System

Abstract

An object of the system according to the embodiment is to allow anyone to easily create a high-quality POP or text banner.SOLUTION: A system according to an embodiment includes a generation AI, an instruction analysis unit, and a design generation unit. The instruction analysis unit analyzes an instruction from a user. The design generation unit generates a POP or a text banner based on the instruction analyzed by the instruction analysis 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] With conventional technology, there are problems with the quality and time required to create POPs and text banners depending on the designer's strengths and weaknesses, and there is room for improvement.

[0005] The system according to the embodiment aims to enable anyone to easily create high-quality POPs and text banners. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, an instruction analysis unit, and a design generation unit. The instruction analysis unit analyzes user instructions. The design generation unit generates POPs and text banners based on the instructions analyzed by the instruction analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows anyone to easily create high-quality POPs and text banners. [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 image generation AI system according to the embodiment of the present invention is a system that automatically generates POPs and text banners based on user instructions. This allows anyone to easily create POPs and text banners that match their image.

[0029] An image generation AI system according to an embodiment includes a generation AI, an instruction analysis unit, and a design generation unit. The generation AI receives user instructions. For example, the user inputs an instruction such as, "Please create a bright POP for a summer sale." The instruction analysis unit analyzes the instructions received by the generation AI. For example, the instruction analysis unit analyzes the user's instructions using text analysis technology and extracts necessary design elements. The instruction analysis unit can also convert voice instructions into text and analyze it using voice recognition technology. The design generation unit generates a POP or text banner based on the instructions analyzed by the instruction analysis unit. For example, the design generation unit customizes a design according to the user's instructions based on a template provided by the generation AI. The design generation unit can also automatically generate a design tailored to the user's preferences. This allows the image generation AI system according to an embodiment to automatically generate a POP or text banner based on the user's instructions.

[0030] The instruction analysis unit can learn the user's past design history and automatically generate designs that suit the user's preferences. For example, the instruction analysis unit allows the generation AI to analyze the user's past design history and learn the user's preferred colors, fonts, and layouts. For example, it can automatically generate designs that suit the user's preferences based on data from designs created in the past. The instruction analysis unit also stores the history of designs created by the user in a database, and the generation AI proposes new designs based on that data. For example, it can prioritize the incorporation of design elements that the user frequently uses. The instruction analysis unit also allows the generation AI to learn the user's past design history and automatically generate templates that suit the user's preferences. For example, it provides templates that reflect the user's preferred color tones and font styles. This makes it possible to automatically generate designs that suit the user's preferences.

[0031] The instruction analysis unit can analyze the user's voice instructions and generate POPs and text banners based on the voice input. For example, if the user verbally instructs, "I want a bright POP for a summer sale," the instruction analysis unit analyzes the voice and automatically generates an appropriate design. For example, it uses voice recognition technology to convert the instructions into text and reflect them in the design. The instruction analysis unit also analyzes the voice input and builds a system that understands the user's intentions and generates designs. For example, it automatically sets colors, fonts, and layout based on the voice instructions. The instruction analysis unit also allows the user to specify design elements via voice, and the generation AI generates POPs and text banners based on those instructions. For example, it analyzes a voice instruction such as, "A simple design with a red base." This makes it possible to generate POPs and text banners based on voice input.

[0032] The design generation unit can automatically generate POPs and text banners in different languages, providing multilingual designs. For example, the design generation unit uses a generation AI to translate user instructions into multiple languages ​​and automatically generate POPs and text banners for each language. For example, it provides designs in languages ​​such as English, French, and Chinese. The design generation unit also prepares multilingual design templates, and the generation AI generates designs suitable for each language based on the user's instructions. For example, the same design can be deployed in different languages. The design generation unit also uses the generation AI to take into account the cultures and customs of different languages ​​and automatically generate designs accordingly. For example, it reflects fonts and colors that suit the characteristics of each language. This makes it possible to provide multilingual designs.

[0033] The instruction analysis unit can analyze a user's handwritten sketch and generate a design based on it. For example, the instruction analysis unit scans a sketch drawn by the user, and the generation AI analyzes the sketch to generate a design. For example, it creates a POP based on handwritten illustrations and layouts. The instruction analysis unit also analyzes the handwritten sketch using image recognition technology, and the generation AI converts the content into a digital design. For example, it replaces handwritten characters and shapes with digital fonts and graphics. The instruction analysis unit also analyzes the user's handwritten sketch in real time, and the generation AI generates a design on the spot. For example, handwritten input can be performed on a tablet or smartphone, and the results can be instantly reflected in the design. This allows designs to be generated based on handwritten sketches.

[0034] The design generation unit can analyze a user's past sales data and propose designs that contribute to increased sales. For example, the design generation unit's generation AI analyzes a user's past sales data and extracts the characteristics of designs that have resulted in high sales. For example, if a particular color or layout contributes to sales, the unit proposes a design that incorporates those elements. The design generation unit also analyzes designs that were effective during a particular season or event based on the sales data, and the generation AI proposes a design based on that. For example, it takes designs that were successful during Christmas sales as reference. The design generation unit also analyzes past sales data using the generation AI to identify design elements that contribute to increased sales. For example, if a particular font or image has an impact on sales, the unit proposes a design that incorporates those elements. This makes it possible to propose designs that contribute to increased sales.

[0035] The design generation unit can analyze a user's social media activity and propose designs that match the latest trends. For example, the generation AI in the design generation unit analyzes a user's social media activity and proposes designs that match current trends. For example, it generates designs based on popular hashtags and trending words. The design generation unit also analyzes users' posts and reactions on social media, and the generation AI proposes designs based on that. For example, it incorporates design elements from posts that have received many likes from users. The design generation unit also analyzes social media trend data using the generation AI to make proposals that reflect the latest design trends. For example, it proposes designs that incorporate popular colors and font styles. This makes it possible to propose designs that match the latest trends.

[0036] The design generation unit can analyze the designs of the user's competitors and propose a competing design. For example, the design generation unit uses a generation AI to analyze the designs of the user's competitors and propose a competing design. For example, it analyzes a competitor's POP or banner and generates a design that surpasses them. The design generation unit also collects design data on competitors and uses that data to propose a competing design. For example, it generates a design based on the success stories of competitors. The design generation unit also uses a generation AI to analyze a competitor's design, understand the strengths and weaknesses of that design, and then propose an advantageous design to the user. For example, it incorporates elements that complement the weaknesses of the competitor's design. This makes it possible to propose a design that competes with competitors.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The instruction analysis unit can analyze a user's past purchasing history and generate designs based on the user's preferred products and services. For example, it can extract related design elements based on data on products and services the user has purchased in the past and reflect these in POPs and text banners. The instruction analysis unit can also learn the user's purchasing history and suggest designs tailored to specific seasons or events. For example, it can automatically generate designs for the Christmas season or suitable for summer sales. Furthermore, the instruction analysis unit can identify design elements that contribute to increased sales based on the user's purchasing history and generate designs that reflect these elements. This makes it possible to provide effective designs based on the user's purchasing history.

[0039] The design generation unit can collect user feedback and improve the design based on it. For example, a function could be added that allows users to provide ratings and comments on generated designs, and that feedback could be analyzed to improve the quality of the design. The design generation unit can also reflect user feedback in real time and instantly modify the design. For example, if a user requests that the color be a little brighter, the generation AI can adjust the design accordingly. Furthermore, the design generation unit can accumulate user feedback and use it in future design generation. This enables flexible design generation based on user feedback.

[0040] The design generation unit can analyze the user's geographical location information and generate designs specialized for that region. For example, if the user is in a specific region, it will provide a design that matches the culture and customs of that region. The design generation unit can also automatically generate designs according to the season and weather. For example, it will propose a warm design for cold winter regions and a cool design for hot summer regions. Furthermore, the design generation unit can generate designs that match local events and festivals. For example, it will provide POPs and text banners that incorporate design elements related to local festivals and events. This makes it possible to provide effective designs based on the user's geographical location information.

[0041] The design generation unit can analyze a user's social media activity and suggest designs that match the latest trends. For example, the generation AI analyzes a user's social media activity and suggests designs that match current trends. For example, it generates designs based on popular hashtags and trending words. The design generation unit also analyzes users' posts and reactions on social media, and the generation AI suggests designs based on that. For example, it incorporates design elements from posts that have received many likes from users. The design generation unit also analyzes social media trend data and makes suggestions that reflect the latest design trends. For example, it suggests designs that incorporate popular colors and font styles. This makes it possible to suggest designs that match the latest trends.

[0042] The design generation unit can analyze the designs of the user's competitors and propose a competing design. For example, the generation AI can analyze the designs of the user's competitors and propose a competing design. For example, it can analyze a competitor's POP or banner and generate a design that surpasses them. The design generation unit also collects design data on competitors, and the generation AI uses that data to propose a competing design. For example, it generates a design based on the success stories of competitors. The design generation unit can also analyze a competitor's design using the generation AI to understand the strengths and weaknesses of that design and propose an advantageous design to the user. For example, it can incorporate elements that complement the weaknesses of the competitor's design. This makes it possible to propose a design that competes with competitors.

[0043] The design generation unit can automatically generate POPs and text banners in different languages, providing multilingual designs. For example, the design generation unit uses a generation AI to translate user instructions into multiple languages ​​and automatically generate POPs and text banners for each language. For example, it provides designs in languages ​​such as English, French, and Chinese. The design generation unit also prepares multilingual design templates, and the generation AI generates designs suitable for each language based on the user's instructions. For example, the same design can be deployed in different languages. The design generation unit also uses the generation AI to take into account the cultures and customs of different languages ​​and automatically generate designs accordingly. For example, it reflects fonts and colors that suit the characteristics of each language. This makes it possible to provide multilingual designs.

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

[0045] Step 1: The generation AI receives instructions from the user. For example, the user might say, "I want you to create a bright POP for our summer sale." Step 2: The instruction analysis unit analyzes the instructions received by the generation AI. For example, the instruction analysis unit may use text analysis technology to analyze the user's instructions and extract the necessary design elements. The instruction analysis unit may also use voice recognition technology to convert voice instructions into text and analyze it. Step 3: The design generation unit generates POPs and text banners based on the instructions analyzed by the instruction analysis unit. For example, the design generation unit customizes the design according to the user's instructions based on a template provided by the generation AI. The design generation unit can also automatically generate designs tailored to the user's preferences.

[0046] (Example 2) The image generation AI system according to the embodiment of the present invention is a system that automatically generates POPs and text banners based on user instructions. This allows anyone to easily create POPs and text banners that match their image.

[0047] An image generation AI system according to an embodiment includes a generation AI, an instruction analysis unit, and a design generation unit. The generation AI receives user instructions. For example, the user inputs an instruction such as, "Please create a bright POP for a summer sale." The instruction analysis unit analyzes the instructions received by the generation AI. For example, the instruction analysis unit analyzes the user's instructions using text analysis technology and extracts necessary design elements. The instruction analysis unit can also convert voice instructions into text and analyze it using voice recognition technology. The design generation unit generates a POP or text banner based on the instructions analyzed by the instruction analysis unit. For example, the design generation unit customizes a design according to the user's instructions based on a template provided by the generation AI. The design generation unit can also automatically generate a design tailored to the user's preferences. This allows the image generation AI system according to an embodiment to automatically generate a POP or text banner based on the user's instructions.

[0048] The instruction analysis unit can learn the user's past design history and automatically generate designs that suit the user's preferences. For example, the instruction analysis unit allows the generation AI to analyze the user's past design history and learn the user's preferred colors, fonts, and layouts. For example, it can automatically generate designs that suit the user's preferences based on data from designs created in the past. The instruction analysis unit also stores the history of designs created by the user in a database, and the generation AI proposes new designs based on that data. For example, it can prioritize the incorporation of design elements that the user frequently uses. The instruction analysis unit also allows the generation AI to learn the user's past design history and automatically generate templates that suit the user's preferences. For example, it provides templates that reflect the user's preferred color tones and font styles. This makes it possible to automatically generate designs that suit the user's preferences.

[0049] The instruction analysis unit can analyze the user's voice instructions and generate POPs and text banners based on the voice input. For example, if the user verbally instructs, "I want a bright POP for a summer sale," the instruction analysis unit analyzes the voice and automatically generates an appropriate design. For example, it uses voice recognition technology to convert the instructions into text and reflect them in the design. The instruction analysis unit also analyzes the voice input and builds a system that understands the user's intentions and generates designs. For example, it automatically sets colors, fonts, and layout based on the voice instructions. The instruction analysis unit also allows the user to specify design elements via voice, and the generation AI generates POPs and text banners based on those instructions. For example, it analyzes a voice instruction such as, "A simple design with a red base." This makes it possible to generate POPs and text banners based on voice input.

[0050] The instruction analysis unit can use the emotion estimation function to analyze the user's emotional state and generate a design that elicits positive emotions. The instruction analysis unit, for example, analyzes the user's facial expressions and vocal tone and uses the emotion estimation function to understand the user's emotional state. For example, a positive design is generated when the user is relaxed. The instruction analysis unit also uses the emotion estimation function to monitor the user's emotional state in real time when creating a design and proposes a design that elicits positive emotions. For example, a design that has a relaxing effect is generated when the user is feeling stressed. The instruction analysis unit also analyzes the user's emotional state and automatically selects design elements that elicit positive emotions. For example, a design using bright colors and fun illustrations is generated. This makes it possible to generate a design that matches the user's emotional state.

[0051] The design generation unit can automatically generate POPs and text banners in different languages, providing multilingual designs. For example, the design generation unit uses a generation AI to translate user instructions into multiple languages ​​and automatically generate POPs and text banners for each language. For example, it provides designs in languages ​​such as English, French, and Chinese. The design generation unit also prepares multilingual design templates, and the generation AI generates designs suitable for each language based on the user's instructions. For example, the same design can be deployed in different languages. The design generation unit also uses the generation AI to take into account the cultures and customs of different languages ​​and automatically generate designs accordingly. For example, it reflects fonts and colors that suit the characteristics of each language. This makes it possible to provide multilingual designs.

[0052] The instruction analysis unit can analyze a user's handwritten sketch and generate a design based on it. For example, the instruction analysis unit scans a sketch drawn by the user, and the generation AI analyzes the sketch to generate a design. For example, it creates a POP based on handwritten illustrations and layouts. The instruction analysis unit also analyzes the handwritten sketch using image recognition technology, and the generation AI converts the content into a digital design. For example, it replaces handwritten characters and shapes with digital fonts and graphics. The instruction analysis unit also analyzes the user's handwritten sketch in real time, and the generation AI generates a design on the spot. For example, handwritten input can be performed on a tablet or smartphone, and the results can be instantly reflected in the design. This allows designs to be generated based on handwritten sketches.

[0053] The instruction analysis unit uses the emotion estimation function to monitor the user's emotions in real time when creating a design and can make design suggestions based on the emotions. The instruction analysis unit, for example, analyzes the user's facial expressions and voice tone and uses the emotion estimation function to monitor the user's emotional state in real time. For example, it suggests a positive design when the user is relaxed. The instruction analysis unit also uses the emotion estimation function to monitor the user's emotional state in real time when creating a design and makes design suggestions based on the emotions. For example, it suggests a design that has a relaxing effect when the user is feeling stressed. The instruction analysis unit also analyzes the user's emotional state and automatically selects design elements that will elicit positive emotions. For example, it suggests designs that use bright colors and fun illustrations. This makes it possible to make design suggestions based on the user's emotions.

[0054] The design generation unit can analyze a user's past sales data and propose designs that contribute to increased sales. For example, the design generation unit's generation AI analyzes a user's past sales data and extracts the characteristics of designs that have resulted in high sales. For example, if a particular color or layout contributes to sales, the unit proposes a design that incorporates those elements. The design generation unit also analyzes designs that were effective during a particular season or event based on the sales data, and the generation AI proposes a design based on that. For example, it takes designs that were successful during Christmas sales as reference. The design generation unit also analyzes past sales data using the generation AI to identify design elements that contribute to increased sales. For example, if a particular font or image has an impact on sales, the unit proposes a design that incorporates those elements. This makes it possible to propose designs that contribute to increased sales.

[0055] The design generation unit can analyze a user's social media activity and propose designs that match the latest trends. For example, the generation AI in the design generation unit analyzes a user's social media activity and proposes designs that match current trends. For example, it generates designs based on popular hashtags and trending words. The design generation unit also analyzes users' posts and reactions on social media, and the generation AI proposes designs based on that. For example, it incorporates design elements from posts that have received many likes from users. The design generation unit also analyzes social media trend data using the generation AI to make proposals that reflect the latest design trends. For example, it proposes designs that incorporate popular colors and font styles. This makes it possible to propose designs that match the latest trends.

[0056] The design generation unit can use the emotion estimation function to analyze the emotions of the target demographic and propose designs that appeal to those emotions. For example, the design generation unit can use the emotion estimation function to analyze the emotions of the target demographic and propose designs that appeal to those emotions. For example, it can generate designs that elicit positive emotions for young people. The design generation unit can also collect emotional data on the target demographic, and the generation AI can propose designs that appeal to the emotions based on that data. For example, it can propose designs that have a relaxing effect for housewives. The design generation unit can also use the emotion estimation function to analyze the emotional responses of the target demographic and propose designs based on the results. For example, it can generate designs that give a sense of security to elderly people. This makes it possible to propose designs that appeal to the emotions of the target demographic.

[0057] The design generation unit can analyze the designs of the user's competitors and propose a competing design. For example, the design generation unit uses a generation AI to analyze the designs of the user's competitors and propose a competing design. For example, it analyzes a competitor's POP or banner and generates a design that surpasses them. The design generation unit also collects design data on competitors and uses that data to propose a competing design. For example, it generates a design based on the success stories of competitors. The design generation unit also uses a generation AI to analyze a competitor's design, understand the strengths and weaknesses of that design, and then propose an advantageous design to the user. For example, it incorporates elements that complement the weaknesses of the competitor's design. This makes it possible to propose a design that competes with competitors.

[0058] The design generation unit can use the emotion estimation function to monitor the emotional responses of the target demographic in real time and continuously propose optimal designs. For example, the design generation unit can use the emotion estimation function to monitor the emotional responses of the target demographic in real time and propose optimal designs based on that data. For example, it can prioritize designs that evoke a high number of positive emotional responses. The design generation unit also collects emotional response data of the target demographic in real time, and the generation AI continuously proposes designs based on that data. For example, it updates designs every time the emotional responses change. The design generation unit also uses the emotion estimation function to analyze the emotional responses of the target demographic and build a system that proposes designs based on the results. For example, it can automatically select designs with high emotional scores. This makes it possible to continuously propose optimal designs based on the emotional responses of the target demographic.

[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 instruction analysis unit can analyze a user's past purchasing history and generate designs based on the user's preferred products and services. For example, it can extract related design elements based on data on products and services the user has purchased in the past and reflect these in POPs and text banners. The instruction analysis unit can also learn the user's purchasing history and suggest designs tailored to specific seasons or events. For example, it can automatically generate designs for the Christmas season or suitable for summer sales. Furthermore, the instruction analysis unit can identify design elements that contribute to increased sales based on the user's purchasing history and generate designs that reflect these elements. This makes it possible to provide effective designs based on the user's purchasing history.

[0061] The design generation unit can collect user feedback and improve the design based on it. For example, a function could be added that allows users to provide ratings and comments on generated designs, and that feedback could be analyzed to improve the quality of the design. The design generation unit can also reflect user feedback in real time and instantly modify the design. For example, if a user requests that the color be a little brighter, the generation AI can adjust the design accordingly. Furthermore, the design generation unit can accumulate user feedback and use it in future design generation. This enables flexible design generation based on user feedback.

[0062] The design generation unit can analyze the user's geographical location information and generate designs specialized for that region. For example, if the user is in a specific region, it will provide a design that matches the culture and customs of that region. The design generation unit can also automatically generate designs according to the season and weather. For example, it will propose a warm design for cold winter regions and a cool design for hot summer regions. Furthermore, the design generation unit can generate designs that match local events and festivals. For example, it will provide POPs and text banners that incorporate design elements related to local festivals and events. This makes it possible to provide effective designs based on the user's geographical location information.

[0063] The design generation unit can analyze a user's social media activity and suggest designs that match the latest trends. For example, the generation AI analyzes a user's social media activity and suggests designs that match current trends. For example, it generates designs based on popular hashtags and trending words. The design generation unit also analyzes users' posts and reactions on social media, and the generation AI suggests designs based on that. For example, it incorporates design elements from posts that have received many likes from users. The design generation unit also analyzes social media trend data and makes suggestions that reflect the latest design trends. For example, it suggests designs that incorporate popular colors and font styles. This makes it possible to suggest designs that match the latest trends.

[0064] The design generation unit can analyze the designs of the user's competitors and propose a competing design. For example, the generation AI can analyze the designs of the user's competitors and propose a competing design. For example, it can analyze a competitor's POP or banner and generate a design that surpasses them. The design generation unit also collects design data on competitors, and the generation AI uses that data to propose a competing design. For example, it generates a design based on the success stories of competitors. The design generation unit can also analyze a competitor's design using the generation AI to understand the strengths and weaknesses of that design and propose an advantageous design to the user. For example, it can incorporate elements that complement the weaknesses of the competitor's design. This makes it possible to propose a design that competes with competitors.

[0065] The instruction analysis unit can use the emotion estimation function to analyze the user's emotional state and generate a design that elicits positive emotions. The instruction analysis unit, for example, analyzes the user's facial expressions and vocal tone and uses the emotion estimation function to understand the user's emotional state. For example, a positive design is generated when the user is relaxed. The instruction analysis unit also uses the emotion estimation function to monitor the user's emotional state in real time when creating a design and proposes a design that elicits positive emotions. For example, a design that has a relaxing effect is generated when the user is feeling stressed. The instruction analysis unit also analyzes the user's emotional state and automatically selects design elements that elicit positive emotions. For example, a design using bright colors and fun illustrations is generated. This makes it possible to generate a design that matches the user's emotional state.

[0066] The design generation unit can use the emotion estimation function to analyze the emotions of the target demographic and propose designs that appeal to those emotions. For example, the design generation unit can use the emotion estimation function to analyze the emotions of the target demographic and propose designs that appeal to those emotions. For example, it can generate designs that elicit positive emotions for young people. The design generation unit can also collect emotional data on the target demographic, and the generation AI can propose designs that appeal to the emotions based on that data. For example, it can propose designs that have a relaxing effect for housewives. The design generation unit can also use the emotion estimation function to analyze the emotional responses of the target demographic and propose designs based on the results. For example, it can generate designs that give a sense of security to elderly people. This makes it possible to propose designs that appeal to the emotions of the target demographic.

[0067] The design generation unit can use the emotion estimation function to monitor the emotional responses of the target demographic in real time and continuously propose optimal designs. For example, the design generation unit can use the emotion estimation function to monitor the emotional responses of the target demographic in real time and propose optimal designs based on that data. For example, it can prioritize designs that evoke a high number of positive emotional responses. The design generation unit also collects emotional response data of the target demographic in real time, and the generation AI continuously proposes designs based on that data. For example, it updates designs every time the emotional responses change. The design generation unit also uses the emotion estimation function to analyze the emotional responses of the target demographic and build a system that proposes designs based on the results. For example, it can automatically select designs with high emotional scores. This makes it possible to continuously propose optimal designs based on the emotional responses of the target demographic.

[0068] The instruction analysis unit uses the emotion estimation function to monitor the user's emotions in real time when creating a design and can make design suggestions based on the emotions. The instruction analysis unit, for example, analyzes the user's facial expressions and voice tone and uses the emotion estimation function to monitor the user's emotional state in real time. For example, it suggests a positive design when the user is relaxed. The instruction analysis unit also uses the emotion estimation function to monitor the user's emotional state in real time when creating a design and makes design suggestions based on the emotions. For example, it suggests a design that has a relaxing effect when the user is feeling stressed. The instruction analysis unit also analyzes the user's emotional state and automatically selects design elements that will elicit positive emotions. For example, it suggests designs that use bright colors and fun illustrations. This makes it possible to make design suggestions based on the user's emotions.

[0069] The design generation unit can automatically generate POPs and text banners in different languages, providing multilingual designs. For example, the design generation unit uses a generation AI to translate user instructions into multiple languages ​​and automatically generate POPs and text banners for each language. For example, it provides designs in languages ​​such as English, French, and Chinese. The design generation unit also prepares multilingual design templates, and the generation AI generates designs suitable for each language based on the user's instructions. For example, the same design can be deployed in different languages. The design generation unit also uses the generation AI to take into account the cultures and customs of different languages ​​and automatically generate designs accordingly. For example, it reflects fonts and colors that suit the characteristics of each language. This makes it possible to provide multilingual designs.

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

[0071] Step 1: The generation AI receives instructions from the user. For example, the user might say, "I want you to create a bright POP for our summer sale." Step 2: The instruction analysis unit analyzes the instructions received by the generation AI. For example, the instruction analysis unit may use text analysis technology to analyze the user's instructions and extract the necessary design elements. The instruction analysis unit may also use voice recognition technology to convert voice instructions into text and analyze it. Step 3: The design generation unit generates POPs and text banners based on the instructions analyzed by the instruction analysis unit. For example, the design generation unit customizes the design according to the user's instructions based on a template provided by the generation AI. The design generation unit can also automatically generate designs tailored to the user's preferences.

[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 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.

[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 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.

[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 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.

[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 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.

[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. Generative AI and an instruction analysis unit that analyzes an instruction from a user; a design generation unit that generates a POP or a text banner based on the instruction analyzed by the instruction analysis unit. A system characterized by:

2. The instruction analysis unit Learns the user's past design history and automatically generates designs that match the user's preferences 2. The system of claim 1.

3. The instruction analysis unit Analyzing the user's voice instructions and generating POPs and text banners based on the voice input.

2. The system of claim 1.

4. The instruction analysis unit Analyzing the user's emotional state and generating a design that elicits positive emotions 2. The system of claim 1.

5. The design generation unit Automatically generate POP and text banners in different languages ​​to provide multilingual designs 2. The system of claim 1.

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