System

Abstract

An object of the system according to the embodiment is to analyze hobbies and purchase behaviors of users, provide brand name recognition and reputation in real time, create a price adjustment strategy in accordance with an early market trend, and predict a future trend.SOLUTION: A system according to an embodiment includes a target audience attributes obtainer, a real-time information provider, a price adjustment strategies generator, and a AI forecasting. The target audience attributes grasping unit analyzes hobbies and purchase behaviors of users using the generated AI and grasps attributes of the target audience in detail. The real-time information providing unit provides information on brand name recognition and reputation in real time. The price adjustment strategy generation unit generates a strategy for adjusting a price in accordance with an early market trend. The AI forecasting unit predicts a future trend of the brand.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] Previous technologies had issues in that they were not able to fully grasp users' hobbies and purchasing behavior, provide real-time brand awareness and reputation, create price adjustment strategies in line with early market trends, and predict future trends.

[0005] The system of the embodiment aims to analyze users' hobbies and purchasing behavior, provide brand awareness and reputation in real time, create price adjustment strategies in line with early market trends, and predict future trends. [Means for solving the problem]

[0006] The system according to the embodiment includes a target audience attribute understanding unit, a real-time information providing unit, a price adjustment strategy creation unit, and an AI forecasting unit. The target audience attribute understanding unit uses a generation AI to analyze users' hobbies and purchasing behavior to understand the target audience's attributes in detail. The real-time information providing unit provides information on brand awareness and reputation in real time. The price adjustment strategy creation unit creates a strategy for adjusting prices in line with early market trends. The AI ​​forecasting unit predicts future trends for the brand. [Effects of the Invention]

[0007] The system according to the embodiment can analyze users' hobbies and purchasing behavior, provide real-time brand awareness and reputation, create price adjustment strategies in line with early market trends, and predict future trends. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The InsightAI system according to an embodiment of the present invention utilizes generative AI to gain real-time insights into brand awareness, reputation, and target audiences, enhancing marketing and sales strategies. This allows businesses to develop competitive content and promotional strategies to increase repeat customers. Furthermore, by utilizing AI forecasting capabilities, the system can predict future trends and facilitate strategic decision-making.

[0029] The InsightAI system according to the embodiment includes a target audience attribute understanding unit, a real-time information provision unit, a price adjustment strategy creation unit, and an AI forecasting unit. The target audience attribute understanding unit uses a generation AI to analyze users' hobbies and purchasing behaviors to understand the attributes of the target audience in detail. For example, data on products purchased by users through online shopping and pages viewed by users is collected, and the generation AI analyzes the data. The target audience attribute understanding unit also identifies users' attributes, such as age, gender, and interests, and reflects them in a company's marketing strategy. For example, the generation AI performs analysis based on user behavioral data and purchase history. The real-time information provision unit uses the generation AI to provide information on brand awareness and reputation in real time. For example, the generation AI analyzes data collected from social media and news sites to understand positive and negative comments about the brand in real time. The real-time information provision unit also enables companies to take prompt countermeasures. For example, the generation AI performs analysis based on data collected from social media and news sites. The price adjustment strategy creation unit uses the generation AI to create a strategy for adjusting prices in line with early market trends. For example, it analyzes competitors' price trends and consumer purchasing intent to propose optimal pricing. The price adjustment strategy creation unit also enables companies to increase repeat customers. For example, the generation AI performs analysis based on competitors' price data and consumer purchasing data. The AI ​​forecasting unit uses the generation AI to provide a forecasting function for predicting future brand trends. For example, it analyzes past data to predict changes in brand awareness, reputation, and target audience several months into the future. The AI ​​forecasting unit also enables companies to make strategic decisions. For example, the generation AI makes predictions based on past brand data and market data. As a result, the InsightAI system according to the embodiment can grasp brand awareness, reputation, and target audience insights in real time, thereby enhancing marketing and sales strategies.For example, companies can develop competitive content and promotional strategies to increase repeat customers, and by utilizing AI forecasting capabilities, they can predict future trends and make strategic decisions.

[0030] The target audience attribute understanding unit can analyze a user's social media activities and comments in online communities to understand their potential interests and concerns in detail. The target audience attribute understanding unit, for example, analyzes the content of social media posts to identify the user's potential interests and concerns. For example, it understands interests based on specific hashtags and keywords. The target audience attribute understanding unit also analyzes comments in online communities to identify the user's potential interests and concerns. For example, it analyzes the content of comments on forums and message boards to understand their interests. The target audience attribute understanding unit also analyzes social media activities to identify the user's potential interests and concerns. For example, it understands interests based on accounts followed and posts liked. This makes it possible to understand the user's potential interests and concerns in detail and reflect them in marketing strategies.

[0031] The target audience attribute understanding unit can analyze user location information data and perform targeting that takes into account the characteristics and cultural background of each region. The target audience attribute understanding unit, for example, analyzes user location information data and understands the characteristics of each region. For example, targeting is performed based on purchasing behavior data in a specific region. The target audience attribute understanding unit also performs targeting that takes into account the cultural background of each region based on the location information data. For example, a marketing strategy tailored to local events and festivals is created. The target audience attribute understanding unit also analyzes user location information data and performs targeting that takes into account the characteristics and cultural background of each region. For example, a marketing strategy is created based on local specialties and popular products. This makes it possible to perform targeting that takes into account the characteristics and cultural background of each region.

[0032] The target audience attribute understanding unit can analyze health data and fitness data in addition to user purchasing behavior data to identify a health-conscious target audience. The target audience attribute understanding unit, for example, analyzes user purchasing behavior data and health data to identify a health-conscious target audience. For example, targeting is performed based on purchase history of health foods and fitness equipment. The target audience attribute understanding unit also analyzes fitness data to identify a health-conscious target audience. For example, targeting is performed based on fitness app usage data. The target audience attribute understanding unit also analyzes health data to identify a health-conscious target audience. For example, targeting is performed based on health checkup data and wearable device data. This allows the health-conscious target audience to be identified and reflected in marketing strategies.

[0033] The target audience attribute understanding unit can propose cross-promotions with products and services from different industries based on the user's hobbies and purchasing behavior. The target audience attribute understanding unit, for example, analyzes data on the user's hobbies and purchasing behavior to propose cross-promotions with products and services from different industries. For example, a cross-promotion between sporting goods and health foods is carried out. The target audience attribute understanding unit also proposes cross-promotions with products and services from different industries based on the user's hobbies and purchasing behavior. For example, a cross-promotion between travel goods and cameras is carried out. The target audience attribute understanding unit also proposes cross-promotions with products and services from different industries based on the user's purchasing behavior data. For example, a cross-promotion between fashion items and beauty products is carried out. This allows the breadth of marketing strategies to be broadened by proposing cross-promotions with products and services from different industries.

[0034] The real-time information providing unit can analyze not only social media and news sites, but also users' email and chat histories, to provide omnidirectional information about a brand. The real-time information providing unit, for example, analyzes users' email and chat histories to collect information about a brand. For example, it analyzes the content of emails and chat conversations to understand opinions and feelings about a brand. The real-time information providing unit also analyzes users' email and chat histories in addition to social media and news sites to provide omnidirectional information about a brand. For example, it understands brand reputation in real time based on email and chat data. The real-time information providing unit also builds a system that analyzes users' email and chat histories to collect information about a brand. For example, it analyzes the content of emails and chats to understand opinions and feelings about a brand in real time. This allows for the provision of omnidirectional information about a brand, making it possible to provide more comprehensive information.

[0035] When providing real-time information, the real-time information providing unit can simultaneously analyze the trends of competitors and changes in the market, and provide comprehensive information. For example, the real-time information providing unit analyzes the trends of competitors in real time and provides this information together with information about the brand. For example, the brand strategy is adjusted based on price changes and new product information from competitors. The real-time information providing unit also analyzes market changes in real time and provides this information together with information about the brand. For example, the brand strategy is adjusted based on market trends and consumer trends. The real-time information providing unit also analyzes the trends of competitors and changes in the market in real time, and builds a system that provides comprehensive information. For example, the brand strategy is adjusted based on the trends of competitors and changes in the market. This makes it possible to provide comprehensive information by simultaneously analyzing the trends of competitors and changes in the market.

[0036] The real-time information providing unit can analyze visual data when providing real-time information and perform visual brand evaluation. The real-time information providing unit, for example, analyzes images and videos on social media or news sites and performs visual brand evaluation. For example, it analyzes the content of posted images and videos to understand opinions and emotions about the brand. The real-time information providing unit also analyzes images and videos posted by users and performs visual brand evaluation. For example, it analyzes the content of images and videos to understand opinions and emotions about the brand in real time. The real-time information providing unit also analyzes visual data and builds a system for performing visual brand evaluation. For example, it analyzes the content of images and videos to understand opinions and emotions about the brand in real time. In this way, visual brand evaluation becomes possible by analyzing visual data.

[0037] The real-time information providing unit can translate brand evaluations in different languages ​​in real time and provide information from a global perspective. The real-time information providing unit, for example, translates brand evaluations in different languages ​​in real time and provides information from a global perspective. For example, translation is performed into multiple languages ​​such as English, French, and Chinese. The real-time information providing unit also translates posts in different languages ​​on social media and news sites in real time and provides brand evaluations. For example, comments and reviews in different languages ​​are translated to understand opinions and emotions about the brand. The real-time information providing unit also builds a system that translates brand evaluations in different languages ​​in real time and provides information from a global perspective. For example, posts in different languages ​​are translated to understand opinions and emotions about the brand in real time. This makes it possible to provide information from a global perspective by translating brand evaluations in different languages ​​in real time.

[0038] The price adjustment strategy creation unit can analyze not only the price trends of competitors, but also consumer purchasing history and seasonal trends, and propose a composite price strategy. The price adjustment strategy creation unit, for example, analyzes the price trends of competitors and proposes a price strategy that takes into account consumer purchasing history and seasonal trends. For example, it adjusts its own prices in accordance with competitors' price changes. The price adjustment strategy creation unit also analyzes consumer purchasing history and proposes a price strategy that takes into account seasonal trends. For example, it adjusts prices for products that are in high demand during specific seasons. The price adjustment strategy creation unit also constructs a system that comprehensively analyzes competitors' price trends, consumer purchasing history, and seasonal trends, and proposes an optimal price strategy. For example, it performs price adjustments that take multiple factors into account. This allows for more effective price adjustments by proposing a price strategy that takes multiple factors into account.

[0039] The price adjustment strategy creation unit can simultaneously propose promotions and discount campaigns to increase users' purchasing motivation when adjusting prices. The price adjustment strategy creation unit, for example, proposes promotions and discount campaigns to increase users' purchasing motivation when adjusting prices. For example, it can lower the price and provide special discount coupons at the same time. The price adjustment strategy creation unit also analyzes users' purchasing history and proposes promotions to increase users' purchasing motivation when adjusting prices. For example, it can provide a discount on the next purchase for users who have purchased a specific product. The price adjustment strategy creation unit also builds a system that proposes discount campaigns to increase users' purchasing motivation when adjusting prices. For example, it can implement a special promotion after a price change. In this way, by proposing promotions and discount campaigns at the same time as adjusting prices, it is possible to increase users' purchasing motivation.

[0040] The price adjustment strategy creation unit can take into account the characteristics of different regions and markets when creating a price adjustment strategy, and set optimal prices for each region. The price adjustment strategy creation unit, for example, analyzes the market characteristics of different regions and sets optimal prices for each region. For example, it adjusts prices taking into account the economic situation and purchasing power of the region. The price adjustment strategy creation unit also builds a system that takes into account market characteristics and sets optimal prices for each region. For example, it sets prices based on the demand and competitive situation of each region. The price adjustment strategy creation unit also analyzes the characteristics of different regions and markets and sets optimal prices. For example, it adjusts prices taking into account the culture and consumer preferences of the region. This makes it possible to set optimal prices that take into account the characteristics of each region.

[0041] The price adjustment strategy creation unit can propose new sales formats, such as subscription models and bundle sales, when adjusting prices. For example, the price adjustment strategy creation unit proposes a subscription model when adjusting prices. For example, a regular purchase plan can be introduced to acquire long-term customers. The price adjustment strategy creation unit can also propose bundle sales, selling multiple products as a set at the same time as adjusting prices. For example, related products can be bundled and offered at a discounted price. The price adjustment strategy creation unit can also propose new sales formats to increase customer purchasing motivation at the same time as adjusting prices. For example, a subscription model or bundle sales can be introduced. In this way, by proposing new sales formats, it is possible to increase users' purchasing motivation.

[0042] When predicting a brand's future trends, the AI ​​forecasting department can simulate different scenarios and propose the most promising strategy. The AI ​​forecasting department, for example, simulates different scenarios to predict a brand's future trends. For example, it proposes the most promising strategy based on multiple scenarios. The AI ​​forecasting department also conducts scenario simulations and builds a system to predict a brand's future trends. For example, it proposes the optimal strategy based on different scenarios. The AI ​​forecasting department also simulates different scenarios to predict a brand's future trends. For example, it proposes the most promising strategy based on multiple scenarios. In this way, by simulating different scenarios and proposing the most promising strategy, a more effective strategy can be created.

[0043] The AI ​​forecasting unit can visualize the results of forecasting, allowing them to be intuitively understood using graphs and charts. The AI ​​forecasting unit, for example, visualizes the results of forecasting, allowing them to be intuitively understood using graphs and charts. For example, future trends are displayed in graphs. The AI ​​forecasting unit also builds a system that visualizes the results of forecasting, allowing them to be intuitively understood using graphs and charts. For example, future trends are displayed in charts. The AI ​​forecasting unit also visualizes the results of forecasting, allowing them to be intuitively understood using graphs and charts. For example, future trends are displayed in graphs. In this way, by visualizing the results of forecasting, they can be intuitively understood.

[0044] The AI ​​Forecasting Department uses the forecasting function to simultaneously predict future trends in different industries and markets and propose cross-industry strategies. The AI ​​Forecasting Department, for example, predicts future trends in different industries and markets and proposes cross-industry strategies. For example, it proposes a new business strategy based on trends in different industries. The AI ​​Forecasting Department also uses the forecasting function to build a system that predicts future trends in different industries and markets. For example, it proposes a new business strategy based on trends in different industries. The AI ​​Forecasting Department also predicts future trends in different industries and markets and proposes cross-industry strategies. For example, it proposes a new business strategy based on trends in different industries. In this way, by predicting future trends in different industries and markets and proposing cross-industry strategies, more effective strategies can be created.

[0045] The AI ​​forecasting unit can formulate comprehensive strategies by taking into account the user's lifestyle and social changes when predicting future trends. The AI ​​forecasting unit, for example, predicts future trends by taking into account the user's lifestyle and social changes. For example, it predicts future trends based on lifestyle changes. The AI ​​forecasting unit also builds a system that predicts future trends by taking into account social changes. For example, it formulates future strategies based on social trends. The AI ​​forecasting unit also builds comprehensive strategies by taking into account the user's lifestyle and social changes. For example, it predicts future trends based on lifestyle changes. This makes it possible to formulate comprehensive strategies that take into account the user's lifestyle and social changes.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The target audience attribute understanding unit can analyze health data and fitness data in addition to user purchasing behavior data to identify a health-conscious target audience. For example, the target audience attribute understanding unit analyzes user purchasing behavior data and health data to identify a health-conscious target audience. For example, targeting is performed based on purchase history of health foods and fitness equipment. The target audience attribute understanding unit also analyzes fitness data to identify a health-conscious target audience. For example, targeting is performed based on fitness app usage data. The target audience attribute understanding unit also analyzes health data to identify a health-conscious target audience. For example, targeting is performed based on health checkup data and wearable device data. This allows the health-conscious target audience to be identified and reflected in marketing strategies.

[0048] The target audience attribute understanding unit can analyze user location information data and perform targeting that takes into account the characteristics and cultural background of each region. For example, the user location information data is analyzed to understand the characteristics of each region. For example, targeting is performed based on purchasing behavior data in a specific region. The target audience attribute understanding unit also performs targeting that takes into account the cultural background of each region based on the location information data. For example, a marketing strategy tailored to local events and festivals is created. The target audience attribute understanding unit also analyzes user location information data and performs targeting that takes into account the characteristics and cultural background of each region. For example, a marketing strategy is created based on local specialties and popular products. This makes it possible to perform targeting that takes into account the characteristics and cultural background of each region.

[0049] The target audience attribute understanding unit can analyze a user's social media activities and comments in online communities to understand their potential interests and concerns in detail. For example, it can analyze the content of social media posts to identify the user's potential interests and concerns. For example, it can understand interests based on specific hashtags and keywords. The target audience attribute understanding unit can also analyze comments in online communities to identify the user's potential interests and concerns. For example, it can analyze the content of comments on forums and message boards to understand interests. The target audience attribute understanding unit can also analyze social media activities to identify the user's potential interests and concerns. For example, it can understand interests based on accounts followed and posts liked. This makes it possible to understand the user's potential interests and concerns in detail and reflect them in marketing strategies.

[0050] The target audience attribute understanding unit can analyze health data and fitness data in addition to user purchasing behavior data to identify a health-conscious target audience. For example, the target audience attribute understanding unit analyzes user purchasing behavior data and health data to identify a health-conscious target audience. For example, targeting is performed based on purchase history of health foods and fitness equipment. The target audience attribute understanding unit also analyzes fitness data to identify a health-conscious target audience. For example, targeting is performed based on fitness app usage data. The target audience attribute understanding unit also analyzes health data to identify a health-conscious target audience. For example, targeting is performed based on health checkup data and wearable device data. This allows the health-conscious target audience to be identified and reflected in marketing strategies.

[0051] The target audience attribute understanding unit can propose cross-promotions with products and services from different industries based on the user's hobbies and purchasing behavior. For example, by analyzing data on the user's hobbies and purchasing behavior, cross-promotions with products and services from different industries can be proposed. For example, a cross-promotion between sporting goods and health foods can be carried out. The target audience attribute understanding unit can also propose cross-promotions with products and services from different industries based on the user's hobbies and purchasing behavior. For example, a cross-promotion between travel goods and cameras can be carried out. The target audience attribute understanding unit can also propose cross-promotions with products and services from different industries based on the user's purchasing behavior data. For example, a cross-promotion between fashion items and beauty products can be carried out. This allows the breadth of marketing strategies to be broadened by proposing cross-promotions with products and services from different industries.

[0052] The real-time information providing unit can analyze not only social media and news sites, but also users' email and chat histories to provide omnidirectional information about a brand. For example, it can analyze users' email and chat histories to collect information about a brand. For example, it can analyze the content of emails and chat conversations to understand opinions and feelings about a brand. The real-time information providing unit can also analyze users' email and chat histories in addition to social media and news sites to provide omnidirectional information about a brand. For example, it can understand brand reputation in real time based on email and chat data. The real-time information providing unit can also build a system that analyzes users' email and chat histories to collect information about a brand. For example, it can analyze the content of emails and chats to understand opinions and feelings about a brand in real time. This allows for the provision of omnidirectional information about a brand, making it possible to provide more comprehensive information.

[0053] When providing real-time information, the real-time information providing unit can simultaneously analyze the trends of competitors and changes in the market, and provide comprehensive information. For example, it can analyze the trends of competitors in real time and provide this information together with information about the brand. For example, it can adjust the brand strategy based on price changes and new product information from competitors. The real-time information providing unit can also analyze market changes in real time and provide this information together with information about the brand. For example, it can adjust the brand strategy based on market trends and consumer trends. The real-time information providing unit can also analyze the trends of competitors and changes in the market in real time, and build a system that provides comprehensive information. For example, it can adjust the brand strategy based on the trends of competitors and changes in the market. This makes it possible to provide comprehensive information by simultaneously analyzing the trends of competitors and changes in the market.

[0054] The real-time information providing unit can analyze visual data when providing real-time information and perform visual brand evaluation. For example, it can analyze images and videos on social media and news sites to perform visual brand evaluation. For example, it can analyze the content of posted images and videos to understand opinions and emotions about the brand. The real-time information providing unit can also analyze images and videos posted by users to perform visual brand evaluation. For example, it can analyze the content of images and videos to understand opinions and emotions about the brand in real time. The real-time information providing unit can also analyze visual data and build a system to perform visual brand evaluation. For example, it can analyze the content of images and videos to understand opinions and emotions about the brand in real time. This makes it possible to perform visual brand evaluation by analyzing visual data.

[0055] The real-time information providing unit can translate brand evaluations in different languages ​​in real time and provide information from a global perspective. For example, it translates brand evaluations in different languages ​​in real time and provides information from a global perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. The real-time information providing unit also translates posts in different languages ​​on social media and news sites in real time and provides brand evaluations. For example, it translates comments and reviews in different languages ​​to understand opinions and emotions about the brand. The real-time information providing unit also builds a system that translates brand evaluations in different languages ​​in real time and provides information from a global perspective. For example, it translates posts in different languages ​​to understand opinions and emotions about the brand in real time. This makes it possible to provide information from a global perspective by translating brand evaluations in different languages ​​in real time.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The target audience attribute understanding unit uses the generation AI to analyze users' hobbies and purchasing behavior to gain a detailed understanding of the target audience's attributes. For example, data on the products users purchase online and the pages they view is collected, and the generation AI analyzes that data. The target audience attribute understanding unit also identifies users' attributes, such as age, gender, and interests, and reflects them in the company's marketing strategy. Step 2: The real-time information provider uses generative AI to provide real-time information about a brand's popularity and reputation. For example, it analyzes data collected from social media and news sites to identify positive and negative comments about the brand in real time. The real-time information provider also enables companies to take prompt action. Step 3: The price adjustment strategy creation department uses generative AI to create a strategy for adjusting prices to match early market trends. For example, it analyzes competitors' price trends and consumer purchasing intentions to propose optimal pricing. The price adjustment strategy creation department also helps companies increase repeat customers. Step 4: The AI ​​Forecasting Department uses generative AI to provide forecasting capabilities to predict future trends for brands. For example, it analyzes past data to predict changes in brand awareness, reputation, and target audience over the next few months. The AI ​​Forecasting Department also helps companies make strategic decisions.

[0058] (Example 2) The InsightAI system according to an embodiment of the present invention utilizes generative AI to gain real-time insights into brand awareness, reputation, and target audiences, enhancing marketing and sales strategies. This allows businesses to develop competitive content and promotional strategies to increase repeat customers. Furthermore, by utilizing AI forecasting capabilities, the system can predict future trends and facilitate strategic decision-making.

[0059] The InsightAI system according to the embodiment includes a target audience attribute understanding unit, a real-time information provision unit, a price adjustment strategy creation unit, and an AI forecasting unit. The target audience attribute understanding unit uses a generation AI to analyze users' hobbies and purchasing behaviors to understand the attributes of the target audience in detail. For example, data on products purchased by users through online shopping and pages viewed by users is collected, and the generation AI analyzes the data. The target audience attribute understanding unit also identifies users' attributes, such as age, gender, and interests, and reflects them in a company's marketing strategy. For example, the generation AI performs analysis based on user behavioral data and purchase history. The real-time information provision unit uses the generation AI to provide information on brand awareness and reputation in real time. For example, the generation AI analyzes data collected from social media and news sites to understand positive and negative comments about the brand in real time. The real-time information provision unit also enables companies to take prompt countermeasures. For example, the generation AI performs analysis based on data collected from social media and news sites. The price adjustment strategy creation unit uses the generation AI to create a strategy for adjusting prices in line with early market trends. For example, it analyzes competitors' price trends and consumer purchasing intent to propose optimal pricing. The price adjustment strategy creation unit also enables companies to increase repeat customers. For example, the generation AI performs analysis based on competitors' price data and consumer purchasing data. The AI ​​forecasting unit uses the generation AI to provide a forecasting function for predicting future brand trends. For example, it analyzes past data to predict changes in brand awareness, reputation, and target audience several months into the future. The AI ​​forecasting unit also enables companies to make strategic decisions. For example, the generation AI makes predictions based on past brand data and market data. As a result, the InsightAI system according to the embodiment can grasp brand awareness, reputation, and target audience insights in real time, thereby enhancing marketing and sales strategies.For example, companies can develop competitive content and promotional strategies to increase repeat customers, and by utilizing AI forecasting capabilities, they can predict future trends and make strategic decisions.

[0060] The target audience attribute understanding unit utilizes a user's emotion estimation function to identify emotional attributes based on purchasing behavior and hobbies, and can reflect these in marketing strategies. In the target audience attribute understanding unit, for example, the generation AI performs emotion estimation based on the user's purchasing history and hobbies to identify emotional attributes. For example, the emotion score when a specific product is purchased is analyzed to target users with strong positive emotions. The target audience attribute understanding unit also analyzes the user's online activity and identifies emotional attributes using the emotion estimation function. For example, it analyzes comments and posts on social media to calculate an emotion score. In addition, the target audience attribute understanding unit uses the generation AI to perform emotion estimation based on user purchasing behavior data to identify emotional attributes. For example, it analyzes the emotion score when a product in a specific category is purchased and reflects this in marketing strategies. This allows the user's emotional attributes to be identified, making it possible to develop more effective marketing strategies.

[0061] The target audience attribute understanding unit can analyze a user's social media activities and comments in online communities to understand their potential interests and concerns in detail. The target audience attribute understanding unit, for example, analyzes the content of social media posts to identify the user's potential interests and concerns. For example, it understands interests based on specific hashtags and keywords. The target audience attribute understanding unit also analyzes comments in online communities to identify the user's potential interests and concerns. For example, it analyzes the content of comments on forums and message boards to understand their interests. The target audience attribute understanding unit also analyzes social media activities to identify the user's potential interests and concerns. For example, it understands interests based on accounts followed and posts liked. This makes it possible to understand the user's potential interests and concerns in detail and reflect them in marketing strategies.

[0062] The target audience attribute understanding unit can analyze user location information data and perform targeting that takes into account the characteristics and cultural background of each region. The target audience attribute understanding unit, for example, analyzes user location information data and understands the characteristics of each region. For example, targeting is performed based on purchasing behavior data in a specific region. The target audience attribute understanding unit also performs targeting that takes into account the cultural background of each region based on the location information data. For example, a marketing strategy tailored to local events and festivals is created. The target audience attribute understanding unit also analyzes user location information data and performs targeting that takes into account the characteristics and cultural background of each region. For example, a marketing strategy is created based on local specialties and popular products. This makes it possible to perform targeting that takes into account the characteristics and cultural background of each region.

[0063] The target audience attribute understanding unit can analyze health data and fitness data in addition to user purchasing behavior data to identify a health-conscious target audience. The target audience attribute understanding unit, for example, analyzes user purchasing behavior data and health data to identify a health-conscious target audience. For example, targeting is performed based on purchase history of health foods and fitness equipment. The target audience attribute understanding unit also analyzes fitness data to identify a health-conscious target audience. For example, targeting is performed based on fitness app usage data. The target audience attribute understanding unit also analyzes health data to identify a health-conscious target audience. For example, targeting is performed based on health checkup data and wearable device data. This allows the health-conscious target audience to be identified and reflected in marketing strategies.

[0064] The target audience attribute understanding unit can propose cross-promotions with products and services from different industries based on the user's hobbies and purchasing behavior. The target audience attribute understanding unit, for example, analyzes data on the user's hobbies and purchasing behavior to propose cross-promotions with products and services from different industries. For example, a cross-promotion between sporting goods and health foods is carried out. The target audience attribute understanding unit also proposes cross-promotions with products and services from different industries based on the user's hobbies and purchasing behavior. For example, a cross-promotion between travel goods and cameras is carried out. The target audience attribute understanding unit also proposes cross-promotions with products and services from different industries based on the user's purchasing behavior data. For example, a cross-promotion between fashion items and beauty products is carried out. This allows the breadth of marketing strategies to be broadened by proposing cross-promotions with products and services from different industries.

[0065] The target audience attribute understanding unit can use the emotion estimation function to generate personalized advertisements in real time according to the emotional state of the user. The target audience attribute understanding unit, for example, uses the emotion estimation function to generate personalized advertisements in real time according to the emotional state of the user. For example, a bright-toned advertisement is displayed to a user in a positive emotional state. The target audience attribute understanding unit also analyzes the emotional state of the user in real time and generates personalized advertisements based on the results. For example, an advertisement for a product with a relaxing effect is displayed to a user who is feeling stressed. The target audience attribute understanding unit also generates advertisements in real time according to the emotional state of the user based on the emotion estimation data. For example, an advertisement including special offers or discount information is displayed to a user with a high emotion score. In this way, personalized advertisements in real time according to the emotional state of the user can be generated, maximizing the effectiveness of the advertisements.

[0066] The real-time information providing unit can analyze not only social media and news sites, but also users' email and chat histories, to provide omnidirectional information about a brand. The real-time information providing unit, for example, analyzes users' email and chat histories to collect information about a brand. For example, it analyzes the content of emails and chat conversations to understand opinions and feelings about a brand. The real-time information providing unit also analyzes users' email and chat histories in addition to social media and news sites to provide omnidirectional information about a brand. For example, it understands brand reputation in real time based on email and chat data. The real-time information providing unit also builds a system that analyzes users' email and chat histories to collect information about a brand. For example, it analyzes the content of emails and chats to understand opinions and feelings about a brand in real time. This allows for the provision of omnidirectional information about a brand, making it possible to provide more comprehensive information.

[0067] When providing real-time information, the real-time information providing unit can simultaneously analyze the trends of competitors and changes in the market, and provide comprehensive information. For example, the real-time information providing unit analyzes the trends of competitors in real time and provides this information together with information about the brand. For example, the brand strategy is adjusted based on price changes and new product information from competitors. The real-time information providing unit also analyzes market changes in real time and provides this information together with information about the brand. For example, the brand strategy is adjusted based on market trends and consumer trends. The real-time information providing unit also analyzes the trends of competitors and changes in the market in real time, and builds a system that provides comprehensive information. For example, the brand strategy is adjusted based on the trends of competitors and changes in the market. This makes it possible to provide comprehensive information by simultaneously analyzing the trends of competitors and changes in the market.

[0068] The real-time information providing unit can analyze visual data when providing real-time information and perform visual brand evaluation. The real-time information providing unit, for example, analyzes images and videos on social media or news sites and performs visual brand evaluation. For example, it analyzes the content of posted images and videos to understand opinions and emotions about the brand. The real-time information providing unit also analyzes images and videos posted by users and performs visual brand evaluation. For example, it analyzes the content of images and videos to understand opinions and emotions about the brand in real time. The real-time information providing unit also analyzes visual data and builds a system for performing visual brand evaluation. For example, it analyzes the content of images and videos to understand opinions and emotions about the brand in real time. In this way, visual brand evaluation becomes possible by analyzing visual data.

[0069] The real-time information providing unit can translate brand evaluations in different languages ​​in real time and provide information from a global perspective. The real-time information providing unit, for example, translates brand evaluations in different languages ​​in real time and provides information from a global perspective. For example, translation is performed into multiple languages ​​such as English, French, and Chinese. The real-time information providing unit also translates posts in different languages ​​on social media and news sites in real time and provides brand evaluations. For example, comments and reviews in different languages ​​are translated to understand opinions and emotions about the brand. The real-time information providing unit also builds a system that translates brand evaluations in different languages ​​in real time and provides information from a global perspective. For example, posts in different languages ​​are translated to understand opinions and emotions about the brand in real time. This makes it possible to provide information from a global perspective by translating brand evaluations in different languages ​​in real time.

[0070] The real-time information providing unit can use the emotion estimation function to monitor the user's real-time emotional reactions and provide instant feedback to the brand. The real-time information providing unit, for example, uses the emotion estimation function to monitor the user's real-time emotional reactions and provide instant feedback to the brand. For example, it analyzes the user's facial expressions and voice to calculate an emotion score. The real-time information providing unit also monitors the user's real-time emotional reactions and provides instant feedback to the brand based on the results. For example, it provides special offers and discount information to users with strong positive emotions. The real-time information providing unit also builds a system that monitors the user's real-time emotional reactions based on the emotion estimation data and provides instant feedback to the brand. For example, it adjusts the brand's strategy in response to changes in the user's emotions. In this way, it is possible to provide instant feedback to the brand by monitoring the user's real-time emotional reactions.

[0071] The price adjustment strategy creation unit uses generative AI and utilizes a user's emotion estimation function to predict emotional reactions to price changes and make optimal price adjustments. The price adjustment strategy creation unit, for example, analyzes a user's purchase history and emotion data to predict emotional reactions to price changes. For example, it predicts negative emotional reactions when a price increases and makes appropriate price adjustments. The price adjustment strategy creation unit also uses the emotion estimation function to monitor users' emotional reactions to price changes in real time and make optimal price adjustments. For example, it readjusts the price based on the emotion score after the price change. The price adjustment strategy creation unit also builds a system that predicts emotional reactions to price changes based on user emotion data and makes optimal price adjustments. For example, it suggests lowering the price if the emotion score is low. This makes it possible to predict users' emotional reactions to price changes and make optimal price adjustments, thereby improving user satisfaction.

[0072] The price adjustment strategy creation unit can analyze not only the price trends of competitors, but also consumer purchasing history and seasonal trends, and propose a composite price strategy. The price adjustment strategy creation unit, for example, analyzes the price trends of competitors and proposes a price strategy that takes into account consumer purchasing history and seasonal trends. For example, it adjusts its own prices in accordance with competitors' price changes. The price adjustment strategy creation unit also analyzes consumer purchasing history and proposes a price strategy that takes into account seasonal trends. For example, it adjusts prices for products that are in high demand during specific seasons. The price adjustment strategy creation unit also constructs a system that comprehensively analyzes competitors' price trends, consumer purchasing history, and seasonal trends, and proposes an optimal price strategy. For example, it performs price adjustments that take multiple factors into account. This allows for more effective price adjustments by proposing a price strategy that takes multiple factors into account.

[0073] The price adjustment strategy creation unit can simultaneously propose promotions and discount campaigns to increase users' purchasing motivation when adjusting prices. The price adjustment strategy creation unit, for example, proposes promotions and discount campaigns to increase users' purchasing motivation when adjusting prices. For example, it can lower the price and provide special discount coupons at the same time. The price adjustment strategy creation unit also analyzes users' purchasing history and proposes promotions to increase users' purchasing motivation when adjusting prices. For example, it can provide a discount on the next purchase for users who have purchased a specific product. The price adjustment strategy creation unit also builds a system that proposes discount campaigns to increase users' purchasing motivation when adjusting prices. For example, it can implement a special promotion after a price change. In this way, by proposing promotions and discount campaigns at the same time as adjusting prices, it is possible to increase users' purchasing motivation.

[0074] The price adjustment strategy creation unit can take into account the characteristics of different regions and markets when creating a price adjustment strategy, and set optimal prices for each region. The price adjustment strategy creation unit, for example, analyzes the market characteristics of different regions and sets optimal prices for each region. For example, it adjusts prices taking into account the economic situation and purchasing power of the region. The price adjustment strategy creation unit also builds a system that takes into account market characteristics and sets optimal prices for each region. For example, it sets prices based on the demand and competitive situation of each region. The price adjustment strategy creation unit also analyzes the characteristics of different regions and markets and sets optimal prices. For example, it adjusts prices taking into account the culture and consumer preferences of the region. This makes it possible to set optimal prices that take into account the characteristics of each region.

[0075] The price adjustment strategy creation unit can propose new sales formats, such as subscription models and bundle sales, when adjusting prices. For example, the price adjustment strategy creation unit proposes a subscription model when adjusting prices. For example, a regular purchase plan can be introduced to acquire long-term customers. The price adjustment strategy creation unit can also propose bundle sales, selling multiple products as a set at the same time as adjusting prices. For example, related products can be bundled and offered at a discounted price. The price adjustment strategy creation unit can also propose new sales formats to increase customer purchasing motivation at the same time as adjusting prices. For example, a subscription model or bundle sales can be introduced. In this way, by proposing new sales formats, it is possible to increase users' purchasing motivation.

[0076] The AI ​​forecasting unit uses the generative AI to predict future emotional trends by utilizing not only past data but also the user's emotion estimation function. The AI ​​forecasting unit, for example, analyzes past data and user emotion data to predict future emotional trends. For example, it predicts future emotional trends based on past emotion scores. The AI ​​forecasting unit also uses the emotion estimation function to build a system for predicting future emotional trends. For example, it predicts future emotional trends based on user emotion data. The AI ​​forecasting unit also uses the generative AI to analyze past data and emotion data to predict future emotional trends. For example, it predicts future emotional trends based on past emotion scores. This makes it possible to create more effective strategies by analyzing past data and user emotion data and predicting future emotional trends.

[0077] When predicting a brand's future trends, the AI ​​forecasting department can simulate different scenarios and propose the most promising strategy. The AI ​​forecasting department, for example, simulates different scenarios to predict a brand's future trends. For example, it proposes the most promising strategy based on multiple scenarios. The AI ​​forecasting department also conducts scenario simulations and builds a system to predict a brand's future trends. For example, it proposes the optimal strategy based on different scenarios. The AI ​​forecasting department also simulates different scenarios to predict a brand's future trends. For example, it proposes the most promising strategy based on multiple scenarios. In this way, by simulating different scenarios and proposing the most promising strategy, a more effective strategy can be created.

[0078] The AI ​​forecasting unit can visualize the results of forecasting, allowing them to be intuitively understood using graphs and charts. The AI ​​forecasting unit, for example, visualizes the results of forecasting, allowing them to be intuitively understood using graphs and charts. For example, future trends are displayed in graphs. The AI ​​forecasting unit also builds a system that visualizes the results of forecasting, allowing them to be intuitively understood using graphs and charts. For example, future trends are displayed in charts. The AI ​​forecasting unit also visualizes the results of forecasting, allowing them to be intuitively understood using graphs and charts. For example, future trends are displayed in graphs. In this way, by visualizing the results of forecasting, they can be intuitively understood.

[0079] The AI ​​Forecasting Department uses the forecasting function to simultaneously predict future trends in different industries and markets and propose cross-industry strategies. The AI ​​Forecasting Department, for example, predicts future trends in different industries and markets and proposes cross-industry strategies. For example, it proposes a new business strategy based on trends in different industries. The AI ​​Forecasting Department also uses the forecasting function to build a system that predicts future trends in different industries and markets. For example, it proposes a new business strategy based on trends in different industries. The AI ​​Forecasting Department also predicts future trends in different industries and markets and proposes cross-industry strategies. For example, it proposes a new business strategy based on trends in different industries. In this way, by predicting future trends in different industries and markets and proposing cross-industry strategies, more effective strategies can be created.

[0080] The AI ​​forecasting unit can formulate comprehensive strategies by taking into account the user's lifestyle and social changes when predicting future trends. The AI ​​forecasting unit, for example, predicts future trends by taking into account the user's lifestyle and social changes. For example, it predicts future trends based on lifestyle changes. The AI ​​forecasting unit also builds a system that predicts future trends by taking into account social changes. For example, it formulates future strategies based on social trends. The AI ​​forecasting unit also builds comprehensive strategies by taking into account the user's lifestyle and social changes. For example, it predicts future trends based on lifestyle changes. This makes it possible to formulate comprehensive strategies that take into account the user's lifestyle and social changes.

[0081] The AI ​​forecasting unit can use the emotion estimation function to predict users' emotional reactions to future trends and propose strategies that are likely to resonate emotionally. The AI ​​forecasting unit, for example, uses the emotion estimation function to predict users' emotional reactions to future trends. For example, it proposes a strategy based on the emotion score for future trends. The AI ​​forecasting unit also builds a system that predicts users' emotional reactions and proposes strategies that are likely to resonate emotionally. For example, it proposes a strategy based on the emotion score for future trends. The AI ​​forecasting unit also predicts users' emotional reactions to future trends based on the emotion estimation data and proposes strategies that are likely to resonate emotionally. For example, it proposes a strategy based on the emotion score for future trends. In this way, by predicting users' emotional reactions to future trends and proposing strategies that are likely to resonate emotionally, more effective strategies can be developed.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The target audience attribute understanding unit can analyze health data and fitness data in addition to user purchasing behavior data to identify a health-conscious target audience. For example, the target audience attribute understanding unit analyzes user purchasing behavior data and health data to identify a health-conscious target audience. For example, targeting is performed based on purchase history of health foods and fitness equipment. The target audience attribute understanding unit also analyzes fitness data to identify a health-conscious target audience. For example, targeting is performed based on fitness app usage data. The target audience attribute understanding unit also analyzes health data to identify a health-conscious target audience. For example, targeting is performed based on health checkup data and wearable device data. This allows the health-conscious target audience to be identified and reflected in marketing strategies.

[0084] The target audience attribute understanding unit can analyze user location information data and perform targeting that takes into account the characteristics and cultural background of each region. For example, the user location information data is analyzed to understand the characteristics of each region. For example, targeting is performed based on purchasing behavior data in a specific region. The target audience attribute understanding unit also performs targeting that takes into account the cultural background of each region based on the location information data. For example, a marketing strategy tailored to local events and festivals is created. The target audience attribute understanding unit also analyzes user location information data and performs targeting that takes into account the characteristics and cultural background of each region. For example, a marketing strategy is created based on local specialties and popular products. This makes it possible to perform targeting that takes into account the characteristics and cultural background of each region.

[0085] The target audience attribute understanding unit can analyze a user's social media activities and comments in online communities to understand their potential interests and concerns in detail. For example, it can analyze the content of social media posts to identify the user's potential interests and concerns. For example, it can understand interests based on specific hashtags and keywords. The target audience attribute understanding unit can also analyze comments in online communities to identify the user's potential interests and concerns. For example, it can analyze the content of comments on forums and message boards to understand interests. The target audience attribute understanding unit can also analyze social media activities to identify the user's potential interests and concerns. For example, it can understand interests based on accounts followed and posts liked. This makes it possible to understand the user's potential interests and concerns in detail and reflect them in marketing strategies.

[0086] The target audience attribute understanding unit can use the emotion estimation function to generate personalized advertisements in real time according to the emotional state of the user. For example, the emotion estimation function is used to generate personalized advertisements in real time according to the emotional state of the user. For example, a bright-toned advertisement is displayed to a user in a positive emotional state. The target audience attribute understanding unit also analyzes the emotional state of the user in real time and generates personalized advertisements based on the results. For example, an advertisement for a product with a relaxing effect is displayed to a user who is feeling stressed. The target audience attribute understanding unit also generates advertisements in real time according to the emotional state of the user based on the emotion estimation data. For example, an advertisement including special offers or discount information is displayed to a user with a high emotion score. In this way, personalized advertisements in real time according to the emotional state of the user can be generated, maximizing the effectiveness of the advertisements.

[0087] The target audience attribute understanding unit can analyze health data and fitness data in addition to user purchasing behavior data to identify a health-conscious target audience. For example, the target audience attribute understanding unit analyzes user purchasing behavior data and health data to identify a health-conscious target audience. For example, targeting is performed based on purchase history of health foods and fitness equipment. The target audience attribute understanding unit also analyzes fitness data to identify a health-conscious target audience. For example, targeting is performed based on fitness app usage data. The target audience attribute understanding unit also analyzes health data to identify a health-conscious target audience. For example, targeting is performed based on health checkup data and wearable device data. This allows the health-conscious target audience to be identified and reflected in marketing strategies.

[0088] The target audience attribute understanding unit can propose cross-promotions with products and services from different industries based on the user's hobbies and purchasing behavior. For example, by analyzing data on the user's hobbies and purchasing behavior, cross-promotions with products and services from different industries can be proposed. For example, a cross-promotion between sporting goods and health foods can be carried out. The target audience attribute understanding unit can also propose cross-promotions with products and services from different industries based on the user's hobbies and purchasing behavior. For example, a cross-promotion between travel goods and cameras can be carried out. The target audience attribute understanding unit can also propose cross-promotions with products and services from different industries based on the user's purchasing behavior data. For example, a cross-promotion between fashion items and beauty products can be carried out. This allows the breadth of marketing strategies to be broadened by proposing cross-promotions with products and services from different industries.

[0089] The real-time information providing unit can analyze not only social media and news sites, but also users' email and chat histories to provide omnidirectional information about a brand. For example, it can analyze users' email and chat histories to collect information about a brand. For example, it can analyze the content of emails and chat conversations to understand opinions and feelings about a brand. The real-time information providing unit can also analyze users' email and chat histories in addition to social media and news sites to provide omnidirectional information about a brand. For example, it can understand brand reputation in real time based on email and chat data. The real-time information providing unit can also build a system that analyzes users' email and chat histories to collect information about a brand. For example, it can analyze the content of emails and chats to understand opinions and feelings about a brand in real time. This allows for the provision of omnidirectional information about a brand, making it possible to provide more comprehensive information.

[0090] When providing real-time information, the real-time information providing unit can simultaneously analyze the trends of competitors and changes in the market, and provide comprehensive information. For example, it can analyze the trends of competitors in real time and provide this information together with information about the brand. For example, it can adjust the brand strategy based on price changes and new product information from competitors. The real-time information providing unit can also analyze market changes in real time and provide this information together with information about the brand. For example, it can adjust the brand strategy based on market trends and consumer trends. The real-time information providing unit can also analyze the trends of competitors and changes in the market in real time, and build a system that provides comprehensive information. For example, it can adjust the brand strategy based on the trends of competitors and changes in the market. This makes it possible to provide comprehensive information by simultaneously analyzing the trends of competitors and changes in the market.

[0091] The real-time information providing unit can analyze visual data when providing real-time information and perform visual brand evaluation. For example, it can analyze images and videos on social media and news sites to perform visual brand evaluation. For example, it can analyze the content of posted images and videos to understand opinions and emotions about the brand. The real-time information providing unit can also analyze images and videos posted by users to perform visual brand evaluation. For example, it can analyze the content of images and videos to understand opinions and emotions about the brand in real time. The real-time information providing unit can also analyze visual data and build a system to perform visual brand evaluation. For example, it can analyze the content of images and videos to understand opinions and emotions about the brand in real time. This makes it possible to perform visual brand evaluation by analyzing visual data.

[0092] The real-time information providing unit can translate brand evaluations in different languages ​​in real time and provide information from a global perspective. For example, it translates brand evaluations in different languages ​​in real time and provides information from a global perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. The real-time information providing unit also translates posts in different languages ​​on social media and news sites in real time and provides brand evaluations. For example, it translates comments and reviews in different languages ​​to understand opinions and emotions about the brand. The real-time information providing unit also builds a system that translates brand evaluations in different languages ​​in real time and provides information from a global perspective. For example, it translates posts in different languages ​​to understand opinions and emotions about the brand in real time. This makes it possible to provide information from a global perspective by translating brand evaluations in different languages ​​in real time.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The target audience attribute understanding unit uses the generation AI to analyze users' hobbies and purchasing behavior to gain a detailed understanding of the target audience's attributes. For example, data on the products users purchase online and the pages they view is collected, and the generation AI analyzes that data. The target audience attribute understanding unit also identifies users' attributes, such as age, gender, and interests, and reflects them in the company's marketing strategy. Step 2: The real-time information provider uses generative AI to provide real-time information about a brand's popularity and reputation. For example, it analyzes data collected from social media and news sites to identify positive and negative comments about the brand in real time. The real-time information provider also enables companies to take prompt action. Step 3: The price adjustment strategy creation department uses generative AI to create a strategy for adjusting prices to match early market trends. For example, it analyzes competitors' price trends and consumer purchasing intentions to propose optimal pricing. The price adjustment strategy creation department also helps companies increase repeat customers. Step 4: The AI ​​Forecasting Department uses generative AI to provide forecasting capabilities to predict future trends for brands. For example, it analyzes past data to predict changes in brand awareness, reputation, and target audience over the next few months. The AI ​​Forecasting Department also helps companies make strategic decisions.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 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. Using generative AI, a target audience attribute understanding unit that analyzes user hobbies and purchasing behavior and understands the attributes of the target audience in detail; A real-time information provider that provides real-time information on brand awareness and reputation, and a price adjustment strategy development department that develops strategies to adjust prices to early market trends; An AI forecasting unit that predicts future trends of the brand. A system characterized by:

2. The target audience attribute understanding unit By utilizing the user's emotion estimation function, emotional attributes are identified based on the purchasing behavior and hobbies, and are reflected in a marketing strategy.

2. The system of claim 1.

3. The target audience attribute understanding unit Analyzing the user's social media activity and online community comments to gain a detailed understanding of their potential interests and concerns 2. The system of claim 1.

4. The target audience attribute understanding unit Analyze the location data of the user and perform targeting taking into account the characteristics and cultural background of each region.

2. The system of claim 1.

5. The target audience attribute understanding unit In addition to the user's purchasing behavior data, health and fitness data will be analyzed to identify a health-conscious target audience.

2. The system of claim 1.

Images