System

Abstract

An object of a system according to an embodiment is to generate a novel idea based on attributes, hobbies, and preferences of a target audience and provide the idea to a person in charge of marketing or an advertisement creator.SOLUTION: A system according to an embodiment includes a target audience analysis unit, an idea generation unit, and an inspiration providing unit. The target audience analyzing unit analyzes attributes, hobbies, and preferences of the target audience. The idea generation unit generates a novel idea on the basis of the attributes, hobbies, and preferences of the target audience analyzed by the target audience analysis unit. The inspiration providing unit provides the idea generated by the idea generation unit to a marketing person or an advertisement creator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to efficiently generate innovative ideas based on the attributes and hobbies and preferences of target audiences and provide them to marketers and advertising creators.

[0005] The system according to the embodiment aims to generate innovative ideas based on the attributes and hobbies of a target audience and provide them to marketers and advertising creators. [Means for solving the problem]

[0006] The system according to the embodiment includes a target audience analysis unit, an idea generation unit, and an inspiration provision unit. The target audience analysis unit analyzes the attributes and hobbies and preferences of the target audience. The idea generation unit generates innovative ideas based on the attributes and hobbies and preferences of the target audience analyzed by the target audience analysis unit. The inspiration provision unit provides the ideas generated by the idea generation unit to marketers and advertising creators. [Effects of the Invention]

[0007] The system according to the embodiment can generate innovative ideas based on the attributes and hobbies of the target audience and provide them to marketers and advertising creators. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 CreatiMind Navigator system according to an embodiment of the present invention generates innovative ideas based on the attributes and hobbies of a target audience and provides them to marketers and advertising creators, enabling them to mass-produce marketing campaign concepts and gain an advantage over the competition.

[0029] The CreatiMind Navigator system according to the embodiment includes a target audience analysis unit, an idea generation unit, and an inspiration provision unit. The target audience analysis unit analyzes the attributes and hobbies and preferences of the target audience. For example, the target audience analysis unit collects data such as age, gender, region, and interests to create a profile of the target audience. The target audience analysis unit can also analyze social media activity and purchase history to create a profile reflecting the target audience's latest hobbies and preferences. The idea generation unit generates innovative ideas based on the attributes and hobbies and preferences of the target audience analyzed by the target audience analysis unit. For example, the idea generation unit proposes unique advertising concepts and promotion methods for targets of specific age groups or hobbies and preferences. The idea generation unit can also analyze successful examples of past marketing campaigns and combine their elements to generate new ideas. The inspiration provision unit provides the ideas generated by the idea generation unit to marketers and advertising creators. For example, the inspiration provider can compare ideas proposed by the generative AI with marketers' past success stories to provide optimal inspiration. The inspiration provider can also customize the ideas proposed by the generative AI for each different marketing channel. This allows the CreatiMind Navigator system to generate innovative ideas based on the attributes and hobbies of the target audience and provide them to marketers and advertising creators, giving them an advantage over the competition.

[0030] The target audience analysis unit can analyze the social media activity of the target audience in real time and create a profile that reflects their latest hobbies and preferences. For example, the target audience analysis unit uses generative AI to analyze the social media activity of the target audience in real time and extract the latest hobbies and preferences from the content of posts, hashtags, accounts followed, etc. For example, the profile can be updated based on the frequency of posts related to specific brands or trends. This makes it possible to create a profile that reflects the target audience's latest hobbies and preferences in real time.

[0031] The target audience analysis unit can analyze the target audience's past purchasing history and identify detailed attributes based on their purchasing patterns. For example, the target audience analysis unit analyzes the target audience's online shopping history and identifies their hobbies and preferences based on the categories and brands of products purchased. For example, if a person frequently purchases fashion items, it can be determined that they are interested in fashion. This allows the target audience's past purchasing history to be analyzed and detailed attributes to be identified based on their purchasing patterns.

[0032] The target audience analysis unit can create more detailed profiles by introducing multimodal analysis including voice data and image data. The target audience analysis unit, for example, analyzes the voice data of the target audience and identifies their hobbies and preferences from the tone of voice and the content of the topics. For example, if the voice data shows that a particular sport is frequently discussed, it can be determined that the person is interested in that sport. The target audience analysis unit can also analyze image data and identify their hobbies and preferences from visual information. For example, if the person posts many images that include the logo of a particular brand, it can be determined that the person is interested in that brand. This allows for the creation of more detailed profiles by introducing multimodal analysis including voice data and image data.

[0033] The target audience analysis unit can compare and analyze target audiences from different regions and cultural spheres, and perform attribute analysis from a global perspective. For example, the target audience analysis unit collects data on target audiences from different regions and analyzes it taking into account cultural backgrounds and regional characteristics. For example, it compares the hobbies and preferences of target audiences in Asia and Europe, and reflects the characteristics of each region in the profile. This makes it possible to compare and analyze target audiences from different regions and cultural spheres, and perform attribute analysis from a global perspective.

[0034] The idea generation unit can analyze successful cases of past marketing campaigns and combine their elements to generate new ideas. For example, the idea generation unit uses a generation AI to collect successful cases of past marketing campaigns from a database and analyze the factors behind their success. For example, it extracts the factors that made a particular advertising method or promotion strategy successful and generates new ideas based on that. The idea generation unit also combines elements of successful cases to generate ideas for new marketing campaigns. For example, it proposes ideas that combine elements from different successful cases to create synergistic effects. This makes it possible to analyze successful cases of past marketing campaigns and combine their elements to generate new ideas.

[0035] The idea generation unit can generate different scenarios based on the attributes of the target audience and propose the optimal idea for each scenario. For example, the idea generation unit uses a generation AI to generate different scenarios based on the attribute data of the target audience. For example, different marketing scenarios can be created for each age group, gender, and region, and the optimal idea for each can be proposed. The idea generation unit also analyzes the content of the scenario in detail to propose the optimal idea for each scenario. For example, in a specific scenario, it can prioritize the proposal of ideas that have a good response from the target audience. This makes it possible to generate different scenarios based on the attributes of the target audience and propose the optimal idea for each scenario.

[0036] The idea generation unit can refer to marketing methods from different industries and carry out crossover innovation. For example, the generation AI collects and analyzes marketing methods from different industries from a database. For example, it generates new ideas that combine marketing methods from the fashion and technology industries. The idea generation unit can also refer to successful examples from different industries and carry out crossover innovation. For example, it can combine elements from different industries to propose a new marketing campaign. This makes it possible to refer to marketing methods from different industries and carry out crossover innovation.

[0037] The inspiration providing unit can compare ideas proposed by the generative AI with past success stories of marketers and provide optimal inspiration. For example, the inspiration providing unit compares ideas proposed by the generative AI with past success stories of marketers and analyzes similarities and differences. For example, it prioritizes the proposal of ideas that have elements in common with past success stories. The inspiration providing unit also compares ideas proposed by the generative AI with past failure stories of marketers and provides inspiration for avoiding risks. For example, it excludes ideas that have similar elements to past failure stories. This makes it possible to compare ideas proposed by the generative AI with past success stories of marketers and provide optimal inspiration.

[0038] The inspiration providing unit can customize and provide the ideas proposed by the generation AI for each different marketing channel. For example, the inspiration providing unit customizes and provides the ideas proposed by the generation AI for social media. For example, it proposes social media advertisements using short videos or infographics. The inspiration providing unit also customizes and provides the ideas proposed by the generation AI for email marketing. For example, it proposes personalized email content. The inspiration providing unit also customizes and provides the ideas proposed by the generation AI for advertising. For example, it proposes banner advertisements or display advertisements. This allows the ideas proposed by the generation AI to be customized and provided for each different marketing channel.

[0039] The inspiration providing unit can adapt the ideas proposed by the generative AI to different languages ​​and cultures and apply them to international marketing campaigns. For example, the inspiration providing unit translates the ideas proposed by the generative AI into different languages ​​and apply them to international marketing campaigns. For example, it translates into multiple languages ​​such as English, French, and Chinese. The inspiration providing unit also adapts the ideas proposed by the generative AI to different cultures. For example, it modifies the ideas taking into account the values ​​and customs of a particular culture. In this way, the ideas proposed by the generative AI can be adapted to different languages ​​and cultures and applied to international marketing campaigns.

[0040] The inspiration providing unit can convert ideas proposed by the generation AI into visual notes or mind maps to make them easier to understand visually. For example, the inspiration providing unit can convert ideas proposed by the generation AI into visual notes and display them visually. For example, it can show important points with diagrams or icons. The inspiration providing unit can also convert ideas proposed by the generation AI into mind maps to make them easier to understand visually. For example, it can visually arrange ideas related to a central theme. In this way, ideas proposed by the generation AI can be converted into visual notes or mind maps to make them easier to understand visually.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The CreatiMind Navigator system also includes a user behavior prediction unit. The behavior prediction unit analyzes the target audience's past behavior data and can predict future behavior. For example, if there is a tendency for people to purchase certain products during a particular season, it can propose a marketing campaign tailored to that season. The behavior prediction unit can also predict the target audience's life events (marriage, childbirth, moving, etc.) and propose marketing strategies based on those events. This allows for more effective marketing campaigns to be implemented by predicting the target audience's behavior.

[0043] The CreatiMind Navigator system also includes a competitive analysis section. This section analyzes competitors' marketing activities and helps understand their strategies. For example, it analyzes the content and frequency of competitors' advertising campaigns and evaluates their effectiveness. The competitive analysis section can also collect evaluations of competitors' products and services and adjust your own marketing strategies based on the results. This allows you to understand your competitors' trends and gain an advantage over the competition.

[0044] The CreatiMind Navigator system may further include a behavior prediction unit that predicts user behavior and adjusts marketing strategies based on the predicted behavior. The behavior prediction unit, for example, analyzes past behavioral data of the target audience and predicts future behavior. For example, if there is a tendency for people to purchase certain products during a certain season, it can propose a marketing campaign tailored to that season. The behavior prediction unit can also predict the target audience's life events (marriage, childbirth, moving, etc.) and propose a marketing strategy based on that. This allows for predicting the target audience's behavior and implementing more effective marketing campaigns.

[0045] The CreatiMind Navigator system may further include a purchasing analysis unit that analyzes users' purchasing histories and adjusts marketing strategies based on their purchasing patterns. The purchasing analysis unit, for example, analyzes the target audience's online shopping history and identifies their hobbies and preferences based on the categories and brands of products purchased. For example, if a user frequently purchases fashion items, it may be determined that the user is interested in fashion. The purchasing analysis unit may also suggest specific products or services based on the target audience's purchasing patterns. This allows the target audience's purchasing history to be analyzed and marketing strategies to be implemented based on their purchasing patterns.

[0046] The CreatiMind Navigator system may further include a trend analysis unit that analyzes users' social media activities and adjusts marketing strategies based on social media trends. The trend analysis unit, for example, analyzes social media posts and hashtags of the target audience to identify the latest trends. For example, the trend analysis unit updates profiles based on the frequency of posts about specific brands or trends. The trend analysis unit can also adjust the content of marketing campaigns based on social media trends. This allows the system to analyze the social media activities of the target audience and implement marketing strategies based on the latest trends.

[0047] The CreatiMind Navigator system may further include a feedback collection unit that collects user feedback and adjusts marketing strategies based on the feedback. The feedback collection unit, for example, collects surveys and reviews from the target audience and analyzes the data. For example, the content of the next campaign may be adjusted based on the evaluations and opinions of a particular campaign. The feedback collection unit may also identify areas for improvement in products and services based on the target audience's feedback. This allows the system to collect feedback from the target audience and implement marketing strategies based on the feedback.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The target audience analysis unit analyzes the attributes and hobbies of the target audience. For example, it collects data such as age, gender, region, and interests to create a target audience profile. It can also analyze social media activity and purchase history to create a profile that reflects the target audience's latest hobbies and preferences. Step 2: The idea generation unit generates innovative ideas based on the target audience's attributes and hobbies and preferences analyzed by the target audience analysis unit. For example, it can propose unique advertising concepts and promotional methods for targets of a specific age group or hobbies and preferences. It can also analyze successful examples of past marketing campaigns and combine their elements to generate new ideas. Step 3: The inspiration provider provides the ideas generated by the idea generator to marketers and advertising creators. For example, the inspiration provider compares the ideas proposed by the AI ​​generator with past success stories of marketers to provide optimal inspiration. It can also provide ideas customized for each different marketing channel.

[0050] (Example 2) The CreatiMind Navigator system according to an embodiment of the present invention generates innovative ideas based on the attributes and hobbies of a target audience and provides them to marketers and advertising creators, enabling them to mass-produce marketing campaign concepts and gain an advantage over the competition.

[0051] The CreatiMind Navigator system according to the embodiment includes a target audience analysis unit, an idea generation unit, and an inspiration provision unit. The target audience analysis unit analyzes the attributes and hobbies and preferences of the target audience. For example, the target audience analysis unit collects data such as age, gender, region, and interests to create a profile of the target audience. The target audience analysis unit can also analyze social media activity and purchase history to create a profile reflecting the target audience's latest hobbies and preferences. The idea generation unit generates innovative ideas based on the attributes and hobbies and preferences of the target audience analyzed by the target audience analysis unit. For example, the idea generation unit proposes unique advertising concepts and promotion methods for targets of specific age groups or hobbies and preferences. The idea generation unit can also analyze successful examples of past marketing campaigns and combine their elements to generate new ideas. The inspiration provision unit provides the ideas generated by the idea generation unit to marketers and advertising creators. For example, the inspiration provider can compare ideas proposed by the generative AI with marketers' past success stories to provide optimal inspiration. The inspiration provider can also customize the ideas proposed by the generative AI for each different marketing channel. This allows the CreatiMind Navigator system to generate innovative ideas based on the attributes and hobbies of the target audience and provide them to marketers and advertising creators, giving them an advantage over the competition.

[0052] The target audience analysis unit can analyze the social media activity of the target audience in real time and create a profile that reflects their latest hobbies and preferences. For example, the target audience analysis unit uses generative AI to analyze the social media activity of the target audience in real time and extract the latest hobbies and preferences from the content of posts, hashtags, accounts followed, etc. For example, the profile can be updated based on the frequency of posts related to specific brands or trends. This makes it possible to create a profile that reflects the target audience's latest hobbies and preferences in real time.

[0053] The target audience analysis unit can analyze the target audience's past purchasing history and identify detailed attributes based on their purchasing patterns. For example, the target audience analysis unit analyzes the target audience's online shopping history and identifies their hobbies and preferences based on the categories and brands of products purchased. For example, if a person frequently purchases fashion items, it can be determined that they are interested in fashion. This allows the target audience's past purchasing history to be analyzed and detailed attributes to be identified based on their purchasing patterns.

[0054] The target audience analysis unit can use the emotion estimation function to analyze the emotional state of the target audience and create a profile based on their emotions. For example, the target audience analysis unit uses the emotion estimation function to analyze the emotional state of the target audience from their social media posts and comments, and if there are many positive emotions, reflects their hobbies and preferences in the profile. For example, if there are many positive reactions to a particular event, it can determine that the target audience is interested in that event. This allows the target audience's emotional state to be analyzed and a profile based on their emotions to be created.

[0055] The target audience analysis unit can create more detailed profiles by introducing multimodal analysis including voice data and image data. The target audience analysis unit, for example, analyzes the voice data of the target audience and identifies their hobbies and preferences from the tone of voice and the content of the topics. For example, if the voice data shows that a particular sport is frequently discussed, it can be determined that the person is interested in that sport. The target audience analysis unit can also analyze image data and identify their hobbies and preferences from visual information. For example, if the person posts many images that include the logo of a particular brand, it can be determined that the person is interested in that brand. This allows for the creation of more detailed profiles by introducing multimodal analysis including voice data and image data.

[0056] The target audience analysis unit can compare and analyze target audiences from different regions and cultural spheres, and perform attribute analysis from a global perspective. For example, the target audience analysis unit collects data on target audiences from different regions and analyzes it taking into account cultural backgrounds and regional characteristics. For example, it compares the hobbies and preferences of target audiences in Asia and Europe, and reflects the characteristics of each region in the profile. This makes it possible to compare and analyze target audiences from different regions and cultural spheres, and perform attribute analysis from a global perspective.

[0057] The target audience analysis unit can use the emotion estimation function to monitor the emotional responses of the target audience in real time and propose a marketing strategy based on the emotions. For example, the target audience analysis unit can use the emotion estimation function to monitor the emotional responses of the target audience from social media posts and comments in real time and propose a marketing strategy based on themes that have many positive responses. For example, if there are many positive responses to a particular event, the target audience analysis unit can propose a campaign related to that event. This makes it possible to monitor the emotional responses of the target audience in real time and propose a marketing strategy based on the emotions.

[0058] The idea generation unit can analyze successful cases of past marketing campaigns and combine their elements to generate new ideas. For example, the idea generation unit uses a generation AI to collect successful cases of past marketing campaigns from a database and analyze the factors behind their success. For example, it extracts the factors that made a particular advertising method or promotion strategy successful and generates new ideas based on that. The idea generation unit also combines elements of successful cases to generate ideas for new marketing campaigns. For example, it proposes ideas that combine elements from different successful cases to create synergistic effects. This makes it possible to analyze successful cases of past marketing campaigns and combine their elements to generate new ideas.

[0059] The idea generation unit can generate different scenarios based on the attributes of the target audience and propose the optimal idea for each scenario. For example, the idea generation unit uses a generation AI to generate different scenarios based on the attribute data of the target audience. For example, different marketing scenarios can be created for each age group, gender, and region, and the optimal idea for each can be proposed. The idea generation unit also analyzes the content of the scenario in detail to propose the optimal idea for each scenario. For example, in a specific scenario, it can prioritize the proposal of ideas that have a good response from the target audience. This makes it possible to generate different scenarios based on the attributes of the target audience and propose the optimal idea for each scenario.

[0060] The idea generation unit uses the emotion estimation function to generate ideas that resonate with the emotions of the target audience and draw out emotional empathy. The idea generation unit, for example, uses the emotion estimation function to analyze emotional data of the target audience and generate ideas that draw out positive emotions. For example, it proposes advertising concepts that incorporate moving stories or humor. The idea generation unit also uses the emotion estimation function to develop an algorithm for generating ideas that resonate with the emotions of the target audience. For example, it generates ideas based on emotion scores and draws out emotional empathy. This makes it possible to generate ideas that resonate with the emotions of the target audience and draw out emotional empathy.

[0061] The idea generation unit can refer to marketing methods from different industries and carry out crossover innovation. For example, the generation AI collects and analyzes marketing methods from different industries from a database. For example, it generates new ideas that combine marketing methods from the fashion and technology industries. The idea generation unit can also refer to successful examples from different industries and carry out crossover innovation. For example, it can combine elements from different industries to propose a new marketing campaign. This makes it possible to refer to marketing methods from different industries and carry out crossover innovation.

[0062] The idea generation unit uses the emotion estimation function to provide feedback on the user's emotional reactions to the generated ideas in real time, thereby improving the quality of the ideas. The idea generation unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the generated ideas in real time and improves the quality of the ideas based on the data. For example, ideas with a high number of positive emotional reactions are preferentially adopted. The idea generation unit also uses the emotion estimation function to analyze the user's emotional reactions and identify areas for improvement in the ideas. For example, if there are a lot of negative emotional reactions, the cause is analyzed and the idea is modified. In this way, the user's emotional reactions to the generated ideas are provided as feedback in real time, thereby improving the quality of the ideas.

[0063] The inspiration providing unit can compare ideas proposed by the generative AI with past success stories of marketers and provide optimal inspiration. For example, the inspiration providing unit compares ideas proposed by the generative AI with past success stories of marketers and analyzes similarities and differences. For example, it prioritizes the proposal of ideas that have elements in common with past success stories. The inspiration providing unit also compares ideas proposed by the generative AI with past failure stories of marketers and provides inspiration for avoiding risks. For example, it excludes ideas that have similar elements to past failure stories. This makes it possible to compare ideas proposed by the generative AI with past success stories of marketers and provide optimal inspiration.

[0064] The inspiration providing unit can customize and provide the ideas proposed by the generation AI for each different marketing channel. For example, the inspiration providing unit customizes and provides the ideas proposed by the generation AI for social media. For example, it proposes social media advertisements using short videos or infographics. The inspiration providing unit also customizes and provides the ideas proposed by the generation AI for email marketing. For example, it proposes personalized email content. The inspiration providing unit also customizes and provides the ideas proposed by the generation AI for advertising. For example, it proposes banner advertisements or display advertisements. This allows the ideas proposed by the generation AI to be customized and provided for each different marketing channel.

[0065] The inspiration providing unit uses the emotion estimation function to analyze the emotional state of the marketing personnel and provide ideas at the timing when the marketing personnel are most likely to receive inspiration. For example, the inspiration providing unit uses the emotion estimation function to analyze the emotional state of the marketing personnel in real time and provide ideas at the timing when positive emotions are strong. For example, new ideas are proposed when the emotion score is high. The inspiration providing unit also uses the emotion estimation function to analyze the emotional state of the marketing personnel and provide ideas at the timing when stress is low. For example, inspiration is provided when the marketing personnel are relaxed. In this way, the emotional state of the marketing personnel can be analyzed and ideas can be provided at the timing when the marketing personnel are most likely to receive inspiration.

[0066] The inspiration providing unit can adapt the ideas proposed by the generative AI to different languages ​​and cultures and apply them to international marketing campaigns. For example, the inspiration providing unit translates the ideas proposed by the generative AI into different languages ​​and apply them to international marketing campaigns. For example, it translates into multiple languages ​​such as English, French, and Chinese. The inspiration providing unit also adapts the ideas proposed by the generative AI to different cultures. For example, it modifies the ideas taking into account the values ​​and customs of a particular culture. In this way, the ideas proposed by the generative AI can be adapted to different languages ​​and cultures and applied to international marketing campaigns.

[0067] The inspiration providing unit can convert ideas proposed by the generation AI into visual notes or mind maps to make them easier to understand visually. For example, the inspiration providing unit can convert ideas proposed by the generation AI into visual notes and display them visually. For example, it can show important points with diagrams or icons. The inspiration providing unit can also convert ideas proposed by the generation AI into mind maps to make them easier to understand visually. For example, it can visually arrange ideas related to a central theme. In this way, ideas proposed by the generation AI can be converted into visual notes or mind maps to make them easier to understand visually.

[0068] The inspiration providing unit can use the emotion estimation function to monitor the emotional reactions of the marketing personnel in real time and continuously provide optimal inspiration. For example, the inspiration providing unit can use the emotion estimation function to monitor the emotional reactions of the marketing personnel in real time and provide new ideas at a time when positive emotions are strong. For example, inspiration can be provided when the emotion score is high. The inspiration providing unit can also use the emotion estimation function to analyze the emotional reactions of the marketing personnel and provide ideas at a time when negative emotions are low. For example, inspiration can be provided when stress is low. In this way, the emotional reactions of the marketing personnel can be monitored in real time and optimal inspiration can be continuously provided.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The CreatiMind Navigator system also includes a user behavior prediction unit. The behavior prediction unit analyzes the target audience's past behavior data and can predict future behavior. For example, if there is a tendency for people to purchase certain products during a particular season, it can propose a marketing campaign tailored to that season. The behavior prediction unit can also predict the target audience's life events (marriage, childbirth, moving, etc.) and propose marketing strategies based on those events. This allows for more effective marketing campaigns to be implemented by predicting the target audience's behavior.

[0071] The CreatiMind Navigator system also includes a competitive analysis section. This section analyzes competitors' marketing activities and helps understand their strategies. For example, it analyzes the content and frequency of competitors' advertising campaigns and evaluates their effectiveness. The competitive analysis section can also collect evaluations of competitors' products and services and adjust your own marketing strategies based on the results. This allows you to understand your competitors' trends and gain an advantage over the competition.

[0072] The CreatiMind Navigator system may further include an emotion adjustment unit that estimates a user's emotions and adjusts marketing strategies based on the estimated emotions. For example, the emotion adjustment unit analyzes the target audience's emotional data and, if negative emotions are prevalent, suggests content that elicits positive emotions. For example, it suggests advertisements that incorporate moving stories or humor. The emotion adjustment unit can also adjust the tone and content of marketing messages according to the target audience's emotional state. This allows for the adjustment of marketing strategies based on the target audience's emotions, resulting in more effective campaigns.

[0073] The CreatiMind Navigator system may further include a timing adjustment unit that estimates a user's emotions and adjusts the timing of advertisement display based on the estimated emotions. The timing adjustment unit, for example, analyzes the target audience's emotional data and displays advertisements at times when positive emotions are strong. For example, it displays new advertisements when the emotional score is high. The timing adjustment unit can also adjust the frequency of advertisement display based on the target audience's emotional state. This allows for the adjustment of advertisement display timing based on the target audience's emotions, enabling more effective advertising campaigns.

[0074] The CreatiMind Navigator system may further include a personalization unit that estimates a user's emotions and personalizes content based on the estimated emotions. For example, the personalization unit may analyze the target audience's emotional data and suggest content that elicits positive emotions. For example, the personalization unit may suggest content that incorporates moving stories or humor. The personalization unit may also adjust the tone and content of the content depending on the target audience's emotional state. This allows for personalized content based on the target audience's emotions, enabling more effective marketing campaigns.

[0075] The CreatiMind Navigator system may further include a message tailoring unit that estimates a user's emotions and adjusts marketing messages based on the estimated emotions. For example, the message tailoring unit analyzes the target audience's emotional data and, if negative emotions are prevalent, suggests messages that elicit positive emotions. For example, it suggests messages that incorporate encouraging words or inspiring stories. The message tailoring unit can also adjust the tone and content of messages depending on the target audience's emotional state. This allows for the tailoring of marketing messages based on the target audience's emotions, enabling more effective campaigns.

[0076] The CreatiMind Navigator system may further include a behavior prediction unit that predicts user behavior and adjusts marketing strategies based on the predicted behavior. The behavior prediction unit, for example, analyzes past behavioral data of the target audience and predicts future behavior. For example, if there is a tendency for people to purchase certain products during a certain season, it can propose a marketing campaign tailored to that season. The behavior prediction unit can also predict the target audience's life events (marriage, childbirth, moving, etc.) and propose a marketing strategy based on that. This allows for predicting the target audience's behavior and implementing more effective marketing campaigns.

[0077] The CreatiMind Navigator system may further include a purchasing analysis unit that analyzes users' purchasing histories and adjusts marketing strategies based on their purchasing patterns. The purchasing analysis unit, for example, analyzes the target audience's online shopping history and identifies their hobbies and preferences based on the categories and brands of products purchased. For example, if a user frequently purchases fashion items, it may be determined that the user is interested in fashion. The purchasing analysis unit may also suggest specific products or services based on the target audience's purchasing patterns. This allows the target audience's purchasing history to be analyzed and marketing strategies to be implemented based on their purchasing patterns.

[0078] The CreatiMind Navigator system may further include a trend analysis unit that analyzes users' social media activities and adjusts marketing strategies based on social media trends. The trend analysis unit, for example, analyzes social media posts and hashtags of the target audience to identify the latest trends. For example, the trend analysis unit updates profiles based on the frequency of posts about specific brands or trends. The trend analysis unit can also adjust the content of marketing campaigns based on social media trends. This allows the system to analyze the social media activities of the target audience and implement marketing strategies based on the latest trends.

[0079] The CreatiMind Navigator system may further include a feedback collection unit that collects user feedback and adjusts marketing strategies based on the feedback. The feedback collection unit, for example, collects surveys and reviews from the target audience and analyzes the data. For example, the content of the next campaign may be adjusted based on the evaluations and opinions of a particular campaign. The feedback collection unit may also identify areas for improvement in products and services based on the target audience's feedback. This allows the system to collect feedback from the target audience and implement marketing strategies based on the feedback.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The target audience analysis unit analyzes the attributes and hobbies of the target audience. For example, it collects data such as age, gender, region, and interests to create a target audience profile. It can also analyze social media activity and purchase history to create a profile that reflects the target audience's latest hobbies and preferences. Step 2: The idea generation unit generates innovative ideas based on the target audience's attributes and hobbies and preferences analyzed by the target audience analysis unit. For example, it can propose unique advertising concepts and promotional methods for targets of a specific age group or hobbies and preferences. It can also analyze successful examples of past marketing campaigns and combine their elements to generate new ideas. Step 3: The inspiration provider provides the ideas generated by the idea generator to marketers and advertising creators. For example, the inspiration provider compares the ideas proposed by the AI ​​generator with past success stories of marketers to provide optimal inspiration. It can also provide ideas customized for each different marketing channel.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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 target audience analysis unit that analyzes the attributes and hobbies and preferences of the target audience; an idea generation unit that generates novel ideas based on the attributes and hobbies and preferences of the target audience analyzed by the target audience analysis unit; an inspiration providing unit that provides ideas generated by the idea generating unit to marketing personnel and advertising creators; A system characterized by:

2. The target audience analysis unit Analyze the target audience's social media activity in real time to create a profile that reflects their current interests and preferences.

2. The system of claim 1.

3. The target audience analysis unit Introduce multimodal analysis, including audio and image data, to create a more detailed profile.

2. The system of claim 1.

4. The idea generation unit Analyze past marketing campaign successes and combine their elements to generate new ideas 2. The system of claim 1.

5. The inspiration providing unit The ideas proposed by the generative AI are compared with the marketer's past success stories to provide optimal inspiration.

2. The system of claim 1.

6. The target audience analysis unit Analyzing the emotional state of said target audience and creating an emotion-based profile 2. The system of claim 1.

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