System

Abstract

An object of a system according to an embodiment is to efficiently label location information big data.SOLUTION: A system includes a position information collecting part, a generation AI inputting part, an analyzing part, and a labeling part. The position information collection unit collects position information. The generated AI inputting unit inputs the position information collected by the position information collecting unit to the generated AI. The analyzing unit analyzes the position information inputted by the generated AI inputting unit. The labeling unit attaches a label based on the position information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional technology, labeling of location information big data is done manually, which is inefficient and leaves room for improvement.

[0005] The system according to the embodiment aims to efficiently label location information big data. [Means for solving the problem]

[0006] The system according to the embodiment includes a location information collection unit, a generation AI input unit, an analysis unit, and a labeling unit. The location information collection unit collects location information. The generation AI input unit inputs the location information collected by the location information collection unit to the generation AI. The analysis unit analyzes the location information input by the generation AI input unit. The labeling unit attaches a label based on the location information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently label location information big data. [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) A location information labeling system according to an embodiment of the present invention is a system for efficiently labeling location information big data. This system uses a generation AI to analyze location information and automatically recognize and label users' locations and activities. As a result, the location information labeling system efficiently labels location information big data, which can be used for personalized service provision, effective marketing, urban planning, trend analysis, and more.

[0029] A location information labeling system according to an embodiment includes a location information collection unit, a generation AI input unit, an analysis unit, and a labeling unit. The location information collection unit collects location information. For example, it collects GPS data. The location information collection unit can also collect Wi-Fi location information. The location information collection unit can also collect beacon data. The generation AI input unit inputs the location information collected by the location information collection unit to the generation AI. For example, it inputs location information data to the generation AI. The generation AI input unit can also input the location information data and an analysis prompt to the generation AI. The generation AI input unit can also convert the format of the location information in advance and input it to the generation AI. The analysis unit analyzes the location information input by the generation AI input unit. For example, the generation AI analyzes the location information using a text generation AI (e.g., LLM). The generation AI can also analyze the location information using a multimodal generation AI. The generation AI can also analyze the location information using a movement pattern analysis technique. The labeling unit assigns a label based on the location information analyzed by the analysis unit. For example, if a specific coordinate is located within a shopping mall, the generation AI will label it as "stayed at the shopping mall." The generation AI can also label it as "walked" or "traveled by car" based on the user's movement speed and movement pattern. The generation AI can also recognize the user's location and activity and label it based on that. This enables the location information labeling system according to the embodiment to efficiently label location information big data. For example, personalized services can be provided to users based on the labeled location information data. Furthermore, the labeled location information data can be utilized to develop effective marketing strategies. Furthermore, the labeled location information data can be used for urban planning and trend analysis.

[0030] The location information collection unit can collect the user's biometric information in addition to location data and input it to the generation AI input unit. The location information collection unit collects biometric information such as the user's heart rate and body temperature along with location data and inputs it to the generation AI. For example, it uses data obtained from a smartwatch or fitness tracker. The location information collection unit also combines the location data with biometric information to estimate the user's health condition and stress level. For example, a high heart rate is labeled as "exercising." The location information collection unit also labels the user's activity in more detail based on the biometric information. For example, a rising body temperature is recognized as "resting after exercise." This makes it possible to estimate the user's health condition and stress level and reflect them in the labeling.

[0031] The location information collection unit can collect user voice data in addition to location data and input it to the generation AI input unit. The location information collection unit, for example, collects user voice data along with location data and inputs it to the generation AI. For example, it acquires voice data using a smartphone microphone. The location information collection unit also analyzes the voice data and recognizes the content of the user's conversation and the surrounding sound environment. For example, if the sounds of a cafe can be heard, it will label it as "staying at the cafe." The location information collection unit also labels the user's activities in detail based on the voice data. For example, it will recognize the content of the conversation as "in a business meeting." This makes it possible to analyze the content of the user's conversation and the surrounding sound environment and use them for labeling.

[0032] The location information collection unit can collect location information data over a wide area using a drone or an autonomous vehicle and input it to the generation AI input unit. The location information collection unit, for example, uses a drone to collect location information data over a wide area and inputs it to the generation AI. For example, it can grasp the traffic situation throughout the city in real time. The location information collection unit can also use an autonomous vehicle to collect detailed location information data for a specific area. For example, it can grasp the availability of parking spaces throughout the city. The location information collection unit can also input location information data collected from a drone or an autonomous vehicle into the generation AI to efficiently analyze a wide area of ​​data. For example, it can analyze people flow patterns throughout the city. This makes it possible to efficiently collect and analyze a wide area of ​​data.

[0033] The location information collection unit can collect user SNS posts or photo data in addition to location information data and input it to the generation AI input unit. The location information collection unit, for example, collects user SNS posts and photo data along with location information data and inputs it to the generation AI. For example, it analyzes Instagram and Twitter posts. The location information collection unit also performs a detailed analysis of the user's activities and interests based on the SNS posts and photo data. For example, it may label the post as "sightseeing" based on the content of the post. The location information collection unit also uses SNS data to analyze the user's interests and preferences and reflect them in the location information data. For example, it may recognize the user as "staying at a museum" based on the content of a photo. This makes it possible to analyze the user's activities and interests in more detail.

[0034] The analysis unit can perform more accurate labeling by referring to the user's past behavioral history and preference data. For example, when the generation AI analyzes location information, the analysis unit refers to the user's past behavioral history. For example, it may label the user as a "regular customer" based on data on places visited in the past. The analysis unit also labels location information based on the user's preference data. For example, if the analysis unit recognizes a user as a "cafe lover" based on past preference data and the user is staying at a cafe, it may label the user as a "favorite cafe." The analysis unit also combines past behavioral history and preference data to perform more accurate labeling. For example, it may analyze a pattern of visiting specific places at specific times of the day and label the user as "on the way to work." This enables more accurate labeling based on the user's past behavioral history and preference data.

[0035] The analysis unit can refer to weather data and traffic information to recognize user activities in more detail and reflect this in labeling. For example, the analysis unit refers to weather data when the generation AI analyzes location information. For example, if the user is staying in a specific location on a rainy day, it will label the user as "taking shelter from the rain." The analysis unit also analyzes the user's movement patterns based on traffic information and reflects this in labeling. For example, if the user is in a congested road, it will recognize the user as "in a traffic jam." The analysis unit also combines weather data and traffic information to label the user's activities in more detail. For example, if the user is in a park on a sunny day, it will label the user as "taking a walk." This makes it possible to label user activities in more detail based on weather data and traffic information.

[0036] The analysis unit can combine different generative AI models for analysis and perform labeling from multiple perspectives. The analysis unit, for example, combines different generative AI models to analyze location information data. For example, a natural language processing model and an image recognition model are used together. The analysis unit also uses multiple generative AI models to analyze location information data from multiple angles and perform labeling. For example, a voice recognition model and a behavior analysis model are combined. The analysis unit also integrates the analysis results of different generative AI models to perform more accurate labeling. For example, the analysis results of each model are weighted to determine the final label. This makes it possible to perform labeling from multiple perspectives.

[0037] The analysis unit can share data between the user's devices and integrate and analyze information from multiple devices. For example, the analysis unit integrates location data collected from the user's smartphone and wearable devices and inputs it into the generation AI. For example, it links data from a smartwatch with a smartphone. The analysis unit also integrates location data from multiple devices, which the generation AI analyzes. For example, it combines GPS data from a smartphone with data from an in-car navigation system. The analysis unit also shares data between devices, building a system in which the generation AI performs integrated analysis. For example, it centrally manages data from smart devices in the home. This makes it possible to integrate and analyze information from multiple devices.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The location information labeling system can also collect a user's purchasing history and input it into the generation AI input section. For example, data on products a user has purchased in the past can be collected and input into the generation AI. In addition, the user's interests and preferences can be analyzed based on the purchasing history and reflected in the labeling. For example, if a user has a history of frequent purchases at a particular store, the user can be labeled as a "regular customer." In addition, the user's behavioral patterns can be analyzed based on the purchasing history and used for labeling. For example, if a user makes a lot of purchases at a particular store during a particular time period, the user can be labeled as "shopping on the way to work." This allows for more detailed labeling based on the user's purchasing history.

[0040] The location labeling system can also collect the user's calendar data and input it into the generation AI input section. For example, it can collect schedule data from the calendar app on the user's smartphone and input it into the generation AI. It can also analyze the user's schedules and activities based on the calendar data and reflect this in labeling. For example, if the calendar says "meeting," it can label it as "in a meeting." It can also analyze the user's behavioral patterns based on the calendar data and use this for labeling. For example, if there are many appointments at a specific location during a specific time period, it can label it as "regular meeting." This enables more detailed labeling based on the user's calendar data.

[0041] The location labeling system can also collect data from the user's health app and input it into the generation AI input section. For example, step count and sleep data can be collected from the health app on the user's smartphone and input into the generation AI. Furthermore, based on the health app data, the system can analyze the user's health status and activities and reflect this in labeling. For example, a high step count can be labeled as "walking." Furthermore, based on the health app data, the system can analyze the user's behavioral patterns and use this for labeling. For example, a high step count during a specific time period can be labeled as "commuting." This allows for more detailed labeling based on the user's health app data.

[0042] The location information labeling system can also collect a user's music playback history and input it into the generation AI input section. For example, playback history can be collected from a user's music streaming service and input into the generation AI. In addition, based on the music playback history, the user's interests and preferences can be analyzed and reflected in the labeling. For example, if a user plays a lot of music of a particular genre, the user can be labeled as a "music lover." In addition, based on the music playback history, the user's behavioral patterns can be analyzed and used for labeling. For example, if a specific genre of music is played at a specific time of day, the user can be labeled as a "relaxation time." This allows for more detailed labeling based on the user's music playback history.

[0043] The location information labeling system can also collect a user's reading history and input it into the generation AI input section. For example, reading history can be collected from a user's e-book reader and input into the generation AI. In addition, based on the reading history, the user's interests and preferences can be analyzed and reflected in the labeling. For example, if a user reads many books of a specific genre, the user can be labeled as a "book lover." In addition, based on the reading history, the user's behavioral patterns can be analyzed and used in labeling. For example, if a user reads during a specific time period, the user can be labeled as "reading time." This allows for more detailed labeling based on the user's reading history.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The location information collection unit collects location information, such as GPS data, Wi-Fi location information, and beacon data. Step 2: The generation AI input unit inputs the location information collected by the location information collection unit to the generation AI. For example, the location information data can be input to the generation AI and an analysis prompt can be input to the generation AI. Alternatively, the location information data can be converted into a different format in advance and then input to the generation AI. Step 3: The analysis unit analyzes the location information input by the generation AI input unit. For example, the analysis unit uses text generation AI (e.g., LLM), multimodal generation AI, or movement pattern analysis technology. Step 4: The labeling unit assigns labels based on the location information analyzed by the analysis unit. For example, if a specific coordinate is within a shopping mall, it can be labeled as "stayed at the shopping mall." Based on the speed and movement pattern, it can also be labeled as "walked" or "traveled by car." It can also recognize the user's location and activity and assign labels based on that.

[0046] (Example 2) A location information labeling system according to an embodiment of the present invention is a system for efficiently labeling location information big data. This system uses a generation AI to analyze location information and automatically recognize and label users' locations and activities. As a result, the location information labeling system efficiently labels location information big data, which can be used for personalized service provision, effective marketing, urban planning, trend analysis, and more.

[0047] A location information labeling system according to an embodiment includes a location information collection unit, a generation AI input unit, an analysis unit, and a labeling unit. The location information collection unit collects location information. For example, it collects GPS data. The location information collection unit can also collect Wi-Fi location information. The location information collection unit can also collect beacon data. The generation AI input unit inputs the location information collected by the location information collection unit to the generation AI. For example, it inputs location information data to the generation AI. The generation AI input unit can also input the location information data and an analysis prompt to the generation AI. The generation AI input unit can also convert the format of the location information in advance and input it to the generation AI. The analysis unit analyzes the location information input by the generation AI input unit. For example, the generation AI analyzes the location information using a text generation AI (e.g., LLM). The generation AI can also analyze the location information using a multimodal generation AI. The generation AI can also analyze the location information using a movement pattern analysis technique. The labeling unit assigns a label based on the location information analyzed by the analysis unit. For example, if a specific coordinate is located within a shopping mall, the generation AI will label it as "stayed at the shopping mall." The generation AI can also label it as "walked" or "traveled by car" based on the user's movement speed and movement pattern. The generation AI can also recognize the user's location and activity and label it based on that. This enables the location information labeling system according to the embodiment to efficiently label location information big data. For example, personalized services can be provided to users based on the labeled location information data. Furthermore, the labeled location information data can be utilized to develop effective marketing strategies. Furthermore, the labeled location information data can be used for urban planning and trend analysis.

[0048] The location information collection unit can collect the user's biometric information in addition to location data and input it to the generation AI input unit. The location information collection unit collects biometric information such as the user's heart rate and body temperature along with location data and inputs it to the generation AI. For example, it uses data obtained from a smartwatch or fitness tracker. The location information collection unit also combines the location data with biometric information to estimate the user's health condition and stress level. For example, a high heart rate is labeled as "exercising." The location information collection unit also labels the user's activity in more detail based on the biometric information. For example, a rising body temperature is recognized as "resting after exercise." This makes it possible to estimate the user's health condition and stress level and reflect them in the labeling.

[0049] The location information collection unit can collect user voice data in addition to location data and input it to the generation AI input unit. The location information collection unit, for example, collects user voice data along with location data and inputs it to the generation AI. For example, it acquires voice data using a smartphone microphone. The location information collection unit also analyzes the voice data and recognizes the content of the user's conversation and the surrounding sound environment. For example, if the sounds of a cafe can be heard, it will label it as "staying at the cafe." The location information collection unit also labels the user's activities in detail based on the voice data. For example, it will recognize the content of the conversation as "in a business meeting." This makes it possible to analyze the content of the user's conversation and the surrounding sound environment and use them for labeling.

[0050] The location information collection unit can collect data for estimating the user's emotional state in addition to location information data and input it to the generation AI input unit. The location information collection unit, for example, collects data for estimating the user's emotional state along with location information data and inputs it to the generation AI. For example, it analyzes facial expressions and voice tone. The location information collection unit also labels the user's activities based on emotion based on the emotion estimation data. For example, if there are many smiles, it labels the user as "having fun." The location information collection unit also uses an emotion estimation function to analyze the user's emotional state in real time and perform emotion-based labeling. For example, if an angry expression is observed, it recognizes the user as "feeling stressed." This makes it possible to estimate the user's emotional state and perform emotion-based labeling.

[0051] The location information collection unit can collect location information data over a wide area using a drone or an autonomous vehicle and input it to the generation AI input unit. The location information collection unit, for example, uses a drone to collect location information data over a wide area and inputs it to the generation AI. For example, it can grasp the traffic situation throughout the city in real time. The location information collection unit can also use an autonomous vehicle to collect detailed location information data for a specific area. For example, it can grasp the availability of parking spaces throughout the city. The location information collection unit can also input location information data collected from a drone or an autonomous vehicle into the generation AI to efficiently analyze a wide area of ​​data. For example, it can analyze people flow patterns throughout the city. This makes it possible to efficiently collect and analyze a wide area of ​​data.

[0052] The location information collection unit can collect user SNS posts or photo data in addition to location information data and input it to the generation AI input unit. The location information collection unit, for example, collects user SNS posts and photo data along with location information data and inputs it to the generation AI. For example, it analyzes Instagram and Twitter posts. The location information collection unit also performs a detailed analysis of the user's activities and interests based on the SNS posts and photo data. For example, it may label the post as "sightseeing" based on the content of the post. The location information collection unit also uses SNS data to analyze the user's interests and preferences and reflect them in the location information data. For example, it may recognize the user as "staying at a museum" based on the content of a photo. This makes it possible to analyze the user's activities and interests in more detail.

[0053] The location information collection unit can use the emotion estimation function to estimate the user's emotions in real time when collecting location information data and input the data to the generation AI input unit. The location information collection unit, for example, collects data for estimating the user's emotional state in real time along with location information data and inputs the data to the generation AI. For example, it analyzes facial expressions using a smartphone camera. The location information collection unit also provides emotion-based feedback on the user's activities based on the emotion estimation data. For example, if the user is feeling stressed, it can suggest places where the user can relax. The location information collection unit also uses the emotion estimation function to analyze the user's emotional state in real time and provide emotion-based services. For example, if the user is having fun, it can provide information about nearby events. This makes it possible to provide feedback based on the user's emotions.

[0054] The analysis unit can perform more accurate labeling by referring to the user's past behavioral history and preference data. For example, when the generation AI analyzes location information, the analysis unit refers to the user's past behavioral history. For example, it may label the user as a "regular customer" based on data on places visited in the past. The analysis unit also labels location information based on the user's preference data. For example, if the analysis unit recognizes a user as a "cafe lover" based on past preference data and the user is staying at a cafe, it may label the user as a "favorite cafe." The analysis unit also combines past behavioral history and preference data to perform more accurate labeling. For example, it may analyze a pattern of visiting specific places at specific times of the day and label the user as "on the way to work." This enables more accurate labeling based on the user's past behavioral history and preference data.

[0055] The analysis unit can refer to weather data and traffic information to recognize user activities in more detail and reflect this in labeling. For example, the analysis unit refers to weather data when the generation AI analyzes location information. For example, if the user is staying in a specific location on a rainy day, it will label the user as "taking shelter from the rain." The analysis unit also analyzes the user's movement patterns based on traffic information and reflects this in labeling. For example, if the user is in a congested road, it will recognize the user as "in a traffic jam." The analysis unit also combines weather data and traffic information to label the user's activities in more detail. For example, if the user is in a park on a sunny day, it will label the user as "taking a walk." This makes it possible to label user activities in more detail based on weather data and traffic information.

[0056] The analysis unit can use the emotion estimation function to consider the user's emotional state and perform emotion-based labeling. For example, when the generation AI analyzes location information, the analysis unit references data for estimating the user's emotional state. For example, it analyzes facial expressions and vocal tone. The analysis unit also labels the user's activities based on emotion based on the emotion estimation data. For example, if there are many smiles, it will label the activity as "having fun." The analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and perform emotion-based labeling. For example, if an angry expression is seen, it will recognize the activity as "feeling stressed." This makes it possible to consider the user's emotional state and perform emotion-based labeling.

[0057] The analysis unit can combine different generative AI models for analysis and perform labeling from multiple perspectives. The analysis unit, for example, combines different generative AI models to analyze location information data. For example, a natural language processing model and an image recognition model are used together. The analysis unit also uses multiple generative AI models to analyze location information data from multiple angles and perform labeling. For example, a voice recognition model and a behavior analysis model are combined. The analysis unit also integrates the analysis results of different generative AI models to perform more accurate labeling. For example, the analysis results of each model are weighted to determine the final label. This makes it possible to perform labeling from multiple perspectives.

[0058] The analysis unit can share data between the user's devices and integrate and analyze information from multiple devices. For example, the analysis unit integrates location data collected from the user's smartphone and wearable devices and inputs it into the generation AI. For example, it links data from a smartwatch with a smartphone. The analysis unit also integrates location data from multiple devices, which the generation AI analyzes. For example, it combines GPS data from a smartphone with data from an in-car navigation system. The analysis unit also shares data between devices, building a system in which the generation AI performs integrated analysis. For example, it centrally manages data from smart devices in the home. This makes it possible to integrate and analyze information from multiple devices.

[0059] The analysis unit can use the emotion estimation function to monitor the user's emotions in real time and label them based on their emotions. For example, the analysis unit monitors the user's emotional state in real time when the generation AI analyzes location information. For example, it analyzes facial expressions using a smartphone camera. The analysis unit also labels the user's activities based on their emotions based on the emotion estimation data. For example, if the user is feeling stressed, it labels them as "relaxed." The analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and provide emotion-based services. For example, if the user is having fun, it provides information about nearby events. This makes it possible to monitor the user's emotions in real time and label them based on their emotions.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The location information labeling system can also collect a user's purchasing history and input it into the generation AI input section. For example, data on products a user has purchased in the past can be collected and input into the generation AI. In addition, the user's interests and preferences can be analyzed based on the purchasing history and reflected in the labeling. For example, if a user has a history of frequent purchases at a particular store, the user can be labeled as a "regular customer." In addition, the user's behavioral patterns can be analyzed based on the purchasing history and used for labeling. For example, if a user makes a lot of purchases at a particular store during a particular time period, the user can be labeled as "shopping on the way to work." This allows for more detailed labeling based on the user's purchasing history.

[0062] The location labeling system can also collect the user's calendar data and input it into the generation AI input section. For example, it can collect schedule data from the calendar app on the user's smartphone and input it into the generation AI. It can also analyze the user's schedules and activities based on the calendar data and reflect this in labeling. For example, if the calendar says "meeting," it can label it as "in a meeting." It can also analyze the user's behavioral patterns based on the calendar data and use this for labeling. For example, if there are many appointments at a specific location during a specific time period, it can label it as "regular meeting." This enables more detailed labeling based on the user's calendar data.

[0063] The location labeling system can also collect data from the user's health app and input it into the generation AI input section. For example, step count and sleep data can be collected from the health app on the user's smartphone and input into the generation AI. Furthermore, based on the health app data, the system can analyze the user's health status and activities and reflect this in labeling. For example, a high step count can be labeled as "walking." Furthermore, based on the health app data, the system can analyze the user's behavioral patterns and use this for labeling. For example, a high step count during a specific time period can be labeled as "commuting." This allows for more detailed labeling based on the user's health app data.

[0064] The location information labeling system can also collect a user's music playback history and input it into the generation AI input section. For example, playback history can be collected from a user's music streaming service and input into the generation AI. In addition, based on the music playback history, the user's interests and preferences can be analyzed and reflected in the labeling. For example, if a user plays a lot of music of a particular genre, the user can be labeled as a "music lover." In addition, based on the music playback history, the user's behavioral patterns can be analyzed and used for labeling. For example, if a specific genre of music is played at a specific time of day, the user can be labeled as a "relaxation time." This allows for more detailed labeling based on the user's music playback history.

[0065] The location information labeling system can also collect a user's reading history and input it into the generation AI input section. For example, reading history can be collected from a user's e-book reader and input into the generation AI. In addition, based on the reading history, the user's interests and preferences can be analyzed and reflected in the labeling. For example, if a user reads many books of a specific genre, the user can be labeled as a "book lover." In addition, based on the reading history, the user's behavioral patterns can be analyzed and used in labeling. For example, if a user reads during a specific time period, the user can be labeled as "reading time." This allows for more detailed labeling based on the user's reading history.

[0066] The location labeling system can also collect data to estimate a user's emotional state and input it into the generation AI input unit. For example, it can analyze facial expressions using the user's smartphone camera. Based on the emotion estimation data, it can also label the user's activities based on their emotions. For example, if a user smiles frequently, it can label them as "having fun." It can also use the emotion estimation function to analyze the user's emotional state in real time and label them based on their emotions. For example, if an angry expression is seen, it can be recognized as "feeling stressed." This makes it possible to estimate a user's emotional state and label them based on their emotions.

[0067] The location labeling system can also collect the user's voice data and input it into the generation AI input section. For example, voice data can be acquired using the microphone on the user's smartphone. The voice data can also be analyzed to estimate the user's emotional state. For example, the system can label the user as "having fun" based on the tone of voice and speaking style. The system can also label the user's activities based on emotions based on the voice data. For example, if angry voices are heard, the system can recognize the user as "feeling stressed." This makes it possible to label the user's voice data based on emotions.

[0068] The location labeling system can also collect users' social media posts and input them into the generation AI input section. For example, it can analyze users' Twitter and Instagram posts. It can also estimate a user's emotional state based on the social media posts. For example, it can label a user as "having fun" based on the content of the post and comments. It can also label a user's activities based on their emotions based on the social media posts. For example, if there are many negative posts, it can recognize that the user is "feeling stressed." This makes it possible to label users' social media posts based on their emotions.

[0069] The location labeling system can also collect the user's biometric information and input it into the generation AI input section. For example, it can analyze the user's heart rate and body temperature. It can also estimate the user's emotional state based on the biometric information. For example, a high heart rate can be labeled as "nervous." It can also label the user's activity based on emotion based on the biometric information. For example, a rising body temperature can be recognized as "excited." This makes it possible to label based on emotions based on the user's biometric information.

[0070] The location labeling system can also collect data from the user's wearable device and input it into the generation AI input section. For example, it can collect heart rate and activity data from a smartwatch. It can also estimate the user's emotional state based on the data from the wearable device. For example, a stable heart rate can be labeled as "relaxed." It can also label the user's activity based on emotion based on the data from the wearable device. For example, a sudden increase in heart rate can be recognized as "surprised." This makes it possible to label based on emotion based on the data from the user's wearable device.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The location information collection unit collects location information, such as GPS data, Wi-Fi location information, and beacon data. Step 2: The generation AI input unit inputs the location information collected by the location information collection unit to the generation AI. For example, the location information data can be input to the generation AI and an analysis prompt can be input to the generation AI. Alternatively, the location information data can be converted into a different format in advance and then input to the generation AI. Step 3: The analysis unit analyzes the location information input by the generation AI input unit. For example, the analysis unit uses text generation AI (e.g., LLM), multimodal generation AI, or movement pattern analysis technology. Step 4: The labeling unit assigns labels based on the location information analyzed by the analysis unit. For example, if a specific coordinate is within a shopping mall, it can be labeled as "stayed at the shopping mall." Based on the speed and movement pattern, it can also be labeled as "walked" or "traveled by car." It can also recognize the user's location and activity and assign labels based on that.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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 location information collection unit that collects location information; a generation AI input unit that inputs the location information collected by the location information collection unit to a generation AI; an analysis unit that analyzes the location information input by the generation AI input unit; a labeling unit that labels the location information analyzed by the analyzing unit. A system characterized by:

2. The location information collection unit In addition to location information data, data for estimating the user's emotional state is collected and input into the generation AI input unit.

2. The system of claim 1.

3. The location information collection unit Using drones or self-driving cars, wide-area location information data is collected and input into the generation AI input unit.

2. The system of claim 1.

4. The analysis unit Referencing the user's past behavioral history and preference data for more accurate labeling 2. The system of claim 1.

5. The analysis unit Considering the user's emotional state and performing emotion-based labeling 2. The system of claim 1.

6. The location information collection unit The user's emotions at the time of collecting location information data are estimated in real time and input into the generation AI input unit.

2. The system of claim 1.

7. The analysis unit Combine and analyze different generative AI models to label from multiple perspectives 2. The system of claim 1.

8. The analysis unit Monitor user emotions in real time and label them based on their emotions 2. The system of claim 1.

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