system

Abstract

The system according to the embodiment aims to generate an optimal travel schedule based on the user's wishes. [Solution] A system according to an embodiment includes a reception unit that receives input, an analysis unit, a schedule generation unit, and a provision unit. The reception unit receives input from a user about places they want to go, things they want to do, and foods they want to eat. The analysis unit analyzes the information received by the reception unit and calculates travel routes. The schedule generation unit generates a travel schedule based on the travel routes calculated by the analysis unit. The provision unit provides the schedule generated by the schedule generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for users to create optimal travel schedules based on the places they want to go, the things they want to do, and the foods they want to eat.

[0005] The system according to the embodiment aims to generate an optimal travel schedule based on the user's wishes. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit that receives input, an analysis unit, a schedule generation unit, and a provision unit. The reception unit receives input from a user of places they want to go, things they want to do, and foods they want to eat. The analysis unit analyzes the information received by the reception unit and calculates travel routes. The schedule generation unit generates a travel schedule based on the travel routes calculated by the analysis unit. The provision unit provides the schedule generated by the schedule generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate an optimal travel schedule based on the user's wishes. [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) In an embodiment of the present invention, a travel itinerary planning system calculates the shortest travel route and presents a travel schedule when a user inputs the places they want to go, the things they want to do, the foods they want to eat, etc. The travel itinerary planning system allows the user to input the places they want to go, the things they want to do, the foods they want to eat, etc., and the AI ​​analyzes the input information and calculates the optimal travel route. Furthermore, a travel schedule is generated based on the travel route calculated by the AI. This schedule includes the places to visit, the means of transportation, the travel time, and places to eat. For example, if a user inputs "I want to go to tourist spot A," "I want to eat a specific dish," and "I want to visit tourist spot B," the AI ​​analyzes this information and calculates the optimal travel route. Next, based on the calculated travel route, the AI ​​generates a schedule that visits tourist spot A, then travels to a nearby restaurant to eat a specific dish, and then visits tourist spot B. This schedule also includes the means of transportation and the travel time to each location. This allows users to easily create an optimal travel schedule tailored to their preferences. Furthermore, because the AI ​​calculates the travel route, users can travel efficiently and use their time effectively. Furthermore, the system also suggests places to eat, making meal planning easy. In addition to sightseeing, the system can also be applied to business trips and everyday shopping, making it suitable for a wide range of uses. This allows the itinerary planning system to provide the optimal travel schedule tailored to the user's wishes. For example, the system allows the user to efficiently plan the places they want to go and the things they want to do, allowing them to use their time effectively. It also suggests places to eat, making meal plans easy. In addition to sightseeing, the system can also be applied to business trips and everyday shopping, making it suitable for a wide range of uses.

[0029] An itinerary planning system according to an embodiment includes a reception unit, an analysis unit, a schedule generation unit, and a provision unit. The reception unit receives input from a user about places to visit, things to do, and foods to eat. For example, the reception unit can input the user's desired places and activities in text format. The reception unit can also receive the user's requests using voice input. The reception unit can also receive the user's requests using image input. For example, the user can identify a desired place by uploading a photo of the desired place. The analysis unit analyzes the information received by the reception unit and calculates an optimal travel route. For example, the analysis unit calculates the shortest distance and optimal time based on the desired places and activities input by the user. The analysis unit can also calculate an optimal travel route taking into account transportation methods and traffic conditions. The analysis unit can also suggest an optimal route by referring to the user's past travel history. The schedule generation unit generates a travel schedule based on the travel route calculated by the analysis unit. For example, the schedule generation unit generates a schedule including places to visit, transportation methods, travel time, places to eat, etc. The schedule generation unit can also customize the schedule according to the user's wishes. Furthermore, the schedule generation unit can generate schedules for business trips, daily shopping, and the like in addition to sightseeing. The providing unit provides the schedule generated by the schedule generation unit to the user. For example, the providing unit displays the generated schedule on the user's smartphone or tablet. The providing unit can also print and provide the generated schedule. Furthermore, the providing unit can send the generated schedule by email. In this way, the itinerary planning system according to the embodiment can provide an optimal travel schedule tailored to the user's wishes.

[0030] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display as candidates places to go and things to do that the user has frequently input in the past. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest places to go and things to do that the user will use during a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI suggest the optimal input method.

[0031] The reception unit can suggest related places and activities based on the user's current location information when inputting the information. For example, when the user inputs their current location, the reception unit can suggest nearby tourist attractions and restaurants. The reception unit can also suggest optimal places to visit and things to do by taking into account the distance from the user's current location. The reception unit can also update the user's current location in real time and suggest related places and activities when the user uses the app while on the move. This allows more relevant places and activities to be suggested by taking into account the user's current location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's current location information data to the generation AI and cause the generation AI to suggest related places and activities.

[0032] The reception unit can select an input means according to the user's input method at the time of input. For example, the reception unit can automatically set the desired destination simply by the user's voice input of "I want to go to tourist spot A." The reception unit can also allow the user to easily set the desired destination or activity by performing a specific gesture on the smartphone screen. The reception unit can also suggest related destinations or activities based on an image uploaded by the user. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method data into a generation AI and have the generation AI select the optimal input means.

[0033] The reception unit can analyze the user's social media activity at the time of input and suggest related places and activities. For example, the reception unit can suggest places where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related places and activities. The reception unit can also suggest related places and activities by referring to the activities of the user's friends on social media. This enables more relevant suggestions to be made by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related places and activities.

[0034] The reception unit can customize the input method by reflecting the user's past feedback at the time of input. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input interface. This makes it possible to customize the input method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the input method.

[0035] The reception unit can suggest the optimal input method by taking into account the user's device information at the time of input. For example, if the user is using a smartphone, the reception unit can prioritize and suggest voice input or touch input. Furthermore, if the user is using a tablet, the reception unit can also suggest an input method optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can also suggest an input method that is simple and highly visible. This makes it possible to suggest the optimal input method by taking into account the user's device information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's device information data into the generation AI and have the generation AI suggest the optimal input method.

[0036] The analysis unit can propose an optimal route by referring to the user's past movement history during analysis. The analysis unit can propose an optimal route, for example, based on routes the user has used in the past. The analysis unit can also propose a route that avoids congestion based on the user's past movement history. The analysis unit can also analyze the user's past movement history and propose the most efficient route. In this way, the optimal route can be proposed by referring to the user's past movement history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past movement history data into the generation AI and cause the generation AI to propose an optimal route.

[0037] The analysis unit can select a means of transportation based on the user's current traffic conditions during analysis. The analysis unit can, for example, suggest the optimal means of transportation based on real-time traffic congestion information. The analysis unit can also suggest the optimal means of transportation taking into account the real-time operation status of public transportation. The analysis unit can also suggest a detour route based on real-time road construction information. This allows the optimal means of transportation to be selected by taking into account the user's current traffic conditions. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input real-time traffic condition data into the generation AI and have the generation AI select the optimal means of transportation.

[0038] During analysis, the analysis unit can analyze the user's past travel history and propose similar travel plans. For example, the analysis unit can propose similar tourist spots based on tourist spots the user has visited in the past. The analysis unit can also propose tourist spots that the user may be interested in based on the user's past travel history. The analysis unit can also analyze the user's past travel history and propose optimal travel plans. In this way, similar travel plans can be proposed by analyzing the user's past travel history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past travel history data into the generation AI and cause the generation AI to propose similar travel plans.

[0039] The analysis unit can propose a route based on the user's geographical distribution during analysis. For example, if the user is staying in a specific area, the analysis unit can propose an optimal route within that area. Furthermore, if the user is visiting multiple areas, the analysis unit can also propose an optimal travel route between each area. The analysis unit can also analyze the user's geographical distribution and propose the most efficient route. This allows the optimal route to be proposed by taking the user's geographical distribution into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical distribution data into the generation AI and have the generation AI propose an optimal route.

[0040] The analysis unit can analyze the user's social media activity during analysis and suggest related travel routes. For example, the analysis unit can suggest an optimal travel route based on the location where the user checked in on social media. The analysis unit can also analyze the content of the user's social media posts and suggest related travel routes. The analysis unit can also suggest related travel routes by referring to the activities of the user's friends on social media. In this way, related travel routes can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related travel routes.

[0041] The analysis unit can optimize the analysis algorithm by reflecting the user's past feedback during analysis. The analysis unit can optimize the analysis algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also preferentially use a specific analysis method based on the user's past feedback. The analysis unit can also analyze the user's past feedback and customize the analysis algorithm. This makes it possible to optimize the analysis algorithm by reflecting the user's past feedback. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past feedback data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0042] The schedule generation unit can refer to the user's past travel schedule when generating a schedule and propose an optimal schedule. The schedule generation unit can propose an optimal schedule based on, for example, tourist spots that the user has visited in the past. The schedule generation unit can also propose tourist spots that the user may be interested in based on the user's past travel schedule. The schedule generation unit can also analyze the user's past travel schedule and propose the most efficient schedule. In this way, an optimal schedule can be proposed by referring to the user's past travel schedule. Some or all of the above-mentioned processing in the schedule generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule generation unit can input the user's past travel schedule data into the generation AI and have the generation AI propose an optimal schedule.

[0043] The schedule generation unit can customize the schedule based on the user's current living situation when generating the schedule. For example, if the user is busy at work, the schedule generation unit can suggest a short and efficient schedule. Furthermore, if the user is on vacation, the schedule generation unit can suggest a relaxing schedule. Furthermore, if the user is with family, the schedule generation unit can suggest a schedule including family activities. This makes it possible to provide a more appropriate schedule by taking the user's current living situation into consideration. Some or all of the above-described processing in the schedule generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule generation unit can input the user's current living situation data into the generation AI and have the generation AI customize the schedule.

[0044] The schedule generation unit can improve the accuracy of the schedule by reflecting user feedback when generating the schedule. The schedule generation unit improves the accuracy of the schedule, for example, based on feedback provided by the user in the past. The schedule generation unit can also preferentially use a specific schedule generation method based on the user's past feedback. The schedule generation unit can also analyze the user's past feedback and customize the schedule generation algorithm. In this way, the accuracy of the schedule is improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the schedule generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule generation unit can input the user's past feedback data into the generation AI and cause the generation AI to optimize the schedule generation algorithm.

[0045] The schedule generation unit can propose a schedule based on the user's geographical distribution when generating a schedule. For example, if the user is staying in a specific area, the schedule generation unit can propose an optimal schedule within that area. Furthermore, if the user is visiting multiple areas, the schedule generation unit can also propose an optimal travel schedule between each area. Furthermore, the schedule generation unit can analyze the user's geographical distribution and propose the most efficient schedule. In this way, an optimal schedule can be proposed by taking the user's geographical distribution into consideration. Some or all of the above-described processing in the schedule generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the schedule generation unit can input the user's geographical distribution data into the generation AI and have the generation AI propose an optimal schedule.

[0046] The schedule generation unit can analyze the user's social media activity when generating a schedule and propose a related schedule. The schedule generation unit can propose an optimal schedule based on, for example, the locations where the user has checked in on social media. The schedule generation unit can also analyze the content of the user's social media posts and propose a related schedule. The schedule generation unit can also propose a related schedule by referring to the activities of the user's friends on social media. In this way, a related schedule can be proposed by analyzing the user's social media activity. Some or all of the above-described processing in the schedule generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule generation unit can input the user's social media activity data into the generation AI and cause the generation AI to propose a related schedule.

[0047] The schedule generation unit can customize the content of the schedule by reflecting the user's past feedback when generating the schedule. The schedule generation unit customizes the content of the schedule based on, for example, feedback provided by the user in the past. The schedule generation unit can also preferentially suggest specific schedule content based on the user's past feedback. The schedule generation unit can also analyze the user's past feedback and optimize the content of the schedule. In this way, the content of the schedule can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the schedule generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule generation unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the content of the schedule.

[0048] The providing unit can provide information by referring to the user's past usage history when providing the information. The providing unit can, for example, provide optimal information based on information the user has used in the past. The providing unit can also provide information that is likely to be of interest to the user based on the user's past usage history. The providing unit can also analyze the user's past usage history and provide the most efficient information. In this way, optimal information can be provided by referring to the user's past usage history. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past usage history data into a generation AI and cause the generation AI to provide optimal information.

[0049] The providing unit can customize the way information is displayed based on the user's current situation at the time of providing the information. For example, when the user is moving, the providing unit provides concise, highly visible information. Furthermore, when the user is taking a break, the providing unit can also provide detailed information. Furthermore, when the user is in a specific location, the providing unit can also provide information related to that location. This makes it possible to customize the way information is displayed by taking the user's current situation into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's current situation data into the generating AI and cause the generating AI to customize the way information is displayed.

[0050] The providing unit can improve the accuracy of the information to be provided by reflecting user feedback at the time of providing the information. The providing unit can improve the accuracy of the information based on, for example, feedback provided by the user in the past. The providing unit can also preferentially use a specific information provision method based on the user's past feedback. The providing unit can also analyze the user's past feedback and customize the information provision algorithm. This improves the accuracy of the information by reflecting the user's feedback. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to optimize the information provision algorithm.

[0051] The providing unit can provide information based on the user's geographical distribution at the time of providing. For example, if the user is staying in a specific area, the providing unit can provide information related to that area. Furthermore, if the user is visiting multiple areas, the providing unit can also provide information related to each area. Furthermore, the providing unit can analyze the user's geographical distribution and provide the most efficient information. This makes it possible to provide optimal information by taking the user's geographical distribution into consideration. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's geographical distribution data into the generating AI and cause the generating AI to provide optimal information.

[0052] The providing unit can analyze the user's social media activity at the time of providing and provide related information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related information.

[0053] The providing unit can customize the content of the information by reflecting the user's past feedback when providing the information. The providing unit customizes the content of the information based on, for example, feedback provided by the user in the past. The providing unit can also prioritize providing specific information content based on the user's past feedback. The providing unit can also analyze the user's past feedback and optimize the information content. In this way, the information content can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to customize the information content.

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

[0055] The analysis unit can also suggest optimal travel routes based on the user's health data. For example, if the user uses a pedometer or heart rate monitor, the analysis unit can refer to this data and suggest travel routes that suit the user's physical strength. Also, if the user has set specific health goals, the analysis unit can suggest routes that match those goals. Furthermore, the analysis unit can suggest rest points based on the user's health condition. This makes it possible to provide optimal travel routes that take the user's health condition into consideration.

[0056] The schedule generation unit can also customize a schedule based on the user's hobbies and interests. For example, if the user likes music, the schedule generation unit can suggest music events and live music venues. If the user is interested in history, the schedule generation unit can also incorporate historical tourist spots and museums into the schedule. Furthermore, if the user likes outdoor activities, the schedule generation unit can also suggest hiking trails and campsites. This makes it possible to provide a schedule that matches the user's hobbies and interests.

[0057] The analysis unit can also analyze the user's past travel reviews and suggest optimal tourist destinations. For example, it can suggest similar tourist destinations based on tourist destinations that the user has previously given high ratings. It can also suggest tourist destinations that avoid tourist destinations that the user has previously given low ratings. Furthermore, it can analyze the content of the user's reviews and suggest tourist destinations based on specific elements (e.g., scenery, services, access, etc.). This makes it possible to suggest optimal tourist destinations based on the user's past travel reviews.

[0058] The schedule generation unit can also adjust the schedule based on the user's current weather information. For example, if it is raining, indoor tourist spots and activities can be suggested. If it is sunny, outdoor tourist spots and activities can be suggested. Furthermore, if the temperature is high, cool places and waterside activities can be suggested. This makes it possible to provide a schedule that takes the user's current weather information into consideration.

[0059] The analysis unit can also suggest the optimal means of transportation based on the user's past history of transportation usage. For example, it can prioritize suggestions of transportation modes that the user has frequently used in the past. It can also make suggestions taking into account transportation modes that the user has avoided in the past. Furthermore, it can analyze the user's past history of transportation usage and suggest the most efficient means of transportation. This makes it possible to suggest the optimal means of transportation based on the user's past history of transportation usage.

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

[0061] Step 1: The reception unit accepts input from the user about places they want to go, things they want to do, and things they want to eat. For example, the user can input their preferences using text, voice input, or image input. In the case of image input, the user can specify the place they want to go by uploading a photo of the place. Step 2: The analysis unit analyzes the information received by the reception unit and calculates the optimal travel route. For example, it calculates the shortest distance and optimal time based on the destination and activities entered by the user. It can also calculate the optimal travel route taking into account the mode of transportation and traffic conditions. It can also suggest the optimal route by referring to the user's past travel history. Step 3: The schedule generation unit generates a travel schedule based on the travel route calculated by the analysis unit. For example, a schedule including places to visit, means of transportation, travel time, places to eat, etc. can be generated. The schedule can also be customized according to the user's wishes. In addition to sightseeing, schedules can also be generated for business trips, daily shopping, etc. Step 4: The providing unit provides the schedule generated by the schedule generating unit to the user. For example, the generated schedule may be displayed on the user's smartphone or tablet. The generated schedule may also be printed and provided. Furthermore, the generated schedule may also be sent by email.

[0062] (Example 2) In an embodiment of the present invention, a travel itinerary planning system calculates the shortest travel route and presents a travel schedule when a user inputs the places they want to go, the things they want to do, the foods they want to eat, etc. The travel itinerary planning system allows the user to input the places they want to go, the things they want to do, the foods they want to eat, etc., and the AI ​​analyzes the input information and calculates the optimal travel route. Furthermore, a travel schedule is generated based on the travel route calculated by the AI. This schedule includes the places to visit, the means of transportation, the travel time, and places to eat. For example, if a user inputs "I want to go to tourist spot A," "I want to eat a specific dish," and "I want to visit tourist spot B," the AI ​​analyzes this information and calculates the optimal travel route. Next, based on the calculated travel route, the AI ​​generates a schedule that visits tourist spot A, then travels to a nearby restaurant to eat a specific dish, and then visits tourist spot B. This schedule also includes the means of transportation and the travel time to each location. This allows users to easily create an optimal travel schedule tailored to their preferences. Furthermore, because the AI ​​calculates the travel route, users can travel efficiently and use their time effectively. Furthermore, the system also suggests places to eat, making meal planning easy. In addition to sightseeing, the system can also be applied to business trips and everyday shopping, making it suitable for a wide range of uses. This allows the itinerary planning system to provide the optimal travel schedule tailored to the user's wishes. For example, the system allows the user to efficiently plan the places they want to go and the things they want to do, allowing them to use their time effectively. It also suggests places to eat, making meal plans easy. In addition to sightseeing, the system can also be applied to business trips and everyday shopping, making it suitable for a wide range of uses.

[0063] An itinerary planning system according to an embodiment includes a reception unit, an analysis unit, a schedule generation unit, and a provision unit. The reception unit receives input from a user about places to visit, things to do, and foods to eat. For example, the reception unit can input the user's desired places and activities in text format. The reception unit can also receive the user's requests using voice input. The reception unit can also receive the user's requests using image input. For example, the user can identify a desired place by uploading a photo of the desired place. The analysis unit analyzes the information received by the reception unit and calculates an optimal travel route. For example, the analysis unit calculates the shortest distance and optimal time based on the desired places and activities input by the user. The analysis unit can also calculate an optimal travel route taking into account transportation methods and traffic conditions. The analysis unit can also suggest an optimal route by referring to the user's past travel history. The schedule generation unit generates a travel schedule based on the travel route calculated by the analysis unit. For example, the schedule generation unit generates a schedule including places to visit, transportation methods, travel time, places to eat, etc. The schedule generation unit can also customize the schedule according to the user's wishes. Furthermore, the schedule generation unit can generate schedules for business trips, daily shopping, and the like in addition to sightseeing. The providing unit provides the schedule generated by the schedule generation unit to the user. For example, the providing unit displays the generated schedule on the user's smartphone or tablet. The providing unit can also print and provide the generated schedule. Furthermore, the providing unit can send the generated schedule by email. In this way, the itinerary planning system according to the embodiment can provide an optimal travel schedule tailored to the user's wishes.

[0064] The reception unit can estimate the user's emotions and adjust input priority based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly enter places to go or things to do. This allows for adjusting input priority according to the user's emotions, thereby providing a more appropriate input method. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0065] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display as candidates places to go and things to do that the user has frequently input in the past. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest places to go and things to do that the user will use during a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI suggest the optimal input method.

[0066] The reception unit can suggest related places and activities based on the user's current location information when inputting the information. For example, when the user inputs their current location, the reception unit can suggest nearby tourist attractions and restaurants. The reception unit can also suggest optimal places to visit and things to do by taking into account the distance from the user's current location. The reception unit can also update the user's current location in real time and suggest related places and activities when the user uses the app while on the move. This allows more relevant places and activities to be suggested by taking into account the user's current location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's current location information data to the generation AI and cause the generation AI to suggest related places and activities.

[0067] The reception unit can select an input means according to the user's input method at the time of input. For example, the reception unit can automatically set the desired destination simply by the user's voice input of "I want to go to tourist spot A." The reception unit can also allow the user to easily set the desired destination or activity by performing a specific gesture on the smartphone screen. The reception unit can also suggest related destinations or activities based on an image uploaded by the user. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method data into a generation AI and have the generation AI select the optimal input means.

[0068] The reception unit can estimate the user's emotions and filter the input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize suggesting places and activities that allow the user to relax. Furthermore, if the user is having fun, the reception unit can also suggest places and activities that are highly entertaining. Furthermore, if the user is tired, the reception unit can prioritize suggesting places and activities that allow the user to relax. This enables more appropriate suggestions by filtering the input content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] The reception unit can analyze the user's social media activity at the time of input and suggest related places and activities. For example, the reception unit can suggest places where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related places and activities. The reception unit can also suggest related places and activities by referring to the activities of the user's friends on social media. This enables more relevant suggestions to be made by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related places and activities.

[0070] The reception unit can customize the input method by reflecting the user's past feedback at the time of input. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input interface. This makes it possible to customize the input method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the input method.

[0071] The reception unit can suggest the optimal input method by taking into account the user's device information at the time of input. For example, if the user is using a smartphone, the reception unit can prioritize and suggest voice input or touch input. Furthermore, if the user is using a tablet, the reception unit can also suggest an input method optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can also suggest an input method that is simple and highly visible. This makes it possible to suggest the optimal input method by taking into account the user's device information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's device information data into the generation AI and have the generation AI suggest the optimal input method.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and suggest an optimal travel route. If the user is in a hurry, the analysis unit can also perform a quick analysis and suggest the shortest route. If the user is excited, the analysis unit can provide analysis results with visually stimulating effects. This allows for adjusting the analysis priority according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] The analysis unit can propose an optimal route by referring to the user's past movement history during analysis. The analysis unit can propose an optimal route, for example, based on routes the user has used in the past. The analysis unit can also propose a route that avoids congestion based on the user's past movement history. The analysis unit can also analyze the user's past movement history and propose the most efficient route. In this way, the optimal route can be proposed by referring to the user's past movement history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past movement history data into the generation AI and cause the generation AI to propose an optimal route.

[0074] The analysis unit can select a means of transportation based on the user's current traffic conditions during analysis. The analysis unit can, for example, suggest the optimal means of transportation based on real-time traffic congestion information. The analysis unit can also suggest the optimal means of transportation taking into account the real-time operation status of public transportation. The analysis unit can also suggest a detour route based on real-time road construction information. This allows the optimal means of transportation to be selected by taking into account the user's current traffic conditions. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input real-time traffic condition data into the generation AI and have the generation AI select the optimal means of transportation.

[0075] During analysis, the analysis unit can analyze the user's past travel history and propose similar travel plans. For example, the analysis unit can propose similar tourist spots based on tourist spots the user has visited in the past. The analysis unit can also propose tourist spots that the user may be interested in based on the user's past travel history. The analysis unit can also analyze the user's past travel history and propose optimal travel plans. In this way, similar travel plans can be proposed by analyzing the user's past travel history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past travel history data into the generation AI and cause the generation AI to propose similar travel plans.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] The analysis unit can propose a route based on the user's geographical distribution during analysis. For example, if the user is staying in a specific area, the analysis unit can propose an optimal route within that area. Furthermore, if the user is visiting multiple areas, the analysis unit can also propose an optimal travel route between each area. The analysis unit can also analyze the user's geographical distribution and propose the most efficient route. This allows the optimal route to be proposed by taking the user's geographical distribution into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical distribution data into the generation AI and have the generation AI propose an optimal route.

[0078] The analysis unit can analyze the user's social media activity during analysis and suggest related travel routes. For example, the analysis unit can suggest an optimal travel route based on the location where the user checked in on social media. The analysis unit can also analyze the content of the user's social media posts and suggest related travel routes. The analysis unit can also suggest related travel routes by referring to the activities of the user's friends on social media. In this way, related travel routes can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related travel routes.

[0079] The analysis unit can optimize the analysis algorithm by reflecting the user's past feedback during analysis. The analysis unit can optimize the analysis algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also preferentially use a specific analysis method based on the user's past feedback. The analysis unit can also analyze the user's past feedback and customize the analysis algorithm. This makes it possible to optimize the analysis algorithm by reflecting the user's past feedback. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past feedback data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0080] The schedule generation unit can estimate the user's emotions and adjust the schedule priority based on the estimated user emotions. For example, if the user is relaxed, the schedule generation unit can suggest a relaxed schedule. Furthermore, if the user is in a hurry, the schedule generation unit can suggest an efficient schedule. Furthermore, if the user is excited, the schedule generation unit can suggest a schedule that includes many activities. This allows for adjusting the schedule priority according to the user's emotions, thereby providing a more appropriate schedule. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the schedule generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the schedule generation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotions.

[0081] The schedule generation unit can refer to the user's past travel schedule when generating a schedule and propose an optimal schedule. The schedule generation unit can propose an optimal schedule based on, for example, tourist spots that the user has visited in the past. The schedule generation unit can also propose tourist spots that the user may be interested in based on the user's past travel schedule. The schedule generation unit can also analyze the user's past travel schedule and propose the most efficient schedule. In this way, an optimal schedule can be proposed by referring to the user's past travel schedule. Some or all of the above-mentioned processing in the schedule generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule generation unit can input the user's past travel schedule data into the generation AI and have the generation AI propose an optimal schedule.

[0082] The schedule generation unit can customize the schedule based on the user's current living situation when generating the schedule. For example, if the user is busy at work, the schedule generation unit can suggest a short and efficient schedule. Furthermore, if the user is on vacation, the schedule generation unit can suggest a relaxing schedule. Furthermore, if the user is with family, the schedule generation unit can suggest a schedule including family activities. This makes it possible to provide a more appropriate schedule by taking the user's current living situation into consideration. Some or all of the above-described processing in the schedule generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule generation unit can input the user's current living situation data into the generation AI and have the generation AI customize the schedule.

[0083] The schedule generation unit can improve the accuracy of the schedule by reflecting user feedback when generating the schedule. The schedule generation unit improves the accuracy of the schedule, for example, based on feedback provided by the user in the past. The schedule generation unit can also preferentially use a specific schedule generation method based on the user's past feedback. The schedule generation unit can also analyze the user's past feedback and customize the schedule generation algorithm. In this way, the accuracy of the schedule is improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the schedule generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule generation unit can input the user's past feedback data into the generation AI and cause the generation AI to optimize the schedule generation algorithm.

[0084] The schedule generation unit can estimate the user's emotions and adjust the schedule display method based on the estimated user emotions. For example, if the user is nervous, the schedule generation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the schedule generation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the schedule generation unit can provide a display method that focuses on the main points. This allows for a more appropriate display by adjusting the schedule display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the schedule generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the schedule generation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotions.

[0085] The schedule generation unit can propose a schedule based on the user's geographical distribution when generating a schedule. For example, if the user is staying in a specific area, the schedule generation unit can propose an optimal schedule within that area. Furthermore, if the user is visiting multiple areas, the schedule generation unit can also propose an optimal travel schedule between each area. Furthermore, the schedule generation unit can analyze the user's geographical distribution and propose the most efficient schedule. In this way, an optimal schedule can be proposed by taking the user's geographical distribution into consideration. Some or all of the above-described processing in the schedule generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the schedule generation unit can input the user's geographical distribution data into the generation AI and have the generation AI propose an optimal schedule.

[0086] The schedule generation unit can analyze the user's social media activity when generating a schedule and propose a related schedule. The schedule generation unit can propose an optimal schedule based on, for example, the locations where the user has checked in on social media. The schedule generation unit can also analyze the content of the user's social media posts and propose a related schedule. The schedule generation unit can also propose a related schedule by referring to the activities of the user's friends on social media. In this way, a related schedule can be proposed by analyzing the user's social media activity. Some or all of the above-described processing in the schedule generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule generation unit can input the user's social media activity data into the generation AI and cause the generation AI to propose a related schedule.

[0087] The schedule generation unit can customize the content of the schedule by reflecting the user's past feedback when generating the schedule. The schedule generation unit customizes the content of the schedule based on, for example, feedback provided by the user in the past. The schedule generation unit can also preferentially suggest specific schedule content based on the user's past feedback. The schedule generation unit can also analyze the user's past feedback and optimize the content of the schedule. In this way, the content of the schedule can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the schedule generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the schedule generation unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the content of the schedule.

[0088] The providing unit can estimate the user's emotions and adjust the priority of information to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the providing unit can prioritize providing information that focuses on the main points. Furthermore, when the user is excited, the providing unit can prioritize providing visually stimulating information. This allows for adjusting the priority of information according to the user's emotions, thereby providing more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] The providing unit can provide information by referring to the user's past usage history when providing the information. The providing unit can, for example, provide optimal information based on information the user has used in the past. The providing unit can also provide information that is likely to be of interest to the user based on the user's past usage history. The providing unit can also analyze the user's past usage history and provide the most efficient information. In this way, optimal information can be provided by referring to the user's past usage history. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past usage history data into a generation AI and cause the generation AI to provide optimal information.

[0090] The providing unit can customize the way information is displayed based on the user's current situation at the time of providing the information. For example, when the user is moving, the providing unit provides concise, highly visible information. Furthermore, when the user is taking a break, the providing unit can also provide detailed information. Furthermore, when the user is in a specific location, the providing unit can also provide information related to that location. This makes it possible to customize the way information is displayed by taking the user's current situation into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's current situation data into the generating AI and cause the generating AI to customize the way information is displayed.

[0091] The providing unit can improve the accuracy of the information to be provided by reflecting user feedback at the time of providing the information. The providing unit can improve the accuracy of the information based on, for example, feedback provided by the user in the past. The providing unit can also preferentially use a specific information provision method based on the user's past feedback. The providing unit can also analyze the user's past feedback and customize the information provision algorithm. This improves the accuracy of the information by reflecting the user's feedback. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to optimize the information provision algorithm.

[0092] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the information display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0093] The providing unit can provide information based on the user's geographical distribution at the time of providing. For example, if the user is staying in a specific area, the providing unit can provide information related to that area. Furthermore, if the user is visiting multiple areas, the providing unit can also provide information related to each area. Furthermore, the providing unit can analyze the user's geographical distribution and provide the most efficient information. This makes it possible to provide optimal information by taking the user's geographical distribution into consideration. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's geographical distribution data into the generating AI and cause the generating AI to provide optimal information.

[0094] The providing unit can analyze the user's social media activity at the time of providing and provide related information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related information.

[0095] The providing unit can customize the content of the information by reflecting the user's past feedback when providing the information. The providing unit customizes the content of the information based on, for example, feedback provided by the user in the past. The providing unit can also prioritize providing specific information content based on the user's past feedback. The providing unit can also analyze the user's past feedback and optimize the information content. In this way, the information content can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to customize the information content. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, schedule generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives user input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's input information. The schedule generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal travel schedule. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated schedule to the user. The reception unit can use, for example, the camera 42 or microphone 38B of the smart device 14 to estimate the user's emotions. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, schedule generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives user input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's input information. The schedule generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal travel schedule. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated schedule to the user. The reception unit can use, for example, the camera 42 and microphone 238 of the smart glasses 214 to estimate the user's emotions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, schedule generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives user input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's input information. The schedule generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal travel schedule. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the generated schedule to the user. For example, the reception unit can use the camera 42 or microphone 238 of the headset type terminal 314 to estimate the user's emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, schedule generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives user input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input information. The schedule generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal travel schedule. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated schedule to the user. The reception unit can use, for example, the camera 42 and microphone 238 of the robot 414 to estimate the user's emotions.

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

[0097] The analysis unit can also suggest optimal travel routes based on the user's health data. For example, if the user uses a pedometer or heart rate monitor, the analysis unit can refer to this data and suggest travel routes that suit the user's physical strength. Also, if the user has set specific health goals, the analysis unit can suggest routes that match those goals. Furthermore, the analysis unit can suggest rest points based on the user's health condition. This makes it possible to provide optimal travel routes that take the user's health condition into consideration.

[0098] The reception unit can also estimate the user's emotions and provide input feedback based on the estimated user emotions. For example, if the user feels anxious, the reception unit can display an encouraging message. Also, if the user feels excited, the reception unit can display a message sharing the user's emotions. Furthermore, if the user feels tired, the reception unit can suggest simplifying the input. In this way, by providing feedback according to the user's emotions, a more comfortable input experience can be provided.

[0099] The schedule generation unit can also customize a schedule based on the user's hobbies and interests. For example, if the user likes music, the schedule generation unit can suggest music events and live music venues. If the user is interested in history, the schedule generation unit can also incorporate historical tourist spots and museums into the schedule. Furthermore, if the user likes outdoor activities, the schedule generation unit can also suggest hiking trails and campsites. This makes it possible to provide a schedule that matches the user's hobbies and interests.

[0100] The providing unit can also estimate the user's emotions and adjust the timing of displaying information based on the estimated user's emotions. For example, if the user is relaxed, the information can be displayed slowly. If the user is in a hurry, the information can be displayed quickly. Furthermore, if the user is excited, the information can be displayed in a visually appealing manner. This makes it possible to provide more appropriate information by adjusting the timing of displaying information according to the user's emotions.

[0101] The analysis unit can also analyze the user's past travel reviews and suggest optimal tourist destinations. For example, it can suggest similar tourist destinations based on tourist destinations that the user has previously given high ratings. It can also suggest tourist destinations that avoid tourist destinations that the user has previously given low ratings. Furthermore, it can analyze the content of the user's reviews and suggest tourist destinations based on specific elements (e.g., scenery, services, access, etc.). This makes it possible to suggest optimal tourist destinations based on the user's past travel reviews.

[0102] The reception unit can also estimate the user's emotions and change the design of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple and calm design can be provided. If the user is having fun, a colorful and fun design can be provided. Furthermore, if the user is tired, a highly visible design can be provided. In this way, by providing an input interface design that corresponds to the user's emotions, a more comfortable input experience can be provided.

[0103] The schedule generation unit can also adjust the schedule based on the user's current weather information. For example, if it is raining, indoor tourist spots and activities can be suggested. If it is sunny, outdoor tourist spots and activities can be suggested. Furthermore, if the temperature is high, cool places and waterside activities can be suggested. This makes it possible to provide a schedule that takes the user's current weather information into consideration.

[0104] The providing unit can also estimate the user's emotions and adjust the format of the information based on the estimated user's emotions. For example, if the user is relaxed, detailed text information can be provided. If the user is in a hurry, concise information using bullet points and charts can be provided. Furthermore, if the user is excited, information using visually appealing graphics and videos can be provided. In this way, by adjusting the format of information according to the user's emotions, more appropriate information can be provided.

[0105] The analysis unit can also suggest the optimal means of transportation based on the user's past history of transportation usage. For example, it can prioritize suggestions of transportation modes that the user has frequently used in the past. It can also make suggestions taking into account transportation modes that the user has avoided in the past. Furthermore, it can analyze the user's past history of transportation usage and suggest the most efficient means of transportation. This makes it possible to suggest the optimal means of transportation based on the user's past history of transportation usage.

[0106] The schedule generation unit can also estimate the user's emotions and adjust the flexibility of the schedule based on the estimated user's emotions. For example, if the user is relaxed, it can suggest a schedule with ample time. If the user is in a hurry, it can also suggest an efficient schedule. Furthermore, if the user is excited, it can also suggest a schedule that includes many activities. In this way, it is possible to provide a more appropriate schedule by adjusting the flexibility of the schedule according to the user's emotions.

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

[0108] Step 1: The reception unit accepts input from the user about places they want to go, things they want to do, and things they want to eat. For example, the user can input their preferences using text, voice input, or image input. In the case of image input, the user can specify the place they want to go by uploading a photo of the place. Step 2: The analysis unit analyzes the information received by the reception unit and calculates the optimal travel route. For example, it calculates the shortest distance and optimal time based on the destination and activities entered by the user. It can also calculate the optimal travel route taking into account the mode of transportation and traffic conditions. It can also suggest the optimal route by referring to the user's past travel history. Step 3: The schedule generation unit generates a travel schedule based on the travel route calculated by the analysis unit. For example, a schedule including places to visit, means of transportation, travel time, places to eat, etc. can be generated. The schedule can also be customized according to the user's wishes. In addition to sightseeing, schedules can also be generated for business trips, daily shopping, etc. Step 4: The providing unit provides the schedule generated by the schedule generating unit to the user. For example, the generated schedule may be displayed on the user's smartphone or tablet. The generated schedule may also be printed and provided. Furthermore, the generated schedule may also be sent by email.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

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

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

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

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives input from users about places they want to go, things they want to do, and things they want to eat; an analysis unit that analyzes the information received by the reception unit and calculates a travel route; a schedule generation unit that generates a travel schedule based on the travel route calculated by the analysis unit; a providing unit that provides the schedule generated by the schedule generating unit. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the priority of inputs based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyzes the user's past input history and suggests input methods 2. The system of claim 1.

4. The reception unit Suggests relevant places and activities based on your current location as you type 2. The system of claim 1.

5. The reception unit Select an input method according to the user's input method when inputting 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and filter the input content based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyzes your social media activity as you type to suggest relevant places and activities 2. The system of claim 1.

8. The reception unit Customize your input experience by incorporating your users' past feedback as they type 2. The system of claim 1.

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