System

Abstract

An object of the system according to the embodiment is to provide a value of a new discovery or encounter to a user.SOLUTION: A system includes a search condition analysis part, a proposal part, and a dynamic DigitalTwin map. The search condition analysis unit analyzes a search condition input by a user in a natural language. The proposal unit proposes an optimum place or service on the basis of the search condition analyzed by the search condition analysis unit. The Dynamic DigitalTwin map displays the locations or services suggested by the suggester.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 only offers limited search methods, such as addresses and names, making it difficult for users to make new discoveries or encounters.

[0005] The system according to the embodiment aims to provide users with the value of new discoveries and encounters. [Means for solving the problem]

[0006] A system according to an embodiment includes a search condition analysis unit, a suggestion unit, and a Dynamic Digital Twin map. The search condition analysis unit analyzes search conditions entered by a user in natural language. The suggestion unit suggests optimal places or services based on the search conditions analyzed by the search condition analysis unit. The Dynamic Digital Twin map displays the places or services suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide users with the value of new discoveries and encounters. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The search system according to the embodiment of the present invention is a system that provides a new search method that replaces the conventional map search by address or name by incorporating a chat function of generated AI on a dynamic digital twin map. This allows the search system to provide users with the value of new discoveries and encounters.

[0029] A search system according to an embodiment includes a generation AI, a search condition analysis unit, a suggestion unit, and a dynamic digital twin map. The generation AI analyzes search conditions entered by a user in natural language. For example, if a user enters, "Tell me about a popular restaurant within five minutes of my current location that a family of four can enjoy for half a day," the generation AI analyzes the search conditions and makes appropriate suggestions. The search condition analysis unit suggests optimal places and services based on the search conditions analyzed by the generation AI. For example, the search condition analysis unit suggests nearby popular restaurants and services based on the user's input. The suggestion unit displays the places and services suggested by the search condition analysis unit. For example, the suggestion unit displays information such as the location of the suggested restaurant, surrounding traffic conditions, and congestion level. The dynamic digital twin map displays the places and services suggested by the suggestion unit. For example, the dynamic digital twin map reflects real-world information in real time, allowing the user to intuitively grasp information about their current location and destination. This allows the search system according to an embodiment to provide users with the value of new discoveries and encounters. For example, by suggesting new shops, events, tourist spots, etc. that users were not aware of, it can provide new enjoyment to everyday life.

[0030] The search condition analysis unit learns the user's past search history and behavioral patterns to make more personalized suggestions. For example, the search condition analysis unit uses a generation AI to analyze the user's past search history and learn frequently searched places and preferences. For example, it prioritizes suggestions of cafes that the user has searched for many times in the past. The search condition analysis unit also learns the user's behavioral patterns to suggest places to visit at specific times of the day. For example, it learns that the user visits a park every weekend and suggests parks for weekends. The search condition analysis unit also combines the user's past search history and behavioral patterns to make suggestions tailored to specific events or seasons. For example, it learns that the user has visited the beach in the summer and suggests beaches in the summer. In this way, by learning the user's past search history and behavioral patterns, it is possible to make more personalized suggestions.

[0031] The search condition analysis unit can take into account the user's hobbies and interests and suggest related events and activities. For example, the search condition analysis unit uses a generation AI to learn the user's hobbies and suggest related events. For example, it learns that the user likes music and suggests nearby concerts and live events. The search condition analysis unit also takes into account the user's interests and suggests related activities. For example, it learns that the user likes the outdoors and suggests nearby hiking trails and campsites. The search condition analysis unit also suggests seasonal events and activities based on the user's hobbies and interests. For example, it learns that the user enjoys skiing in the winter and suggests ski resorts in the winter. In this way, it is possible to suggest related events and activities by taking the user's hobbies and interests into account.

[0032] The search condition analysis unit can analyze the content of a user's social media posts and suggest places based on their interests. For example, the search condition analysis unit uses a generation AI to analyze the content of a user's social media posts and suggest places based on their interests. For example, if a user frequently posts photos of cafes, popular nearby cafes will be suggested. The search condition analysis unit also analyzes the content of a user's social media posts and learns that the user is interested in specific events or activities. For example, if a user posts many sporting events, nearby sporting events will be suggested. The search condition analysis unit also analyzes the content of a user's social media posts and suggests places based on seasonal interests. For example, if a user posts many photos of beaches in the summer, beaches will be suggested in the summer. In this way, by analyzing the content of a user's social media posts, it is possible to suggest places based on their interests.

[0033] Dynamic Digital Twin maps reflect environmental data in real time and can suggest optimal routes or locations based on weather or traffic conditions. For example, Dynamic Digital Twin maps reflect weather data in real time and suggest indoor activities on rainy days. For example, they might suggest nearby museums or shopping malls on rainy days. Dynamic Digital Twin maps also reflect traffic conditions in real time and suggest optimal routes to avoid traffic jams. For example, they might suggest detour routes based on traffic congestion information. Dynamic Digital Twin maps also reflect environmental data in real time and suggest less crowded locations. For example, they might suggest quiet parks or cafes to avoid crowded tourist spots. In this way, optimal routes and locations can be suggested by reflecting environmental data in real time.

[0034] A dynamic digital twin map can learn a user's past movement history and suggest more efficient routes. For example, a dynamic digital twin map can learn a user's past movement history and suggest the optimal route to frequently visited places. For example, it can learn the route a user takes to commute daily and suggest the optimal commute route. A dynamic digital twin map can also suggest the optimal route to places visited at a specific time of day based on the user's past movement history. For example, it can suggest the optimal route to places the user visits on the weekend. A dynamic digital twin map can also learn a user's past movement history and suggest the optimal route tailored to a specific event or season. For example, it can learn that the user visits the beach in the summer and suggest the optimal route to the beach in the summer. In this way, by learning a user's past movement history, it can suggest more efficient routes.

[0035] Dynamic Digital Twin maps can seamlessly link different devices, allowing users to access the same information from any device. Dynamic Digital Twin maps, for example, can seamlessly link smartphones and car navigation systems, allowing users to access the same information from any device. For example, a location searched on a smartphone can be automatically transferred to the car navigation system. Dynamic Digital Twin maps also strengthen the link between different devices, allowing users to access the same information from tablets and PCs. For example, a route planned on a PC can be transferred to a smartphone or car navigation system. Dynamic Digital Twin maps also enable real-time linkage between devices, allowing users to access the latest information from any device. For example, information updated on a smartphone is instantly updated in the car navigation system. This seamless linkage between different devices allows users to access the same information from any device.

[0036] Dynamic Digital Twin maps can work with a user's social network to display the current locations of friends and family as well as recommended places. Dynamic Digital Twin maps, for example, can work with a user's social network to display the current locations of friends and family. For example, it can display the current locations of friends on a map. Dynamic Digital Twin maps can also obtain recommended places from the user's social network and display them on the map. For example, it can suggest recommended places based on places that friends have visited and reviews. Dynamic Digital Twin maps can also work with social networks to suggest places to visit with friends and family. For example, it can suggest places to visit together if a friend is nearby. In this way, by working with a user's social network, it can display the current locations of friends and family as well as recommended places.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The search system can also obtain the user's health data and make suggestions based on their health condition. For example, it can obtain heart rate and step count data from the user's smartwatch and suggest nearby gyms and walking courses to users who are not getting enough exercise. It can also analyze the user's sleep data and suggest places where users can relax if they are sleep-deprived. It can also obtain the user's dietary data and suggest healthy restaurants if their nutritional balance is unbalanced. This allows for more personalized service by making suggestions based on the user's health condition.

[0039] The search system can also learn the user's travel history and make new suggestions based on places visited in the past. For example, it can learn which cities or countries the user has visited in the past and suggest new tourist spots in the same area. It can also learn which events or activities the user has participated in in the past and suggest similar events. It can also suggest recommended travel destinations for each season based on the user's travel history. This makes it possible to provide a more fulfilling travel experience by making new suggestions based on the user's travel history.

[0040] The search system can also analyze a user's purchasing history and suggest related products and services. For example, it can learn about products the user has purchased in the past and suggest related products. It can also prioritize suggestions for products from specific brands or categories based on the user's purchasing history. It can also analyze a user's purchasing history and suggest recommended products for each season. This makes it possible to provide a more personalized shopping experience by making suggestions based on the user's purchasing history.

[0041] The search system can also collect user feedback to improve the accuracy of its suggestions. For example, users can rate suggested places and services, and the system can use that rating to improve future suggestions. It can also analyze user feedback to learn how much a particular suggestion increased user satisfaction. Furthermore, it can develop new suggestion algorithms based on user feedback to improve the accuracy of its suggestions. This allows the system to make suggestions that are more satisfying by utilizing user feedback.

[0042] The search system can also obtain the user's location information in real time and make suggestions based on the user's current location. For example, it can suggest nearby restaurants and tourist attractions based on the user's current location. It can also suggest events and activities being held in a specific area based on the user's location information. It can also obtain the user's location information in real time and suggest optimal routes and rest spots for users who are on the move. This makes it possible to provide a more convenient service by making suggestions based on the user's location information.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The search condition analysis unit analyzes the search conditions entered by the user in natural language. For example, if a user enters, "Tell me about a popular restaurant within five minutes of my current location where a family of four can spend half a day," the search condition analysis unit analyzes the content and makes appropriate suggestions. Step 2: The suggestion unit suggests optimal places and services based on the search conditions analyzed by the search condition analysis unit. For example, the search condition analysis unit suggests nearby popular shops and services based on the user's input. Step 3: The Dynamic Digital Twin map displays the places and services suggested by the suggestion module. For example, it displays information such as the location of the suggested store, the surrounding traffic conditions, and the level of congestion. The Dynamic Digital Twin map reflects real-world information in real time, allowing users to intuitively grasp information about their current location and destination.

[0045] (Example 2) The search system according to the embodiment of the present invention is a system that provides a new search method that replaces the conventional map search by address or name by incorporating a chat function of generated AI on a dynamic digital twin map. This allows the search system to provide users with the value of new discoveries and encounters.

[0046] A search system according to an embodiment includes a generation AI, a search condition analysis unit, a suggestion unit, and a dynamic digital twin map. The generation AI analyzes search conditions entered by a user in natural language. For example, if a user enters, "Tell me about a popular restaurant within five minutes of my current location that a family of four can enjoy for half a day," the generation AI analyzes the search conditions and makes appropriate suggestions. The search condition analysis unit suggests optimal places and services based on the search conditions analyzed by the generation AI. For example, the search condition analysis unit suggests nearby popular restaurants and services based on the user's input. The suggestion unit displays the places and services suggested by the search condition analysis unit. For example, the suggestion unit displays information such as the location of the suggested restaurant, surrounding traffic conditions, and congestion level. The dynamic digital twin map displays the places and services suggested by the suggestion unit. For example, the dynamic digital twin map reflects real-world information in real time, allowing the user to intuitively grasp information about their current location and destination. This allows the search system according to an embodiment to provide users with the value of new discoveries and encounters. For example, by suggesting new shops, events, tourist spots, etc. that users were not aware of, it can provide new enjoyment to everyday life.

[0047] The search condition analysis unit can estimate a user's emotions and provide optimal search results based on those emotions. For example, the search condition analysis unit uses a generation AI to analyze emotions from user input and suggest relaxing places for stressed users. For example, if a user inputs "I'm tired," the search condition analysis unit suggests nearby spas and cafes. Furthermore, if a user inputs an indication that they are in a fun mood, the search condition analysis unit suggests activities and events based on that emotion. For example, if a user inputs "Tell me some fun places," the search condition analysis unit suggests nearby amusement parks and events. Furthermore, if a user inputs an indication that they are sad, the search condition analysis unit suggests comforting places based on that emotion. For example, if a user inputs "I'm feeling depressed," the search condition analysis unit suggests quiet parks and art museums. This improves user convenience by providing optimal search results based on the user's emotions.

[0048] The search condition analysis unit learns the user's past search history and behavioral patterns to make more personalized suggestions. For example, the search condition analysis unit uses a generation AI to analyze the user's past search history and learn frequently searched places and preferences. For example, it prioritizes suggestions of cafes that the user has searched for many times in the past. The search condition analysis unit also learns the user's behavioral patterns to suggest places to visit at specific times of the day. For example, it learns that the user visits a park every weekend and suggests parks for weekends. The search condition analysis unit also combines the user's past search history and behavioral patterns to make suggestions tailored to specific events or seasons. For example, it learns that the user has visited the beach in the summer and suggests beaches in the summer. In this way, by learning the user's past search history and behavioral patterns, it is possible to make more personalized suggestions.

[0049] The search condition analysis unit can analyze the user's voice tone or facial expression to provide search results that correspond to their emotional state. For example, the generation AI analyzes the user's voice tone and suggests active activities if the user is excited. For example, if the user says, "Tell me some fun places" in an excited voice, the search condition analysis unit suggests nearby amusement parks or sports facilities. The search condition analysis unit also analyzes the user's facial expression and suggests fun places if the user is smiling. For example, if the user says, "Is there anywhere fun?" with a smile, the search condition analysis unit suggests nearby events or festivals. The search condition analysis unit also analyzes the user's voice tone and facial expression in combination to provide search results that correspond to their emotional state. For example, if the user says, "Tell me some places that will cheer me up" in a depressed voice and sad expression, the search condition analysis unit suggests quiet parks or art museums. In this way, by analyzing the user's voice tone and facial expression, search results that correspond to their emotional state can be provided.

[0050] The search condition analysis unit can take into account the user's hobbies and interests and suggest related events and activities. For example, the search condition analysis unit uses a generation AI to learn the user's hobbies and suggest related events. For example, it learns that the user likes music and suggests nearby concerts and live events. The search condition analysis unit also takes into account the user's interests and suggests related activities. For example, it learns that the user likes the outdoors and suggests nearby hiking trails and campsites. The search condition analysis unit also suggests seasonal events and activities based on the user's hobbies and interests. For example, it learns that the user enjoys skiing in the winter and suggests ski resorts in the winter. In this way, it is possible to suggest related events and activities by taking the user's hobbies and interests into account.

[0051] The search condition analysis unit can analyze the content of a user's social media posts and suggest places based on their interests. For example, the search condition analysis unit uses a generation AI to analyze the content of a user's social media posts and suggest places based on their interests. For example, if a user frequently posts photos of cafes, popular nearby cafes will be suggested. The search condition analysis unit also analyzes the content of a user's social media posts and learns that the user is interested in specific events or activities. For example, if a user posts many sporting events, nearby sporting events will be suggested. The search condition analysis unit also analyzes the content of a user's social media posts and suggests places based on seasonal interests. For example, if a user posts many photos of beaches in the summer, beaches will be suggested in the summer. In this way, by analyzing the content of a user's social media posts, it is possible to suggest places based on their interests.

[0052] The search condition analysis unit can estimate a user's emotions in real time and provide search results that correspond to their emotions. For example, the search condition analysis unit uses a generative AI to estimate emotions in real time from user input and provide search results that correspond to their emotions. For example, if a user inputs "I'm tired," the system will suggest nearby spas and cafes. The search condition analysis unit also analyzes the user's voice tone and facial expression in real time to provide search results that correspond to their emotions. For example, if a user says "Tell me some fun places" in an excited voice, the system will suggest nearby amusement parks and sports facilities. The search condition analysis unit also builds a system that estimates a user's emotions in real time and provides search results that correspond to their emotions. For example, if a user says "Tell me some places that will cheer me up" in a depressed voice and with a sad expression, the system will suggest quiet parks and art museums. This allows the system to estimate a user's emotions in real time and provide search results that correspond to their emotions.

[0053] Dynamic Digital Twin maps can estimate a user's emotions and customize the information on the map based on their emotions. For example, a Dynamic Digital Twin map can estimate a user's emotions and highlight rest spots for a tired user. For example, if a user inputs "tired," nearby cafes and parks will be highlighted. A Dynamic Digital Twin map can also estimate a user's emotions and highlight activities and events if the user is in the mood for fun. For example, if a user inputs "Tell me some fun places," nearby amusement parks and events will be highlighted. A Dynamic Digital Twin map can also estimate a user's emotions and highlight comforting places if the user is in a sad mood. For example, if a user inputs "I'm feeling depressed," quiet parks and art museums will be highlighted. This improves user convenience by customizing the information on the map based on the user's emotions.

[0054] Dynamic Digital Twin maps reflect environmental data in real time and can suggest optimal routes or locations based on weather or traffic conditions. For example, Dynamic Digital Twin maps reflect weather data in real time and suggest indoor activities on rainy days. For example, they might suggest nearby museums or shopping malls on rainy days. Dynamic Digital Twin maps also reflect traffic conditions in real time and suggest optimal routes to avoid traffic jams. For example, they might suggest detour routes based on traffic congestion information. Dynamic Digital Twin maps also reflect environmental data in real time and suggest less crowded locations. For example, they might suggest quiet parks or cafes to avoid crowded tourist spots. In this way, optimal routes and locations can be suggested by reflecting environmental data in real time.

[0055] A dynamic digital twin map can learn a user's past movement history and suggest more efficient routes. For example, a dynamic digital twin map can learn a user's past movement history and suggest the optimal route to frequently visited places. For example, it can learn the route a user takes to commute daily and suggest the optimal commute route. A dynamic digital twin map can also suggest the optimal route to places visited at a specific time of day based on the user's past movement history. For example, it can suggest the optimal route to places the user visits on the weekend. A dynamic digital twin map can also learn a user's past movement history and suggest the optimal route tailored to a specific event or season. For example, it can learn that the user visits the beach in the summer and suggest the optimal route to the beach in the summer. In this way, by learning a user's past movement history, it can suggest more efficient routes.

[0056] Dynamic Digital Twin maps can seamlessly link different devices, allowing users to access the same information from any device. Dynamic Digital Twin maps, for example, can seamlessly link smartphones and car navigation systems, allowing users to access the same information from any device. For example, a location searched on a smartphone can be automatically transferred to the car navigation system. Dynamic Digital Twin maps also strengthen the link between different devices, allowing users to access the same information from tablets and PCs. For example, a route planned on a PC can be transferred to a smartphone or car navigation system. Dynamic Digital Twin maps also enable real-time linkage between devices, allowing users to access the latest information from any device. For example, information updated on a smartphone is instantly updated in the car navigation system. This seamless linkage between different devices allows users to access the same information from any device.

[0057] Dynamic Digital Twin maps can work with a user's social network to display the current locations of friends and family as well as recommended places. Dynamic Digital Twin maps, for example, can work with a user's social network to display the current locations of friends and family. For example, it can display the current locations of friends on a map. Dynamic Digital Twin maps can also obtain recommended places from the user's social network and display them on the map. For example, it can suggest recommended places based on places that friends have visited and reviews. Dynamic Digital Twin maps can also work with social networks to suggest places to visit with friends and family. For example, it can suggest places to visit together if a friend is nearby. In this way, by working with a user's social network, it can display the current locations of friends and family as well as recommended places.

[0058] Dynamic Digital Twin maps can estimate a user's emotions in real time and provide map information tailored to their emotions. For example, if a user inputs "I'm tired," nearby cafes and parks are highlighted. Dynamic Digital Twin maps also analyze the user's tone of voice and facial expressions in real time to provide map information tailored to their emotions. For example, if a user says "Tell me some fun places" in an excited voice, nearby amusement parks and sports facilities are highlighted. Dynamic Digital Twin maps also build a system that estimates a user's emotions in real time and provides map information tailored to their emotions. For example, if a user says "Tell me some places that will cheer me up" in a depressed voice and with a sad expression, quiet parks and art museums are highlighted. This makes it possible to provide map information tailored to a user's emotions by estimating their emotions in real time.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The search system can also obtain the user's health data and make suggestions based on their health condition. For example, it can obtain heart rate and step count data from the user's smartwatch and suggest nearby gyms and walking courses to users who are not getting enough exercise. It can also analyze the user's sleep data and suggest places where users can relax if they are sleep-deprived. It can also obtain the user's dietary data and suggest healthy restaurants if their nutritional balance is unbalanced. This allows for more personalized service by making suggestions based on the user's health condition.

[0061] The search system can also learn the user's travel history and make new suggestions based on places visited in the past. For example, it can learn which cities or countries the user has visited in the past and suggest new tourist spots in the same area. It can also learn which events or activities the user has participated in in the past and suggest similar events. It can also suggest recommended travel destinations for each season based on the user's travel history. This makes it possible to provide a more fulfilling travel experience by making new suggestions based on the user's travel history.

[0062] The search system can also analyze a user's purchasing history and suggest related products and services. For example, it can learn about products the user has purchased in the past and suggest related products. It can also prioritize suggestions for products from specific brands or categories based on the user's purchasing history. It can also analyze a user's purchasing history and suggest recommended products for each season. This makes it possible to provide a more personalized shopping experience by making suggestions based on the user's purchasing history.

[0063] The search system can also collect user feedback to improve the accuracy of its suggestions. For example, users can rate suggested places and services, and the system can use that rating to improve future suggestions. It can also analyze user feedback to learn how much a particular suggestion increased user satisfaction. Furthermore, it can develop new suggestion algorithms based on user feedback to improve the accuracy of its suggestions. This allows the system to make suggestions that are more satisfying by utilizing user feedback.

[0064] The search system can also obtain the user's location information in real time and make suggestions based on the user's current location. For example, it can suggest nearby restaurants and tourist attractions based on the user's current location. It can also suggest events and activities being held in a specific area based on the user's location information. It can also obtain the user's location information in real time and suggest optimal routes and rest spots for users who are on the move. This makes it possible to provide a more convenient service by making suggestions based on the user's location information.

[0065] The search system can also estimate the user's emotions and suggest music and video content based on the estimated emotions. For example, if the user feels like relaxing, relaxing music and videos can be suggested. If the user feels like cheering up, uplifting music and videos can be suggested. Furthermore, if the user feels like concentrating, music and videos that will help improve concentration can be suggested. In this way, by suggesting music and video content based on the user's emotions, a more fulfilling entertainment experience can be provided.

[0066] The search system can also estimate the user's emotions and suggest meals based on the estimated emotions. For example, if the user is feeling stressed, the search system can suggest meals that will help them relax. If the user is feeling energized, the search system can suggest meals that will replenish their energy. Furthermore, if the user is feeling depressed, the search system can suggest meals that will lift their spirits. In this way, by suggesting meals based on the user's emotions, the search system can provide a healthier and more satisfying dining experience.

[0067] The search system can also estimate the user's emotions and suggest exercise or relaxation activities based on the estimated emotions. For example, if the user is feeling stressed, the system can suggest yoga or meditation for relaxation. If the user feels like releasing energy, the system can suggest running or gym workouts. If the user feels like refreshing themselves, the system can suggest hiking or walking in nature. In this way, by suggesting exercise and relaxation activities based on the user's emotions, the system can provide a healthier and more fulfilling lifestyle.

[0068] The search system can also estimate the user's emotions and suggest reading and learning activities based on the estimated emotions. For example, if the user feels like relaxing, it can suggest books and learning content that will help them relax. If the user feels like concentrating, it can suggest books and learning content that will help them concentrate. Furthermore, if the user feels like gaining new knowledge, it can suggest interesting books and learning content. In this way, by suggesting reading and learning activities based on the user's emotions, it is possible to provide a more fulfilling knowledge acquisition experience.

[0069] The search system can also estimate the user's emotions and suggest travel destinations based on the estimated emotions. For example, if the user feels like relaxing, it can suggest resorts and hot springs. If the user feels like adventuring, it can suggest travel destinations where active activities can be enjoyed. Furthermore, if the user feels like enjoying culture, it can suggest travel destinations with historical tourist sites and art museums. In this way, by suggesting travel destinations based on the user's emotions, it is possible to provide a more satisfying travel experience.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The search condition analysis unit analyzes the search conditions entered by the user in natural language. For example, if a user enters, "Tell me about a popular restaurant within five minutes of my current location where a family of four can spend half a day," the search condition analysis unit analyzes the content and makes appropriate suggestions. Step 2: The suggestion unit suggests optimal places and services based on the search conditions analyzed by the search condition analysis unit. For example, the search condition analysis unit suggests nearby popular shops and services based on the user's input. Step 3: The Dynamic Digital Twin map displays the places and services suggested by the suggestion module. For example, it displays information such as the location of the suggested store, the surrounding traffic conditions, and the level of congestion. The Dynamic Digital Twin map reflects real-world information in real time, allowing users to intuitively grasp information about their current location and destination.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0085] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0092] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0093] The 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.

[0094] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0095] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0096] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0097] Fig. 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.

[0098] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0100] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0101] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0102] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0103] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] The data processing system 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 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.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The 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.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system equipped with a generative AI, a search condition analysis unit that analyzes search conditions entered by a user in natural language; a suggestion unit that suggests an optimal place or service based on the search conditions analyzed by the search condition analysis unit; a Dynamic Digital Twin map that displays the places or services suggested by the suggestion unit. A system characterized by:

2. The search condition analysis unit Inferring the user's emotions and providing optimal search results based on the emotions 2. The system of claim 1.

3. The search condition analysis unit Considering the user's hobbies and interests, suggesting relevant events and activities 2. The system of claim 1.

4. The Digital Twin map is Reflects real-time environmental data and suggests optimal routes or locations based on weather or traffic conditions 2. The system of claim 1.

5. The Digital Twin map is Inferring the user's emotion and customizing the information on the map based on the emotion.

2. The system of claim 1.

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