automatically obtains trend information from social media and news sites and reflects it in the design. The data collection unit also tracks market trends in real time, and the generation AI proposes designs based on that data. For example, it generates advertising designs that incorporate the latest fashion and interior design trends. The data collection unit also collects market trend data in real time, and the generation AI learns the effectiveness of designs based on that data. For example, it analyzes trend keywords and popular color patterns and reflects them in the design. This makes it possible to propose designs that reflect the latest market trends.

[0031] The data collection unit can classify the collected data by different industries and cultural spheres, and learn design trends based on the industry and cultural sphere. For example, the data collection unit classifies the data collected by the generation AI by different industries to learn industry-specific design trends. For example, design data from the medical industry and the entertainment industry can be analyzed separately. The data collection unit also classifies the data collected by the generation AI by cultural sphere to learn region-specific design trends. For example, design data from Asia and Europe can be analyzed separately to reflect regional preferences. The data collection unit also classifies the data by different industries and cultural spheres, and the generation AI can learn the effectiveness of designs based on that data. For example, design elements popular in a particular cultural sphere can be extracted and reflected in proposals. This allows region-specific design trends to be learned and reflected in proposals.

[0032] The data collection unit collects multimodal information including audio data and video data, and can learn the effectiveness of a design based on the multimodal information. In the data collection unit, for example, the generation AI collects audio data and learns the effectiveness of a design. For example, the data collection unit analyzes the narration and musical tone of an advertisement and reflects this in the design. In addition, the data collection unit collects video data, and the generation AI learns the effectiveness of a design based on that data. For example, the data collection unit analyzes viewing data of video advertisements and proposes designs based on viewer reactions. In addition, the data collection unit collects multimodal information including audio and video data, and the generation AI learns the effectiveness of a design based on that data. For example, the data collection unit uses voice recognition technology to analyze the content of user utterances and reflect this in the design. In this way, the multimodal information including audio and video data can be learned and the effectiveness of a design can be improved.

[0033] The data collection unit can share data with other generative AIs and exchange data between them. For example, the data collection unit shares data collected by a generative AI with other generative AIs and exchanges data. For example, design data can be shared with generative AIs in different fields to deepen mutual learning. The data collection unit also exchanges data between different generative AIs, allowing the generative AIs to learn about the effectiveness of designs based on that data. For example, advertising design AI and product design AI can share data and mutually improve their proposals. The data collection unit also shares data collected by a generative AI with other generative AIs, building a system for data exchange between different generative AIs. For example, generative AIs in different industries can share data to increase design diversity. This allows data exchange between different generative AIs to increase design diversity.

[0034] The suggestion unit can reflect the user's past design history and make personalized suggestions. For example, the suggestion unit uses a generation AI to analyze the user's past design history and make personalized design suggestions based on that data. For example, the suggestion unit reflects the style and colors of designs created by the user in the past. The suggestion unit also builds a system in which the generation AI makes personalized design suggestions based on the user's design history. For example, the suggestion unit prioritizes suggesting design elements that the user prefers. The suggestion unit also uses a generation AI to learn the user's past design history and make personalized design suggestions based on that data. For example, the suggestion unit reflects fonts and layouts that the user frequently uses. In this way, personalized suggestions can be made by reflecting the user's past design history.

[0035] The proposal unit can automatically reflect requirements and regulations specific to the user's industry. For example, the proposal unit has a generation AI that learns the requirements and regulations specific to the user's industry and makes design proposals based on that data. For example, legal regulations are automatically reflected in advertising designs for the medical industry. The proposal unit also builds a system in which the generation AI makes design proposals based on the requirements and regulations specific to the user's industry. For example, compliance requirements are reflected in advertising designs for the financial industry. The proposal unit also builds a system in which the generation AI makes design proposals that automatically reflect the requirements and regulations specific to the user's industry. For example, information that must be displayed is automatically included in advertising designs for the food industry. This makes it possible to propose appropriate designs by automatically reflecting industry-specific requirements and regulations.

[0036] The proposal unit can add a function to optimize for different devices and platforms. For example, the proposal unit adds a function to optimize the design proposed by the generation AI for different devices and platforms. For example, it automatically generates designs compatible with devices such as smartphones, tablets, and PCs. The proposal unit also builds a system that proposes designs optimized for different platforms. For example, it generates designs compatible with platforms such as social media ads, websites, and printed materials. The proposal unit also adds a function to optimize the design proposed by the generation AI for different devices and platforms, allowing users to easily customize it. For example, it automatically adjusts the layout and changes the font size for each device. This makes it possible to propose designs optimized for different devices and platforms.

[0037] The proposal unit can add a function that allows designs to be shared with other users and customized collaboratively. For example, the proposal unit adds a function that allows designs proposed by the generative AI to be shared with other users and customized collaboratively. For example, it provides a collaboration tool that allows team members to edit designs simultaneously. The proposal unit also builds a design proposal system that allows collaborative customization. For example, it provides an interface that allows multiple users to edit designs in real time and exchange opinions. The proposal unit also adds a function that allows designs proposed by the generative AI to be shared with other users and customized collaboratively. For example, it provides a system that can track design version management and change history. This makes it easier to create designs in a team by adding a function that allows designs to be shared with other users and customized collaboratively.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The marketing support system can also analyze a user's past purchasing history and make personalized design suggestions. For example, it can suggest highly relevant designs based on data on products and services the user has purchased in the past. In addition, the generative AI can suggest designs that are optimal for promoting specific products based on the user's purchasing history. Furthermore, it can analyze a user's purchasing history and make design suggestions that match the season or events. This allows for more effective marketing by making personalized design suggestions based on the user's purchasing history.

[0040] The marketing support system can also analyze users' social media activity and make personalized design suggestions. For example, it can suggest highly relevant designs based on the content and comments the user has shared on social media. It can also analyze the activities of the user's followers and friends to make design suggestions that match trends. Furthermore, the generative AI can suggest the best design for a specific campaign based on the user's social media activity. This allows for more effective marketing by making personalized design suggestions based on the user's social media activity.

[0041] The marketing support system can also collect user feedback and improve design proposals based on that feedback. For example, users can comment on and rate proposed designs, and the generative AI can use that data to improve its next proposal. Specific design elements can also be enhanced or removed based on user feedback. Furthermore, it is possible to add a function that collects user feedback in real time and instantly reflects it in design proposals. This allows design proposals based on user feedback to provide designs that are more satisfying.

[0042] The marketing support system can also analyze the user's geographical location information to propose region-specific designs. For example, it can propose designs that reflect the culture and trends of the user's region. Furthermore, based on the user's location information, the generative AI can propose designs that match local events or seasons. Furthermore, it is possible to add a function that collects the user's geographical location information in real time and immediately reflects it in design proposals. This allows for more effective marketing by proposing designs based on the user's geographical location information.

[0043] The marketing support system can also analyze a user's browsing history to make personalized design suggestions. For example, it can suggest highly relevant designs based on the websites the user has previously visited and the content they have viewed. The generative AI can also suggest designs that are optimal for promoting specific products based on the user's browsing history. Furthermore, it can analyze a user's browsing history to make design suggestions tailored to the season or event. This allows for more effective marketing by making personalized design suggestions based on the user's browsing history.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The data collection department collects various design data and marketing data, such as design data on graphic design, web design, and product design, and marketing data on consumer behavior, advertising effectiveness, and sales. Step 2: The learning unit learns from the data collected by the data collection unit. For example, it analyzes the data using machine learning algorithms or deep learning techniques to learn the effectiveness of the design. Step 3: The suggestion unit proposes the optimal design to the user based on the data learned by the learning unit. For example, it analyzes the prompt entered by the user and proposes an appropriate design. It can also propose designs that match the user's needs and preferences. Step 4: The customization unit allows the user to customize the design proposed by the suggestion unit, for example, by allowing the user to change the color and layout of the design, and to add or remove elements of the design.

[0046] (Example 2) The marketing support system according to an embodiment of the present invention collects various design data and marketing data, uses a generation AI to learn, proposes optimal designs to users, and allows users to customize them. This allows the marketing support system to reduce design man-hours and request costs, and also allows users to create designs that satisfy them.

[0047] A marketing support system according to an embodiment includes a data collection unit, a learning unit, a proposal unit, and a customization unit. The data collection unit collects various design data and marketing data. For example, the data collection unit collects design data such as graphic design, web design, and product design. The data collection unit also collects marketing data such as consumer behavior data, advertising effectiveness data, and sales data. The learning unit learns from the data collected by the data collection unit. For example, the learning unit analyzes the data using a machine learning algorithm to learn the effectiveness of the design. The learning unit can also learn from the data using deep learning technology. The proposal unit proposes an optimal design to a user based on the data learned by the learning unit. For example, the proposal unit analyzes prompts entered by the user and proposes an appropriate design. The proposal unit can also propose designs tailored to the user's needs and preferences. The customization unit allows the user to customize the design proposed by the proposal unit. For example, the customization unit allows the user to change the color or layout of the design. The customization unit can also allow the user to add or delete elements of the design. As a result, the marketing support system according to the embodiment can reduce the number of design steps and the cost of designing, and can also create designs that satisfy the user.

[0048] The data collection unit collects users' emotional response data and can learn the effectiveness of designs based on the emotional response data. For example, the data collection unit allows the generation AI to collect users' emotional response data and learn the effectiveness of designs. For example, the data collection unit analyzes users' facial expressions and voices when viewing designs and prioritizes learning designs that elicit positive reactions. The data collection unit also collects emotional data when users evaluate designs, and the generation AI learns the effectiveness of designs based on that data. For example, it extracts features of designs that users have given high ratings to and reflects them in the next design proposal. The data collection unit also uses emotion analysis technology to collect users' emotional response data, and the generation AI learns the effectiveness of designs based on that data. For example, it analyzes the user's heart rate and galvanic skin response when viewing a design to measure the intensity of their emotions. This allows the generation AI to learn the effectiveness of designs based on users' emotions and propose more effective designs.

[0049] The data collection unit tracks market trends in real time and can reflect design trends based on market trends. For example, the data collection unit allows the generation AI to collect market trend data in real time and learn the latest design trends. For example, it automatically obtains trend information from social media and news sites and reflects it in the design. The data collection unit also tracks market trends in real time, and the generation AI proposes designs based on that data. For example, it generates advertising designs that incorporate the latest fashion and interior design trends. The data collection unit also collects market trend data in real time, and the generation AI learns the effectiveness of designs based on that data. For example, it analyzes trend keywords and popular color patterns and reflects them in the design. This makes it possible to propose designs that reflect the latest market trends.

[0050] The data collection unit can classify the collected data by different industries and cultural spheres, and learn design trends based on the industry and cultural sphere. For example, the data collection unit classifies the data collected by the generation AI by different industries to learn industry-specific design trends. For example, design data from the medical industry and the entertainment industry can be analyzed separately. The data collection unit also classifies the data collected by the generation AI by cultural sphere to learn region-specific design trends. For example, design data from Asia and Europe can be analyzed separately to reflect regional preferences. The data collection unit also classifies the data by different industries and cultural spheres, and the generation AI can learn the effectiveness of designs based on that data. For example, design elements popular in a particular cultural sphere can be extracted and reflected in proposals. This allows region-specific design trends to be learned and reflected in proposals.

[0051] The data collection unit collects multimodal information including audio data and video data, and can learn the effectiveness of a design based on the multimodal information. In the data collection unit, for example, the generation AI collects audio data and learns the effectiveness of a design. For example, the data collection unit analyzes the narration and musical tone of an advertisement and reflects this in the design. In addition, the data collection unit collects video data, and the generation AI learns the effectiveness of a design based on that data. For example, the data collection unit analyzes viewing data of video advertisements and proposes designs based on viewer reactions. In addition, the data collection unit collects multimodal information including audio and video data, and the generation AI learns the effectiveness of a design based on that data. For example, the data collection unit uses voice recognition technology to analyze the content of user utterances and reflect this in the design. In this way, the multimodal information including audio and video data can be learned and the effectiveness of a design can be improved.

[0052] The data collection unit can share data with other generative AIs and exchange data between them. For example, the data collection unit shares data collected by a generative AI with other generative AIs and exchanges data. For example, design data can be shared with generative AIs in different fields to deepen mutual learning. The data collection unit also exchanges data between different generative AIs, allowing the generative AIs to learn about the effectiveness of designs based on that data. For example, advertising design AI and product design AI can share data and mutually improve their proposals. The data collection unit also shares data collected by a generative AI with other generative AIs, building a system for data exchange between different generative AIs. For example, generative AIs in different industries can share data to increase design diversity. This allows data exchange between different generative AIs to increase design diversity.

[0053] The suggestion unit can reflect the user's past design history and make personalized suggestions. For example, the suggestion unit uses a generation AI to analyze the user's past design history and make personalized design suggestions based on that data. For example, the suggestion unit reflects the style and colors of designs created by the user in the past. The suggestion unit also builds a system in which the generation AI makes personalized design suggestions based on the user's design history. For example, the suggestion unit prioritizes suggesting design elements that the user prefers. The suggestion unit also uses a generation AI to learn the user's past design history and make personalized design suggestions based on that data. For example, the suggestion unit reflects fonts and layouts that the user frequently uses. In this way, personalized suggestions can be made by reflecting the user's past design history.

[0054] The proposal unit can automatically reflect requirements and regulations specific to the user's industry. For example, the proposal unit has a generation AI that learns the requirements and regulations specific to the user's industry and makes design proposals based on that data. For example, legal regulations are automatically reflected in advertising designs for the medical industry. The proposal unit also builds a system in which the generation AI makes design proposals based on the requirements and regulations specific to the user's industry. For example, compliance requirements are reflected in advertising designs for the financial industry. The proposal unit also builds a system in which the generation AI makes design proposals that automatically reflect the requirements and regulations specific to the user's industry. For example, information that must be displayed is automatically included in advertising designs for the food industry. This makes it possible to propose appropriate designs by automatically reflecting industry-specific requirements and regulations.

[0055] The suggestion unit uses the emotion estimation function to make design suggestions based on the user's emotions, and can prioritize proposing designs that evoke the user's most positive emotions. The suggestion unit, for example, uses the emotion estimation function to make design suggestions based on the user's emotions. For example, it prioritizes proposing design elements that the user has expressed positive emotions about. The suggestion unit also builds a system in which the generation AI proposes designs that evoke the most positive emotions based on the user's emotion data. For example, it reflects features of designs that the user has given high ratings about. The suggestion unit also uses the emotion estimation function to make design suggestions based on the user's emotions, and prioritizes proposing designs that evoke the user's most positive emotions. For example, it analyzes the user's facial expressions and voice and proposes designs with high emotion scores. In this way, by making design suggestions based on the user's emotions, it is possible to prioritize proposing designs that evoke the user's most positive emotions.

[0056] The proposal unit can add a function to optimize for different devices and platforms. For example, the proposal unit adds a function to optimize the design proposed by the generation AI for different devices and platforms. For example, it automatically generates designs compatible with devices such as smartphones, tablets, and PCs. The proposal unit also builds a system that proposes designs optimized for different platforms. For example, it generates designs compatible with platforms such as social media ads, websites, and printed materials. The proposal unit also adds a function to optimize the design proposed by the generation AI for different devices and platforms, allowing users to easily customize it. For example, it automatically adjusts the layout and changes the font size for each device. This makes it possible to propose designs optimized for different devices and platforms.

[0057] The proposal unit can add a function that allows designs to be shared with other users and customized collaboratively. For example, the proposal unit adds a function that allows designs proposed by the generative AI to be shared with other users and customized collaboratively. For example, it provides a collaboration tool that allows team members to edit designs simultaneously. The proposal unit also builds a design proposal system that allows collaborative customization. For example, it provides an interface that allows multiple users to edit designs in real time and exchange opinions. The proposal unit also adds a function that allows designs proposed by the generative AI to be shared with other users and customized collaboratively. For example, it provides a system that can track design version management and change history. This makes it easier to create designs in a team by adding a function that allows designs to be shared with other users and customized collaboratively.

[0058] The suggestion unit can use the emotion estimation function to provide real-time feedback on the emotions of the user when customizing the design, and support customization based on the emotions. The suggestion unit, for example, uses the emotion estimation function to provide real-time feedback on the emotions of the user when customizing the design. For example, by analyzing the user's facial expressions and voice and displaying an emotion score, it preferentially suggests designs that evoke positive emotions in the user. The suggestion unit also builds a system in which a generative AI supports optimal customization based on the user's emotion data. For example, if the user expresses negative emotions, it suggests changes to the design. The suggestion unit also uses the emotion estimation function to provide real-time feedback on the emotions of the user when customizing the design, and support optimal customization. For example, it makes suggestions to adjust the color or layout of the design according to changes in the user's emotions. This allows optimal customization to be supported based on the user's emotions, making it possible to create a design that satisfies the user.

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

[0060] The marketing support system can also analyze a user's past purchasing history and make personalized design suggestions. For example, it can suggest highly relevant designs based on data on products and services the user has purchased in the past. In addition, the generative AI can suggest designs that are optimal for promoting specific products based on the user's purchasing history. Furthermore, it can analyze a user's purchasing history and make design suggestions that match the season or events. This allows for more effective marketing by making personalized design suggestions based on the user's purchasing history.

[0061] The marketing support system can also estimate the user's emotions and adjust the design colors based on the estimated emotions. For example, if the user expresses positive emotions, it will suggest bright colors, and if they express negative emotions, it will suggest subdued colors. The generative AI can also suggest the optimal color palette based on the user's emotional data. Furthermore, it is possible to add a function that adjusts colors in real time according to changes in the user's emotions. This allows for more effective design proposals by adjusting colors based on the user's emotions.

[0062] The marketing support system can also analyze users' social media activity and make personalized design suggestions. For example, it can suggest highly relevant designs based on the content and comments the user has shared on social media. It can also analyze the activities of the user's followers and friends to make design suggestions that match trends. Furthermore, the generative AI can suggest the best design for a specific campaign based on the user's social media activity. This allows for more effective marketing by making personalized design suggestions based on the user's social media activity.

[0063] The marketing support system can also estimate the user's emotions and adjust the design layout based on the estimated emotions. For example, if the user expresses positive emotions, it will suggest an open layout, and if they express negative emotions, it will suggest a simple layout. The generative AI can also suggest the optimal layout based on the user's emotional data. Furthermore, it is possible to add a function that adjusts the layout in real time according to changes in the user's emotions. This allows for more effective design proposals by adjusting the layout based on the user's emotions.

[0064] The marketing support system can also collect user feedback and improve design proposals based on that feedback. For example, users can comment on and rate proposed designs, and the generative AI can use that data to improve its next proposal. Specific design elements can also be enhanced or removed based on user feedback. Furthermore, it is possible to add a function that collects user feedback in real time and instantly reflects it in design proposals. This allows design proposals based on user feedback to provide designs that are more satisfying.

[0065] The marketing support system can also estimate the user's emotions and adjust the font of the design based on the estimated emotions. For example, if the user expresses positive emotions, it will suggest a soft font, and if they express negative emotions, it will suggest a simple font. The generative AI can also suggest the most suitable font based on the user's emotional data. Furthermore, it is possible to add a function that adjusts the font in real time according to changes in the user's emotions. This allows for more effective design proposals by adjusting the font based on the user's emotions.

[0066] The marketing support system can also analyze the user's geographical location information to propose region-specific designs. For example, it can propose designs that reflect the culture and trends of the user's region. Furthermore, based on the user's location information, the generative AI can propose designs that match local events or seasons. Furthermore, it is possible to add a function that collects the user's geographical location information in real time and immediately reflects it in design proposals. This allows for more effective marketing by proposing designs based on the user's geographical location information.

[0067] The marketing support system can also estimate the user's emotions and adjust the design animation based on the estimated emotions. For example, if the user expresses positive emotions, it will suggest a dynamic animation, and if they express negative emotions, it will suggest a static animation. The generative AI can also suggest the optimal animation based on the user's emotional data. Furthermore, it is possible to add a function that adjusts animations in real time according to changes in the user's emotions. This allows for more effective design proposals by adjusting animations based on the user's emotions.

[0068] The marketing support system can also analyze a user's browsing history to make personalized design suggestions. For example, it can suggest highly relevant designs based on the websites the user has previously visited and the content they have viewed. The generative AI can also suggest designs that are optimal for promoting specific products based on the user's browsing history. Furthermore, it can analyze a user's browsing history to make design suggestions tailored to the season or event. This allows for more effective marketing by making personalized design suggestions based on the user's browsing history.

[0069] The marketing support system can also estimate the user's emotions and adjust the design interactions based on the estimated emotions. For example, if the user expresses positive emotions, it can propose a design with many interactive elements, and if the user expresses negative emotions, it can propose simple interactions. The generative AI can also propose optimal interactions based on the user's emotional data. Furthermore, it is possible to add a function that adjusts interactions in real time according to changes in the user's emotions. This allows for more effective design proposals by adjusting interactions based on the user's emotions.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The data collection department collects various design data and marketing data, such as design data on graphic design, web design, and product design, and marketing data on consumer behavior, advertising effectiveness, and sales. Step 2: The learning unit learns from the data collected by the data collection unit. For example, it analyzes the data using machine learning algorithms or deep learning techniques to learn the effectiveness of the design. Step 3: The suggestion unit proposes the optimal design to the user based on the data learned by the learning unit. For example, it analyzes the prompt entered by the user and proposes an appropriate design. It can also propose designs that match the user's needs and preferences. Step 4: The customization unit allows the user to customize the design proposed by the suggestion unit, for example, by allowing the user to change the color and layout of the design, and to add or remove elements of the design.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 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.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection department that collects various design and marketing data, a learning unit that learns the data collected by the data collection unit; a suggestion unit that suggests an optimal design to a user based on the data learned by the learning unit; a customization unit that enables a user to customize the design proposed by the proposal unit. A system characterized by:

2. The data collection unit Collecting user emotional response data and learning about the effectiveness of the design based on the emotional response data.

2. The system of claim 1.

3. The data collection unit Track market trends in real time and reflect design trends based on said market trends 2. The system of claim 1.

4. The data collection unit Categorize the collected data into different industries and cultures, and learn design trends based on those industries and cultures.

2. The system of claim 1.

5. The data collection unit Collecting multimodal information including audio data and video data and learning the effectiveness of the design based on the multimodal information 2. The system of claim 1.

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