system

Abstract

The system according to this embodiment aims to provide users with an experience that evokes a sense of serendipity. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, an explicit unit, and a scoring unit. The collection unit collects data on the user's past behavior. The analysis unit analyzes the data collected by the collection unit and discovers the user's potential relationships. The suggestion unit suggests future actions based on the relationships discovered by the analysis unit. The explicit unit reveals serendipity when the user performs the future action suggested by the suggestion unit. The scoring unit scores a serendipity score based on the serendipity revealed by the explicit unit.

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 character of the chatbot, 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 conventional technology, it cannot be said that a sufficient experience of making the user feel serendipity has been provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a user with an experience of feeling serendipity.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, an explicit unit, and a scoring unit. The data collection unit collects data on the user's past behavior. The analysis unit analyzes the data collected by the data collection unit and discovers the user's potential relationships. The proposal unit proposes future actions based on the relationships discovered by the analysis unit. The explicit unit reveals serendipity when the user performs the future action proposed by the proposal unit. The scoring unit scores a serendipity score based on the serendipity revealed by the explicit unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide users with an experience that gives them a sense of serendipity. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 serendipity experience provision system according to an embodiment of the present invention is a system that provides users with an experience that makes them feel serendipitous by utilizing generative AI. This serendipity experience provision system provides users with an experience that makes them feel serendipitous by collecting and analyzing the user's past and present behavioral data, proposing future actions, revealing serendipity, and scoring it. For example, the serendipity experience provision system collects past actions such as the user reading a specific book on an e-book service and current actions such as searching for accommodation on a travel booking service. Next, the serendipity experience provision system analyzes the collected data and proposes future actions that the user should take next to feel serendipitous. For example, the generative AI discovers that there is an inn nearby that is the setting for a specific book and proposes that the user book this inn. When the user actually takes the future action, the serendipity is revealed, and the user can gain a new discovery or a special experience. For example, when the user actually stays at the inn, it is revealed that it is the setting for a specific book. Furthermore, the serendipity score is scored from multiple perspectives using generative AI. For example, the system scores factors such as the joy and satisfaction a user feels, the rarity of the quest, its difficulty of completion, how well it suits their current interests and circumstances, and its novelty. A user's serendipity score is visualized in the form of rankings and badges, allowing users to compete with each other. Furthermore, community features enable users who have experienced similar serendipity to form communities, fostering shared emotions and the creation of new communities and friendships. This allows companies to conduct promotions at the optimal time based on users' potential interests and behavioral data, reducing marketing costs. Users' daily lives become more fortunate and exciting, and their attachment to products and services increases. Corporate groups see users who want to experience serendipity actively using their services. In this way, the serendipity experience delivery system can leverage user behavioral data to provide experiences that evoke a sense of serendipity.

[0029] The serendipity experience provision system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a specification unit, and a scoring unit. The collection unit collects the user's past behavioral data. The collection unit can collect, for example, the user's purchase history, browsing history, location information, etc. The collection unit can collect, for example, the history of products and services that the user has purchased in the past. The collection unit can also collect the history of web pages and content that the user has viewed in the past. Furthermore, the collection unit can collect the user's location information and collect the history of places the user has visited. The analysis unit analyzes the data collected by the collection unit and discovers the user's potential relationships. The analysis unit can analyze the data using, for example, data mining or machine learning algorithms. The analysis unit can, for example, analyze the user's behavioral patterns and discover common interests and concerns. Furthermore, the analysis unit can analyze the user's behavioral data and discover potential relationships. Furthermore, the analysis unit can analyze the user's behavioral data and predict future behavior. The suggestion unit proposes future behavior based on the relationships discovered by the analysis unit. The suggestion unit can, for example, suggest future actions based on the user's past actions. For example, the suggestion unit can suggest the next product to purchase based on the products the user has purchased in the past. The suggestion unit can also suggest the next place to visit based on the places the user has visited in the past. Furthermore, the suggestion unit can also suggest future actions based on the user's current actions. The revealing unit reveals serendipity when the user performs a future action suggested by the suggestion unit. The revealing unit can reveal serendipity using, for example, notifications or pop-up messages. For example, the revealing unit displays a message revealing serendipity when the user performs a future action. The revealing unit can also send a notification revealing serendipity when the user performs a future action. Furthermore, the revealing unit can display a pop-up message revealing serendipity when the user performs a future action. The scoring unit scores a serendipity score based on the serendipity revealed by the revealing unit.The scoring unit can, for example, score the joy and satisfaction felt by the user. The scoring unit evaluates the joy and satisfaction felt by the user and calculates a serendipity score. The scoring unit can also score the rarity and difficulty of completing a quest. Furthermore, the scoring unit can score how well it suits the user's current interests and circumstances. As a result, the serendipity experience provision system according to this embodiment can provide an experience that evokes a sense of serendipity by utilizing the user's behavioral data.

[0030] The data collection unit collects data on users' past behavior. Specifically, it can collect user purchase history, browsing history, location information, and more. For example, when collecting a user's past purchase history of goods and services, it obtains data from online shopping sites and service providers. This allows for an understanding of what kinds of goods and services users are interested in. The data collection unit can also collect a history of web pages and content that users have viewed in the past. This includes browser history data and log data from applications used by the user. Furthermore, the data collection unit can collect user location information and a history of places the user has visited. Location information can be obtained from smartphone GPS data and applications that use location services. This allows for a detailed understanding of what kinds of places users are interested in and what their behavioral patterns are. The data collection unit centrally manages this data and makes it available to the analysis and proposal units. By adjusting the frequency and accuracy of data collection, flexible responses can be made to specific situations and conditions. For example, during specific events or campaigns, increasing the data collection frequency can obtain more detailed user behavior data. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes data collected by the data collection unit to discover potential user relationships. Specifically, it can analyze data using data mining and machine learning algorithms. For example, it can use clustering algorithms to analyze user behavior patterns and discover common interests. This allows it to identify user groups with similar behavior patterns and provide optimal suggestions to each group. It can also use association rule mining to analyze user behavior data and discover potential relationships. This reveals how specific behaviors relate to other behaviors. Furthermore, the analysis unit can use time series analysis and predictive models to analyze user behavior data and predict future behavior. This allows it to predict what actions users are likely to take next and provide appropriate suggestions based on that prediction. The analysis unit can also leverage historical data and statistical information to grasp long-term trends and patterns. For example, it can predict user behavior patterns in specific seasons or events based on historical data and provide suggestions based on that prediction. This allows the analysis unit to quickly and accurately analyze user behavior data and predict potential relationships and future behavior.

[0032] The suggestion unit proposes future actions based on the relationships discovered by the analysis unit. Specifically, it can propose future actions based on the user's past behavior. For example, it can use a recommendation engine to suggest products to buy next based on products the user has purchased in the past. This can suggest products and services that the user might be interested in, thereby increasing their purchase intent. The suggestion unit can also suggest places to visit next based on places the user has visited in the past. This includes analyzing location data to suggest tourist destinations and restaurants that the user might be interested in. Furthermore, the suggestion unit can propose future actions based on the user's current behavior. For example, it can suggest relevant information and products based on the web pages and content the user is currently viewing. The suggestion unit can improve the user experience by providing personalized suggestions that match the user's interests and preferences. The suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can track whether the user purchased a suggested product or visited a suggested place, and adjust the suggestion algorithm based on the results. This allows the suggestion unit to provide more appropriate and effective suggestions to the user.

[0033] The explicit section reveals serendipity when the user takes a future action suggested by the suggestion section. Specifically, serendipity can be revealed using notifications and pop-up messages. For example, when a user purchases a suggested product, the explicit section can notify them that the product has a special discount or benefit. Also, when a user visits a suggested location, a pop-up message can inform them that a special event or service is being offered at that location. The explicit section can provide users with surprise and delight by displaying messages that reveal serendipity when they take a future action. For example, when a user visits a suggested restaurant, the explicit section can notify them that the restaurant is offering a special menu or service. Also, when a user visits a suggested tourist destination, the explicit section can notify them that a special event or activity is being held at that tourist destination. By sending notifications that reveal serendipity when users take a future action, the explicit section can provide users with a positive experience. In this way, the explicit section can provide users with an experience that makes them feel serendipitous and improve user satisfaction.

[0034] The scoring unit calculates a serendipity score based on the serendipity revealed by the explicit unit. Specifically, it can score the joy and satisfaction felt by the user. For example, when a user purchases a suggested product, a survey is conducted to evaluate their satisfaction with that product, and a serendipity score is calculated based on the results. Similarly, when a user visits a suggested location, a survey is conducted to evaluate their satisfaction with the experience at that location, and a serendipity score is calculated based on the results. The scoring unit can also score the rarity and difficulty of achieving a quest. For example, a quest can be set that can only be completed if the user meets certain conditions, and a score can be assigned according to the degree of completion. Furthermore, the scoring unit can score how well something is suited to the user's current interests and situation. For example, it can evaluate how well an action suggested based on the user's current interests and concerns is suited, and calculate a score based on the results. In this way, the scoring unit can quantitatively evaluate the user's experience and calculate a serendipity score. Based on these scores, the scoring unit can provide feedback to the user and provide data to improve future suggestions. This allows the scoring unit to continuously improve the user experience.

[0035] The data collection unit can collect data on the user's past and present behavior. For example, the data collection unit can collect purchase history and browsing history as data on the user's past behavior. The data collection unit can also collect real-time location information and current browsing history as data on the user's current behavior. For example, the data collection unit can collect the history of the web pages the user is currently viewing. The data collection unit can also collect the user's current location information and collect data on the places the user is currently visiting. By collecting data on the user's past and present behavior, more accurate analysis becomes possible. 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 the user's past and present behavior data into AI and have the AI ​​perform the data collection.

[0036] The analysis unit can analyze the collected data and discover potential user relationships. For example, the analysis unit can analyze the data using data mining or machine learning algorithms. For example, the analysis unit can analyze user behavior patterns and discover common interests. Furthermore, the analysis unit can analyze user behavior data and discover potential relationships. In addition, the analysis unit can analyze user behavior data and predict future behavior. This improves the accuracy of suggesting future behavior by analyzing the collected data and discovering potential user relationships. 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 collected data into AI and have the AI ​​perform the data analysis.

[0037] The suggestion unit can propose future actions based on the analyzed data. For example, the suggestion unit can propose future actions based on the user's past actions. For example, the suggestion unit can propose the next product to purchase based on the products the user has purchased in the past. The suggestion unit can also propose the next place to visit based on the places the user has visited in the past. Furthermore, the suggestion unit can propose future actions based on the user's current actions. This provides the user with a serendipitous experience by proposing future actions based on the analyzed data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the analyzed data into AI and have the AI ​​execute the proposal of future actions.

[0038] The explicit part can reveal serendipity when the user takes a future action. The explicit part reveals serendipity, for example, by using notifications or pop-up messages. The explicit part can, for example, display a message revealing serendipity when the user takes a future action. The explicit part can also send a notification revealing serendipity when the user takes a future action. Furthermore, the explicit part can display a pop-up message revealing serendipity when the user takes a future action. This provides the user with new discoveries and special experiences by revealing serendipity when the user takes a future action. Some or all of the above processing in the explicit part may be performed using AI, for example, or not using AI. For example, the explicit part can have AI generate a message revealing serendipity when the user takes a future action.

[0039] The scoring unit can score the serendipity score from multiple perspectives. For example, the scoring unit can score the joy and satisfaction felt by the user. The scoring unit can evaluate the joy and satisfaction felt by the user and calculate the serendipity score. The scoring unit can also score the rarity and difficulty of the quest. Furthermore, the scoring unit can score how well it suits the user's current interests and circumstances. By scoring the serendipity score from multiple perspectives, the user's experience can be evaluated in more detail. Some or all of the above processing in the scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit can input the joy and satisfaction felt by the user into the AI ​​and have the AI ​​calculate the serendipity score.

[0040] The scoring unit can visualize a user's serendipity score in the form of rankings or badges. For example, the scoring unit can display a user's serendipity score in a ranking format. For example, the scoring unit can display a ranking of a user against other users based on their serendipity score. The scoring unit can also award badges according to a user's serendipity score. For example, the scoring unit can display badges according to the user's level of achievement based on their serendipity score. This allows users to compete with each other by visualizing their serendipity scores in the form of rankings or badges. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input a user's serendipity score into an AI and have the AI ​​generate rankings and badges.

[0041] The system can form communities where users who have experienced the same serendipitous events can gather through its community features. For example, the system can form groups where users who have experienced the same serendipitous events can gather. For example, the system can provide communities where users who have experienced the same serendipitous events can interact with each other. The system can also help users share their experiences and form new communities and friendships. For example, the system can provide online forums where users who have experienced the same serendipitous events can gather. This allows for the sharing of experiences and the creation of new communities and friendships by forming communities where users who have experienced the same serendipitous events can gather. Some or all of the processes described above in the system may be performed using AI, for example, or not using AI. For example, the system can have AI perform the task of forming communities where users who have experienced the same serendipitous events can gather.

[0042] The data collection unit can analyze the user's past behavioral data and select the optimal acquisition method. For example, the data collection unit may prioritize acquiring data from services that the user frequently uses. For example, the data collection unit may acquire data at specific time periods based on the user's behavioral patterns. The data collection unit can also acquire relevant data based on the user's interests. This allows for efficient data collection by analyzing the user's past behavioral data and selecting the optimal acquisition method. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into AI and have the AI ​​select the optimal acquisition method.

[0043] The data collection unit can filter past behavioral data based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize data related to areas the user is currently interested in. For example, the data collection unit can acquire highly relevant data based on the user's current lifestyle. The data collection unit can also filter and acquire data related to the user's current activities. This allows for the acquisition of highly relevant data by filtering the data based on the user's current lifestyle and areas of interest. 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 the user's current lifestyle and areas of interest into the AI ​​and have the AI ​​perform the data filtering.

[0044] The data collection unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring behavioral data. For example, if the user is in a specific region, the data collection unit will prioritize the acquisition of data related to that region. For example, if the user is traveling, the data collection unit will prioritize the acquisition of data related to the travel destination. Furthermore, if the user is at home, the data collection unit can prioritize the acquisition of data related to the area around the user's home. In this way, by acquiring data while considering the user's geographical location, highly relevant data can be prioritized. 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 the user's geographical location information into AI and have the AI ​​perform the acquisition of highly relevant data.

[0045] The data collection unit can analyze a user's social media activity and obtain relevant data when acquiring behavioral data. For example, the data collection unit can obtain relevant data based on information shared by the user on social media. For example, the data collection unit can obtain relevant data based on accounts followed by the user on social media. The data collection unit can also obtain relevant data based on groups the user participates in on social media. This allows for the efficient acquisition of relevant data by analyzing the user's social media activity. 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 the user's social media activity into AI and have AI perform the acquisition of relevant data.

[0046] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between past and present data during the analysis. For example, the analysis unit can improve accuracy by analyzing the correlation between past and present data. For example, the analysis unit can improve accuracy by comparing trends between past and present data. Furthermore, the analysis unit can also improve accuracy by extracting patterns from past and present data. In this way, the accuracy of the analysis is improved by considering the interrelationships between past and present data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past and present data into AI and have the AI ​​perform the analysis of the interrelationships.

[0047] The analysis unit can perform analysis while taking user attribute information into consideration. For example, the analysis unit can analyze data based on the user's age and gender. For example, the analysis unit can analyze data based on the user's occupation and hobbies. The analysis unit can also analyze data based on the user's lifestyle. By considering user attribute information, more personalized analysis becomes possible. 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 user attribute information into AI and have the AI ​​perform the data analysis.

[0048] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can analyze data based on the user's location. For example, the analysis unit can analyze data based on the user's travel destinations. The analysis unit can also analyze data based on the user's activity range. By considering the geographical distribution of the data, 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 user's geographical data into AI and have AI perform geographical distribution analysis.

[0049] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant research papers. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant industry reports. The analysis unit can also improve the accuracy of its analysis by referring to relevant patent documents. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature into the AI ​​and have the AI ​​perform the literature referencing and analysis.

[0050] The proposal unit can adjust the level of detail of its proposals based on the importance of the action. For example, the proposal unit will provide detailed proposals for important actions. For example, it will provide simplified proposals for actions of low importance. It can also provide proposals with a moderate level of detail for actions of moderate importance. By adjusting the level of detail of proposals based on the importance of the action, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the action into the AI ​​and have the AI ​​adjust the level of detail of the proposals.

[0051] The suggestion unit can apply different suggestion algorithms depending on the category of the activity when making suggestions. For example, for travel-related activities, the suggestion unit applies a suggestion algorithm specifically for travel. For example, for shopping-related activities, the suggestion unit applies a suggestion algorithm specifically for shopping. Furthermore, for entertainment-related activities, the suggestion unit can also apply a suggestion algorithm specifically for entertainment. By applying different suggestion algorithms depending on the category of activity, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the category of activity into the AI ​​and have the AI ​​perform the application of the suggestion algorithm.

[0052] The proposal department can determine the priority of proposals based on the timing of action submissions. For example, the proposal department will prioritize proposals for highly urgent actions. For example, it will postpone proposals for less urgent actions. The proposal department can also propose actions of moderate urgency with an appropriate priority. This allows for more appropriate proposals by determining the priority of proposals based on the timing of action submissions. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the timing of action submissions into the AI ​​and have the AI ​​determine the priority of proposals.

[0053] The suggestion unit can adjust the order of suggestions based on the relevance of the actions when making suggestions. For example, the suggestion unit will suggest highly relevant actions first. For example, it will suggest less relevant actions later. It can also suggest moderately relevant actions in an appropriate order. By adjusting the order of suggestions based on the relevance of the actions, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of the actions into the AI ​​and have the AI ​​adjust the order of suggestions.

[0054] The explicit function can select the optimal explicit method by referring to the user's past behavior history during explicitness. For example, the explicit function may prioritize using explicit methods that the user has preferred in the past. For example, the explicit function may select a highly relevant explicit method based on the user's past behavior history. The explicit function can also analyze the user's past behavior patterns and select the optimal explicit method. This allows for more appropriate explicitness by selecting the optimal explicit method by referring to the user's past behavior history. Some or all of the above processing in the explicit function may be performed using AI, for example, or without AI. For example, the explicit function can input the user's past behavior history into AI and have the AI ​​select the optimal explicit method.

[0055] The explicit function can customize the explicit method based on the user's current living situation at the time of explicitness. For example, if the user is busy, the explicit function may use a concise explicit method. If the user is relaxed, the explicit function may use a detailed explicit method. The explicit function may also use an explicit method relevant to the user's travel destination if the user is traveling. This allows for more appropriate explicitness by customizing the explicit method based on the user's current living situation. Some or all of the above processing in the explicit function may be performed using AI, for example, or not using AI. For example, the explicit function can input the user's current living situation into AI and have AI perform the customization of the explicit method.

[0056] The expliciting unit can select the optimal expliciting method at the time of expliciting, taking into account the user's geographical location information. For example, if the user is in a specific region, the expliciting unit will explicit serendipity related to that region. For example, if the user is traveling, the expliciting unit will explicit serendipity related to the travel destination. Furthermore, if the user is at home, the expliciting unit can also explicit serendipity related to the area around the user's home. This allows for more appropriate expliciting by selecting the optimal expliciting method considering the user's geographical location information. Some or all of the above processing in the expliciting unit may be performed using AI, for example, or without AI. For example, the expliciting unit can input the user's geographical location information into AI and have the AI ​​select the optimal expliciting method.

[0057] The expliciting unit can analyze the user's social media activity and suggest expliciting methods at the time of expliciting. For example, the expliciting unit can explicit relevant serendipity based on information the user has shared on social media. For example, the expliciting unit can explicit relevant serendipity based on accounts the user follows on social media. The expliciting unit can also explicit relevant serendipity based on groups the user participates in on social media. This allows for more appropriate expliciting by analyzing the user's social media activity and suggesting expliciting methods. Some or all of the above processing in the expliciting unit may be performed using AI, for example, or not using AI. For example, the expliciting unit can input the user's social media activity into AI and have the AI ​​suggest expliciting methods.

[0058] The scoring unit can optimize its scoring algorithm by referring to past scoring data during the scoring process. For example, the scoring unit can analyze past scoring data and optimize the scoring algorithm. For example, the scoring unit can compare past scoring data with current data and optimize the scoring algorithm. The scoring unit can also extract trends from past scoring data and optimize the scoring algorithm. This improves the accuracy of scoring by optimizing the scoring algorithm by referring to past scoring data. Some or all of the above processes in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input past scoring data into AI and have AI perform the optimization of the scoring algorithm.

[0059] The scoring unit can perform scoring while considering the user's attribute information. For example, the scoring unit can perform scoring based on the user's age and gender. For example, the scoring unit can perform scoring based on the user's occupation and hobbies. Furthermore, the scoring unit can also perform scoring based on the user's lifestyle. This allows for more personalized scoring by considering the user's attribute information. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the user's attribute information into AI and have the AI ​​perform the scoring.

[0060] The scoring unit can perform scoring while considering the geographical distribution of the data. For example, the scoring unit can perform scoring based on the user's location. For example, the scoring unit can perform scoring based on the user's travel destinations. The scoring unit can also perform scoring based on the user's activity range. By considering the geographical distribution of the data, more accurate scoring becomes possible. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the user's geographical data into AI and have AI perform geographical distribution scoring.

[0061] The scoring unit can improve the accuracy of its scoring by referring to relevant literature during the scoring process. For example, the scoring unit can improve the accuracy of its scoring by referring to relevant research papers. For example, the scoring unit can improve the accuracy of its scoring by referring to relevant industry reports. The scoring unit can also improve the accuracy of its scoring by referring to relevant patent documents. In this way, the accuracy of the scoring is improved by referring to relevant literature. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without using AI. For example, the scoring unit can input relevant literature into AI and have AI perform the literature referencing and scoring.

[0062] The community function can select the optimal display method when displaying communities by referring to the user's past participation history. For example, the community function may prioritize display methods that the user has preferred in the past. For example, the community function may select a highly relevant display method based on the user's past participation history. The community function can also analyze the user's past participation patterns and select the optimal display method. This allows for more appropriate display by selecting the optimal display method by referring to the user's past participation history. Some or all of the above processing in the community function may be performed using AI, for example, or without AI. For example, the community function can input the user's past participation history into AI and have the AI ​​select the optimal display method.

[0063] The community feature can customize the displayed content based on the user's current interests when displaying communities. For example, the community feature can prioritize displaying communities related to the user's current areas of interest. For example, the community feature can display highly relevant communities based on the user's current interests. The community feature can also display communities related to the user's current activities. This allows for more appropriate displays by customizing the displayed content based on the user's current interests. Some or all of the above processing in the community feature may be performed using AI, for example, or not. For example, the community feature can input the user's current interests into AI and have the AI ​​perform the customization of the displayed content.

[0064] The community feature can select the optimal display method when displaying community content, taking into account the user's device information. For example, if the user is using a smartphone, the community feature provides a display method that matches the screen size. If the user is using a tablet, the community feature provides a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the community feature can provide a concise and highly visible display method. This allows for more appropriate display by selecting the optimal display method considering the user's device information. Some or all of the above processing in the community feature may be performed using AI, for example, or without AI. For example, the community feature can input the user's device information into AI and have the AI ​​select the optimal display method.

[0065] The community feature can analyze a user's social media activity and suggest relevant communities when displaying communities. For example, the community feature can suggest relevant communities based on information the user has shared on social media. For example, the community feature can suggest relevant communities based on accounts the user follows on social media. Furthermore, the community feature can also suggest relevant communities based on groups the user participates in on social media. This allows for the suggestion of more appropriate communities by analyzing the user's social media activity and suggesting relevant communities. Some or all of the above processing in the community feature may be performed using AI, or not. For example, the community feature can input the user's social media activity into AI and have the AI ​​suggest relevant communities.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The data collection unit can analyze users' social media activity and acquire relevant data. For example, it can acquire relevant data based on information users share on social media. It can also acquire relevant data based on accounts users follow on social media. Furthermore, it can acquire relevant data based on groups users participate in on social media. This allows for the efficient acquisition of relevant data by analyzing users' social media activity.

[0068] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between past and present data. For example, it can improve accuracy by analyzing the correlation between past and present data. It can also improve accuracy by comparing trends in past and present data. Furthermore, it can improve accuracy by extracting patterns from past and present data. In this way, the accuracy of the analysis is improved by considering the interrelationships between past and present data.

[0069] The proposal department can adjust the level of detail in its proposals based on the importance of the action. For example, for important actions, it can provide detailed proposals. For less important actions, it can provide simplified proposals. Furthermore, for actions of moderate importance, it can provide proposals with an appropriate level of detail. By adjusting the level of detail in proposals based on the importance of the action, more appropriate proposals can be made.

[0070] The explicit information section can customize the explicit information based on the user's current life situation. For example, if the user is busy, a concise explicit information method can be used. Conversely, if the user is relaxed, a more detailed explicit information method can be used. Furthermore, if the user is traveling, an explicit information method related to their travel destination can be used. This allows for more appropriate explicit information by customizing the explicit information based on the user's current life situation.

[0071] The scoring unit can optimize the scoring algorithm by referring to past scoring data. For example, it can analyze past scoring data and optimize the scoring algorithm. It can also compare past scoring data with current data and optimize the scoring algorithm. Furthermore, it can extract trends from past scoring data and optimize the scoring algorithm. As a result, the accuracy of scoring is improved by optimizing the scoring algorithm by referring to past scoring data.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The data collection unit collects data on the user's past behavior. For example, it can collect the user's purchase history, browsing history, location information, etc. The data collection unit collects the history of products and services the user has purchased in the past, the history of web pages and content viewed, and the history of places visited. Step 2: The analysis unit analyzes the data collected by the collection unit to discover potential user relationships. For example, it analyzes the data using data mining and machine learning algorithms to analyze user behavior patterns and discover common interests and concerns. Step 3: The proposal unit suggests future actions based on the relationships discovered by the analysis unit. For example, it suggests the next product to purchase or the next place to visit based on the user's past behavior. Step 4: The explicit part reveals serendipity when the user takes the future action suggested by the suggestive part. For example, serendipity is revealed using notifications or pop-up messages. Step 5: The scoring unit scores the serendipity score based on the serendipity revealed by the explicit unit. For example, it evaluates the joy and satisfaction felt by the user and calculates the serendipity score.

[0074] (Example of form 2) The serendipity experience provision system according to an embodiment of the present invention is a system that provides users with an experience that makes them feel serendipitous by utilizing generative AI. This serendipity experience provision system provides users with an experience that makes them feel serendipitous by collecting and analyzing the user's past and present behavioral data, proposing future actions, revealing serendipity, and scoring it. For example, the serendipity experience provision system collects past actions such as the user reading a specific book on an e-book service and current actions such as searching for accommodation on a travel booking service. Next, the serendipity experience provision system analyzes the collected data and proposes future actions that the user should take next to feel serendipitous. For example, the generative AI discovers that there is an inn nearby that is the setting for a specific book and proposes that the user book this inn. When the user actually takes the future action, the serendipity is revealed, and the user can gain a new discovery or a special experience. For example, when the user actually stays at the inn, it is revealed that it is the setting for a specific book. Furthermore, the serendipity score is scored from multiple perspectives using generative AI. For example, the system scores factors such as the joy and satisfaction a user feels, the rarity of the quest, its difficulty of completion, how well it suits their current interests and circumstances, and its novelty. A user's serendipity score is visualized in the form of rankings and badges, allowing users to compete with each other. Furthermore, community features enable users who have experienced similar serendipity to form communities, fostering shared emotions and the creation of new communities and friendships. This allows companies to conduct promotions at the optimal time based on users' potential interests and behavioral data, reducing marketing costs. Users' daily lives become more fortunate and exciting, and their attachment to products and services increases. Corporate groups see users who want to experience serendipity actively using their services. In this way, the serendipity experience delivery system can leverage user behavioral data to provide experiences that evoke a sense of serendipity.

[0075] The serendipity experience provision system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a specification unit, and a scoring unit. The collection unit collects the user's past behavioral data. The collection unit can collect, for example, the user's purchase history, browsing history, location information, etc. The collection unit can collect, for example, the history of products and services that the user has purchased in the past. The collection unit can also collect the history of web pages and content that the user has viewed in the past. Furthermore, the collection unit can collect the user's location information and collect the history of places the user has visited. The analysis unit analyzes the data collected by the collection unit and discovers the user's potential relationships. The analysis unit can analyze the data using, for example, data mining or machine learning algorithms. The analysis unit can, for example, analyze the user's behavioral patterns and discover common interests and concerns. Furthermore, the analysis unit can analyze the user's behavioral data and discover potential relationships. Furthermore, the analysis unit can analyze the user's behavioral data and predict future behavior. The suggestion unit proposes future behavior based on the relationships discovered by the analysis unit. The suggestion unit can, for example, suggest future actions based on the user's past actions. For example, the suggestion unit can suggest the next product to purchase based on the products the user has purchased in the past. The suggestion unit can also suggest the next place to visit based on the places the user has visited in the past. Furthermore, the suggestion unit can also suggest future actions based on the user's current actions. The revealing unit reveals serendipity when the user performs a future action suggested by the suggestion unit. The revealing unit can reveal serendipity using, for example, notifications or pop-up messages. For example, the revealing unit displays a message revealing serendipity when the user performs a future action. The revealing unit can also send a notification revealing serendipity when the user performs a future action. Furthermore, the revealing unit can display a pop-up message revealing serendipity when the user performs a future action. The scoring unit scores a serendipity score based on the serendipity revealed by the revealing unit.The scoring unit can, for example, score the joy and satisfaction felt by the user. The scoring unit evaluates the joy and satisfaction felt by the user and calculates a serendipity score. The scoring unit can also score the rarity and difficulty of completing a quest. Furthermore, the scoring unit can score how well it suits the user's current interests and circumstances. As a result, the serendipity experience provision system according to this embodiment can provide an experience that evokes a sense of serendipity by utilizing the user's behavioral data.

[0076] The data collection unit collects data on users' past behavior. Specifically, it can collect user purchase history, browsing history, location information, and more. For example, when collecting a user's past purchase history of goods and services, it obtains data from online shopping sites and service providers. This allows for an understanding of what kinds of goods and services users are interested in. The data collection unit can also collect a history of web pages and content that users have viewed in the past. This includes browser history data and log data from applications used by the user. Furthermore, the data collection unit can collect user location information and a history of places the user has visited. Location information can be obtained from smartphone GPS data and applications that use location services. This allows for a detailed understanding of what kinds of places users are interested in and what their behavioral patterns are. The data collection unit centrally manages this data and makes it available to the analysis and proposal units. By adjusting the frequency and accuracy of data collection, flexible responses can be made to specific situations and conditions. For example, during specific events or campaigns, increasing the data collection frequency can obtain more detailed user behavior data. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0077] The analysis unit analyzes data collected by the data collection unit to discover potential user relationships. Specifically, it can analyze data using data mining and machine learning algorithms. For example, it can use clustering algorithms to analyze user behavior patterns and discover common interests. This allows it to identify user groups with similar behavior patterns and provide optimal suggestions to each group. It can also use association rule mining to analyze user behavior data and discover potential relationships. This reveals how specific behaviors relate to other behaviors. Furthermore, the analysis unit can use time series analysis and predictive models to analyze user behavior data and predict future behavior. This allows it to predict what actions users are likely to take next and provide appropriate suggestions based on that prediction. The analysis unit can also leverage historical data and statistical information to grasp long-term trends and patterns. For example, it can predict user behavior patterns in specific seasons or events based on historical data and provide suggestions based on that prediction. This allows the analysis unit to quickly and accurately analyze user behavior data and predict potential relationships and future behavior.

[0078] The suggestion unit proposes future actions based on the relationships discovered by the analysis unit. Specifically, it can propose future actions based on the user's past behavior. For example, it can use a recommendation engine to suggest products to buy next based on products the user has purchased in the past. This can suggest products and services that the user might be interested in, thereby increasing their purchase intent. The suggestion unit can also suggest places to visit next based on places the user has visited in the past. This includes analyzing location data to suggest tourist destinations and restaurants that the user might be interested in. Furthermore, the suggestion unit can propose future actions based on the user's current behavior. For example, it can suggest relevant information and products based on the web pages and content the user is currently viewing. The suggestion unit can improve the user experience by providing personalized suggestions that match the user's interests and preferences. The suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can track whether the user purchased a suggested product or visited a suggested place, and adjust the suggestion algorithm based on the results. This allows the suggestion unit to provide more appropriate and effective suggestions to the user.

[0079] The explicit section reveals serendipity when the user takes a future action suggested by the suggestion section. Specifically, serendipity can be revealed using notifications and pop-up messages. For example, when a user purchases a suggested product, the explicit section can notify them that the product has a special discount or benefit. Also, when a user visits a suggested location, a pop-up message can inform them that a special event or service is being offered at that location. The explicit section can provide users with surprise and delight by displaying messages that reveal serendipity when they take a future action. For example, when a user visits a suggested restaurant, the explicit section can notify them that the restaurant is offering a special menu or service. Also, when a user visits a suggested tourist destination, the explicit section can notify them that a special event or activity is being held at that tourist destination. By sending notifications that reveal serendipity when users take a future action, the explicit section can provide users with a positive experience. In this way, the explicit section can provide users with an experience that makes them feel serendipitous and improve user satisfaction.

[0080] The scoring unit calculates a serendipity score based on the serendipity revealed by the explicit unit. Specifically, it can score the joy and satisfaction felt by the user. For example, when a user purchases a suggested product, a survey is conducted to evaluate their satisfaction with that product, and a serendipity score is calculated based on the results. Similarly, when a user visits a suggested location, a survey is conducted to evaluate their satisfaction with the experience at that location, and a serendipity score is calculated based on the results. The scoring unit can also score the rarity and difficulty of achieving a quest. For example, a quest can be set that can only be completed if the user meets certain conditions, and a score can be assigned according to the degree of completion. Furthermore, the scoring unit can score how well something is suited to the user's current interests and situation. For example, it can evaluate how well an action suggested based on the user's current interests and concerns is suited, and calculate a score based on the results. In this way, the scoring unit can quantitatively evaluate the user's experience and calculate a serendipity score. Based on these scores, the scoring unit can provide feedback to the user and provide data to improve future suggestions. This allows the scoring unit to continuously improve the user experience.

[0081] The data collection unit can collect data on the user's past and present behavior. For example, the data collection unit can collect purchase history and browsing history as data on the user's past behavior. The data collection unit can also collect real-time location information and current browsing history as data on the user's current behavior. For example, the data collection unit can collect the history of the web pages the user is currently viewing. The data collection unit can also collect the user's current location information and collect data on the places the user is currently visiting. By collecting data on the user's past and present behavior, more accurate analysis becomes possible. 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 the user's past and present behavior data into AI and have the AI ​​perform the data collection.

[0082] The analysis unit can analyze the collected data and discover potential user relationships. For example, the analysis unit can analyze the data using data mining or machine learning algorithms. For example, the analysis unit can analyze user behavior patterns and discover common interests. Furthermore, the analysis unit can analyze user behavior data and discover potential relationships. In addition, the analysis unit can analyze user behavior data and predict future behavior. This improves the accuracy of suggesting future behavior by analyzing the collected data and discovering potential user relationships. 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 collected data into AI and have the AI ​​perform the data analysis.

[0083] The suggestion unit can propose future actions based on the analyzed data. For example, the suggestion unit can propose future actions based on the user's past actions. For example, the suggestion unit can propose the next product to purchase based on the products the user has purchased in the past. The suggestion unit can also propose the next place to visit based on the places the user has visited in the past. Furthermore, the suggestion unit can propose future actions based on the user's current actions. This provides the user with a serendipitous experience by proposing future actions based on the analyzed data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the analyzed data into AI and have the AI ​​execute the proposal of future actions.

[0084] The explicit part can reveal serendipity when the user takes a future action. The explicit part reveals serendipity, for example, by using notifications or pop-up messages. The explicit part can, for example, display a message revealing serendipity when the user takes a future action. The explicit part can also send a notification revealing serendipity when the user takes a future action. Furthermore, the explicit part can display a pop-up message revealing serendipity when the user takes a future action. This provides the user with new discoveries and special experiences by revealing serendipity when the user takes a future action. Some or all of the above processing in the explicit part may be performed using AI, for example, or not using AI. For example, the explicit part can have AI generate a message revealing serendipity when the user takes a future action.

[0085] The scoring unit can score the serendipity score from multiple perspectives. For example, the scoring unit can score the joy and satisfaction felt by the user. The scoring unit can evaluate the joy and satisfaction felt by the user and calculate the serendipity score. The scoring unit can also score the rarity and difficulty of the quest. Furthermore, the scoring unit can score how well it suits the user's current interests and circumstances. By scoring the serendipity score from multiple perspectives, the user's experience can be evaluated in more detail. Some or all of the above processing in the scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit can input the joy and satisfaction felt by the user into the AI ​​and have the AI ​​calculate the serendipity score.

[0086] The scoring unit can visualize a user's serendipity score in the form of rankings or badges. For example, the scoring unit can display a user's serendipity score in a ranking format. For example, the scoring unit can display a ranking of a user against other users based on their serendipity score. The scoring unit can also award badges according to a user's serendipity score. For example, the scoring unit can display badges according to the user's level of achievement based on their serendipity score. This allows users to compete with each other by visualizing their serendipity scores in the form of rankings or badges. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input a user's serendipity score into an AI and have the AI ​​generate rankings and badges.

[0087] The system can form communities where users who have experienced the same serendipitous events can gather through its community features. For example, the system can form groups where users who have experienced the same serendipitous events can gather. For example, the system can provide communities where users who have experienced the same serendipitous events can interact with each other. The system can also help users share their experiences and form new communities and friendships. For example, the system can provide online forums where users who have experienced the same serendipitous events can gather. This allows for the sharing of experiences and the creation of new communities and friendships by forming communities where users who have experienced the same serendipitous events can gather. Some or all of the processes described above in the system may be performed using AI, for example, or not using AI. For example, the system can have AI perform the task of forming communities where users who have experienced the same serendipitous events can gather.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of acquiring past behavioral data based on the estimated emotions. For example, if the user is stressed, the data collection unit will acquire past behavioral data during periods of relaxation. If the user is excited, the data collection unit will acquire past behavioral data after the excitement has subsided. Furthermore, if the user is tired, the data collection unit can acquire past behavioral data after rest. This allows for data acquisition at a more appropriate time by adjusting the timing of acquisition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The data collection unit can analyze the user's past behavioral data and select the optimal acquisition method. For example, the data collection unit may prioritize acquiring data from services that the user frequently uses. For example, the data collection unit may acquire data at specific time periods based on the user's behavioral patterns. The data collection unit can also acquire relevant data based on the user's interests. This allows for efficient data collection by analyzing the user's past behavioral data and selecting the optimal acquisition method. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into AI and have the AI ​​select the optimal acquisition method.

[0090] The data collection unit can filter past behavioral data based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize data related to areas the user is currently interested in. For example, the data collection unit can acquire highly relevant data based on the user's current lifestyle. The data collection unit can also filter and acquire data related to the user's current activities. This allows for the acquisition of highly relevant data by filtering the data based on the user's current lifestyle and areas of interest. 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 the user's current lifestyle and areas of interest into the AI ​​and have the AI ​​perform the data filtering.

[0091] The data collection unit can estimate the user's emotions and determine the priority of behavioral data to acquire based on the estimated user emotions. For example, if the user is relaxed, the data collection unit will prioritize acquiring behavioral data related to relaxation. For example, if the user is excited, the data collection unit will prioritize acquiring behavioral data related to excitement. The data collection unit can also prioritize acquiring behavioral data related to fatigue if the user is tired. By prioritizing behavioral data based on the user's emotions, more important data can be acquired preferentially. 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 a generative AI and have the generative AI perform emotion estimation.

[0092] The data collection unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring behavioral data. For example, if the user is in a specific region, the data collection unit will prioritize the acquisition of data related to that region. For example, if the user is traveling, the data collection unit will prioritize the acquisition of data related to the travel destination. Furthermore, if the user is at home, the data collection unit can prioritize the acquisition of data related to the area around the user's home. In this way, by acquiring data while considering the user's geographical location, highly relevant data can be prioritized. 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 the user's geographical location information into AI and have the AI ​​perform the acquisition of highly relevant data.

[0093] The data collection unit can analyze a user's social media activity and obtain relevant data when acquiring behavioral data. For example, the data collection unit can obtain relevant data based on information shared by the user on social media. For example, the data collection unit can obtain relevant data based on accounts followed by the user on social media. The data collection unit can also obtain relevant data based on groups the user participates in on social media. This allows for the efficient acquisition of relevant data by analyzing the user's social media activity. 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 the user's social media activity into AI and have AI perform the acquisition of relevant data.

[0094] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit performs a detailed data analysis. If the user is in a hurry, for example, the analysis unit performs a simplified data analysis. The analysis unit can also perform a visually stimulating data analysis if the user is excited. By adjusting the data analysis method based on the user's emotions, a more appropriate analysis becomes possible. 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 not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between past and present data during the analysis. For example, the analysis unit can improve accuracy by analyzing the correlation between past and present data. For example, the analysis unit can improve accuracy by comparing trends between past and present data. Furthermore, the analysis unit can also improve accuracy by extracting patterns from past and present data. In this way, the accuracy of the analysis is improved by considering the interrelationships between past and present data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past and present data into AI and have the AI ​​perform the analysis of the interrelationships.

[0096] The analysis unit can perform analysis while taking user attribute information into consideration. For example, the analysis unit can analyze data based on the user's age and gender. For example, the analysis unit can analyze data based on the user's occupation and hobbies. The analysis unit can also analyze data based on the user's lifestyle. By considering user attribute information, more personalized analysis becomes possible. 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 user attribute information into AI and have the AI ​​perform the data analysis.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. The analysis unit can also provide a concise display method if the user is in a hurry. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. 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, a text generation AI (e.g., LLM) or a 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 a generative AI and have the generative AI perform emotion estimation.

[0098] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can analyze data based on the user's location. For example, the analysis unit can analyze data based on the user's travel destinations. The analysis unit can also analyze data based on the user's activity range. By considering the geographical distribution of the data, 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 user's geographical data into AI and have AI perform geographical distribution analysis.

[0099] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant research papers. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant industry reports. The analysis unit can also improve the accuracy of its analysis by referring to relevant patent documents. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature into the AI ​​and have the AI ​​perform the literature referencing and analysis.

[0100] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will present suggestions in a gentle manner. If the user is in a hurry, the suggestion unit will present concise and quick suggestions. Furthermore, if the user is excited, the suggestion unit can present visually stimulating suggestions. This allows for more appropriate suggestions by adjusting the presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The proposal unit can adjust the level of detail of its proposals based on the importance of the action. For example, the proposal unit will provide detailed proposals for important actions. For example, it will provide simplified proposals for actions of low importance. It can also provide proposals with a moderate level of detail for actions of moderate importance. By adjusting the level of detail of proposals based on the importance of the action, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the action into the AI ​​and have the AI ​​adjust the level of detail of the proposals.

[0102] The suggestion unit can apply different suggestion algorithms depending on the category of the activity when making suggestions. For example, for travel-related activities, the suggestion unit applies a suggestion algorithm specifically for travel. For example, for shopping-related activities, the suggestion unit applies a suggestion algorithm specifically for shopping. Furthermore, for entertainment-related activities, the suggestion unit can also apply a suggestion algorithm specifically for entertainment. By applying different suggestion algorithms depending on the category of activity, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the category of activity into the AI ​​and have the AI ​​perform the application of the suggestion algorithm.

[0103] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. The suggestion unit can also provide visually stimulating suggestions if the user is excited. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0104] The proposal department can determine the priority of proposals based on the timing of action submissions. For example, the proposal department will prioritize proposals for highly urgent actions. For example, it will postpone proposals for less urgent actions. The proposal department can also propose actions of moderate urgency with an appropriate priority. This allows for more appropriate proposals by determining the priority of proposals based on the timing of action submissions. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the timing of action submissions into the AI ​​and have the AI ​​determine the priority of proposals.

[0105] The suggestion unit can adjust the order of suggestions based on the relevance of the actions when making suggestions. For example, the suggestion unit will suggest highly relevant actions first. For example, it will suggest less relevant actions later. It can also suggest moderately relevant actions in an appropriate order. By adjusting the order of suggestions based on the relevance of the actions, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of the actions into the AI ​​and have the AI ​​adjust the order of suggestions.

[0106] The explicit unit can estimate the user's emotions and adjust the way serendipity is expressed based on the estimated emotions. For example, if the user is relaxed, the explicit unit will express serendipity in a gentle way. If the user is excited, the explicit unit will express serendipity in a visually stimulating way. If the user is tired, the explicit unit can also express serendipity in a simple and easily visible way. This allows for more appropriate expression by adjusting the way serendipity is expressed 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 explicit unit may be performed using AI or not using AI. For example, the explicit unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The explicit function can select the optimal explicit method by referring to the user's past behavior history during explicitness. For example, the explicit function may prioritize using explicit methods that the user has preferred in the past. For example, the explicit function may select a highly relevant explicit method based on the user's past behavior history. The explicit function can also analyze the user's past behavior patterns and select the optimal explicit method. This allows for more appropriate explicitness by selecting the optimal explicit method by referring to the user's past behavior history. Some or all of the above processing in the explicit function may be performed using AI, for example, or without AI. For example, the explicit function can input the user's past behavior history into AI and have the AI ​​select the optimal explicit method.

[0108] The explicit function can customize the explicit method based on the user's current living situation at the time of explicitness. For example, if the user is busy, the explicit function may use a concise explicit method. If the user is relaxed, the explicit function may use a detailed explicit method. The explicit function may also use an explicit method relevant to the user's travel destination if the user is traveling. This allows for more appropriate explicitness by customizing the explicit method based on the user's current living situation. Some or all of the above processing in the explicit function may be performed using AI, for example, or not using AI. For example, the explicit function can input the user's current living situation into AI and have AI perform the customization of the explicit method.

[0109] The explicit unit can estimate the user's emotions and determine the priority of serendipity based on the estimated user emotions. For example, if the user is relaxed, the explicit unit will prioritize the expliciting of serendipity associated with relaxation. For example, if the user is excited, the explicit unit will prioritize the expliciting of serendipity associated with excitement. Furthermore, if the user is tired, the explicit unit can also prioritize the expliciting of serendipity associated with fatigue. This allows for more appropriate expliciting by determining the priority of serendipity 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 explicit unit may be performed using AI, for example, or without AI. For example, the explicit unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The expliciting unit can select the optimal expliciting method at the time of expliciting, taking into account the user's geographical location information. For example, if the user is in a specific region, the expliciting unit will explicit serendipity related to that region. For example, if the user is traveling, the expliciting unit will explicit serendipity related to the travel destination. Furthermore, if the user is at home, the expliciting unit can also explicit serendipity related to the area around the user's home. This allows for more appropriate expliciting by selecting the optimal expliciting method considering the user's geographical location information. Some or all of the above processing in the expliciting unit may be performed using AI, for example, or without AI. For example, the expliciting unit can input the user's geographical location information into AI and have the AI ​​select the optimal expliciting method.

[0111] The expliciting unit can analyze the user's social media activity and suggest expliciting methods at the time of expliciting. For example, the expliciting unit can explicit relevant serendipity based on information the user has shared on social media. For example, the expliciting unit can explicit relevant serendipity based on accounts the user follows on social media. The expliciting unit can also explicit relevant serendipity based on groups the user participates in on social media. This allows for more appropriate expliciting by analyzing the user's social media activity and suggesting expliciting methods. Some or all of the above processing in the expliciting unit may be performed using AI, for example, or not using AI. For example, the expliciting unit can input the user's social media activity into AI and have the AI ​​suggest expliciting methods.

[0112] The scoring unit can estimate the user's emotions and adjust the scoring criteria based on the estimated emotions. For example, if the user is relaxed, the scoring unit will prioritize emotions associated with relaxation. If the user is excited, the scoring unit will prioritize emotions associated with excitement. The scoring unit can also prioritize emotions associated with fatigue if the user is tired. By adjusting the scoring criteria based on the user's emotions, more appropriate scoring becomes possible. 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 scoring unit may be performed using AI, or not using AI. For example, the scoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0113] The scoring unit can optimize its scoring algorithm by referring to past scoring data during the scoring process. For example, the scoring unit can analyze past scoring data and optimize the scoring algorithm. For example, the scoring unit can compare past scoring data with current data and optimize the scoring algorithm. The scoring unit can also extract trends from past scoring data and optimize the scoring algorithm. This improves the accuracy of scoring by optimizing the scoring algorithm by referring to past scoring data. Some or all of the above processes in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input past scoring data into AI and have AI perform the optimization of the scoring algorithm.

[0114] The scoring unit can perform scoring while considering the user's attribute information. For example, the scoring unit can perform scoring based on the user's age and gender. For example, the scoring unit can perform scoring based on the user's occupation and hobbies. Furthermore, the scoring unit can also perform scoring based on the user's lifestyle. This allows for more personalized scoring by considering the user's attribute information. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the user's attribute information into AI and have the AI ​​perform the scoring.

[0115] The scoring unit can estimate the user's emotions and adjust the order in which the scoring results are displayed based on the estimated emotions. For example, if the user is relaxed, the scoring unit will prioritize displaying the score for relaxation. For example, if the user is excited, the scoring unit will prioritize displaying the score for excitement. The scoring unit can also prioritize displaying the score for fatigue if the user is tired. By adjusting the order in which the scoring results are displayed based on the user's emotions, a more appropriate display becomes possible. 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 scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0116] The scoring unit can perform scoring while considering the geographical distribution of the data. For example, the scoring unit can perform scoring based on the user's location. For example, the scoring unit can perform scoring based on the user's travel destinations. The scoring unit can also perform scoring based on the user's activity range. By considering the geographical distribution of the data, more accurate scoring becomes possible. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the user's geographical data into AI and have AI perform geographical distribution scoring.

[0117] The scoring unit can improve the accuracy of its scoring by referring to relevant literature during the scoring process. For example, the scoring unit can improve the accuracy of its scoring by referring to relevant research papers. For example, the scoring unit can improve the accuracy of its scoring by referring to relevant industry reports. The scoring unit can also improve the accuracy of its scoring by referring to relevant patent documents. In this way, the accuracy of the scoring is improved by referring to relevant literature. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without using AI. For example, the scoring unit can input relevant literature into AI and have AI perform the literature referencing and scoring.

[0118] The community feature can estimate the user's emotions and adjust how the community is displayed based on those emotions. For example, if the user is relaxed, the community feature may provide a display with soft colors. If the user is excited, the community feature may provide a visually stimulating display. Furthermore, if the user is tired, the community feature may provide a simple and highly visible display. This allows for a more appropriate display by adjusting the community's display based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the community feature may be performed using AI, or not. For example, the community feature can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0119] The community function can select the optimal display method when displaying communities by referring to the user's past participation history. For example, the community function may prioritize display methods that the user has preferred in the past. For example, the community function may select a highly relevant display method based on the user's past participation history. The community function can also analyze the user's past participation patterns and select the optimal display method. This allows for more appropriate display by selecting the optimal display method by referring to the user's past participation history. Some or all of the above processing in the community function may be performed using AI, for example, or without AI. For example, the community function can input the user's past participation history into AI and have the AI ​​select the optimal display method.

[0120] The community feature can customize the displayed content based on the user's current interests when displaying communities. For example, the community feature can prioritize displaying communities related to the user's current areas of interest. For example, the community feature can display highly relevant communities based on the user's current interests. The community feature can also display communities related to the user's current activities. This allows for more appropriate displays by customizing the displayed content based on the user's current interests. Some or all of the above processing in the community feature may be performed using AI, for example, or not. For example, the community feature can input the user's current interests into AI and have the AI ​​perform the customization of the displayed content.

[0121] The community feature can estimate the user's emotions and adjust the community's operation procedures based on those emotions. For example, if the user is relaxed, the community feature may simplify the operation procedures. If the user is excited, the community feature may provide visually stimulating operation procedures. Furthermore, if the user is tired, the community feature may provide simple and highly visible operation procedures. This allows for more appropriate operation by adjusting the community's operation procedures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the community feature may be performed using AI, or not. For example, the community feature can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0122] The community feature can select the optimal display method when displaying community content, taking into account the user's device information. For example, if the user is using a smartphone, the community feature provides a display method that matches the screen size. If the user is using a tablet, the community feature provides a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the community feature can provide a concise and highly visible display method. This allows for more appropriate display by selecting the optimal display method considering the user's device information. Some or all of the above processing in the community feature may be performed using AI, for example, or without AI. For example, the community feature can input the user's device information into AI and have the AI ​​select the optimal display method.

[0123] The community feature can analyze a user's social media activity and suggest relevant communities when displaying communities. For example, the community feature can suggest relevant communities based on information the user has shared on social media. For example, the community feature can suggest relevant communities based on accounts the user follows on social media. Furthermore, the community feature can also suggest relevant communities based on groups the user participates in on social media. This allows for the suggestion of more appropriate communities by analyzing the user's social media activity and suggesting relevant communities. Some or all of the above processing in the community feature may be performed using AI, or not. For example, the community feature can input the user's social media activity into AI and have the AI ​​suggest relevant communities.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can make suggestions during times when the user is relaxed. If the user is busy, the suggestion function can make suggestions during times when the user is calm. Furthermore, if the user is excited, the suggestion function can make suggestions after the excitement has subsided. By adjusting the timing of suggestions based on the user's emotions, suggestions can be made at more appropriate times.

[0126] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on those emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is busy, the analysis unit can perform a simplified analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. By adjusting the depth of the analysis based on the user's emotions, a more appropriate analysis becomes possible.

[0127] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on those estimates. For example, if the user is relaxed, the unit can collect data more frequently. If the user is busy, the unit can reduce the frequency of data collection. Furthermore, if the user is excited, the unit can collect data after the excitement has subsided. By adjusting the frequency of data collection based on the user's emotions, data can be collected at a more appropriate time.

[0128] The explicit function can estimate the user's emotions and adjust how serendipity is presented based on those emotions. For example, if the user is relaxed, the explicit function can present serendipity in a gentle manner. If the user is excited, the explicit function can present serendipity in a visually stimulating way. Furthermore, if the user is tired, the explicit function can present serendipity in a simple and easily visible way. This allows for more appropriate presentation by adjusting the method of serendipity presentation based on the user's emotions.

[0129] The scoring unit can estimate the user's emotions and adjust the scoring criteria based on those estimated emotions. For example, if the user is relaxed, the scoring can be weighted more heavily on the emotions associated with relaxation. Similarly, if the user is excited, the scoring can be weighted more heavily on the emotions associated with excitement. Furthermore, if the user is tired, the scoring can be weighted more heavily on the emotions associated with fatigue. By adjusting the scoring criteria based on the user's emotions, more appropriate scoring becomes possible.

[0130] The data collection unit can analyze users' social media activity and acquire relevant data. For example, it can acquire relevant data based on information users share on social media. It can also acquire relevant data based on accounts users follow on social media. Furthermore, it can acquire relevant data based on groups users participate in on social media. This allows for the efficient acquisition of relevant data by analyzing users' social media activity.

[0131] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between past and present data. For example, it can improve accuracy by analyzing the correlation between past and present data. It can also improve accuracy by comparing trends in past and present data. Furthermore, it can improve accuracy by extracting patterns from past and present data. In this way, the accuracy of the analysis is improved by considering the interrelationships between past and present data.

[0132] The proposal department can adjust the level of detail in its proposals based on the importance of the action. For example, for important actions, it can provide detailed proposals. For less important actions, it can provide simplified proposals. Furthermore, for actions of moderate importance, it can provide proposals with an appropriate level of detail. By adjusting the level of detail in proposals based on the importance of the action, more appropriate proposals can be made.

[0133] The explicit information section can customize the explicit information based on the user's current life situation. For example, if the user is busy, a concise explicit information method can be used. Conversely, if the user is relaxed, a more detailed explicit information method can be used. Furthermore, if the user is traveling, an explicit information method related to their travel destination can be used. This allows for more appropriate explicit information by customizing the explicit information based on the user's current life situation.

[0134] The scoring unit can optimize the scoring algorithm by referring to past scoring data. For example, it can analyze past scoring data and optimize the scoring algorithm. It can also compare past scoring data with current data and optimize the scoring algorithm. Furthermore, it can extract trends from past scoring data and optimize the scoring algorithm. As a result, the accuracy of scoring is improved by optimizing the scoring algorithm by referring to past scoring data.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The data collection unit collects data on the user's past behavior. For example, it can collect the user's purchase history, browsing history, location information, etc. The data collection unit collects the history of products and services the user has purchased in the past, the history of web pages and content viewed, and the history of places visited. Step 2: The analysis unit analyzes the data collected by the collection unit to discover potential user relationships. For example, it analyzes the data using data mining and machine learning algorithms to analyze user behavior patterns and discover common interests and concerns. Step 3: The proposal unit suggests future actions based on the relationships discovered by the analysis unit. For example, it suggests the next product to purchase or the next place to visit based on the user's past behavior. Step 4: The explicit part reveals serendipity when the user takes the future action suggested by the suggestive part. For example, serendipity is revealed using notifications or pop-up messages. Step 5: The scoring unit scores the serendipity score based on the serendipity revealed by the explicit unit. For example, it evaluates the joy and satisfaction felt by the user and calculates the serendipity score.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, explicit unit, and scoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to discover the user's potential relationships. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes future actions based on the analysis results. The explicit unit is implemented, for example, by the control unit 46A of the smart device 14, and reveals serendipity when the user performs the proposed future action. The scoring unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and scores a serendipity score based on the explicit serendipity. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, disclosure unit, and scoring unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to discover the user's potential relationships. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and suggests future actions based on the analysis results. The disclosure unit is implemented, for example, by the control unit 46A of the smart glasses 214, and reveals serendipity when the user performs the suggested future action. The scoring unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and scores a serendipity score based on the revealed serendipity. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, manifestation unit, and scoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to discover the user's potential relationships. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and suggests future actions based on the analysis results. The manifestation unit is implemented, for example, by the control unit 46A of the headset terminal 314, and reveals serendipity when the user performs the suggested future action. The scoring unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and scores a serendipity score based on the revealed serendipity. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, manifestation unit, and scoring unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to discover the user's potential relationships. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes future actions based on the analysis results. The manifestation unit is implemented, for example, by the control unit 46A of the robot 414, and reveals serendipity when the user performs the proposed future action. The scoring unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and scores a serendipity score based on the revealed serendipity. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A data collection unit that collects data on the user's past behavior, The data collected by the aforementioned collection unit is analyzed by an analysis unit that discovers potential relationships between users, Based on the relationships discovered by the aforementioned analysis unit, the proposal unit proposes future actions, An explicit unit that reveals serendipity when the user performs a future action proposed by the aforementioned proposal unit, The system includes a scoring unit that scores a serendipity score based on the serendipity revealed by the explicit unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data on the user's past and current behavior. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected data to discover potential user relationships. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analyzed data, we propose future actions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned explicit section is, Reveal serendipity when users take future actions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The scoring unit is, Serendipity scores are scored from multiple perspectives. The system described in Appendix 1, characterized by the features described herein. (Note 7) The scoring unit is, Visualize users' serendipity scores in the form of rankings and badges. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned system, Through community features, users who have experienced similar serendipitous events can form communities. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of acquiring past behavioral data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past behavioral data and select the optimal method for acquiring it. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When acquiring past behavioral data, 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 12) The aforementioned collection unit is It estimates the user's emotions and determines the priority of behavioral data to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When acquiring behavioral data, the system prioritizes acquiring highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When acquiring behavioral data, we analyze the user's social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by considering the interrelationship between past and present data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, user attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, When performing analysis, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the action being taken. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the action. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on when the actions will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the actions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned explicit section is, We estimate the user's emotions and adjust the way serendipity is revealed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned explicit section is, When explicit, the system selects the optimal explicit method by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned explicit section is, When explicit, customize the explicit method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned explicit section is, It estimates user emotions and prioritizes serendipity based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned explicit section is, When specifying a user's location, the system will select the most appropriate method of specification, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned explicit section is, When explicit, we analyze the user's social media activity and suggest methods for explicit disclosure. The system described in Appendix 1, characterized by the features described herein. (Note 33) The scoring unit is, The system estimates the user's emotions and adjusts the scoring criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The scoring unit is, During the scoring process, the scoring algorithm is optimized by referring to past scoring data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The scoring unit is, When scoring, the scoring process takes into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The scoring unit is, It estimates the user's emotions and adjusts the order in which the scoring results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The scoring unit is, When scoring, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 38) The scoring unit is, When scoring, refer to relevant literature to improve the accuracy of the scoring. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned Community function is It estimates user sentiment and adjusts how communities are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned Community function is When displaying communities, the system selects the optimal display method by referring to the user's past participation history. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned Community function is When displaying communities, customize the content shown based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned Community function is It estimates user sentiment and adjusts community interaction procedures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned Community function is When displaying community content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned Community function is When displaying communities, the system analyzes the user's social media activity and suggests relevant communities. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0209] 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 data collection unit that collects data on the user's past behavior, The data collected by the aforementioned collection unit is analyzed by an analysis unit that discovers potential relationships between users, Based on the relationships discovered by the aforementioned analysis unit, the proposal unit proposes future actions, An explicit unit that reveals serendipity when the user performs a future action proposed by the aforementioned proposal unit, The system includes a scoring unit that scores a serendipity score based on the serendipity revealed by the explicit unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data on the user's past and current behavior. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected data to discover potential user relationships. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analyzed data, we propose future actions. The system according to feature 1.

5. The aforementioned explicit section is, Revealing serendipity when users take future actions. The system according to feature 1.

6. The scoring unit is, Serendipity scores are scored from multiple perspectives. The system according to feature 1.

7. The scoring unit is, Visualize users' serendipity scores in the form of rankings and badges. The system according to feature 1.

8. The aforementioned system, Through community features, users who have experienced similar serendipitous events can form communities. The system according to feature 1.

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