System

Abstract

To provide a system that provides customized marketing messages and content on the basis of the user information.SOLUTION: A system according to an embodiment comprises: a provision unit; an interactive feedback unit; an interactive provision unit; a demonstration unit; and a generation unit. The provision unit provides customized marketing messages or content on the basis of user information. The interactive feedback unit conducts interactive questionnaires or surveys in order to collect feedback or opinions from users on marketing subjects. The interactive provision unit provides users with interactive games or interactive quizzes with privileges. The demonstration unit conducts demonstrations of new products or services through interactive conversations and responds to user questions in real time. The generation unit generates content of the marketing subjects on the basis of user stories and information about the marketing subjects.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have not adequately addressed effectively delivering marketing messages and content tailored to a user's individual needs and interests, and there is room for improvement.

[0005] The system according to the embodiment aims to provide customized marketing messages and content based on user information. [Means for solving the problem]

[0006] The system according to the embodiment includes a providing unit, an interactive feedback unit, an interactive providing unit, a demo unit, and a generating unit. The providing unit provides customized marketing messages or content based on user information. The interactive feedback unit conducts an interactive questionnaire or survey to collect feedback or opinions from users on the marketing target. The interactive providing unit provides interactive games or interactive quizzes with rewards to users. The demo unit demonstrates new products or services through dialogue and answers users' questions in real time. The generating unit generates content for the marketing target based on the user's story and information on the marketing target. Effect of the Invention

[0007] The system according to the embodiment can provide customized marketing messages and content based on the user's information. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The interactive approach AI assistant system for marketing campaigns according to an embodiment of the present invention is a system that provides customized marketing messages and content based on the user's past behavior, interests, and preferences. The system includes a providing unit that provides customized marketing messages and content based on user information, an interactive feedback unit that conducts interactive questionnaires and surveys to collect feedback and opinions from users on the marketing target, an interactive providing unit that provides users with interactive games and quizzes with rewards, a demo unit that demonstrates new products and services through dialogue and answers users' questions in real time, and a generating unit that generates content based on the user's story and information on the marketing target. For example, it is possible to suggest products that the user is likely to be interested in next based on products that the user has previously purchased and content that the user has viewed. In addition, it is possible to collect users' opinions through interactive questionnaires and improve marketing strategies based on the feedback. Furthermore, it is possible to increase user engagement by providing games and quizzes with rewards. It is possible to deepen the user's understanding by interactively demonstrating new products and services and answering users' questions in real time. It is also possible for users to create their own stories and share them with brands to jointly create content. This system can provide users with a more personalized experience and improve marketing effectiveness. For example, the content of the next game provided can be customized based on the history of games the user has previously participated in. It is also possible to customize the progress of the story using the user's real-time location information. In addition, it is possible to provide optimal demonstrations by recognizing the user's eye tracking data, movements, and gestures. This allows the conversational approach AI assistant system of marketing campaigns to provide a more personalized experience for users and improve marketing effectiveness.

[0029] The interactive approach AI assistant system for a marketing campaign according to the embodiment includes a providing unit, an interactive feedback unit, an interactive providing unit, a demo unit, and a generating unit. The providing unit provides a marketing message or content customized based on user information. The user information includes, for example, past behavior, interests, preferences, and the like, but is not limited to such examples. The providing unit, for example, suggests products that the user is likely to be interested in next based on the user's past purchase history. The providing unit can also provide related content based on the user's browsing history. Furthermore, the providing unit can also provide a marketing message customized based on the user's attribute information. For example, the providing unit generates a message for a specific target group based on the user's attribute information such as age, gender, and place of residence. The interactive feedback unit conducts an interactive questionnaire or survey to collect feedback and opinions from users regarding the marketing target. The interactive feedback unit conducts a questionnaire while interacting with the user using, for example, a chatbot. The interactive feedback unit can also collect user opinions using a voice assistant. Furthermore, the interactive feedback unit can analyze the user's feedback in real time and use it to improve the marketing strategy. For example, the interactive feedback unit analyzes the content of the user's answers and extracts certain trends and patterns. The interactive providing unit provides the user with interactive games and quizzes with rewards. The interactive providing unit provides, for example, points or coupons as rewards. The interactive providing unit can also provide customized game content based on the user's past game participation history. For example, the interactive providing unit customizes the content of the next game to be provided based on the history of games the user has previously participated in. The demo unit demonstrates new products and services through dialogue and answers the user's questions in real time. For example, the demo unit provides an optimal demonstration using eye tracking data of the user. The demo unit can also recognize the user's movements and gestures to perform interactive demonstrations.Furthermore, the demo unit can customize the demo content by using the user's real-time location information. The generation unit generates content based on the user's story and marketing target information. For example, the generation unit generates new content based on a story created by the user in the past. The generation unit can also generate customized content based on the user's travel history. For example, the generation unit generates travel guide content based on places the user has visited in the past. In this way, the interactive approach AI assistant system for marketing campaigns according to the embodiment can provide a more personalized experience for the user and enhance marketing effectiveness.

[0030] The providing unit provides a marketing message or content customized based on the user's information. The user's information includes, but is not limited to, past behavior, interests, preferences, and the like. For example, the providing unit suggests products that the user is likely to be interested in next based on the user's past purchase history. The providing unit can also provide related content based on the user's browsing history. Furthermore, the providing unit can provide a marketing message customized based on the user's attribute information. For example, the providing unit generates a message for a specific target group based on the user's attribute information such as age, sex, and place of residence. The providing unit collects this information and analyzes it using AI to grasp the user's preferences and behavioral patterns in detail. For example, AI analyzes the user's past purchase history and predicts the product that is likely to be purchased next. Based on this prediction, the providing unit suggests the most suitable product to the user. Also, related content can be provided by analyzing the user's browsing history. For example, if the user frequently browses products in a specific category, new products and sale information related to that category is provided. Furthermore, the providing unit generates a marketing message customized based on the user's attribute information. For example, a message that is sensitive to trends can be provided to younger users, while a message that emphasizes reliability and a sense of security can be provided to senior users. This allows the provider to provide a more personalized experience to users and improve marketing effectiveness.

[0031] The interactive feedback unit conducts an interactive questionnaire or survey to collect feedback and opinions from users regarding the marketing target. The interactive feedback unit, for example, uses a chatbot to conduct a questionnaire while interacting with the user. The interactive feedback unit can also collect user opinions using a voice assistant. Furthermore, the interactive feedback unit can analyze the user's feedback in real time and use it to improve the marketing strategy. For example, the interactive feedback unit analyzes the content of the user's answer and extracts specific trends and patterns. The interactive feedback unit uses AI to analyze the user's feedback in real time and use it to improve the marketing strategy. For example, the chatbot uses natural language processing technology to understand the user's answer and continues to ask appropriate questions to collect more detailed feedback. Furthermore, the voice assistant uses voice recognition technology to collect the user's opinion and convert it into text data. This allows the interactive feedback unit to quickly and accurately collect the user's feedback and analyze it in real time. Furthermore, the interactive feedback unit extracts specific trends and patterns based on the collected feedback and use it to improve the marketing strategy. For example, if a particular product receives a low user rating, the interactive feedback unit can identify areas for improvement for that product and reflect them in the next campaign. This allows the interactive feedback unit to realize a marketing strategy that reflects the user's opinions and develop an effective campaign.

[0032] The interactive providing unit provides the user with an interactive game or quiz with a reward. The interactive providing unit provides, for example, points or coupons as rewards. The interactive providing unit can also provide game content customized based on the user's past game participation history. For example, the interactive providing unit customizes the content of the next game to be provided based on the history of games in which the user has participated in the past. The interactive providing unit uses AI to analyze the user's past game participation history and customizes the content of the next game to be provided. For example, the interactive providing unit analyzes the genre and difficulty of games in which the user has participated in the past and adjusts the content of the next game to be provided to suit the user's preferences. The interactive providing unit can also provide rewards in real time according to the progress of the game. For example, when a user clears a specific stage, points or coupons are instantly provided. This allows the user to earn rewards while enjoying the game, and the interactive providing unit can increase the user's engagement. Furthermore, the interactive providing unit can continuously improve the content of the game based on the user's feedback. For example, if the user feels that a specific stage is difficult, the difficulty of that stage is adjusted. The interactive providing unit can also develop new game content for the next campaign based on the user's game participation history. This allows the interactive providing unit to constantly provide new experiences to users, thereby enhancing marketing effectiveness.

[0033] The demo unit demonstrates new products and services through dialogue and answers the user's questions in real time. For example, the demo unit uses the user's gaze tracking data to provide an optimal demonstration. The demo unit can also recognize the user's movements and gestures to provide an interactive demonstration. Furthermore, the demo unit can customize the content of the demo using the user's real-time location information. The demo unit uses AI to analyze the user's gaze tracking data and provide a demonstration that focuses on the part that the user is most interested in. For example, if the user focuses his or her gaze on a specific product function, a detailed explanation of that function is provided. The demo unit can also recognize the user's movements and gestures and provide an interactive demonstration. For example, if the user raises his or her hand, the content of the demo is changed according to that action. Furthermore, the demo unit can customize the content of the demo using the user's real-time location information. For example, if the user is in a specific area, the demo unit provides a demonstration of products and services related to that area. This allows the demo unit to provide a more personalized demonstration to the user and deepen the user's understanding of the product or service. Furthermore, the demo unit can answer the user's questions in real time. For example, if a user asks a question during a demo, the AI ​​will analyze the question and provide an appropriate answer, allowing the demo department to quickly resolve users' doubts and increase their trust in the product or service.

[0034] The generation unit generates content based on the user's story and information on the marketing target. For example, the generation unit generates new content based on a story created by the user in the past. The generation unit can also generate customized content based on the user's travel history. For example, the generation unit generates travel guide content based on places the user has visited in the past. The generation unit uses AI to analyze the user's story and travel history to generate optimal content for the user. For example, the generation unit analyzes a story created by the user in the past and generates new content based on the theme and style of the story. The generation unit also analyzes the user's travel history and generates customized travel guide content based on places visited and experiences. This allows the generation unit to provide more personalized content to the user and improve marketing effectiveness. Furthermore, the generation unit can continuously improve the generated content based on user feedback. For example, if the user provides opinions or impressions on the generated content, the feedback is analyzed and reflected in the next content generation. The generation unit can also adjust the theme and style of the content according to the user's interests and preferences. This allows the generation unit to always provide optimal content to the user and increase user engagement.

[0035] The suggestion unit can suggest marketing targets based on the user's questions and reactions. The suggestion unit, for example, collects the user's questions and reactions through real-time chat. For example, the suggestion unit analyzes questions asked by the user to the chatbot and suggests appropriate marketing targets. The suggestion unit can also collect the user's reactions based on survey results and suggest marketing targets. For example, the suggestion unit analyzes the content of the user's answers to the survey and suggests products and services that match the user's interests and preferences. This makes it possible to suggest appropriate marketing targets based on the user's questions and reactions. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's questions and reactions to AI, which can then suggest appropriate marketing targets.

[0036] The experience providing unit can develop a story that is the target of marketing through a dialogue with the user, and provide an experience in which the user becomes a part of the story. The experience providing unit develops a story based on, for example, a storyboard. For example, the experience providing unit determines the progress of the story through a dialogue with the user. The experience providing unit can also develop a story based on a scenario. For example, the experience providing unit changes the scenario based on the user's selection and customizes the progress of the story. This can increase the user's engagement by providing an experience in which the user becomes a part of the story. Some or all of the above-mentioned processing in the experience providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the experience providing unit can input the content of the dialogue with the user to AI, and the AI ​​can determine the progress of the story.

[0037] The recommendation unit can recommend customized products or services in real time based on the user's past behavior, interests, or preferences. The recommendation unit, for example, recommends products or services based on the user's browsing history. For example, the recommendation unit analyzes product pages that the user has viewed in the past and recommends related products. The recommendation unit can also recommend products or services based on the user's purchase history. For example, the recommendation unit recommends products that the user is likely to purchase next based on products that the user has purchased in the past. Furthermore, the recommendation unit can also recommend products or services based on the results of a survey of the user. For example, the recommendation unit recommends customized products or services based on the interests and preferences that the user has answered in the survey. This makes it possible to recommend appropriate products or services in real time based on the user's past behavior, interests, or preferences. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the user's behavior data into AI, which can then recommend appropriate products or services.

[0038] The providing unit can provide customized content based on the geographical location information of the user. The providing unit, for example, acquires the user's location information based on GPS data. For example, when the user is in a specific area, the providing unit provides content related to the area. The providing unit can also estimate the user's location information based on the IP address. For example, the providing unit analyzes the user's IP address and provides content specific to the area. Furthermore, the providing unit can also acquire the user's location information based on beacon data. For example, when the user is in a specific store, the providing unit provides content related to the store. This makes it possible to provide appropriate content based on the user's geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's location information to AI, and the AI ​​can provide appropriate content.

[0039] The demo unit can provide an optimal product or service demonstration by utilizing the user's gaze tracking data. The demo unit, for example, collects the user's gaze data using an eye tracking device. For example, the demo unit provides a demonstration that focuses on the part where the user directs his / her gaze. The demo unit can also track the user's gaze using camera data. For example, the demo unit provides a demonstration that adds detailed explanations to the part where the user fixes his / her gaze for a long time. Furthermore, the demo unit can provide a demonstration that overlooks the whole when the user frequently moves his / her gaze. This allows the user's gaze tracking data to be used to provide an optimal demonstration. A part or all of the above-mentioned processing in the demo unit may be performed, for example, using AI, or may be performed without using AI. For example, the demo unit can input the user's gaze data into AI, and the AI ​​can provide an optimal demonstration.

[0040] The experience providing unit can change the progress of the story based on the user's selection. The experience providing unit changes the progress of the story, for example, by the user selecting an option. For example, the experience providing unit changes the scenario of the story based on the option selected by the user. The experience providing unit can also save the user's selection result and reflect it in the next progress of the story. For example, the experience providing unit customizes the progress of the story based on the option selected by the user in the past. This makes it possible to increase the engagement of the user by changing the progress of the story based on the user's selection. A part or all of the above-mentioned processing in the experience providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the experience providing unit can input the user's selection data into AI, which can determine the progress of the story.

[0041] The interactive providing unit can provide customized game content based on the user's past game participation history. The interactive providing unit customizes the content of the next game to be provided based on, for example, the history of games in which the user has participated in the past. For example, the interactive providing unit provides optimal game content based on the genre of games that the user has liked in the past. The interactive providing unit can also provide game content that attracts the user's interest based on the user's past game participation history. For example, the interactive providing unit customizes the next game based on the content of games in which the user has obtained a high score in the past. This makes it possible to provide appropriate game content based on the user's past game participation history. A part or all of the above-mentioned processing in the interactive providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the interactive providing unit inputs the user's game participation history into an AI, and the AI ​​can provide optimal game content.

[0042] The demo unit can recognize the user's movement or gesture and perform an interactive demonstration. The demo unit, for example, recognizes the user's movement using a motion sensor. For example, when the user raises his / her hand, the demo unit provides a demonstration according to the movement. The demo unit can also recognize the user's gesture using camera data. For example, when the user shakes his / her head, the demo unit provides a demonstration according to the movement. Furthermore, when the user walks around, the demo unit can also provide a demonstration according to the movement. This makes it possible to recognize the user's movement or gesture and provide an interactive demonstration. A part or all of the above-mentioned processing in the demo unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the demo unit can input the user's movement or gesture data into the AI, which can provide an interactive demonstration.

[0043] The experience providing unit can customize the progress of the story by using the real-time location information of the user. The experience providing unit acquires the user's location information based on, for example, GPS data. For example, when the user is in a specific area, the experience providing unit provides the progress of the story related to the area. The experience providing unit can also acquire the user's location information based on beacon data. For example, when the user is in a specific store, the experience providing unit provides the progress of the story related to the store. Furthermore, the experience providing unit can also customize the progress of the story by using the user's real-time location information. For example, when the user is traveling, the experience providing unit provides the progress of the story related to the travel destination. This makes it possible to customize the progress of the story by using the user's real-time location information. A part or all of the above-mentioned processing in the experience providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the experience providing unit inputs the user's location information into AI, and the AI ​​can customize the progress of the story.

[0044] The generating unit can generate customized content based on the user's travel history. The generating unit generates travel guide content based on places the user has visited in the past, for example. For example, the generating unit generates related event introduction content based on events the user has participated in in the past. The generating unit can also generate content that suggests a place to visit next based on the user's travel history. For example, the generating unit suggests a next travel destination based on data on tourist spots the user has visited in the past. This makes it possible to generate appropriate content based on the user's travel history. Some or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the user's travel history data into AI, which can generate customized content.

[0045] The providing unit can analyze the user's past purchase history and select the optimal content provision timing. The providing unit selects the optimal content provision timing based on, for example, the user's purchase date and time. For example, the providing unit provides content according to a time period based on a history of the user's past purchases during that time period. The providing unit can also select the optimal content provision timing based on the user's purchased product. For example, the providing unit provides related content according to the timing when the user purchased a specific product. Furthermore, the providing unit can also select the optimal content provision timing based on the user's purchase frequency. For example, if the user tends to make purchases at a specific frequency, the providing unit provides content according to that frequency. This makes it possible to provide content at an appropriate timing based on the user's past purchase history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's purchase history data into AI, and the AI ​​can select the optimal content provision timing.

[0046] The providing unit can dynamically change the type of content to be provided based on the user's current activity status. The providing unit, for example, uses an activity tracker to collect the user's activity status. For example, when the user is exercising, the providing unit provides health-related content. The providing unit can also grasp the user's activity status based on the usage status of the app. For example, when the user is at work, the providing unit provides business-related content. Furthermore, the providing unit can also provide entertainment-related content based on the user's activity status. For example, when the user is relaxing, the providing unit provides entertainment-related content. This makes it possible to provide appropriate content based on the user's current activity status. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's activity status data into AI, and the AI ​​can provide optimal content.

[0047] The providing unit can provide a region-specific marketing message based on the geographical location information of the user. The providing unit, for example, acquires the user's location information based on GPS data. For example, when the user is in a specific region, the providing unit provides event information for that region. The providing unit can also estimate the user's location information based on the IP address. For example, when the user is traveling, the providing unit provides tourist information for the travel destination. Furthermore, the providing unit can also acquire the user's location information based on beacon data. For example, when the user is at home, the providing unit provides sale information for nearby stores. This makes it possible to provide an appropriate marketing message based on the user's geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's location information to AI, which can generate a region-specific marketing message.

[0048] The providing unit can analyze the social media activity of the user and provide related content. For example, when the user uses a specific hashtag, the providing unit provides content related to the hashtag. For example, when the user follows a specific account, the providing unit provides content related to the account. In addition, when the user likes a specific post, the providing unit can also provide content related to the post. For example, when the user shows an interest in a specific topic, the providing unit provides content related to the topic. This makes it possible to provide appropriate content based on the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI, and the AI ​​can provide related content.

[0049] The interactive feedback unit can optimize the order of questions based on the user's past answer history when collecting feedback. The interactive feedback unit, for example, suggests an optimal question order based on the order of questions answered by the user in the past. For example, the interactive feedback unit preferentially provides questions related to the content of the user's past answers. The interactive feedback unit can also suggest an order that is easy to answer based on the user's past answer history. For example, the interactive feedback unit analyzes the history of questions answered by the user in the past and determines an optimal question order. This makes it possible to provide an appropriate question order based on the user's past answer history. A part or all of the above-mentioned processing in the interactive feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the interactive feedback unit can input the user's answer history data into AI, which can determine the optimal question order.

[0050] The interactive feedback unit can collect region-specific feedback based on the geographical location information of the user. The interactive feedback unit, for example, acquires the user's location information based on GPS data. For example, when the user is in a specific area, the interactive feedback unit collects feedback related to the area. The interactive feedback unit can also estimate the user's location information based on the IP address. For example, when the user is traveling, the interactive feedback unit collects feedback related to the travel destination. Furthermore, the interactive feedback unit can also acquire the user's location information based on beacon data. For example, when the user is at home, the interactive feedback unit collects feedback related to nearby stores. This makes it possible to collect appropriate feedback based on the user's geographical location information. A part or all of the above-mentioned processing in the interactive feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the interactive feedback unit inputs the user's location information into AI, which can collect region-specific feedback.

[0051] The interactive providing unit can analyze the user's past game participation history and provide optimal game content. The interactive providing unit customizes the content of the next game to be provided based on, for example, the history of games in which the user has participated in the past. For example, the interactive providing unit provides optimal game content based on the genre of games that the user has liked in the past. The interactive providing unit can also provide game content that attracts the user's interest based on the user's past game participation history. For example, the interactive providing unit customizes the next game based on the content of games in which the user has obtained a high score in the past. This makes it possible to provide appropriate game content based on the user's past game participation history. A part or all of the above-mentioned processing in the interactive providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the interactive providing unit inputs the user's game participation history into AI, and the AI ​​can provide optimal game content.

[0052] The interactive providing unit can provide region-specific games and quizzes in consideration of the geographical location information of the user. The interactive providing unit, for example, acquires the user's location information based on GPS data. For example, when the user is in a specific region, the interactive providing unit provides games and quizzes related to the region. The interactive providing unit can also estimate the user's location information based on the IP address. For example, when the user is traveling, the interactive providing unit provides games and quizzes related to the travel destination. Furthermore, the interactive providing unit can also acquire the user's location information based on beacon data. For example, when the user is at home, the interactive providing unit provides games and quizzes related to nearby stores. This makes it possible to provide appropriate games and quizzes based on the user's geographical location information. A part or all of the above-mentioned processing in the interactive providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the interactive providing unit can input the user's location information to AI, which can generate region-specific games and quizzes.

[0053] The demo unit can provide an optimal demonstration by utilizing the user's gaze tracking data. The demo unit, for example, collects the user's gaze data using an eye-tracking device. For example, the demo unit provides a demonstration that focuses on the part where the user directs his / her gaze. The demo unit can also track the user's gaze using camera data. For example, the demo unit provides a demonstration that adds detailed explanations to the part where the user fixes his / her gaze for a long time. Furthermore, the demo unit can provide a demonstration that overlooks the whole when the user frequently moves his / her gaze. This allows the user's gaze tracking data to be used to provide an optimal demonstration. A part or all of the above-mentioned processing in the demo unit may be performed, for example, using AI, or may be performed without using AI. For example, the demo unit can input the user's gaze data into AI, and the AI ​​can provide an optimal demonstration.

[0054] The demo unit can recognize the user's movement or gesture and perform an interactive demonstration. The demo unit, for example, recognizes the user's movement using a motion sensor. For example, when the user raises his / her hand, the demo unit provides a demonstration according to the movement. The demo unit can also recognize the user's gesture using camera data. For example, when the user shakes his / her head, the demo unit provides a demonstration according to the movement. Furthermore, when the user walks around, the demo unit can also provide a demonstration according to the movement. This makes it possible to recognize the user's movement or gesture and provide an interactive demonstration. A part or all of the above-mentioned processing in the demo unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the demo unit can input the user's movement or gesture data into the AI, which can provide an interactive demonstration.

[0055] The generation unit can analyze the user's past behavior history and apply an optimal content generation algorithm. The generation unit, for example, generates related content based on content previously viewed by the user. For example, the generation unit generates related product introduction content based on products previously purchased by the user. The generation unit can also generate interesting content from the user's past behavior history. For example, the generation unit suggests an article to be read next based on the content of an article previously viewed by the user. This makes it possible to generate appropriate content based on the user's past behavior history. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs the user's behavior history data into AI, and the AI ​​can apply an optimal content generation algorithm.

[0056] The generating unit can generate customized content based on the user's travel history. The generating unit generates travel guide content based on places the user has visited in the past, for example. For example, the generating unit generates related event introduction content based on events the user has participated in in the past. The generating unit can also generate content that suggests a place to visit next based on the user's travel history. For example, the generating unit suggests a next travel destination based on data on tourist spots the user has visited in the past. This makes it possible to generate appropriate content based on the user's travel history. Some or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the user's travel history data into AI, which can generate customized content.

[0057] The suggestion unit can analyze the user's past behavior history and select the optimal suggestion timing. The suggestion unit, for example, makes a suggestion based on a history of the user accepting a suggestion in a specific time period in the past, according to that time period. For example, if the user has a tendency to accept a suggestion on a specific day of the week, the suggestion unit makes a suggestion based on that day of the week. In addition, if the user accepts a suggestion during a specific event (e.g., a sale period), the suggestion unit can also make a suggestion based on that event. For example, the suggestion unit makes the next suggestion based on a product that the user purchased in the past at a specific event. This allows the suggestion to be made at an appropriate timing based on the user's past behavior history. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit inputs the user's behavior history data into an AI, which can select the optimal suggestion timing.

[0058] The suggestion unit can make a region-specific suggestion based on the geographical location information of the user. The suggestion unit acquires the user's location information based on, for example, GPS data. For example, when the user is in a specific region, the suggestion unit proposes event information for that region. The suggestion unit can also estimate the user's location information based on the IP address. For example, when the user is traveling, the suggestion unit proposes tourist information for the travel destination. Furthermore, the suggestion unit can acquire the user's location information based on beacon data. For example, when the user is at home, the suggestion unit proposes sale information for nearby stores. This makes it possible to make appropriate suggestions based on the user's geographical location information. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's location information to an AI, which can generate region-specific suggestions.

[0059] The experience providing unit can optimize the development of the story based on the user's past behavior history. The experience providing unit provides a related development of the story based on, for example, the history of stories in which the user has participated in the past. For example, the experience providing unit provides an optimal development of the story based on the genre of stories that the user has liked in the past. The experience providing unit can also provide an interesting development of the story based on the user's past behavior history. For example, the experience providing unit customizes the next story based on the content of the story in which the user has participated in the past. This makes it possible to provide an appropriate development of the story based on the user's past behavior history. A part or all of the above-mentioned processing in the experience providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the experience providing unit inputs the user's behavior history data into AI, and the AI ​​can generate an optimal development of the story.

[0060] The experience providing unit can customize the progress of the story by using the real-time location information of the user. The experience providing unit acquires the user's location information based on, for example, GPS data. For example, when the user is in a specific area, the experience providing unit provides the progress of the story related to the area. The experience providing unit can also acquire the user's location information based on beacon data. For example, when the user is in a specific store, the experience providing unit provides the progress of the story related to the store. Furthermore, the experience providing unit can also customize the progress of the story by using the user's real-time location information. For example, when the user is traveling, the experience providing unit provides the progress of the story related to the travel destination. This makes it possible to customize the progress of the story by using the user's real-time location information. A part or all of the above-mentioned processing in the experience providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the experience providing unit inputs the user's location information into AI, and the AI ​​can customize the progress of the story.

[0061] The recommendation unit can analyze the user's past behavior history and select the optimal recommendation timing. The recommendation unit, for example, makes a recommendation based on a history of the user accepting recommendations in a specific time period in the past, according to that time period. For example, if the user has a tendency to accept recommendations on a specific day of the week, the recommendation unit makes a recommendation based on that day of the week. In addition, if the user accepts recommendations during a specific event (e.g., a sale period), the recommendation unit can also make a recommendation based on that event. For example, the recommendation unit makes the next recommendation based on a product that the user purchased in the past at a specific event. This allows recommendations to be made at an appropriate time based on the user's past behavior history. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit inputs the user's behavior history data into an AI, which can select the optimal recommendation timing.

[0062] The recommendation unit can make region-specific recommendations based on the geographical location information of the user. The recommendation unit, for example, acquires the user's location information based on GPS data. For example, when the user is in a specific region, the recommendation unit recommends event information for that region. The recommendation unit can also estimate the user's location information based on the IP address. For example, when the user is traveling, the recommendation unit recommends tourist information for the travel destination. Furthermore, the recommendation unit can acquire the user's location information based on beacon data. For example, when the user is at home, the recommendation unit recommends sale information for nearby stores. This makes it possible to make appropriate recommendations based on the user's geographical location information. Part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's location information to AI, which can generate region-specific recommendations.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The providing unit can suggest products that are likely to be purchased next based on the user's purchasing history. For example, the providing unit can suggest products similar to products previously purchased by the user. The providing unit can also suggest complementary products to products previously purchased by the user. Furthermore, the providing unit can also suggest consumables for products previously purchased by the user. This makes it possible to suggest appropriate products based on the user's purchasing history.

[0065] The suggestion unit can suggest optimal marketing targets based on the user's real-time location information. For example, when the user is in a particular store, the suggestion unit can suggest products from that store. Also, when the user is in a particular area, the suggestion unit can suggest event information for that area. Furthermore, when the user is traveling, the suggestion unit can suggest tourist information for the travel destination. In this way, it is possible to suggest appropriate marketing targets based on the user's real-time location information.

[0066] The experience providing unit can customize the progress of the story based on the user's past behavior history. For example, the experience providing unit determines the progress of the next story based on the history of stories in which the user has participated in the past. The experience providing unit can also customize the progress of the story based on the genre of stories that the user has liked in the past. Furthermore, the experience providing unit can also provide an interesting story progress based on the user's past behavior history. This makes it possible to provide an appropriate story progress based on the user's past behavior history.

[0067] The recommendation unit can analyze the user's social media activity and recommend related products and services. For example, if the user uses a specific hashtag, the recommendation unit can recommend products and services related to the hashtag. Also, if the user follows a specific account, the recommendation unit can recommend products and services related to the account. Furthermore, if the user likes a specific post, the recommendation unit can recommend products and services related to the post. This makes it possible to recommend appropriate products and services based on the user's social media activity.

[0068] The providing unit can dynamically change the type of content to be provided based on the user's current activity status. For example, the providing unit can provide health-related content when the user is exercising. The providing unit can also provide business-related content when the user is working. Furthermore, the providing unit can provide entertainment-related content when the user is relaxing. In this way, appropriate content can be provided based on the user's current activity status.

[0069] The process flow of the first embodiment will be briefly described below.

[0070] Step 1: The provision unit provides customized marketing messages or content based on user information. User information includes past behavior, interests, preferences, purchase history, browsing history, and attribute information (age, gender, place of residence, etc.). Based on this information, the provision unit suggests products and content relevant to the user and generates messages for specific target groups. Step 2: The interactive feedback department conducts interactive questionnaires and surveys to collect feedback and opinions from users regarding the marketing target. The interactive feedback department uses chatbots and voice assistants to conduct surveys while interacting with users, and analyzes user feedback in real time to help improve marketing strategies. Step 3: The interactive providing unit provides the user with interactive games and quizzes with rewards. The interactive providing unit provides points and coupons as rewards, and provides game content customized based on the user's past game participation history. Step 4: The Demo Unit interactively demonstrates the new product or service and answers the user's questions in real time. The Demo Unit uses the user's eye-tracking data, movements, gestures, and real-time location information to create an interactive demonstration. Step 5: The generator generates content based on the user's story and marketing target information. The generator generates customized content based on the user's past stories and travel history, and provides travel guide content, for example.

[0071] (Example 2) The interactive approach AI assistant system for marketing campaigns according to an embodiment of the present invention is a system that provides customized marketing messages and content based on the user's past behavior, interests, and preferences. The system includes a providing unit that provides customized marketing messages and content based on user information, an interactive feedback unit that conducts interactive questionnaires and surveys to collect feedback and opinions from users on the marketing target, an interactive providing unit that provides users with interactive games and quizzes with rewards, a demo unit that demonstrates new products and services through dialogue and answers users' questions in real time, and a generating unit that generates content based on the user's story and information on the marketing target. For example, it is possible to suggest products that the user is likely to be interested in next based on products that the user has previously purchased and content that the user has viewed. In addition, it is possible to collect users' opinions through interactive questionnaires and improve marketing strategies based on the feedback. Furthermore, it is possible to increase user engagement by providing games and quizzes with rewards. It is possible to deepen the user's understanding by interactively demonstrating new products and services and answering users' questions in real time. It is also possible for users to create their own stories and share them with brands to jointly create content. This system can provide users with a more personalized experience and improve marketing effectiveness. For example, the content of the next game provided can be customized based on the history of games the user has previously participated in. It is also possible to customize the progress of the story using the user's real-time location information. In addition, it is possible to provide optimal demonstrations by recognizing the user's eye tracking data, movements, and gestures. This allows the conversational approach AI assistant system of marketing campaigns to provide a more personalized experience for users and improve marketing effectiveness.

[0072] The interactive approach AI assistant system for a marketing campaign according to the embodiment includes a providing unit, an interactive feedback unit, an interactive providing unit, a demo unit, and a generating unit. The providing unit provides a marketing message or content customized based on user information. The user information includes, for example, past behavior, interests, preferences, and the like, but is not limited to such examples. The providing unit, for example, suggests products that the user is likely to be interested in next based on the user's past purchase history. The providing unit can also provide related content based on the user's browsing history. Furthermore, the providing unit can also provide a marketing message customized based on the user's attribute information. For example, the providing unit generates a message for a specific target group based on the user's attribute information such as age, gender, and place of residence. The interactive feedback unit conducts an interactive questionnaire or survey to collect feedback and opinions from users regarding the marketing target. The interactive feedback unit conducts a questionnaire while interacting with the user using, for example, a chatbot. The interactive feedback unit can also collect user opinions using a voice assistant. Furthermore, the interactive feedback unit can analyze the user's feedback in real time and use it to improve the marketing strategy. For example, the interactive feedback unit analyzes the content of the user's answers and extracts certain trends and patterns. The interactive providing unit provides the user with interactive games and quizzes with rewards. The interactive providing unit provides, for example, points or coupons as rewards. The interactive providing unit can also provide customized game content based on the user's past game participation history. For example, the interactive providing unit customizes the content of the next game to be provided based on the history of games the user has previously participated in. The demo unit demonstrates new products and services through dialogue and answers the user's questions in real time. For example, the demo unit provides an optimal demonstration using eye tracking data of the user. The demo unit can also recognize the user's movements and gestures to perform interactive demonstrations.Furthermore, the demo unit can customize the demo content by using the user's real-time location information. The generation unit generates content based on the user's story and marketing target information. For example, the generation unit generates new content based on a story created by the user in the past. The generation unit can also generate customized content based on the user's travel history. For example, the generation unit generates travel guide content based on places the user has visited in the past. As a result, the interactive approach AI assistant system for marketing campaigns according to the embodiment can provide a more personalized experience for the user and enhance marketing effectiveness.

[0073] The providing unit provides a marketing message or content customized based on the user's information. The user's information includes, but is not limited to, past behavior, interests, preferences, and the like. For example, the providing unit suggests products that the user is likely to be interested in next based on the user's past purchase history. The providing unit can also provide related content based on the user's browsing history. Furthermore, the providing unit can provide a marketing message customized based on the user's attribute information. For example, the providing unit generates a message for a specific target group based on the user's attribute information such as age, sex, and place of residence. The providing unit collects this information and analyzes it using AI to grasp the user's preferences and behavioral patterns in detail. For example, AI analyzes the user's past purchase history and predicts the product that is likely to be purchased next. Based on this prediction, the providing unit suggests the most suitable product to the user. Also, related content can be provided by analyzing the user's browsing history. For example, if the user frequently browses products in a specific category, new products and sale information related to that category is provided. Furthermore, the providing unit generates a marketing message customized based on the user's attribute information. For example, a message that is sensitive to trends can be provided to younger users, while a message that emphasizes reliability and a sense of security can be provided to senior users. This allows the provider to provide a more personalized experience to users and improve marketing effectiveness.

[0074] The interactive feedback unit conducts an interactive questionnaire or survey to collect feedback and opinions from users regarding the marketing target. The interactive feedback unit, for example, uses a chatbot to conduct a questionnaire while interacting with the user. The interactive feedback unit can also collect user opinions using a voice assistant. Furthermore, the interactive feedback unit can analyze the user's feedback in real time and use it to improve the marketing strategy. For example, the interactive feedback unit analyzes the content of the user's answer and extracts specific trends and patterns. The interactive feedback unit uses AI to analyze the user's feedback in real time and use it to improve the marketing strategy. For example, the chatbot uses natural language processing technology to understand the user's answer and continues to ask appropriate questions to collect more detailed feedback. Furthermore, the voice assistant uses voice recognition technology to collect the user's opinion and convert it into text data. This allows the interactive feedback unit to quickly and accurately collect the user's feedback and analyze it in real time. Furthermore, the interactive feedback unit extracts specific trends and patterns based on the collected feedback and use it to improve the marketing strategy. For example, if a particular product receives a low user rating, the interactive feedback unit can identify areas for improvement for that product and reflect them in the next campaign. This allows the interactive feedback unit to realize a marketing strategy that reflects the user's opinions and develop an effective campaign.

[0075] The interactive providing unit provides the user with an interactive game or quiz with a reward. The interactive providing unit provides, for example, points or coupons as rewards. The interactive providing unit can also provide game content customized based on the user's past game participation history. For example, the interactive providing unit customizes the content of the next game to be provided based on the history of games in which the user has participated in the past. The interactive providing unit uses AI to analyze the user's past game participation history and customizes the content of the next game to be provided. For example, the interactive providing unit analyzes the genre and difficulty of games in which the user has participated in the past and adjusts the content of the next game to be provided to suit the user's preferences. The interactive providing unit can also provide rewards in real time according to the progress of the game. For example, when a user clears a specific stage, points or coupons are instantly provided. This allows the user to earn rewards while enjoying the game, and the interactive providing unit can increase the user's engagement. Furthermore, the interactive providing unit can continuously improve the content of the game based on the user's feedback. For example, if the user feels that a specific stage is difficult, the difficulty of that stage is adjusted. The interactive providing unit can also develop new game content for the next campaign based on the user's game participation history. This allows the interactive providing unit to constantly provide new experiences to users, thereby enhancing marketing effectiveness.

[0076] The demo unit demonstrates new products and services through dialogue and answers the user's questions in real time. For example, the demo unit uses the user's gaze tracking data to provide an optimal demonstration. The demo unit can also recognize the user's movements and gestures to provide an interactive demonstration. Furthermore, the demo unit can customize the content of the demo using the user's real-time location information. The demo unit uses AI to analyze the user's gaze tracking data and provide a demonstration that focuses on the part that the user is most interested in. For example, if the user focuses his or her gaze on a specific product function, a detailed explanation of that function is provided. The demo unit can also recognize the user's movements and gestures and provide an interactive demonstration. For example, if the user raises his or her hand, the content of the demo is changed according to that action. Furthermore, the demo unit can customize the content of the demo using the user's real-time location information. For example, if the user is in a specific area, the demo unit provides a demonstration of products and services related to that area. This allows the demo unit to provide a more personalized demonstration to the user and deepen the user's understanding of the product or service. Furthermore, the demo unit can answer the user's questions in real time. For example, if a user asks a question during a demo, the AI ​​will analyze the question and provide an appropriate answer, allowing the demo department to quickly resolve users' doubts and increase their trust in the product or service.

[0077] The generation unit generates content based on the user's story and information on the marketing target. For example, the generation unit generates new content based on a story created by the user in the past. The generation unit can also generate customized content based on the user's travel history. For example, the generation unit generates travel guide content based on places the user has visited in the past. The generation unit uses AI to analyze the user's story and travel history to generate optimal content for the user. For example, the generation unit analyzes a story created by the user in the past and generates new content based on the theme and style of the story. The generation unit also analyzes the user's travel history and generates customized travel guide content based on places visited and experiences. This allows the generation unit to provide more personalized content to the user and improve marketing effectiveness. Furthermore, the generation unit can continuously improve the generated content based on user feedback. For example, if the user provides opinions or impressions on the generated content, the feedback is analyzed and reflected in the next content generation. The generation unit can also adjust the theme and style of the content according to the user's interests and preferences. This allows the generation unit to always provide optimal content to the user and increase user engagement.

[0078] The suggestion unit can suggest marketing targets based on the user's questions and reactions. The suggestion unit, for example, collects the user's questions and reactions through real-time chat. For example, the suggestion unit analyzes questions asked by the user to the chatbot and suggests appropriate marketing targets. The suggestion unit can also collect the user's reactions based on survey results and suggest marketing targets. For example, the suggestion unit analyzes the content of the user's answers to the survey and suggests products and services that match the user's interests and preferences. This makes it possible to suggest appropriate marketing targets based on the user's questions and reactions. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's questions and reactions to AI, which can then suggest appropriate marketing targets.

[0079] The experience providing unit can develop a story that is the target of marketing through a dialogue with the user, and provide an experience in which the user becomes a part of the story. The experience providing unit develops a story based on, for example, a storyboard. For example, the experience providing unit determines the progress of the story through a dialogue with the user. The experience providing unit can also develop a story based on a scenario. For example, the experience providing unit changes the scenario based on the user's selection and customizes the progress of the story. This can increase the user's engagement by providing an experience in which the user becomes a part of the story. Some or all of the above-mentioned processing in the experience providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the experience providing unit can input the content of the dialogue with the user to AI, and the AI ​​can determine the progress of the story.

[0080] The recommendation unit can recommend customized products or services in real time based on the user's past behavior, interests, or preferences. The recommendation unit, for example, recommends products or services based on the user's browsing history. For example, the recommendation unit analyzes product pages that the user has viewed in the past and recommends related products. The recommendation unit can also recommend products or services based on the user's purchase history. For example, the recommendation unit recommends products that the user is likely to purchase next based on products that the user has purchased in the past. Furthermore, the recommendation unit can also recommend products or services based on the results of a survey of the user. For example, the recommendation unit recommends customized products or services based on the interests and preferences that the user has answered in the survey. This makes it possible to recommend appropriate products or services in real time based on the user's past behavior, interests, or preferences. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the user's behavior data into AI, which can then recommend appropriate products or services.

[0081] The providing unit can provide customized content based on the geographical location information of the user. The providing unit, for example, acquires the user's location information based on GPS data. For example, when the user is in a specific area, the providing unit provides content related to the area. The providing unit can also estimate the user's location information based on the IP address. For example, the providing unit analyzes the user's IP address and provides content specific to the area. Furthermore, the providing unit can also acquire the user's location information based on beacon data. For example, when the user is in a specific store, the providing unit provides content related to the store. This makes it possible to provide appropriate content based on the user's geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's location information to AI, and the AI ​​can provide appropriate content.

[0082] The demo unit can provide an optimal product or service demonstration by utilizing the user's gaze tracking data. The demo unit, for example, collects the user's gaze data using an eye tracking device. For example, the demo unit provides a demonstration that focuses on the part where the user directs his / her gaze. The demo unit can also track the user's gaze using camera data. For example, the demo unit provides a demonstration that adds detailed explanations to the part where the user fixes his / her gaze for a long time. Furthermore, the demo unit can provide a demonstration that overlooks the whole when the user frequently moves his / her gaze. This allows the user's gaze tracking data to be used to provide an optimal demonstration. A part or all of the above-mentioned processing in the demo unit may be performed, for example, using AI, or may be performed without using AI. For example, the demo unit can input the user's gaze data into AI, and the AI ​​can provide an optimal demonstration.

[0083] The experience providing unit can change the progress of the story based on the user's selection. The experience providing unit changes the progress of the story, for example, by the user selecting an option. For example, the experience providing unit changes the scenario of the story based on the option selected by the user. The experience providing unit can also save the user's selection result and reflect it in the next progress of the story. For example, the experience providing unit customizes the progress of the story based on the option selected by the user in the past. This makes it possible to increase the engagement of the user by changing the progress of the story based on the user's selection. A part or all of the above-mentioned processing in the experience providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the experience providing unit can input the user's selection data into AI, which can determine the progress of the story.

[0084] The interactive providing unit can provide customized game content based on the user's past game participation history. The interactive providing unit customizes the content of the next game to be provided based on, for example, the history of games in which the user has participated in the past. For example, the interactive providing unit provides optimal game content based on the genre of games that the user has liked in the past. The interactive providing unit can also provide game content that attracts the user's interest based on the user's past game participation history. For example, the interactive providing unit customizes the next game based on the content of games in which the user has obtained a high score in the past. This makes it possible to provide appropriate game content based on the user's past game participation history. A part or all of the above-mentioned processing in the interactive providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the interactive providing unit inputs the user's game participation history into an AI, and the AI ​​can provide optimal game content.

[0085] The demo unit can recognize the user's movement or gesture and perform an interactive demonstration. The demo unit, for example, recognizes the user's movement using a motion sensor. For example, when the user raises his / her hand, the demo unit provides a demonstration according to the movement. The demo unit can also recognize the user's gesture using camera data. For example, when the user shakes his / her head, the demo unit provides a demonstration according to the movement. Furthermore, when the user walks around, the demo unit can also provide a demonstration according to the movement. This makes it possible to recognize the user's movement or gesture and provide an interactive demonstration. A part or all of the above-mentioned processing in the demo unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the demo unit can input the user's movement or gesture data into the AI, which can provide an interactive demonstration.

[0086] The experience providing unit can customize the progress of the story by using the real-time location information of the user. The experience providing unit acquires the user's location information based on, for example, GPS data. For example, when the user is in a specific area, the experience providing unit provides the progress of the story related to the area. The experience providing unit can also acquire the user's location information based on beacon data. For example, when the user is in a specific store, the experience providing unit provides the progress of the story related to the store. Furthermore, the experience providing unit can also customize the progress of the story by using the user's real-time location information. For example, when the user is traveling, the experience providing unit provides the progress of the story related to the travel destination. This makes it possible to customize the progress of the story by using the user's real-time location information. A part or all of the above-mentioned processing in the experience providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the experience providing unit inputs the user's location information into AI, and the AI ​​can customize the progress of the story.

[0087] The generating unit can generate customized content based on the user's travel history. The generating unit generates travel guide content based on places the user has visited in the past, for example. For example, the generating unit generates related event introduction content based on events the user has participated in in the past. The generating unit can also generate content that suggests a place to visit next based on the user's travel history. For example, the generating unit suggests a next travel destination based on data on tourist spots the user has visited in the past. This makes it possible to generate appropriate content based on the user's travel history. Some or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the user's travel history data into AI, which can generate customized content.

[0088] The providing unit can estimate the user's emotion and adjust the tone of the marketing message based on the estimated user's emotion. The providing unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the providing unit analyzes the user's facial expression data and calculates an emotion score. The providing unit can also estimate the user's emotion using a voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the providing unit can also estimate the user's emotion using a text analysis technology. For example, the providing unit analyzes text data input by the user and calculates an emotion score. This makes it possible to provide a marketing message with an appropriate tone based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the provider can input a user's emotional data into the AI, which can then generate a marketing message with an appropriate tone.

[0089] The providing unit can analyze the user's past purchase history and select the optimal content provision timing. The providing unit selects the optimal content provision timing based on, for example, the user's purchase date and time. For example, the providing unit provides content according to a time period based on a history of the user's past purchases during that time period. The providing unit can also select the optimal content provision timing based on the user's purchased product. For example, the providing unit provides related content according to the timing when the user purchased a specific product. Furthermore, the providing unit can also select the optimal content provision timing based on the user's purchase frequency. For example, if the user tends to make purchases at a specific frequency, the providing unit provides content according to that frequency. This makes it possible to provide content at an appropriate timing based on the user's past purchase history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's purchase history data into AI, and the AI ​​can select the optimal content provision timing.

[0090] The providing unit can dynamically change the type of content to be provided based on the user's current activity status. The providing unit, for example, uses an activity tracker to collect the user's activity status. For example, when the user is exercising, the providing unit provides health-related content. The providing unit can also grasp the user's activity status based on the usage status of the app. For example, when the user is at work, the providing unit provides business-related content. Furthermore, the providing unit can also provide entertainment-related content based on the user's activity status. For example, when the user is relaxing, the providing unit provides entertainment-related content. This makes it possible to provide appropriate content based on the user's current activity status. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's activity status data into AI, and the AI ​​can provide optimal content.

[0091] The providing unit can estimate the user's emotion and determine the priority of the content to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the providing unit analyzes the user's facial expression data and calculates an emotion score. The providing unit can also estimate the user's emotion using a voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the providing unit can also estimate the user's emotion using a text analysis technology. For example, the providing unit analyzes text data input by the user and calculates an emotion score. This makes it possible to provide appropriate content preferentially based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the provider can input user emotional data into the AI, which can then determine the priority order of appropriate content.

[0092] The providing unit can provide a region-specific marketing message based on the geographical location information of the user. The providing unit, for example, acquires the user's location information based on GPS data. For example, when the user is in a specific region, the providing unit provides event information for that region. The providing unit can also estimate the user's location information based on the IP address. For example, when the user is traveling, the providing unit provides tourist information for the travel destination. Furthermore, the providing unit can also acquire the user's location information based on beacon data. For example, when the user is at home, the providing unit provides sale information for nearby stores. This makes it possible to provide an appropriate marketing message based on the user's geographical location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's location information to AI, which can generate a region-specific marketing message.

[0093] The providing unit can analyze the social media activity of the user and provide related content. For example, when the user uses a specific hashtag, the providing unit provides content related to the hashtag. For example, when the user follows a specific account, the providing unit provides content related to the account. In addition, when the user likes a specific post, the providing unit can also provide content related to the post. For example, when the user shows an interest in a specific topic, the providing unit provides content related to the topic. This makes it possible to provide appropriate content based on the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI, and the AI ​​can provide related content.

[0094] The interactive feedback unit can estimate the user's emotion and adjust the questionnaire question content based on the estimated user's emotion. The interactive feedback unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the interactive feedback unit analyzes the user's facial expression data and calculates an emotion score. The interactive feedback unit can also estimate the user's emotion using a voice analysis technology. For example, the interactive feedback unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the interactive feedback unit can also estimate the user's emotion using a text analysis technology. For example, the interactive feedback unit analyzes text data input by the user and calculates an emotion score. This makes it possible to provide appropriate question content based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the interactive feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the interactive feedback unit can input the user's emotional data into the AI, which can then generate appropriate questions.

[0095] The interactive feedback unit can optimize the order of questions based on the user's past answer history when collecting feedback. The interactive feedback unit, for example, suggests an optimal question order based on the order of questions answered by the user in the past. For example, the interactive feedback unit preferentially provides questions related to the content of the user's past answers. The interactive feedback unit can also suggest an order that is easy to answer based on the user's past answer history. For example, the interactive feedback unit analyzes the history of questions answered by the user in the past and determines an optimal question order. This makes it possible to provide an appropriate question order based on the user's past answer history. A part or all of the above-mentioned processing in the interactive feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the interactive feedback unit can input the user's answer history data into AI, which can determine the optimal question order.

[0096] The interactive feedback unit can estimate the user's emotion and adjust the importance of the feedback based on the estimated user's emotion. The interactive feedback unit estimates the user's emotion using, for example, a facial expression recognition technology. For example, the interactive feedback unit analyzes the user's facial expression data and calculates an emotion score. The interactive feedback unit can also estimate the user's emotion using a voice analysis technology. For example, the interactive feedback unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the interactive feedback unit can also estimate the user's emotion using a text analysis technology. For example, the interactive feedback unit analyzes text data input by the user and calculates an emotion score. This makes it possible to collect feedback with an appropriate importance based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the interactive feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the interactive feedback unit can input a user's emotional data into the AI, which can then collect feedback of appropriate importance.

[0097] The interactive feedback unit can collect region-specific feedback based on the geographical location information of the user. The interactive feedback unit, for example, acquires the user's location information based on GPS data. For example, when the user is in a specific area, the interactive feedback unit collects feedback related to the area. The interactive feedback unit can also estimate the user's location information based on the IP address. For example, when the user is traveling, the interactive feedback unit collects feedback related to the travel destination. Furthermore, the interactive feedback unit can also acquire the user's location information based on beacon data. For example, when the user is at home, the interactive feedback unit collects feedback related to nearby stores. This makes it possible to collect appropriate feedback based on the user's geographical location information. A part or all of the above-mentioned processing in the interactive feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the interactive feedback unit inputs the user's location information into AI, which can collect region-specific feedback.

[0098] The dialogue-type providing unit can estimate the user's emotion and adjust the difficulty of the game or quiz based on the estimated user's emotion. The dialogue-type providing unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the dialogue-type providing unit analyzes the user's facial expression data and calculates an emotion score. The dialogue-type providing unit can also estimate the user's emotion using a voice analysis technology. For example, the dialogue-type providing unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the dialogue-type providing unit can also estimate the user's emotion using a text analysis technology. For example, the dialogue-type providing unit analyzes text data input by the user and calculates an emotion score. This makes it possible to provide a game or quiz of appropriate difficulty based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue-type providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the interactive providing unit can input a user's emotional data into the AI, which can then generate games or quizzes of appropriate difficulty.

[0099] The interactive providing unit can analyze the user's past game participation history and provide optimal game content. The interactive providing unit customizes the content of the next game to be provided based on, for example, the history of games in which the user has participated in the past. For example, the interactive providing unit provides optimal game content based on the genre of games that the user has liked in the past. The interactive providing unit can also provide game content that attracts the user's interest based on the user's past game participation history. For example, the interactive providing unit customizes the next game based on the content of games in which the user has obtained a high score in the past. This makes it possible to provide appropriate game content based on the user's past game participation history. A part or all of the above-mentioned processing in the interactive providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the interactive providing unit inputs the user's game participation history into AI, and the AI ​​can provide optimal game content.

[0100] The dialogue-type providing unit can estimate the user's emotion and adjust the type of benefit based on the estimated user's emotion. The dialogue-type providing unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the dialogue-type providing unit analyzes the user's facial expression data and calculates an emotion score. The dialogue-type providing unit can also estimate the user's emotion using a voice analysis technology. For example, the dialogue-type providing unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the dialogue-type providing unit can also estimate the user's emotion using a text analysis technology. For example, the dialogue-type providing unit analyzes text data input by the user and calculates an emotion score. This makes it possible to provide an appropriate benefit based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue-type providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the interactive provision unit can input a user's emotional data into the AI, which can then generate appropriate rewards.

[0101] The interactive providing unit can provide region-specific games and quizzes in consideration of the geographical location information of the user. The interactive providing unit, for example, acquires the user's location information based on GPS data. For example, when the user is in a specific region, the interactive providing unit provides games and quizzes related to the region. The interactive providing unit can also estimate the user's location information based on the IP address. For example, when the user is traveling, the interactive providing unit provides games and quizzes related to the travel destination. Furthermore, the interactive providing unit can also acquire the user's location information based on beacon data. For example, when the user is at home, the interactive providing unit provides games and quizzes related to nearby stores. This makes it possible to provide appropriate games and quizzes based on the user's geographical location information. A part or all of the above-mentioned processing in the interactive providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the interactive providing unit can input the user's location information to AI, which can generate region-specific games and quizzes.

[0102] The demo unit can estimate the user's emotion and adjust the progress speed of the demo based on the estimated user's emotion. The demo unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the demo unit analyzes the user's facial expression data and calculates an emotion score. The demo unit can also estimate the user's emotion using voice analysis technology. For example, the demo unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the demo unit can also estimate the user's emotion using text analysis technology. For example, the demo unit analyzes text data input by the user and calculates an emotion score. This makes it possible to provide a demo with an appropriate progress speed based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the demo unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the demo unit can input a user's emotional data into the AI, which can then generate a demo with an appropriate progression speed.

[0103] The demo unit can provide an optimal demonstration by utilizing the user's gaze tracking data. The demo unit, for example, collects the user's gaze data using an eye-tracking device. For example, the demo unit provides a demonstration that focuses on the part where the user directs his / her gaze. The demo unit can also track the user's gaze using camera data. For example, the demo unit provides a demonstration that adds detailed explanations to the part where the user fixes his / her gaze for a long time. Furthermore, the demo unit can provide a demonstration that overlooks the whole when the user frequently moves his / her gaze. This allows the user's gaze tracking data to be used to provide an optimal demonstration. A part or all of the above-mentioned processing in the demo unit may be performed, for example, using AI, or may be performed without using AI. For example, the demo unit can input the user's gaze data into AI, and the AI ​​can provide an optimal demonstration.

[0104] The demo unit can estimate the user's emotion and customize the content of the demo based on the estimated user's emotion. The demo unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the demo unit analyzes the user's facial expression data and calculates an emotion score. The demo unit can also estimate the user's emotion using voice analysis technology. For example, the demo unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the demo unit can estimate the user's emotion using text analysis technology. For example, the demo unit analyzes text data input by the user and calculates an emotion score. This makes it possible to provide a demo with appropriate content based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the demo unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the demo unit inputs the user's emotion data into an AI, and the AI ​​can generate a demo with appropriate content.

[0105] The demo unit can recognize the user's movement or gesture and perform an interactive demonstration. The demo unit, for example, recognizes the user's movement using a motion sensor. For example, when the user raises his / her hand, the demo unit provides a demonstration according to the movement. The demo unit can also recognize the user's gesture using camera data. For example, when the user shakes his / her head, the demo unit provides a demonstration according to the movement. Furthermore, when the user walks around, the demo unit can also provide a demonstration according to the movement. This makes it possible to recognize the user's movement or gesture and provide an interactive demonstration. A part or all of the above-mentioned processing in the demo unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the demo unit can input the user's movement or gesture data into the AI, which can provide an interactive demonstration.

[0106] The generation unit can estimate the user's emotion and adjust the tone of the content to be generated based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the generation unit analyzes the user's facial expression data and calculates an emotion score. The generation unit can also estimate the user's emotion using a voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the generation unit can also estimate the user's emotion using a text analysis technology. For example, the generation unit analyzes text data input by the user and calculates an emotion score. This makes it possible to generate content with an appropriate tone based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotional data into the AI, which can then generate content with an appropriate tone.

[0107] The generation unit can analyze the user's past behavior history and apply an optimal content generation algorithm. The generation unit, for example, generates related content based on content previously viewed by the user. For example, the generation unit generates related product introduction content based on products previously purchased by the user. The generation unit can also generate interesting content from the user's past behavior history. For example, the generation unit suggests an article to be read next based on the content of an article previously viewed by the user. This makes it possible to generate appropriate content based on the user's past behavior history. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs the user's behavior history data into AI, and the AI ​​can apply an optimal content generation algorithm.

[0108] The generation unit can estimate the user's emotion and determine the priority of the content to be generated based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the generation unit analyzes the user's facial expression data and calculates an emotion score. The generation unit can also estimate the user's emotion using a voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the generation unit can also estimate the user's emotion using a text analysis technology. For example, the generation unit analyzes text data input by the user and calculates an emotion score. This makes it possible to generate appropriate content preferentially based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotional data into the AI, which can then determine the priority order of appropriate content.

[0109] The generating unit can generate customized content based on the user's travel history. The generating unit generates travel guide content based on places the user has visited in the past, for example. For example, the generating unit generates related event introduction content based on events the user has participated in in the past. The generating unit can also generate content that suggests a place to visit next based on the user's travel history. For example, the generating unit suggests a next travel destination based on data on tourist spots the user has visited in the past. This makes it possible to generate appropriate content based on the user's travel history. Some or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the user's travel history data into AI, which can generate customized content.

[0110] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. The suggestion unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the suggestion unit analyzes the user's facial expression data and calculates an emotion score. The suggestion unit can also estimate the user's emotion using a voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the suggestion unit can also estimate the user's emotion using a text analysis technology. For example, the suggestion unit analyzes text data input by the user and calculates an emotion score. This allows a suggestion to be made in an appropriate expression method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input a user's emotional data into the AI, which can then make suggestions in an appropriate manner of expression.

[0111] The suggestion unit can analyze the user's past behavior history and select the optimal suggestion timing. The suggestion unit, for example, makes a suggestion based on a history of the user accepting a suggestion in a specific time period in the past, according to that time period. For example, if the user has a tendency to accept a suggestion on a specific day of the week, the suggestion unit makes a suggestion based on that day of the week. In addition, if the user accepts a suggestion during a specific event (e.g., a sale period), the suggestion unit can also make a suggestion based on that event. For example, the suggestion unit makes the next suggestion based on a product that the user purchased in the past at a specific event. This allows the suggestion to be made at an appropriate timing based on the user's past behavior history. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit inputs the user's behavior history data into an AI, which can select the optimal suggestion timing.

[0112] The suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. The suggestion unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the suggestion unit analyzes the user's facial expression data and calculates an emotion score. The suggestion unit can also estimate the user's emotion using a voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the suggestion unit can also estimate the user's emotion using a text analysis technology. For example, the suggestion unit analyzes text data input by the user and calculates an emotion score. This makes it possible to prioritize appropriate suggestions based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input a user's emotional data into the AI, which can then determine the priority order of appropriate suggestions.

[0113] The suggestion unit can make a region-specific suggestion based on the geographical location information of the user. The suggestion unit acquires the user's location information based on, for example, GPS data. For example, when the user is in a specific region, the suggestion unit proposes event information for that region. The suggestion unit can also estimate the user's location information based on the IP address. For example, when the user is traveling, the suggestion unit proposes tourist information for the travel destination. Furthermore, the suggestion unit can acquire the user's location information based on beacon data. For example, when the user is at home, the suggestion unit proposes sale information for nearby stores. This makes it possible to make appropriate suggestions based on the user's geographical location information. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's location information to an AI, which can generate region-specific suggestions.

[0114] The experience providing unit can estimate the user's emotion and adjust the progress of the story based on the estimated user's emotion. The experience providing unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the experience providing unit analyzes the user's facial expression data and calculates an emotion score. The experience providing unit can also estimate the user's emotion using voice analysis technology. For example, the experience providing unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the experience providing unit can also estimate the user's emotion using text analysis technology. For example, the experience providing unit analyzes text data input by the user and calculates an emotion score. This makes it possible to provide a story with an appropriate progress based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the experience providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the experience provider can input a user's emotional data into the AI, which can then generate a story with an appropriate progression.

[0115] The experience providing unit can optimize the development of the story based on the user's past behavior history. The experience providing unit provides a related development of the story based on, for example, the history of stories in which the user has participated in the past. For example, the experience providing unit provides an optimal development of the story based on the genre of stories that the user has liked in the past. The experience providing unit can also provide an interesting development of the story based on the user's past behavior history. For example, the experience providing unit customizes the next story based on the content of the story in which the user has participated in the past. This makes it possible to provide an appropriate development of the story based on the user's past behavior history. A part or all of the above-mentioned processing in the experience providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the experience providing unit inputs the user's behavior history data into AI, and the AI ​​can generate an optimal development of the story.

[0116] The experience providing unit can estimate the user's emotion and adjust the importance of the story based on the estimated user's emotion. The experience providing unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the experience providing unit analyzes the user's facial expression data and calculates an emotion score. The experience providing unit can also estimate the user's emotion using voice analysis technology. For example, the experience providing unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the experience providing unit can also estimate the user's emotion using text analysis technology. For example, the experience providing unit analyzes text data input by the user and calculates an emotion score. This makes it possible to provide a story with an appropriate importance based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the experience providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the experience provider can input a user's emotional data into the AI, which can then generate a story with an appropriate level of importance.

[0117] The experience providing unit can customize the progress of the story by using the real-time location information of the user. The experience providing unit acquires the user's location information based on, for example, GPS data. For example, when the user is in a specific area, the experience providing unit provides the progress of the story related to the area. The experience providing unit can also acquire the user's location information based on beacon data. For example, when the user is in a specific store, the experience providing unit provides the progress of the story related to the store. Furthermore, the experience providing unit can also customize the progress of the story by using the user's real-time location information. For example, when the user is traveling, the experience providing unit provides the progress of the story related to the travel destination. This makes it possible to customize the progress of the story by using the user's real-time location information. A part or all of the above-mentioned processing in the experience providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the experience providing unit inputs the user's location information into AI, and the AI ​​can customize the progress of the story.

[0118] The recommendation unit can estimate the user's emotion and adjust the recommended expression method based on the estimated user's emotion. The recommendation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the recommendation unit analyzes the user's facial expression data and calculates an emotion score. The recommendation unit can also estimate the user's emotion using voice analysis technology. For example, the recommendation unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the recommendation unit can also estimate the user's emotion using text analysis technology. For example, the recommendation unit analyzes text data input by the user and calculates an emotion score. This allows the recommendation to be made in an appropriate expression method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input a user's emotional data into the AI, which can then make recommendations in an appropriate manner.

[0119] The recommendation unit can analyze the user's past behavior history and select the optimal recommendation timing. The recommendation unit, for example, makes a recommendation based on a history of the user accepting recommendations in a specific time period in the past, according to that time period. For example, if the user has a tendency to accept recommendations on a specific day of the week, the recommendation unit makes a recommendation based on that day of the week. In addition, if the user accepts recommendations during a specific event (e.g., a sale period), the recommendation unit can also make a recommendation based on that event. For example, the recommendation unit makes the next recommendation based on a product that the user purchased in the past at a specific event. This allows recommendations to be made at an appropriate time based on the user's past behavior history. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit inputs the user's behavior history data into an AI, which can select the optimal recommendation timing.

[0120] The recommendation unit can estimate the user's emotion and determine the priority of recommendations based on the estimated user's emotion. The recommendation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the recommendation unit analyzes the user's facial expression data and calculates an emotion score. The recommendation unit can also estimate the user's emotion using voice analysis technology. For example, the recommendation unit analyzes the tone and speed of the user's voice and calculates an emotion score. Furthermore, the recommendation unit can also estimate the user's emotion using text analysis technology. For example, the recommendation unit analyzes text data input by the user and calculates an emotion score. This makes it possible to prioritize appropriate recommendations based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input a user's emotional data into the AI, which can then determine appropriate recommendation priorities.

[0121] The recommendation unit can make region-specific recommendations based on the geographical location information of the user. The recommendation unit, for example, acquires the user's location information based on GPS data. For example, when the user is in a specific region, the recommendation unit recommends event information for that region. The recommendation unit can also estimate the user's location information based on the IP address. For example, when the user is traveling, the recommendation unit recommends tourist information for the travel destination. Furthermore, the recommendation unit can acquire the user's location information based on beacon data. For example, when the user is at home, the recommendation unit recommends sale information for nearby stores. This makes it possible to make appropriate recommendations based on the user's geographical location information. Part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's location information to AI, which can generate region-specific recommendations.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The providing unit can suggest products that are likely to be purchased next based on the user's purchasing history. For example, the providing unit can suggest products similar to products previously purchased by the user. The providing unit can also suggest complementary products to products previously purchased by the user. Furthermore, the providing unit can also suggest consumables for products previously purchased by the user. This makes it possible to suggest appropriate products based on the user's purchasing history.

[0124] The suggestion unit can suggest optimal marketing targets based on the user's real-time location information. For example, when the user is in a particular store, the suggestion unit can suggest products from that store. Also, when the user is in a particular area, the suggestion unit can suggest event information for that area. Furthermore, when the user is traveling, the suggestion unit can suggest tourist information for the travel destination. In this way, it is possible to suggest appropriate marketing targets based on the user's real-time location information.

[0125] The experience providing unit can customize the progress of the story based on the user's past behavior history. For example, the experience providing unit determines the progress of the next story based on the history of stories in which the user has participated in the past. The experience providing unit can also customize the progress of the story based on the genre of stories that the user has liked in the past. Furthermore, the experience providing unit can also provide an interesting story progress based on the user's past behavior history. This makes it possible to provide an appropriate story progress based on the user's past behavior history.

[0126] The recommendation unit can analyze the user's social media activity and recommend related products and services. For example, if the user uses a specific hashtag, the recommendation unit can recommend products and services related to the hashtag. Also, if the user follows a specific account, the recommendation unit can recommend products and services related to the account. Furthermore, if the user likes a specific post, the recommendation unit can recommend products and services related to the post. This makes it possible to recommend appropriate products and services based on the user's social media activity.

[0127] The providing unit can dynamically change the type of content to be provided based on the user's current activity status. For example, the providing unit can provide health-related content when the user is exercising. The providing unit can also provide business-related content when the user is working. Furthermore, the providing unit can provide entertainment-related content when the user is relaxing. In this way, appropriate content can be provided based on the user's current activity status.

[0128] The providing unit can estimate the user's emotion and adjust the tone of the marketing message based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion using facial expression recognition technology. The providing unit can also estimate the user's emotion using voice analysis technology. Furthermore, the providing unit can also estimate the user's emotion using text analysis technology. This makes it possible to provide a marketing message with an appropriate tone based on the user's emotion.

[0129] The interactive feedback unit can estimate the user's emotion and adjust the questionnaire question content based on the estimated user's emotion. For example, the interactive feedback unit can estimate the user's emotion using facial expression recognition technology. The interactive feedback unit can also estimate the user's emotion using voice analysis technology. Furthermore, the interactive feedback unit can also estimate the user's emotion using text analysis technology. This makes it possible to provide appropriate question content based on the user's emotion.

[0130] The dialogue providing unit can estimate the user's emotions and adjust the difficulty of the game or quiz based on the estimated user's emotions. For example, the dialogue providing unit can estimate the user's emotions using facial expression recognition technology. The dialogue providing unit can also estimate the user's emotions using voice analysis technology. Furthermore, the dialogue providing unit can also estimate the user's emotions using text analysis technology. This makes it possible to provide games or quizzes of an appropriate level of difficulty based on the user's emotions.

[0131] The demo unit can estimate the user's emotion and adjust the demo progress speed based on the estimated user's emotion. For example, the demo unit estimates the user's emotion using facial expression recognition technology. The demo unit can also estimate the user's emotion using voice analysis technology. Furthermore, the demo unit can also estimate the user's emotion using text analysis technology. This makes it possible to provide a demo with an appropriate progress speed based on the user's emotion.

[0132] The generation unit can estimate the user's emotion and adjust the tone of the generated content based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion using facial expression recognition technology. The generation unit can also estimate the user's emotion using voice analysis technology. Furthermore, the generation unit can also estimate the user's emotion using text analysis technology. This makes it possible to generate content with an appropriate tone based on the user's emotion.

[0133] The process flow of the second embodiment will be briefly described below.

[0134] Step 1: The provision unit provides customized marketing messages or content based on user information. User information includes past behavior, interests, preferences, purchase history, browsing history, and attribute information (age, gender, place of residence, etc.). Based on this information, the provision unit suggests products and content relevant to the user and generates messages for specific target groups. Step 2: The interactive feedback department conducts interactive questionnaires and surveys to collect feedback and opinions from users regarding the marketing target. The interactive feedback department uses chatbots and voice assistants to conduct surveys while interacting with users, and analyzes user feedback in real time to help improve marketing strategies. Step 3: The interactive providing unit provides the user with interactive games and quizzes with rewards. The interactive providing unit provides points and coupons as rewards, and provides game content customized based on the user's past game participation history. Step 4: The Demo Unit interactively demonstrates the new product or service and answers the user's questions in real time. The Demo Unit uses the user's eye-tracking data, movements, gestures, and real-time location information to create an interactive demonstration. Step 5: The generator generates content based on the user's story and marketing target information. The generator generates customized content based on the user's past stories and travel history, and provides travel guide content, for example.

[0135] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0137] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0138] For example, the providing unit, the interactive feedback unit, the interactive providing unit, the demo unit, and the generating unit are realized by at least one of the smart device 14 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The interactive feedback unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The interactive providing unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The demo unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The generating unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0146] Fig. 4 shows an example of 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0149] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0151] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0152] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0153] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0154] For example, the providing unit, the interactive feedback unit, the interactive providing unit, the demo unit, and the generating unit are realized by at least one of the smart glasses 214 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The interactive feedback unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The interactive providing unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The demo unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The generating unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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, the RAM 30, and the 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.

[0158] 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.

[0159] 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 the voice according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0161] 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 performed in a secure state.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0165] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0166] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0167] 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 voice indicating a user input for 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.

[0168] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0169] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0170] For example, the providing unit, the interactive feedback unit, the interactive providing unit, the demo unit, and the generating unit are realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The interactive feedback unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The interactive providing unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The demo unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The generating unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] 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, the RAM 30, and the 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.

[0174] 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.

[0175] 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 the voice according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0177] 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 performed in a secure state.

[0178] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0179] 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.

[0180] 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.

[0181] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0182] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0183] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0184] 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 a voice indicating a user input for 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.

[0185] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0186] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0187] For example, the providing unit, the interactive feedback unit, the interactive providing unit, the demonstration unit, and the generating unit are realized by at least one of the robot 414 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The interactive feedback unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The interactive providing unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The demonstration unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The generating unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes are possible.

[0188] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0189] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0190] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0191] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0192] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0193] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0195] 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.

[0196] 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 Universal Serial Bus (USB) 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.

[0197] In addition, 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 upon request from the data processing device 12.

[0198] 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.

[0199] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0200] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0201] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0202] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0203] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0204] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0205] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0206] (Appendix 1) a providing unit that provides customized marketing messages or content based on the user's information; an interactive feedback unit that conducts an interactive questionnaire or survey to collect feedback or opinions from the user regarding marketing targets; an interactive providing unit for providing interactive games or interactive quizzes with rewards to the user; a demo unit that interactively demonstrates new products and services and answers questions from the user in real time; A generation unit that generates content for the marketing target based on the user's story and information on the marketing target. A system characterized by: (Appendix 2) A suggestion unit that proposes the marketing target based on the user's questions and responses is provided. 2. The system of claim 1, (Appendix 3) An experience providing unit that develops a story that is the subject of marketing through dialogue with the user and provides the user with an experience of becoming a part of the story. 2. The system of claim 1, (Appendix 4) A recommendation component that provides customized product or service recommendations in real time based on a user's past behavior, interests, or preferences. 2. The system of claim 1, (Appendix 5) The providing unit is Providing customized content based on a user's geographic location 2. The system of claim 1, (Appendix 6) The demo section includes: Leverage user eye-tracking data to provide appropriate product or service demonstrations 2. The system of claim 1, (Appendix 7) The experience providing unit includes: Change the story progression based on user choices 4. The system of claim 3. (Appendix 8) The interactive providing unit is Providing customized game content based on a user's past game participation history 2. The system of claim 1, (Appendix 9) The demo section includes: Recognize user movements or gestures to provide interactive demonstrations 2. The system of claim 1, (Appendix 10) The experience providing unit includes: Use the user's real-time location to customize the story progression 4. The system of claim 3. (Appendix 11) The generation unit is Generate customized content based on a user's travel history 2. The system of claim 1, (Appendix 12) The providing unit is Infer user sentiment and adjust the tone of your marketing messages based on that sentiment 2. The system of claim 1, (Appendix 13) The providing unit is Analyze users' past purchase history and select the appropriate timing for providing content 2. The system of claim 1, (Appendix 14) The providing unit is Dynamically change the type of content you serve based on the user's current activity 2. The system of claim 1, (Appendix 15) The providing unit is To estimate a user's emotions and determine the priority of contents to be provided based on the estimated user's emotions 2. The system of claim 1, (Appendix 16) The providing unit is Deliver localized marketing messages based on a user's geographic location 2. The system of claim 1, (Appendix 17) The providing unit is Analyze your social media activity and provide you with relevant content 2. The system of claim 1, (Appendix 18) The interactive feedback unit includes: Inferring user emotions and adjusting survey questions based on the estimated user emotions 2. The system of claim 1, (Appendix 19) The interactive feedback unit includes: Optimize question order when collecting feedback based on user's past answers 2. The system of claim 1, (Appendix 20) The interactive feedback unit includes: Inferring user emotions and adjusting the importance of feedback based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The interactive feedback unit includes: Collect location-specific feedback based on users' geographic location 2. The system of claim 1, (Appendix 22) The interactive providing unit is To estimate a user's emotion and adjust the difficulty of a game or quiz based on the estimated user's emotion 2. The system of claim 1, (Appendix 23) The interactive providing unit is Analyzing users' past game participation history and providing appropriate game content 2. The system of claim 1, (Appendix 24) The interactive providing unit is To estimate a user's emotion and adjust a reward type based on the estimated user's emotion. 2. The system of claim 1, (Appendix 25) The interactive providing unit is Taking into account the user's geographic location, provide region-specific games or quizzes 2. The system of claim 1, (Appendix 26) The demo section includes: Estimate the user's emotions and adjust the speed of the demo based on the estimated user emotions. 2. The system of claim 1, (Appendix 27) The demo section includes: Use user eye-tracking data to provide relevant demonstrations 2. The system of claim 1, (Appendix 28) The demo section includes: Infer user emotions and customize demo content based on the inferred user emotions 2. The system of claim 1, (Appendix 29) The demo section includes: Recognize user movements or gestures to provide interactive demonstrations 2. The system of claim 1, (Appendix 30) The generation unit is Inferring a user's emotions and adjusting the tone of generated content based on the estimated user's emotions 2. The system of claim 1, (Appendix 31) The generation unit is Analyze users' past behavior and apply appropriate content generation algorithms 2. The system of claim 1, (Appendix 32) The generation unit is Estimating user emotions and determining the priority of content to be generated based on the estimated user emotions 2. The system of claim 1, (Appendix 33) The generation unit is Generate customized content based on a user's travel history 2. The system of claim 1, (Appendix 34) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 3. The system of claim 2, (Appendix 35) The suggestion unit, Analyze the user's past behavior history and select the appropriate timing for making suggestions 3. The system of claim 2, (Appendix 36) The suggestion unit, Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 3. The system of claim 2, (Appendix 37) The suggestion unit, Providing localized suggestions based on the user's geographic location 3. The system of claim 2, (Appendix 38) The experience providing unit includes: Infer the user's emotions and adjust the story progression based on the estimated user emotions. 4. The system of claim 3. (Appendix 39) The experience providing unit includes: Optimize story development based on user's past behavior 4. The system of claim 3. (Appendix 40) The experience providing unit includes: Infer user emotions and adjust the importance of stories based on the estimated user emotions 4. The system of claim 3. (Appendix 41) The experience providing unit includes: Use the user's real-time location to customize the story progression 4. The system of claim 3. (Appendix 42) The recommendation unit is Infer user sentiment and tailor recommendation language based on the inferred user sentiment 5. The system of claim 4. (Appendix 43) The recommendation unit is Analyze the user's past behavior history and select the appropriate recommendation timing 5. The system of claim 4. (Appendix 44) The recommendation unit is Infer user sentiment and prioritize recommendations based on the inferred user sentiment 5. The system of claim 4. (Appendix 45) The recommendation unit is Make location-specific recommendations based on the user's geographic location 5. The system of claim 4. [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a providing unit that provides customized marketing messages or content based on the user's information; an interactive feedback unit that conducts an interactive questionnaire or survey to collect feedback or opinions from the user regarding marketing targets; an interactive providing unit for providing interactive games or interactive quizzes with rewards to the user; a demo unit that interactively demonstrates new products and services and answers questions from the user in real time; A generation unit that generates content for the marketing target based on the user's story and information on the marketing target. A system characterized by:

2. A proposal unit that proposes the marketing target based on the user's questions and responses.

2. The system of claim 1 .

3. An experience providing unit that develops a story of the marketing target through dialogue with the user and provides the user with an experience of becoming a part of the story.

2. The system of claim 1 .

4. a recommendation unit that provides customized product or service recommendations in real time based on the user's past behavior, interests, or preferences; 2. The system of claim 1 .

5. The providing unit is Providing customized content based on the user's geographic location 2. The system of claim 1 .

6. The demo section includes: Utilizing the user's eye-tracking data to provide appropriate product or service demonstrations 2. The system of claim 1 .

7. The experience providing unit includes: Varying the progression of the story based on the user's selection 4. The system of claim 3.

8. The interactive providing unit is Providing customized game content based on the user's past game participation history 2. The system of claim 1 .

9. The demo section includes: Recognizing the user's movements or gestures and providing an interactive demonstration 2. The system of claim 1 .

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