system

Abstract

The system according to the embodiment aims to provide a personalized travel plan based on the user's past behavior and preferences. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects a user's past behavior or preferences. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a personalized itinerary based on the analysis results obtained by the analysis unit. The provision unit provides the itinerary generated by the generation unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately addressed providing personalized travel plans based on a user's past behavior and preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized travel plan based on the user's past behavior and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects a user's past behavior or preferences. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a personalized itinerary based on the analysis results obtained by the analysis unit. The provision unit provides the itinerary generated by the generation unit. [Effects of the Invention]

[0007] An embodiment of the system can provide personalized travel plans based on a user's past behavior and preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information provision system for event participants according to an embodiment of the present invention collects and analyzes a user's past behavior and preferences using AI to provide a personalized travel plan. This system uses AI to analyze the user's past behavior and preferences, as well as data from social media and other sources, and then proposes a personalized travel plan to the user based on the analysis results. This travel plan includes subcultures and fan activities related to professional baseball and other events, providing a unique experience. For example, the system collects the user's past behavior and preferences, such as events attended in the past, places visited, and social media posts. This data is input into AI. The AI ​​then analyzes the collected data. The AI ​​analyzes the user's behavioral patterns and preferences and generates a travel plan optimal for the user. For example, for a user who previously enjoyed watching baseball games, a travel plan including watching a professional baseball game is proposed. The generated travel plan includes subcultures and fan activities related to professional baseball and other events, such as fan events held before and after the game and visits to related tourist spots. This allows the user to enjoy a unique experience. Furthermore, the AI ​​also analyzes data from social media and other sources. For example, if a user frequently uses keywords such as "watching a baseball game" or "attending a live concert" on social media, the travel plan can be optimized based on that information. This makes it possible to provide a personalized travel plan tailored to the user's interests. This system allows users to easily obtain travel plans tailored to their preferences, allowing them to further enjoy events such as watching a baseball game or attending a live concert. This allows the information provision system for event participants to provide personalized travel plans based on the user's past actions and preferences.

[0029] An information provision system for event participants according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects a user's past behavior or preferences. The user's past behavior includes, but is not limited to, past travel history, purchase history, browsing history, and the like. The preferences include, but are not limited to, favorite places, activities of interest, and food preferences. The collection unit collects, for example, the user's past event participation history and content posted on social media. For example, the collection unit collects data such as the type, date, time, and location of events the user has previously participated in. The collection unit can also collect content posted on social media. For example, if a user frequently uses keywords such as "watching baseball games" or "attending live events" on social media, the collection unit collects such information. The analysis unit analyzes the collected data to identify the user's behavioral patterns and preferences. The behavioral patterns include, but are not limited to, frequency, time period, and location. The preferences include, but are not limited to, favorite places, activities of interest, and food preferences. The analysis unit, for example, analyzes the collected data and identifies the user's behavioral patterns and preferences. The generation unit generates a personalized travel plan based on the analysis results obtained by the analysis unit. The generation unit generates a travel plan that includes, for example, subcultures and fan activities related to professional baseball or other events. Examples of subcultures include, but are not limited to, specific anime, music, sports, etc. Examples of fan activities include, but are not limited to, fan meetings, event participation, and merchandise purchases. The provision unit provides the user with the travel plan generated by the generation unit. The provision unit provides the user with the generated travel plan, for example, via email, app notification, website, etc. In this way, the information provision system for event participants according to the embodiment can provide a personalized travel plan based on the user's past behavior and preferences.

[0030] The collection unit can collect the user's past event participation history or the content of posts on social media. The collection unit collects data such as the type, date, time, and location of events the user has previously attended. Event participation history includes, but is not limited to, concerts, sporting events, and festivals previously attended. The collection unit can also collect the content of posts on social media. For example, if a user frequently uses keywords such as "watching a baseball game" or "attending a live concert" on social media, the collection unit collects that information. The content of posts on social media includes, but is not limited to, the type of post, the social media platform on which the information is collected, and the collection period. By collecting the user's past event participation history and the content of posts on social media, more accurate personalization is possible. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the content of posts on social media into AI, which then analyzes and collects the content.

[0031] The analysis unit can analyze the collected data to identify the user's behavioral patterns and preferences. The analysis unit, for example, analyzes the collected data to identify the user's behavioral patterns and preferences. Examples of behavioral patterns include, but are not limited to, frequency, time period, and location. Examples of preferences include, but are not limited to, favorite places, activities of interest, and food preferences. The analysis unit, for example, analyzes the user's past event participation history and posts on social media to identify the user's behavioral patterns and preferences. For example, data such as the type, date, time, and location of events the user has participated in can be analyzed to identify the user's behavioral patterns. The analysis unit can also analyze posts on social media to identify the user's preferences. For example, if a user frequently uses keywords such as "watching a baseball game" or "attending a live concert" on social media, the analysis unit can analyze that information to identify the user's preferences. By identifying the user's behavioral patterns and preferences, a more appropriate travel plan can be generated. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analytics unit can input collected data into AI, which can then analyze the data to identify behavioral patterns and preferences.

[0032] The generation unit can generate travel plans that include subcultures or fan activities related to professional baseball or other events. The generation unit generates travel plans that include subcultures or fan activities related to professional baseball or other events. Subcultures include, but are not limited to, specific anime, music, and sports. Fan activities include, but are not limited to, fan meetings, event participation, and merchandise purchases. The generation unit generates travel plans that include, for example, watching professional baseball games. Examples include fan events held before and after the game and visits to related tourist spots. The generation unit can also generate travel plans that include subcultures or fan activities related to other events. Examples include activities related to music festivals and visits to tourist spots related to anime events. This allows for the generation of travel plans that include subcultures or fan activities related to professional baseball or other events, thereby providing a unique experience. Some or all of the above-described processing by the generation unit may be performed using, or without, AI. For example, the generation unit can input data related to professional baseball or other events into AI, which then analyzes the data to generate a travel plan.

[0033] The providing unit can provide the generated travel plan to the user. For example, the providing unit provides the generated travel plan to the user. Examples of providing include, but are not limited to, email, app notification, and website. For example, the providing unit can send the generated travel plan to the user by email. The providing unit can also provide the travel plan to the user through an app notification. For example, the travel plan can be displayed by sending a notification to an app installed by the user. The providing unit can also provide the travel plan to the user through a website. For example, the travel plan can be displayed on a website accessed by the user. By providing the generated travel plan to the user, the user can easily obtain the travel plan. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the generated travel plan to AI, which can select the optimal delivery method to provide the travel plan to the user.

[0034] The analysis unit can analyze SNS data to identify a user's interests. The analysis unit can, for example, analyze SNS data to identify a user's interests. SNS data includes, but is not limited to, postings, likes, and comments. Interests include, but are not limited to, specific topics, hobbies, and activities. For example, if a user frequently uses keywords such as "watching baseball games" or "attending a live concert" on SNS, the analysis unit can analyze the information and identify the user's interests. The analysis unit can also analyze postings from accounts the user follows to identify the user's interests. For example, the analysis unit can analyze topics and keywords frequently posted by accounts the user follows to identify the user's interests. By analyzing the SNS data, it is possible to provide travel plans based on the user's interests. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input SNS data into AI, which can analyze the data to identify the user's interests.

[0035] The collection unit can analyze the user's past event participation history and select the optimal data collection method. For example, the collection unit prioritizes collecting related data based on the type of event the user has participated in in the past. For example, the collection unit analyzes data on concerts and sporting events the user has participated in in the past and prioritizes collecting related data. The collection unit can also focus on collecting data on places the user frequently visits. For example, the collection unit collects data on tourist spots and event venues frequently visited by the user and optimizes a travel plan based on that data. If the user tends to participate in a particular event, the collection unit can also intensively collect data related to that event. For example, if the user participates in a particular music festival every year, data related to that festival can be collected and reflected in the travel plan. This allows the optimal data collection method to be selected by analyzing the user's past event participation history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past event participation history into AI, which can then analyze the data and select the optimal data collection method.

[0036] When collecting data, the collection unit can filter the data based on the user's current areas of interest and lifestyle. For example, the collection unit prioritizes collecting data related to events in which the user is currently interested. For example, the collection unit collects data related to sporting events and music festivals in which the user is currently interested and optimizes a travel plan based on the collected data. The collection unit can also selectively collect appropriate data based on the user's lifestyle (e.g., how busy the user is at work). For example, if the user is busy at work, the collection unit collects information about relaxing events that suit the user's lifestyle. The collection unit can also collect highly relevant data based on the user's current geographic location. For example, the collection unit prioritizes collecting information about events held near the user's current location. This allows more relevant data to be collected by filtering the data based on the user's current areas of interest and lifestyle. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data about the user's current areas of interest and lifestyle into AI, which then analyzes and filters the data.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting event information held near the user's current location. For example, the collection unit collects information on concerts and sporting events held in the user's current city and optimizes the travel plan based on that data. The collection unit can also collect related event information based on data on places the user has visited in the past. For example, the collection unit collects data on tourist spots and event venues the user has visited in the past and optimizes the travel plan based on that data. The collection unit can also prioritize collecting event information for travel destinations the user is planning. For example, the collection unit collects information on events held in the user's planned travel destination and optimizes the travel plan based on that data. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which analyzes the data and prioritizes collecting highly relevant data.

[0038] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, if the user frequently uses keywords such as "watching a baseball game" or "attending a live event" on an SNS, the collection unit can collect related data based on that information. For example, if the user frequently uses keywords such as "watching a baseball game" or "attending a live event" on an SNS, the collection unit can collect that information and reflect it in a travel plan. The collection unit can also analyze the content of posts from accounts the user follows and collect related event information. For example, the collection unit can analyze topics and keywords frequently posted by accounts the user follows and collect related event information. The collection unit can also collect related data based on event information shared by the user on an SNS. For example, the collection unit can collect event information shared by the user on an SNS and optimize a travel plan based on that data. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the user's social media activities into AI, which can analyze the data and collect related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance and provides the result to the user. For example, the analysis unit performs a detailed analysis on data related to an event in which the user is interested and provides the result to the user. The analysis unit can also perform a simplified analysis on data of low importance and provide the result to the user. For example, the analysis unit can perform a simplified analysis on data in which the user is not very interested and provide the result to the user. The analysis unit can also perform an analysis with a moderate level of detail on data of medium importance and provide the result to the user. For example, the analysis unit can perform an analysis with a moderate level of detail on data in which the user is moderately interested and provide the result to the user. In this way, by adjusting the level of detail of the analysis based on the importance of the data, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI, which can analyze the data and adjust the level of detail.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies an algorithm that analyzes past participation trends to event participation history data. For example, the analysis unit analyzes the types and frequency of events a user has participated in in the past and performs analysis based on those trends. The analysis unit can also apply a text analysis algorithm to SNS data to identify a user's interests. For example, the analysis unit can analyze keywords and phrases frequently used by a user on SNS to identify the user's interests. The analysis unit can also apply a location information analysis algorithm to geographic location data to identify related event information. For example, the analysis unit can analyze data on the user's current location and places the user has visited in the past to identify related event information. By applying different analysis algorithms depending on the data category, more accurate analysis results can be provided. Some or all of the above-mentioned processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the data category into AI, which can select and apply an appropriate analysis algorithm.

[0041] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data and provides the results to the user. For example, the analysis unit prioritizes analyzing data recently collected by the user and provides the results to the user. The analysis unit can also determine the analysis priority for past data based on its importance. For example, the analysis unit can evaluate the importance of data collected by the user in the past and determine the analysis priority based on that importance. The analysis unit can also prioritize analysis related to events during a specific period of time for data collected during that period. For example, the analysis unit can analyze data collected by the user during a specific period of time and provide information related to events during that period. This allows for determining the analysis priority based on the time when the data was collected, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the time when the data was collected into AI, which can then analyze the data and determine the priority.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data most relevant to the user's current interests. For example, the analysis unit prioritizes analysis of data related to events in which the user is currently interested and provides the results to the user. The analysis unit can also prioritize analysis of highly relevant data based on the user's past behavioral patterns. For example, the analysis unit analyzes data of events the user has previously attended and adjusts the order of analysis based on the relevance. The analysis unit can also prioritize analysis of highly relevant data based on the user's geographic location. For example, the analysis unit analyzes data of the user's current location and places the user has previously visited and adjusts the order of analysis based on the relevance. This allows for adjusting the order of analysis based on the relevance of the data, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the relevance of the data into AI, which then analyzes the data and adjusts the order.

[0043] The generation unit can generate an appropriate travel plan by analyzing the user's past behavioral patterns. For example, the generation unit generates a travel plan that includes related tourist spots based on places the user has visited in the past. For example, the generation unit analyzes data on tourist spots the user has visited in the past and generates a travel plan based on the associations between the tourist spots. The generation unit can also generate a travel plan that includes related events based on the user's past event participation history. For example, the generation unit analyzes data on events the user has participated in in the past and generates a travel plan based on the associations between the tourist spots. The generation unit can also analyze the user's past behavioral patterns and generate the most efficient travel plan. For example, the generation unit analyzes the user's past behavioral patterns and generates a travel plan based on the efficiency of the events. This makes it possible to provide an optimal travel plan by analyzing the user's past behavioral patterns. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's past behavioral patterns into AI, which then analyzes the data to generate an appropriate travel plan.

[0044] The generation unit can customize the travel plan based on the user's current areas of interest at the time of generation. For example, the generation unit generates a travel plan that includes tourist spots related to events in which the user is currently interested. For example, the generation unit generates a travel plan that includes tourist spots related to sporting events or music festivals in which the user is currently interested. The generation unit can also generate a travel plan that includes related activities based on the user's current hobbies and interests. For example, the generation unit generates a travel plan that includes activities related to the user's current hobbies and interests. The generation unit can also customize an optimal travel plan based on the user's current areas of interest. For example, the generation unit customizes an optimal travel plan based on topics or activities in which the user is currently interested. This makes it possible to provide a more appropriate travel plan by customizing the travel plan based on the user's current areas of interest. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the user's current areas of interest into AI, which then analyzes the data to customize the travel plan.

[0045] The generation unit can generate an appropriate travel plan by taking into account the user's geographical location information. For example, the generation unit generates a travel plan that includes events held near the user's current location. For example, the generation unit generates a travel plan that includes information on concerts and sporting events held in the user's current city. The generation unit can also generate a travel plan that includes event information for a travel destination the user is planning. For example, the generation unit generates a travel plan that includes information on events held in the travel destination the user is planning. The generation unit can also generate a travel plan that includes related tourist spots based on the user's past visit history. For example, the generation unit generates a travel plan that includes related tourist spots based on data on tourist spots the user has previously visited. This makes it possible to provide an optimal travel plan by taking into account the user's geographical location information. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information into AI, which then analyzes the data to generate an appropriate travel plan.

[0046] The generation unit can optimize the travel plan by analyzing the user's social media activity during generation. For example, if the user frequently uses keywords such as "watching a baseball game" or "attending a live event" on social media, the generation unit can optimize the travel plan based on that information. For example, if the user frequently uses keywords such as "watching a baseball game" or "attending a live event" on social media, the generation unit can optimize the travel plan based on that information. The generation unit can also analyze posts from accounts followed by the user to generate a travel plan that includes related events. For example, the generation unit can analyze topics and keywords frequently posted by accounts followed by the user to generate a travel plan that includes related events. The generation unit can also generate a travel plan that includes related tourist spots based on event information shared by the user on social media. For example, the generation unit generates a travel plan that includes related tourist spots based on event information shared by the user on social media. This makes it possible to provide an optimal travel plan by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the user's social media activity into AI, which can analyze the data to optimize the travel plan.

[0047] When providing the travel plans, the providing unit can select the optimal providing method by referring to the user's past usage history. For example, the providing unit preferentially selects a providing method (e.g., email, app notification) that the user has previously preferred. For example, if the user has previously preferred to receive travel plans via email, the providing unit preferentially selects that providing method. The providing unit can also provide the travel plans at the optimal timing based on the time period used by the user in the past. For example, if the user has previously preferred to receive travel plans during a specific time period, the providing unit provides the travel plans at that time period. The providing unit can also analyze the user's past usage history and select the most effective providing method. For example, the providing unit analyzes the user's past usage history and selects the most effective providing method based on the results. In this way, the travel plans can be provided in the optimal manner by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's past usage history into AI, which analyzes the data and selects the optimal providing method.

[0048] The providing unit can select the optimal providing method by taking into consideration the user's device information when providing the travel plan. For example, if the user is using a smartphone, the providing unit provides the travel plan in a display method tailored to the screen size. For example, if the user is using a smartphone, the providing unit provides the travel plan in a display method optimized for the screen size of the device. Furthermore, if the user is using a tablet, the providing unit can also provide the travel plan in a display method optimized for a large screen. For example, if the user is using a tablet, the providing unit can provide the travel plan in a display method optimized for the large screen of the device. Furthermore, if the user is using a smartwatch, the providing unit can also provide the travel plan in a concise and highly visible display method. For example, if the user is using a smartwatch, the providing unit provides the travel plan in a concise and highly visible display method optimized for the device. This makes it possible to provide the travel plan in the optimal manner by taking into consideration the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, which can analyze the data and select the optimal providing method.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The collection unit can also collect the user's health data in addition to the user's past behavioral data. For example, data such as the user's number of steps, heart rate, and sleep patterns can be collected and used to optimize the travel plan. The analysis unit can analyze the collected health data and adjust the travel plan based on the user's physical condition and health status. For example, if the user is tired, a travel plan including relaxing activities can be suggested. Also, if the user is active, a travel plan including activities that require a lot of physical activity can be suggested. This makes it possible to provide a personalized travel plan tailored to the user's health status.

[0051] The collection unit can also collect the user's purchase history in addition to the user's past event participation history. For example, data on products and services purchased by the user in the past can be collected and used to optimize travel plans. The analysis unit can analyze the collected purchase history and adjust travel plans based on the user's consumption trends and preferences. For example, if the user likes a particular brand or product, the analysis unit can suggest travel plans that include tourist spots and events related to that brand or product. It can also suggest travel plans that include events and activities related to products the user has purchased in the past. This makes it possible to provide personalized travel plans based on the user's purchase history.

[0052] The generation unit can generate travel plans that include activities related to the user's hobbies and special skills, in addition to subcultures and fan activities related to professional baseball and other events. For example, if the user's hobby is cooking, a travel plan that includes local cooking classes and gourmet tours can be proposed. Also, if the user's hobby is photography, a travel plan that includes visiting photogenic spots can be proposed. Furthermore, if the user likes a particular sport, a travel plan that includes activities and events related to that sport can be proposed. This makes it possible to provide personalized travel plans tailored to the user's hobbies and special skills.

[0053] The providing unit can take into consideration the user's device usage status when providing the generated travel plan to the user. For example, if the user frequently uses a smartphone, the travel plan can be provided in a format optimized for the smartphone. Also, if the user uses a tablet or a PC, the travel plan can be provided in a format optimized for each device. Furthermore, if the user uses a smartwatch, the travel plan can be provided in a concise and highly visible format. This makes it possible to provide the travel plan in an optimal format that matches the user's device usage status.

[0054] The collection unit can analyze the user's past event participation history and select the optimal data collection method. For example, it can prioritize collection of related data based on the types of events the user has participated in in the past. It can also focus on collecting data about places the user frequently visits. Furthermore, if the user tends to participate in a particular event, it can also intensively collect data related to that event. In this way, it is possible to select the optimal data collection method by analyzing the user's past event participation history.

[0055] When collecting data, the collection unit can filter the data based on the user's current areas of interest and lifestyle. For example, it can prioritize collection of data related to events in which the user is currently interested. It can also selectively collect appropriate data depending on the user's lifestyle (e.g., how busy they are at work). It can also collect highly relevant data based on the user's current geographic location. This allows more relevant data to be collected by filtering data based on the user's current areas of interest and lifestyle.

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

[0057] Step 1: The collection unit collects the user's past behavior or preferences. The user's past behavior includes, but is not limited to, past travel history, purchase history, browsing history, etc. Preferences include, but are not limited to, favorite places, activities of interest, and food preferences, etc. The collection unit collects, for example, the user's past event participation history and content posted on SNS. For example, data such as the type, date, time, and location of events the user has participated in in the past is collected. The collection unit can also collect content posted on SNS. For example, if the user frequently uses keywords such as "watching baseball games" or "attending live events" on SNS, that information is collected. Step 2: The analysis unit analyzes the collected data and identifies the user's behavioral patterns and preferences. Examples of behavioral patterns include, but are not limited to, frequency, time period, and location. Examples of preferences include, but are not limited to, favorite places, activities of interest, and food preferences. For example, the analysis unit analyzes the collected data and identifies the user's behavioral patterns and preferences. Step 3: The generation unit generates a personalized travel plan based on the analysis results obtained by the analysis unit. The generation unit generates a travel plan that includes subcultures and fan activities related to, for example, professional baseball games and other events. Subcultures include, but are not limited to, specific anime, music, sports, etc. Fan activities include, but are not limited to, fan meetings, event participation, and merchandise purchases. Step 4: The providing unit provides the travel plan generated by the generating unit to the user. For example, the providing unit provides the generated travel plan to the user via email, app notification, website, etc.

[0058] (Example 2) An information provision system for event participants according to an embodiment of the present invention collects and analyzes a user's past behavior and preferences using AI to provide a personalized travel plan. This system uses AI to analyze the user's past behavior and preferences, as well as data from social media and other sources, and then proposes a personalized travel plan to the user based on the analysis results. This travel plan includes subcultures and fan activities related to professional baseball and other events, providing a unique experience. For example, the system collects the user's past behavior and preferences, such as events attended in the past, places visited, and social media posts. This data is input into AI. The AI ​​then analyzes the collected data. The AI ​​analyzes the user's behavioral patterns and preferences and generates a travel plan optimal for the user. For example, for a user who previously enjoyed watching baseball games, a travel plan including watching a professional baseball game is proposed. The generated travel plan includes subcultures and fan activities related to professional baseball and other events, such as fan events held before and after the game and visits to related tourist spots. This allows the user to enjoy a unique experience. Furthermore, the AI ​​also analyzes data from social media and other sources. For example, if a user frequently uses keywords such as "watching a baseball game" or "attending a live concert" on social media, the travel plan can be optimized based on that information. This makes it possible to provide a personalized travel plan tailored to the user's interests. This system allows users to easily obtain travel plans tailored to their preferences, allowing them to further enjoy events such as watching a baseball game or attending a live concert. This allows the information provision system for event participants to provide personalized travel plans based on the user's past actions and preferences.

[0059] An information provision system for event participants according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects a user's past behavior or preferences. The user's past behavior includes, but is not limited to, past travel history, purchase history, browsing history, and the like. The preferences include, but are not limited to, favorite places, activities of interest, and food preferences. The collection unit collects, for example, the user's past event participation history and content posted on social media. For example, the collection unit collects data such as the type, date, time, and location of events the user has previously participated in. The collection unit can also collect content posted on social media. For example, if a user frequently uses keywords such as "watching baseball games" or "attending live events" on social media, the collection unit collects such information. The analysis unit analyzes the collected data to identify the user's behavioral patterns and preferences. The behavioral patterns include, but are not limited to, frequency, time period, and location. The preferences include, but are not limited to, favorite places, activities of interest, and food preferences. The analysis unit, for example, analyzes the collected data and identifies the user's behavioral patterns and preferences. The generation unit generates a personalized travel plan based on the analysis results obtained by the analysis unit. The generation unit generates a travel plan that includes, for example, subcultures and fan activities related to professional baseball or other events. Examples of subcultures include, but are not limited to, specific anime, music, sports, etc. Examples of fan activities include, but are not limited to, fan meetings, event participation, and merchandise purchases. The provision unit provides the user with the travel plan generated by the generation unit. The provision unit provides the user with the generated travel plan, for example, via email, app notification, website, etc. In this way, the information provision system for event participants according to the embodiment can provide a personalized travel plan based on the user's past behavior and preferences.

[0060] The collection unit can collect the user's past event participation history or the content of posts on social media. The collection unit collects data such as the type, date, time, and location of events the user has previously attended. Event participation history includes, but is not limited to, concerts, sporting events, and festivals previously attended. The collection unit can also collect the content of posts on social media. For example, if a user frequently uses keywords such as "watching a baseball game" or "attending a live concert" on social media, the collection unit collects that information. The content of posts on social media includes, but is not limited to, the type of post, the social media platform on which the information is collected, and the collection period. By collecting the user's past event participation history and the content of posts on social media, more accurate personalization is possible. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the content of posts on social media into AI, which then analyzes and collects the content.

[0061] The analysis unit can analyze the collected data to identify the user's behavioral patterns and preferences. The analysis unit, for example, analyzes the collected data to identify the user's behavioral patterns and preferences. Examples of behavioral patterns include, but are not limited to, frequency, time period, and location. Examples of preferences include, but are not limited to, favorite places, activities of interest, and food preferences. The analysis unit, for example, analyzes the user's past event participation history and posts on social media to identify the user's behavioral patterns and preferences. For example, data such as the type, date, time, and location of events the user has participated in can be analyzed to identify the user's behavioral patterns. The analysis unit can also analyze posts on social media to identify the user's preferences. For example, if a user frequently uses keywords such as "watching a baseball game" or "attending a live concert" on social media, the analysis unit can analyze that information to identify the user's preferences. By identifying the user's behavioral patterns and preferences, a more appropriate travel plan can be generated. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analytics unit can input collected data into AI, which can then analyze the data to identify behavioral patterns and preferences.

[0062] The generation unit can generate travel plans that include subcultures or fan activities related to professional baseball or other events. The generation unit generates travel plans that include subcultures or fan activities related to professional baseball or other events. Subcultures include, but are not limited to, specific anime, music, and sports. Fan activities include, but are not limited to, fan meetings, event participation, and merchandise purchases. The generation unit generates travel plans that include, for example, watching professional baseball games. Examples include fan events held before and after the game and visits to related tourist spots. The generation unit can also generate travel plans that include subcultures or fan activities related to other events. Examples include activities related to music festivals and visits to tourist spots related to anime events. This allows for the generation of travel plans that include subcultures or fan activities related to professional baseball or other events, thereby providing a unique experience. Some or all of the above-described processing by the generation unit may be performed using, or without, AI. For example, the generation unit can input data related to professional baseball or other events into AI, which then analyzes the data to generate a travel plan.

[0063] The providing unit can provide the generated travel plan to the user. For example, the providing unit provides the generated travel plan to the user. Examples of providing include, but are not limited to, email, app notification, and website. For example, the providing unit can send the generated travel plan to the user by email. The providing unit can also provide the travel plan to the user through an app notification. For example, the travel plan can be displayed by sending a notification to an app installed by the user. The providing unit can also provide the travel plan to the user through a website. For example, the travel plan can be displayed on a website accessed by the user. By providing the generated travel plan to the user, the user can easily obtain the travel plan. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the generated travel plan to AI, which can select the optimal delivery method to provide the travel plan to the user.

[0064] The analysis unit can analyze SNS data to identify a user's interests. The analysis unit can, for example, analyze SNS data to identify a user's interests. SNS data includes, but is not limited to, postings, likes, and comments. Interests include, but are not limited to, specific topics, hobbies, and activities. For example, if a user frequently uses keywords such as "watching baseball games" or "attending a live concert" on SNS, the analysis unit can analyze the information and identify the user's interests. The analysis unit can also analyze postings from accounts the user follows to identify the user's interests. For example, the analysis unit can analyze topics and keywords frequently posted by accounts the user follows to identify the user's interests. By analyzing the SNS data, it is possible to provide travel plans based on the user's interests. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input SNS data into AI, which can analyze the data to identify the user's interests.

[0065] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is excited, the collection unit collects data in real time and reflects it immediately. For example, if the user is excited while watching a sporting event, the collection unit captures the user's emotions at that moment, collects data, and reflects it in the travel plan. The collection unit can also periodically collect data when the user is relaxed and provide stable information. For example, data can be collected during times when the user is relaxed and the travel plan can be optimized based on that data. The collection unit can also reduce the frequency of data collection when the user is stressed, thereby reducing the user's burden. For example, if the user is stressed, the collection unit adjusts the frequency of data collection taking into account the user's emotions, thereby reducing the user's burden. This allows data to be collected at a more appropriate time by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into AI, which may analyze the emotion and adjust the timing of data collection.

[0066] The collection unit can analyze the user's past event participation history and select the optimal data collection method. For example, the collection unit prioritizes collecting related data based on the type of event the user has participated in in the past. For example, the collection unit analyzes data on concerts and sporting events the user has participated in in the past and prioritizes collecting related data. The collection unit can also focus on collecting data on places the user frequently visits. For example, the collection unit collects data on tourist spots and event venues frequently visited by the user and optimizes a travel plan based on that data. If the user tends to participate in a particular event, the collection unit can also intensively collect data related to that event. For example, if the user participates in a particular music festival every year, data related to that festival can be collected and reflected in the travel plan. This allows the optimal data collection method to be selected by analyzing the user's past event participation history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past event participation history into AI, which can then analyze the data and select the optimal data collection method.

[0067] When collecting data, the collection unit can filter the data based on the user's current areas of interest and lifestyle. For example, the collection unit prioritizes collecting data related to events in which the user is currently interested. For example, the collection unit collects data related to sporting events and music festivals in which the user is currently interested and optimizes a travel plan based on the collected data. The collection unit can also selectively collect appropriate data based on the user's lifestyle (e.g., how busy the user is at work). For example, if the user is busy at work, the collection unit collects information about relaxing events that suit the user's lifestyle. The collection unit can also collect highly relevant data based on the user's current geographic location. For example, the collection unit prioritizes collecting information about events held near the user's current location. This allows more relevant data to be collected by filtering the data based on the user's current areas of interest and lifestyle. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data about the user's current areas of interest and lifestyle into AI, which then analyzes and filters the data.

[0068] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit prioritizes collecting the latest event information. For example, if the user is excited while watching a sporting event, the collection unit captures the user's emotions at that moment and collects the latest event information. Furthermore, if the user is relaxed, the collection unit can prioritize collecting reviews and ratings of past events. For example, reviews and ratings of past events are collected during times when the user is relaxed, and a travel plan is optimized based on the collected data. Furthermore, if the user is stressed, the collection unit can prioritize collecting information about events that can help the user relax. For example, if the user is stressed, information about events that can help the user relax is collected taking into account the user's emotions. This allows for more appropriate data to be collected by determining the priority of data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotional data into the AI, which can then analyze the emotions and determine the priority of the data.

[0069] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting event information held near the user's current location. For example, the collection unit collects information on concerts and sporting events held in the user's current city and optimizes the travel plan based on that data. The collection unit can also collect related event information based on data on places the user has visited in the past. For example, the collection unit collects data on tourist spots and event venues the user has visited in the past and optimizes the travel plan based on that data. The collection unit can also prioritize collecting event information for travel destinations the user is planning. For example, the collection unit collects information on events held in the user's planned travel destination and optimizes the travel plan based on that data. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which analyzes the data and prioritizes collecting highly relevant data.

[0070] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, if the user frequently uses keywords such as "watching a baseball game" or "attending a live event" on an SNS, the collection unit can collect related data based on that information. For example, if the user frequently uses keywords such as "watching a baseball game" or "attending a live event" on an SNS, the collection unit can collect that information and reflect it in a travel plan. The collection unit can also analyze the content of posts from accounts the user follows and collect related event information. For example, the collection unit can analyze topics and keywords frequently posted by accounts the user follows and collect related event information. The collection unit can also collect related data based on event information shared by the user on an SNS. For example, the collection unit can collect event information shared by the user on an SNS and optimize a travel plan based on that data. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the user's social media activities into AI, which can analyze the data and collect related data.

[0071] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is excited, the analysis unit displays the analysis results using visually stimulating graphics. For example, if the user is excited while watching a sporting event, the analysis results can be displayed using visually stimulating graphics that reflect the user's emotions. The analysis unit can also display the analysis results using graphics in subdued colors if the user is relaxed. For example, subdued colors are used when displaying the analysis results during a time when the user is relaxed. The analysis unit can also display the analysis results using simple, highly visible graphics if the user is stressed. For example, if the user is stressed, the analysis results can be displayed using simple, highly visible graphics that take the user's emotions into consideration. This allows for adjusting the presentation method of the analysis based on the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI, which may analyze the emotion and adjust the way the analysis is expressed.

[0072] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance and provides the result to the user. For example, the analysis unit performs a detailed analysis on data related to an event in which the user is interested and provides the result to the user. The analysis unit can also perform a simplified analysis on data of low importance and provide the result to the user. For example, the analysis unit can perform a simplified analysis on data in which the user is not very interested and provide the result to the user. The analysis unit can also perform an analysis with a moderate level of detail on data of medium importance and provide the result to the user. For example, the analysis unit can perform an analysis with a moderate level of detail on data in which the user is moderately interested and provide the result to the user. In this way, by adjusting the level of detail of the analysis based on the importance of the data, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI, which can analyze the data and adjust the level of detail.

[0073] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies an algorithm that analyzes past participation trends to event participation history data. For example, the analysis unit analyzes the types and frequency of events a user has participated in in the past and performs analysis based on those trends. The analysis unit can also apply a text analysis algorithm to SNS data to identify a user's interests. For example, the analysis unit can analyze keywords and phrases frequently used by a user on SNS to identify the user's interests. The analysis unit can also apply a location information analysis algorithm to geographic location data to identify related event information. For example, the analysis unit can analyze data on the user's current location and places the user has visited in the past to identify related event information. By applying different analysis algorithms depending on the data category, more accurate analysis results can be provided. Some or all of the above-mentioned processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the data category into AI, which can select and apply an appropriate analysis algorithm.

[0074] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result that reflects the user's emotions. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit provides a detailed analysis result during a time period when the user is relaxed. The analysis unit can also provide an analysis result with visually stimulating effects if the user is excited. For example, if the user is excited, the analysis unit provides an analysis result with visually stimulating effects that reflect the user's emotions. This allows for adjusting the length of the analysis based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotional data into the AI, which can then analyze the emotions and adjust the length of the analysis.

[0075] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data and provides the results to the user. For example, the analysis unit prioritizes analyzing data recently collected by the user and provides the results to the user. The analysis unit can also determine the analysis priority for past data based on its importance. For example, the analysis unit can evaluate the importance of data collected by the user in the past and determine the analysis priority based on that importance. The analysis unit can also prioritize analysis related to events during a specific period of time for data collected during that period. For example, the analysis unit can analyze data collected by the user during a specific period of time and provide information related to events during that period. This allows for determining the analysis priority based on the time when the data was collected, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the time when the data was collected into AI, which can then analyze the data and determine the priority.

[0076] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data most relevant to the user's current interests. For example, the analysis unit prioritizes analysis of data related to events in which the user is currently interested and provides the results to the user. The analysis unit can also prioritize analysis of highly relevant data based on the user's past behavioral patterns. For example, the analysis unit analyzes data of events the user has previously attended and adjusts the order of analysis based on the relevance. The analysis unit can also prioritize analysis of highly relevant data based on the user's geographic location. For example, the analysis unit analyzes data of the user's current location and places the user has previously visited and adjusts the order of analysis based on the relevance. This allows for adjusting the order of analysis based on the relevance of the data, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the relevance of the data into AI, which then analyzes the data and adjusts the order.

[0077] The generation unit can estimate the user's emotions and adjust the method for generating the itinerary based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates an itinerary that proceeds at a leisurely pace. For example, if the user is relaxed, the generation unit generates an itinerary that proceeds at a leisurely pace, reflecting the user's emotions. Furthermore, if the user is in a hurry, the generation unit can generate an itinerary that emphasizes the shortest route. For example, if the user is in a hurry, the generation unit generates an itinerary that emphasizes the shortest route, reflecting the user's emotions. Furthermore, if the user is excited, the generation unit can generate an itinerary that adds visually stimulating effects. For example, if the user is excited, the generation unit generates an itinerary that adds visually stimulating effects, reflecting the user's emotions. This allows the generation method for the itinerary to be adjusted based on the user's emotions, thereby providing a more appropriate itinerary. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotional data into the AI, which can then analyze the emotions and adjust how it generates the travel plan.

[0078] The generation unit can generate an appropriate travel plan by analyzing the user's past behavioral patterns. For example, the generation unit generates a travel plan that includes related tourist spots based on places the user has visited in the past. For example, the generation unit analyzes data on tourist spots the user has visited in the past and generates a travel plan based on the associations between the tourist spots. The generation unit can also generate a travel plan that includes related events based on the user's past event participation history. For example, the generation unit analyzes data on events the user has participated in in the past and generates a travel plan based on the associations between the tourist spots. The generation unit can also analyze the user's past behavioral patterns and generate the most efficient travel plan. For example, the generation unit analyzes the user's past behavioral patterns and generates a travel plan based on the efficiency of the events. This makes it possible to provide an optimal travel plan by analyzing the user's past behavioral patterns. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's past behavioral patterns into AI, which then analyzes the data to generate an appropriate travel plan.

[0079] The generation unit can customize the travel plan based on the user's current areas of interest at the time of generation. For example, the generation unit generates a travel plan that includes tourist spots related to events in which the user is currently interested. For example, the generation unit generates a travel plan that includes tourist spots related to sporting events or music festivals in which the user is currently interested. The generation unit can also generate a travel plan that includes related activities based on the user's current hobbies and interests. For example, the generation unit generates a travel plan that includes activities related to the user's current hobbies and interests. The generation unit can also customize an optimal travel plan based on the user's current areas of interest. For example, the generation unit customizes an optimal travel plan based on topics or activities in which the user is currently interested. This makes it possible to provide a more appropriate travel plan by customizing the travel plan based on the user's current areas of interest. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the user's current areas of interest into AI, which then analyzes the data to customize the travel plan.

[0080] The generation unit can estimate the user's emotions and prioritize the itinerary to be generated based on the estimated user emotions. For example, if the user is excited, the generation unit generates an itinerary that prioritizes the latest event information. For example, if the user is excited while watching a sporting event, the generation unit generates an itinerary that prioritizes the latest event information, reflecting the user's emotions. Furthermore, if the user is relaxed, the generation unit can generate an itinerary that prioritizes reviews and ratings of past events. For example, the generation unit generates an itinerary that prioritizes reviews and ratings of past events for the time period when the user is relaxing. Furthermore, if the user is stressed, the generation unit can generate an itinerary that prioritizes relaxing activities. For example, if the user is stressed, the generation unit generates an itinerary that prioritizes relaxing activities, taking the user's emotions into consideration. This allows the user to prioritize the itinerary based on the user's emotions, thereby providing a more appropriate itinerary. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data into AI, which may analyze the emotions and determine the priorities of the travel plan.

[0081] The generation unit can generate an appropriate travel plan by taking into account the user's geographical location information. For example, the generation unit generates a travel plan that includes events held near the user's current location. For example, the generation unit generates a travel plan that includes information on concerts and sporting events held in the user's current city. The generation unit can also generate a travel plan that includes event information for a travel destination the user is planning. For example, the generation unit generates a travel plan that includes information on events held in the travel destination the user is planning. The generation unit can also generate a travel plan that includes related tourist spots based on the user's past visit history. For example, the generation unit generates a travel plan that includes related tourist spots based on data on tourist spots the user has previously visited. This makes it possible to provide an optimal travel plan by taking into account the user's geographical location information. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information into AI, which then analyzes the data to generate an appropriate travel plan.

[0082] The generation unit can optimize the travel plan by analyzing the user's social media activity during generation. For example, if the user frequently uses keywords such as "watching a baseball game" or "attending a live event" on social media, the generation unit can optimize the travel plan based on that information. For example, if the user frequently uses keywords such as "watching a baseball game" or "attending a live event" on social media, the generation unit can optimize the travel plan based on that information. The generation unit can also analyze posts from accounts followed by the user to generate a travel plan that includes related events. For example, the generation unit can analyze topics and keywords frequently posted by accounts followed by the user to generate a travel plan that includes related events. The generation unit can also generate a travel plan that includes related tourist spots based on event information shared by the user on social media. For example, the generation unit generates a travel plan that includes related tourist spots based on event information shared by the user on social media. This makes it possible to provide an optimal travel plan by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data on the user's social media activity into AI, which can analyze the data to optimize the travel plan.

[0083] The providing unit can estimate the user's emotions and adjust the way in which the travel plan is provided based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide the travel plan using visually stimulating graphics. For example, if the user is excited while watching a sporting event, the providing unit can provide the travel plan using visually stimulating graphics that reflect the user's emotions. Furthermore, if the user is relaxed, the providing unit can provide the travel plan using graphics in subdued colors. For example, the providing unit can provide the travel plan using graphics in subdued colors during times when the user is relaxing. Furthermore, if the user is stressed, the providing unit can provide the travel plan using simple, highly visible graphics. For example, if the user is stressed, the providing unit can provide the travel plan using simple, highly visible graphics that take the user's emotions into consideration. This allows the travel plan to be provided in a more appropriate manner by adjusting the way in which the travel plan is provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotional data into the AI, which can then analyze the emotions and adjust the providing method.

[0084] When providing the travel plans, the providing unit can select the optimal providing method by referring to the user's past usage history. For example, the providing unit preferentially selects a providing method (e.g., email, app notification) that the user has previously preferred. For example, if the user has previously preferred to receive travel plans via email, the providing unit preferentially selects that providing method. The providing unit can also provide the travel plans at the optimal timing based on the time period used by the user in the past. For example, if the user has previously preferred to receive travel plans during a specific time period, the providing unit provides the travel plans at that time period. The providing unit can also analyze the user's past usage history and select the most effective providing method. For example, the providing unit analyzes the user's past usage history and selects the most effective providing method based on the results. In this way, the travel plans can be provided in the optimal manner by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's past usage history into AI, which analyzes the data and selects the optimal providing method.

[0085] The providing unit can estimate the user's emotions and adjust the order in which travel planners are provided based on the estimated user emotions. For example, if the user is excited, the providing unit can provide the latest event information first. For example, if the user is excited while watching a sporting event, the providing unit can provide the latest event information first, reflecting the user's emotions. Furthermore, if the user is relaxed, the providing unit can provide reviews and ratings of past events first. For example, reviews and ratings of past events can be provided first during times when the user is relaxing. Furthermore, if the user is stressed, the providing unit can provide information on relaxing activities first. For example, if the user is stressed, the providing unit can provide information on relaxing activities first, taking the user's emotions into consideration. In this way, by adjusting the order in which travel planners are provided based on the user's emotions, travel planners can be provided in a more appropriate order. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotional data into the AI, which can then analyze the emotions and adjust the order of presentation.

[0086] The providing unit can select the optimal providing method by taking into consideration the user's device information when providing the travel plan. For example, if the user is using a smartphone, the providing unit provides the travel plan in a display method tailored to the screen size. For example, if the user is using a smartphone, the providing unit provides the travel plan in a display method optimized for the screen size of the device. Furthermore, if the user is using a tablet, the providing unit can also provide the travel plan in a display method optimized for a large screen. For example, if the user is using a tablet, the providing unit can provide the travel plan in a display method optimized for the large screen of the device. Furthermore, if the user is using a smartwatch, the providing unit can also provide the travel plan in a concise and highly visible display method. For example, if the user is using a smartwatch, the providing unit provides the travel plan in a concise and highly visible display method optimized for the device. This makes it possible to provide the travel plan in the optimal manner by taking into consideration the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into AI, which can analyze the data and select the optimal providing method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's past behavior and preferences using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data to identify the user's behavioral patterns and preferences. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a personalized travel plan based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides the generated travel plan to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's past behavior and preferences using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the user's behavioral patterns and preferences. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a personalized travel plan based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated travel plan to the user. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's past behavior and preferences using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the user's behavioral patterns and preferences. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a personalized travel plan based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the generated travel plan to the user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's past behavior and preferences using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the user's behavioral patterns and preferences. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a personalized travel plan based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated travel plan to the user.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The collection unit can also collect the user's health data in addition to the user's past behavioral data. For example, data such as the user's number of steps, heart rate, and sleep patterns can be collected and used to optimize the travel plan. The analysis unit can analyze the collected health data and adjust the travel plan based on the user's physical condition and health status. For example, if the user is tired, a travel plan including relaxing activities can be suggested. Also, if the user is active, a travel plan including activities that require a lot of physical activity can be suggested. This makes it possible to provide a personalized travel plan tailored to the user's health status.

[0089] The collection unit can also collect the user's purchase history in addition to the user's past event participation history. For example, data on products and services purchased by the user in the past can be collected and used to optimize travel plans. The analysis unit can analyze the collected purchase history and adjust travel plans based on the user's consumption trends and preferences. For example, if the user likes a particular brand or product, the analysis unit can suggest travel plans that include tourist spots and events related to that brand or product. It can also suggest travel plans that include events and activities related to products the user has purchased in the past. This makes it possible to provide personalized travel plans based on the user's purchase history.

[0090] In addition to the collected data, the analysis unit can analyze the user's voice data. For example, if the user uses a smart speaker or voice assistant, that voice data can be collected and used for analysis. The analysis unit can analyze the voice data to identify the user's interests. For example, if the user asks the voice assistant questions such as "Tell me where to go next" or "What restaurants do you recommend?", the analysis unit can optimize the travel plan based on that information. The analysis unit can also infer the user's emotions from their voice tone and speaking style and adjust the travel plan based on those emotions. This makes it possible to provide a personalized travel plan based on the user's voice data.

[0091] The generation unit can generate travel plans that include activities related to the user's hobbies and special skills, in addition to subcultures and fan activities related to professional baseball and other events. For example, if the user's hobby is cooking, a travel plan that includes local cooking classes and gourmet tours can be proposed. Also, if the user's hobby is photography, a travel plan that includes visiting photogenic spots can be proposed. Furthermore, if the user likes a particular sport, a travel plan that includes activities and events related to that sport can be proposed. This makes it possible to provide personalized travel plans tailored to the user's hobbies and special skills.

[0092] The providing unit can take into consideration the user's device usage status when providing the generated travel plan to the user. For example, if the user frequently uses a smartphone, the travel plan can be provided in a format optimized for the smartphone. Also, if the user uses a tablet or a PC, the travel plan can be provided in a format optimized for each device. Furthermore, if the user uses a smartwatch, the travel plan can be provided in a concise and highly visible format. This makes it possible to provide the travel plan in an optimal format that matches the user's device usage status.

[0093] When analyzing SNS data, the analysis unit can estimate the user's emotions and adjust the analysis priority based on those emotions. For example, if a user expresses positive emotions on SNS, data related to those emotions can be analyzed with priority. Also, if a user expresses negative emotions, the analysis unit can take those emotions into consideration when analyzing the data. Furthermore, if a user frequently expresses a particular emotion, the analysis unit can focus on analyzing data related to that emotion. This makes it possible to provide analysis results based on the user's emotions.

[0094] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is excited, data can be collected in real time and reflected immediately. Also, if the user is relaxed, data can be collected periodically to provide stable information. Furthermore, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden on the user. In this way, by adjusting the timing of data collection based on the user's emotions, data can be collected at more appropriate times.

[0095] The collection unit can analyze the user's past event participation history and select the optimal data collection method. For example, it can prioritize collection of related data based on the types of events the user has participated in in the past. It can also focus on collecting data about places the user frequently visits. Furthermore, if the user tends to participate in a particular event, it can also intensively collect data related to that event. In this way, it is possible to select the optimal data collection method by analyzing the user's past event participation history.

[0096] When collecting data, the collection unit can filter the data based on the user's current areas of interest and lifestyle. For example, it can prioritize collection of data related to events in which the user is currently interested. It can also selectively collect appropriate data depending on the user's lifestyle (e.g., how busy they are at work). It can also collect highly relevant data based on the user's current geographic location. This allows more relevant data to be collected by filtering data based on the user's current areas of interest and lifestyle.

[0097] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, if the user is excited, it can prioritize collecting the latest event information. Also, if the user is relaxed, it can prioritize collecting reviews and ratings of past events. Furthermore, if the user is stressed, it can prioritize collecting event information that will help the user relax. In this way, by determining the priority of data to be collected based on the user's emotions, it is possible to collect more appropriate data.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The collection unit collects the user's past behavior or preferences. The user's past behavior includes, but is not limited to, past travel history, purchase history, browsing history, etc. Preferences include, but are not limited to, favorite places, activities of interest, and food preferences, etc. The collection unit collects, for example, the user's past event participation history and content posted on SNS. For example, data such as the type, date, time, and location of events the user has participated in in the past is collected. The collection unit can also collect content posted on SNS. For example, if the user frequently uses keywords such as "watching baseball games" or "attending live events" on SNS, that information is collected. Step 2: The analysis unit analyzes the collected data and identifies the user's behavioral patterns and preferences. Examples of behavioral patterns include, but are not limited to, frequency, time period, and location. Examples of preferences include, but are not limited to, favorite places, activities of interest, and food preferences. For example, the analysis unit analyzes the collected data and identifies the user's behavioral patterns and preferences. Step 3: The generation unit generates a personalized travel plan based on the analysis results obtained by the analysis unit. The generation unit generates a travel plan that includes subcultures and fan activities related to, for example, professional baseball games and other events. Subcultures include, but are not limited to, specific anime, music, sports, etc. Fan activities include, but are not limited to, fan meetings, event participation, and merchandise purchases. Step 4: The providing unit provides the travel plan generated by the generating unit to the user. For example, the providing unit provides the generated travel plan to the user via email, app notification, website, etc.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0171] [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects past behavior or preferences of users; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates a personalized travel plan based on the analysis results obtained by the analysis unit; a provider unit that provides the travel plan generated by the generator unit. A system characterized by:

2. The collecting unit Collecting users' past event participation history or social media posts 2. The system of claim 1.

3. The analysis unit Analyzing collected data to identify user behavior patterns and preferences 2. The system of claim 1.

4. The generation unit Generate travel itineraries that include subculture or fan activities related to professional baseball or other events 2. The system of claim 1.

5. The providing unit Providing the generated travel plan to the user 2. The system of claim 1.

6. The analysis unit Analyzing social media data to identify user interests 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze users' past event participation history and select the appropriate data collection method 2. The system of claim 1.

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