system

Abstract

The system according to the embodiment aims to generate a personal guide based on the user's preferences and past history, and to provide entertainment content. [Solution] A system according to an embodiment includes a generation unit, a provision unit, and a suggestion unit. The generation unit generates a personal guide based on a user's preferences and past history. The provision unit provides entertainment content based on the personal guide generated by the generation unit. The suggestion unit collects traffic information and suggests routes.

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 provide a personalized guide based on a user's preferences and past history, and there is room for improvement.

[0005] The system according to the embodiment aims to generate a personal guide based on the user's preferences and past history, and to provide entertainment content. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a provision unit, and a suggestion unit. The generation unit generates a personal guide based on a user's preferences and past history. The provision unit provides entertainment content based on the personal guide generated by the generation unit. The suggestion unit collects traffic information and suggests routes. [Effects of the Invention]

[0007] The system according to the embodiment can generate a personal guide based on the user's preferences and past history, and provide entertainment content. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A car navigation system according to an embodiment of the present invention provides a personalized guide using a car navigation system equipped with a generation AI, allowing users to enjoy their car even during traffic jams. This system begins when a user inputs their destination into the car navigation system. The generation AI then calculates a route to the destination based on the user's preferences and past travel history, and generates a personalized guide along that route. This personalized guide provides information tailored to the user's interests. For example, it can include information about tourist spots and restaurants along the route, as well as historical background. Furthermore, to help users enjoy their waiting time during traffic jams, the generation AI provides entertainment content tailored to the user's preferences. For example, it can provide music, audiobooks, podcasts, quizzes, and more. This helps users avoid boredom while stuck in traffic jams. The generation AI can also collect traffic information in real time and suggest optimal routes. This allows users to avoid traffic jams and reach their destination smoothly. This system allows users to spend their time in the car meaningfully and enjoy themselves even during traffic jams. For example, when a user inputs a destination into the car navigation system, the generation AI calculates the optimal route based on the user's past travel history and preferences. The generation AI then collects information about tourist spots and restaurants along the route and generates a personalized guide. Furthermore, the generative AI provides entertainment content tailored to the user's preferences. For example, if the user likes music, the generative AI will play music that matches the user's preferences. If the user likes audiobooks, the generative AI will play audiobooks that match the user's preferences. Furthermore, the generative AI collects traffic information in real time and suggests optimal routes. For example, the generative AI will suggest optimal detour routes to the user based on traffic congestion information. This allows the user to avoid traffic jams and reach their destination smoothly. This allows the car navigation system to generate a personal guide based on the user's preferences and past history, provide entertainment content, and suggest optimal routes, allowing the user to spend their time in the car more meaningfully.

[0029] A car navigation system according to an embodiment includes a generating unit, a providing unit, and a suggesting unit. The generating unit generates a personal guide based on a user's preferences and past history. The generating unit collects, for example, the user's past travel history, viewing history, and purchase history, and estimates the user's preferences based on this data. The generating unit suggests new spots and restaurants that the user may be interested in, for example, based on information about tourist spots and restaurants the user has visited in the past. The generating unit also generates a personal guide including information about tourist spots and restaurants, historical background, and the like, based on the user's preferences. For example, if the user is interested in history, the generating unit provides information about historical tourist spots along the route. The providing unit provides entertainment content based on the personal guide generated by the generating unit. The providing unit provides entertainment content such as music, audiobooks, podcasts, and quizzes. The providing unit selects optimal entertainment content according to the user's preferences. For example, if the user wants to relax, the providing unit provides relaxing music or audiobooks. If the user is excited, the providing unit can also provide energetic music or quizzes. The suggesting unit collects traffic information and suggests an optimal route. The suggestion unit, for example, collects traffic information in real time and calculates the optimal route based on congestion information and accident information. The suggestion unit suggests the optimal route based on the user's current location and destination. For example, the suggestion unit suggests the shortest route if the user is in a hurry. The suggestion unit can also suggest a scenic route if the user wants to relax. As a result, the car navigation system according to the embodiment can generate a personal guide based on the user's preferences and past history, provide entertainment content, and suggest the optimal route.

[0030] The providing unit can provide entertainment content such as music, audiobooks, podcasts, and quizzes. The providing unit, for example, provides music tailored to the user's preferences. For example, if the user wants to relax, the providing unit can provide relaxing music. Furthermore, if the user is excited, the providing unit can provide energetic music. Furthermore, if the user likes audiobooks, the providing unit can provide audiobooks that match the user's preferences. For example, the providing unit can suggest new works based on the authors of audiobooks the user has listened to in the past. Furthermore, if the user likes podcasts, the providing unit can provide podcasts that match the user's preferences. For example, the providing unit can suggest podcasts that match the user's preferences based on the user's past listening history. Furthermore, if the user likes quizzes, the providing unit can provide quizzes that match the user's preferences. For example, the providing unit can provide quizzes on topics that the user is likely to be interested in. In this way, the providing unit can provide entertainment content tailored to the user's preferences.

[0031] The suggestion unit can collect traffic information in real time and suggest a route. For example, the suggestion unit collects traffic information in real time and calculates an optimal route based on congestion information and accident information. The suggestion unit suggests an optimal route based on the user's current location and destination. For example, the suggestion unit suggests the shortest route if the user is in a hurry. The suggestion unit can also suggest a scenic route if the user wants to relax. Furthermore, the suggestion unit analyzes the user's past driving history and selects an optimal route. For example, the suggestion unit suggests an optimal route based on routes the user has used in the past. The suggestion unit can also suggest a route that avoids congestion based on the user's past driving history. Furthermore, the suggestion unit customizes the route based on the user's current driving situation and destination. For example, the suggestion unit suggests the shortest route if the user is in a hurry. The suggestion unit can also suggest a scenic route if the user is relaxed. In this way, the suggestion unit can collect traffic information in real time and suggest an optimal route.

[0032] The generation unit can generate a personal guide including information on tourist spots and restaurants, as well as historical background, based on the user's preferences and past history. The generation unit, for example, collects the user's past travel history, viewing history, purchase history, etc., and estimates the user's preferences based on this data. The generation unit, for example, suggests new spots and restaurants that the user may be interested in based on information on tourist spots and restaurants the user has visited in the past. The generation unit also generates a personal guide including information on tourist spots and restaurants, as well as historical background, based on the user's preferences. For example, if the user is interested in history, the generation unit provides information on historical tourist spots along the route. The generation unit also filters information based on the user's current interests. For example, the generation unit selects tourist spots based on themes that the user is currently interested in (e.g., history, nature). The generation unit can also provide related information based on keywords recently searched by the user. This allows the generation unit to generate a personal guide based on the user's preferences and past history.

[0033] The generation unit can analyze the user's past travel history and select information on tourist spots and restaurants. The generation unit, for example, analyzes the user's past travel history and selects information on optimal tourist spots and restaurants. For example, the generation unit can suggest similar spots based on ratings of tourist spots the user has previously visited. The generation unit can also analyze the menus of restaurants the user has previously visited and suggest new restaurants that match the user's preferences. Furthermore, the generation unit can prioritize information related to a specific area from the user's past travel history. This allows the generation unit to analyze the user's past travel history and provide information on optimal tourist spots and restaurants. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's past travel history data into the generation AI and cause the generation AI to select information on tourist spots and restaurants.

[0034] When generating a personal guide, the generation unit can filter information based on the user's current interests. The generation unit filters information based on the user's current interests, for example. For example, the generation unit selects tourist spots based on themes (e.g., history, nature) that the user is currently interested in. The generation unit can also provide related information based on keywords recently searched by the user. Furthermore, the generation unit can suggest spots that match the user's interests by referring to information on accounts the user follows on social media. This allows the generation unit to filter information based on the user's current interests and provide more relevant information. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the user's current interests into the generation AI and have the generation AI perform information filtering.

[0035] When generating a personal guide, the generation unit can prioritize providing highly relevant information by taking into account the user's geographical location information. For example, the generation unit can prioritize providing highly relevant information by taking into account the user's geographical location information. For example, the generation unit can prioritize suggesting tourist spots close to the user's current location. The generation unit can also suggest restaurants that are easily accessible from the user's current location. Furthermore, the generation unit can provide information along an optimal route based on the user's current location. This allows the generation unit to provide more relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information data into the generation AI and cause the generation AI to provide highly relevant information.

[0036] When generating a personal guide, the generation unit can analyze the user's social media activity and provide related information. For example, the generation unit can analyze the user's social media activity and provide related information. For example, the generation unit can provide related information based on places and events that the user has "liked" on social media. The generation unit can also analyze the content of posts from accounts the user follows and suggest spots that match the user's interests. Furthermore, the generation unit can provide information on related tourist spots and restaurants based on posts shared by the user. This allows the generation unit to analyze the user's social media activity and provide more relevant information. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related information.

[0037] When selecting entertainment content to provide, the providing unit can select the content by analyzing the user's past viewing history. The providing unit, for example, analyzes the user's past viewing history and selects optimal entertainment content. For example, the providing unit can suggest similar music based on the genre of music the user has listened to in the past. The providing unit can also suggest new works based on the authors of audiobooks the user has listened to in the past. Furthermore, the providing unit can suggest podcasts that match the user's preferences based on the user's past viewing history. In this way, the providing unit can analyze the user's past viewing history and provide optimal entertainment content. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past viewing history data into a generation AI and cause the generation AI to select entertainment content.

[0038] When selecting entertainment content to provide, the providing unit can prioritize providing highly relevant content by taking into account the user's geographical location information. The providing unit, for example, provides highly relevant entertainment content by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide music or podcasts related to that area. Furthermore, if the user is at a specific tourist spot, the providing unit can provide an audiobook related to that spot. Furthermore, if the user is participating in a specific event, the providing unit can provide a quiz related to the event. In this way, the providing unit can provide more relevant entertainment content by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into a generation AI and cause the generation AI to provide highly relevant content.

[0039] When selecting entertainment content to provide, the providing unit can analyze the user's social media activity and provide relevant content. The providing unit, for example, analyzes the user's social media activity and provides relevant content. For example, the providing unit can provide relevant content based on music or podcasts that the user has "liked" on social media. The providing unit can also analyze posts from accounts the user follows and provide content that matches the user's interests. Furthermore, the providing unit can provide relevant entertainment content based on posts shared by the user. This allows the providing unit to analyze the user's social media activity and provide more relevant entertainment content. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide relevant content.

[0040] When selecting a route to be proposed, the suggestion unit can select the route by analyzing the user's past driving history. The suggestion unit, for example, analyzes the user's past driving history and selects an optimal route. For example, the suggestion unit can suggest an optimal route based on routes the user has used in the past. The suggestion unit can also suggest a route that avoids congestion based on the user's past driving history. Furthermore, the suggestion unit can analyze the user's past driving history and suggest the most efficient route. In this way, the suggestion unit can analyze the user's past driving history and provide an optimal route. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past driving history data into the generation AI and cause the generation AI to select a route.

[0041] When proposing a route, the suggestion unit can customize the route based on the user's current driving situation and destination. The suggestion unit customizes the route based on, for example, the user's current driving situation and destination. For example, if the user is in a hurry, the suggestion unit can suggest the shortest route. Also, if the user is relaxed, the suggestion unit can suggest a scenic route. Furthermore, if the user is heading to a specific destination, the suggestion unit can suggest the optimal route to that destination. This allows the suggestion unit to customize the route based on the user's current driving situation and destination. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input the user's driving situation data into the generation AI and cause the generation AI to customize the route.

[0042] The suggestion unit may propose a route taking into consideration the user's geographical location information when selecting a proposed route. The suggestion unit may, for example, propose an optimal route taking into consideration the user's geographical location information. For example, the suggestion unit may propose the most efficient route from the user's current location. The suggestion unit may also propose a route that is easily accessible from the user's current location. Furthermore, the suggestion unit may also propose an optimal route based on the user's current location. This allows the suggestion unit to provide a more appropriate route taking into consideration the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input the user's geographical location information data into a generation AI and cause the generation AI to propose a route.

[0043] When selecting a route to suggest, the suggestion unit can analyze the user's social media activity and suggest related routes. The suggestion unit, for example, analyzes the user's social media activity and suggests related routes. For example, the suggestion unit can suggest related routes based on places and events that the user has "liked" on social media. The suggestion unit can also analyze the content of posts from accounts the user follows and suggest routes that match the user's interests. Furthermore, the suggestion unit can suggest related routes based on posts shared by the user. This allows the suggestion unit to analyze the user's social media activity and provide more relevant routes. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest routes.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The generation unit can generate a personal guide based on the user's health data in addition to the user's preferences and past history. For example, the generation unit analyzes data obtained from the user's fitness tracker or smartwatch to suggest tourist spots and restaurants according to the user's health condition. If the user likes healthy eating, the generation unit can preferentially suggest health-oriented restaurants. If the user likes exercise, the generation unit can suggest tourist spots that include hiking trails and jogging trails. Furthermore, the generation unit can suggest places to relax and activities that help relieve stress based on the user's health data. In this way, the generation unit can generate a personal guide according to the user's health condition.

[0046] In addition to providing entertainment content, the providing unit can also provide educational content tailored to the user's learning needs. For example, the providing unit can suggest online courses or educational podcasts based on the topic the user wants to learn. If the user wants to learn a language, the providing unit can provide language learning apps or audio lessons. If the user wants to master a specific skill, the providing unit can suggest video tutorials or e-books related to that skill. Furthermore, the providing unit can suggest content that the user should learn next based on the user's learning progress. This allows the providing unit to provide educational content tailored to the user's learning needs.

[0047] The suggestion unit not only collects traffic information in real time but also suggests routes based on the user's driving style. For example, the suggestion unit analyzes the speed and frequency of braking when the user has driven in the past, and suggests routes that are optimal for the user's driving style. If the user prioritizes safe driving, the suggestion unit can suggest routes with less traffic and fewer traffic lights. Also, if the user prefers smooth driving, the suggestion unit can prioritize routes with fewer curves and expressways. Furthermore, the suggestion unit can provide information on rest spots and service areas based on the user's driving style. This allows the suggestion unit to suggest optimal routes that suit the user's driving style.

[0048] The generation unit can generate a personal guide based on feedback from the user's social network in addition to the user's preferences and past history. For example, the generation unit can collect ratings of tourist spots and restaurants visited by the user's friends and family and make suggestions based on that. It can prioritize suggestions of spots highly rated by the user's friends. It can also analyze the number of comments and "likes" on posts shared by the user on social media to suggest popular spots. Furthermore, the generation unit can grasp trends within the user's social network and provide the latest information on popular spots and events. This allows the generation unit to generate a more relevant personal guide based on feedback from the user's social network.

[0049] The suggestion unit not only collects traffic information in real time but also suggests routes based on the user's driving style. For example, the suggestion unit analyzes the speed and frequency of braking when the user has driven in the past, and suggests routes that are optimal for the user's driving style. If the user prioritizes safe driving, the suggestion unit can suggest routes with less traffic and fewer traffic lights. Also, if the user prefers smooth driving, the suggestion unit can prioritize routes with fewer curves and expressways. Furthermore, the suggestion unit can provide information on rest spots and service areas based on the user's driving style. This allows the suggestion unit to suggest optimal routes that suit the user's driving style.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The generator generates a personal guide based on the user's preferences and past history. The generator collects the user's past travel history, viewing history, purchase history, etc., and estimates the user's preferences based on this data. For example, based on information about tourist spots and restaurants the user has visited in the past, the generator suggests new spots and restaurants that the user might be interested in. The generator also generates a personal guide that includes information about tourist spots and restaurants, historical background, etc., based on the user's preferences. For example, if the user is interested in history, the generator provides information about historical tourist spots along the route. Step 2: The providing unit provides entertainment content based on the personal guide generated by the generating unit. The providing unit provides entertainment content such as music, audiobooks, podcasts, and quizzes, and selects the most suitable entertainment content according to the user's preferences. For example, if the user wants to relax, the providing unit provides relaxing music and audiobooks, and if the user is excited, the providing unit provides energetic music and quizzes. Step 3: The suggestion unit collects traffic information and proposes the optimal route. The suggestion unit collects traffic information in real time and calculates the optimal route based on congestion and accident information. The suggestion unit proposes the optimal route based on the user's current location and destination. For example, if the user is in a hurry, it proposes the shortest route, and if the user wants to relax, it proposes a scenic route.

[0052] (Example 2) A car navigation system according to an embodiment of the present invention provides a personalized guide using a car navigation system equipped with a generation AI, allowing users to enjoy their car even during traffic jams. This system begins when a user inputs their destination into the car navigation system. The generation AI then calculates a route to the destination based on the user's preferences and past travel history, and generates a personalized guide along that route. This personalized guide provides information tailored to the user's interests. For example, it can include information about tourist spots and restaurants along the route, as well as historical background. Furthermore, to help users enjoy their waiting time during traffic jams, the generation AI provides entertainment content tailored to the user's preferences. For example, it can provide music, audiobooks, podcasts, quizzes, and more. This helps users avoid boredom while stuck in traffic jams. The generation AI can also collect traffic information in real time and suggest optimal routes. This allows users to avoid traffic jams and reach their destination smoothly. This system allows users to spend their time in the car meaningfully and enjoy themselves even during traffic jams. For example, when a user inputs a destination into the car navigation system, the generation AI calculates the optimal route based on the user's past travel history and preferences. The generation AI then collects information about tourist spots and restaurants along the route and generates a personalized guide. Furthermore, the generative AI provides entertainment content tailored to the user's preferences. For example, if the user likes music, the generative AI will play music that matches the user's preferences. If the user likes audiobooks, the generative AI will play audiobooks that match the user's preferences. Furthermore, the generative AI collects traffic information in real time and suggests optimal routes. For example, the generative AI will suggest optimal detour routes to the user based on traffic congestion information. This allows the user to avoid traffic jams and reach their destination smoothly. This allows the car navigation system to generate a personal guide based on the user's preferences and past history, provide entertainment content, and suggest optimal routes, allowing the user to spend their time in the car more meaningfully.

[0053] A car navigation system according to an embodiment includes a generating unit, a providing unit, and a suggesting unit. The generating unit generates a personal guide based on a user's preferences and past history. The generating unit collects, for example, the user's past travel history, viewing history, and purchase history, and estimates the user's preferences based on this data. The generating unit suggests new spots and restaurants that the user may be interested in, for example, based on information about tourist spots and restaurants the user has visited in the past. The generating unit also generates a personal guide including information about tourist spots and restaurants, historical background, and the like, based on the user's preferences. For example, if the user is interested in history, the generating unit provides information about historical tourist spots along the route. The providing unit provides entertainment content based on the personal guide generated by the generating unit. The providing unit provides entertainment content such as music, audiobooks, podcasts, and quizzes. The providing unit selects optimal entertainment content according to the user's preferences. For example, if the user wants to relax, the providing unit provides relaxing music or audiobooks. If the user is excited, the providing unit can also provide energetic music or quizzes. The suggesting unit collects traffic information and suggests an optimal route. The suggestion unit, for example, collects traffic information in real time and calculates the optimal route based on congestion information and accident information. The suggestion unit suggests the optimal route based on the user's current location and destination. For example, the suggestion unit suggests the shortest route if the user is in a hurry. The suggestion unit can also suggest a scenic route if the user wants to relax. As a result, the car navigation system according to the embodiment can generate a personal guide based on the user's preferences and past history, provide entertainment content, and suggest the optimal route.

[0054] The providing unit can provide entertainment content such as music, audiobooks, podcasts, and quizzes. The providing unit, for example, provides music tailored to the user's preferences. For example, if the user wants to relax, the providing unit can provide relaxing music. Furthermore, if the user is excited, the providing unit can provide energetic music. Furthermore, if the user likes audiobooks, the providing unit can provide audiobooks that match the user's preferences. For example, the providing unit can suggest new works based on the authors of audiobooks the user has listened to in the past. Furthermore, if the user likes podcasts, the providing unit can provide podcasts that match the user's preferences. For example, the providing unit can suggest podcasts that match the user's preferences based on the user's past listening history. Furthermore, if the user likes quizzes, the providing unit can provide quizzes that match the user's preferences. For example, the providing unit can provide quizzes on topics that the user is likely to be interested in. In this way, the providing unit can provide entertainment content tailored to the user's preferences.

[0055] The suggestion unit can collect traffic information in real time and suggest a route. For example, the suggestion unit collects traffic information in real time and calculates an optimal route based on congestion information and accident information. The suggestion unit suggests an optimal route based on the user's current location and destination. For example, the suggestion unit suggests the shortest route if the user is in a hurry. The suggestion unit can also suggest a scenic route if the user wants to relax. Furthermore, the suggestion unit analyzes the user's past driving history and selects an optimal route. For example, the suggestion unit suggests an optimal route based on routes the user has used in the past. The suggestion unit can also suggest a route that avoids congestion based on the user's past driving history. Furthermore, the suggestion unit customizes the route based on the user's current driving situation and destination. For example, the suggestion unit suggests the shortest route if the user is in a hurry. The suggestion unit can also suggest a scenic route if the user is relaxed. In this way, the suggestion unit can collect traffic information in real time and suggest an optimal route.

[0056] The generation unit can generate a personal guide including information on tourist spots and restaurants, as well as historical background, based on the user's preferences and past history. The generation unit, for example, collects the user's past travel history, viewing history, purchase history, etc., and estimates the user's preferences based on this data. The generation unit, for example, suggests new spots and restaurants that the user may be interested in based on information on tourist spots and restaurants the user has visited in the past. The generation unit also generates a personal guide including information on tourist spots and restaurants, as well as historical background, based on the user's preferences. For example, if the user is interested in history, the generation unit provides information on historical tourist spots along the route. The generation unit also filters information based on the user's current interests. For example, the generation unit selects tourist spots based on themes that the user is currently interested in (e.g., history, nature). The generation unit can also provide related information based on keywords recently searched by the user. This allows the generation unit to generate a personal guide based on the user's preferences and past history.

[0057] The generation unit can estimate the user's emotions and adjust the content of the personal guide based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the content of the personal guide based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit prioritizes providing information on relaxing tourist spots and restaurants. Furthermore, if the user is excited, the generation unit can generate a personal guide that includes a lot of activity and event information. Furthermore, if the user is tired, the generation unit primarily provides information on rest spots and places where the user can relax. This allows the generation unit to adjust the content of the personal guide based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the content of the personal guide based on the emotion.

[0058] The generation unit can analyze the user's past travel history and select information on tourist spots and restaurants. The generation unit, for example, analyzes the user's past travel history and selects information on optimal tourist spots and restaurants. For example, the generation unit can suggest similar spots based on ratings of tourist spots the user has previously visited. The generation unit can also analyze the menus of restaurants the user has previously visited and suggest new restaurants that match the user's preferences. Furthermore, the generation unit can prioritize information related to a specific area from the user's past travel history. This allows the generation unit to analyze the user's past travel history and provide information on optimal tourist spots and restaurants. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's past travel history data into the generation AI and cause the generation AI to select information on tourist spots and restaurants.

[0059] When generating a personal guide, the generation unit can filter information based on the user's current interests. The generation unit filters information based on the user's current interests, for example. For example, the generation unit selects tourist spots based on themes (e.g., history, nature) that the user is currently interested in. The generation unit can also provide related information based on keywords recently searched by the user. Furthermore, the generation unit can suggest spots that match the user's interests by referring to information on accounts the user follows on social media. This allows the generation unit to filter information based on the user's current interests and provide more relevant information. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the user's current interests into the generation AI and have the generation AI perform information filtering.

[0060] The generation unit can estimate the user's emotions and adjust the display order of the personal guide based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the display order of the personal guide based on the estimated user emotions. For example, if the user is relaxed, the generation unit can first display information about relaxing spots. Furthermore, if the user is in a hurry, the generation unit can prioritize displaying information along the most efficient route. Furthermore, if the user is excited, the generation unit can first display activity or event information. This allows the generation unit to adjust the display order of the personal guide based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display order based on the emotion.

[0061] When generating a personal guide, the generation unit can prioritize providing highly relevant information by taking into account the user's geographical location information. For example, the generation unit can prioritize providing highly relevant information by taking into account the user's geographical location information. For example, the generation unit can prioritize suggesting tourist spots close to the user's current location. The generation unit can also suggest restaurants that are easily accessible from the user's current location. Furthermore, the generation unit can provide information along an optimal route based on the user's current location. This allows the generation unit to provide more relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information data into the generation AI and cause the generation AI to provide highly relevant information.

[0062] When generating a personal guide, the generation unit can analyze the user's social media activity and provide related information. For example, the generation unit can analyze the user's social media activity and provide related information. For example, the generation unit can provide related information based on places and events that the user has "liked" on social media. The generation unit can also analyze the content of posts from accounts the user follows and suggest spots that match the user's interests. Furthermore, the generation unit can provide information on related tourist spots and restaurants based on posts shared by the user. This allows the generation unit to analyze the user's social media activity and provide more relevant information. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related information.

[0063] The providing unit can estimate the user's emotions and adjust the type of entertainment content based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the type of entertainment content based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide relaxing music or an audiobook. If the user is excited, the providing unit can provide energetic music or a quiz. If the user is tired, the providing unit can provide relaxing podcasts or meditation audio. This allows the providing unit to adjust the type of entertainment content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the type of entertainment content based on the emotion.

[0064] When selecting entertainment content to provide, the providing unit can select the content by analyzing the user's past viewing history. The providing unit, for example, analyzes the user's past viewing history and selects optimal entertainment content. For example, the providing unit can suggest similar music based on the genre of music the user has listened to in the past. The providing unit can also suggest new works based on the authors of audiobooks the user has listened to in the past. Furthermore, the providing unit can suggest podcasts that match the user's preferences based on the user's past viewing history. In this way, the providing unit can analyze the user's past viewing history and provide optimal entertainment content. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past viewing history data into a generation AI and cause the generation AI to select entertainment content.

[0065] When providing entertainment content, the providing unit can customize the content based on the user's current mood and situation. The providing unit customizes the content based on, for example, the user's current mood and situation. For example, if the user is relaxed, the providing unit can provide relaxing music or an audiobook. Furthermore, if the user is excited, the providing unit can provide energetic music or a quiz. Furthermore, if the user is tired, the providing unit can provide relaxing podcasts or meditation audio. This allows the providing unit to customize the content based on the user's current mood and situation. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's mood and situation data into the generation AI and have the generation AI customize the content.

[0066] The providing unit can estimate the user's emotion and adjust the playback order of entertainment content based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the playback order of entertainment content based on the estimated user's emotion. For example, if the user is relaxed, the providing unit can first play relaxing content. Also, if the user is excited, the providing unit can first play energetic content. Furthermore, if the user is tired, the providing unit can first play relaxing content. This allows the providing unit to adjust the playback order of entertainment content according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the playback order based on the emotion.

[0067] When selecting entertainment content to provide, the providing unit can prioritize providing highly relevant content by taking into account the user's geographical location information. The providing unit, for example, provides highly relevant entertainment content by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide music or podcasts related to that area. Furthermore, if the user is at a specific tourist spot, the providing unit can provide an audiobook related to that spot. Furthermore, if the user is participating in a specific event, the providing unit can provide a quiz related to the event. In this way, the providing unit can provide more relevant entertainment content by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into a generation AI and cause the generation AI to provide highly relevant content.

[0068] When selecting entertainment content to provide, the providing unit can analyze the user's social media activity and provide relevant content. The providing unit, for example, analyzes the user's social media activity and provides relevant content. For example, the providing unit can provide relevant content based on music or podcasts that the user has "liked" on social media. The providing unit can also analyze posts from accounts the user follows and provide content that matches the user's interests. Furthermore, the providing unit can provide relevant entertainment content based on posts shared by the user. This allows the providing unit to analyze the user's social media activity and provide more relevant entertainment content. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide relevant content.

[0069] The suggestion unit can estimate the user's emotions and adjust the method for proposing an optimal route based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the method for proposing an optimal route based on the estimated user emotions. For example, the suggestion unit can suggest a scenic route when the user is relaxed. The suggestion unit can also suggest the shortest route when the user is in a hurry. Furthermore, the suggestion unit can suggest a route with a lot of activity when the user is excited. This allows the suggestion unit to adjust the method for proposing an optimal route according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, for example, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the route suggestion method based on the emotion.

[0070] When selecting a route to be proposed, the suggestion unit can select the route by analyzing the user's past driving history. The suggestion unit, for example, analyzes the user's past driving history and selects an optimal route. For example, the suggestion unit can suggest an optimal route based on routes the user has used in the past. The suggestion unit can also suggest a route that avoids congestion based on the user's past driving history. Furthermore, the suggestion unit can analyze the user's past driving history and suggest the most efficient route. In this way, the suggestion unit can analyze the user's past driving history and provide an optimal route. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past driving history data into the generation AI and cause the generation AI to select a route.

[0071] When proposing a route, the suggestion unit can customize the route based on the user's current driving situation and destination. The suggestion unit customizes the route based on, for example, the user's current driving situation and destination. For example, if the user is in a hurry, the suggestion unit can suggest the shortest route. Also, if the user is relaxed, the suggestion unit can suggest a scenic route. Furthermore, if the user is heading to a specific destination, the suggestion unit can suggest the optimal route to that destination. This allows the suggestion unit to customize the route based on the user's current driving situation and destination. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input the user's driving situation data into the generation AI and cause the generation AI to customize the route.

[0072] The suggestion unit can estimate the user's emotions and prioritize route suggestions based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and prioritizes route suggestions based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can prioritize scenic routes. Furthermore, if the user is in a hurry, the suggestion unit can prioritize the shortest route. Furthermore, if the user is excited, the suggestion unit can prioritize routes with a lot of activity. This allows the suggestion unit to prioritize route suggestions based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using an AI, or may be performed without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to prioritize route suggestions based on emotions.

[0073] The suggestion unit may propose a route taking into consideration the user's geographical location information when selecting a proposed route. The suggestion unit may, for example, propose an optimal route taking into consideration the user's geographical location information. For example, the suggestion unit may propose the most efficient route from the user's current location. The suggestion unit may also propose a route that is easily accessible from the user's current location. Furthermore, the suggestion unit may also propose an optimal route based on the user's current location. This allows the suggestion unit to provide a more appropriate route taking into consideration the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input the user's geographical location information data into a generation AI and cause the generation AI to propose a route.

[0074] When selecting a route to suggest, the suggestion unit can analyze the user's social media activity and suggest related routes. The suggestion unit, for example, analyzes the user's social media activity and suggests related routes. For example, the suggestion unit can suggest related routes based on places and events that the user has "liked" on social media. The suggestion unit can also analyze the content of posts from accounts the user follows and suggest routes that match the user's interests. Furthermore, the suggestion unit can suggest related routes based on posts shared by the user. This allows the suggestion unit to analyze the user's social media activity and provide more relevant routes. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest routes. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned generation unit, provision unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14, and collects the user's past travel history, viewing history, purchase history, etc., and estimates the user's preferences based on this data. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides entertainment content such as music, audiobooks, podcasts, and quizzes. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects traffic information in real time and suggests optimal routes. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned generation unit, provision unit, and suggestion unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214, and collects the user's past travel history, viewing history, purchase history, etc., and estimates the user's preferences based on this data. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides entertainment content such as music, audiobooks, podcasts, and quizzes. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects traffic information in real time and suggests an optimal route. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned generation unit, provision unit, and suggestion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset-type terminal 314, and collects the user's past travel history, viewing history, purchase history, etc., and estimates the user's preferences based on this data. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides entertainment content such as music, audiobooks, podcasts, and quizzes. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects traffic information in real time and suggests optimal routes. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned generation unit, provision unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414, and collects the user's past travel history, viewing history, purchase history, etc., and estimates the user's preferences based on this data. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides entertainment content such as music, audiobooks, podcasts, and quizzes. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects traffic information in real time and suggests the optimal route.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The generation unit can generate a personal guide based on the user's health data in addition to the user's preferences and past history. For example, the generation unit analyzes data obtained from the user's fitness tracker or smartwatch to suggest tourist spots and restaurants according to the user's health condition. If the user likes healthy eating, the generation unit can preferentially suggest health-oriented restaurants. If the user likes exercise, the generation unit can suggest tourist spots that include hiking trails and jogging trails. Furthermore, the generation unit can suggest places to relax and activities that help relieve stress based on the user's health data. In this way, the generation unit can generate a personal guide according to the user's health condition.

[0077] In addition to providing entertainment content, the providing unit can also provide educational content tailored to the user's learning needs. For example, the providing unit can suggest online courses or educational podcasts based on the topic the user wants to learn. If the user wants to learn a language, the providing unit can provide language learning apps or audio lessons. If the user wants to master a specific skill, the providing unit can suggest video tutorials or e-books related to that skill. Furthermore, the providing unit can suggest content that the user should learn next based on the user's learning progress. This allows the providing unit to provide educational content tailored to the user's learning needs.

[0078] The suggestion unit not only collects traffic information in real time but also suggests routes based on the user's driving style. For example, the suggestion unit analyzes the speed and frequency of braking when the user has driven in the past, and suggests routes that are optimal for the user's driving style. If the user prioritizes safe driving, the suggestion unit can suggest routes with less traffic and fewer traffic lights. Also, if the user prefers smooth driving, the suggestion unit can prioritize routes with fewer curves and expressways. Furthermore, the suggestion unit can provide information on rest spots and service areas based on the user's driving style. This allows the suggestion unit to suggest optimal routes that suit the user's driving style.

[0079] The generation unit can generate a personal guide based on feedback from the user's social network in addition to the user's preferences and past history. For example, the generation unit can collect ratings of tourist spots and restaurants visited by the user's friends and family and make suggestions based on that. It can prioritize suggestions of spots highly rated by the user's friends. It can also analyze the number of comments and "likes" on posts shared by the user on social media to suggest popular spots. Furthermore, the generation unit can grasp trends within the user's social network and provide the latest information on popular spots and events. This allows the generation unit to generate a more relevant personal guide based on feedback from the user's social network.

[0080] The generation unit can estimate the user's emotions and adjust the content of the personal guide based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can prioritize providing information on tourist spots and restaurants where the user can relax. Furthermore, if the user is excited, the generation unit can generate a personal guide that includes a lot of activity and event information. Furthermore, if the user is tired, the generation unit can mainly provide information on rest spots and places where the user can relax. This allows the generation unit to adjust the content of the personal guide based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the content of the personal guide based on the emotion.

[0081] In addition to providing entertainment content, the providing unit can also provide relaxation content according to the user's health condition. For example, the providing unit monitors the user's heart rate and stress level and provides relaxing music and meditation guides. If the user shows a high stress level, the providing unit can provide relaxation music and deep breathing guides. Also, if the user is relaxed, the providing unit can provide content to maintain relaxation. Furthermore, the providing unit can suggest appropriate times to take a break based on the user's health data. This allows the providing unit to provide relaxation content according to the user's health condition.

[0082] The suggestion unit not only collects traffic information in real time but also suggests routes based on the user's driving style. For example, the suggestion unit analyzes the speed and frequency of braking when the user has driven in the past, and suggests routes that are optimal for the user's driving style. If the user prioritizes safe driving, the suggestion unit can suggest routes with less traffic and fewer traffic lights. Also, if the user prefers smooth driving, the suggestion unit can prioritize routes with fewer curves and expressways. Furthermore, the suggestion unit can provide information on rest spots and service areas based on the user's driving style. This allows the suggestion unit to suggest optimal routes that suit the user's driving style.

[0083] The generation unit can estimate the user's emotions and adjust the display order of the personal guide based on the estimated user emotions. For example, if the user is relaxed, the generation unit can first display information about relaxing spots. Furthermore, if the user is in a hurry, the generation unit can prioritize displaying information along the most efficient route. Furthermore, if the user is excited, the generation unit can first display activity or event information. This allows the generation unit to adjust the display order of the personal guide based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display order based on the emotion.

[0084] In addition to providing entertainment content, the providing unit can also provide relaxation content according to the user's health condition. For example, the providing unit monitors the user's heart rate and stress level and provides relaxing music and meditation guides. If the user shows a high stress level, the providing unit can provide relaxation music and deep breathing guides. Also, if the user is relaxed, the providing unit can provide content to maintain relaxation. Furthermore, the providing unit can suggest appropriate times to take a break based on the user's health data. This allows the providing unit to provide relaxation content according to the user's health condition.

[0085] The suggestion unit can estimate the user's emotions and adjust the optimal route suggestion method based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can suggest a scenic route. Furthermore, if the user is in a hurry, the suggestion unit can suggest a route with a lot of activity. This allows the suggestion unit to adjust the optimal route suggestion method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the route suggestion method based on the emotion.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The generator generates a personal guide based on the user's preferences and past history. The generator collects the user's past travel history, viewing history, purchase history, etc., and estimates the user's preferences based on this data. For example, based on information about tourist spots and restaurants the user has visited in the past, the generator suggests new spots and restaurants that the user might be interested in. The generator also generates a personal guide that includes information about tourist spots and restaurants, historical background, etc., based on the user's preferences. For example, if the user is interested in history, the generator provides information about historical tourist spots along the route. Step 2: The providing unit provides entertainment content based on the personal guide generated by the generating unit. The providing unit provides entertainment content such as music, audiobooks, podcasts, and quizzes, and selects the most suitable entertainment content according to the user's preferences. For example, if the user wants to relax, the providing unit provides relaxing music and audiobooks, and if the user is excited, the providing unit provides energetic music and quizzes. Step 3: The suggestion unit collects traffic information and proposes the optimal route. The suggestion unit collects traffic information in real time and calculates the optimal route based on congestion and accident information. The suggestion unit proposes the optimal route based on the user's current location and destination. For example, if the user is in a hurry, it proposes the shortest route, and if the user wants to relax, it proposes a scenic route.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0090] 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.

[0091] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0103] 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.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] [Explanation of symbols]

[0160] 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 generation unit that generates a personal guide based on the user's preferences and past history; a providing unit that provides entertainment content based on the personal guide generated by the generating unit; a proposal unit that collects traffic information and proposes a route; A system characterized by:

2. The providing unit Providing entertainment content such as music, audiobooks, podcasts, and quizzes 2. The system of claim 1.

3. The proposal unit Collects real-time traffic information and suggests routes 2. The system of claim 1.

4. The generation unit Generates a personal guide based on the user's preferences and past history, including information on tourist spots, restaurants, and historical background.

2. The system of claim 1.

5. The generation unit Estimate the user's emotions and adjust the content of the personal guide based on the estimated user emotions.

2. The system of claim 1.

6. The generation unit Analyze the user's past travel history and select information on tourist attractions and restaurants 2. The system of claim 1.

7. The generation unit When generating a personal guide, filter information based on the user's current interests.

2. The system of claim 1.

8. The generation unit Estimates the user's emotions and adjusts the display order of the personal guide based on the estimated user emotions.

2. The system of claim 1.

9. The generation unit When generating a personal guide, information is provided preferentially based on the user's geographical location.

2. The system of claim 1.

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