System

Abstract

An object of a system according to an embodiment is to propose an optimal outing course based on a user's preference, budget, and available time.SOLUTION: A system includes a user input analysis part, a climate information consideration part, an event information consideration part, a congestion degree consideration part, and a review consideration part. The user input analysis unit analyzes user input information. The climate information consideration unit considers climate information and season based on the user input information analyzed by the user input analysis unit. The event information consideration unit considers local event information based on the climate information considered by the climate information consideration unit and the season. The congestion degree consideration unit considers the congestion degree based on the event information of the area considered by the event information consideration unit. The review consideration unit considers the user review based on the congestion degree considered by the congestion degree consideration unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately propose optimal outing itineraries based on a user's preferences, budget, and available time, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal outing course based on the user's preferences, budget, and available time. [Means for solving the problem]

[0006] A system according to an embodiment includes a user input analysis unit, a climate information consideration unit, an event information consideration unit, a congestion level consideration unit, and a review consideration unit. The user input analysis unit analyzes user input information. The climate information consideration unit considers climate information and season based on the user input information analyzed by the user input analysis unit. The event information consideration unit considers local event information based on the climate information and season considered by the climate information consideration unit. The congestion level consideration unit considers congestion level based on the local event information considered by the event information consideration unit. The review consideration unit considers user reviews based on congestion level considered by the congestion level consideration unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal outing course based on the user's preferences, budget, and available time. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The outing itinerary suggestion system according to an embodiment of the present invention is a system that suggests optimal holiday outing itineraries based on preferences, budget, and available time input by a user. In this system, a generation AI analyzes the information input by the user and generates optimal outing itineraries by taking into consideration detailed weather information, seasons, local event information, crowd levels, user reviews, etc. held by Yahoo. This allows the outing itinerary suggestion system to easily find optimal outing itineraries that match the user's preferences and conditions.

[0029] An outing itinerary suggestion system according to an embodiment includes a user input analysis unit, a weather information consideration unit, an event information consideration unit, a congestion level consideration unit, and a review consideration unit. The user input analysis unit analyzes user input information. For example, if a user inputs "I like nature, my budget is 5,000 yen or less, and I have 5 hours of available time," the user input analysis unit suggests an optimal outing itinerary based on this information. The weather information consideration unit considers weather information and the season based on the user input information analyzed by the user input analysis unit. For example, if a user plans to go out in the spring, the weather information consideration unit suggests tourist spots and activities suitable for the spring weather. The event information consideration unit considers local event information based on the weather information and the season considered by the weather information consideration unit. For example, if a festival or event is being held in an area the user is visiting, the event information consideration unit suggests a route to participate in the event based on that information. The congestion level consideration unit considers the congestion level based on the local event information considered by the event information consideration unit. For example, if a popular tourist spot is crowded, the congestion level consideration unit suggests an alternative spot to avoid the crowds. The review consideration unit considers user reviews based on the congestion level considered by the congestion level consideration unit. For example, if a user desires to visit a highly rated tourist spot, the review consideration unit will preferentially suggest spots with high user reviews. This allows the outing course suggestion system to suggest an optimal outing course based on the user's input information, taking into consideration weather information, season, local event information, congestion level, and user reviews.

[0030] The user input analysis unit analyzes a user's past behavioral history and search history to propose an outing itinerary optimized for each individual user. The user input analysis unit, for example, analyzes a user's past behavioral history to understand frequently visited places and preference trends. For example, based on data on tourist spots and restaurants visited in the past, it proposes new spots that match the user's preferences. The user input analysis unit also analyzes search history to identify themes and activities in which the user is interested. For example, based on keywords searched in the past and the content of pages viewed, it proposes related outing itineraries. The user input analysis unit also analyzes a user's behavioral patterns to understand places that are often visited during specific times of the day or on specific days of the week. For example, it prioritizes the proposal of spots that are often visited on weekends. This makes it possible to propose outing itineraries optimized for each individual user based on the user's past behavioral history and search history.

[0031] The user input analysis unit can dynamically generate an optimal outing itinerary based on the current location using real-time location information. The user input analysis unit, for example, acquires the user's current location in real time and suggests an optimal outing itinerary based on that location information. For example, it suggests tourist spots and restaurants within walking distance of the current location. The user input analysis unit also suggests an optimal route while the user is traveling based on real-time location information. For example, it dynamically generates tourist spots and rest areas that can be stopped off during the trip. The user input analysis unit also adjusts the optimal outing itinerary in real time, taking into account weather information and traffic conditions at the current location. For example, it suggests indoor spots if it is raining. This makes it possible to dynamically generate an optimal outing itinerary based on the user's real-time location information.

[0032] The climate information consideration unit can analyze past weather data in addition to climate information and propose an outing itinerary that corresponds to predicted climate change. The climate information consideration unit, for example, analyzes past weather data and proposes an outing itinerary that corresponds to predicted climate change. For example, based on past data, it proposes indoor spots in areas where there is a lot of rain in a particular season. The climate information consideration unit also proposes an outing itinerary that corresponds to future climate conditions based on climate change forecast data. For example, if a rise in temperature is predicted, it proposes cool places or waterside spots. The climate information consideration unit also proposes activities that are suitable for specific climate conditions based on past weather data. For example, on a windy day, it proposes activities such as windsurfing. In this way, it is possible to propose an outing itinerary that corresponds to predicted climate change based on past weather data.

[0033] The climate information consideration unit can suggest optimal tourist spots by taking into account natural phenomena specific to each season. The climate information consideration unit, for example, can suggest optimal tourist spots by taking into account natural phenomena specific to each season. For example, it can suggest famous cherry blossom viewing spots in spring and famous autumn foliage spots in autumn. The climate information consideration unit can also suggest optimal outing courses by taking into account event information according to natural phenomena in each season. For example, it can suggest courses that include cherry blossom festivals and autumn leaf viewing events. The climate information consideration unit can also suggest tourist spots that suit the user's preferences based on natural phenomena in each season. For example, it can suggest spots where seasonal flowers can be seen for a user who likes flowers. In this way, it is possible to suggest optimal tourist spots by taking into account natural phenomena specific to each season.

[0034] The event information consideration unit can analyze feedback from past event participants in addition to event information, and prioritize suggesting highly rated events. The event information consideration unit, for example, analyzes feedback from past event participants and prioritize suggesting highly rated events. For example, it suggests popular events based on user reviews and rating scores. The event information consideration unit also analyzes the appeal and points to note of a particular event based on feedback from event participants, and suggests an event that is suitable for the user. For example, it suggests an event for families or an event for couples. The event information consideration unit also analyzes areas for improvement and success factors of an event based on the feedback data, and suggests the most suitable event for the user. For example, it extracts the characteristics of events that have received high ratings in the past, and suggests similar events. This makes it possible to prioritize suggesting highly rated events based on feedback from past event participants.

[0035] The event information consideration unit is capable of proposing a course that allows efficient participation in multiple events, taking into account the event times and locations. The event information consideration unit, for example, proposes a course that allows efficient participation in multiple events, taking into account the event times and locations. For example, it creates a schedule that allows participation in consecutive events held in the same area. The event information consideration unit also proposes a course that minimizes travel time based on the event times. For example, it combines events held within walking distance. The event information consideration unit also proposes a course that optimizes transportation means and travel routes, taking into account the location of the events. For example, it proposes a course that allows efficient travel using public transportation. In this way, it is possible to propose a course that allows efficient participation in multiple events, taking into account the event times and locations.

[0036] The congestion degree consideration unit can analyze past congestion patterns in addition to congestion degree data and propose a route that corresponds to the predicted congestion situation. The congestion degree consideration unit, for example, analyzes past congestion patterns and proposes a route that corresponds to the predicted congestion situation. For example, if congestion is predicted during a specific time period or day of the week, it proposes alternative spots to avoid the congestion. Furthermore, the congestion degree consideration unit proposes a route that allows the user to spend comfortably based on the congestion prediction data. For example, it proposes spots that can be visited during times when it is less crowded. Furthermore, if congestion is predicted during a specific event or season based on past congestion data, it proposes a route that avoids the congestion. For example, it proposes spots that can be visited during times when it is less crowded. In this way, it is possible to propose a route that corresponds to the predicted congestion situation based on past congestion patterns.

[0037] The congestion level consideration unit monitors the congestion level in real time and can immediately suggest an alternative route if congestion occurs. The congestion level consideration unit, for example, monitors the congestion level in real time and can immediately suggest an alternative route if congestion occurs. For example, it can suggest alternative spots to avoid spots that are predicted to be crowded. The congestion level consideration unit also suggests a route that the user can take comfortably based on real-time congestion data. For example, it can suggest spots that can be visited during times when congestion is less. The congestion level consideration unit also builds a system that notifies the user if congestion occurs and suggests an alternative route. For example, it can notify the user to avoid spots that are predicted to be crowded. This makes it possible to monitor the congestion level in real time and immediately suggest an alternative route if congestion occurs.

[0038] The review consideration unit can evaluate the reliability of reviews in addition to user reviews and prioritize highly reliable reviews. The review consideration unit, for example, evaluates the reliability of user reviews and prioritizes highly reliable reviews. For example, it determines reliability based on the review poster's history and evaluation score. The review consideration unit also suggests an optimal outing course for the user based on highly reliable reviews. For example, it prioritizes suggesting spots with many highly reliable reviews. The review consideration unit also develops an algorithm to evaluate the reliability of reviews and automatically extracts highly reliable reviews. For example, it evaluates reliability by analyzing the content of the review and information about the poster. In this way, by evaluating the reliability of reviews and prioritizing the highly reliable reviews, it is possible to suggest a more accurate outing course.

[0039] The review consideration unit can analyze the content of reviews and suggest courses based on specific keywords. The review consideration unit, for example, analyzes the content of user reviews and suggests courses based on specific keywords. For example, it suggests spots that frequently contain the keyword "beautiful scenery." The review consideration unit also suggests courses that suit the user's preferences based on the keywords in the reviews. For example, it preferentially suggests spots that frequently contain the keyword "friendly staff." The review consideration unit also performs keyword analysis and suggests courses based on specific themes. For example, it suggests courses based on keywords such as "family-friendly" or "couple-friendly." In this way, by analyzing the content of reviews and suggesting courses based on specific keywords, it is possible to provide outing courses that suit the user's preferences.

[0040] The review consideration unit can suggest similar spots in different regions or countries based on user reviews. The review consideration unit, for example, suggests similar spots in different regions or countries based on user reviews. For example, if spots with the same theme exist in other regions or countries, the review consideration unit provides that information. The review consideration unit also analyzes reviews of similar spots and suggests spots that match the user's preferences. For example, if spots of the same genre exist in other regions or countries, the review consideration unit provides that information. The review consideration unit also compares reviews from different regions or countries and suggests the most suitable spots to the user. For example, the review consideration unit selects the most suitable spots based on the review's rating score and content. This makes it possible to suggest similar spots in different regions or countries based on user reviews.

[0041] The review consideration unit can propose a course that combines surrounding tourist spots and activities with a spot that has a high review rating as its center. The review consideration unit, for example, proposes a course that combines surrounding tourist spots and activities with a spot that has a high review rating as its center. For example, it combines a highly rated restaurant with its surrounding tourist spots. The review consideration unit also analyzes reviews of highly rated spots to propose a course that suits the user's preferences. For example, it combines spots that the user is likely to be interested in based on the content of the reviews. The review consideration unit also proposes an optimal course based on the review rating score. For example, it proposes a course that prioritizes the combination of highly rated spots. This makes it possible to propose a course that combines surrounding tourist spots and activities with a spot that has a high review rating as its center.

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

[0043] The user input analysis unit can also analyze the user's health data and suggest outing itineraries based on the user's health condition. For example, if the user uses a fitness device, it can suggest a course that allows for moderate exercise based on that data. Also, if the user has specific health goals, it can suggest a course that includes activities that match those goals. For example, it can suggest a hiking course that burns a lot of calories to a user who is on a diet. It can also suggest relaxing spots and activities that help relieve stress based on the user's health condition. This makes it possible to suggest the optimal outing itinerary based on the user's health data.

[0044] The user input analysis unit can also analyze the user's hobbies and interests and suggest outing courses based on a specific theme. For example, if the user is interested in art, a course touring art museums and galleries can be suggested. If the user is interested in history, a course including historical landmarks and museums can be suggested. Furthermore, if the user likes outdoor activities, a course including hiking and camping can be suggested. This makes it possible to suggest outing courses based on a specific theme based on the user's hobbies and interests.

[0045] The user input analysis unit can also analyze the user's social media posts and suggest outing itineraries based on trends. For example, it can analyze photos and hashtags posted by the user on Instagram to suggest popular spots and activities. It can also suggest similar spots based on places visited by the user's friends and followers. It can also analyze popular events and spots on social media in real time to suggest itineraries based on the latest trends. This allows it to suggest outing itineraries that follow trends based on the user's social media posts.

[0046] In addition to weather information, the weather information consideration unit can also analyze the user's allergy data and suggest an outing itinerary that avoids allergies. For example, if the user has hay fever, it can suggest spots suitable for areas or seasons with low pollen levels. Also, if the user is sensitive to a specific allergen, it can suggest places with low levels of that allergen. Furthermore, the weather information consideration unit can suggest an outing itinerary that will keep the user comfortable based on the allergy information. This makes it possible to suggest an outing itinerary that avoids allergies based on the user's allergy data.

[0047] The climate information consideration unit can also propose optimal outing itineraries by taking into account not only natural phenomena unique to each season but also local specialties and food culture. For example, in spring, it can propose spots where you can enjoy sakura mochi (cherry blossom rice cakes) and hanami bento (cherry blossom viewing bento) along with famous cherry blossom viewing spots. In autumn, it can also propose restaurants where you can enjoy local autumn flavors along with famous autumn foliage viewing spots. Furthermore, the climate information consideration unit can propose outing itineraries that suit the user's preferences based on seasonal specialties and food culture. This makes it possible to propose optimal outing itineraries that take into account natural phenomena unique to each season and local specialties and food culture.

[0048] In addition to event information, the event information consideration unit can analyze the user's interests and concerns and suggest events based on a specific theme. For example, if the user is interested in music, music festivals and concerts can be suggested. Also, if the user is interested in art, art exhibitions and workshops can be suggested. Furthermore, based on the user's interests and concerns, it can also suggest outing courses that combine related events. This makes it possible to suggest events that fit a specific theme based on the user's interests and concerns.

[0049] The event information consideration unit can analyze the user's schedule in addition to the event time and location, and can also suggest a route that allows the user to efficiently participate in the event. For example, based on the user's calendar information, it can suggest events that can be attended during free time. It can also suggest a route that minimizes travel time based on the user's schedule. Furthermore, it can combine the user's schedule and event information to suggest a route that allows the user to participate in the event at the optimal time. This makes it possible to suggest an outing route that allows the user to efficiently participate in the event based on the user's schedule.

[0050] In addition to user reviews, the review consideration unit can also evaluate the reliability of reviews and prioritize highly reliable reviews. For example, it can determine reliability based on the review poster's history and rating score. It can also analyze the content of reviews and suggest optimal outing itineraries for users based on highly reliable reviews. Furthermore, it can develop an algorithm to evaluate the reliability of reviews and automatically extract highly reliable reviews. This allows it to suggest more accurate outing itineraries by evaluating the reliability of reviews and prioritizing highly reliable reviews.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The user input analysis unit analyzes the user input information. For example, if the user inputs "I like nature, my budget is 5,000 yen or less, and I have 5 hours available," the user input analysis unit will use this information to suggest the optimal outing itinerary. Step 2: The climate information consideration unit considers the climate information and the season based on the user input information analyzed by the user input analysis unit. For example, if the user plans to go out in spring, the climate information consideration unit suggests tourist spots and activities suitable for the spring weather. Step 3: The event information consideration unit considers local event information based on the weather information and season considered by the weather information consideration unit. For example, if a festival or event is being held in the area the user is visiting, the event information consideration unit suggests a course to participate in the event based on that information. Step 4: The congestion level consideration unit considers the congestion level based on the event information of the area considered by the event information consideration unit. For example, if a popular tourist spot is crowded, the congestion level consideration unit suggests an alternative spot to avoid the crowds. Step 5: The review consideration unit considers user reviews based on the congestion level considered by the congestion level consideration unit. For example, if the user desires a highly rated tourist spot, the review consideration unit preferentially suggests spots with high user reviews.

[0053] (Example 2) The outing itinerary suggestion system according to an embodiment of the present invention is a system that suggests optimal holiday outing itineraries based on preferences, budget, and available time input by a user. In this system, a generation AI analyzes the information input by the user and generates optimal outing itineraries by taking into consideration detailed weather information, seasons, local event information, crowd levels, user reviews, etc. held by Yahoo. This allows the outing itinerary suggestion system to easily find optimal outing itineraries that match the user's preferences and conditions.

[0054] An outing itinerary suggestion system according to an embodiment includes a user input analysis unit, a weather information consideration unit, an event information consideration unit, a congestion level consideration unit, and a review consideration unit. The user input analysis unit analyzes user input information. For example, if a user inputs "I like nature, my budget is 5,000 yen or less, and I have 5 hours of available time," the user input analysis unit suggests an optimal outing itinerary based on this information. The weather information consideration unit considers weather information and the season based on the user input information analyzed by the user input analysis unit. For example, if a user plans to go out in the spring, the weather information consideration unit suggests tourist spots and activities suitable for the spring weather. The event information consideration unit considers local event information based on the weather information and the season considered by the weather information consideration unit. For example, if a festival or event is being held in an area the user is visiting, the event information consideration unit suggests a route to participate in the event based on that information. The congestion level consideration unit considers the congestion level based on the local event information considered by the event information consideration unit. For example, if a popular tourist spot is crowded, the congestion level consideration unit suggests an alternative spot to avoid the crowds. The review consideration unit considers user reviews based on the congestion level considered by the congestion level consideration unit. For example, if a user desires to visit a highly rated tourist spot, the review consideration unit will preferentially suggest spots with high user reviews. This allows the outing course suggestion system to suggest an optimal outing course based on the user's input information, taking into consideration weather information, season, local event information, congestion level, and user reviews.

[0055] The user input analysis unit analyzes a user's past behavioral history and search history to propose an outing itinerary optimized for each individual user. The user input analysis unit, for example, analyzes a user's past behavioral history to understand frequently visited places and preference trends. For example, based on data on tourist spots and restaurants visited in the past, it proposes new spots that match the user's preferences. The user input analysis unit also analyzes search history to identify themes and activities in which the user is interested. For example, based on keywords searched in the past and the content of pages viewed, it proposes related outing itineraries. The user input analysis unit also analyzes a user's behavioral patterns to understand places that are often visited during specific times of the day or on specific days of the week. For example, it prioritizes the proposal of spots that are often visited on weekends. This makes it possible to propose outing itineraries optimized for each individual user based on the user's past behavioral history and search history.

[0056] The user input analysis unit can dynamically generate an optimal outing itinerary based on the current location using real-time location information. The user input analysis unit, for example, acquires the user's current location in real time and suggests an optimal outing itinerary based on that location information. For example, it suggests tourist spots and restaurants within walking distance of the current location. The user input analysis unit also suggests an optimal route while the user is traveling based on real-time location information. For example, it dynamically generates tourist spots and rest areas that can be stopped off during the trip. The user input analysis unit also adjusts the optimal outing itinerary in real time, taking into account weather information and traffic conditions at the current location. For example, it suggests indoor spots if it is raining. This makes it possible to dynamically generate an optimal outing itinerary based on the user's real-time location information.

[0057] The user input analysis unit uses the emotion estimation function to analyze the emotion of the user at the time of input and can suggest an outing itinerary that elicits positive emotions. The user input analysis unit, for example, analyzes the facial expression and voice of the user at the time of input and calculates an emotion score. For example, if a smile or an excited voice is detected, a course that elicits positive emotions is suggested. The user input analysis unit also uses the emotion estimation function to provide real-time feedback on the emotional response to the information entered by the user. For example, if the user has a happy expression, it suggests an even more enjoyable activity. The user input analysis unit also prioritizes suggesting courses that have elicited positive emotions in the past based on the user's emotion data. For example, it re-suggests courses that have received high ratings in the past. In this way, the emotion of the user at the time of input can be analyzed and an outing itinerary that elicits positive emotions can be suggested.

[0058] The climate information consideration unit can analyze past weather data in addition to climate information and propose an outing itinerary that corresponds to predicted climate change. The climate information consideration unit, for example, analyzes past weather data and proposes an outing itinerary that corresponds to predicted climate change. For example, based on past data, it proposes indoor spots in areas where there is a lot of rain in a particular season. The climate information consideration unit also proposes an outing itinerary that corresponds to future climate conditions based on climate change forecast data. For example, if a rise in temperature is predicted, it proposes cool places or waterside spots. The climate information consideration unit also proposes activities that are suitable for specific climate conditions based on past weather data. For example, on a windy day, it proposes activities such as windsurfing. In this way, it is possible to propose an outing itinerary that corresponds to predicted climate change based on past weather data.

[0059] The climate information consideration unit can suggest optimal tourist spots by taking into account natural phenomena specific to each season. The climate information consideration unit, for example, can suggest optimal tourist spots by taking into account natural phenomena specific to each season. For example, it can suggest famous cherry blossom viewing spots in spring and famous autumn foliage spots in autumn. The climate information consideration unit can also suggest optimal outing courses by taking into account event information according to natural phenomena in each season. For example, it can suggest courses that include cherry blossom festivals and autumn leaf viewing events. The climate information consideration unit can also suggest tourist spots that suit the user's preferences based on natural phenomena in each season. For example, it can suggest spots where seasonal flowers can be seen for a user who likes flowers. In this way, it is possible to suggest optimal tourist spots by taking into account natural phenomena specific to each season.

[0060] The climate information consideration unit can use the emotion estimation function to analyze the user's preferred weather conditions and suggest an outing itinerary that is optimal for those conditions. The climate information consideration unit, for example, uses the emotion estimation function to analyze the user's preferred weather conditions. For example, if the user shows positive emotions on sunny days, it suggests an outing itinerary that is optimal for sunny days. The climate information consideration unit also identifies weather conditions that have previously elicited positive emotions based on the user's emotion data and suggests an outing itinerary that is optimal for those conditions. For example, if a user prefers cool weather, it suggests a cool place. The climate information consideration unit also combines the user's input information with the emotion data to suggest an outing itinerary that is optimal for the weather conditions. For example, if the user dislikes heat, it suggests a cool place or an indoor spot. In this way, it is possible to analyze the user's preferred weather conditions and suggest an outing itinerary that is optimal for those conditions.

[0061] The event information consideration unit can analyze feedback from past event participants in addition to event information, and prioritize suggesting highly rated events. The event information consideration unit, for example, analyzes feedback from past event participants and prioritize suggesting highly rated events. For example, it suggests popular events based on user reviews and rating scores. The event information consideration unit also analyzes the appeal and points to note of a particular event based on feedback from event participants, and suggests an event that is suitable for the user. For example, it suggests an event for families or an event for couples. The event information consideration unit also analyzes areas for improvement and success factors of an event based on the feedback data, and suggests the most suitable event for the user. For example, it extracts the characteristics of events that have received high ratings in the past, and suggests similar events. This makes it possible to prioritize suggesting highly rated events based on feedback from past event participants.

[0062] The event information consideration unit is capable of proposing a course that allows efficient participation in multiple events, taking into account the event times and locations. The event information consideration unit, for example, proposes a course that allows efficient participation in multiple events, taking into account the event times and locations. For example, it creates a schedule that allows participation in consecutive events held in the same area. The event information consideration unit also proposes a course that minimizes travel time based on the event times. For example, it combines events held within walking distance. The event information consideration unit also proposes a course that optimizes transportation means and travel routes, taking into account the location of the events. For example, it proposes a course that allows efficient travel using public transportation. In this way, it is possible to propose a course that allows efficient participation in multiple events, taking into account the event times and locations.

[0063] The congestion degree consideration unit can analyze past congestion patterns in addition to congestion degree data and propose a route that corresponds to the predicted congestion situation. The congestion degree consideration unit, for example, analyzes past congestion patterns and proposes a route that corresponds to the predicted congestion situation. For example, if congestion is predicted during a specific time period or day of the week, it proposes alternative spots to avoid the congestion. Furthermore, the congestion degree consideration unit proposes a route that allows the user to spend comfortably based on the congestion prediction data. For example, it proposes spots that can be visited during times when it is less crowded. Furthermore, if congestion is predicted during a specific event or season based on past congestion data, it proposes a route that avoids the congestion. For example, it proposes spots that can be visited during times when it is less crowded. In this way, it is possible to propose a route that corresponds to the predicted congestion situation based on past congestion patterns.

[0064] The congestion level consideration unit monitors the congestion level in real time and can immediately suggest an alternative route if congestion occurs. The congestion level consideration unit, for example, monitors the congestion level in real time and can immediately suggest an alternative route if congestion occurs. For example, it can suggest alternative spots to avoid spots that are predicted to be crowded. The congestion level consideration unit also suggests a route that the user can take comfortably based on real-time congestion data. For example, it can suggest spots that can be visited during times when congestion is less. The congestion level consideration unit also builds a system that notifies the user if congestion occurs and suggests an alternative route. For example, it can notify the user to avoid spots that are predicted to be crowded. This makes it possible to monitor the congestion level in real time and immediately suggest an alternative route if congestion occurs.

[0065] The congestion degree consideration unit uses the emotion estimation function to analyze the user's desire to avoid crowds and can suggest a route that avoids crowds. The congestion degree consideration unit, for example, uses the emotion estimation function to analyze the user's desire to avoid crowds. For example, if the user dislikes crowds, it suggests spots that are less crowded. Furthermore, the congestion degree consideration unit suggests a route that avoids crowds if the user has previously expressed a desire to avoid crowds based on the user's emotion data. For example, it suggests spots that can be visited during less crowded times. Furthermore, the congestion degree consideration unit combines the user's input information with the emotion data to suggest an optimal route that avoids crowds. For example, if the user dislikes crowds, it suggests spots and times that are less crowded. In this way, it is possible to analyze the user's desire to avoid crowds and suggest a route that avoids crowds.

[0066] The review consideration unit can evaluate the reliability of reviews in addition to user reviews and prioritize highly reliable reviews. The review consideration unit, for example, evaluates the reliability of user reviews and prioritizes highly reliable reviews. For example, it determines reliability based on the review poster's history and evaluation score. The review consideration unit also suggests an optimal outing course for the user based on highly reliable reviews. For example, it prioritizes suggesting spots with many highly reliable reviews. The review consideration unit also develops an algorithm to evaluate the reliability of reviews and automatically extracts highly reliable reviews. For example, it evaluates reliability by analyzing the content of the review and information about the poster. In this way, by evaluating the reliability of reviews and prioritizing the highly reliable reviews, it is possible to suggest a more accurate outing course.

[0067] The review consideration unit can analyze the content of reviews and suggest courses based on specific keywords. The review consideration unit, for example, analyzes the content of user reviews and suggests courses based on specific keywords. For example, it suggests spots that frequently contain the keyword "beautiful scenery." The review consideration unit also suggests courses that suit the user's preferences based on the keywords in the reviews. For example, it preferentially suggests spots that frequently contain the keyword "friendly staff." The review consideration unit also performs keyword analysis and suggests courses based on specific themes. For example, it suggests courses based on keywords such as "family-friendly" or "couple-friendly." In this way, by analyzing the content of reviews and suggesting courses based on specific keywords, it is possible to provide outing courses that suit the user's preferences.

[0068] The review consideration unit can use the emotion estimation function to analyze the emotion a user has toward a review and suggest a course that elicits positive emotions. The review consideration unit, for example, uses the emotion estimation function to analyze the emotion a user has toward a review. For example, it suggests a course based on reviews in which the user expresses positive emotions. The review consideration unit also identifies reviews that have elicited positive emotions in the past based on the user's emotion data and suggests a course based on those reviews. For example, it suggests spots with many highly rated reviews. The review consideration unit also combines the user's input information with the emotion data to suggest a course based on the optimal review. For example, it preferentially considers reviews in which the user expresses positive emotions. This makes it possible to analyze the emotion a user has toward a review and suggest a course that elicits positive emotions.

[0069] The review consideration unit can suggest similar spots in different regions or countries based on user reviews. The review consideration unit, for example, suggests similar spots in different regions or countries based on user reviews. For example, if spots with the same theme exist in other regions or countries, the review consideration unit provides that information. The review consideration unit also analyzes reviews of similar spots and suggests spots that match the user's preferences. For example, if spots of the same genre exist in other regions or countries, the review consideration unit provides that information. The review consideration unit also compares reviews from different regions or countries and suggests the most suitable spots to the user. For example, the review consideration unit selects the most suitable spots based on the review's rating score and content. This makes it possible to suggest similar spots in different regions or countries based on user reviews.

[0070] The review consideration unit can propose a course that combines surrounding tourist spots and activities with a spot that has a high review rating as its center. The review consideration unit, for example, proposes a course that combines surrounding tourist spots and activities with a spot that has a high review rating as its center. For example, it combines a highly rated restaurant with its surrounding tourist spots. The review consideration unit also analyzes reviews of highly rated spots to propose a course that suits the user's preferences. For example, it combines spots that the user is likely to be interested in based on the content of the reviews. The review consideration unit also proposes an optimal course based on the review rating score. For example, it proposes a course that prioritizes the combination of highly rated spots. This makes it possible to propose a course that combines surrounding tourist spots and activities with a spot that has a high review rating as its center.

[0071] The review consideration unit uses the emotion estimation function to analyze the emotions the user has toward the reviews and can propose an optimal outing course. The review consideration unit, for example, uses the emotion estimation function to analyze the emotions the user has toward the reviews. For example, it proposes a course based on reviews in which the user expresses positive emotions. The review consideration unit also identifies reviews that have elicited positive emotions in the past based on the user's emotion data and proposes a course based on those reviews. For example, it proposes spots with many highly rated reviews. The review consideration unit also combines the user's input information with the emotion data to propose a course based on the optimal reviews. For example, it preferentially considers reviews in which the user expresses positive emotions. This makes it possible to analyze the emotions the user has toward the reviews and propose an optimal outing course.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The user input analysis unit can also analyze the user's health data and suggest outing itineraries based on the user's health condition. For example, if the user uses a fitness device, it can suggest a course that allows for moderate exercise based on that data. Also, if the user has specific health goals, it can suggest a course that includes activities that match those goals. For example, it can suggest a hiking course that burns a lot of calories to a user who is on a diet. It can also suggest relaxing spots and activities that help relieve stress based on the user's health condition. This makes it possible to suggest the optimal outing itinerary based on the user's health data.

[0074] The user input analysis unit can also analyze the user's hobbies and interests and suggest outing courses based on a specific theme. For example, if the user is interested in art, a course touring art museums and galleries can be suggested. If the user is interested in history, a course including historical landmarks and museums can be suggested. Furthermore, if the user likes outdoor activities, a course including hiking and camping can be suggested. This makes it possible to suggest outing courses based on a specific theme based on the user's hobbies and interests.

[0075] The user input analysis unit can also analyze the user's social media posts and suggest outing itineraries based on trends. For example, it can analyze photos and hashtags posted by the user on Instagram to suggest popular spots and activities. It can also suggest similar spots based on places visited by the user's friends and followers. It can also analyze popular events and spots on social media in real time to suggest itineraries based on the latest trends. This allows it to suggest outing itineraries that follow trends based on the user's social media posts.

[0076] The user input analysis unit can also use the emotion estimation function to suggest relaxing outing itineraries for users when they are feeling stressed. For example, if a user is feeling stressed, it can suggest spots where they can relax in nature, spas, hot springs, etc. It can also suggest spots that offer music or aromatherapy that will help the user relax. Furthermore, it can re-suggest spots and activities that have helped the user relax in the past based on the user's emotion data. This makes it possible to suggest relaxing outing itineraries for users when they are feeling stressed.

[0077] In addition to weather information, the weather information consideration unit can also analyze the user's allergy data and suggest an outing itinerary that avoids allergies. For example, if the user has hay fever, it can suggest spots suitable for areas or seasons with low pollen levels. Also, if the user is sensitive to a specific allergen, it can suggest places with low levels of that allergen. Furthermore, the weather information consideration unit can suggest an outing itinerary that will keep the user comfortable based on the allergy information. This makes it possible to suggest an outing itinerary that avoids allergies based on the user's allergy data.

[0078] The climate information consideration unit can also propose optimal outing itineraries by taking into account not only natural phenomena unique to each season but also local specialties and food culture. For example, in spring, it can propose spots where you can enjoy sakura mochi (cherry blossom rice cakes) and hanami bento (cherry blossom viewing bento) along with famous cherry blossom viewing spots. In autumn, it can also propose restaurants where you can enjoy local autumn flavors along with famous autumn foliage viewing spots. Furthermore, the climate information consideration unit can propose outing itineraries that suit the user's preferences based on seasonal specialties and food culture. This makes it possible to propose optimal outing itineraries that take into account natural phenomena unique to each season and local specialties and food culture.

[0079] The weather information consideration unit can use the emotion estimation function to suggest an outing itinerary that is optimal for specific weather conditions when the user expresses positive emotions under those conditions. For example, if the user expresses positive emotions on a sunny day, outdoor activities suitable for sunny days can be suggested. Also, if the user enjoys relaxation on rainy days, indoor spots suitable for rainy days can be suggested. Furthermore, based on the user's emotion data, weather conditions that have previously elicited positive emotions can be identified and an outing itinerary that is optimal for those conditions can be suggested. In this way, if the user expresses positive emotions under specific weather conditions, an outing itinerary that is optimal for those weather conditions can be suggested.

[0080] In addition to event information, the event information consideration unit can analyze the user's interests and concerns and suggest events based on a specific theme. For example, if the user is interested in music, music festivals and concerts can be suggested. Also, if the user is interested in art, art exhibitions and workshops can be suggested. Furthermore, based on the user's interests and concerns, it can also suggest outing courses that combine related events. This makes it possible to suggest events that fit a specific theme based on the user's interests and concerns.

[0081] The event information consideration unit can analyze the user's schedule in addition to the event time and location, and can also suggest a route that allows the user to efficiently participate in the event. For example, based on the user's calendar information, it can suggest events that can be attended during free time. It can also suggest a route that minimizes travel time based on the user's schedule. Furthermore, it can combine the user's schedule and event information to suggest a route that allows the user to participate in the event at the optimal time. This makes it possible to suggest an outing route that allows the user to efficiently participate in the event based on the user's schedule.

[0082] The congestion level consideration unit can also use the emotion estimation function to analyze the user's desire to avoid crowds and suggest a route that avoids crowds. For example, if the user dislikes crowds, it can suggest less crowded spots. Also, based on the user's emotion data, if the user has previously expressed a desire to avoid crowds, it can suggest a route that avoids crowds. Furthermore, it can combine the user's input information with the emotion data to suggest an optimal route that avoids crowds. In this way, it is possible to analyze the user's desire to avoid crowds and suggest a route that avoids crowds.

[0083] In addition to user reviews, the review consideration unit can also evaluate the reliability of reviews and prioritize highly reliable reviews. For example, it can determine reliability based on the review poster's history and rating score. It can also analyze the content of reviews and suggest optimal outing itineraries for users based on highly reliable reviews. Furthermore, it can develop an algorithm to evaluate the reliability of reviews and automatically extract highly reliable reviews. This allows it to suggest more accurate outing itineraries by evaluating the reliability of reviews and prioritizing highly reliable reviews.

[0084] The review consideration unit can also use the emotion estimation function to analyze the emotions a user has toward a review and suggest a course that elicits positive emotions. For example, it can suggest a course based on reviews that the user has expressed positive emotions about. It can also identify reviews that have elicited positive emotions in the past based on the user's emotion data and suggest a course based on those reviews. It can also combine the user's input information with the emotion data to suggest a course based on the most appropriate review. This makes it possible to analyze the emotions a user has toward a review and suggest a course that elicits positive emotions.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The user input analysis unit analyzes the user input information. For example, if the user inputs "I like nature, my budget is 5,000 yen or less, and I have 5 hours available," the user input analysis unit will use this information to suggest the optimal outing itinerary. Step 2: The climate information consideration unit considers the climate information and the season based on the user input information analyzed by the user input analysis unit. For example, if the user plans to go out in spring, the climate information consideration unit suggests tourist spots and activities suitable for the spring weather. Step 3: The event information consideration unit considers local event information based on the weather information and season considered by the weather information consideration unit. For example, if a festival or event is being held in the area the user is visiting, the event information consideration unit suggests a course to participate in the event based on that information. Step 4: The congestion level consideration unit considers the congestion level based on the event information of the area considered by the event information consideration unit. For example, if a popular tourist spot is crowded, the congestion level consideration unit suggests an alternative spot to avoid the crowds. Step 5: The review consideration unit considers user reviews based on the congestion level considered by the congestion level consideration unit. For example, if the user desires a highly rated tourist spot, the review consideration unit preferentially suggests spots with high user reviews.

[0087] 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.

[0088] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0100] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0154] 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 user input analysis unit that analyzes user input information; a climate information consideration unit that considers climate information and seasons based on the user input information analyzed by the user input analysis unit; an event information consideration unit that considers local event information based on the climate information considered by the climate information consideration unit and the season; a congestion degree consideration unit that considers a congestion degree based on event information of the area considered by the event information consideration unit; a review consideration unit that considers user reviews based on the congestion degree considered by the congestion degree consideration unit. A system characterized by:

2. The user input analysis unit Using real-time location information, the system dynamically generates the optimal itinerary based on your current location.

2. The system of claim 1.

3. The climate information consideration unit In addition to the weather information, the app analyzes past weather data and suggests outing itineraries that correspond to predicted climate changes.

2. The system of claim 1.

4. The event information consideration unit In addition to the event information, feedback from past event participants is analyzed to prioritize highly rated events.

2. The system of claim 1.

5. The congestion degree consideration unit The congestion level is monitored in real time, and an alternative route is immediately suggested if congestion occurs.

2. The system of claim 1.

6. The review consideration unit: Analyzes the emotions users have about reviews and suggests courses that elicit positive emotions 2. The system of claim 1.

7. The user input analysis unit Analyzes the user's emotions when inputting information and suggests outing itineraries that elicit positive emotions 2. The system of claim 1.

8. The climate information consideration unit Analyzes the user's preferred weather conditions and suggests the best route for going out under those conditions 2. The system of claim 1.

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