System

Abstract

An object of a system according to an embodiment is to provide a sightseeing guide customized based on a user's interest and life experience.SOLUTION: A system includes an interest collection part, an analysis part, a guide generation part, and a provision part. The interest collection unit collects information on the user's interest and life experience. The analysis unit analyzes the information collected by the interest collection unit. The guide generation unit generates a sightseeing guide customized based on the information analyzed by the analysis unit. The providing section provides the sightseeing guide generated by the guide generating section as a voice guide or a text guide.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that tourist guides are not customized to the interests and life experiences of individual users, making them difficult to understand.

[0005] The system according to the embodiment aims to provide a tourist guide that is customized based on the user's interests and life experiences. [Means for solving the problem]

[0006] The system according to the embodiment includes an interest collection unit, an analysis unit, a guide generation unit, and a provision unit. The interest collection unit collects information about a user's interests and life experiences. The analysis unit analyzes the information collected by the interest collection unit. The guide generation unit generates a customized tour guide based on the information analyzed by the analysis unit. The provision unit provides the tour guide generated by the guide generation unit as an audio guide or a text guide. [Effects of the Invention]

[0007] The system according to the embodiment can provide a customized tourist guide based on the user's interests and life experiences. [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 tourist guide system according to an embodiment of the present invention is a system that uses a generation AI to provide a tourist guide customized based on the user's interests and life experiences. This allows the tourist guide system to provide a tourist guide customized according to the user's interests, making it easier for the user to acquire knowledge.

[0029] A tourist guide system according to an embodiment includes an interest collection unit, an analysis unit, a guide generation unit, and a provision unit. The interest collection unit collects information about a user's interests and life experiences. For example, the user can input information such as places visited in the past, topics of interest, and hobbies. The interest collection unit can also collect public information from the user's social media accounts and analyze their interests and preferences. The analysis unit analyzes the information collected by the interest collection unit. For example, the analysis unit analyzes the user's interests using data mining technology or statistical analysis. The guide generation unit generates a customized tourist guide based on the information analyzed by the analysis unit. For example, a user interested in history can be provided with a guide that explains the detailed historical background of castles and historical buildings. The provision unit provides the tourist guide generated by the guide generation unit as an audio guide or a text guide. For example, when a user visits a tourist facility using a smartphone or tablet, the guide generated by the generation AI can be played back as audio. It is also possible to display the guide in text format. This allows the tourist guide system to provide a customized tourist guide based on the user's interests.

[0030] The interest collection unit can collect publicly available information from SNS accounts and analyze interests and preferences using natural language processing technology. The interest collection unit, for example, collects publicly available posts and profile information from a user's SNS account and analyzes the interests and preferences using natural language processing technology. For example, the interest collection unit identifies interests based on the themes the user frequently posts on and the accounts the user follows. The interest collection unit also collects behavioral data such as the user's "likes" and "shares" on SNS and analyzes the interests and preferences. For example, the interest collection unit estimates the user's interests based on reactions to posts in a specific genre. The interest collection unit also analyzes image data collected from the user's SNS account to identify the interests and preferences. For example, the interest collection unit estimates the user's areas of interest based on photos of travel destinations and images of events. This makes it possible to analyze the user's interests and preferences from the user's SNS account.

[0031] The interest collection unit can analyze sightseeing history and reviews to track changes in interests. For example, the interest collection unit collects historical data on tourist spots visited by the user in the past and analyzes changes in interests. For example, it tracks changes in the user's interests based on the frequency of visits and length of stay. The interest collection unit also analyzes reviews and ratings of tourist spots posted by the user to identify changes in interests. For example, it estimates changes in the user's interests based on the review content and rating score. The interest collection unit also compares the user's past sightseeing history with their current interests to analyze changes in interests. For example, it identifies changes in interests by associating places visited in the past with current interests. This makes it possible to track changes in the user's interests.

[0032] The interest collection unit can collect health data and propose a sightseeing plan based on the health data. The interest collection unit collects health data such as the number of steps and heart rate from the user's smartwatch or fitness tracker, for example, and proposes a sightseeing plan tailored to the user's physical condition. For example, the sightseeing route can be adjusted according to the user's fatigue level. The interest collection unit also proposes tourist spots that are best suited to the user's physical condition based on the health data. For example, if the user's heart rate is high, it can suggest places to relax, and if the user's number of steps is high, it can suggest rest spots. The interest collection unit also monitors the user's health data in real time and dynamically adjusts the sightseeing plan tailored to the user's physical condition. For example, if the user's number of steps is high, it can suggest a shorter sightseeing route. This makes it possible to propose a sightseeing plan tailored to the user's physical condition.

[0033] The interest collection unit can simultaneously collect the interests of family and friends and generate a guide that is optimal for the entire group. The interest collection unit, for example, collects interest information from the user's family and friends and generates a tourist guide that is optimal for the entire group. For example, it suggests tourist spots that take into account the interests of each member. The interest collection unit also analyzes the interests of the entire group and suggests tourist spots that share common interests. For example, it prioritizes the introduction of activities and exhibits that everyone can enjoy. The interest collection unit also generates a tourist route that is optimal for the entire group based on the interest information of family and friends. For example, it suggests a route that incorporates the interests of each member in a balanced manner. This makes it possible to generate a tourist guide that is optimal for the entire group.

[0034] The guide generation unit can automatically insert videos and music related to the tourist guide based on the user's interests. The guide generation unit automatically inserts videos related to the tourist guide based on the user's interests, for example, by providing documentary footage related to explanations of historical buildings. The guide generation unit also automatically inserts music related to the tourist guide based on the user's interests, for example, by playing classical music related to the exhibits at an art museum. The guide generation unit also builds a system that automatically inserts videos and music related to the tourist guide, and provides a multimedia guide tailored to the user's interests. For example, it incorporates videos and music related to themes in which the user has shown interest. This makes it possible to automatically insert videos and music related to the tourist guide based on the user's interests.

[0035] The guide generation unit can provide guide content at multiple difficulty levels according to the user's interests. The guide generation unit provides tourist guide content at multiple difficulty levels according to the user's interests, for example. For example, a simple explanation for beginners and a detailed explanation for experts are prepared. The guide generation unit also builds a system that adjusts the tourist guide content to the user's knowledge level. For example, the depth of the explanation and the use of technical terms are adjusted according to the user's interests and knowledge. The guide generation unit also dynamically changes the difficulty level of the tourist guide based on the user's interests. For example, if the user requests more detailed information, a more specialized explanation is provided. This makes it possible to provide guide content at multiple difficulty levels according to the user's interests.

[0036] The guide generation unit can provide information on related events and workshops based on the user's interests. The guide generation unit, for example, builds a system that provides information on related events and workshops based on the user's interests. For example, it introduces lectures and workshops related to a specific exhibition. The guide generation unit also automatically inserts information on related events and workshops into a tourist guide. For example, it provides event information related to a theme that interests the user. The guide generation unit also provides information on events and workshops held at tourist facilities based on the user's interests. For example, it introduces activities related to a specific exhibition. This makes it possible to provide information on related events and workshops based on the user's interests.

[0037] The providing unit can insert the user's name into the audio guide to provide a personalized experience. The providing unit, for example, builds a system that inserts the user's name into the audio guide to provide a personalized experience. For example, the providing unit calls out the user's name at the beginning of the guide. The providing unit also inserts the user's name into the audio guide to provide an individualized guide. For example, the providing unit uses the user's name during the explanation of a particular exhibit. The providing unit also inserts the user's name into the audio guide to provide a personalized experience. For example, the providing unit uses the user's name to provide an individual message for a particular exhibit. In this way, the user's name can be inserted into the audio guide to provide a personalized experience.

[0038] The providing unit can adjust the speed and tone of the audio guide to suit the user's preferences. The providing unit, for example, builds a system that adjusts the speed of the audio guide according to the user's preferences. For example, if the user wants to receive information at a fast pace, the speed of the guide is increased. The providing unit also adjusts the tone of the audio guide according to the user's preferences. For example, the audio guide is provided in accordance with the user's preferences, such as a calm tone or a lively tone. The providing unit also develops a system that dynamically adjusts the speed and tone of the audio guide based on user feedback. For example, if the user wants to change the speed of the guide, the adjustment is made in real time. This allows the speed and tone of the audio guide to be adjusted to suit the user's preferences.

[0039] The providing unit can link the audio guide and the text guide and provide information in a format selected by the user. The providing unit, for example, links the audio guide and the text guide and builds a system that provides information in a format selected by the user. For example, the text guide is displayed while the user is listening to the audio guide. The providing unit also allows the user to switch between the audio guide and the text guide. For example, the audio guide can be paused to read the text guide. The providing unit also links the audio guide and the text guide and provides information in a format selected by the user. For example, the content of the audio guide can be displayed in text format so that the user can refer to it as needed. In this way, the audio guide and the text guide can be linked and information can be provided in a format selected by the user.

[0040] The providing unit can combine AR technology with the audio guide and simultaneously provide visual information. The providing unit, for example, builds a system that combines AR technology with the audio guide and simultaneously provides visual information. For example, AR content related to exhibits is displayed through a smartphone camera. The providing unit also uses AR technology to visually complement the content of the audio guide. For example, a 3D model of a building is displayed during an explanation of a historical building. The providing unit also combines AR technology with the audio guide and simultaneously provides visual information. For example, AR content related to an exhibit at an art museum is displayed, allowing the user to visually understand the details of the work. This makes it possible to combine AR technology with the audio guide and simultaneously provide visual information.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The tourist guide system can also be equipped with a health management unit that collects the user's health data and proposes sightseeing plans based on their health condition. For example, the system can collect data such as the number of steps and heart rate from the user's smartwatch or fitness tracker and propose sightseeing routes tailored to their physical condition. The system can adjust the sightseeing route according to the user's fatigue level, or suggest places to relax if the user's heart rate is high. It can also monitor health data in real time and dynamically adjust sightseeing plans to suit the user's physical condition. This allows the system to provide sightseeing plans tailored to the user's physical condition.

[0043] The tourist guide system can also be equipped with a group interest collection unit that collects interest information from the user's family and friends and generates a tourist guide that is optimal for the entire group. For example, it can suggest tourist spots that take into account the interests of each member and prioritize activities and exhibits that everyone can enjoy. It can also generate an optimal tourist route for the entire group based on the interest information of family and friends, proposing a route that incorporates the interests of each member in a balanced way. This makes it possible to provide a tourist guide that is optimal for the entire group.

[0044] The tourist guide system can also include a multimedia generation unit that automatically inserts videos and music related to the tourist guide based on the user's interests. For example, it can provide documentary videos related to explanations of historical buildings or play classical music related to exhibits in art museums. Furthermore, incorporating videos and music related to themes that the user has shown interest in can provide a richer tourist experience. This allows for the automatic insertion of videos and music related to the tourist guide based on the user's interests.

[0045] The tourist guide system may further include an event information provider that provides information on related events and workshops based on the user's interests. For example, it may introduce lectures and workshops related to a specific exhibition and provide event information related to themes that interest the user. It may also provide information on events and workshops held at tourist facilities and introduce activities related to a specific exhibition. This makes it possible to provide information on related events and workshops based on the user's interests.

[0046] The tourist guide system may further include a name insertion unit that inserts the user's name into the audio guide to provide a personalized experience. For example, the user's name may be called out at the beginning of the guide or used during the explanation of a particular exhibit. The user's name may also be used to provide a personalized message for a particular exhibit. This allows the user's name to be inserted into the audio guide to provide a personalized experience.

[0047] The tourist guide system can also be equipped with an audio adjustment unit that adjusts the speed and tone of the audio guide to suit the user's preferences. For example, if the user wants to receive information at a fast pace, the audio guide can be provided in a speedier manner, or in a calmer or more energetic tone, depending on the user's preferences. The audio guide speed and tone can also be dynamically adjusted based on user feedback. This allows the audio guide speed and tone to be adjusted to suit the user's preferences.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The interest collection unit collects information about the user's interests and life experiences. For example, the user can enter information such as places they have visited in the past, topics they are interested in, and hobbies. The interest collection unit can also collect public information from the user's social media accounts and analyze their interests and preferences. Step 2: The analysis unit analyzes the information collected by the interest collection unit, for example, by using data mining techniques or statistical analysis to analyze the user's interests. Step 3: The guide generator generates a customized tourist guide based on the information analyzed by the analyzer. For example, a user who is interested in history can receive a guide that explains the detailed historical background of castles and historical buildings. Step 4: The providing unit provides the tourist guide generated by the guide generation unit as an audio guide or text guide. For example, when a user visits a tourist facility using a smartphone or tablet, the guide generated by the generation AI can be played back as audio. It is also possible to display the guide in text format.

[0050] (Example 2) A tourist guide system according to an embodiment of the present invention is a system that uses a generation AI to provide a tourist guide customized based on the user's interests and life experiences. This allows the tourist guide system to provide a tourist guide customized according to the user's interests, making it easier for the user to acquire knowledge.

[0051] A tourist guide system according to an embodiment includes an interest collection unit, an analysis unit, a guide generation unit, and a provision unit. The interest collection unit collects information about a user's interests and life experiences. For example, the user can input information such as places visited in the past, topics of interest, and hobbies. The interest collection unit can also collect public information from the user's social media accounts and analyze their interests and preferences. The analysis unit analyzes the information collected by the interest collection unit. For example, the analysis unit analyzes the user's interests using data mining technology or statistical analysis. The guide generation unit generates a customized tourist guide based on the information analyzed by the analysis unit. For example, a user interested in history can be provided with a guide that explains the detailed historical background of castles and historical buildings. The provision unit provides the tourist guide generated by the guide generation unit as an audio guide or a text guide. For example, when a user visits a tourist facility using a smartphone or tablet, the guide generated by the generation AI can be played back as audio. It is also possible to display the guide in text format. This allows the tourist guide system to provide a customized tourist guide based on the user's interests.

[0052] The interest collection unit can collect publicly available information from SNS accounts and analyze interests and preferences using natural language processing technology. The interest collection unit, for example, collects publicly available posts and profile information from a user's SNS account and analyzes the interests and preferences using natural language processing technology. For example, the interest collection unit identifies interests based on the themes the user frequently posts on and the accounts the user follows. The interest collection unit also collects behavioral data such as the user's "likes" and "shares" on SNS and analyzes the interests and preferences. For example, the interest collection unit estimates the user's interests based on reactions to posts in a specific genre. The interest collection unit also analyzes image data collected from the user's SNS account to identify the interests and preferences. For example, the interest collection unit estimates the user's areas of interest based on photos of travel destinations and images of events. This makes it possible to analyze the user's interests and preferences from the user's SNS account.

[0053] The interest collection unit can analyze sightseeing history and reviews to track changes in interests. For example, the interest collection unit collects historical data on tourist spots visited by the user in the past and analyzes changes in interests. For example, it tracks changes in the user's interests based on the frequency of visits and length of stay. The interest collection unit also analyzes reviews and ratings of tourist spots posted by the user to identify changes in interests. For example, it estimates changes in the user's interests based on the review content and rating score. The interest collection unit also compares the user's past sightseeing history with their current interests to analyze changes in interests. For example, it identifies changes in interests by associating places visited in the past with current interests. This makes it possible to track changes in the user's interests.

[0054] The analysis unit can use the emotion estimation function to analyze emotions regarding the information entered by the user and generate prompts for eliciting positive emotions. The analysis unit, for example, performs emotion estimation on the information entered by the user and generates prompts for eliciting positive emotions. For example, it provides positive information related to a topic in which the user has shown interest. The analysis unit also uses the emotion estimation function to analyze emotions regarding the information entered by the user and provides feedback for eliciting positive emotions. For example, it displays an encouraging message regarding a topic in which the user has shown interest. The analysis unit also generates prompts for eliciting positive emotions based on emotion estimation data regarding the information entered by the user. For example, it provides success stories and positive episodes regarding a topic in which the user has shown interest. In this way, it is possible to generate prompts that elicit positive emotions from the user.

[0055] The interest collection unit can collect health data and propose a sightseeing plan based on the health data. The interest collection unit collects health data such as the number of steps and heart rate from the user's smartwatch or fitness tracker, for example, and proposes a sightseeing plan tailored to the user's physical condition. For example, the sightseeing route can be adjusted according to the user's fatigue level. The interest collection unit also proposes tourist spots that are best suited to the user's physical condition based on the health data. For example, if the user's heart rate is high, it can suggest places to relax, and if the user's number of steps is high, it can suggest rest spots. The interest collection unit also monitors the user's health data in real time and dynamically adjusts the sightseeing plan tailored to the user's physical condition. For example, if the user's number of steps is high, it can suggest a shorter sightseeing route. This makes it possible to propose a sightseeing plan tailored to the user's physical condition.

[0056] The interest collection unit can simultaneously collect the interests of family and friends and generate a guide that is optimal for the entire group. The interest collection unit, for example, collects interest information from the user's family and friends and generates a tourist guide that is optimal for the entire group. For example, it suggests tourist spots that take into account the interests of each member. The interest collection unit also analyzes the interests of the entire group and suggests tourist spots that share common interests. For example, it prioritizes the introduction of activities and exhibits that everyone can enjoy. The interest collection unit also generates a tourist route that is optimal for the entire group based on the interest information of family and friends. For example, it suggests a route that incorporates the interests of each member in a balanced manner. This makes it possible to generate a tourist guide that is optimal for the entire group.

[0057] The analysis unit can use the emotion estimation function to analyze the emotions felt by the user while sightseeing in real time and suggest the next tourist spot. For example, the analysis unit can use the emotion estimation function to analyze the emotions felt by the user while sightseeing in real time and suggest the next tourist spot. For example, if the user has a strong positive emotion, it can suggest spots with a similar theme. The analysis unit can also analyze changes in emotions while sightseeing based on the user's emotion data and dynamically adjust the next tourist spot. For example, if the user has a strong negative emotion, it can suggest a place where the user can relax. The analysis unit can also collect emotion data during sightseeing in real time and build a system that optimizes the next tourist spot. For example, it can prioritize and suggest spots with a high emotion score. This makes it possible to suggest the next tourist spot based on the user's emotions.

[0058] The guide generation unit can automatically insert videos and music related to the tourist guide based on the user's interests. The guide generation unit automatically inserts videos related to the tourist guide based on the user's interests, for example, by providing documentary footage related to explanations of historical buildings. The guide generation unit also automatically inserts music related to the tourist guide based on the user's interests, for example, by playing classical music related to the exhibits at an art museum. The guide generation unit also builds a system that automatically inserts videos and music related to the tourist guide, and provides a multimedia guide tailored to the user's interests. For example, it incorporates videos and music related to themes in which the user has shown interest. This makes it possible to automatically insert videos and music related to the tourist guide based on the user's interests.

[0059] The guide generation unit can provide guide content at multiple difficulty levels according to the user's interests. The guide generation unit provides tourist guide content at multiple difficulty levels according to the user's interests, for example. For example, a simple explanation for beginners and a detailed explanation for experts are prepared. The guide generation unit also builds a system that adjusts the tourist guide content to the user's knowledge level. For example, the depth of the explanation and the use of technical terms are adjusted according to the user's interests and knowledge. The guide generation unit also dynamically changes the difficulty level of the tourist guide based on the user's interests. For example, if the user requests more detailed information, a more specialized explanation is provided. This makes it possible to provide guide content at multiple difficulty levels according to the user's interests.

[0060] The guide generation unit can use the emotion estimation function to adjust the guide content in real time according to the emotion. The guide generation unit, for example, uses the emotion estimation function to adjust the guide content in real time according to the user's emotion. For example, if the user is excited, more interesting information is provided. The guide generation unit also builds a system that dynamically changes the guide content based on the user's emotion data. For example, if the user is tired, information that helps the user to relax is provided. The guide generation unit also uses the emotion estimation function to adjust the guide content in real time according to the user's emotion. For example, if the user has lost interest, new information that attracts the user's interest is provided. In this way, the guide content can be adjusted in real time according to the user's emotion.

[0061] The guide generation unit can provide information on related events and workshops based on the user's interests. The guide generation unit, for example, builds a system that provides information on related events and workshops based on the user's interests. For example, it introduces lectures and workshops related to a specific exhibition. The guide generation unit also automatically inserts information on related events and workshops into a tourist guide. For example, it provides event information related to a theme that interests the user. The guide generation unit also provides information on events and workshops held at tourist facilities based on the user's interests. For example, it introduces activities related to a specific exhibition. This makes it possible to provide information on related events and workshops based on the user's interests.

[0062] The guide generation unit can use the emotion estimation function to prioritize introducing exhibits that are likely to be of most interest to the user. The guide generation unit, for example, uses the emotion estimation function to build a system that prioritizes introducing exhibits that are likely to be of most interest to the user. For example, exhibits with high emotion scores are displayed preferentially. The guide generation unit also identifies exhibits that are likely to be of most interest to the user based on the user's emotion data and introduces them preferentially. For example, exhibits related to a theme in which the user has shown interest are provided preferentially. The guide generation unit also uses the emotion estimation function to identify exhibits that are likely to be of most interest to the user in real time and introduce them preferentially. For example, exhibits are dynamically adjusted according to changes in the user's emotions. This makes it possible to prioritize introducing exhibits that are likely to be of most interest to the user.

[0063] The providing unit can insert the user's name into the audio guide to provide a personalized experience. The providing unit, for example, builds a system that inserts the user's name into the audio guide to provide a personalized experience. For example, the providing unit calls out the user's name at the beginning of the guide. The providing unit also inserts the user's name into the audio guide to provide an individualized guide. For example, the providing unit uses the user's name during the explanation of a particular exhibit. The providing unit also inserts the user's name into the audio guide to provide a personalized experience. For example, the providing unit uses the user's name to provide an individual message for a particular exhibit. In this way, the user's name can be inserted into the audio guide to provide a personalized experience.

[0064] The providing unit can adjust the speed and tone of the audio guide to suit the user's preferences. The providing unit, for example, builds a system that adjusts the speed of the audio guide according to the user's preferences. For example, if the user wants to receive information at a fast pace, the speed of the guide is increased. The providing unit also adjusts the tone of the audio guide according to the user's preferences. For example, the audio guide is provided in accordance with the user's preferences, such as a calm tone or a lively tone. The providing unit also develops a system that dynamically adjusts the speed and tone of the audio guide based on user feedback. For example, if the user wants to change the speed of the guide, the adjustment is made in real time. This allows the speed and tone of the audio guide to be adjusted to suit the user's preferences.

[0065] The providing unit can use the emotion estimation function to change the tone and content of the audio guide in real time according to the user's emotion. For example, the providing unit uses the emotion estimation function to build a system that changes the tone and content of the audio guide in real time according to the user's emotion. For example, if the user is excited, the audio guide is provided in a lively tone. The providing unit also dynamically changes the tone and content of the audio guide based on the user's emotion data. For example, if the user is relaxed, the audio guide is provided in a calm tone. The providing unit also uses the emotion estimation function to change the tone and content of the audio guide in real time according to the user's emotion. For example, if the user is losing interest, new information that will pique their interest is provided. This makes it possible to change the tone and content of the audio guide in real time according to the user's emotion.

[0066] The providing unit can link the audio guide and the text guide and provide information in a format selected by the user. The providing unit, for example, links the audio guide and the text guide and builds a system that provides information in a format selected by the user. For example, the text guide is displayed while the user is listening to the audio guide. The providing unit also allows the user to switch between the audio guide and the text guide. For example, the audio guide can be paused to read the text guide. The providing unit also links the audio guide and the text guide and provides information in a format selected by the user. For example, the content of the audio guide can be displayed in text format so that the user can refer to it as needed. In this way, the audio guide and the text guide can be linked and information can be provided in a format selected by the user.

[0067] The providing unit can combine AR technology with the audio guide and simultaneously provide visual information. The providing unit, for example, builds a system that combines AR technology with the audio guide and simultaneously provides visual information. For example, AR content related to exhibits is displayed through a smartphone camera. The providing unit also uses AR technology to visually complement the content of the audio guide. For example, a 3D model of a building is displayed during an explanation of a historical building. The providing unit also combines AR technology with the audio guide and simultaneously provides visual information. For example, AR content related to an exhibit at an art museum is displayed, allowing the user to visually understand the details of the work. This makes it possible to combine AR technology with the audio guide and simultaneously provide visual information.

[0068] The providing unit can use the emotion estimation function to select a tone of the audio guide that will relax the user. For example, the providing unit uses the emotion estimation function to build a system that selects a tone of the audio guide that will most relax the user. For example, the providing unit selects a tone that has a high relaxing effect based on the user's emotion data. The providing unit also dynamically adjusts the tone of the audio guide that will relax the user based on the user's emotion data. For example, if the user is feeling stressed, the providing unit provides guidance in a calm tone. The providing unit also uses the emotion estimation function to select a tone of the audio guide that will most relax the user. For example, the providing unit selects a tone that has a high relaxing effect based on changes in the user's emotion. This makes it possible to select a tone of the audio guide that will most relax the user.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The tourist guide system can also be equipped with a health management unit that collects the user's health data and proposes sightseeing plans based on their health condition. For example, the system can collect data such as the number of steps and heart rate from the user's smartwatch or fitness tracker and propose sightseeing routes tailored to their physical condition. The system can adjust the sightseeing route according to the user's fatigue level, or suggest places to relax if the user's heart rate is high. It can also monitor health data in real time and dynamically adjust sightseeing plans to suit the user's physical condition. This allows the system to provide sightseeing plans tailored to the user's physical condition.

[0071] The tourist guide system can also be equipped with a group interest collection unit that collects interest information from the user's family and friends and generates a tourist guide that is optimal for the entire group. For example, it can suggest tourist spots that take into account the interests of each member and prioritize activities and exhibits that everyone can enjoy. It can also generate an optimal tourist route for the entire group based on the interest information of family and friends, proposing a route that incorporates the interests of each member in a balanced way. This makes it possible to provide a tourist guide that is optimal for the entire group.

[0072] The tourist guide system can also be equipped with an emotion analysis unit that estimates the user's emotions and suggests tourist spots based on the estimated emotions. For example, the system can analyze the emotions felt by the user while sightseeing in real time, and if the emotion is strong positive, suggest spots with a similar theme. If the emotion is strong negative, it can suggest places where the user can relax. It can also analyze changes in emotions during sightseeing based on the emotion data and dynamically adjust the next tourist spot. This allows the system to suggest the next tourist spot based on the user's emotions.

[0073] The tourist guide system can also include a multimedia generation unit that automatically inserts videos and music related to the tourist guide based on the user's interests. For example, it can provide documentary videos related to explanations of historical buildings or play classical music related to exhibits in art museums. Furthermore, incorporating videos and music related to themes that the user has shown interest in can provide a richer tourist experience. This allows for the automatic insertion of videos and music related to the tourist guide based on the user's interests.

[0074] The tourist guide system can also be equipped with an emotion adjustment unit that estimates the user's emotions and adjusts the guide content in real time based on the estimated emotions. For example, if the user is excited, more interesting information can be provided, and if the user is tired, information that will help them relax can be provided. Also, if the user loses interest, it is possible to provide new information that will attract their interest. In this way, the guide content can be adjusted in real time according to the user's emotions.

[0075] The tourist guide system may further include an event information provider that provides information on related events and workshops based on the user's interests. For example, it may introduce lectures and workshops related to a specific exhibition and provide event information related to themes that interest the user. It may also provide information on events and workshops held at tourist facilities and introduce activities related to a specific exhibition. This makes it possible to provide information on related events and workshops based on the user's interests.

[0076] The tourist guide system may further include an emotion prioritization unit that estimates the user's emotion and prioritizes introducing exhibits that are likely to be of the user's most interest based on the estimated emotion. For example, exhibits with high emotion scores may be displayed preferentially, and exhibits related to themes that the user is interested in may be provided preferentially. It may also be possible to dynamically adjust exhibits according to changes in the user's emotion. This allows exhibits that are likely to be of the user's most interest to be introduced preferentially.

[0077] The tourist guide system may further include a name insertion unit that inserts the user's name into the audio guide to provide a personalized experience. For example, the user's name may be called out at the beginning of the guide or used during the explanation of a particular exhibit. The user's name may also be used to provide a personalized message for a particular exhibit. This allows the user's name to be inserted into the audio guide to provide a personalized experience.

[0078] The tourist guide system can also be equipped with an audio adjustment unit that adjusts the speed and tone of the audio guide to suit the user's preferences. For example, if the user wants to receive information at a fast pace, the audio guide can be provided in a speedier manner, or in a calmer or more energetic tone, depending on the user's preferences. The audio guide speed and tone can also be dynamically adjusted based on user feedback. This allows the audio guide speed and tone to be adjusted to suit the user's preferences.

[0079] The tourist guide system can also be equipped with a relaxing voice unit that uses an emotion estimation function to select the voice guide tone that will most relax the user. For example, a tone with a high relaxing effect can be selected based on the user's emotion data, and if the user is feeling stressed, a calm tone can be used to provide guidance. It is also possible to select a tone with a high relaxing effect according to changes in the user's emotion. This allows the voice guide tone that will most relax the user to be selected.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The interest collection unit collects information about the user's interests and life experiences. For example, the user can enter information such as places they have visited in the past, topics they are interested in, and hobbies. The interest collection unit can also collect public information from the user's social media accounts and analyze their interests and preferences. Step 2: The analysis unit analyzes the information collected by the interest collection unit, for example, by using data mining techniques or statistical analysis to analyze the user's interests. Step 3: The guide generator generates a customized tourist guide based on the information analyzed by the analyzer. For example, a user who is interested in history can receive a guide that explains the detailed historical background of castles and historical buildings. Step 4: The providing unit provides the tourist guide generated by the guide generation unit as an audio guide or text guide. For example, when a user visits a tourist facility using a smartphone or tablet, the guide generated by the generation AI can be played back as audio. It is also possible to display the guide in text format.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 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. an interest gathering unit that gathers information about the user's interests and life experiences; an analysis unit that analyzes the information collected by the interest collection unit; a guide generation unit that generates a customized tourist guide based on the information analyzed by the analysis unit; a providing unit that provides the tourist guide generated by the guide generating unit as an audio guide or a text guide. A system characterized by:

2. The interest collection unit Collect publicly available information from the SNS account and analyze the interests and preferences using natural language processing technology.

2. The system of claim 1.

3. The interest collection unit Analyze travel history and reviews to track changes in these interests 2. The system of claim 1.

4. The analysis unit Analyzes the emotions associated with the information entered by the user and generates prompts to elicit positive emotions.

2. The system of claim 1.

5. The interest collection unit Collecting user health data and proposing sightseeing plans based on said health data 2. The system of claim 1.

6. The interest collection unit It also collects the interests of the user's family and friends to generate the best guide for the whole group.

2. The system of claim 1.

7. The analysis unit Analyzes the user's emotions in real time while sightseeing and suggests the next tourist spot 2. The system of claim 1.

8. The guide generation unit Based on the interests, videos and music related to the tourist guide are automatically inserted.

2. The system of claim 1.

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