System

Abstract

The system according to the embodiment aims to provide a personalized travel plan based on a user's past travel reservation or shopping purchase history.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection part inputs the ID of the user and collects the history of past travel reservations and shopping purchase commodities. The analysis unit analyzes the data collected by the collection unit and specifies a user's preference and a behavior pattern. The generation unit generates a travel plan based on the preference and the behavior pattern specified by the analysis unit. The providing unit provides the travel plan generated by the generating unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide personalized travel plans based on a user's past travel bookings and shopping purchase history, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized travel plan based on a user's past travel reservations and shopping purchase history. [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 inputs a user's ID and collects a history of past travel reservations and shopping purchases. The analysis unit analyzes the data collected by the collection unit and identifies the user's preferences and behavioral patterns. The generation unit generates a travel plan based on the preferences and behavioral patterns identified by the analysis unit. The provision unit provides the travel plan generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide a personalized travel plan based on the user's past travel reservations and shopping purchase history. [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) A travel plan generation system according to an embodiment of the present invention inputs a user's ID, collects a history of past travel reservations and shopping purchases, and uses a generation AI to analyze these histories to identify the user's preferences and behavioral patterns, thereby automatically generating an optimal travel plan. The travel plan generation system inputs a user's ID, searches the user's past travel reservations and shopping purchases, and uses the generation AI to analyze these histories to identify the user's preferences and behavioral patterns. For example, if a user has frequently visited beach resorts in the past, a travel plan including a beach resort is suggested. Furthermore, when a user books accommodation, a recommended travel plan is automatically generated. In this case, the generation AI uses search big data to analyze local trends and popular spots and proposes them as candidates. For example, if a user books accommodation in Tokyo, a travel plan including the latest information on Tokyo's tourist attractions and events is suggested. This allows the travel plan generation system to easily obtain an optimal travel plan based on the user's preferences and behavioral patterns. Furthermore, suggestions that take local trends and popular spots into consideration improve travel satisfaction. This allows the travel plan generation system to provide an optimal travel plan based on the user's preferences and behavioral patterns. For example, detailed data on places a user has visited and products they have purchased is collected, and Generative AI analyzes this data to identify the user's preferences and behavioral patterns. Generative AI then uses big search data to analyze local trends and popular spots and incorporate them into travel plans. This allows users to plan their trips based on the most up-to-date information, improving travel satisfaction.

[0029] An embodiment of a travel plan generation system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit inputs a user's ID and collects a history of past travel reservations and shopping purchases. For example, the collection unit collects data such as travel destinations visited by the user and product categories purchased by the user. The collection unit can also acquire information from multiple data sources to collect the user's history of past travel reservations and shopping purchases. For example, the collection unit can collect data from travel reservation sites and shopping sites. The analysis unit uses a generation AI to analyze the data collected by the collection unit and identify the user's preferences and behavioral patterns. For example, if the user has visited beach resorts frequently in the past, the generation AI determines that the user prefers beach resorts. The analysis unit can also use the generation AI to identify the user's behavioral patterns. For example, the generation AI determines that the user tends to visit certain places in certain seasons. The generation unit uses the generation AI to generate a travel plan based on the preferences and behavioral patterns identified by the analysis unit. For example, if the user prefers beach resorts, the generation unit generates a travel plan that includes beach resorts. The generation unit can also customize the travel plan based on the user's preferences and behavioral patterns using the generation AI. For example, the generation unit generates an optimal travel plan for the user based on data on places the user has visited in the past and products the user has purchased. The provision unit provides the user with the travel plan generated by the generation unit. For example, the provision unit displays the generated travel plan on the user's device. The provision unit can also notify the user of the generated travel plan. For example, the provision unit sends the generated travel plan to the user by email or message. In this way, the travel plan generation system according to the embodiment can provide an optimal travel plan based on the user's preferences and behavioral patterns. For example, the generation AI can identify the user's preferences and behavioral patterns based on data on places the user has visited in the past and products the user has purchased, and generate an optimal travel plan.

[0030] The generation unit can use search big data to analyze local trends and popular spots and reflect them in travel plans. The generation unit, for example, uses search big data to analyze local trends and popular spots. For example, the generation unit collects data such as search keywords, search frequency, and search areas to identify trends and popular spots. The generation unit can also identify trends and popular spots based on data such as the number of visitors, review ratings, and the number of mentions on social media. For example, the generation unit identifies tourist spots with a high number of mentions on social media as trending. The generation unit can also use search big data to analyze local trends and popular spots. For example, the generation unit analyzes search keywords and search frequency in a specific area to identify local trends and popular spots. This allows the generation unit to provide travel plans that take local trends and popular spots into consideration. For example, if a user books accommodation in Tokyo, the generation unit can propose a travel plan that includes the latest tourist spots and event information in Tokyo. This allows the user to plan a trip based on the latest information, improving travel satisfaction.

[0031] The collection unit can analyze the user's past travel reservations and shopping purchase history and select the optimal collection method. For example, the collection unit can analyze the user's past travel reservations and shopping purchase history and select the optimal collection method. For example, the collection unit can prioritize collecting data from travel reservation sites that the user has frequently used in the past. The collection unit can also analyze the categories of products the user has previously purchased and collect related data. The collection unit can also analyze the user's past travel destinations and collect data about similar travel destinations. For example, the collection unit can collect related data based on data about travel destinations the user has previously visited. This allows the collection unit to collect data in the optimal way based on the user's past history. Some or all of the above-described processing in the collection unit can be performed, for example, using AI, or can be performed without AI. For example, the collection unit can input the user's past history data into a generation AI and have the generation AI select the optimal collection method.

[0032] The collection unit can filter the history based on the user's current living situation and areas of interest when collecting the history. For example, the collection unit filters the history based on the user's current living situation and areas of interest when collecting the history. For example, the collection unit preferentially collects related history based on categories in which the user is currently interested. The collection unit can also filter related history based on the user's current living situation (e.g., family composition or work situation). The collection unit can also collect related history based on the user's current areas of interest (e.g., hobbies and interests). For example, the collection unit preferentially collects data related to the user's current hobbies and interests. This allows the collection unit to preferentially collect related history based on the user's current situation and interests. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's current living situation and areas of interest to the generation AI and cause the generation AI to perform filtering.

[0033] The collection unit can select the optimal collection means depending on the user's input method when collecting the history. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting the history. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Also, if the user is using text input, the collection unit can prioritize collecting text data. Also, if the user is using image input, the collection unit can prioritize collecting image data. For example, the collection unit collects related data based on image data taken by the user. This allows the collection unit to collect the history using the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data depending on the user's input method to the generation AI and cause the generation AI to select the optimal collection means.

[0034] When collecting history, the collection unit can prioritize collecting highly relevant history by taking into account the user's geographical location information. For example, when collecting history, the collection unit prioritizes collecting highly relevant history by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting travel reservation history related to the user's current location. The collection unit can also prioritize collecting shopping purchase history related to areas the user has previously visited. The collection unit can also collect highly relevant history based on the user's current geographical location information. For example, the collection unit prioritizes collecting history of places close to the user's current location. This allows the collection unit to collect highly relevant history based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant history.

[0035] The collection unit can analyze the user's social media activities and collect related histories when collecting the history. For example, the collection unit can analyze the user's social media activities and collect related histories when collecting the history. For example, the collection unit can collect travel reservation histories related to places where the user checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related shopping purchase histories. The collection unit can also collect related histories by referring to the activities of the user's friends on social media. For example, the collection unit can collect related histories based on the check-in information of the user's friends on social media. This allows the collection unit to collect related histories based on the user's social media activities. Some or all of the above-mentioned processing in 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 a generation AI and cause the generation AI to collect related histories.

[0036] The collection unit can customize the collection method by reflecting the user's past feedback when collecting history. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting history. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit can also preferentially use a specific data collection means based on the user's past feedback. The collection unit can also customize the collection method by reflecting the user's feedback. For example, the collection unit adjusts the collection method based on the user's feedback. This allows the collection unit to customize the collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0037] The analysis unit can adjust the level of detail of the analysis based on the importance of the history during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the history during analysis. For example, the analysis unit performs a detailed analysis on important history. The analysis unit can also perform a brief analysis on less important history. The analysis unit can also adjust the level of detail of the analysis according to the importance of the history. For example, the analysis unit adjusts the level of detail of the analysis based on the frequency and impact of the history and the user's interest. This allows the analysis unit to perform the analysis at an optimal level of detail according to the importance of the history. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input history importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0038] The analysis unit can apply different analysis algorithms depending on the category of the history during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the history during analysis. For example, the analysis unit can apply a travel-related analysis algorithm to travel reservation history. The analysis unit can also apply a shopping-related analysis algorithm to shopping purchase history. The analysis unit can also select an optimal analysis algorithm depending on the category of the history. For example, the analysis unit can apply different analysis algorithms depending on categories such as travel history, shopping history, and social media history. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of the history. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input history category data into the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0039] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by reflecting the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis based on the user's past reports and database records. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0040] The analysis unit can determine the priority of analysis based on the time of submission of the history during analysis. The analysis unit, for example, determines the priority of analysis based on the time of submission of the history during analysis. For example, the analysis unit prioritizes analysis of recent history. The analysis unit can also postpone analysis of history that was submitted earlier. The analysis unit can also determine the priority of analysis based on the time of submission. For example, the analysis unit determines the priority of analysis based on the submission date and time or the frequency of submission. This allows the analysis unit to perform analysis in an optimal order based on the time of submission of the history. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input history submission time data into the generation AI and have the generation AI determine the priority of analysis.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the histories during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the histories during analysis. For example, the analysis unit prioritizes analysis of highly relevant histories. The analysis unit can also postpone analysis of less relevant histories. The analysis unit can also adjust the order of analysis based on the relevance of the histories. For example, the analysis unit adjusts the order of analysis based on related keywords or common themes. This allows the analysis unit to perform analysis in an optimal order based on the relevance of the histories. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input history relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0042] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. The analysis unit can also avoid technical terms if the user does not have technical expertise. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terms based on the user's occupation, educational background, and past experience. This allows the analysis unit to provide analysis results using technical terms that are optimal for the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0043] The generation unit can improve the accuracy of generation by referring to the user's past travel plans during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past travel plans during generation. For example, the generation unit can improve the accuracy of the current travel plan based on the user's past travel plans. The generation unit can also adjust the generation algorithm by referring to the user's past travel plans. The generation unit can also improve the accuracy of generation by reflecting the user's past travel plans. For example, the generation unit can improve the accuracy of generation based on the user's past travel itineraries, visited destinations, reviews, etc. In this way, the generation unit can improve the accuracy of generation by referring to the user's past travel plans. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past travel plan data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0044] The generation unit can customize the travel plan based on the user's current living situation and areas of interest at the time of generation. The generation unit, for example, customizes the travel plan based on the user's current living situation and areas of interest at the time of generation. For example, the generation unit customizes the travel plan based on the user's current living situation (e.g., family composition and work situation). The generation unit can also customize the travel plan based on the user's current areas of interest (e.g., hobbies and interests). The generation unit can also provide an optimal travel plan based on the user's current living situation and areas of interest. For example, the generation unit provides a travel plan related to the user's current hobbies and interests. This allows the generation unit to provide an optimal travel plan based on the user's current situation and areas of interest. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input data on the user's current living situation and areas of interest into the generation AI and cause the generation AI to customize the travel plan.

[0045] The generation unit can improve the generation method by reflecting user feedback during generation. The generation unit can improve the generation method by reflecting user feedback during generation, for example. For example, the generation unit adjusts the generation algorithm based on user feedback. The generation unit can also improve the generation method by reflecting user feedback. The generation unit can also improve the accuracy of generation by referring to user feedback. For example, the generation unit improves the generation method based on user ratings, comments, and survey results. This allows the generation unit to improve the generation method based on user feedback. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI, for example. For example, the generation unit can input user feedback data into the generation AI and cause the generation AI to improve the generation method.

[0046] The generation unit can generate an optimal travel plan taking into account the user's geographical location information at the time of generation. The generation unit, for example, generates an optimal travel plan taking into account the user's geographical location information at the time of generation. For example, the generation unit generates an optimal travel plan based on the user's current location. The generation unit can also generate related travel plans based on areas the user has previously visited. The generation unit can also provide an optimal travel plan based on the user's current geographical location information. For example, the generation unit generates a travel plan including nearby tourist spots and event information based on the user's current location. This allows the generation unit to provide an optimal travel plan based on the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's geographical location information data into the generation AI and cause the generation AI to generate an optimal travel plan.

[0047] The generation unit can analyze the user's social media activity and propose a travel plan at the time of generation. For example, the generation unit can analyze the user's social media activity and propose a travel plan at the time of generation. For example, the generation unit can propose a travel plan based on locations where the user has checked in on social media. The generation unit can also analyze the content of the user's social media posts and propose related travel plans. The generation unit can also propose a travel plan based on the activities of the user's friends on social media. For example, the generation unit can propose related travel plans based on the check-in information of the user's friends on social media. This allows the generation unit to provide an optimal travel plan based on the user's social media activity. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's social media activity data into the generation AI and cause the generation AI to propose a travel plan.

[0048] The generation unit can customize the travel plan by reflecting the user's past feedback at the time of generation. The generation unit, for example, customizes the travel plan by reflecting the user's past feedback at the time of generation. For example, the generation unit customizes the travel plan based on the user's past feedback. The generation unit can also adjust the travel plan by reflecting the user's past feedback. The generation unit can also provide an optimal travel plan by referring to the user's feedback. For example, the generation unit customizes the travel plan based on the user's ratings, comments, and survey results. This allows the generation unit to provide an optimal travel plan based on the user's past feedback. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the travel plan.

[0049] The providing unit can select the optimal display method by referring to the user's past operation history when providing the plan. For example, the providing unit selects the optimal display method by referring to the user's past operation history when providing the plan. For example, the providing unit selects the optimal display method based on the user's past operation history. The providing unit can also adjust the display method by referring to the user's past operation history. The providing unit can also provide the optimal display method by reflecting the user's past operation history. For example, the providing unit selects the display method based on the user's click history, operation time, and operation frequency. This allows the providing unit to provide the travel plan using the optimal display method based on the user's past operation 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 using AI. For example, the providing unit can input the user's past operation history data into the generation AI and cause the generation AI to select the optimal display method.

[0050] The providing unit can customize the display content according to the user's current task at the time of providing. The providing unit, for example, customizes the display content according to the user's current task at the time of providing. For example, the providing unit customizes the display content based on the task the user is currently performing. The providing unit can also adjust the display content taking into account the user's current task. The providing unit can also provide optimal display content according to the user's current task. For example, when the user is planning a trip, the providing unit prioritizes displaying detailed information about the trip plan. This allows the providing unit to provide the trip plan with optimal display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data to the generating AI and cause the generating AI to customize the display content.

[0051] The providing unit can select the optimal display method by taking into account the user's device information when providing the information. For example, the providing unit selects the optimal display method by taking into account the user's device information when providing the information. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a simple and highly visible display method. For example, the providing unit selects the display method based on the type, screen size, and OS version of the user's device. This allows the providing unit to provide the travel plan in the optimal display method based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0052] The providing unit can make the display content multilingual based on the user's language setting when providing the content. The providing unit, for example, makes the display content multilingual based on the user's language setting when providing the content. For example, the providing unit automatically sets the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. For example, the providing unit makes the display content multilingual based on the language setting of the user's device or the language selected by the user. This allows the providing unit to provide a travel plan in the optimal language based on the user's language setting. 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 language setting data to a generation AI and cause the generation AI to execute multilingual display content.

[0053] The providing unit can analyze the user's social media activity and provide related information at the time of providing. For example, the providing unit can analyze the user's social media activity and provide related information at the time of providing. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. For example, the providing unit can provide related information based on the check-in information of the user's friends on social media. This allows the providing unit to provide related information based on the user's social media activity. Some or all of the above-mentioned processing in 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 user's social media activity data into a generation AI and cause the generation AI to provide related information.

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

[0055] The collection unit can collect the user's health data and reflect it in travel plan suggestions. For example, it can collect the user's step count, heart rate, and sleep data and suggest travel plans based on the user's health condition. If the user is interested in health, it can also suggest travel plans that include healthy activities and meals. Furthermore, if the user has specific health goals, it can provide travel plans tailored to those goals. This allows the collection unit to provide optimal travel plans based on the user's health data.

[0056] The providing unit can collect the user's past travel plan ratings and improve the method for proposing travel plans based on the ratings. For example, the unit can analyze the characteristics of travel plans that the user has previously given high ratings and preferentially suggest travel plans with similar characteristics. The accuracy of suggestions can also be improved by avoiding the characteristics of travel plans that the user has given low ratings. Furthermore, the providing unit can customize the suggestion method based on the user's ratings and provide travel plans that suit the user's preferences. This allows the providing unit to suggest optimal travel plans based on the user's past ratings.

[0057] The generation unit can analyze the success rate of the user's past travel plans and generate a new travel plan based on the success rate. For example, a new plan can be generated that incorporates elements of travel plans that have been successful in the past. The success rate can also be improved by avoiding elements of plans that have failed in the past. Furthermore, the generation unit can provide a plan that will provide a higher level of satisfaction by referring to the user's evaluations of past travel plans. This allows the generation unit to provide an optimal travel plan based on the success rate of the user's past travel plans.

[0058] The collection unit can analyze the user's social media activity and reflect it in suggesting travel plans. For example, it can analyze the places the user has checked in and the content of posts on social media to suggest related travel plans. It can also customize travel plans by referring to the social media activity of the user's friends. Furthermore, it can suggest travel plans based on events and places the user has shown interest in on social media. This allows the collection unit to provide optimal travel plans based on the user's social media activity.

[0059] The providing unit can analyze the user's past travel plan history and optimize the timing of proposing a travel plan. For example, it can analyze the time and frequency at which the user planned trips in the past and propose a travel plan at the optimal timing. It can also adjust the proposal timing by referring to the success rate of the user's past travel plans. Furthermore, it can propose a travel plan at the optimal timing by taking into account the user's current schedule and plans. This allows the providing unit to propose a travel plan at the optimal timing based on the user's past history.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit inputs the user's ID and collects the user's past travel reservations and shopping purchase history. For example, the collection unit collects data such as the travel destinations the user has visited in the past and the categories of products purchased. The collection unit can also obtain information from multiple data sources. For example, the collection unit collects data from travel reservation sites and shopping sites. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and identify the user's preferences and behavioral patterns. For example, the generation AI determines that the user prefers beach resorts if the user has visited beach resorts many times in the past. The generation AI also determines that the user tends to visit certain places during certain seasons. Step 3: The generation unit uses the generation AI to generate a travel plan based on the preferences and behavioral patterns identified by the analysis unit. For example, if the user prefers beach resorts, the generation unit generates a travel plan that includes beach resorts. The generation unit can also customize the travel plan based on the user's preferences and behavioral patterns. Step 4: The providing unit provides the travel plan generated by the generating unit to the user. For example, the providing unit displays the generated travel plan on the user's device. The providing unit can also send the generated travel plan to the user by email or message.

[0062] (Example 2) A travel plan generation system according to an embodiment of the present invention inputs a user's ID, collects a history of past travel reservations and shopping purchases, and uses a generation AI to analyze these histories to identify the user's preferences and behavioral patterns, thereby automatically generating an optimal travel plan. The travel plan generation system inputs a user's ID, searches the user's past travel reservations and shopping purchases, and uses the generation AI to analyze these histories to identify the user's preferences and behavioral patterns. For example, if a user has frequently visited beach resorts in the past, a travel plan including a beach resort is suggested. Furthermore, when a user books accommodation, a recommended travel plan is automatically generated. In this case, the generation AI uses search big data to analyze local trends and popular spots and proposes them as candidates. For example, if a user books accommodation in Tokyo, a travel plan including the latest information on Tokyo's tourist attractions and events is suggested. This allows the travel plan generation system to easily obtain an optimal travel plan based on the user's preferences and behavioral patterns. Furthermore, suggestions that take local trends and popular spots into consideration improve travel satisfaction. This allows the travel plan generation system to provide an optimal travel plan based on the user's preferences and behavioral patterns. For example, detailed data on places a user has visited and products they have purchased is collected, and Generative AI analyzes this data to identify the user's preferences and behavioral patterns. Generative AI then uses big search data to analyze local trends and popular spots and incorporate them into travel plans. This allows users to plan their trips based on the most up-to-date information, improving travel satisfaction.

[0063] An embodiment of a travel plan generation system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit inputs a user's ID and collects a history of past travel reservations and shopping purchases. For example, the collection unit collects data such as travel destinations visited by the user and product categories purchased by the user. The collection unit can also acquire information from multiple data sources to collect the user's history of past travel reservations and shopping purchases. For example, the collection unit can collect data from travel reservation sites and shopping sites. The analysis unit uses a generation AI to analyze the data collected by the collection unit and identify the user's preferences and behavioral patterns. For example, if the user has visited beach resorts frequently in the past, the generation AI determines that the user prefers beach resorts. The analysis unit can also use the generation AI to identify the user's behavioral patterns. For example, the generation AI determines that the user tends to visit certain places in certain seasons. The generation unit uses the generation AI to generate a travel plan based on the preferences and behavioral patterns identified by the analysis unit. For example, if the user prefers beach resorts, the generation unit generates a travel plan that includes beach resorts. The generation unit can also customize the travel plan based on the user's preferences and behavioral patterns using the generation AI. For example, the generation unit generates an optimal travel plan for the user based on data on places the user has visited in the past and products the user has purchased. The provision unit provides the user with the travel plan generated by the generation unit. For example, the provision unit displays the generated travel plan on the user's device. The provision unit can also notify the user of the generated travel plan. For example, the provision unit sends the generated travel plan to the user by email or message. In this way, the travel plan generation system according to the embodiment can provide an optimal travel plan based on the user's preferences and behavioral patterns. For example, the generation AI can identify the user's preferences and behavioral patterns based on data on places the user has visited in the past and products the user has purchased, and generate an optimal travel plan.

[0064] The generation unit can use search big data to analyze local trends and popular spots and reflect them in travel plans. The generation unit, for example, uses search big data to analyze local trends and popular spots. For example, the generation unit collects data such as search keywords, search frequency, and search areas to identify trends and popular spots. The generation unit can also identify trends and popular spots based on data such as the number of visitors, review ratings, and the number of mentions on social media. For example, the generation unit identifies tourist spots with a high number of mentions on social media as trending. The generation unit can also use search big data to analyze local trends and popular spots. For example, the generation unit analyzes search keywords and search frequency in a specific area to identify local trends and popular spots. This allows the generation unit to provide travel plans that take local trends and popular spots into consideration. For example, if a user books accommodation in Tokyo, the generation unit can propose a travel plan that includes the latest tourist spots and event information in Tokyo. This allows the user to plan a trip based on the latest information, improving travel satisfaction.

[0065] The collection unit can estimate the user's emotions and adjust the timing of history collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of history collection based on the estimated user emotions. For example, when the user is relaxed, the collection unit delays the collection timing to collect history when the user is calm. Furthermore, when the user is stressed, the collection unit can advance the collection timing to quickly collect history. Furthermore, when the user is busy, the collection unit can adjust the collection timing to collect history according to the user's schedule. For example, the collection unit adjusts the collection timing based on the user's activity time or when their emotions change. This allows the collection unit to collect history at the optimal timing according to the user's emotions. Emotion estimation is realized 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. For example, the collection unit can estimate emotions using techniques such as facial expression recognition, voice analysis, and text analysis. This allows the collection unit to collect history at the optimal timing based on the user's emotions.

[0066] The collection unit can analyze the user's past travel reservations and shopping purchase history and select the optimal collection method. For example, the collection unit can analyze the user's past travel reservations and shopping purchase history and select the optimal collection method. For example, the collection unit can prioritize collecting data from travel reservation sites that the user has frequently used in the past. The collection unit can also analyze the categories of products the user has previously purchased and collect related data. The collection unit can also analyze the user's past travel destinations and collect data about similar travel destinations. For example, the collection unit can collect related data based on data about travel destinations the user has previously visited. This allows the collection unit to collect data in the optimal way based on the user's past history. Some or all of the above-described processing in the collection unit can be performed, for example, using AI, or can be performed without AI. For example, the collection unit can input the user's past history data into a generation AI and have the generation AI select the optimal collection method.

[0067] The collection unit can filter the history based on the user's current living situation and areas of interest when collecting the history. For example, the collection unit filters the history based on the user's current living situation and areas of interest when collecting the history. For example, the collection unit preferentially collects related history based on categories in which the user is currently interested. The collection unit can also filter related history based on the user's current living situation (e.g., family composition or work situation). The collection unit can also collect related history based on the user's current areas of interest (e.g., hobbies and interests). For example, the collection unit preferentially collects data related to the user's current hobbies and interests. This allows the collection unit to preferentially collect related history based on the user's current situation and interests. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's current living situation and areas of interest to the generation AI and cause the generation AI to perform filtering.

[0068] The collection unit can select the optimal collection means depending on the user's input method when collecting the history. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting the history. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Also, if the user is using text input, the collection unit can prioritize collecting text data. Also, if the user is using image input, the collection unit can prioritize collecting image data. For example, the collection unit collects related data based on image data taken by the user. This allows the collection unit to collect the history using the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data depending on the user's input method to the generation AI and cause the generation AI to select the optimal collection means.

[0069] The collection unit can estimate the user's emotions and determine the priority of the history to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of the history to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit can prioritize collecting past travel reservation history. Also, when the user is stressed, the collection unit can prioritize collecting shopping purchase history. Also, when the user is busy, the collection unit can prioritize collecting important history. For example, the collection unit can prioritize the history to be collected based on the intensity of the user's emotions and the importance of the history. In this way, the collection unit can prioritize the history to be collected according to the user's emotions. Emotion estimation is realized 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. For example, the collection unit can estimate emotions using techniques such as facial expression recognition, voice analysis, and text analysis. This allows the collection unit to determine the priority of the history to be collected based on the user's emotions.

[0070] When collecting history, the collection unit can prioritize collecting highly relevant history by taking into account the user's geographical location information. For example, when collecting history, the collection unit prioritizes collecting highly relevant history by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting travel reservation history related to the user's current location. The collection unit can also prioritize collecting shopping purchase history related to areas the user has previously visited. The collection unit can also collect highly relevant history based on the user's current geographical location information. For example, the collection unit prioritizes collecting history of places close to the user's current location. This allows the collection unit to collect highly relevant history based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant history.

[0071] The collection unit can analyze the user's social media activities and collect related histories when collecting the history. For example, the collection unit can analyze the user's social media activities and collect related histories when collecting the history. For example, the collection unit can collect travel reservation histories related to places where the user checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related shopping purchase histories. The collection unit can also collect related histories by referring to the activities of the user's friends on social media. For example, the collection unit can collect related histories based on the check-in information of the user's friends on social media. This allows the collection unit to collect related histories based on the user's social media activities. Some or all of the above-mentioned processing in 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 a generation AI and cause the generation AI to collect related histories.

[0072] The collection unit can customize the collection method by reflecting the user's past feedback when collecting history. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting history. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit can also preferentially use a specific data collection means based on the user's past feedback. The collection unit can also customize the collection method by reflecting the user's feedback. For example, the collection unit adjusts the collection method based on the user's feedback. This allows the collection unit to customize the collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0073] 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, 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, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. The analysis unit can also provide analysis results that are concise when the user is busy. For example, the analysis unit can adjust the presentation method of the analysis based on the intensity of the user's emotions and the situation. This allows the analysis unit to provide analysis results in an optimal presentation method depending 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the analysis unit can estimate emotions using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the analysis unit to adjust the presentation method of the analysis based on the user's emotions.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the history during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the history during analysis. For example, the analysis unit performs a detailed analysis on important history. The analysis unit can also perform a brief analysis on less important history. The analysis unit can also adjust the level of detail of the analysis according to the importance of the history. For example, the analysis unit adjusts the level of detail of the analysis based on the frequency and impact of the history and the user's interest. This allows the analysis unit to perform the analysis at an optimal level of detail according to the importance of the history. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input history importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the category of the history during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the history during analysis. For example, the analysis unit can apply a travel-related analysis algorithm to travel reservation history. The analysis unit can also apply a shopping-related analysis algorithm to shopping purchase history. The analysis unit can also select an optimal analysis algorithm depending on the category of the history. For example, the analysis unit can apply different analysis algorithms depending on categories such as travel history, shopping history, and social media history. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of the history. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input history category data into the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0076] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by reflecting the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis based on the user's past reports and database records. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can provide a detailed analysis when the user is relaxed. The analysis unit can also provide a concise analysis when the user is stressed. The analysis unit can also provide a short analysis that focuses on the main points when the user is busy. For example, the analysis unit can adjust the length of the analysis based on the intensity of the user's emotions and the situation. This allows the analysis unit to provide analysis results with an optimal length depending 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the analysis unit can estimate emotions using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the analysis unit to adjust the length of the analysis based on the user's emotions.

[0078] The analysis unit can determine the priority of analysis based on the time of submission of the history during analysis. The analysis unit, for example, determines the priority of analysis based on the time of submission of the history during analysis. For example, the analysis unit prioritizes analysis of recent history. The analysis unit can also postpone analysis of history that was submitted earlier. The analysis unit can also determine the priority of analysis based on the time of submission. For example, the analysis unit determines the priority of analysis based on the submission date and time or the frequency of submission. This allows the analysis unit to perform analysis in an optimal order based on the time of submission of the history. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input history submission time data into the generation AI and have the generation AI determine the priority of analysis.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the histories during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the histories during analysis. For example, the analysis unit prioritizes analysis of highly relevant histories. The analysis unit can also postpone analysis of less relevant histories. The analysis unit can also adjust the order of analysis based on the relevance of the histories. For example, the analysis unit adjusts the order of analysis based on related keywords or common themes. This allows the analysis unit to perform analysis in an optimal order based on the relevance of the histories. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input history relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0080] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. The analysis unit can also avoid technical terms if the user does not have technical expertise. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terms based on the user's occupation, educational background, and past experience. This allows the analysis unit to provide analysis results using technical terms that are optimal for the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0081] The generation unit can estimate the user's emotions and adjust the representation of the generated travel plan based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the representation of the generated travel plan based on the estimated user emotions. For example, the generation unit provides a detailed travel plan when the user is relaxed. The generation unit can also provide a concise travel plan when the user is stressed. The generation unit can also provide a travel plan that focuses on the main points when the user is busy. For example, the generation unit adjusts the representation of the travel plan based on the intensity of the user's emotions and the situation. This allows the generation unit to provide a travel plan in an optimal representation depending on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the generation unit can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the generation unit to adjust the representation of the generated travel plan based on the user's emotions.

[0082] The generation unit can improve the accuracy of generation by referring to the user's past travel plans during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past travel plans during generation. For example, the generation unit can improve the accuracy of the current travel plan based on the user's past travel plans. The generation unit can also adjust the generation algorithm by referring to the user's past travel plans. The generation unit can also improve the accuracy of generation by reflecting the user's past travel plans. For example, the generation unit can improve the accuracy of generation based on the user's past travel itineraries, visited destinations, reviews, etc. In this way, the generation unit can improve the accuracy of generation by referring to the user's past travel plans. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past travel plan data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0083] The generation unit can customize the travel plan based on the user's current living situation and areas of interest at the time of generation. The generation unit, for example, customizes the travel plan based on the user's current living situation and areas of interest at the time of generation. For example, the generation unit customizes the travel plan based on the user's current living situation (e.g., family composition and work situation). The generation unit can also customize the travel plan based on the user's current areas of interest (e.g., hobbies and interests). The generation unit can also provide an optimal travel plan based on the user's current living situation and areas of interest. For example, the generation unit provides a travel plan related to the user's current hobbies and interests. This allows the generation unit to provide an optimal travel plan based on the user's current situation and areas of interest. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input data on the user's current living situation and areas of interest into the generation AI and cause the generation AI to customize the travel plan.

[0084] The generation unit can improve the generation method by reflecting user feedback during generation. The generation unit can improve the generation method by reflecting user feedback during generation, for example. For example, the generation unit adjusts the generation algorithm based on user feedback. The generation unit can also improve the generation method by reflecting user feedback. The generation unit can also improve the accuracy of generation by referring to user feedback. For example, the generation unit improves the generation method based on user ratings, comments, and survey results. This allows the generation unit to improve the generation method based on user feedback. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI, for example. For example, the generation unit can input user feedback data into the generation AI and cause the generation AI to improve the generation method.

[0085] The generation unit can estimate the user's emotions and prioritize the itineraries to be generated based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and prioritizes the itineraries to be generated based on the estimated user emotions. For example, the generation unit can prioritize generating detailed itineraries when the user is relaxed. The generation unit can also prioritize generating concise itineraries when the user is stressed. The generation unit can also prioritize generating concise itineraries when the user is busy. For example, the generation unit can prioritize the itineraries based on the intensity of the user's emotions and the situation. This allows the generation unit to provide itineraries with optimal priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the generation unit can estimate the user's emotions using techniques such as facial expression recognition, voice analysis, and text analysis. This allows the generation unit to determine the priority of the travel plans to be generated based on the user's emotions.

[0086] The generation unit can generate an optimal travel plan taking into account the user's geographical location information at the time of generation. The generation unit, for example, generates an optimal travel plan taking into account the user's geographical location information at the time of generation. For example, the generation unit generates an optimal travel plan based on the user's current location. The generation unit can also generate related travel plans based on areas the user has previously visited. The generation unit can also provide an optimal travel plan based on the user's current geographical location information. For example, the generation unit generates a travel plan including nearby tourist spots and event information based on the user's current location. This allows the generation unit to provide an optimal travel plan based on the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's geographical location information data into the generation AI and cause the generation AI to generate an optimal travel plan.

[0087] The generation unit can analyze the user's social media activity and propose a travel plan at the time of generation. For example, the generation unit can analyze the user's social media activity and propose a travel plan at the time of generation. For example, the generation unit can propose a travel plan based on locations where the user has checked in on social media. The generation unit can also analyze the content of the user's social media posts and propose related travel plans. The generation unit can also propose a travel plan based on the activities of the user's friends on social media. For example, the generation unit can propose related travel plans based on the check-in information of the user's friends on social media. This allows the generation unit to provide an optimal travel plan based on the user's social media activity. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's social media activity data into the generation AI and cause the generation AI to propose a travel plan.

[0088] The generation unit can customize the travel plan by reflecting the user's past feedback at the time of generation. The generation unit, for example, customizes the travel plan by reflecting the user's past feedback at the time of generation. For example, the generation unit customizes the travel plan based on the user's past feedback. The generation unit can also adjust the travel plan by reflecting the user's past feedback. The generation unit can also provide an optimal travel plan by referring to the user's feedback. For example, the generation unit customizes the travel plan based on the user's ratings, comments, and survey results. This allows the generation unit to provide an optimal travel plan based on the user's past feedback. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the travel plan.

[0089] The providing unit can estimate the user's emotions and adjust the display method of the travel plan to be provided based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the display method of the travel plan to be provided based on the estimated user emotions. For example, the providing unit can provide a detailed display method when the user is relaxed. Furthermore, the providing unit can provide a concise display method when the user is stressed. Furthermore, the providing unit can provide a display method that focuses on the main points when the user is busy. For example, the providing unit can adjust the display method based on the intensity of the user's emotions and the situation. This allows the providing unit to provide the travel plan in an optimal display method depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the providing unit can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the providing unit to adjust the display method of the travel plan to be provided based on the user's emotions.

[0090] The providing unit can select the optimal display method by referring to the user's past operation history when providing the plan. For example, the providing unit selects the optimal display method by referring to the user's past operation history when providing the plan. For example, the providing unit selects the optimal display method based on the user's past operation history. The providing unit can also adjust the display method by referring to the user's past operation history. The providing unit can also provide the optimal display method by reflecting the user's past operation history. For example, the providing unit selects the display method based on the user's click history, operation time, and operation frequency. This allows the providing unit to provide the travel plan using the optimal display method based on the user's past operation 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 using AI. For example, the providing unit can input the user's past operation history data into the generation AI and cause the generation AI to select the optimal display method.

[0091] The providing unit can customize the display content according to the user's current task at the time of providing. The providing unit, for example, customizes the display content according to the user's current task at the time of providing. For example, the providing unit customizes the display content based on the task the user is currently performing. The providing unit can also adjust the display content taking into account the user's current task. The providing unit can also provide optimal display content according to the user's current task. For example, when the user is planning a trip, the providing unit prioritizes displaying detailed information about the trip plan. This allows the providing unit to provide the trip plan with optimal display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data to the generating AI and cause the generating AI to customize the display content.

[0092] The providing unit can estimate the user's emotions and adjust the operation procedures of the travel plan to be provided based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the operation procedures of the travel plan to be provided based on the estimated user's emotions. For example, the providing unit can provide detailed operation procedures when the user is relaxed. The providing unit can also provide concise operation procedures when the user is stressed. The providing unit can also provide operation procedures that focus on the main points when the user is busy. For example, the providing unit can adjust the operation procedures based on the intensity of the user's emotions and the situation. This allows the providing unit to provide a travel plan with optimal operation procedures according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the providing unit can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the providing unit to adjust the operation procedures of the travel plan to be provided based on the user's emotions.

[0093] The providing unit can select the optimal display method by taking into account the user's device information when providing the information. For example, the providing unit selects the optimal display method by taking into account the user's device information when providing the information. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a simple and highly visible display method. For example, the providing unit selects the display method based on the type, screen size, and OS version of the user's device. This allows the providing unit to provide the travel plan in the optimal display method based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0094] The providing unit can make the display content multilingual based on the user's language setting when providing the content. The providing unit, for example, makes the display content multilingual based on the user's language setting when providing the content. For example, the providing unit automatically sets the display content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. For example, the providing unit makes the display content multilingual based on the language setting of the user's device or the language selected by the user. This allows the providing unit to provide a travel plan in the optimal language based on the user's language setting. 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 language setting data to a generation AI and cause the generation AI to execute multilingual display content.

[0095] The providing unit can analyze the user's social media activity and provide related information at the time of providing. For example, the providing unit can analyze the user's social media activity and provide related information at the time of providing. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. For example, the providing unit can provide related information based on the check-in information of the user's friends on social media. This allows the providing unit to provide related information based on the user's social media activity. Some or all of the above-mentioned processing in 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 user's social media activity data into a generation AI and cause the generation AI to provide related information. === 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 inputs a user's ID via the control unit 46A of the smart device 14 and collects a history of past travel reservations and shopping purchases. The analysis unit analyzes the collected data using a generation AI via the specific processing unit 290 of the data processing device 12, for example, and identifies the user's preferences and behavioral patterns. The generation unit generates a travel plan using a generation AI via the specific processing unit 290 of the data processing device 12, for example. The provision unit provides the travel plan generated by the control unit 46A of the smart device 14 to the user, for example. === 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 inputs a user's ID via the control unit 46A of the smart glasses 214 and collects past travel reservations and shopping purchase histories. The analysis unit analyzes the collected data using a generation AI via the specific processing unit 290 of the data processing device 12, for example, and identifies the user's preferences and behavioral patterns. The generation unit generates a travel plan using a generation AI via the specific processing unit 290 of the data processing device 12, for example. The provision unit provides the travel plan generated by the control unit 46A of the smart glasses 214 to the user, for example. === 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 inputs the user's ID via the control unit 46A of the headset type terminal 314 and collects the history of past travel reservations and shopping purchases. For example, the analysis unit analyzes the collected data using a generation AI via the specific processing unit 290 of the data processing device 12 to identify the user's preferences and behavioral patterns. For example, the generation unit generates a travel plan using a generation AI via the specific processing unit 290 of the data processing device 12. For example, the provision unit provides the travel plan generated by the control unit 46A of the headset type terminal 314 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 inputs the user's ID via the control unit 46A of the robot 414 and collects the history of past travel reservations and shopping purchases. The analysis unit analyzes the collected data using a generation AI via the specific processing unit 290 of the data processing device 12, for example, and identifies the user's preferences and behavioral patterns. The generation unit generates a travel plan using a generation AI via the specific processing unit 290 of the data processing device 12, for example. The provision unit provides the travel plan generated by the control unit 46A of the robot 414 to the user, for example.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The providing unit can estimate the user's emotions and adjust the frequency of travel plan suggestions based on the estimated user emotions. For example, if the user is relaxed, travel plans can be suggested more frequently. If the user is stressed, the frequency of suggestions can be reduced to reduce the user's burden. Furthermore, if the user is busy, only important suggestions can be given priority. This allows the providing unit to suggest travel plans at an optimal frequency depending on the user's emotions. Emotion estimation is achieved using technologies such as facial expression recognition, voice analysis, and text analysis.

[0098] The collection unit can collect the user's health data and reflect it in travel plan suggestions. For example, it can collect the user's step count, heart rate, and sleep data and suggest travel plans based on the user's health condition. If the user is interested in health, it can also suggest travel plans that include healthy activities and meals. Furthermore, if the user has specific health goals, it can provide travel plans tailored to those goals. This allows the collection unit to provide optimal travel plans based on the user's health data.

[0099] The generation unit can estimate the user's emotions and adjust the contents of the travel plan based on the estimated user emotions. For example, if the user is relaxed, a travel plan including many relaxing activities can be generated. Also, if the user is stressed, a travel plan including many activities that help relieve stress can be generated. Furthermore, if the user is seeking adventure, a travel plan including many active activities can be provided. In this way, the generation unit can provide a travel plan with optimal content according to the user's emotions. Emotion estimation is achieved using technologies such as facial expression recognition, voice analysis, and text analysis.

[0100] The providing unit can collect the user's past travel plan ratings and improve the method for proposing travel plans based on the ratings. For example, the unit can analyze the characteristics of travel plans that the user has previously given high ratings and preferentially suggest travel plans with similar characteristics. The accuracy of suggestions can also be improved by avoiding the characteristics of travel plans that the user has given low ratings. Furthermore, the providing unit can customize the suggestion method based on the user's ratings and provide travel plans that suit the user's preferences. This allows the providing unit to suggest optimal travel plans based on the user's past ratings.

[0101] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is relaxed, detailed data can be collected. If the user is stressed, only the minimum amount of data necessary can be collected. Furthermore, if the user is busy, important data can be collected preferentially. This allows the collection unit to optimally collect data according to the user's emotions. Emotion estimation is achieved using technologies such as facial expression recognition, voice analysis, and text analysis.

[0102] The generation unit can analyze the success rate of the user's past travel plans and generate a new travel plan based on the success rate. For example, a new plan can be generated that incorporates elements of travel plans that have been successful in the past. The success rate can also be improved by avoiding elements of plans that have failed in the past. Furthermore, the generation unit can provide a plan that will provide a higher level of satisfaction by referring to the user's evaluations of past travel plans. This allows the generation unit to provide an optimal travel plan based on the success rate of the user's past travel plans.

[0103] The providing unit can estimate the user's emotions and adjust the notification method for the travel plan based on the estimated user's emotions. For example, if the user is relaxed, the travel plan can be suggested using a pop-up notification. If the user is stressed, the travel plan can be quietly notified via email or message. Furthermore, if the user is busy, only important notifications can be prioritized. This allows the providing unit to notify the user of the travel plan in the most optimal way depending on the user's emotions. Emotion estimation is achieved using technologies such as facial expression recognition, voice analysis, and text analysis.

[0104] The collection unit can analyze the user's social media activity and reflect it in suggesting travel plans. For example, it can analyze the places the user has checked in and the content of posts on social media to suggest related travel plans. It can also customize travel plans by referring to the social media activity of the user's friends. Furthermore, it can suggest travel plans based on events and places the user has shown interest in on social media. This allows the collection unit to provide optimal travel plans based on the user's social media activity.

[0105] The generation unit can estimate the user's emotions and adjust the budget of the travel plan based on the estimated user emotions. For example, if the user is relaxed, a luxurious travel plan with a higher budget can be proposed. If the user is stressed, a reasonable travel plan with a lower budget can be proposed. Furthermore, if the user is busy, an efficient budget allocation can be made to provide a travel plan that allows the user to use their time effectively. This allows the generation unit to provide a travel plan with an optimal budget according to the user's emotions. Emotion estimation is achieved using technologies such as facial expression recognition, voice analysis, and text analysis.

[0106] The providing unit can analyze the user's past travel plan history and optimize the timing of proposing a travel plan. For example, it can analyze the time and frequency at which the user planned trips in the past and propose a travel plan at the optimal timing. It can also adjust the proposal timing by referring to the success rate of the user's past travel plans. Furthermore, it can propose a travel plan at the optimal timing by taking into account the user's current schedule and plans. This allows the providing unit to propose a travel plan at the optimal timing based on the user's past history.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The collection unit inputs the user's ID and collects the user's past travel reservations and shopping purchase history. For example, the collection unit collects data such as the travel destinations the user has visited in the past and the categories of products purchased. The collection unit can also obtain information from multiple data sources. For example, the collection unit collects data from travel reservation sites and shopping sites. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and identify the user's preferences and behavioral patterns. For example, the generation AI determines that the user prefers beach resorts if the user has visited beach resorts many times in the past. The generation AI also determines that the user tends to visit certain places during certain seasons. Step 3: The generation unit uses the generation AI to generate a travel plan based on the preferences and behavioral patterns identified by the analysis unit. For example, if the user prefers beach resorts, the generation unit generates a travel plan that includes beach resorts. The generation unit can also customize the travel plan based on the user's preferences and behavioral patterns. Step 4: The providing unit provides the travel plan generated by the generating unit to the user. For example, the providing unit displays the generated travel plan on the user's device. The providing unit can also send the generated travel plan to the user by email or message.

[0109] 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.

[0110] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 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.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the 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.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the 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.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 inputs a user's ID and collects the history of past travel reservations and shopping purchases; an analysis unit that analyzes the data collected by the collection unit and identifies user preferences and behavior patterns; a generation unit that generates a travel plan based on the preferences and behavioral patterns identified by the analysis unit; a providing unit that provides the travel plan generated by the generating unit to the user. A system characterized by:

2. The generation unit Use big search data to analyze local trends and hotspots and plan your trip accordingly 2. The system of claim 1.

3. The collecting unit Estimate the user's emotions and adjust the timing of history collection based on the estimated user emotions.

2. The system of claim 1.

4. The collecting unit Analyze the user's past travel reservations and shopping purchase history to select the optimal collection method 2. The system of claim 1.

5. The collecting unit When collecting history, filter it based on the user's current life situation and interests.

2. The system of claim 1.

6. The collecting unit When collecting history, select the optimal collection method depending on the user's input method.

2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and prioritize the history to be collected based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit When collecting history, the system takes into account the user's geographic location information to prioritize collecting history that is highly relevant.

2. The system of claim 1.

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