System

Abstract

An object of a system according to an embodiment is to enable a traveler to quickly make an optimal travel plan.SOLUTION: A system according to an embodiment includes a needs collection unit, an analysis unit, a planning unit, and an information integration unit. The needs collection unit collects needs of travelers. The analysis unit analyzes the needs of the traveler collected by the needs collection unit. A plan making part makes an optimum travel plan based on the needs of the traveler analyzed by the analysis part. The information integrator collects and integrates data from a plurality of information sources.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of requiring a lot of time for travelers to plan optimal travel plans, and information is siloed.

[0005] The system according to the embodiment aims to enable travelers to quickly create optimal travel plans. [Means for solving the problem]

[0006] The system according to the embodiment comprises a needs collection unit, an analysis unit, a planning unit, and an information integration unit. The needs collection unit collects traveler needs. The analysis unit analyzes the traveler needs collected by the needs collection unit. The planning unit creates an optimal travel plan based on the traveler needs analyzed by the analysis unit. The information integration unit collects and integrates data from multiple information sources. [Effects of the Invention]

[0007] The system according to the embodiment can enable travelers to quickly create optimal travel plans. [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) The travel planning system according to the embodiment of the present invention is a system that collects and analyzes traveler needs, creates an optimal travel plan, and integrates the information, thereby enabling the travel planning system to provide a personalized travel plan to the traveler.

[0029] A travel planning system according to an embodiment includes a needs collection unit, an analysis unit, a planner, and an information integration unit. The needs collection unit collects traveler needs. For example, when a traveler inputs a prompt such as, "I would like to plan a 3-night, 4-day trip within a budget of 100,000 yen. I would like to visit tourist spots, but I also want some time to relax," the needs collection unit collects the information. The analysis unit analyzes the traveler's needs collected by the needs collection unit. For example, the generation AI analyzes the traveler's input information and understands the traveler's needs. The planner creates an optimal travel plan based on the traveler's needs analyzed by the analysis unit. For example, the generation AI suggests optimal transportation, accommodations, and tourist spots based on the traveler's budget, time, and preferences. The information integration unit collects and integrates data from multiple sources. For example, the generation AI collects data from multiple flight comparison sites and hotel reservation sites and displays the optimal options in a unified manner. This allows the travel planning system to provide travelers with personalized travel plans.

[0030] The needs gathering unit can analyze the traveler's past online activity and extract latent needs. For example, the needs gathering unit analyzes the traveler's social media posts and extracts places the traveler has visited in the past and activities that the traveler has been interested in. For example, the needs gathering unit suggests tourist spots that the traveler may be interested in based on photos and comments that the traveler has posted in the past. The needs gathering unit also analyzes the traveler's search history and extracts latent needs. For example, the needs gathering unit suggests plans that the traveler may be interested in based on tourist spots and activities that the traveler has searched for in the past. The needs gathering unit also analyzes the traveler's online activity and extracts latent needs. For example, the needs gathering unit suggests tourist spots and activities that the traveler may be interested in based on travel blogs and reviews that the traveler has viewed in the past. In this way, the traveler's past online activity can be analyzed and latent needs can be extracted.

[0031] The needs gathering unit can ask follow-up questions in a dialogue format in response to the prompt entered by the traveler to elicit more detailed needs. For example, in the needs gathering unit, the generation AI asks follow-up questions in a dialogue format in response to the prompt entered by the traveler to elicit more detailed needs. For example, it may ask, "What tourist spots would you like to visit?" to confirm specific wishes. In addition, in the needs gathering unit, the generation AI asks follow-up questions in a dialogue format in response to the prompt entered by the traveler to elicit more detailed needs. For example, it may ask, "What activities would you like to enjoy within your budget?" to confirm specific wishes. In addition, in the needs gathering unit, the generation AI asks follow-up questions in a dialogue format in response to the prompt entered by the traveler to elicit more detailed needs. For example, it may ask, "How much time would you like to have to relax?" to confirm specific wishes. In this way, it is possible to elicit more detailed needs from the traveler.

[0032] The needs collection unit can also analyze needs from visual or auditory information using voice input or image input. For example, when a traveler inputs their needs using voice input, the generation AI analyzes the voice data to understand the traveler's wishes. For example, if the traveler inputs "I want to go to a beach resort," the generation AI will suggest beach resorts based on that information. Furthermore, when a traveler inputs their needs using image input, the generation AI analyzes the image data to understand the traveler's wishes. For example, if a traveler uploads a photo of a beach, the generation AI will suggest beach resorts based on that information. Furthermore, when a traveler inputs their needs using voice input or image input, the generation AI analyzes the visual and auditory information to understand the traveler's wishes. For example, if a traveler uploads a photo of a tourist spot and inputs "I want to go there" by voice, the generation AI will suggest tourist spots based on that information. This makes it possible to analyze needs from visual and auditory information as well.

[0033] The needs collection unit introduces an interactive questionnaire format, allowing travelers to input their needs in a game-like manner. For example, when travelers input their needs in an interactive questionnaire format, the generation AI asks them questions in a game-like manner. For example, it may ask a quiz-style question such as, "Please choose one of the following tourist destinations that you would like to visit." Furthermore, when travelers input their needs in an interactive questionnaire format, the generation AI asks them questions in a game-like manner. For example, it may present options such as, "Please choose an activity that you can enjoy within your budget." Furthermore, when travelers input their needs in an interactive questionnaire format, the generation AI asks them questions in a game-like manner. For example, it may ask a question such as, "How much time do you want to have for relaxation?", allowing them to earn points. This allows travelers to input their needs in a game-like manner.

[0034] The planning unit can analyze weather and traffic conditions in real time and dynamically update the optimal travel plan. In the planning unit, for example, the generation AI analyzes the weather in real time and dynamically updates the optimal travel plan. For example, if the weather at the travel destination worsens, indoor activities are suggested. In addition, in the planning unit, the generation AI analyzes traffic conditions in real time and dynamically updates the optimal travel plan. For example, if traffic congestion occurs, alternative routes and means of transportation are suggested. In addition, in the planning unit, the generation AI analyzes weather and traffic conditions in real time and dynamically updates the optimal travel plan. For example, if the weather is good, outdoor activities are suggested and travel time is adjusted according to traffic conditions. This makes it possible to dynamically update a travel plan that takes weather and traffic conditions into account in real time.

[0035] The planning unit can propose a reasonable plan by taking into account the health condition and physical condition of the traveler. For example, in the planning unit, the generation AI takes into account the health condition of the traveler and proposes a reasonable travel plan. For example, if a traveler inputs that they are not feeling well, the generation AI will propose a relaxing plan. In addition, the planning unit can propose a reasonable travel plan by taking into account the traveler's physical condition. For example, if a traveler inputs that they are not good at traveling for long periods of time, the generation AI will propose a plan that shortens travel time. In addition, the planning unit can propose a reasonable travel plan by taking into account the health condition and physical condition of the traveler. For example, if a traveler inputs that they are not confident in their physical strength, the generation AI will propose an activity that does not require physical strength. In this way, it is possible to propose a reasonable plan that takes into account the health condition and physical condition of the traveler.

[0036] When creating a travel plan, the planning unit also collects the opinions of the traveler's friends and family, and can consider the needs of the entire group. For example, when the generation AI creates a travel plan, the planning unit also collects the opinions of the traveler's friends and family, and considers the needs of the entire group. For example, it suggests activities that everyone in the group can enjoy. In addition, when the generation AI creates a travel plan, the planning unit also collects the opinions of the traveler's friends and family, and considers the needs of the entire group. For example, it suggests accommodation that will satisfy everyone. In addition, when the generation AI creates a travel plan, the planning unit also collects the opinions of the traveler's friends and family, and considers the needs of the entire group. For example, it suggests tourist spots that everyone can enjoy. This makes it possible to create a travel plan that takes the needs of the entire group into consideration.

[0037] The planning unit can suggest special activities that reflect the hobbies and interests of travelers. For example, when the generation AI plans a trip, the planning unit suggests special activities that reflect the hobbies and interests of travelers. For example, if a traveler inputs that they are interested in history, the planning unit suggests historical tourist spots. Furthermore, when the generation AI plans a trip, the planning unit suggests special activities that reflect the hobbies and interests of travelers. For example, if a traveler inputs that they like the outdoors, the planning unit suggests hiking and camping. Furthermore, when the generation AI plans a trip, the planning unit suggests special activities that reflect the hobbies and interests of travelers. For example, if a traveler inputs that they are interested in art, the planning unit suggests museums and galleries. This makes it possible to suggest special activities that reflect the hobbies and interests of travelers.

[0038] When integrating data from different information sources, the information integration unit can evaluate the reliability of the data and preferentially display highly reliable information. For example, when the generation AI integrates data from different information sources, the information integration unit evaluates the reliability of the data and preferentially display highly reliable information. For example, it preferentially displays information from official websites and reliable review sites. Furthermore, when the generation AI integrates data from different information sources, the information integration unit evaluates the reliability of the data and preferentially display highly reliable information. For example, it preferentially displays data from information sources with high user ratings. Furthermore, when the generation AI integrates data from different information sources, the information integration unit evaluates the reliability of the data and preferentially display highly reliable information. For example, it evaluates reliability based on the accuracy of past data and displays highly reliable information. This allows highly reliable information to be preferentially displayed.

[0039] The information integration unit can learn from travelers' past feedback and optimize the method of information integration. For example, in the information integration unit, the generation AI learns from travelers' past feedback and optimizes the method of information integration. For example, if past feedback was that "there is too much information and it is difficult to understand," the information is displayed in a concise manner. The information integration unit also learns from travelers' past feedback and optimizes the method of information integration. For example, if past feedback was that "I want information that is visually easy to understand," the information is displayed using infographics and charts. The information integration unit also learns from travelers' past feedback and optimizes the method of information integration. For example, if past feedback was that "images are easier to see than text," the information is displayed using a lot of images. In this way, past feedback can be learned and the method of information integration can be optimized.

[0040] When integrating information, the information integration unit can generate infographics and dashboards that are visually easy to understand. For example, when the generation AI integrates information, the information integration unit generates infographics that are visually easy to understand. For example, it displays an overview of a travel plan using diagrams and icons so that it can be understood at a glance. In addition, when the generation AI integrates information, the information integration unit generates dashboards that are visually easy to understand. For example, it displays the progress of the travel plan and important information so that it can be confirmed at a glance. In addition, when the generation AI integrates information, the information integration unit generates infographics and dashboards that are visually easy to understand. For example, it displays each element of the travel plan in a different color to highlight important information. This makes it possible to generate infographics and dashboards that are visually easy to understand.

[0041] When integrating information, the information integration unit can customize the display to suit the traveler's language and culture. For example, when the generation AI integrates information, the information integration unit customizes the display to suit the traveler's language. For example, if the traveler selects Japanese, all information is displayed in Japanese. Furthermore, when the generation AI integrates information, the information integration unit customizes the display to suit the traveler's culture. For example, if the traveler is familiar with a particular culture, information related to that culture is displayed preferentially. Furthermore, when the generation AI integrates information, the information integration unit customizes the display to suit the traveler's language and culture. For example, if the traveler selects English and is familiar with American culture, information related to American culture is displayed in English. This makes it possible to customize the display to suit the traveler's language and culture.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The needs gathering unit can also monitor the health status of travelers and suggest travel plans that take their health into consideration. For example, if a traveler has a chronic illness, it can suggest a reasonable plan based on that information. Also, if a traveler has allergies, it can suggest meals and accommodations that take allergies into consideration. Furthermore, if a traveler places importance on fitness, it can suggest plans that include fitness facilities and activities. This makes it possible to provide travel plans that take the traveler's health status into consideration.

[0044] The analysis unit analyzes the travel history of the traveler and can propose plans that suit the traveler's preferences based on the places the traveler has visited and the activities the traveler has experienced. For example, if the traveler has visited a beach resort in the past, a similar beach resort can be proposed. Also, if the traveler has enjoyed adventure activities in the past, a plan that includes adventure activities can be proposed. Furthermore, based on reviews of tourist spots that the traveler has visited in the past, it can also propose new tourist spots that the traveler may be interested in. This makes it possible to provide personalized travel plans that utilize the traveler's past travel history.

[0045] The information integration unit can customize the display to suit the traveler's language and culture. For example, if the traveler selects Japanese, all information will be displayed in Japanese. Also, if the traveler is familiar with a particular culture, information related to that culture can be displayed preferentially. Furthermore, if the traveler selects English and is familiar with American culture, information related to American culture can be displayed in English. This makes it possible to customize the display to suit the traveler's language and culture.

[0046] When integrating data from different information sources, the information integration unit can evaluate the reliability of the data and prioritize displaying highly reliable information. For example, it can prioritize displaying information from official websites and highly reliable review sites. It can also prioritize displaying data from information sources with high user ratings. Furthermore, it can evaluate reliability based on the accuracy of past data and display highly reliable information. This allows highly reliable information to be prioritized.

[0047] The information integration unit can learn from past feedback from travelers and optimize the method of information integration. For example, if past feedback was that there was too much information and it was difficult to understand, the information can be displayed in a concise manner. Also, if feedback was received that users wanted information that was easy to understand visually, the information can be displayed using infographics and charts. Furthermore, if feedback was received that images are easier to read than text, the information can be displayed using a lot of images. In this way, the unit can learn from past feedback and optimize the method of information integration.

[0048] When integrating information, the information integration unit can generate infographics and dashboards that are visually easy to understand. For example, it can display an overview of a travel plan using diagrams and icons so that it can be understood at a glance. It can also display the progress of the travel plan and important information so that it can be checked at a glance. Furthermore, it can display each element of the travel plan in a different color to highlight important information. This makes it possible to generate infographics and dashboards that are visually easy to understand.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The needs gathering section gathers the needs of the traveler. For example, if a traveler inputs a prompt such as, "I want to plan a 3-night, 4-day trip with a budget of 100,000 yen or less. I want to visit tourist spots, but I also want some time to relax," the needs gathering section will collect that information. Step 2: The analysis unit analyzes the traveler needs collected by the needs collection unit. For example, the generation AI analyzes the traveler's input information and understands their needs. Step 3: The planning section creates an optimal travel plan based on the traveler's needs analyzed by the analysis section. For example, the generation AI suggests the best means of transportation, accommodation, and tourist attractions based on the traveler's budget, time, and preferences. Step 4: The information integration unit collects and integrates data from multiple sources. For example, the generation AI collects data from multiple flight comparison sites and hotel booking sites, and displays the best options in a unified view.

[0051] (Example 2) The travel planning system according to the embodiment of the present invention is a system that collects and analyzes traveler needs, creates an optimal travel plan, and integrates the information, thereby enabling the travel planning system to provide a personalized travel plan to the traveler.

[0052] A travel planning system according to an embodiment includes a needs collection unit, an analysis unit, a planner, and an information integration unit. The needs collection unit collects traveler needs. For example, when a traveler inputs a prompt such as, "I would like to plan a 3-night, 4-day trip within a budget of 100,000 yen. I would like to visit tourist spots, but I also want some time to relax," the needs collection unit collects the information. The analysis unit analyzes the traveler's needs collected by the needs collection unit. For example, the generation AI analyzes the traveler's input information and understands the traveler's needs. The planner creates an optimal travel plan based on the traveler's needs analyzed by the analysis unit. For example, the generation AI suggests optimal transportation, accommodations, and tourist spots based on the traveler's budget, time, and preferences. The information integration unit collects and integrates data from multiple sources. For example, the generation AI collects data from multiple flight comparison sites and hotel reservation sites and displays the optimal options in a unified manner. This allows the travel planning system to provide travelers with personalized travel plans.

[0053] The needs collection unit performs emotional analysis on the information input by the traveler and can perform needs analysis based on the traveler's emotional state. For example, in the needs collection unit, the generation AI performs emotional analysis on the prompt input by the traveler and quantifies the traveler's emotional state. For example, if the traveler inputs "I want to relax," the generation AI prioritizes suggestions for relaxation plans based on that emotional score. The needs collection unit also performs emotional analysis on the information input by the traveler and performs needs analysis taking into account the traveler's emotional state. For example, if the traveler inputs "I want to relieve stress," the generation AI suggests activities suitable for relieving stress based on that emotional state. The needs collection unit also performs emotional analysis on the prompt input by the traveler and performs needs analysis taking into account the traveler's emotional state. For example, if the traveler inputs "I want to have a good time," the generation AI suggests fun activities based on that emotional state. This makes it possible to perform needs analysis taking into account the traveler's emotional state.

[0054] The needs gathering unit can analyze the traveler's past online activity and extract latent needs. For example, the needs gathering unit analyzes the traveler's social media posts and extracts places the traveler has visited in the past and activities that the traveler has been interested in. For example, the needs gathering unit suggests tourist spots that the traveler may be interested in based on photos and comments that the traveler has posted in the past. The needs gathering unit also analyzes the traveler's search history and extracts latent needs. For example, the needs gathering unit suggests plans that the traveler may be interested in based on tourist spots and activities that the traveler has searched for in the past. The needs gathering unit also analyzes the traveler's online activity and extracts latent needs. For example, the needs gathering unit suggests tourist spots and activities that the traveler may be interested in based on travel blogs and reviews that the traveler has viewed in the past. In this way, the traveler's past online activity can be analyzed and latent needs can be extracted.

[0055] The needs gathering unit can ask follow-up questions in a dialogue format in response to the prompt entered by the traveler to elicit more detailed needs. For example, in the needs gathering unit, the generation AI asks follow-up questions in a dialogue format in response to the prompt entered by the traveler to elicit more detailed needs. For example, it may ask, "What tourist spots would you like to visit?" to confirm specific wishes. In addition, in the needs gathering unit, the generation AI asks follow-up questions in a dialogue format in response to the prompt entered by the traveler to elicit more detailed needs. For example, it may ask, "What activities would you like to enjoy within your budget?" to confirm specific wishes. In addition, in the needs gathering unit, the generation AI asks follow-up questions in a dialogue format in response to the prompt entered by the traveler to elicit more detailed needs. For example, it may ask, "How much time would you like to have to relax?" to confirm specific wishes. In this way, it is possible to elicit more detailed needs from the traveler.

[0056] The needs collection unit can also analyze needs from visual or auditory information using voice input or image input. For example, when a traveler inputs their needs using voice input, the generation AI analyzes the voice data to understand the traveler's wishes. For example, if the traveler inputs "I want to go to a beach resort," the generation AI will suggest beach resorts based on that information. Furthermore, when a traveler inputs their needs using image input, the generation AI analyzes the image data to understand the traveler's wishes. For example, if a traveler uploads a photo of a beach, the generation AI will suggest beach resorts based on that information. Furthermore, when a traveler inputs their needs using voice input or image input, the generation AI analyzes the visual and auditory information to understand the traveler's wishes. For example, if a traveler uploads a photo of a tourist spot and inputs "I want to go there" by voice, the generation AI will suggest tourist spots based on that information. This makes it possible to analyze needs from visual and auditory information as well.

[0057] The needs collection unit introduces an interactive questionnaire format, allowing travelers to input their needs in a game-like manner. For example, when travelers input their needs in an interactive questionnaire format, the generation AI asks them questions in a game-like manner. For example, it may ask a quiz-style question such as, "Please choose one of the following tourist destinations that you would like to visit." Furthermore, when travelers input their needs in an interactive questionnaire format, the generation AI asks them questions in a game-like manner. For example, it may present options such as, "Please choose an activity that you can enjoy within your budget." Furthermore, when travelers input their needs in an interactive questionnaire format, the generation AI asks them questions in a game-like manner. For example, it may ask a question such as, "How much time do you want to have for relaxation?", allowing them to earn points. This allows travelers to input their needs in a game-like manner.

[0058] The needs collection unit can use the emotion estimation function to estimate the emotion of the traveler when he or she enters his or her needs in real time and generate questions that elicit positive emotions. For example, when a traveler enters his or her needs, the generation AI in the needs collection unit uses the emotion estimation function to estimate the emotion in real time and generate questions that elicit positive emotions. For example, if a traveler enters "I want to relax," the generation AI asks, "What is your favorite way to relax?" Furthermore, when a traveler enters his or her needs, the generation AI in the needs collection unit uses the emotion estimation function to estimate the emotion in real time and generate questions that elicit positive emotions. For example, if a traveler enters "I want to have a good time," the generation AI asks, "What activities do you enjoy?" Furthermore, when a traveler enters his or her needs, the generation AI in the needs collection unit uses the emotion estimation function to estimate the emotion in real time and generate questions that elicit positive emotions. For example, if a traveler enters "I want to relieve stress," the generation AI asks, "How do you relieve stress?" This makes it possible to generate questions that elicit positive emotions from travelers.

[0059] The planning unit can take into account the emotional state of the traveler and create a travel plan that is emotionally satisfying. For example, in the planning unit, the generation AI takes into account the emotional state of the traveler and creates a travel plan that is emotionally satisfying. For example, if a traveler inputs that they want to "relax," the generation AI will suggest relaxing spas and beach resorts. In addition, the planning unit takes into account the emotional state of the traveler and creates a travel plan that is emotionally satisfying. For example, if a traveler inputs that they want to "have a good time," the generation AI will suggest a plan that is full of theme parks and activities. In addition, the planning unit takes into account the emotional state of the traveler and creates a travel plan that is emotionally satisfying. For example, if a traveler inputs that they want to "relieve stress," the generation AI will suggest relaxation facilities and tourist spots rich in nature. In this way, a travel plan that takes into account the emotional state of the traveler can be created.

[0060] The planning unit can analyze weather and traffic conditions in real time and dynamically update the optimal travel plan. In the planning unit, for example, the generation AI analyzes the weather in real time and dynamically updates the optimal travel plan. For example, if the weather at the travel destination worsens, indoor activities are suggested. In addition, in the planning unit, the generation AI analyzes traffic conditions in real time and dynamically updates the optimal travel plan. For example, if traffic congestion occurs, alternative routes and means of transportation are suggested. In addition, in the planning unit, the generation AI analyzes weather and traffic conditions in real time and dynamically updates the optimal travel plan. For example, if the weather is good, outdoor activities are suggested and travel time is adjusted according to traffic conditions. This makes it possible to dynamically update a travel plan that takes weather and traffic conditions into account in real time.

[0061] The planning unit can propose a reasonable plan by taking into account the health condition and physical condition of the traveler. For example, in the planning unit, the generation AI takes into account the health condition of the traveler and proposes a reasonable travel plan. For example, if a traveler inputs that they are not feeling well, the generation AI will propose a relaxing plan. In addition, the planning unit can propose a reasonable travel plan by taking into account the traveler's physical condition. For example, if a traveler inputs that they are not good at traveling for long periods of time, the generation AI will propose a plan that shortens travel time. In addition, the planning unit can propose a reasonable travel plan by taking into account the health condition and physical condition of the traveler. For example, if a traveler inputs that they are not confident in their physical strength, the generation AI will propose an activity that does not require physical strength. In this way, it is possible to propose a reasonable plan that takes into account the health condition and physical condition of the traveler.

[0062] When creating a travel plan, the planning unit also collects the opinions of the traveler's friends and family, and can consider the needs of the entire group. For example, when the generation AI creates a travel plan, the planning unit also collects the opinions of the traveler's friends and family, and considers the needs of the entire group. For example, it suggests activities that everyone in the group can enjoy. In addition, when the generation AI creates a travel plan, the planning unit also collects the opinions of the traveler's friends and family, and considers the needs of the entire group. For example, it suggests accommodation that will satisfy everyone. In addition, when the generation AI creates a travel plan, the planning unit also collects the opinions of the traveler's friends and family, and considers the needs of the entire group. For example, it suggests tourist spots that everyone can enjoy. This makes it possible to create a travel plan that takes the needs of the entire group into consideration.

[0063] The planning unit can suggest special activities that reflect the hobbies and interests of travelers. For example, when the generation AI plans a trip, the planning unit suggests special activities that reflect the hobbies and interests of travelers. For example, if a traveler inputs that they are interested in history, the planning unit suggests historical tourist spots. Furthermore, when the generation AI plans a trip, the planning unit suggests special activities that reflect the hobbies and interests of travelers. For example, if a traveler inputs that they like the outdoors, the planning unit suggests hiking and camping. Furthermore, when the generation AI plans a trip, the planning unit suggests special activities that reflect the hobbies and interests of travelers. For example, if a traveler inputs that they are interested in art, the planning unit suggests museums and galleries. This makes it possible to suggest special activities that reflect the hobbies and interests of travelers.

[0064] The planner can use the emotion estimation function to predict the traveler's emotional response to each element of the travel plan and generate a plan that will elicit the most positive response. For example, the planner can use the emotion estimation function to predict the traveler's emotional response to each element of the travel plan and generate a plan that will elicit the most positive response. For example, if the traveler inputs "I want to relax," the planner can prioritize suggesting relaxing activities. The planner can also use the emotion estimation function to predict the traveler's emotional response to each element of the travel plan and generate a plan that will elicit the most positive response. For example, if the traveler inputs "I want to have a good time," the planner can prioritize suggesting fun activities. The planner can also use the emotion estimation function to predict the traveler's emotional response to each element of the travel plan and generate a plan that will elicit the most positive response. For example, if the traveler inputs "I want to relieve stress," the planner can prioritize suggesting activities that are suitable for relieving stress. In this way, the planner can predict the traveler's emotional response to each element of the travel plan and generate a plan that will elicit the most positive response.

[0065] The information integration unit can perform sentiment analysis and provide a method of displaying information that causes the least stress for travelers. For example, if a traveler inputs, "There is too much information and I'm confused," the information integration unit can display information in a concise and concise manner. The information integration unit can also perform sentiment analysis and provide a method of displaying information that causes the least stress for travelers. For example, if a traveler inputs, "I want information that is visually easy to understand," the information integration unit can display information using infographics and charts. The information integration unit can also perform sentiment analysis and provide a method of displaying information that causes the least stress for travelers. For example, if a traveler inputs, "I find images easier to read than text," the information integration unit can display information that makes the most use of images. This can provide a method of displaying information that causes the least stress for travelers.

[0066] When integrating data from different information sources, the information integration unit can evaluate the reliability of the data and preferentially display highly reliable information. For example, when the generation AI integrates data from different information sources, the information integration unit evaluates the reliability of the data and preferentially display highly reliable information. For example, it preferentially displays information from official websites and reliable review sites. Furthermore, when the generation AI integrates data from different information sources, the information integration unit evaluates the reliability of the data and preferentially display highly reliable information. For example, it preferentially displays data from information sources with high user ratings. Furthermore, when the generation AI integrates data from different information sources, the information integration unit evaluates the reliability of the data and preferentially display highly reliable information. For example, it evaluates reliability based on the accuracy of past data and displays highly reliable information. This allows highly reliable information to be preferentially displayed.

[0067] The information integration unit can learn from travelers' past feedback and optimize the method of information integration. For example, in the information integration unit, the generation AI learns from travelers' past feedback and optimizes the method of information integration. For example, if past feedback was that "there is too much information and it is difficult to understand," the information is displayed in a concise manner. The information integration unit also learns from travelers' past feedback and optimizes the method of information integration. For example, if past feedback was that "I want information that is visually easy to understand," the information is displayed using infographics and charts. The information integration unit also learns from travelers' past feedback and optimizes the method of information integration. For example, if past feedback was that "images are easier to see than text," the information is displayed using a lot of images. In this way, past feedback can be learned and the method of information integration can be optimized.

[0068] When integrating information, the information integration unit can generate infographics and dashboards that are visually easy to understand. For example, when the generation AI integrates information, the information integration unit generates infographics that are visually easy to understand. For example, it displays an overview of a travel plan using diagrams and icons so that it can be understood at a glance. In addition, when the generation AI integrates information, the information integration unit generates dashboards that are visually easy to understand. For example, it displays the progress of the travel plan and important information so that it can be confirmed at a glance. In addition, when the generation AI integrates information, the information integration unit generates infographics and dashboards that are visually easy to understand. For example, it displays each element of the travel plan in a different color to highlight important information. This makes it possible to generate infographics and dashboards that are visually easy to understand.

[0069] When integrating information, the information integration unit can customize the display to suit the traveler's language and culture. For example, when the generation AI integrates information, the information integration unit customizes the display to suit the traveler's language. For example, if the traveler selects Japanese, all information is displayed in Japanese. Furthermore, when the generation AI integrates information, the information integration unit customizes the display to suit the traveler's culture. For example, if the traveler is familiar with a particular culture, information related to that culture is displayed preferentially. Furthermore, when the generation AI integrates information, the information integration unit customizes the display to suit the traveler's language and culture. For example, if the traveler selects English and is familiar with American culture, information related to American culture is displayed in English. This makes it possible to customize the display to suit the traveler's language and culture.

[0070] The information integration unit can use the emotion estimation function to monitor the emotional reactions of travelers when they view information and continuously adjust the optimal information display method. For example, the information integration unit can use the emotion estimation function to monitor the emotional reactions of travelers when they view information and continuously adjust the optimal information display method. For example, if a traveler feels that there is too much information and is confused, the information can be displayed in a concise and summarized form. The information integration unit can also use the emotion estimation function to monitor the emotional reactions of travelers when they view information and continuously adjust the optimal information display method. For example, if a traveler feels that they want information that is visually easy to understand, the information can be displayed using infographics and charts. The information integration unit can also use the emotion estimation function to monitor the emotional reactions of travelers when they view information and continuously adjust the optimal information display method. For example, if a traveler feels that images are easier to read than text, the information can be displayed with a greater use of images. This makes it possible to monitor the emotional reactions of travelers and continuously adjust the optimal information display method.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The needs gathering unit can also monitor the health status of travelers and suggest travel plans that take their health into consideration. For example, if a traveler has a chronic illness, it can suggest a reasonable plan based on that information. Also, if a traveler has allergies, it can suggest meals and accommodations that take allergies into consideration. Furthermore, if a traveler places importance on fitness, it can suggest plans that include fitness facilities and activities. This makes it possible to provide travel plans that take the traveler's health status into consideration.

[0073] The needs gathering unit can estimate the emotional state of the traveler and make suggestions to provide an environment where the traveler can relax. For example, if the traveler is feeling stressed, it can suggest a relaxing spa or a place rich in nature. If the traveler wants to have fun, it can suggest a plan full of activities. Furthermore, if the traveler is tired, it can suggest a plan that emphasizes rest. In this way, it is possible to provide an optimal travel plan based on the traveler's emotional state.

[0074] The analysis unit analyzes the travel history of the traveler and can propose plans that suit the traveler's preferences based on the places the traveler has visited and the activities the traveler has experienced. For example, if the traveler has visited a beach resort in the past, a similar beach resort can be proposed. Also, if the traveler has enjoyed adventure activities in the past, a plan that includes adventure activities can be proposed. Furthermore, based on reviews of tourist spots that the traveler has visited in the past, it can also propose new tourist spots that the traveler may be interested in. This makes it possible to provide personalized travel plans that utilize the traveler's past travel history.

[0075] The planning department can take into account the emotional state of the traveler and suggest activities that the traveler will enjoy most. For example, if the traveler wants to relax, it can suggest relaxing spas and beach resorts. If the traveler wants to be active, it can suggest hiking or sports activities. Furthermore, if the traveler wants to enjoy a cultural experience, it can suggest tourist spots where they can learn about local culture and history. This makes it possible to provide the optimal activities based on the traveler's emotional state.

[0076] The information integration unit can customize the display to suit the traveler's language and culture. For example, if the traveler selects Japanese, all information will be displayed in Japanese. Also, if the traveler is familiar with a particular culture, information related to that culture can be displayed preferentially. Furthermore, if the traveler selects English and is familiar with American culture, information related to American culture can be displayed in English. This makes it possible to customize the display to suit the traveler's language and culture.

[0077] The information integration unit can monitor travelers' emotional responses and continuously adjust the optimal information display method. For example, if a traveler feels confused by too much information, the information can be displayed in a concise manner. If a traveler feels that they want information that is easy to understand visually, the information can be displayed using infographics and charts. Furthermore, if a traveler finds it easier to read images than text, the information can be displayed using a lot of images. This makes it possible to monitor travelers' emotional responses and continuously adjust the optimal information display method.

[0078] When integrating data from different information sources, the information integration unit can evaluate the reliability of the data and prioritize displaying highly reliable information. For example, it can prioritize displaying information from official websites and highly reliable review sites. It can also prioritize displaying data from information sources with high user ratings. Furthermore, it can evaluate reliability based on the accuracy of past data and display highly reliable information. This allows highly reliable information to be prioritized.

[0079] The information integration unit can learn from past feedback from travelers and optimize the method of information integration. For example, if past feedback was that there was too much information and it was difficult to understand, the information can be displayed in a concise manner. Also, if feedback was received that users wanted information that was easy to understand visually, the information can be displayed using infographics and charts. Furthermore, if feedback was received that images are easier to read than text, the information can be displayed using a lot of images. In this way, the unit can learn from past feedback and optimize the method of information integration.

[0080] When integrating information, the information integration unit can generate infographics and dashboards that are visually easy to understand. For example, it can display an overview of a travel plan using diagrams and icons so that it can be understood at a glance. It can also display the progress of the travel plan and important information so that it can be checked at a glance. Furthermore, it can display each element of the travel plan in a different color to highlight important information. This makes it possible to generate infographics and dashboards that are visually easy to understand.

[0081] The information integration unit uses its emotion estimation function to monitor the emotional reactions of travelers when they view information, and can continuously adjust the optimal information display method. For example, if a traveler feels confused by too much information, the information can be displayed in a concise manner. If a traveler desires information that is easy to understand visually, the information can be displayed using infographics and charts. Furthermore, if a traveler finds images easier to read than text, the information can be displayed using a lot of images. This makes it possible to monitor the emotional reactions of travelers and continuously adjust the optimal information display method.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The needs gathering section gathers the needs of the traveler. For example, if a traveler inputs a prompt such as, "I want to plan a 3-night, 4-day trip with a budget of 100,000 yen or less. I want to visit tourist spots, but I also want some time to relax," the needs gathering section will collect that information. Step 2: The analysis unit analyzes the traveler needs collected by the needs collection unit. For example, the generation AI analyzes the traveler's input information and understands their needs. Step 3: The planning section creates an optimal travel plan based on the traveler's needs analyzed by the analysis section. For example, the generation AI suggests the best means of transportation, accommodation, and tourist attractions based on the traveler's budget, time, and preferences. Step 4: The information integration unit collects and integrates data from multiple sources. For example, the generation AI collects data from multiple flight comparison sites and hotel booking sites, and displays the best options in a unified view.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. [Explanation of symbols]

[0151] 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 needs gathering department that gathers information on traveler needs; an analysis unit that analyzes the needs of the travelers collected by the needs collection unit; a planning unit that creates an optimal travel plan based on the needs of the traveler analyzed by the analysis unit; and an information integration unit that collects and integrates data from multiple information sources. A system characterized by:

2. The needs collection unit A sentiment analysis is performed on the input information of the traveler, and a needs analysis is performed based on the emotional state of the traveler.

2. The system of claim 1.

3. The needs collection unit Analyzing the traveler's past online activities and extracting potential needs 2. The system of claim 1.

4. The needs collection unit The system asks follow-up questions interactively based on the prompts entered by the traveler to elicit more detailed needs.

2. The system of claim 1.

5. The needs collection unit Analyze needs from visual or auditory information using voice or image input 2. The system of claim 1.

Images