System

Abstract

A system according to an embodiment aims to provide personalized advertisements based on marketing needs.SOLUTION: A system according to an embodiment includes a marketing needs analysis unit, an influencer generation unit, and an advertisement provision unit. The marketing needs analysis unit analyzes marketing needs. The influencer generation unit generates an AI influencer based on the result analyzed by the marketing needs analysis unit. The advertisement provider provides personalized advertisements using the AI influencers created by the influencer generator.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 not been able to provide effective advertising based on marketing needs, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized advertisements based on marketing needs. [Means for solving the problem]

[0006] A system according to an embodiment includes a marketing needs analysis unit, an influencer generation unit, and an advertisement provision unit. The marketing needs analysis unit analyzes marketing needs. The influencer generation unit creates an AI influencer based on the results of the analysis by the marketing needs analysis unit. The advertisement provision unit provides personalized advertisements using the AI ​​influencer created by the influencer generation unit. [Effects of the Invention]

[0007] An embodiment of the system can provide personalized advertisements based on marketing needs. [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) A system according to an embodiment of the present invention is a production system that uses generative AI to create "AI influencers." This production system analyzes a company's marketing needs, creates AI influencers, and provides personalized advertising. This allows the production system to reduce advertising costs and provide consumers with personalized information and engagement. For example, the production system analyzes a company's marketing needs. For example, it collects and analyzes information on the company's products and services and target customer demographics. The production system then creates an AI influencer based on the analysis results. For example, for a product targeted at young people, an AI influencer with a youthful image is created. The created AI influencer is then used in the company's advertising campaigns. For example, the AI ​​influencer introduces the product in advertising distribution on social media and video platforms. Furthermore, the production system enriches consumers' lives through personalized information and engagement. For example, the AI ​​influencer provides product and service information based on consumers' interests. This allows the production system to revolutionize communication between companies and consumers and promote digital transformation.

[0029] A production system according to an embodiment includes a marketing needs analysis unit, an influencer generation unit, and an advertisement provision unit. The marketing needs analysis unit analyzes a company's marketing needs. For example, it collects and analyzes information on the products and services offered by the company and its target customer demographic. The marketing needs analysis unit can, for example, analyze consumer purchasing behavior and trends. The marketing needs analysis unit can also analyze data from the company's past marketing campaigns and select an optimal analysis algorithm. The influencer generation unit uses a generation AI to create an AI influencer based on the results of the analysis by the marketing needs analysis unit. For example, the AI ​​influencer can be customized to match the company's brand image and product characteristics. For example, for a product targeted at young people, the influencer generation unit can create an AI influencer with a youthful image. The influencer generation unit can also create a backstory for the influencer based on the company's brand story. The advertisement provision unit provides personalized advertisements using the AI ​​influencer created by the influencer generation unit. For example, the AI ​​influencer can introduce products through advertisement distribution on social media or video platforms. The advertisement provision unit can provide personalized advertisements based on, for example, the consumer's interests. The advertisement provision unit can also analyze the consumer's past purchase history and select the most appropriate advertisement. This allows the production system according to the embodiment to reduce advertising costs for companies and provide individual information and engagement to consumers.

[0030] The marketing needs analysis unit can collect and analyze information on products or services offered by a company and target customer segments. The marketing needs analysis unit, for example, collects and analyzes information on products or services offered by a company. For example, it analyzes marketing needs based on the characteristics of a company's products and target customer segments. The marketing needs analysis unit can also collect and analyze information on target customer segments. For example, it collects and analyzes information such as the target customer segment's age group, interests, and purchasing history. Furthermore, the marketing needs analysis unit can analyze data from a company's past marketing campaigns and select the optimal analysis algorithm. This allows for more accurate analysis of marketing needs by collecting and analyzing information on a company's products or services and target customer segments.

[0031] The influencer generation unit can customize AI influencers to match a company's brand image and product characteristics. The influencer generation unit customizes AI influencers based on a company's brand image, for example. For example, it creates AI influencers to match a company's brand values ​​and visual style. The influencer generation unit can also customize AI influencers based on a company's product characteristics. For example, it creates AI influencers to match the product's functions, design, and target market. Furthermore, the influencer generation unit can create influencer backstories based on a company's brand story. For example, it creates influencer backstories based on the company's founding story, mission, and vision. This allows for more effective advertising campaigns by customizing AI influencers to match a company's brand image and product characteristics.

[0032] The ad serving unit can have an AI influencer introduce a product when distributing ads on social media or video platforms. For example, the ad serving unit can have an AI influencer introduce a product when distributing ads on social media. For example, the AI ​​influencer introduces a product on social media platforms such as Facebook (registered trademark), Instagram (registered trademark), and Twitter (registered trademark). The ad serving unit can also have an AI influencer introduce a product when distributing ads on video platforms. For example, the AI ​​influencer introduces a product on video platforms such as YouTube (registered trademark) and TikTok (registered trademark). Furthermore, the ad serving unit can provide personalized ads based on consumers' interests. For example, it can provide ads for related products based on consumers' past purchasing history and behavioral data. This allows the AI ​​influencer to introduce products when distributing ads on social media or video platforms, effectively reaching a target customer demographic.

[0033] The advertisement providing unit can provide advertisements personalized based on the interests and concerns of consumers. The advertisement providing unit provides advertisements personalized based on, for example, the interests and concerns of consumers. For example, advertisements for related products are provided based on consumer questionnaire surveys and behavioral data analysis. The advertisement providing unit can also analyze the consumer's past purchase history and select optimal advertisements. For example, advertisements for products that the consumer may be interested in are provided based on the consumer's past purchase history. Furthermore, the advertisement providing unit can customize advertisement content based on the consumer's current living situation. For example, the advertisement content is customized based on the consumer's family structure, occupation, and lifestyle. This makes it possible to increase consumer engagement by providing advertisements personalized based on the consumer's interests and concerns.

[0034] The marketing needs analysis unit can analyze data from a company's past marketing campaigns and select the optimal analysis algorithm. The marketing needs analysis unit, for example, analyzes data from a company's past marketing campaigns and selects the optimal analysis algorithm. For example, it identifies elements that were successful in past campaigns and selects an analysis algorithm based on those elements. It can also analyze elements that failed in past campaigns and select an algorithm to avoid those elements. It can also evaluate the ROI (return on investment) of past campaigns and select the most effective algorithm. In this way, it is possible to select the optimal analysis algorithm by analyzing data from a company's past marketing campaigns.

[0035] The marketing needs analysis unit can compare and analyze marketing needs by referring to data on the company's competitors during analysis. The marketing needs analysis unit, for example, compares and analyzes marketing needs by referring to data on the company's competitors during analysis. For example, it can analyze the marketing strategies of competitors and compare them with its own strategy. It can also refer to evaluations of competitors' products and services to identify areas for improvement in its own products and services. It can also analyze competitors' market share and develop strategies to increase its own market share. In this way, by comparing and analyzing marketing needs by referring to data on the company's competitors, it is possible to develop a more competitive marketing strategy.

[0036] The marketing needs analysis unit can dynamically adjust the marketing needs based on the company's product lifecycle during analysis. The marketing needs analysis unit dynamically adjusts the marketing needs based on the company's product lifecycle during analysis, for example. For example, during the product introduction phase, analysis can be performed that emphasizes increasing awareness. Also, during the product growth phase, analysis can be performed that emphasizes strengthening competitiveness. Furthermore, during the product maturity phase, analysis can be performed that emphasizes customer retention. In this way, by dynamically adjusting the marketing needs based on the company's product lifecycle, a more effective marketing strategy can be implemented.

[0037] The marketing needs analysis unit can identify marketing needs by taking into account the geographical market data of the company during analysis. For example, the marketing needs analysis unit identifies marketing needs by taking into account the geographical market data of the company during analysis. For example, the marketing needs for each region can be identified based on the geographical market data. The situation of competitors in each region can also be analyzed based on the geographical market data. Furthermore, consumer behavior in each region can also be analyzed based on the geographical market data. In this way, by identifying marketing needs by taking into account the geographical market data of the company, it is possible to optimize marketing strategies for each region.

[0038] The marketing needs analysis unit can analyze the social media activities of a company during analysis to complement its marketing needs. The marketing needs analysis unit, for example, analyzes the social media activities of a company during analysis to complement its marketing needs. For example, it analyzes the content of a company's posts on social media to identify its marketing needs. It can also analyze consumer responses on social media to complement its marketing needs. It can also analyze competitors' activities on social media to complement its marketing needs. In this way, by analyzing a company's social media activities, it can complement its marketing needs and develop more effective marketing strategies.

[0039] The marketing needs analysis unit can customize the analysis method by reflecting the company's past feedback during analysis. The marketing needs analysis unit, for example, customizes the analysis method by reflecting the company's past feedback during analysis. For example, the analysis method can be improved based on past feedback. The analysis algorithm can also be adjusted based on past feedback. Furthermore, the method for displaying the analysis results can be improved based on past feedback. In this way, by customizing the analysis method by reflecting the company's past feedback, more accurate analysis results can be obtained.

[0040] The influencer generation unit can create a backstory for an influencer based on a company's brand story at the time of generation. For example, the influencer generation unit creates a backstory for an influencer based on a company's brand story at the time of generation. For example, the influencer's backstory can be created based on the company's founding story. The influencer's backstory can also be created based on the company's mission and vision. Furthermore, the influencer's backstory can be created based on the company's past success stories. This allows for a more consistent advertising campaign by creating an influencer's backstory based on the company's brand story.

[0041] The influencer generation unit can customize influencers at the time of generation, taking into account attribute information of a company's target customer segment. The influencer generation unit, for example, customizes influencers at the time of generation, taking into account attribute information of a company's target customer segment. For example, the influencer is customized to match the age group of the target customer segment. Influencers can also be customized to match the interests and concerns of the target customer segment. Influencers can also be customized based on the purchasing behavior of the target customer segment. This enables more effective advertising campaigns by customizing influencers taking into account attribute information of a company's target customer segment.

[0042] The influencer generation unit can dynamically adjust the content of the influencer's statements based on the characteristics of the company's products at the time of generation. For example, the influencer generation unit dynamically adjusts the content of the influencer's statements based on the characteristics of the company's products at the time of generation. For example, the content of the influencer's statements can be adjusted based on the characteristics of the product. The content of the influencer's statements can also be adjusted based on how the product is used. The content of the influencer's statements can also be adjusted based on the benefits and features of the product. This makes it possible to dynamically adjust the content of the influencer's statements based on the characteristics of the company's products, thereby enabling more effective advertising campaigns.

[0043] The influencer generation unit can customize influencers at the time of generation, taking into account the geographical market characteristics of a company. The influencer generation unit, for example, customizes influencers at the time of generation, taking into account the geographical market characteristics of a company. For example, the influencer's appearance can be customized based on the geographical market characteristics. The content of the influencer's statements can also be customized based on the geographical market characteristics. The influencer's behavior pattern can also be customized based on the geographical market characteristics. In this way, by customizing influencers taking into account the geographical market characteristics of a company, it is possible to optimize marketing strategies for each region.

[0044] The influencer generation unit can complement the influencer character setting by referring to literature related to the company at the time of generation. For example, the influencer generation unit complements the influencer character setting by referring to literature related to the company at the time of generation. For example, the influencer character setting is complemented based on literature related to the company. The influencer character setting can also be complemented based on past marketing materials of the company. The influencer character setting can also be complemented based on product manuals of the company. In this way, by complementing the influencer character setting by referring to literature related to the company, a more consistent advertising campaign can be achieved.

[0045] The influencer generation unit can determine the range of influencer activity taking into account the market value of a company at the time of generation. The influencer generation unit, for example, determines the range of influencer activity taking into account the market value of a company at the time of generation. For example, the range of influencer activity is determined based on the market value of a company. The frequency of influencer activity can also be determined based on the market value of a company. The content of influencer activity can also be determined based on the market value of a company. Thus, by determining the range of influencer activity taking into account the market value of a company, more effective advertising campaigns can be achieved.

[0046] The advertisement provision unit can select the most appropriate advertisement by analyzing the consumer's past purchase history when providing an advertisement. For example, the advertisement provision unit selects the most appropriate advertisement by analyzing the consumer's past purchase history when providing an advertisement. For example, advertisements for related products are provided based on the consumer's past purchase history. Also, advertisements for products that the consumer is likely to be interested in can be provided based on the consumer's past purchase history. Also, advertisements for products that the consumer is likely to repurchase can be provided based on the consumer's past purchase history. In this way, by analyzing the consumer's past purchase history, more relevant advertisements can be provided.

[0047] The advertisement providing unit can customize advertisement content based on the consumer's current living situation when providing an advertisement. The advertisement providing unit customizes advertisement content based on the consumer's current living situation when providing an advertisement. For example, the advertisement providing unit provides advertisements for related products based on the consumer's current living situation. Furthermore, the advertisement providing unit can provide advertisements for products that the consumer is likely to be interested in based on the consumer's current living situation. Furthermore, the advertisement providing unit can provide advertisements for products that the consumer is likely to need based on the consumer's current living situation. In this way, by customizing advertisement content based on the consumer's current living situation, more effective advertisements can be provided.

[0048] The advertisement provision unit can improve the advertisement display method by reflecting consumer feedback when providing an advertisement. The advertisement provision unit, for example, improves the advertisement display method by reflecting consumer feedback when providing an advertisement. For example, the advertisement display method is improved based on consumer feedback. The advertisement content can also be adjusted based on consumer feedback. The advertisement timing can also be optimized based on consumer feedback. In this way, by improving the advertisement display method by reflecting consumer feedback, more effective advertisement display is possible.

[0049] The advertisement provision unit can provide an optimal advertisement by taking into consideration the geographical location information of the consumer when providing the advertisement. The advertisement provision unit, for example, provides an optimal advertisement by taking into consideration the geographical location information of the consumer when providing the advertisement. For example, the advertisement provision unit provides advertisements for nearby stores based on the consumer's current location. It is also possible to provide area-specific campaign information based on the consumer's current location. It is also possible to provide local event information based on the consumer's current location. In this way, by providing an optimal advertisement by taking into consideration the consumer's geographical location information, it is possible to provide more relevant advertisements.

[0050] The advertisement provision unit can analyze the consumer's social media activity and provide relevant advertisements when providing advertisements. For example, the advertisement provision unit can analyze the consumer's social media activity and provide relevant advertisements when providing advertisements. For example, the advertisement provision unit can analyze the content of the consumer's social media posts and provide advertisements for related products. It can also provide advertisements for related products based on the activity of the consumer's friends on social media. It can also provide advertisements for related stores based on the consumer's check-in information on social media. In this way, it is possible to provide more relevant advertisements by analyzing the consumer's social media activity.

[0051] The advertisement provision unit can customize advertisement content by reflecting the consumer's past feedback when providing an advertisement. The advertisement provision unit, for example, customizes advertisement content by reflecting the consumer's past feedback when providing an advertisement. For example, the advertisement content is customized based on the consumer's past feedback. Also, the advertisement display method can be adjusted based on the consumer's past feedback. Also, the advertisement timing can be optimized based on the consumer's past feedback. In this way, by customizing advertisement content by reflecting the consumer's past feedback, more effective advertisements can be provided.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] When analyzing a company's marketing needs, the Marketing Needs Analysis Department can collect feedback from the company's employees and reflect it in the analysis. For example, it can collect market changes and customer reactions that employees perceive on a daily basis and use them in the analysis. It can also incorporate new marketing strategies and ideas proposed by employees. Furthermore, based on employee feedback, the Marketing Needs Analysis Department can adjust its analysis algorithms to obtain more realistic and effective analysis results. This makes it possible to utilize the knowledge of a company's employees to analyze marketing needs with greater accuracy.

[0054] During analysis, the marketing needs analysis unit can identify marketing needs by referencing a company's supply chain data. For example, it analyzes inventory status and shipping data at each stage of the supply chain to forecast demand. It can also identify bottlenecks in the supply chain and adjust marketing strategies based on those bottlenecks. It can also make proposals to improve supply chain efficiency and reflect them in marketing needs. This makes it possible to utilize a company's supply chain data to develop more effective marketing strategies.

[0055] When generating influencers, the influencer generation unit can set influencer characters based on the company's CSR (corporate social responsibility) activities. For example, if a company is engaged in environmental protection activities, the unit can set an environmentally friendly character. Also, if a company is engaged in activities that contribute to the local community, the unit can set a character that is friendly to the local community. Furthermore, if a company places importance on diversity and inclusion, the unit can set characters with diverse backgrounds. In this way, by setting influencer characters based on the company's CSR activities, the company's brand image can be strengthened.

[0056] The ad serving unit can customize the content of the ad by taking into consideration the consumer's health data when serving the ad. For example, an ad for a health-related product can be served based on the consumer's fitness data. An ad for a nutritional supplement can also be served based on the consumer's dietary data. Furthermore, an ad for a relaxation product can be served based on the consumer's sleep data. This makes it possible to utilize the consumer's health data to serve more relevant ads and increase consumer engagement.

[0057] The advertisement provision unit can estimate the consumer's purchasing willingness when providing an advertisement and adjust the advertisement display frequency based on the estimated purchasing willingness. For example, if the consumer's purchasing willingness is high, the advertisement display frequency can be increased. Also, if the consumer's purchasing willingness is low, the advertisement display frequency can be decreased. Furthermore, if the consumer's purchasing willingness is medium, the advertisement can be displayed at an appropriate frequency. In this way, by adjusting the advertisement display frequency based on the consumer's purchasing willingness, more effective advertisement display is possible.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The Marketing Needs Analysis Department analyzes the company's marketing needs. Specifically, it collects and analyzes information on the products and services offered by the company and their target customer demographics. It also analyzes consumer purchasing behavior and trends, and analyzes data from the company's past marketing campaigns to select the optimal analysis algorithm. Step 2: The influencer generation unit uses generation AI to create an AI influencer based on the results of the analysis by the marketing needs analysis unit. Specifically, the AI ​​influencer is customized to match the company's brand image and product characteristics, and if the product is aimed at younger people, an AI influencer with a youthful image is created. It is also possible to create a backstory for the influencer based on the company's brand story. Step 3: The advertising provider uses the AI ​​influencers created by the influencer generator to provide personalized ads. Specifically, the AI ​​influencers introduce products through ad distribution on social media and video platforms, and provide personalized ads based on consumers' interests. The system can also analyze consumers' past purchase history to select the most suitable ads.

[0060] (Example 2) A system according to an embodiment of the present invention is a production system that uses generative AI to create "AI influencers." This production system analyzes a company's marketing needs, creates AI influencers, and provides personalized advertising. This allows the production system to reduce advertising costs and provide consumers with personalized information and engagement. For example, the production system analyzes a company's marketing needs. For example, it collects and analyzes information on the company's products and services and target customer demographics. The production system then creates an AI influencer based on the analysis results. For example, for a product targeted at young people, an AI influencer with a youthful image is created. The created AI influencer is then used in the company's advertising campaigns. For example, the AI ​​influencer introduces the product in advertising distribution on social media and video platforms. Furthermore, the production system enriches consumers' lives through personalized information and engagement. For example, the AI ​​influencer provides product and service information based on consumers' interests. This allows the production system to revolutionize communication between companies and consumers and promote digital transformation.

[0061] A production system according to an embodiment includes a marketing needs analysis unit, an influencer generation unit, and an advertisement provision unit. The marketing needs analysis unit analyzes a company's marketing needs. For example, it collects and analyzes information on the products and services offered by the company and its target customer demographic. The marketing needs analysis unit can, for example, analyze consumer purchasing behavior and trends. The marketing needs analysis unit can also analyze data from the company's past marketing campaigns and select an optimal analysis algorithm. The influencer generation unit uses a generation AI to create an AI influencer based on the results of the analysis by the marketing needs analysis unit. For example, the AI ​​influencer can be customized to match the company's brand image and product characteristics. For example, for a product targeted at young people, the influencer generation unit can create an AI influencer with a youthful image. The influencer generation unit can also create a backstory for the influencer based on the company's brand story. The advertisement provision unit provides personalized advertisements using the AI ​​influencer created by the influencer generation unit. For example, the AI ​​influencer can introduce products through advertisement distribution on social media or video platforms. The advertisement provision unit can provide personalized advertisements based on, for example, the consumer's interests. The advertisement provision unit can also analyze the consumer's past purchase history and select the most appropriate advertisement. This allows the production system according to the embodiment to reduce advertising costs for companies and provide individual information and engagement to consumers.

[0062] The marketing needs analysis unit can collect and analyze information on products or services offered by a company and target customer segments. The marketing needs analysis unit, for example, collects and analyzes information on products or services offered by a company. For example, it analyzes marketing needs based on the characteristics of a company's products and target customer segments. The marketing needs analysis unit can also collect and analyze information on target customer segments. For example, it collects and analyzes information such as the target customer segment's age group, interests, and purchasing history. Furthermore, the marketing needs analysis unit can analyze data from a company's past marketing campaigns and select the optimal analysis algorithm. This allows for more accurate analysis of marketing needs by collecting and analyzing information on a company's products or services and target customer segments.

[0063] The influencer generation unit can customize AI influencers to match a company's brand image and product characteristics. The influencer generation unit customizes AI influencers based on a company's brand image, for example. For example, it creates AI influencers to match a company's brand values ​​and visual style. The influencer generation unit can also customize AI influencers based on a company's product characteristics. For example, it creates AI influencers to match the product's functions, design, and target market. Furthermore, the influencer generation unit can create influencer backstories based on a company's brand story. For example, it creates influencer backstories based on the company's founding story, mission, and vision. This allows for more effective advertising campaigns by customizing AI influencers to match a company's brand image and product characteristics.

[0064] The ad serving unit can have an AI influencer introduce a product when distributing ads on social media or video platforms. The ad serving unit, for example, has an AI influencer introduce a product when distributing ads on social media. For example, the AI ​​influencer introduces a product on social media platforms such as Facebook, Instagram, and Twitter. The ad serving unit can also have an AI influencer introduce a product when distributing ads on video platforms. For example, the AI ​​influencer introduces a product on video platforms such as YouTube and TikTok. Furthermore, the ad serving unit can provide personalized ads based on consumers' interests. For example, it provides ads for related products based on consumers' past purchasing history and behavioral data. This allows the target customer demographic to be effectively reached by having the AI ​​influencer introduce a product when distributing ads on social media or video platforms.

[0065] The advertisement providing unit can provide advertisements personalized based on the interests and concerns of consumers. The advertisement providing unit provides advertisements personalized based on, for example, the interests and concerns of consumers. For example, advertisements for related products are provided based on consumer questionnaire surveys and behavioral data analysis. The advertisement providing unit can also analyze the consumer's past purchase history and select optimal advertisements. For example, advertisements for products that the consumer may be interested in are provided based on the consumer's past purchase history. Furthermore, the advertisement providing unit can customize advertisement content based on the consumer's current living situation. For example, the advertisement content is customized based on the consumer's family structure, occupation, and lifestyle. This makes it possible to increase consumer engagement by providing advertisements personalized based on the consumer's interests and concerns.

[0066] The marketing needs analysis unit can estimate the user's emotions and adjust the method of analyzing marketing needs based on the estimated user emotions. The marketing needs analysis unit, for example, estimates the user's emotions and adjusts the method of analyzing marketing needs based on the estimated user emotions. For example, if the user is excited, a detailed analysis can be performed, taking into account more data points. Alternatively, if the user is relaxed, a concise analysis can be performed, focusing on key data points. Alternatively, if the user is stressed, the analysis method can be simplified, providing results more quickly. This allows for more appropriate analysis results to be obtained by adjusting the method of analyzing marketing needs based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The marketing needs analysis unit can analyze data from a company's past marketing campaigns and select the optimal analysis algorithm. The marketing needs analysis unit, for example, analyzes data from a company's past marketing campaigns and selects the optimal analysis algorithm. For example, it identifies elements that were successful in past campaigns and selects an analysis algorithm based on those elements. It can also analyze elements that failed in past campaigns and select an algorithm to avoid those elements. It can also evaluate the ROI (return on investment) of past campaigns and select the most effective algorithm. In this way, it is possible to select the optimal analysis algorithm by analyzing data from a company's past marketing campaigns.

[0068] The marketing needs analysis unit can compare and analyze marketing needs by referring to data on the company's competitors during analysis. The marketing needs analysis unit, for example, compares and analyzes marketing needs by referring to data on the company's competitors during analysis. For example, it can analyze the marketing strategies of competitors and compare them with its own strategy. It can also refer to evaluations of competitors' products and services to identify areas for improvement in its own products and services. It can also analyze competitors' market share and develop strategies to increase its own market share. In this way, by comparing and analyzing marketing needs by referring to data on the company's competitors, it is possible to develop a more competitive marketing strategy.

[0069] The marketing needs analysis unit can dynamically adjust the marketing needs based on the company's product lifecycle during analysis. The marketing needs analysis unit dynamically adjusts the marketing needs based on the company's product lifecycle during analysis, for example. For example, during the product introduction phase, analysis can be performed that emphasizes increasing awareness. Also, during the product growth phase, analysis can be performed that emphasizes strengthening competitiveness. Furthermore, during the product maturity phase, analysis can be performed that emphasizes customer retention. In this way, by dynamically adjusting the marketing needs based on the company's product lifecycle, a more effective marketing strategy can be implemented.

[0070] The marketing needs analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The marketing needs analysis unit, for example, estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. For example, if the user is excited, detailed analysis results can be provided preferentially. Alternatively, if the user is relaxed, concise analysis results can be provided preferentially. Alternatively, if the user is stressed, key data points can be prioritized to provide results quickly. This allows for more appropriate analysis results to be provided by prioritizing the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The marketing needs analysis unit can identify marketing needs by taking into account the geographical market data of the company during analysis. For example, the marketing needs analysis unit identifies marketing needs by taking into account the geographical market data of the company during analysis. For example, the marketing needs for each region can be identified based on the geographical market data. The situation of competitors in each region can also be analyzed based on the geographical market data. Furthermore, consumer behavior in each region can also be analyzed based on the geographical market data. In this way, by identifying marketing needs by taking into account the geographical market data of the company, it is possible to optimize marketing strategies for each region.

[0072] The marketing needs analysis unit can analyze the social media activities of a company during analysis to complement its marketing needs. The marketing needs analysis unit, for example, analyzes the social media activities of a company during analysis to complement its marketing needs. For example, it analyzes the content of a company's posts on social media to identify its marketing needs. It can also analyze consumer responses on social media to complement its marketing needs. It can also analyze competitors' activities on social media to complement its marketing needs. In this way, by analyzing a company's social media activities, it can complement its marketing needs and develop more effective marketing strategies.

[0073] The marketing needs analysis unit can customize the analysis method by reflecting the company's past feedback during analysis. The marketing needs analysis unit, for example, customizes the analysis method by reflecting the company's past feedback during analysis. For example, the analysis method can be improved based on past feedback. The analysis algorithm can also be adjusted based on past feedback. Furthermore, the method for displaying the analysis results can be improved based on past feedback. In this way, by customizing the analysis method by reflecting the company's past feedback, more accurate analysis results can be obtained.

[0074] The influencer generation unit can estimate the user's emotions and adjust the influencer's character setting based on the estimated user's emotions. The influencer generation unit, for example, estimates the user's emotions and adjusts the influencer's character setting based on the estimated user's emotions. For example, if the user is relaxed, a calm character setting can be used. Also, if the user is excited, a lively character setting can be used. Also, if the user is stressed, a calm character setting can be used. This allows for more effective advertising campaigns by adjusting the influencer's character setting based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The influencer generation unit can create a backstory for an influencer based on a company's brand story at the time of generation. For example, the influencer generation unit creates a backstory for an influencer based on a company's brand story at the time of generation. For example, the influencer's backstory can be created based on the company's founding story. The influencer's backstory can also be created based on the company's mission and vision. Furthermore, the influencer's backstory can be created based on the company's past success stories. This allows for a more consistent advertising campaign by creating an influencer's backstory based on the company's brand story.

[0076] The influencer generation unit can customize influencers at the time of generation, taking into account attribute information of a company's target customer segment. The influencer generation unit, for example, customizes influencers at the time of generation, taking into account attribute information of a company's target customer segment. For example, the influencer is customized to match the age group of the target customer segment. Influencers can also be customized to match the interests and concerns of the target customer segment. Influencers can also be customized based on the purchasing behavior of the target customer segment. This enables more effective advertising campaigns by customizing influencers taking into account attribute information of a company's target customer segment.

[0077] The influencer generation unit can dynamically adjust the content of the influencer's statements based on the characteristics of the company's products at the time of generation. For example, the influencer generation unit dynamically adjusts the content of the influencer's statements based on the characteristics of the company's products at the time of generation. For example, the content of the influencer's statements can be adjusted based on the characteristics of the product. The content of the influencer's statements can also be adjusted based on how the product is used. The content of the influencer's statements can also be adjusted based on the benefits and features of the product. This makes it possible to dynamically adjust the content of the influencer's statements based on the characteristics of the company's products, thereby enabling more effective advertising campaigns.

[0078] The influencer generation unit can estimate the user's emotions and adjust the timing of the influencer's remarks based on the estimated user emotions. The influencer generation unit, for example, estimates the user's emotions and adjusts the timing of the influencer's remarks based on the estimated user emotions. For example, if the user is relaxed, the influencer can make remarks at a calm timing. Also, if the user is excited, the influencer can make remarks at an active timing. Also, if the user is stressed, the influencer can make remarks at a calm timing. This allows for more effective advertising campaigns by adjusting the timing of the influencer's remarks based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The influencer generation unit can customize influencers at the time of generation, taking into account the geographical market characteristics of a company. The influencer generation unit, for example, customizes influencers at the time of generation, taking into account the geographical market characteristics of a company. For example, the influencer's appearance can be customized based on the geographical market characteristics. The content of the influencer's statements can also be customized based on the geographical market characteristics. The influencer's behavior pattern can also be customized based on the geographical market characteristics. In this way, by customizing influencers taking into account the geographical market characteristics of a company, it is possible to optimize marketing strategies for each region.

[0080] The influencer generation unit can complement the influencer character setting by referring to literature related to the company at the time of generation. For example, the influencer generation unit complements the influencer character setting by referring to literature related to the company at the time of generation. For example, the influencer character setting is complemented based on literature related to the company. The influencer character setting can also be complemented based on past marketing materials of the company. The influencer character setting can also be complemented based on product manuals of the company. In this way, by complementing the influencer character setting by referring to literature related to the company, a more consistent advertising campaign can be achieved.

[0081] The influencer generation unit can determine the range of influencer activity taking into account the market value of a company at the time of generation. The influencer generation unit, for example, determines the range of influencer activity taking into account the market value of a company at the time of generation. For example, the range of influencer activity is determined based on the market value of a company. The frequency of influencer activity can also be determined based on the market value of a company. The content of influencer activity can also be determined based on the market value of a company. Thus, by determining the range of influencer activity taking into account the market value of a company, more effective advertising campaigns can be achieved.

[0082] The advertisement provision unit can estimate a user's emotion and adjust the advertisement display method based on the estimated user's emotion. The advertisement provision unit, for example, estimates a user's emotion and adjusts the advertisement display method based on the estimated user's emotion. For example, if the user is relaxed, a gentle advertisement display method can be provided. Also, if the user is excited, a lively advertisement display method can be provided. Also, if the user is stressed, a simple and highly visible advertisement display method can be provided. This enables more effective advertisement display by adjusting the advertisement display method based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] The advertisement provision unit can select the most appropriate advertisement by analyzing the consumer's past purchase history when providing an advertisement. For example, the advertisement provision unit selects the most appropriate advertisement by analyzing the consumer's past purchase history when providing an advertisement. For example, advertisements for related products are provided based on the consumer's past purchase history. Also, advertisements for products that the consumer is likely to be interested in can be provided based on the consumer's past purchase history. Also, advertisements for products that the consumer is likely to repurchase can be provided based on the consumer's past purchase history. In this way, by analyzing the consumer's past purchase history, more relevant advertisements can be provided.

[0084] The advertisement providing unit can customize advertisement content based on the consumer's current living situation when providing an advertisement. The advertisement providing unit customizes advertisement content based on the consumer's current living situation when providing an advertisement. For example, the advertisement providing unit provides advertisements for related products based on the consumer's current living situation. Furthermore, the advertisement providing unit can provide advertisements for products that the consumer is likely to be interested in based on the consumer's current living situation. Furthermore, the advertisement providing unit can provide advertisements for products that the consumer is likely to need based on the consumer's current living situation. In this way, by customizing advertisement content based on the consumer's current living situation, more effective advertisements can be provided.

[0085] The advertisement provision unit can improve the advertisement display method by reflecting consumer feedback when providing an advertisement. The advertisement provision unit, for example, improves the advertisement display method by reflecting consumer feedback when providing an advertisement. For example, the advertisement display method is improved based on consumer feedback. The advertisement content can also be adjusted based on consumer feedback. The advertisement timing can also be optimized based on consumer feedback. In this way, by improving the advertisement display method by reflecting consumer feedback, more effective advertisement display is possible.

[0086] The advertisement serving unit can estimate a user's emotions and determine the priority of advertisements based on the estimated user's emotions. The advertisement serving unit, for example, estimates a user's emotions and determines the priority of advertisements based on the estimated user's emotions. For example, if the user is relaxed, a calm advertisement can be preferentially displayed. Also, if the user is excited, an active advertisement can be preferentially displayed. Also, if the user is stressed, a simple advertisement with high visibility can be preferentially displayed. This enables more effective advertisement display by determining the priority of advertisements based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The advertisement provision unit can provide an optimal advertisement by taking into consideration the geographical location information of the consumer when providing the advertisement. The advertisement provision unit, for example, provides an optimal advertisement by taking into consideration the geographical location information of the consumer when providing the advertisement. For example, the advertisement provision unit provides advertisements for nearby stores based on the consumer's current location. It is also possible to provide area-specific campaign information based on the consumer's current location. It is also possible to provide local event information based on the consumer's current location. In this way, by providing an optimal advertisement by taking into consideration the consumer's geographical location information, it is possible to provide more relevant advertisements.

[0088] The advertisement provision unit can analyze the consumer's social media activity and provide relevant advertisements when providing advertisements. For example, the advertisement provision unit can analyze the consumer's social media activity and provide relevant advertisements when providing advertisements. For example, the advertisement provision unit can analyze the content of the consumer's social media posts and provide advertisements for related products. It can also provide advertisements for related products based on the activity of the consumer's friends on social media. It can also provide advertisements for related stores based on the consumer's check-in information on social media. In this way, it is possible to provide more relevant advertisements by analyzing the consumer's social media activity.

[0089] The advertisement provision unit can customize advertisement content by reflecting the consumer's past feedback when providing an advertisement. The advertisement provision unit, for example, customizes advertisement content by reflecting the consumer's past feedback when providing an advertisement. For example, the advertisement content is customized based on the consumer's past feedback. Also, the advertisement display method can be adjusted based on the consumer's past feedback. Also, the advertisement timing can be optimized based on the consumer's past feedback. In this way, by customizing advertisement content by reflecting the consumer's past feedback, more effective advertisements can be provided. === Hard Collateral 1-1 === Each of the multiple elements including the above-described marketing needs analysis unit, influencer generation unit, and advertisement provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the marketing needs analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes a company's marketing needs. The influencer generation unit is realized, for example, by the control unit 46A of the smart device 14 and creates an AI influencer using a generation AI. The advertisement provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides personalized advertisements. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned marketing needs analysis unit, influencer generation unit, and advertisement provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the marketing needs analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes a company's marketing needs. The influencer generation unit is realized, for example, by the control unit 46A of the smart glasses 214 and creates an AI influencer using a generation AI. The advertisement provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides personalized advertisements. === Hard Collateral 1-3 === Each of the multiple elements including the above-described marketing needs analysis unit, influencer generation unit, and advertisement provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the marketing needs analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes a company's marketing needs. The influencer generation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and creates an AI influencer using a generation AI. The advertisement provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides personalized advertisements. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned marketing needs analysis unit, influencer generation unit, and advertisement provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the marketing needs analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the marketing needs of a company. The influencer generation unit is realized, for example, by the control unit 46A of the robot 414 and creates an AI influencer using a generation AI. The advertisement provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides personalized advertisements.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] When analyzing a company's marketing needs, the Marketing Needs Analysis Department can collect feedback from the company's employees and reflect it in the analysis. For example, it can collect market changes and customer reactions that employees perceive on a daily basis and use them in the analysis. It can also incorporate new marketing strategies and ideas proposed by employees. Furthermore, based on employee feedback, the Marketing Needs Analysis Department can adjust its analysis algorithms to obtain more realistic and effective analysis results. This makes it possible to utilize the knowledge of a company's employees to analyze marketing needs with greater accuracy.

[0092] During analysis, the marketing needs analysis unit can identify marketing needs by referencing a company's supply chain data. For example, it analyzes inventory status and shipping data at each stage of the supply chain to forecast demand. It can also identify bottlenecks in the supply chain and adjust marketing strategies based on those bottlenecks. It can also make proposals to improve supply chain efficiency and reflect them in marketing needs. This makes it possible to utilize a company's supply chain data to develop more effective marketing strategies.

[0093] When generating influencers, the influencer generation unit can set influencer characters based on the company's CSR (corporate social responsibility) activities. For example, if a company is engaged in environmental protection activities, the unit can set an environmentally friendly character. Also, if a company is engaged in activities that contribute to the local community, the unit can set a character that is friendly to the local community. Furthermore, if a company places importance on diversity and inclusion, the unit can set characters with diverse backgrounds. In this way, by setting influencer characters based on the company's CSR activities, the company's brand image can be strengthened.

[0094] The ad serving unit can customize the content of the ad by taking into consideration the consumer's health data when serving the ad. For example, an ad for a health-related product can be served based on the consumer's fitness data. An ad for a nutritional supplement can also be served based on the consumer's dietary data. Furthermore, an ad for a relaxation product can be served based on the consumer's sleep data. This makes it possible to utilize the consumer's health data to serve more relevant ads and increase consumer engagement.

[0095] The advertisement provision unit can estimate the consumer's purchasing willingness when providing an advertisement and adjust the advertisement display frequency based on the estimated purchasing willingness. For example, if the consumer's purchasing willingness is high, the advertisement display frequency can be increased. Also, if the consumer's purchasing willingness is low, the advertisement display frequency can be decreased. Furthermore, if the consumer's purchasing willingness is medium, the advertisement can be displayed at an appropriate frequency. In this way, by adjusting the advertisement display frequency based on the consumer's purchasing willingness, more effective advertisement display is possible.

[0096] The marketing needs analysis unit can estimate the user's emotions and customize the analysis results of marketing needs based on the estimated user emotions. For example, if the user has positive emotions, detailed analysis results can be provided. If the user has negative emotions, concise analysis results can be provided. Furthermore, if the user has neutral emotions, balanced analysis results can be provided. In this way, by customizing the analysis results of marketing needs based on the user's emotions, more appropriate information can be provided.

[0097] The marketing needs analysis unit can estimate the user's emotions and propose a marketing strategy based on the estimated user's emotions. For example, if the user is excited, an aggressive marketing strategy can be proposed. If the user is relaxed, a calm marketing strategy can be proposed. Furthermore, if the user is feeling stressed, a simple but effective marketing strategy can be proposed. In this way, by proposing a marketing strategy based on the user's emotions, more effective marketing activities can be carried out.

[0098] The influencer generation unit can estimate the user's emotions and dynamically change the influencer's appearance based on the estimated user's emotions. For example, if the user is relaxed, an influencer with a calm appearance can be displayed. Alternatively, if the user is excited, an influencer with a lively appearance can be displayed. Furthermore, if the user is stressed, an influencer with a calm appearance can be displayed. This allows for more effective advertising campaigns by dynamically changing the influencer's appearance based on the user's emotions.

[0099] The advertisement provision unit can estimate the user's emotions and dynamically change the content of the advertisement based on the estimated user's emotions. For example, if the user is relaxed, an advertisement with calm content can be provided. If the user is excited, an advertisement with lively content can be provided. Furthermore, if the user is stressed, an advertisement with simple, highly visible content can be provided. This makes it possible to dynamically change the content of the advertisement based on the user's emotions, thereby enabling more effective advertisement display.

[0100] The advertisement provision unit can estimate the user's emotions and adjust the timing of advertisement display based on the estimated user's emotions. For example, if the user is relaxed, the advertisement can be displayed at a calm timing. If the user is excited, the advertisement can be displayed at an active timing. Furthermore, if the user is stressed, the advertisement can be displayed at a calm timing. In this way, by adjusting the timing of advertisement display based on the user's emotions, more effective advertisement display becomes possible.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The Marketing Needs Analysis Department analyzes the company's marketing needs. Specifically, it collects and analyzes information on the products and services offered by the company and their target customer demographics. It also analyzes consumer purchasing behavior and trends, and analyzes data from the company's past marketing campaigns to select the optimal analysis algorithm. Step 2: The influencer generation unit uses generation AI to create an AI influencer based on the results of the analysis by the marketing needs analysis unit. Specifically, the AI ​​influencer is customized to match the company's brand image and product characteristics, and if the product is aimed at younger people, an AI influencer with a youthful image is created. It is also possible to create a backstory for the influencer based on the company's brand story. Step 3: The advertising provider uses the AI ​​influencers created by the influencer generator to provide personalized ads. Specifically, the AI ​​influencers introduce products through ad distribution on social media and video platforms, and provide personalized ads based on consumers' interests. The system can also analyze consumers' past purchase history to select the most suitable ads.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 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 identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 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 marketing needs analysis department that analyzes marketing needs; an influencer generation unit that generates an AI influencer based on the results of the analysis by the marketing needs analysis unit; an advertisement providing unit that provides personalized advertisements using the AI ​​influencer created by the influencer generating unit; A system characterized by:

2. The marketing needs analysis unit Collect and analyze information about the products or services offered by the company and the target customer demographic 2. The system of claim 1.

3. The influencer generation unit Customize AI influencers to match your company's brand image and product characteristics 2. The system of claim 1.

4. The advertisement providing unit AI influencers introduce products through advertising on social media or video platforms 2. The system of claim 1.

5. The advertisement providing unit Providing personalized advertising based on consumer interests 2. The system of claim 1.

6. The marketing needs analysis unit Inferring user emotions and specifically adjusting the analysis method for marketing needs based on the estimated user emotions 2. The system of claim 1.

7. The marketing needs analysis unit Analyze data from a company's past marketing campaigns and select the optimal analysis algorithm 2. The system of claim 1.

8. The marketing needs analysis unit During analysis, refer to data on a company's competitors to compare and analyze marketing needs 2. The system of claim 1.

9. The marketing needs analysis unit During analysis, dynamically adjust marketing needs based on the company's product lifecycle 2. The system of claim 1.

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