system

Abstract

The system according to this embodiment aims to propose the optimal travel plan, taking into account the user's interests, budget, time, weather, and other factors. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a proposal unit. The collection unit collects information such as the user's interests, budget, time, and weather. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a travel plan based on the information analyzed by the analysis unit. The proposal unit proposes the travel plan generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to automatically generate an optimal travel plan considering the user's interests, budget, time, weather, etc.

[0005] The system according to the embodiment aims to propose an optimal travel plan considering the user's interests, budget, time, weather, etc.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a proposal unit. The collection unit collects information such as the user's interests, budget, time, and weather. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a travel plan based on the information analyzed by the analysis unit. The proposal unit proposes the travel plan generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can suggest an optimal travel plan, taking into account the user's interests, budget, time, weather, and other factors. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The travel plan suggestion system according to an embodiment of the present invention is a system that suggests an optimal travel plan considering the user's interests, budget, time, weather, etc. This system collects information such as the user's interests, budget, time, and weather, and the AI ​​analyzes tourist information and reviews from an extensive database to generate an optimal travel plan for the user. The generated travel plan is dynamically suggested based on the user's preferences and is also integrated with real-time weather information. For example, it collects information such as the user's interests, budget, time, and weather. At this time, the information entered by the user is collected and stored in a database. Next, based on the collected information, the AI ​​analyzes tourist information and reviews from an extensive database. The AI ​​selects the optimal tourist destinations and accommodations based on the user's interests and budget. Furthermore, the generating AI generates an optimal travel plan for the user based on the analysis results. The generated travel plan is dynamically suggested based on the user's preferences. For example, it prioritizes suggesting tourist destinations and accommodations that the user is interested in. It is also integrated with real-time weather information to suggest an optimal plan according to the weather. As a result, the user can efficiently plan their trip and the quality of their trip is improved. As a result, the travel plan suggestion system is expected to reduce the time spent on travel planning by an average of 70%, improve customer satisfaction by 30%, and increase repeat customer rates by 20%.

[0029] The travel plan suggestion system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a suggestion unit. The collection unit collects information such as the user's interests, budget, time, and weather. For example, the collection unit collects information entered by the user and stores it in a database. The collection unit can also use AI to automatically collect information such as the user's interests, budget, time, and weather. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses AI to analyze tourist information and reviews from a wide range of databases. The analysis unit selects the most suitable tourist destinations and accommodations based on the user's interests and budget. The generation unit generates a travel plan based on the information analyzed by the analysis unit. For example, the generation unit uses generation AI to generate a travel plan based on the analysis results. The generation unit can also dynamically generate a travel plan based on the user's preferences. The suggestion unit proposes the travel plan generated by the generation unit to the user. For example, the suggestion unit presents the generated travel plan to the user and receives user feedback. The suggestion unit can also integrate real-time weather information and propose the most suitable plan according to the weather. This allows the travel plan suggestion system to propose the most suitable travel plan based on information such as the user's interests, budget, time, and weather.

[0030] The data collection unit collects information such as user interests, budget, time, and weather. Specifically, it collects information entered by the user and stores it in a database. For example, it provides a form where the user enters their desired travel destination, purpose of travel, budget, duration of travel, preferred activities, etc., and collects this information. The data collection unit can also use AI to automatically collect information such as user interests, budget, time, and weather. For example, it analyzes the user's past search history, booking history, and social media posts to infer the user's interests and preferences. Furthermore, the data collection unit obtains real-time weather information from a weather forecast database and considers the weather during the travel period. This allows the data collection unit to create a more accurate user profile by combining the user's explicit input information with implicit behavioral data. The collected information is stored in a secure database and managed so that the analysis and generation units can access it. To protect user privacy, the data collection unit anonymizes and encrypts the data to ensure security. This allows the data collection unit to efficiently collect diverse user information and improve the accuracy and reliability of the entire system.

[0031] The analysis unit analyzes the information collected by the data collection unit. Specifically, it uses AI to analyze tourist information and reviews from a wide database. For example, it evaluates the popularity of tourist destinations, accessibility, and seasonal appeal, and selects the most suitable tourist destinations and accommodations based on the user's interests and budget. The analysis unit uses natural language processing technology to analyze user input information and review text data to identify tourist destinations and activities that match the user's preferences and expectations. It also uses machine learning algorithms to learn from past user data and make optimal suggestions to users with similar interests and budgets. Furthermore, the analysis unit considers real-time weather information and selects the most suitable tourist destinations and activities according to the weather. For example, it prioritizes suggesting indoor tourist destinations and activities during rainy weather and outdoor tourist destinations and activities during sunny weather. In this way, the analysis unit can build a foundation for responding to the diverse needs of users and providing optimal travel plans.

[0032] The generation unit generates travel plans based on the information analyzed by the analysis unit. Specifically, it uses a generation AI to generate travel plans based on the analysis results. The generation AI takes into account information such as the user's interests, budget, time, and weather, and automatically creates the optimal travel plan. For example, it combines the tourist destinations and activities desired by the user to generate a detailed travel plan including itinerary, transportation, and accommodation. The generation unit can also dynamically generate travel plans based on the user's preferences. For example, if the user wants to add a specific activity or change the budget, the generation AI will regenerate the plan in real time, providing the optimal plan that meets the user's needs. Furthermore, the generation unit learns from past user data and feedback to continuously improve the accuracy of the generation AI. As a result, the generation unit can respond to the diverse needs of users and provide flexible and highly accurate travel plans.

[0033] The suggestion unit proposes travel plans generated by the generation unit to the user. Specifically, it presents the generated travel plan to the user and receives user feedback. The suggestion unit visually displays the details of the travel plan through the user interface, making it easy for the user to understand. For example, it displays details of each day and activity in the travel plan, accommodation information, and transportation options using maps, images, and text. The suggestion unit can also integrate real-time weather information and propose the optimal plan according to the weather. For example, based on the weather forecast for the travel period, it suggests indoor activities in case of rain and outdoor activities in case of sunny weather. Furthermore, the suggestion unit collects user feedback and provides it to the generation unit to improve the accuracy of the travel plan and user satisfaction. For example, if a user wants to change the proposed plan, the suggestion unit communicates the details to the generation unit, and the generation AI regenerates the plan. In this way, the suggestion unit can provide the user with the optimal travel plan and increase user satisfaction.

[0034] The proposal unit can integrate real-time weather information and propose the optimal plan according to the weather. For example, the proposal unit obtains real-time weather information from weather data providers and reflects it in the travel plan. The proposal unit can also adjust the plan based on the latest weather information, taking into account the frequency of weather information updates. The proposal unit obtains the weather forecast for the user's travel destination and proposes tourist spots and activities according to the weather. For example, it proposes indoor tourist spots in case of rain and outdoor activities in case of sunny weather. In this way, by taking real-time weather information into consideration, it can propose the optimal travel plan according to the weather.

[0035] The data collection unit can analyze the user's past travel history and select the optimal information collection method. For example, the data collection unit prioritizes collecting information on relevant tourist destinations based on places the user has visited in the past. The data collection unit analyzes the user's past travel patterns and determines the optimal timing for information collection. The data collection unit collects information on similar accommodations based on reviews of accommodations the user has used in the past. This allows the optimal information collection method to be selected by analyzing the user's past travel history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0036] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, the data collection unit may prioritize collecting information on family-friendly tourist destinations based on the user's current living situation. The data collection unit may collect information on specific theme parks or museums based on the user's areas of interest. The data collection unit may collect information on accommodations that fit the user's budget based on the user's current living situation. By filtering information based on the user's current living situation and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0037] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit prioritizes the collection of information on tourist destinations close to the user's current location. Based on the user's geographical location, the data collection unit collects information on easily accessible accommodations. The data collection unit collects information on the most suitable means of transportation, considering the distance from the user's current location. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0038] The data collection unit can collect relevant information by analyzing the user's social media activity during information gathering. For example, the data collection unit can collect information on relevant tourist destinations based on information about travel destinations shared by the user on social media. The data collection unit can analyze the content of the user's social media posts and collect information related to themes of interest. The data collection unit can collect information on relevant tourist destinations by referring to information about places visited by the user's social media followers. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on important tourist destination information. For less important information, the analysis unit performs a concise analysis. For information of high user interest, the analysis unit performs a detailed analysis. By adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0040] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a dedicated analysis algorithm for tourist destinations to tourist destination information. The analysis unit applies a dedicated analysis algorithm for accommodation facilities to

[0041] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit lowers the priority of analysis for older information. The analysis unit prioritizes the analysis of information recently collected by the user. By determining the priority of analysis based on when the information was collected, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize analyzing information related to the user's interests, information related to the user's budget, or information related to the user's time. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0043] The generation unit can adjust the level of detail generated based on the importance of the travel plan during generation. For example, the generation unit generates a detailed travel plan for important tourist destinations. For less important tourist destinations, the generation unit generates a concise travel plan. For tourist destinations of high user interest, the generation unit generates a detailed travel plan. By adjusting the level of detail generated based on the importance of the travel plan, a more appropriate travel plan can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0044] The generation unit can apply different generation algorithms depending on the category of the travel plan during generation. For example, the generation unit applies a generation algorithm specifically for tourist destinations to tourist destination information. The generation unit applies a generation algorithm specifically for accommodation facilities to accommodation facilities to accommodation facilities to accommodation facilities to accommodation facilities to accommodation facilities to accommodation facilities to transportation

[0045] The generation unit can determine the generation priority based on the submission timing of the travel plans during generation. For example, the generation unit may prioritize generating the most recent travel plans. The generation unit may prioritize generating travel plans with approaching submission deadlines. If the user is in a hurry, the generation unit will quickly generate a travel plan. This allows for the provision of more appropriate travel plans by prioritizing generation based on the submission timing of the travel plans. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0046] The generation unit can adjust the order of generation based on the relevance of the travel plans during generation. For example, the generation unit may prioritize generating travel plans related to the user's interests, travel plans related to the user's budget, or travel plans related to the user's time. By adjusting the order of generation based on the relevance of the travel plans, it is possible to provide more appropriate travel plans. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0047] The suggestion function can adjust the level of detail in its suggestions based on the importance of the travel plan. For example, it can provide detailed suggestions for important tourist destinations, concise suggestions for less important destinations, and detailed suggestions for destinations of high user interest. By adjusting the level of detail based on the importance of the travel plan, it can provide more appropriate suggestions. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the travel plan when making suggestions. For example, the suggestion unit applies a suggestion algorithm specifically for tourist destinations to tourist destination information. The suggestion unit applies a suggestion algorithm specifically for accommodations to accommodation information. The suggestion unit applies a suggestion algorithm specifically for transportation to transportation information. By applying different suggestion algorithms depending on the category of the travel plan, it is possible to provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0049] The proposal department can prioritize proposals based on when the travel plan is submitted. For example, the proposal department may prioritize proposals for the most recent travel plans. The proposal department may also prioritize proposals for travel plans with approaching submission deadlines. If the user is in a hurry, the proposal department will provide proposals quickly. This allows the proposal department to provide more appropriate proposals by prioritizing proposals based on when the travel plan is submitted. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the travel plans. For example, the suggestion unit may prioritize suggesting travel plans related to the user's interests, travel plans related to the user's budget, or travel plans related to the user's time. By adjusting the order of suggestions based on the relevance of the travel plans, the suggestion unit can provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The data collection unit can monitor the user's health status and adjust the timing of information collection based on that status. For example, if the user is fatigued, information collection will begin after they have rested. If the user is healthy and active, information collection will begin immediately. If the user is ill, information collection will be withheld until their health condition improves. This allows for more appropriate information collection by adjusting the timing of information collection according to the user's health status.

[0053] The generation unit can analyze users' past reviews and generate travel plans based on the review content. For example, it can prioritize including tourist destinations that users have given high ratings to in the past, exclude accommodations that users have expressed dissatisfaction with, and suggest activities that users particularly enjoyed again. In this way, by analyzing users' past reviews, it is possible to generate more satisfying travel plans.

[0054] The data collection unit can monitor the user's current activity status and adjust the timing of information collection based on that status. For example, if the user is at work, data collection will be withheld until work is finished. If the user is on vacation, data collection will begin immediately. If the user is traveling, data collection will be withheld until travel is complete. By adjusting the timing of data collection according to the user's current activity status, more appropriate data collection becomes possible.

[0055] The generation unit can create travel plans that take into account the user's current weather information. For example, it might include indoor tourist attractions in case of rain, outdoor activities in case of sunny weather, and ski resorts on snowy days. By considering the user's current weather information, it can generate more appropriate travel plans.

[0056] The data collection unit can monitor the user's current device usage and adjust the timing of data collection based on that usage. For example, if the user is using a smartphone, data collection will be suspended. If the user is using a computer, data collection will begin immediately. If the user is not using a device, a notification will be sent prompting them to use it. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's current device usage.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects information such as the user's interests, budget, time, and weather. For example, the data collection unit collects information entered by the user and stores it in a database. The data collection unit can also use AI to automatically collect information such as the user's interests, budget, time, and weather. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses AI, for example, to analyze tourist information and reviews from a wide database. The analysis unit selects the most suitable tourist destinations and accommodations based on the user's interests and budget. Step 3: The generation unit generates a travel plan based on the information analyzed by the analysis unit. The generation unit generates a travel plan based on the analysis results, for example, using a generation AI. The generation unit can also dynamically generate a travel plan based on the user's preferences. Step 4: The suggestion unit proposes the travel plan generated by the generation unit to the user. For example, the suggestion unit presents the generated travel plan to the user and receives user feedback. The suggestion unit can also integrate real-time weather information and propose the optimal plan according to the weather.

[0059] (Example of form 2) The travel plan suggestion system according to an embodiment of the present invention is a system that suggests an optimal travel plan considering the user's interests, budget, time, weather, etc. This system collects information such as the user's interests, budget, time, and weather, and the AI ​​analyzes tourist information and reviews from an extensive database to generate an optimal travel plan for the user. The generated travel plan is dynamically suggested based on the user's preferences and is also integrated with real-time weather information. For example, it collects information such as the user's interests, budget, time, and weather. At this time, the information entered by the user is collected and stored in a database. Next, based on the collected information, the AI ​​analyzes tourist information and reviews from an extensive database. The AI ​​selects the optimal tourist destinations and accommodations based on the user's interests and budget. Furthermore, the generating AI generates an optimal travel plan for the user based on the analysis results. The generated travel plan is dynamically suggested based on the user's preferences. For example, it prioritizes suggesting tourist destinations and accommodations that the user is interested in. It is also integrated with real-time weather information to suggest an optimal plan according to the weather. As a result, the user can efficiently plan their trip and the quality of their trip is improved. As a result, the travel plan suggestion system is expected to reduce the time spent on travel planning by an average of 70%, improve customer satisfaction by 30%, and increase repeat customer rates by 20%.

[0060] The travel plan suggestion system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a suggestion unit. The collection unit collects information such as the user's interests, budget, time, and weather. For example, the collection unit collects information entered by the user and stores it in a database. The collection unit can also use AI to automatically collect information such as the user's interests, budget, time, and weather. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses AI to analyze tourist information and reviews from a wide range of databases. The analysis unit selects the most suitable tourist destinations and accommodations based on the user's interests and budget. The generation unit generates a travel plan based on the information analyzed by the analysis unit. For example, the generation unit uses generation AI to generate a travel plan based on the analysis results. The generation unit can also dynamically generate a travel plan based on the user's preferences. The suggestion unit proposes the travel plan generated by the generation unit to the user. For example, the suggestion unit presents the generated travel plan to the user and receives user feedback. The suggestion unit can also integrate real-time weather information and propose the most suitable plan according to the weather. This allows the travel plan suggestion system to propose the most suitable travel plan based on information such as the user's interests, budget, time, and weather.

[0061] The data collection unit collects information such as user interests, budget, time, and weather. Specifically, it collects information entered by the user and stores it in a database. For example, it provides a form where the user enters their desired travel destination, purpose of travel, budget, duration of travel, preferred activities, etc., and collects this information. The data collection unit can also use AI to automatically collect information such as user interests, budget, time, and weather. For example, it analyzes the user's past search history, booking history, and social media posts to infer the user's interests and preferences. Furthermore, the data collection unit obtains real-time weather information from a weather forecast database and considers the weather during the travel period. This allows the data collection unit to create a more accurate user profile by combining the user's explicit input information with implicit behavioral data. The collected information is stored in a secure database and managed so that the analysis and generation units can access it. To protect user privacy, the data collection unit anonymizes and encrypts the data to ensure security. This allows the data collection unit to efficiently collect diverse user information and improve the accuracy and reliability of the entire system.

[0062] The analysis unit analyzes the information collected by the data collection unit. Specifically, it uses AI to analyze tourist information and reviews from a wide database. For example, it evaluates the popularity of tourist destinations, accessibility, and seasonal appeal, and selects the most suitable tourist destinations and accommodations based on the user's interests and budget. The analysis unit uses natural language processing technology to analyze user input information and review text data to identify tourist destinations and activities that match the user's preferences and expectations. It also uses machine learning algorithms to learn from past user data and make optimal suggestions to users with similar interests and budgets. Furthermore, the analysis unit considers real-time weather information and selects the most suitable tourist destinations and activities according to the weather. For example, it prioritizes suggesting indoor tourist destinations and activities during rainy weather and outdoor tourist destinations and activities during sunny weather. In this way, the analysis unit can build a foundation for responding to the diverse needs of users and providing optimal travel plans.

[0063] The generation unit generates travel plans based on the information analyzed by the analysis unit. Specifically, it uses a generation AI to generate travel plans based on the analysis results. The generation AI takes into account information such as the user's interests, budget, time, and weather, and automatically creates the optimal travel plan. For example, it combines the tourist destinations and activities desired by the user to generate a detailed travel plan including itinerary, transportation, and accommodation. The generation unit can also dynamically generate travel plans based on the user's preferences. For example, if the user wants to add a specific activity or change the budget, the generation AI will regenerate the plan in real time, providing the optimal plan that meets the user's needs. Furthermore, the generation unit learns from past user data and feedback to continuously improve the accuracy of the generation AI. As a result, the generation unit can respond to the diverse needs of users and provide flexible and highly accurate travel plans.

[0064] The suggestion unit proposes travel plans generated by the generation unit to the user. Specifically, it presents the generated travel plan to the user and receives user feedback. The suggestion unit visually displays the details of the travel plan through the user interface, making it easy for the user to understand. For example, it displays details of each day and activity in the travel plan, accommodation information, and transportation options using maps, images, and text. The suggestion unit can also integrate real-time weather information and propose the optimal plan according to the weather. For example, based on the weather forecast for the travel period, it suggests indoor activities in case of rain and outdoor activities in case of sunny weather. Furthermore, the suggestion unit collects user feedback and provides it to the generation unit to improve the accuracy of the travel plan and user satisfaction. For example, if a user wants to change the proposed plan, the suggestion unit communicates the details to the generation unit, and the generation AI regenerates the plan. In this way, the suggestion unit can provide the user with the optimal travel plan and increase user satisfaction.

[0065] The proposal unit can integrate real-time weather information and propose the optimal plan according to the weather. For example, the proposal unit obtains real-time weather information from weather data providers and reflects it in the travel plan. The proposal unit can also adjust the plan based on the latest weather information, taking into account the frequency of weather information updates. The proposal unit obtains the weather forecast for the user's travel destination and proposes tourist spots and activities according to the weather. For example, it proposes indoor tourist spots in case of rain and outdoor activities in case of sunny weather. In this way, by taking real-time weather information into consideration, it can propose the optimal travel plan according to the weather.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, the data collection unit estimates the user's emotions using facial recognition technology. The data collection unit acquires the user's facial data with a camera and estimates the emotions using an emotion estimation algorithm. If the user is feeling stressed, the data collection unit collects information during times when the user can relax. If the user is excited, the data collection unit starts collecting information immediately and provides results quickly. If the user is tired, the data collection unit collects information after the user has rested. This allows for more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The data collection unit can analyze the user's past travel history and select the optimal information collection method. For example, the data collection unit prioritizes collecting information on relevant tourist destinations based on places the user has visited in the past. The data collection unit analyzes the user's past travel patterns and determines the optimal timing for information collection. The data collection unit collects information on similar accommodations based on reviews of accommodations the user has used in the past. This allows the optimal information collection method to be selected by analyzing the user's past travel history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0068] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, the data collection unit may prioritize collecting information on family-friendly tourist destinations based on the user's current living situation. The data collection unit may collect information on specific theme parks or museums based on the user's areas of interest. The data collection unit may collect information on accommodations that fit the user's budget based on the user's current living situation. By filtering information based on the user's current living situation and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0069] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, the data collection unit estimates the user's emotions using facial recognition technology. The data collection unit acquires the user's facial data with a camera and estimates the emotions using an emotion estimation algorithm. If the user is excited, the data collection unit prioritizes collecting activity information. If the user is relaxed, the data collection unit prioritizes collecting information about relaxing tourist destinations. If the user is stressed, the data collection unit prioritizes collecting information that helps relieve stress. In this way, more appropriate information can be collected by determining the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit prioritizes the collection of information on tourist destinations close to the user's current location. Based on the user's geographical location, the data collection unit collects information on easily accessible accommodations. The data collection unit collects information on the most suitable means of transportation, considering the distance from the user's current location. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0071] The data collection unit can collect relevant information by analyzing the user's social media activity during information gathering. For example, the data collection unit can collect information on relevant tourist destinations based on information about travel destinations shared by the user on social media. The data collection unit can analyze the content of the user's social media posts and collect information related to themes of interest. The data collection unit can collect information on relevant tourist destinations by referring to information about places visited by the user's social media followers. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions using facial recognition technology. The analysis unit acquires the user's facial data with a camera and estimates the emotions using an emotion estimation algorithm. When the user is relaxed, the analysis unit provides detailed analysis results. When the user is in a hurry, the analysis unit provides concise analysis results. When the user is excited, the analysis unit provides visually appealing analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on important tourist destination information. For less important information, the analysis unit performs a concise analysis. For information of high user interest, the analysis unit performs a detailed analysis. By adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0074] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a dedicated analysis algorithm for tourist destinations to tourist destination information. The analysis unit applies a dedicated analysis algorithm for accommodation facilities to

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions using facial recognition technology. The analysis unit acquires the user's facial data via a camera and estimates the emotions using an emotion estimation algorithm. If the user is in a hurry, the analysis unit provides a short, concise analysis result. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is excited, the analysis unit provides a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit lowers the priority of analysis for older information. The analysis unit prioritizes the analysis of information recently collected by the user. By determining the priority of analysis based on when the information was collected, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize analyzing information related to the user's interests, information related to the user's budget, or information related to the user's time. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0078] The generation unit can estimate the user's emotions and adjust the way the generated travel plan is presented based on the estimated emotions. For example, the generation unit estimates the user's emotions using facial recognition technology. The generation unit acquires the user's facial data with a camera and estimates the emotions using an emotion estimation algorithm. If the user is relaxed, the generation unit generates a detailed travel plan. If the user is in a hurry, the generation unit generates a concise travel plan. If the user is excited, the generation unit generates a visually appealing travel plan. This allows for the provision of more appropriate travel plans by adjusting the presentation of the travel plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The generation unit can adjust the level of detail generated based on the importance of the travel plan during generation. For example, the generation unit generates a detailed travel plan for important tourist destinations. For less important tourist destinations, the generation unit generates a concise travel plan. For tourist destinations of high user interest, the generation unit generates a detailed travel plan. By adjusting the level of detail generated based on the importance of the travel plan, a more appropriate travel plan can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0080] The generation unit can apply different generation algorithms depending on the category of the travel plan during generation. For example, the generation unit applies a generation algorithm specifically for tourist destinations to tourist destination information. The generation unit applies a generation algorithm specifically for accommodation facilities to accommodation facilities to accommodation facilities to accommodation facilities to accommodation facilities to accommodation facilities to accommodation facilities to transportation

[0081] The generation unit can estimate the user's emotions and adjust the length of the generated travel plan based on the estimated emotions. For example, the generation unit estimates the user's emotions using facial recognition technology. The generation unit acquires the user's facial data via a camera and estimates emotions using an emotion estimation algorithm. If the user is in a hurry, the generation unit generates a short, concise travel plan. If the user is relaxed, the generation unit generates a detailed travel plan. If the user is excited, the generation unit generates a visually appealing travel plan. This allows for the provision of more appropriate travel plans by adjusting the length of the travel plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The generation unit can determine the generation priority based on the submission timing of the travel plans during generation. For example, the generation unit may prioritize generating the most recent travel plans. The generation unit may prioritize generating travel plans with approaching submission deadlines. If the user is in a hurry, the generation unit will quickly generate a travel plan. This allows for the provision of more appropriate travel plans by prioritizing generation based on the submission timing of the travel plans. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0083] The generation unit can adjust the order of generation based on the relevance of the travel plans during generation. For example, the generation unit may prioritize generating travel plans related to the user's interests, travel plans related to the user's budget, or travel plans related to the user's time. By adjusting the order of generation based on the relevance of the travel plans, it is possible to provide more appropriate travel plans. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0084] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, the suggestion unit might use facial recognition technology to estimate the user's emotions. The suggestion unit might acquire the user's facial data using a camera and estimate the emotions using an emotion estimation algorithm. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. If the user is excited, the suggestion unit will provide visually appealing suggestions. By adjusting the way it presents suggestions according to the user's emotions, it can provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The suggestion function can adjust the level of detail in its suggestions based on the importance of the travel plan. For example, it can provide detailed suggestions for important tourist destinations, concise suggestions for less important destinations, and detailed suggestions for destinations of high user interest. By adjusting the level of detail based on the importance of the travel plan, it can provide more appropriate suggestions. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI.

[0086] The suggestion unit can apply different suggestion algorithms depending on the category of the travel plan when making suggestions. For example, the suggestion unit applies a suggestion algorithm specifically for tourist destinations to tourist destination information. The suggestion unit applies a suggestion algorithm specifically for accommodations to accommodation information. The suggestion unit applies a suggestion algorithm specifically for transportation to transportation information. By applying different suggestion algorithms depending on the category of the travel plan, it is possible to provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, the suggestion unit estimates the user's emotions using facial recognition technology. The suggestion unit acquires the user's facial data with a camera and estimates the emotions using an emotion estimation algorithm. If the user is in a hurry, the suggestion unit makes short, to-the-point suggestions. If the user is relaxed, the suggestion unit makes detailed suggestions. If the user is excited, the suggestion unit makes visually appealing suggestions. By adjusting the length of the suggestion according to the user's emotions, it is possible to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The proposal department can prioritize proposals based on when the travel plan is submitted. For example, the proposal department may prioritize proposals for the most recent travel plans. The proposal department may also prioritize proposals for travel plans with approaching submission deadlines. If the user is in a hurry, the proposal department will provide proposals quickly. This allows the proposal department to provide more appropriate proposals by prioritizing proposals based on when the travel plan is submitted. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI.

[0089] The suggestion unit can adjust the order of suggestions based on the relevance of the travel plans. For example, the suggestion unit may prioritize suggesting travel plans related to the user's interests, travel plans related to the user's budget, or travel plans related to the user's time. By adjusting the order of suggestions based on the relevance of the travel plans, the suggestion unit can provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, it will prioritize analyzing information on relaxing tourist destinations. If the user is excited, it will prioritize analyzing information on activities. If the user is tired, it will prioritize analyzing information on accommodations suitable for rest. By prioritizing analysis according to the user's emotions, it can provide more appropriate analysis results. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0092] The data collection unit can monitor the user's health status and adjust the timing of information collection based on that status. For example, if the user is fatigued, information collection will begin after they have rested. If the user is healthy and active, information collection will begin immediately. If the user is ill, information collection will be withheld until their health condition improves. This allows for more appropriate information collection by adjusting the timing of information collection according to the user's health status.

[0093] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is relaxed, it will provide detailed suggestions. If the user is in a hurry, it will provide concise suggestions. If the user is excited, it will provide visually appealing suggestions. By adjusting the timing of suggestions according to the user's emotions, it can provide more appropriate suggestions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0094] The generation unit can analyze users' past reviews and generate travel plans based on the review content. For example, it can prioritize including tourist destinations that users have given high ratings to in the past, exclude accommodations that users have expressed dissatisfaction with, and suggest activities that users particularly enjoyed again. In this way, by analyzing users' past reviews, it is possible to generate more satisfying travel plans.

[0095] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated emotions. For example, if the user is relaxed, it provides detailed analysis results. If the user is in a hurry, it provides concise analysis results. If the user is excited, it provides visually appealing analysis results. By adjusting the depth of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0096] The data collection unit can monitor the user's current activity status and adjust the timing of information collection based on that status. For example, if the user is at work, data collection will be withheld until work is finished. If the user is on vacation, data collection will begin immediately. If the user is traveling, data collection will be withheld until travel is complete. By adjusting the timing of data collection according to the user's current activity status, more appropriate data collection becomes possible.

[0097] The suggestion function can estimate the user's emotions and adjust the content of suggestions based on those emotions. For example, if the user is relaxed, it will suggest relaxing tourist destinations. If the user is excited, it will suggest activity information. If the user is stressed, it will suggest information that can help relieve stress. By adjusting the content of suggestions according to the user's emotions, it can provide more appropriate suggestions. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0098] The generation unit can create travel plans that take into account the user's current weather information. For example, it might include indoor tourist attractions in case of rain, outdoor activities in case of sunny weather, and ski resorts on snowy days. By considering the user's current weather information, it can generate more appropriate travel plans.

[0099] The analysis unit can estimate the user's emotions and adjust the order of analysis based on the estimated emotions. For example, if the user is relaxed, detailed analysis results are prioritized. If the user is in a hurry, concise analysis results are prioritized. If the user is excited, visually appealing analysis results are prioritized. By adjusting the order of analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0100] The data collection unit can monitor the user's current device usage and adjust the timing of data collection based on that usage. For example, if the user is using a smartphone, data collection will be suspended. If the user is using a computer, data collection will begin immediately. If the user is not using a device, a notification will be sent prompting them to use it. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's current device usage.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection unit collects information such as the user's interests, budget, time, and weather. For example, the data collection unit collects information entered by the user and stores it in a database. The data collection unit can also use AI to automatically collect information such as the user's interests, budget, time, and weather. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses AI, for example, to analyze tourist information and reviews from a wide database. The analysis unit selects the most suitable tourist destinations and accommodations based on the user's interests and budget. Step 3: The generation unit generates a travel plan based on the information analyzed by the analysis unit. The generation unit generates a travel plan based on the analysis results, for example, using a generation AI. The generation unit can also dynamically generate a travel plan based on the user's preferences. Step 4: The suggestion unit proposes the travel plan generated by the generation unit to the user. For example, the suggestion unit presents the generated travel plan to the user and receives user feedback. The suggestion unit can also integrate real-time weather information and propose the optimal plan according to the weather.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information such as the user's interests, budget, time, and weather using the control unit 46A of the smart device 14 and stores it in the database 24. The analysis unit analyzes the collected information using the specific processing unit 290 of the data processing unit 12 and analyzes tourist information and reviews from an extensive database. The generation unit generates a travel plan based on the analysis results using the specific processing unit 290 of the data processing unit 12. The proposal unit can present the travel plan generated by the control unit 46A of the smart device 14 to the user and propose an optimal plan by integrating real-time weather information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information such as the user's interests, budget, time, and weather using the control unit 46A of the smart glasses 214 and stores it in the database 24. The analysis unit analyzes the collected information using the specific processing unit 290 of the data processing unit 12, for example, and analyzes tourist information and reviews from an extensive database. The generation unit generates a travel plan based on the analysis results using the specific processing unit 290 of the data processing unit 12. The proposal unit can, for example, present the travel plan generated by the control unit 46A of the smart glasses 214 to the user and propose an optimal plan by integrating real-time weather information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information such as the user's interests, budget, time, and weather using the control unit 46A of the headset terminal 314 and stores it in the database 24. The analysis unit analyzes the collected information using the specific processing unit 290 of the data processing unit 12 and analyzes tourist information and reviews from an extensive database. The generation unit generates a travel plan based on the analysis results using the specific processing unit 290 of the data processing unit 12. The proposal unit can present the travel plan generated by the control unit 46A of the headset terminal 314 to the user and propose an optimal plan by integrating real-time weather information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and proposal unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information such as the user's interests, budget, time, and weather using the control unit 46A of the robot 414 and stores it in the database 24. The analysis unit analyzes the collected information using, for example, the specific processing unit 290 of the data processing unit 12, and analyzes tourist information and reviews from an extensive database. The generation unit generates a travel plan based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The proposal unit can, for example, present the travel plan generated by the control unit 46A of the robot 414 to the user and propose an optimal plan by integrating real-time weather information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) A data collection unit that collects information such as user interests, budget, time, and weather, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit generates a travel plan based on the information analyzed by the analysis unit, The system includes a proposal unit that proposes the travel plan generated by the generation unit to the user. A system characterized by the following features. (Note 2) The aforementioned proposal section is, It integrates real-time weather information and proposes the optimal plan according to the weather. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Analyze the user's past travel history to select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is We estimate the user's emotions and adjust how the travel plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, adjust the level of detail based on the importance of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, different generation algorithms are applied depending on the category of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the length of the travel plan generated based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the priority of generation is determined based on when the travel plan was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the order of generation is adjusted based on the relevance of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When submitting proposals, we will prioritize them based on when the travel plan is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on the relevance of the travel plan. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects information such as user interests, budget, time, and weather, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit generates a travel plan based on the information analyzed by the analysis unit, The system includes a proposal unit that proposes the travel plan generated by the generation unit to the user. A system characterized by the following features.

2. The aforementioned proposal section is, It integrates real-time weather information and proposes the optimal plan according to the weather. The system according to feature 1.

3. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

4. The aforementioned collection unit is Analyze the user's past travel history to select the most suitable information gathering method. The system according to feature 1.

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

6. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

8. The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system according to feature 1.

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