system

Abstract

The system according to this embodiment aims to provide personalized gift tickets based on the user's interests and preferences. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's social media posts. The analysis unit analyzes the posts collected by the collection unit and identifies the user's interests and preferences. The generation unit generates personalized gift tickets based on the interests and preferences identified by the analysis unit. The provision unit provides the gift tickets generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, personalized gift tickets based on the interests and concerns of users have not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized gift ticket based on the interests and concerns of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's social media posts. The analysis unit analyzes the posts collected by the collection unit to identify the user's interests and preferences. The generation unit generates personalized gift tickets based on the interests and preferences identified by the analysis unit. The provision unit provides the gift tickets generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized gift tickets based on the user's interests and preferences. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The gift ticket generation system according to an embodiment of the present invention is a system that analyzes a user's social media posts and generates personalized gift tickets. This gift ticket generation system collects and analyzes the user's social media posts, generates personalized gift tickets, and provides them to the user. For example, the gift ticket generation system collects photos, comments, check-in information, etc., that the user has posted on social media. Next, the gift ticket generation system analyzes the collected posts using a generation AI to identify the user's interests and preferences. For example, it identifies places the user frequently visits, favorite foods, hobbies, etc. Based on the analysis results, the gift ticket generation system generates personalized gift tickets using a generation AI. For example, it suggests gift tickets for accommodations, restaurants, and spas in areas the user frequently visits. It also includes special plans and discount information tailored to the user's preferences. Finally, the gift ticket generation system provides the generated gift tickets to the user. The user can select the suggested gift tickets and make reservations or purchases. This allows the user to easily obtain gift tickets that match their interests and preferences. The gift ticket generation system can provide more personalized gift tickets by utilizing the user's social media posts. Users will be more satisfied because they can easily obtain gift tickets tailored to their preferences. Furthermore, combining this with booking services for accommodations, restaurants, and spas will enable a wider range of suggestions and expand user choices. This allows the gift ticket generation system to leverage users' social media posts to provide personalized gift tickets.

[0029] The gift ticket generation system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects posts from users' social media. The collection unit collects, for example, photos, comments, and check-in information posted by users on social media. The collection unit can collect, for example, photos posted by users on social media. The collection unit can collect, for example, comments posted by users on social media. The collection unit can collect, for example, check-in information posted by users on social media. The analysis unit analyzes the posts collected by the collection unit to identify the user's interests and preferences. The analysis unit can, for example, analyze the collected posts in detail to identify the user's interests, preferences, and tastes. The analysis unit can, for example, analyze the collected posts using natural language processing technology to identify the user's interests and preferences. The analysis unit can, for example, analyze the collected posts using image recognition technology to identify the user's interests and preferences. The analysis unit can, for example, analyze the collected posts using sentiment analysis technology to identify the user's interests and preferences. The generation unit generates personalized gift tickets based on interests and preferences identified by the analysis unit. The generation unit can, for example, generate gift tickets for accommodations, restaurants, and spas in areas the user frequently visits. The generation unit can, for example, generate gift tickets for accommodations in areas the user frequently visits. The generation unit can, for example, generate gift tickets for restaurants in areas the user frequently visits. The generation unit can, for example, generate gift tickets for spas in areas the user frequently visits. The generation unit can, for example, generate gift tickets that include special plans and discount information tailored to the user's preferences. The generation unit can, for example, generate gift tickets that include special plans tailored to the user's preferences. The generation unit can, for example, generate gift tickets that include discount information tailored to the user's preferences. The provision unit provides the gift tickets generated by the generation unit to the user. The provision unit provides the generated gift tickets to the user, for example, allowing the user to select, book, or purchase them. The provision unit provides the generated gift tickets to the user, for example, allowing the user to select them.The provisioning unit, for example, provides the generated gift ticket to the user, enabling the user to make a reservation. The provisioning unit, for example, provides the generated gift ticket to the user, enabling the user to make a purchase. As a result, the gift ticket generation system according to the embodiment can provide personalized gift tickets by utilizing the user's social media posts.

[0030] The data collection unit collects users' social media posts. Specifically, it collects photos, comments, and check-in information that users post on social media. For example, it can collect photos taken by users while traveling, photos of meals at restaurants, and group photos with friends. This allows for a visual understanding of users' hobbies and interests. Comments and reviews posted by users are also collected. This includes impressions and evaluations of places users have visited and services they have experienced. Furthermore, information on users checking in to specific locations is also collected. This allows for the identification of places users frequently visit and areas of interest. The data collection unit centrally manages this data and stores it in a database for provision to the analytics unit. The data collection unit can connect with various social media platforms via APIs to obtain data in real time. In addition, to ensure data privacy and security, the data collection unit collects data only with the user's consent and protects the data using encryption technology. As a result, the data collection unit can collect a wealth of data from users' diverse social media activities and provide it to the analytics unit.

[0031] The analysis unit analyzes posts collected by the collection unit to identify user interests and preferences. Specifically, it analyzes collected posts in detail to identify user interests, preferences, and tastes. The analysis unit uses natural language processing technology to analyze user comments and reviews and extract specific keywords and phrases. For example, if a user frequently uses positive expressions such as "delicious," "fun," and "recommended," it can be determined that they have a high level of interest in that place or service. In addition, image recognition technology is used to analyze photos posted by users to identify specific places and objects. For example, if a user frequently posts photos of cafes, it can be determined that cafe hopping is their hobby. Furthermore, sentiment analysis technology is used to analyze emotional trends from user posts. This allows for an understanding of what kinds of experiences users have positive feelings towards. The analysis unit combines these technologies to analyze user interests and preferences from multiple angles and perform highly accurate profiling. The analysis results are provided to the generation unit and used to generate personalized gift tickets. In this way, the analysis unit can comprehensively analyze diverse user data and accurately identify user interests and preferences.

[0032] The generation unit generates personalized gift tickets based on the user's interests and preferences identified by the analysis unit. Specifically, it generates gift tickets for accommodations, restaurants, and spas in areas the user frequently visits. For example, if the user enjoys traveling, it can generate gift tickets for accommodations in popular tourist destinations. If the user is a foodie, it can generate gift tickets for highly-rated restaurants. Furthermore, if the user prefers relaxation, it can generate gift tickets for luxury spas. The generation unit can also generate gift tickets that include special plans and discount information tailored to the user's preferences. For example, if the user likes a particular type of cuisine, it can generate a gift ticket that includes special menus and discount information for restaurants serving that cuisine. The generation unit generates these gift tickets in digital format, making them easily accessible to the user. The generation unit customizes the design and content of the gift tickets, making them appealing to the user. This allows the generation unit to generate and provide personalized gift tickets based on the user's interests and preferences.

[0033] The service provider delivers the gift tickets generated by the generation unit to the user. Specifically, it provides the generated gift tickets to the user, allowing the user to select, book, or purchase them. The service provider displays detailed information about the gift tickets to the user and offers options. For example, if the user selects a gift ticket for accommodation, it displays detailed information about the facility, available plans, and prices. If the user selects a gift ticket for a restaurant, it provides information such as the menu, opening hours, and booking methods. The service provider provides an intuitive interface to allow users to easily book or purchase gift tickets. For example, users can select, book, or purchase gift tickets through a website or app accessible from their smartphone or computer. The service provider offers multiple payment and booking methods, taking user convenience into consideration. This allows the service provider to quickly and reliably deliver generated gift tickets to users, enabling them to select, book, or purchase them. Furthermore, the service provider can collect user feedback and continuously improve the content and services of the gift tickets it offers. This allows the service provider to provide high-quality service to users and improve their satisfaction.

[0034] The data collection unit can collect photos, comments, and check-in information posted by users on social media. For example, the data collection unit can collect photos posted by users on social media. For example, the data collection unit can collect comments posted by users on social media. For example, the data collection unit can collect check-in information posted by users on social media. By collecting users' social media posts, more detailed information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input photos posted by users on social media into AI and analyze the content of the photos.

[0035] The analysis unit can analyze collected posts in detail to identify users' interests, preferences, and tastes. For example, the analysis unit can analyze collected posts using natural language processing technology to identify users' interests. For example, the analysis unit can analyze collected posts using image recognition technology to identify users' interests. For example, the analysis unit can analyze collected posts using sentiment analysis technology to identify users' interests. This allows for the provision of more personalized gift tickets by precisely identifying users' interests. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected posts into AI to identify users' interests.

[0036] The generation unit can generate gift tickets for accommodations, restaurants, and spas in areas frequently visited by the user. For example, the generation unit can generate gift tickets for accommodations in areas frequently visited by the user. For example, the generation unit can generate gift tickets for restaurants in areas frequently visited by the user. For example, the generation unit can generate gift tickets for spas in areas frequently visited by the user. This allows for the provision of gift tickets based on the areas visited by the user. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input information about areas frequently visited by the user into a generation AI and generate gift tickets.

[0037] The generation unit can generate gift tickets that include special plans and discount information tailored to the user's preferences. For example, the generation unit can generate gift tickets that include special plans tailored to the user's preferences. For example, the generation unit can generate gift tickets that include discount information tailored to the user's preferences. This allows for the provision of special plans and discount information tailored to the user's preferences. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input information about the user's preferences into a generation AI and generate gift tickets that include special plans and discount information.

[0038] The service provider provides the generated gift tickets to the user, allowing the user to select, reserve, or purchase them. The service provider provides the generated gift tickets to the user, allowing the user to select them. The service provider provides the generated gift tickets to the user, allowing the user to reserve them. The service provider provides the generated gift tickets to the user, allowing the user to purchase them. This allows the user to select the generated gift tickets and make reservations or purchases. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated gift tickets into AI and provide them to the user.

[0039] The data collection unit can analyze a user's past posting history and select the optimal collection method. For example, the data collection unit can identify the time periods when a user frequently posted in the past and collect posts during those times. For example, the data collection unit can analyze hashtags a user has used in the past and collect related posts. For example, the data collection unit can collect related posts based on locations a user has checked into in the past. This allows the optimal collection method to be selected by analyzing a user's past posting history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a user's past posting history into AI and select the optimal collection method.

[0040] The collection unit can filter posts based on the user's current lifestyle and areas of interest when collecting them. For example, if the user is currently traveling, the collection unit will prioritize collecting posts related to travel. For example, if the user is interested in a particular hobby, the collection unit will collect posts related to that hobby. For example, if the user is participating in a particular event, the collection unit will collect posts related to that event. By filtering posts based on the user's current lifestyle and areas of interest, highly relevant information can be collected. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input information about the user's current lifestyle and areas of interest into AI and filter posts.

[0041] The collection unit can prioritize collecting posts that are highly relevant, taking into account the user's geographical location information when collecting posts. For example, if the user is in a specific region, the collection unit will prioritize collecting posts related to that region. For example, if the user is traveling, the collection unit will prioritize collecting posts related to their travel destination. For example, if the user is attending a specific event, the collection unit will prioritize collecting posts related to that event. This allows for the collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into AI and prioritize the collection of highly relevant posts.

[0042] The collection unit can analyze a user's social media activity when collecting posts and collect relevant posts. For example, if a user frequently uses a particular hashtag, the collection unit can collect posts related to that hashtag. For example, if a user follows a particular account, the collection unit can collect posts related to that account. For example, if a user belongs to a particular group, the collection unit can collect posts related to that group. In this way, relevant information can be collected by analyzing a user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into AI and collect relevant posts.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the posts during the analysis. For example, the analysis unit performs a detailed analysis for posts of high importance. For example, the analysis unit performs a simplified analysis for posts of low importance. The analysis unit adjusts the depth and scope of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the posts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input post importance data into AI and adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the post during analysis. For example, the analysis unit applies a travel-related analysis algorithm to posts about travel. For example, the analysis unit applies a food-related analysis algorithm to posts about food. For example, the analysis unit applies a hobby-related analysis algorithm to posts about hobbies. By applying different analysis algorithms depending on the category of the post, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category data of the posts into AI and apply different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the submission date of posts during the analysis process. For example, the analysis unit may prioritize analyzing the most recent posts. For example, the analysis unit may prioritize analyzing posts submitted within a specific period. For example, if a user is participating in a specific event, the analysis unit may prioritize analyzing posts related to that event. This allows for the prioritization of the analysis of the latest information by determining the priority of analysis based on the submission date of posts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input post submission date data into AI to determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of posts during analysis. For example, the analysis unit may prioritize analyzing posts related to the user's interests. For example, the analysis unit may prioritize analyzing posts related to places the user frequently visits. For example, the analysis unit may prioritize analyzing posts in which the user uses a specific hashtag. This allows for efficient analysis by adjusting the order of analysis based on the relevance of posts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input post relevance data into AI and adjust the order of analysis.

[0047] The generation unit can generate the optimal gift ticket by analyzing the user's past purchase history during the generation process. For example, the generation unit can generate relevant gift tickets based on the gift tickets the user has previously purchased. For example, the generation unit can analyze the user's preferences from their past purchase history and generate gift tickets based on that. For example, the generation unit can generate the optimal gift ticket based on the user's ratings of gift tickets they have previously purchased. In this way, the optimal gift ticket can be provided by analyzing the user's past purchase history. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's past purchase history data into a generation AI and generate the optimal gift ticket.

[0048] The generation unit can customize the content of the gift ticket based on the user's current living situation during generation. For example, if the user is currently traveling, the generation unit will generate a gift ticket that can be used at the travel destination. For example, if the user is participating in a specific event, the generation unit will generate a gift ticket related to that event. For example, if the user has shown interest in a particular hobby, the generation unit will generate a gift ticket related to that hobby. This allows for the provision of more appropriate gift tickets by customizing the content of the gift ticket based on the user's current living situation. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's current living situation data into the generation AI to customize the content of the gift ticket.

[0049] The generation unit can generate the most suitable gift ticket by considering the user's geographical location information during the generation process. For example, if the user is in a specific region, the generation unit will generate a gift ticket usable in that region. For example, if the user is traveling, the generation unit will generate a gift ticket usable at their travel destination. For example, if the user is participating in a specific event, the generation unit will generate a gift ticket related to that event. This allows the generation unit to provide the most suitable gift ticket by considering the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and generate the most suitable gift ticket.

[0050] The generation unit can analyze the user's social media activity during generation and suggest the content of the gift ticket. For example, if the user frequently uses a particular hashtag, the generation unit will suggest a gift ticket related to that hashtag. For example, if the user follows a particular account, the generation unit will suggest a gift ticket related to that account. For example, if the user belongs to a particular group, the generation unit will suggest a gift ticket related to that group. In this way, by analyzing the user's social media activity, the optimal gift ticket can be suggested. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and suggest the content of the gift ticket.

[0051] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has preferred to use in the past. For example, the service provider may provide display methods optimized for devices that the user has used in the past. For example, the service provider may select the most efficient display method from the user's past operation history. In this way, the service provider can provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past operation history data into AI and select the optimal display method.

[0052] The service provider can select the optimal display method at the time of delivery, taking into account the user's current device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider will provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider will provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by taking into account the user's current device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI and select the optimal display method.

[0053] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider will provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider will provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI and select the optimal display method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The gift ticket generation system may also include a history analysis unit that analyzes the user's past purchase history. The history analysis unit can, for example, analyze the types and frequency of gift tickets the user has purchased in the past to identify the user's preferences and tendencies. It can also, for example, collect ratings of gift tickets the user has purchased in the past and prioritize suggesting highly rated gift tickets. Furthermore, it can analyze the usage history of gift tickets the user has purchased in the past and suggest frequently used services. This allows for the provision of more personalized gift tickets by leveraging the user's past purchase history.

[0056] The data collection unit can collect posts while considering the user's current geographical location. For example, if the user is traveling, it will prioritize collecting posts related to their travel destination. If the user is attending a specific event, the data collection unit can collect posts related to that event. If the user is in a specific region, the data collection unit can collect posts related to that region. By considering the user's current geographical location, it is possible to collect more relevant information.

[0057] The generation unit can customize the content of gift tickets based on the user's current life circumstances. For example, if the user is currently traveling, it can generate a gift ticket that can be used at their travel destination. If the user is participating in a specific event, the generation unit can generate a gift ticket related to that event. If the user has shown interest in a particular hobby, the generation unit can generate a gift ticket related to that hobby. By customizing the content of gift tickets based on the user's current life circumstances, it is possible to provide more appropriate gift tickets.

[0058] The data collection unit can analyze a user's past posting history and select the optimal collection method. For example, it can identify the time periods when a user frequently posted in the past and collect posts from those times. The data collection unit can also analyze hashtags a user has used in the past and collect related posts. For example, it can collect related posts based on locations a user has checked into in the past. By analyzing a user's past posting history, the optimal collection method can be selected.

[0059] The generation unit can analyze a user's past purchase history and generate the most suitable gift ticket. For example, it can generate a relevant gift ticket based on a user's past purchases. The generation unit can analyze a user's preferences from their past purchase history and generate a gift ticket based on that. The generation unit can generate the most suitable gift ticket based on a user's ratings of past purchases. In this way, by analyzing a user's past purchase history, the system can provide the most suitable gift ticket.

[0060] The system can select the optimal display method by considering the user's current device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. If the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the system can provide the optimal display method by considering the user's current device information.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects users' social media posts. For example, it collects photos, comments, and check-in information that users post on social media. Step 2: The analysis unit analyzes the posts collected by the collection unit to identify the user's interests and concerns. For example, it analyzes the collected posts in detail and uses natural language processing, image recognition, and sentiment analysis technologies to identify the user's interests and concerns. Step 3: The generation unit generates personalized gift tickets based on the interests and preferences identified by the analysis unit. For example, it may generate gift tickets for accommodations, restaurants, or spas in areas the user frequently visits, or gift tickets that include special plans and discount information tailored to the user's preferences. Step 4: The providing unit provides the user with the gift ticket generated by the generating unit. For example, the generated gift ticket is provided to the user so that the user can select, reserve, or purchase.

[0063] (Example of form 2) The gift ticket generation system according to an embodiment of the present invention is a system that analyzes a user's social media posts and generates personalized gift tickets. This gift ticket generation system collects and analyzes the user's social media posts, generates personalized gift tickets, and provides them to the user. For example, the gift ticket generation system collects photos, comments, check-in information, etc., that the user has posted on social media. Next, the gift ticket generation system analyzes the collected posts using a generation AI to identify the user's interests and preferences. For example, it identifies places the user frequently visits, favorite foods, hobbies, etc. Based on the analysis results, the gift ticket generation system generates personalized gift tickets using a generation AI. For example, it suggests gift tickets for accommodations, restaurants, and spas in areas the user frequently visits. It also includes special plans and discount information tailored to the user's preferences. Finally, the gift ticket generation system provides the generated gift tickets to the user. The user can select the suggested gift tickets and make reservations or purchases. This allows the user to easily obtain gift tickets that match their interests and preferences. The gift ticket generation system can provide more personalized gift tickets by utilizing the user's social media posts. Users will be more satisfied because they can easily obtain gift tickets tailored to their preferences. Furthermore, combining this with booking services for accommodations, restaurants, and spas will enable a wider range of suggestions and expand user choices. This allows the gift ticket generation system to leverage users' social media posts to provide personalized gift tickets.

[0064] The gift ticket generation system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects posts from users' social media. The collection unit collects, for example, photos, comments, and check-in information posted by users on social media. The collection unit can collect, for example, photos posted by users on social media. The collection unit can collect, for example, comments posted by users on social media. The collection unit can collect, for example, check-in information posted by users on social media. The analysis unit analyzes the posts collected by the collection unit to identify the user's interests and preferences. The analysis unit can, for example, analyze the collected posts in detail to identify the user's interests, preferences, and tastes. The analysis unit can, for example, analyze the collected posts using natural language processing technology to identify the user's interests and preferences. The analysis unit can, for example, analyze the collected posts using image recognition technology to identify the user's interests and preferences. The analysis unit can, for example, analyze the collected posts using sentiment analysis technology to identify the user's interests and preferences. The generation unit generates personalized gift tickets based on interests and preferences identified by the analysis unit. The generation unit can, for example, generate gift tickets for accommodations, restaurants, and spas in areas the user frequently visits. The generation unit can, for example, generate gift tickets for accommodations in areas the user frequently visits. The generation unit can, for example, generate gift tickets for restaurants in areas the user frequently visits. The generation unit can, for example, generate gift tickets for spas in areas the user frequently visits. The generation unit can, for example, generate gift tickets that include special plans and discount information tailored to the user's preferences. The generation unit can, for example, generate gift tickets that include special plans tailored to the user's preferences. The generation unit can, for example, generate gift tickets that include discount information tailored to the user's preferences. The provision unit provides the gift tickets generated by the generation unit to the user. The provision unit provides the generated gift tickets to the user, for example, allowing the user to select, book, or purchase them. The provision unit provides the generated gift tickets to the user, for example, allowing the user to select them.The provisioning unit, for example, provides the generated gift ticket to the user, enabling the user to make a reservation. The provisioning unit, for example, provides the generated gift ticket to the user, enabling the user to make a purchase. As a result, the gift ticket generation system according to the embodiment can provide personalized gift tickets by utilizing the user's social media posts.

[0065] The data collection unit collects users' social media posts. Specifically, it collects photos, comments, and check-in information that users post on social media. For example, it can collect photos taken by users while traveling, photos of meals at restaurants, and group photos with friends. This allows for a visual understanding of users' hobbies and interests. Comments and reviews posted by users are also collected. This includes impressions and evaluations of places users have visited and services they have experienced. Furthermore, information on users checking in to specific locations is also collected. This allows for the identification of places users frequently visit and areas of interest. The data collection unit centrally manages this data and stores it in a database for provision to the analytics unit. The data collection unit can connect with various social media platforms via APIs to obtain data in real time. In addition, to ensure data privacy and security, the data collection unit collects data only with the user's consent and protects the data using encryption technology. As a result, the data collection unit can collect a wealth of data from users' diverse social media activities and provide it to the analytics unit.

[0066] The analysis unit analyzes posts collected by the collection unit to identify user interests and preferences. Specifically, it analyzes collected posts in detail to identify user interests, preferences, and tastes. The analysis unit uses natural language processing technology to analyze user comments and reviews and extract specific keywords and phrases. For example, if a user frequently uses positive expressions such as "delicious," "fun," and "recommended," it can be determined that they have a high level of interest in that place or service. In addition, image recognition technology is used to analyze photos posted by users to identify specific places and objects. For example, if a user frequently posts photos of cafes, it can be determined that cafe hopping is their hobby. Furthermore, sentiment analysis technology is used to analyze emotional trends from user posts. This allows for an understanding of what kinds of experiences users have positive feelings towards. The analysis unit combines these technologies to analyze user interests and preferences from multiple angles and perform highly accurate profiling. The analysis results are provided to the generation unit and used to generate personalized gift tickets. In this way, the analysis unit can comprehensively analyze diverse user data and accurately identify user interests and preferences.

[0067] The generation unit generates personalized gift tickets based on the user's interests and preferences identified by the analysis unit. Specifically, it generates gift tickets for accommodations, restaurants, and spas in areas the user frequently visits. For example, if the user enjoys traveling, it can generate gift tickets for accommodations in popular tourist destinations. If the user is a foodie, it can generate gift tickets for highly-rated restaurants. Furthermore, if the user prefers relaxation, it can generate gift tickets for luxury spas. The generation unit can also generate gift tickets that include special plans and discount information tailored to the user's preferences. For example, if the user likes a particular type of cuisine, it can generate a gift ticket that includes special menus and discount information for restaurants serving that cuisine. The generation unit generates these gift tickets in digital format, making them easily accessible to the user. The generation unit customizes the design and content of the gift tickets, making them appealing to the user. This allows the generation unit to generate and provide personalized gift tickets based on the user's interests and preferences.

[0068] The service provider delivers the gift tickets generated by the generation unit to the user. Specifically, it provides the generated gift tickets to the user, allowing the user to select, book, or purchase them. The service provider displays detailed information about the gift tickets to the user and offers options. For example, if the user selects a gift ticket for accommodation, it displays detailed information about the facility, available plans, and prices. If the user selects a gift ticket for a restaurant, it provides information such as the menu, opening hours, and booking methods. The service provider provides an intuitive interface to allow users to easily book or purchase gift tickets. For example, users can select, book, or purchase gift tickets through a website or app accessible from their smartphone or computer. The service provider offers multiple payment and booking methods, taking user convenience into consideration. This allows the service provider to quickly and reliably deliver generated gift tickets to users, enabling them to select, book, or purchase them. Furthermore, the service provider can collect user feedback and continuously improve the content and services of the gift tickets it offers. This allows the service provider to provide high-quality service to users and improve their satisfaction.

[0069] The data collection unit can collect photos, comments, and check-in information posted by users on social media. For example, the data collection unit can collect photos posted by users on social media. For example, the data collection unit can collect comments posted by users on social media. For example, the data collection unit can collect check-in information posted by users on social media. By collecting users' social media posts, more detailed information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input photos posted by users on social media into AI and analyze the content of the photos.

[0070] The analysis unit can analyze collected posts in detail to identify users' interests, preferences, and tastes. For example, the analysis unit can analyze collected posts using natural language processing technology to identify users' interests. For example, the analysis unit can analyze collected posts using image recognition technology to identify users' interests. For example, the analysis unit can analyze collected posts using sentiment analysis technology to identify users' interests. This allows for the provision of more personalized gift tickets by precisely identifying users' interests. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected posts into AI to identify users' interests.

[0071] The generation unit can generate gift tickets for accommodations, restaurants, and spas in areas frequently visited by the user. For example, the generation unit can generate gift tickets for accommodations in areas frequently visited by the user. For example, the generation unit can generate gift tickets for restaurants in areas frequently visited by the user. For example, the generation unit can generate gift tickets for spas in areas frequently visited by the user. This allows for the provision of gift tickets based on the areas visited by the user. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input information about areas frequently visited by the user into a generation AI and generate gift tickets.

[0072] The generation unit can generate gift tickets that include special plans and discount information tailored to the user's preferences. For example, the generation unit can generate gift tickets that include special plans tailored to the user's preferences. For example, the generation unit can generate gift tickets that include discount information tailored to the user's preferences. This allows for the provision of special plans and discount information tailored to the user's preferences. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input information about the user's preferences into a generation AI and generate gift tickets that include special plans and discount information.

[0073] The service provider provides the generated gift tickets to the user, allowing the user to select, reserve, or purchase them. The service provider provides the generated gift tickets to the user, allowing the user to select them. The service provider provides the generated gift tickets to the user, allowing the user to reserve them. The service provider provides the generated gift tickets to the user, allowing the user to purchase them. This allows the user to select the generated gift tickets and make reservations or purchases. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated gift tickets into AI and provide them to the user.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of post collection based on the estimated emotions. For example, if the user is showing positive emotions, the data collection unit increases the frequency of post collection to gather the latest information. For example, if the user is showing negative emotions, the data collection unit decreases the frequency of post collection to reduce stress. For example, if the user is participating in a particular event, the data collection unit prioritizes collecting posts related to that event. This allows information to be collected at a more appropriate time by adjusting the timing of post collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into AI to adjust the timing of post collection.

[0075] The data collection unit can analyze a user's past posting history and select the optimal collection method. For example, the data collection unit can identify the time periods when a user frequently posted in the past and collect posts during those times. For example, the data collection unit can analyze hashtags a user has used in the past and collect related posts. For example, the data collection unit can collect related posts based on locations a user has checked into in the past. This allows the optimal collection method to be selected by analyzing a user's past posting history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a user's past posting history into AI and select the optimal collection method.

[0076] The collection unit can filter posts based on the user's current lifestyle and areas of interest when collecting them. For example, if the user is currently traveling, the collection unit will prioritize collecting posts related to travel. For example, if the user is interested in a particular hobby, the collection unit will collect posts related to that hobby. For example, if the user is participating in a particular event, the collection unit will collect posts related to that event. By filtering posts based on the user's current lifestyle and areas of interest, highly relevant information can be collected. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input information about the user's current lifestyle and areas of interest into AI and filter posts.

[0077] The data collection unit can estimate the user's emotions and determine the priority of posts to collect based on the estimated user emotions. For example, if the user is showing positive emotions, the data collection unit will prioritize collecting posts with positive content. For example, if the user is showing negative emotions, the data collection unit will prioritize collecting posts with negative content. For example, if the user is showing a specific emotion, the data collection unit will prioritize collecting posts related to that emotion. This allows for the collection of more appropriate information by prioritizing posts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into AI to determine the priority of posts to collect.

[0078] The collection unit can prioritize collecting posts that are highly relevant, taking into account the user's geographical location information when collecting posts. For example, if the user is in a specific region, the collection unit will prioritize collecting posts related to that region. For example, if the user is traveling, the collection unit will prioritize collecting posts related to their travel destination. For example, if the user is attending a specific event, the collection unit will prioritize collecting posts related to that event. This allows for the collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into AI and prioritize the collection of highly relevant posts.

[0079] The collection unit can analyze a user's social media activity when collecting posts and collect relevant posts. For example, if a user frequently uses a particular hashtag, the collection unit can collect posts related to that hashtag. For example, if a user follows a particular account, the collection unit can collect posts related to that account. For example, if a user belongs to a particular group, the collection unit can collect posts related to that group. In this way, relevant information can be collected by analyzing a user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into AI and collect relevant posts.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is showing positive emotions, the analysis unit can display the analysis results using graphs or charts with bright colors. For example, if the user is showing negative emotions, the analysis unit can display the analysis results using graphs or charts with calm colors. For example, if the user is showing a specific emotion, the analysis unit can display the analysis results using a presentation method that matches that emotion. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and adjust the presentation of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the posts during the analysis. For example, the analysis unit performs a detailed analysis for posts of high importance. For example, the analysis unit performs a simplified analysis for posts of low importance. The analysis unit adjusts the depth and scope of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the posts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input post importance data into AI and adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the post during analysis. For example, the analysis unit applies a travel-related analysis algorithm to posts about travel. For example, the analysis unit applies a food-related analysis algorithm to posts about food. For example, the analysis unit applies a hobby-related analysis algorithm to posts about hobbies. By applying different analysis algorithms depending on the category of the post, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category data of the posts into AI and apply different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on the submission date of posts during the analysis process. For example, the analysis unit may prioritize analyzing the most recent posts. For example, the analysis unit may prioritize analyzing posts submitted within a specific period. For example, if a user is participating in a specific event, the analysis unit may prioritize analyzing posts related to that event. This allows for the prioritization of the analysis of the latest information by determining the priority of analysis based on the submission date of posts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input post submission date data into AI to determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of posts during analysis. For example, the analysis unit may prioritize analyzing posts related to the user's interests. For example, the analysis unit may prioritize analyzing posts related to places the user frequently visits. For example, the analysis unit may prioritize analyzing posts in which the user uses a specific hashtag. This allows for efficient analysis by adjusting the order of analysis based on the relevance of posts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input post relevance data into AI and adjust the order of analysis.

[0086] The generation unit can estimate the user's emotions and adjust the content of the gift ticket it generates based on the estimated emotions. For example, if the user is showing positive emotions, the generation unit generates a gift ticket that includes special plans or discount information. For example, if the user is showing negative emotions, the generation unit generates a gift ticket for a relaxing spa. For example, if the user is showing a specific emotion, the generation unit generates a gift ticket tailored to that emotion. This allows for the provision of more appropriate gift tickets by adjusting the content of the gift ticket based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, for example, or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and adjust the content of the gift ticket.

[0087] The generation unit can generate the optimal gift ticket by analyzing the user's past purchase history during the generation process. For example, the generation unit can generate relevant gift tickets based on the gift tickets the user has previously purchased. For example, the generation unit can analyze the user's preferences from their past purchase history and generate gift tickets based on that. For example, the generation unit can generate the optimal gift ticket based on the user's ratings of gift tickets they have previously purchased. In this way, the optimal gift ticket can be provided by analyzing the user's past purchase history. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's past purchase history data into a generation AI and generate the optimal gift ticket.

[0088] The generation unit can customize the content of the gift ticket based on the user's current living situation during generation. For example, if the user is currently traveling, the generation unit will generate a gift ticket that can be used at the travel destination. For example, if the user is participating in a specific event, the generation unit will generate a gift ticket related to that event. For example, if the user has shown interest in a particular hobby, the generation unit will generate a gift ticket related to that hobby. This allows for the provision of more appropriate gift tickets by customizing the content of the gift ticket based on the user's current living situation. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's current living situation data into the generation AI to customize the content of the gift ticket.

[0089] The generation unit can estimate the user's emotions and determine the priority of gift tickets to generate based on the estimated emotions. For example, if the user is showing positive emotions, the generation unit will prioritize generating gift tickets that include special plans or discount information. For example, if the user is showing negative emotions, the generation unit will prioritize generating gift tickets for relaxing spas. For example, if the user is showing a specific emotion, the generation unit will prioritize generating gift tickets that match that emotion. This allows for the provision of more appropriate gift tickets by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI to determine the priority of gift tickets.

[0090] The generation unit can generate the most suitable gift ticket by considering the user's geographical location information during the generation process. For example, if the user is in a specific region, the generation unit will generate a gift ticket usable in that region. For example, if the user is traveling, the generation unit will generate a gift ticket usable at their travel destination. For example, if the user is participating in a specific event, the generation unit will generate a gift ticket related to that event. This allows the generation unit to provide the most suitable gift ticket by considering the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and generate the most suitable gift ticket.

[0091] The generation unit can analyze the user's social media activity during generation and suggest the content of the gift ticket. For example, if the user frequently uses a particular hashtag, the generation unit will suggest a gift ticket related to that hashtag. For example, if the user follows a particular account, the generation unit will suggest a gift ticket related to that account. For example, if the user belongs to a particular group, the generation unit will suggest a gift ticket related to that group. In this way, by analyzing the user's social media activity, the optimal gift ticket can be suggested. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and suggest the content of the gift ticket.

[0092] The service provider can estimate the user's emotions and adjust how the gift ticket is displayed based on the estimated emotions. For example, if the user is showing positive emotions, the service provider will display the gift ticket with a bright-colored interface. For example, if the user is showing negative emotions, the service provider will display the gift ticket with a calm-colored interface. For example, if the user is showing a specific emotion, the service provider will provide a display method that matches that emotion. This allows for a more appropriate display by adjusting the gift ticket display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into AI and adjust the display method.

[0093] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has preferred to use in the past. For example, the service provider may provide display methods optimized for devices that the user has used in the past. For example, the service provider may select the most efficient display method from the user's past operation history. In this way, the service provider can provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past operation history data into AI and select the optimal display method.

[0094] The service provider can select the optimal display method at the time of delivery, taking into account the user's current device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider will provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider will provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by taking into account the user's current device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI and select the optimal display method.

[0095] The service provider can estimate the user's emotions and adjust the procedure for using the gift ticket based on the estimated emotions. For example, if the user is showing positive emotions, the service provider will provide detailed instructions. For example, if the user is showing negative emotions, the service provider will provide simplified instructions. For example, if the user is showing a specific emotion, the service provider will provide instructions tailored to that emotion. This allows for more appropriate operation by adjusting the instructions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into AI and adjust the instructions.

[0096] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider will provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider will provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI and select the optimal display method.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The gift ticket generation system may also include a history analysis unit that analyzes the user's past purchase history. The history analysis unit can, for example, analyze the types and frequency of gift tickets the user has purchased in the past to identify the user's preferences and tendencies. It can also, for example, collect ratings of gift tickets the user has purchased in the past and prioritize suggesting highly rated gift tickets. Furthermore, it can analyze the usage history of gift tickets the user has purchased in the past and suggest frequently used services. This allows for the provision of more personalized gift tickets by leveraging the user's past purchase history.

[0099] The data collection unit can collect posts while considering the user's current geographical location. For example, if the user is traveling, it will prioritize collecting posts related to their travel destination. If the user is attending a specific event, the data collection unit can collect posts related to that event. If the user is in a specific region, the data collection unit can collect posts related to that region. By considering the user's current geographical location, it is possible to collect more relevant information.

[0100] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if a user is showing positive emotions, it will prioritize analyzing posts with positive content. If a user is showing negative emotions, the analysis unit can prioritize analyzing posts with negative content. If a user is showing a specific emotion, the analysis unit can prioritize analyzing posts related to that emotion. By prioritizing analysis based on the user's emotions, more relevant information can be provided.

[0101] The generation unit can customize the content of gift tickets based on the user's current life circumstances. For example, if the user is currently traveling, it can generate a gift ticket that can be used at their travel destination. If the user is participating in a specific event, the generation unit can generate a gift ticket related to that event. If the user has shown interest in a particular hobby, the generation unit can generate a gift ticket related to that hobby. By customizing the content of gift tickets based on the user's current life circumstances, it is possible to provide more appropriate gift tickets.

[0102] The service provider can estimate the user's emotions and adjust how the gift ticket is displayed based on those emotions. For example, if the user is showing positive emotions, the service provider can display the gift ticket with a bright-colored interface. If the user is showing negative emotions, the service provider can display the gift ticket with a calm-colored interface. If the user is showing a specific emotion, the service provider can provide a display method that matches that emotion. By adjusting the display method of the gift ticket based on the user's emotions, a more appropriate display becomes possible.

[0103] The data collection unit can analyze a user's past posting history and select the optimal collection method. For example, it can identify the time periods when a user frequently posted in the past and collect posts from those times. The data collection unit can also analyze hashtags a user has used in the past and collect related posts. For example, it can collect related posts based on locations a user has checked into in the past. By analyzing a user's past posting history, the optimal collection method can be selected.

[0104] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is showing positive emotions, the analysis unit can display the results using graphs or charts with bright colors. If the user is showing negative emotions, the analysis unit can display the results using graphs or charts with calm colors. If the user is showing a specific emotion, the analysis unit can display the results using a presentation method that matches that emotion. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided.

[0105] The generation unit can analyze a user's past purchase history and generate the most suitable gift ticket. For example, it can generate a relevant gift ticket based on a user's past purchases. The generation unit can analyze a user's preferences from their past purchase history and generate a gift ticket based on that. The generation unit can generate the most suitable gift ticket based on a user's ratings of past purchases. In this way, by analyzing a user's past purchase history, the system can provide the most suitable gift ticket.

[0106] The service provider can estimate the user's emotions and adjust the instructions for using the gift ticket based on those emotions. For example, if the user is showing positive emotions, detailed instructions can be provided. If the user is showing negative emotions, simplified instructions can be provided. If the user is showing a specific emotion, instructions tailored to that emotion can be provided. This allows for more appropriate operation by adjusting the instructions based on the user's emotions.

[0107] The system can select the optimal display method by considering the user's current device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. If the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the system can provide the optimal display method by considering the user's current device information.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The collection unit collects users' social media posts. For example, it collects photos, comments, and check-in information that users post on social media. Step 2: The analysis unit analyzes the posts collected by the collection unit to identify the user's interests and concerns. For example, it analyzes the collected posts in detail and uses natural language processing, image recognition, and sentiment analysis technologies to identify the user's interests and concerns. Step 3: The generation unit generates personalized gift tickets based on the interests and preferences identified by the analysis unit. For example, it may generate gift tickets for accommodations, restaurants, or spas in areas the user frequently visits, or gift tickets that include special plans and discount information tailored to the user's preferences. Step 4: The providing unit provides the user with the gift ticket generated by the generating unit. For example, the generated gift ticket is provided to the user so that the user can select, reserve, or purchase.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's social media posts using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, to analyze the collected posts and identify the user's interests and preferences. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, to generate a personalized gift ticket based on the analysis results. The provision unit is implemented in the control unit 46A of the smart device 14, for example, to provide the generated gift ticket to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's social media posts using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, to analyze the collected posts and identify the user's interests and preferences. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, to generate a personalized gift ticket based on the analysis results. The provision unit is implemented in the control unit 46A of the smart glasses 214, for example, to provide the generated gift ticket to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's social media posts using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, to analyze the collected posts and identify the user's interests and preferences. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, to generate a personalized gift ticket based on the analysis results. The provision unit is implemented in the control unit 46A of the headset terminal 314, for example, to provide the generated gift ticket to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's social media posts using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected posts and identifies the user's interests and preferences. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates a personalized gift ticket based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides the generated gift ticket to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A collection unit that collects users' social media posts, An analysis unit analyzes the posts collected by the aforementioned collection unit to identify the user's interests and preferences, A generation unit that generates personalized gift tickets based on the interests and preferences identified by the analysis unit, The system includes a provisioning unit that provides the user with the gift ticket generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects photos, comments, and check-in information that users post on social media. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze collected posts in detail to identify users' interests, preferences, and tastes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate gift certificates for accommodations, restaurants, and spas in areas the user frequently visits. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate gift tickets that include special plans and discount information tailored to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The generated gift tickets are provided to the user, allowing them to select, reserve, or purchase items. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate user sentiment and adjust the timing of post collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past posting history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting posts, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and determines the priority of posts to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting posts, the system prioritizes collecting posts that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting posts, the system analyzes users' social media activity and collects relevant posts. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the posts. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the post. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the submission date of the submissions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the posts. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the content of the gift ticket generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During creation, the system analyzes the user's past purchase history to generate the most suitable gift ticket. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During creation, the contents of the gift ticket are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of gift tickets to be generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the system takes the user's geographical location into consideration to generate the most suitable gift ticket. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the system analyzes the user's social media activity to suggest the contents of the gift ticket. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate the user's emotions and adjust how gift tickets are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's current device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the instructions for using the gift ticket based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects users' social media posts, An analysis unit analyzes the posts collected by the aforementioned collection unit to identify the user's interests and preferences, A generation unit that generates personalized gift tickets based on the interests and preferences identified by the analysis unit, The system includes a provisioning unit that provides the user with the gift ticket generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects photos, comments, and check-in information that users post on social media. The system according to feature 1.

3. The aforementioned analysis unit, We analyze collected posts in detail to identify users' interests, preferences, and tastes. The system according to feature 1.

4. The generating unit is Generate gift certificates for accommodations, restaurants, and spas in areas the user frequently visits. The system according to feature 1.

5. The generating unit is Generate gift tickets that include special plans and discount information tailored to the user's preferences. The system according to feature 1.

6. The aforementioned supply unit is, The generated gift tickets are provided to the user, allowing them to select, reserve, or purchase items. The system according to feature 1.

7. The aforementioned collection unit is We estimate user sentiment and adjust the timing of post collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past posting history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting posts, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates user sentiment and determines the priority of posts to collect based on the estimated user sentiment. The system according to feature 1.

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