system

Abstract

The system according to the embodiment aims to select and provide an optimal travel plan that meets the diverse needs of users. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit and selects an appropriate plan. The provision unit provides the plan selected by the analysis unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to select an optimal travel plan that meets the diverse needs of users.

[0005] The system according to the embodiment aims to select and provide an optimal travel plan that meets the diverse needs of users. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit and selects an appropriate plan. The provision unit provides the user with the plan selected by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can select and provide an optimal travel plan that meets the diverse needs of users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel planning system according to an embodiment of the present invention collects user information, selects an optimal plan, and provides it. This system collects information such as the user's purpose, destination, number of days, budget, preferences, and membership cards, and selects the optimal plan and itinerary based on that information. It analyzes the information using AI and proposes the optimal plan. Furthermore, it also has a function for providing the selected plan and itinerary to the user, collecting user feedback, and reflecting it in future travel plans. For example, for business trips, it proposes plans tailored to the location and time of a meeting. For leisure trips, it proposes plans tailored to tourist spots and activities. It also includes a function for displaying details and maps of the selected plan and itinerary. This allows the user to easily confirm the optimal plan for them. It also collects user feedback and reflects it in future travel plans. For example, by allowing the user to enter ratings and comments on plans, the AI ​​can learn from that feedback and reflect it in future travel plans. This allows for more accurate travel plans. Users can easily select plans tailored to their purpose and requirements, streamlining travel planning. For example, for business trips, it proposes plans tailored to the location and time of a meeting, allowing for efficient travel. In the case of sightseeing trips, plans tailored to tourist spots and activities are proposed, allowing users to enjoy a fulfilling trip. This allows the travel planning system to efficiently collect, analyze, and provide user information.

[0029] A travel planning system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user information. The user information includes, but is not limited to, purpose, destination, number of days, budget, preferences, and membership cards owned. The collection unit, for example, uses AI to select an optimal plan based on information input by the user. The analysis unit analyzes the information collected by the collection unit and selects an optimal plan. The analysis unit, for example, uses AI to analyze the information and proposes an optimal plan. For example, in the case of a business trip, a plan tailored to the location and time of the meeting is proposed. In the case of a sightseeing trip, a plan tailored to tourist spots and activities is proposed. The provision unit provides the user with the plan selected by the analysis unit. The provision unit displays, for example, details of the selected plan and a route map. This allows the user to easily confirm the optimal plan for themselves. Furthermore, the provision unit collects user feedback and reflects it in the next planning. For example, when the user inputs ratings and comments on the plan, the AI ​​can learn the feedback and reflect it in the next planning. This allows the provision of more accurate plans. This allows the travel planning system according to the embodiment to efficiently collect, analyze, and provide user information.

[0030] The system includes a feedback collection unit. The feedback collection unit collects user feedback. For example, the feedback collection unit collects feedback by the user inputting an evaluation or comment on the plan. The feedback collection unit reflects the collected feedback in the next planning. In this way, user feedback can be collected and reflected in the next planning. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit may input user feedback into AI and have the AI ​​analyze the feedback.

[0031] The system includes a learning unit. The learning unit allows the AI ​​to learn based on the feedback collected by the feedback collecting unit. The learning unit, for example, learns the user's feedback and reflects it in the next planning. The learning unit analyzes the feedback using the AI ​​and reflects it in the next planning. This allows the AI ​​to learn the user's feedback and reflect it in the next planning. Some or all of the above-mentioned processing in the learning unit may be performed using the AI, for example, or may be performed without using the AI. For example, the learning unit may input the feedback collected by the feedback collecting unit into the AI ​​and cause the AI ​​to analyze the feedback.

[0032] The collection unit can collect information including the user's purpose, destination, number of days, budget, preferences, and owned membership cards. The collection unit collects information such as the user's purpose, destination, number of days, budget, preferences, and owned membership cards. The collection unit uses AI to select the optimal plan based on the information entered by the user. For example, in the case of a business trip, a plan tailored to the location and time of the meeting is proposed. In the case of a sightseeing trip, a plan tailored to tourist spots and activities is proposed. By collecting detailed information about the user, a more optimal plan can be provided. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's information into AI and have the AI ​​analyze the information.

[0033] The analysis unit can select an appropriate plan based on the collected information. The analysis unit selects the optimal plan based on the collected information. The analysis unit, for example, uses AI to analyze the information and propose the optimal plan. For example, in the case of a business trip, a plan tailored to the location and time of the meeting is proposed. In the case of a sightseeing trip, a plan tailored to the tourist spots and activities is proposed. In this way, by selecting the optimal plan based on the collected information, it is possible to provide the user with an optimal travel plan. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected information into AI and have the AI ​​analyze the information.

[0034] The providing unit can display details of the selected plan and a map of the course. The providing unit displays details of the selected plan and a map of the course. The providing unit displays, for example, details of the selected plan and a map of the course. This allows the user to easily check the plan that is best suited to them. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input details of the selected plan and a map of the course into AI and have the AI ​​execute the display processing.

[0035] The collection unit can analyze the user's past travel history and select the optimal information collection method. For example, the collection unit prioritizes collection of similar plans based on travel plans used by the user in the past. The collection unit suggests new destinations based on places the user has visited in the past. The collection unit collects plans including preferred activities from the user's past travel history. This enables more optimal information collection by analyzing the user's past travel history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past travel history into AI and have the AI ​​analyze the information.

[0036] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, if the user is busy with their current living situation, the collection unit collects plans that can be enjoyed in a short period of time. If the user has a specific area of ​​interest (e.g., history, nature), the collection unit collects plans related to that area. If the user is planning a family trip, the collection unit preferentially collects plans for families. This allows more appropriate information to be collected by filtering information based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's current living situation and areas of interest into AI and have the AI ​​perform information filtering.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information about tourist spots close to the user's current location. If the user is interested in a particular area, the collection unit prioritizes collecting information related to that area. The collection unit prioritizes collecting information about restaurants and hotels near the user's travel destination. This makes it possible to prioritize collecting highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and have the AI ​​perform information filtering.

[0038] The collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit collects related plans based on travel destination information shared by the user on social media. The collection unit collects the latest information from travel-related accounts the user follows. The collection unit collects plans of interest based on posts the user has "liked" on social media. This allows related information to be collected by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI ​​analyze the information.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit performs a brief analysis of information with low importance. The analysis unit performs an analysis with an appropriate level of detail for information with medium importance. In this way, by adjusting the level of detail of the analysis based on the importance of the collected information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the collected information into AI and have the AI ​​perform the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies an analysis algorithm that emphasizes efficiency to information about business travel. The analysis unit applies an analysis algorithm that emphasizes enjoyment to information about sightseeing travel. The analysis unit applies an analysis algorithm that emphasizes safety to information about family travel. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data into AI and have the AI ​​select the analysis algorithm.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit analyzes older information with a lower priority. The analysis unit analyzes information that has been submitted recently with a moderate priority. In this way, by determining the priority of analysis based on the time of submission of information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information into AI and have the AI ​​perform the analysis priority.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information with high relevance. The analysis unit analyzes information with low relevance later. The analysis unit analyzes information with moderate relevance in an appropriate order. 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-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the information into AI and have the AI ​​execute the order of analysis.

[0043] The providing unit can adjust the level of detail of the provided information based on the importance of the plan when providing the information. For example, the providing unit provides detailed information for plans with high importance. The providing unit provides concise information for plans with low importance. The providing unit provides information with an appropriate level of detail for plans with medium importance. In this way, by adjusting the level of detail of the provided information based on the importance of the plan, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input plan importance data into AI and have the AI ​​execute the level of detail of the provided information.

[0044] The providing unit can apply different provision algorithms depending on the category of the plan when providing the plan. For example, the providing unit applies a provision algorithm that emphasizes efficiency to a business trip plan. The providing unit applies a provision algorithm that emphasizes fun to a sightseeing trip plan. The providing unit applies a provision algorithm that emphasizes safety to a family trip plan. In this way, by applying different provision algorithms depending on the category of the plan, more appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input plan category data into AI and have the AI ​​select the provision algorithm.

[0045] The provision unit can determine the priority of provision based on the time of plan submission at the time of provision. For example, the provision unit provides the most recent plan with priority. The provision unit provides older plans with a lower priority. The provision unit provides plans that have been submitted recently with a moderate priority. In this way, by determining the priority of provision based on the time of plan submission, more appropriate information can be provided. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input plan submission time data into AI and have the AI ​​execute the provision priority.

[0046] The providing unit can adjust the order of provision based on the relevance of the plans when providing them. For example, the providing unit provides plans with high relevance preferentially. The providing unit provides plans with low relevance later. The providing unit provides plans with medium relevance in an appropriate order. In this way, by adjusting the order of provision based on the relevance of the plans, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit inputs plan relevance data into AI and causes the AI ​​to execute the order of provision.

[0047] When collecting feedback, the feedback collection unit can select the optimal collection method by referring to the user's past feedback history. For example, if the user provided detailed feedback in the past, the feedback collection unit requests similar detailed feedback. If the user provided brief feedback in the past, the feedback collection unit requests similar brief feedback. The feedback collection unit selects the optimal collection method from the user's past feedback history. This enables more appropriate feedback collection by referring to the user's past feedback history. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the user's past feedback history data into AI and have the AI ​​select the collection method.

[0048] When collecting feedback, the feedback collection unit can select the optimal collection method by taking into account the user's device information. For example, if the user is using a smartphone, the feedback collection unit provides a feedback form optimized for touch operation. If the user is using a tablet, the feedback collection unit provides a feedback form optimized for a large screen. If the user is using a desktop, the feedback collection unit provides a feedback form optimized for keyboard input. This enables more appropriate feedback collection by taking into account the user's device information. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the user's device information data into AI and have the AI ​​select the collection method.

[0049] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit selects the most effective algorithm from past learning data. The learning unit adjusts the parameters of the algorithm based on the past learning data. The learning unit analyzes the past learning data and identifies areas for improvement in the algorithm. In this way, the learning algorithm can be optimized by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into AI and have the AI ​​perform algorithm optimization.

[0050] During learning, the learning unit can weight the learning data based on the timing of feedback submission. For example, the learning unit weights the learning data by prioritizing the most recent feedback. The learning unit reduces the weighting for older feedback. The learning unit weights feedback submitted at a moderate time. In this way, weighting the learning data based on the timing of feedback submission enables more appropriate learning. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input feedback submission timing data into AI and have the AI ​​perform weighting of the learning data.

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

[0052] The travel planning system can further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit collects data on events the user has previously attended, products they have purchased, and websites they have visited, and provides this data to the analysis unit. Based on this data, the analysis unit can select a travel plan that best suits the user's hobbies and interests. For example, if the user is a music lover, the analysis unit can suggest a plan to attend a music festival. If the user is an outdoor enthusiast, the analysis unit can suggest a plan for camping and hiking. This makes it possible to provide the user with an optimal travel plan that matches their hobbies and interests.

[0053] The travel planning system may further include a social network analysis unit that analyzes the user's social network. The social network analysis unit, for example, collects the travel histories and ratings of the user's friends and family and provides them to the analysis unit. Based on this data, the analysis unit can select an optimal travel plan based on the user's social network. For example, the analysis unit may suggest tourist spots that the user's friends have given high ratings to, or activities that the user's family has enjoyed. This makes it possible to provide an optimal travel plan based on the user's social network.

[0054] The travel planning system can further include a predictive analysis unit based on the user's past travel history. The predictive analysis unit, for example, collects data on places the user has visited and activities the user has participated in in the past and provides it to the analysis unit. Based on this data, the analysis unit can predict the user's future travel destinations and activities and select an optimal travel plan. For example, if the user has previously visited beach resorts, the analysis unit can suggest a beach resort for the next trip. If the user has previously participated in cultural events, the analysis unit can suggest a cultural event for the next trip. This makes it possible to provide an optimal travel plan based on the user's past travel history.

[0055] The travel planning system can further include a location information utilization unit that utilizes the user's real-time location information. The location information utilization unit, for example, collects information on tourist spots, restaurants, and hotels near the user's current location and provides it to the analysis unit. Based on this data, the analysis unit can select an optimal travel plan based on the user's current location. For example, the analysis unit can suggest a plan for the user to visit tourist spots near the user's current location. The analysis unit can suggest a plan for the user to eat at a restaurant near the user's current location. This makes it possible to provide an optimal travel plan based on the user's real-time location information.

[0056] The travel planning system may further include a real-time feedback reflecting unit that reflects user feedback in real time. For example, the real-time feedback reflecting unit may instantly provide feedback provided by the user during the trip to the analysis unit, and the analysis unit may adjust the travel plan based on the feedback. For example, if the user is not satisfied with the current plan, the analysis unit may suggest an alternative plan. If the user enjoys a particular activity, the analysis unit may add a similar activity. This makes it possible to provide an optimal travel plan that reflects the user's feedback in real time.

[0057] The travel planning system may further include a behavior monitoring unit that monitors the user's behavior during the trip. The behavior monitoring unit, for example, collects data on the places the user has visited and the activities he or she has participated in, and provides this data to the analysis unit. Based on this data, the analysis unit can select an optimal travel plan based on the user's behavior patterns. For example, the analysis unit may suggest the next travel plan based on the places the user frequently visits, or the next travel plan based on the user's preferred activities. This makes it possible to provide an optimal travel plan based on the user's behavior patterns.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects user information, such as purpose, destination, number of days, budget, preferences, and membership cards owned. Based on the information entered by the user, the collection unit uses AI to select the optimal plan. Step 2: The analysis unit analyzes the information collected by the collection unit and selects the optimal plan. The analysis unit uses AI to analyze the information and proposes plans tailored to the location and time of the meeting in the case of business trips, and plans tailored to tourist spots and activities in the case of sightseeing trips. Step 3: The provider provides the user with the plan selected by the analysis unit. The provider displays details of the selected plan and a map of the course, allowing the user to easily check the plan that is best for them. In addition, the provider collects user feedback and reflects it in the next planning.

[0060] (Example 2) A travel planning system according to an embodiment of the present invention collects user information, selects an optimal plan, and provides it. This system collects information such as the user's purpose, destination, number of days, budget, preferences, and membership cards, and selects the optimal plan and itinerary based on that information. It analyzes the information using AI and proposes the optimal plan. Furthermore, it also has a function for providing the selected plan and itinerary to the user, collecting user feedback, and reflecting it in future travel plans. For example, for business trips, it proposes plans tailored to the location and time of a meeting. For leisure trips, it proposes plans tailored to tourist spots and activities. It also includes a function for displaying details and maps of the selected plan and itinerary. This allows the user to easily confirm the optimal plan for them. It also collects user feedback and reflects it in future travel plans. For example, by allowing the user to enter ratings and comments on plans, the AI ​​can learn from that feedback and reflect it in future travel plans. This allows for more accurate travel plans. Users can easily select plans tailored to their purpose and requirements, streamlining travel planning. For example, for business trips, it proposes plans tailored to the location and time of a meeting, allowing for efficient travel. In the case of sightseeing trips, plans tailored to tourist spots and activities are proposed, allowing users to enjoy a fulfilling trip. This allows the travel planning system to efficiently collect, analyze, and provide user information.

[0061] A travel planning system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user information. The user information includes, but is not limited to, purpose, destination, number of days, budget, preferences, and membership cards owned. The collection unit, for example, uses AI to select an optimal plan based on information input by the user. The analysis unit analyzes the information collected by the collection unit and selects an optimal plan. The analysis unit, for example, uses AI to analyze the information and proposes an optimal plan. For example, in the case of a business trip, a plan tailored to the location and time of the meeting is proposed. In the case of a sightseeing trip, a plan tailored to tourist spots and activities is proposed. The provision unit provides the user with the plan selected by the analysis unit. The provision unit displays, for example, details of the selected plan and a route map. This allows the user to easily confirm the optimal plan for themselves. Furthermore, the provision unit collects user feedback and reflects it in the next planning. For example, when the user inputs ratings and comments on the plan, the AI ​​can learn the feedback and reflect it in the next planning. This allows the provision of more accurate plans. This allows the travel planning system according to the embodiment to efficiently collect, analyze, and provide user information.

[0062] The system includes a feedback collection unit. The feedback collection unit collects user feedback. For example, the feedback collection unit collects feedback by the user inputting an evaluation or comment on the plan. The feedback collection unit reflects the collected feedback in the next planning. In this way, user feedback can be collected and reflected in the next planning. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit may input user feedback into AI and have the AI ​​analyze the feedback.

[0063] The system includes a learning unit. The learning unit allows the AI ​​to learn based on the feedback collected by the feedback collecting unit. The learning unit, for example, learns the user's feedback and reflects it in the next planning. The learning unit analyzes the feedback using the AI ​​and reflects it in the next planning. This allows the AI ​​to learn the user's feedback and reflect it in the next planning. Some or all of the above-mentioned processing in the learning unit may be performed using the AI, for example, or may be performed without using the AI. For example, the learning unit may input the feedback collected by the feedback collecting unit into the AI ​​and cause the AI ​​to analyze the feedback.

[0064] The collection unit can collect information including the user's purpose, destination, number of days, budget, preferences, and owned membership cards. The collection unit collects information such as the user's purpose, destination, number of days, budget, preferences, and owned membership cards. The collection unit uses AI to select the optimal plan based on the information entered by the user. For example, in the case of a business trip, a plan tailored to the location and time of the meeting is proposed. In the case of a sightseeing trip, a plan tailored to tourist spots and activities is proposed. By collecting detailed information about the user, a more optimal plan can be provided. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's information into AI and have the AI ​​analyze the information.

[0065] The analysis unit can select an appropriate plan based on the collected information. The analysis unit selects the optimal plan based on the collected information. The analysis unit, for example, uses AI to analyze the information and propose the optimal plan. For example, in the case of a business trip, a plan tailored to the location and time of the meeting is proposed. In the case of a sightseeing trip, a plan tailored to the tourist spots and activities is proposed. In this way, by selecting the optimal plan based on the collected information, it is possible to provide the user with an optimal travel plan. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected information into AI and have the AI ​​analyze the information.

[0066] The providing unit can display details of the selected plan and a map of the course. The providing unit displays details of the selected plan and a map of the course. The providing unit displays, for example, details of the selected plan and a map of the course. This allows the user to easily check the plan that is best suited to them. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input details of the selected plan and a map of the course into AI and have the AI ​​execute the display processing.

[0067] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects information during a relaxed time period. If the user is excited, the collection unit immediately starts collecting information. If the user is tired, the 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 a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, or may be performed without using an AI. For example, the collection unit may input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0068] The collection unit can analyze the user's past travel history and select the optimal information collection method. For example, the collection unit prioritizes collection of similar plans based on travel plans used by the user in the past. The collection unit suggests new destinations based on places the user has visited in the past. The collection unit collects plans including preferred activities from the user's past travel history. This enables more optimal information collection by analyzing the user's past travel history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past travel history into AI and have the AI ​​analyze the information.

[0069] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, if the user is busy with their current living situation, the collection unit collects plans that can be enjoyed in a short period of time. If the user has a specific area of ​​interest (e.g., history, nature), the collection unit collects plans related to that area. If the user is planning a family trip, the collection unit preferentially collects plans for families. This allows more appropriate information to be collected by filtering information based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's current living situation and areas of interest into AI and have the AI ​​perform information filtering.

[0070] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit prioritizes collecting detailed information. When the user is in a hurry, the collection unit prioritizes collecting information that focuses on the main points. When the user is excited, the collection unit prioritizes collecting visually appealing information. This allows more appropriate information to be collected by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit may input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0071] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information about tourist spots close to the user's current location. If the user is interested in a particular area, the collection unit prioritizes collecting information related to that area. The collection unit prioritizes collecting information about restaurants and hotels near the user's travel destination. This makes it possible to prioritize collecting highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and have the AI ​​perform information filtering.

[0072] The collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit collects related plans based on travel destination information shared by the user on social media. The collection unit collects the latest information from travel-related accounts the user follows. The collection unit collects plans of interest based on posts the user has "liked" on social media. This allows related information to be collected by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI ​​analyze the information.

[0073] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit provides concise analysis results that focus on the main points. If the user is excited, the analysis unit provides visually appealing analysis results. This allows for adjusting the way the analysis is presented according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit performs a brief analysis of information with low importance. The analysis unit performs an analysis with an appropriate level of detail for information with medium importance. In this way, by adjusting the level of detail of the analysis based on the importance of the collected information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the collected information into AI and have the AI ​​perform the level of detail of the analysis.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies an analysis algorithm that emphasizes efficiency to information about business travel. The analysis unit applies an analysis algorithm that emphasizes enjoyment to information about sightseeing travel. The analysis unit applies an analysis algorithm that emphasizes safety to information about family travel. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data into AI and have the AI ​​select the analysis algorithm.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point 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. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit analyzes older information with a lower priority. The analysis unit analyzes information that has been submitted recently with a moderate priority. In this way, by determining the priority of analysis based on the time of submission of information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information into AI and have the AI ​​perform the analysis priority.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of information with high relevance. The analysis unit analyzes information with low relevance later. The analysis unit analyzes information with moderate relevance in an appropriate order. 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-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the information into AI and have the AI ​​execute the order of analysis.

[0079] The providing unit can estimate the user's emotions and adjust the presentation method based on the estimated user's emotions. For example, if the user is relaxed, the providing unit provides detailed information. If the user is in a hurry, the providing unit provides concise information that focuses on the main points. If the user is excited, the providing unit provides visually appealing information. This allows the presentation method to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit may input the user's emotion data into an AI and cause the AI ​​to perform emotion estimation.

[0080] The providing unit can adjust the level of detail of the provided information based on the importance of the plan when providing the information. For example, the providing unit provides detailed information for plans with high importance. The providing unit provides concise information for plans with low importance. The providing unit provides information with an appropriate level of detail for plans with medium importance. In this way, by adjusting the level of detail of the provided information based on the importance of the plan, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input plan importance data into AI and have the AI ​​execute the level of detail of the provided information.

[0081] The providing unit can apply different provision algorithms depending on the category of the plan when providing the plan. For example, the providing unit applies a provision algorithm that emphasizes efficiency to a business trip plan. The providing unit applies a provision algorithm that emphasizes fun to a sightseeing trip plan. The providing unit applies a provision algorithm that emphasizes safety to a family trip plan. In this way, by applying different provision algorithms depending on the category of the plan, more appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input plan category data into AI and have the AI ​​select the provision algorithm.

[0082] The providing unit can estimate the user's emotions and adjust the length of the information provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit provides short, to-the-point information. If the user is relaxed, the providing unit provides detailed information. If the user is excited, the providing unit provides visually appealing information. This allows the length of information provided to be adjusted according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit may input the user's emotion data into an AI and cause the AI ​​to perform emotion estimation.

[0083] The provision unit can determine the priority of provision based on the time of plan submission at the time of provision. For example, the provision unit provides the most recent plan with priority. The provision unit provides older plans with a lower priority. The provision unit provides plans that have been submitted recently with a moderate priority. In this way, by determining the priority of provision based on the time of plan submission, more appropriate information can be provided. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input plan submission time data into AI and have the AI ​​execute the provision priority.

[0084] The providing unit can adjust the order of provision based on the relevance of the plans when providing them. For example, the providing unit provides plans with high relevance preferentially. The providing unit provides plans with low relevance later. The providing unit provides plans with medium relevance in an appropriate order. In this way, by adjusting the order of provision based on the relevance of the plans, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit inputs plan relevance data into AI and causes the AI ​​to execute the order of provision.

[0085] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. For example, if the user is relaxed, the feedback collection unit requests detailed feedback. If the user is in a hurry, the feedback collection unit requests concise feedback. If the user is excited, the feedback collection unit provides a visually appealing feedback form. This allows for more appropriate feedback to be collected by adjusting the feedback collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback collection unit can input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0086] When collecting feedback, the feedback collection unit can select the optimal collection method by referring to the user's past feedback history. For example, if the user provided detailed feedback in the past, the feedback collection unit requests similar detailed feedback. If the user provided brief feedback in the past, the feedback collection unit requests similar brief feedback. The feedback collection unit selects the optimal collection method from the user's past feedback history. This enables more appropriate feedback collection by referring to the user's past feedback history. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the user's past feedback history data into AI and have the AI ​​select the collection method.

[0087] The feedback collection unit can estimate the user's emotions and determine the priority of feedback collection based on the estimated user's emotions. For example, if the user is relaxed, the feedback collection unit prioritizes collecting detailed feedback. If the user is in a hurry, the feedback collection unit prioritizes collecting concise feedback. If the user is excited, the feedback collection unit prioritizes collecting visually appealing feedback. This allows for more appropriate feedback to be collected by determining the priority of feedback collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback collection unit may be performed using an AI, for example, or without an AI. For example, the feedback collection unit may input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0088] When collecting feedback, the feedback collection unit can select the optimal collection method by taking into account the user's device information. For example, if the user is using a smartphone, the feedback collection unit provides a feedback form optimized for touch operation. If the user is using a tablet, the feedback collection unit provides a feedback form optimized for a large screen. If the user is using a desktop, the feedback collection unit provides a feedback form optimized for keyboard input. This enables more appropriate feedback collection by taking into account the user's device information. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the user's device information data into AI and have the AI ​​select the collection method.

[0089] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit selects detailed feedback as training data. If the user is in a hurry, the learning unit selects concise feedback as training data. If the user is excited, the learning unit selects visually appealing feedback as training data. This enables more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using an AI, for example, or without an AI. For example, the learning unit may input the user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0090] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit selects the most effective algorithm from past learning data. The learning unit adjusts the parameters of the algorithm based on the past learning data. The learning unit analyzes the past learning data and identifies areas for improvement in the algorithm. In this way, the learning algorithm can be optimized by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into AI and have the AI ​​perform algorithm optimization.

[0091] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit performs learning more frequently when the user is relaxed. When the user is in a hurry, the learning unit reduces the frequency of learning. When the user is excited, the learning unit increases the frequency of learning. This allows for more appropriate learning by adjusting the frequency of learning 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 may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using an AI, or may be performed without using an AI. For example, the learning unit may input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0092] During learning, the learning unit can weight the learning data based on the timing of feedback submission. For example, the learning unit weights the learning data by prioritizing the most recent feedback. The learning unit reduces the weighting for older feedback. The learning unit weights feedback submitted at a moderate time. In this way, weighting the learning data based on the timing of feedback submission enables more appropriate learning. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input feedback submission timing data into AI and have the AI ​​perform weighting of the learning data. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user information using the reception device 38 of the smart device 14 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information, and selects an optimal plan. The provision unit displays details and a map of the selected plan using, for example, the output device 40 of the smart device 14 and provides them to the user. The provision unit also has a function of collecting user feedback and reflecting it in the next planning. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user information using the microphone 238 of the smart glasses 214 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to select an optimal plan. The provision unit provides the user with details and maps of the selected plan by voice, for example, using the speaker 240 of the smart glasses 214. The provision unit also has a function of collecting user feedback and reflecting it in the next planning. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects user information using the microphone 238 of the headset type terminal 314 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information, and selects an optimal plan. The provision unit displays details and a map of the selected plan using, for example, the display 343 of the headset type terminal 314, and provides them to the user. The provision unit also has a function of collecting user feedback and reflecting it in the next planning. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user information using the microphone 238 of the robot 414 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information, and selects an optimal plan. The provision unit provides the user with details and maps of the selected plan by voice, for example, using the speaker 240 of the robot 414. The provision unit also has a function of collecting user feedback and reflecting it in the next planning.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The travel planning system can further include a health management unit that monitors the user's health condition. The health management unit collects data such as the user's heart rate, blood pressure, and sleep patterns, and provides it to the analysis unit. Based on this data, the analysis unit can select a travel plan that is optimal for the user's health condition. For example, if the user is feeling stressed, the analysis unit can suggest a plan that allows the user to relax. If the user is tired, the analysis unit can suggest a plan that emphasizes rest. This makes it possible to provide the user with an optimal travel plan that suits their health condition.

[0095] The travel planning system can further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit collects data on events the user has previously attended, products they have purchased, and websites they have visited, and provides this data to the analysis unit. Based on this data, the analysis unit can select a travel plan that best suits the user's hobbies and interests. For example, if the user is a music lover, the analysis unit can suggest a plan to attend a music festival. If the user is an outdoor enthusiast, the analysis unit can suggest a plan for camping and hiking. This makes it possible to provide the user with an optimal travel plan that matches their hobbies and interests.

[0096] The travel planning system may further include a social network analysis unit that analyzes the user's social network. The social network analysis unit, for example, collects the travel histories and ratings of the user's friends and family and provides them to the analysis unit. Based on this data, the analysis unit can select an optimal travel plan based on the user's social network. For example, the analysis unit may suggest tourist spots that the user's friends have given high ratings to, or activities that the user's family has enjoyed. This makes it possible to provide an optimal travel plan based on the user's social network.

[0097] The travel planning system may further include an emotion monitoring unit that monitors the user's emotions in real time. The emotion monitoring unit, for example, analyzes the user's facial expressions, tone of voice, and the content of text input to estimate the user's emotions. Based on this data, the analysis unit can select the optimal travel plan according to the user's emotions. For example, if the user is having fun, it will suggest active activities. If the user is tired, it will suggest relaxing activities. This makes it possible to provide the optimal travel plan according to the user's emotions.

[0098] The travel planning system can further include a predictive analysis unit based on the user's past travel history. The predictive analysis unit, for example, collects data on places the user has visited and activities the user has participated in in the past and provides it to the analysis unit. Based on this data, the analysis unit can predict the user's future travel destinations and activities and select an optimal travel plan. For example, if the user has previously visited beach resorts, the analysis unit can suggest a beach resort for the next trip. If the user has previously participated in cultural events, the analysis unit can suggest a cultural event for the next trip. This makes it possible to provide an optimal travel plan based on the user's past travel history.

[0099] The travel planning system can further include a location information utilization unit that utilizes the user's real-time location information. The location information utilization unit, for example, collects information on tourist spots, restaurants, and hotels near the user's current location and provides it to the analysis unit. Based on this data, the analysis unit can select an optimal travel plan based on the user's current location. For example, the analysis unit can suggest a plan for the user to visit tourist spots near the user's current location. The analysis unit can suggest a plan for the user to eat at a restaurant near the user's current location. This makes it possible to provide an optimal travel plan based on the user's real-time location information.

[0100] The travel planning system can further estimate the user's emotions and adjust the way it proposes travel plans based on the estimated emotions. For example, if the user is feeling stressed, it will prioritize suggestions of relaxing plans. If the user is excited, it will suggest plans that include active activities. If the user is tired, it will suggest plans that emphasize rest. This makes it possible to provide the optimal travel plan according to the user's emotions.

[0101] The travel planning system may further include a real-time feedback reflecting unit that reflects user feedback in real time. For example, the real-time feedback reflecting unit may instantly provide feedback provided by the user during the trip to the analysis unit, and the analysis unit may adjust the travel plan based on the feedback. For example, if the user is not satisfied with the current plan, the analysis unit may suggest an alternative plan. If the user enjoys a particular activity, the analysis unit may add a similar activity. This makes it possible to provide an optimal travel plan that reflects the user's feedback in real time.

[0102] The travel planning system can further estimate the user's emotions and adjust the level of detail of the travel plan based on the estimated emotions. For example, if the user is relaxed, a detailed plan is provided; if the user is in a hurry, a concise plan is provided; if the user is excited, a visually appealing plan is provided. This makes it possible to provide an optimal travel plan according to the user's emotions.

[0103] The travel planning system may further include a behavior monitoring unit that monitors the user's behavior during the trip. The behavior monitoring unit, for example, collects data on the places the user has visited and the activities he or she has participated in, and provides this data to the analysis unit. Based on this data, the analysis unit can select an optimal travel plan based on the user's behavior patterns. For example, the analysis unit may suggest the next travel plan based on the places the user frequently visits, or the next travel plan based on the user's preferred activities. This makes it possible to provide an optimal travel plan based on the user's behavior patterns.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit collects user information, such as purpose, destination, number of days, budget, preferences, and membership cards owned. Based on the information entered by the user, the collection unit uses AI to select the optimal plan. Step 2: The analysis unit analyzes the information collected by the collection unit and selects the optimal plan. The analysis unit uses AI to analyze the information and proposes plans tailored to the location and time of the meeting in the case of business trips, and plans tailored to tourist spots and activities in the case of sightseeing trips. Step 3: The provider provides the user with the plan selected by the analysis unit. The provider displays details of the selected plan and a map of the course, allowing the user to easily check the plan that is best for them. In addition, the provider collects user feedback and reflects it in the next planning.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user information; an analysis unit that analyzes the information collected by the collection unit and selects an appropriate plan; a providing unit that provides a plan selected by the analysis unit to a user; Equipped with A system characterized by:

2. A feedback collection unit is provided to collect user feedback.

2. The system of claim 1.

3. A learning unit that allows the AI ​​to learn based on the feedback collected by the feedback collection unit 3. The system of claim 2.

4. The collecting unit Collect information including user purpose, destination, number of days, budget, preferences, and membership cards owned 2. The system of claim 1.

5. The analysis unit Select the appropriate plan based on the collected information 2. The system of claim 1.

6. The providing unit View details of selected plans and course maps 2. The system of claim 1.

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

2. The system of claim 1.

8. The collecting unit Analyze the user's past travel history and select the optimal information gathering method 2. The system of claim 1.

9. The collecting unit When collecting information, filter it based on the user's current life situation and areas of interest.

2. The system of claim 1.

Images