system

Abstract

An object of a system according to an embodiment is to enable a user to easily install preference data in a generated API.SOLUTION: A system according to an embodiment includes a taste data storage unit, an upload / download unit, and an installation unit. The taste data storage unit stores taste data. An upload / download part uploads / downloads the taste data stored by the taste data storage part. The installation unit installs the preference data downloaded by the upload / download unit in the generated API.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of requiring a lot of time and effort to train a generation API to learn your preferences.

[0005] The system according to the embodiment aims to enable users to easily install preference data into a generation API. [Means for solving the problem]

[0006] The system according to the embodiment includes a preference data storage unit, an upload / download unit, and an installation unit. The preference data storage unit stores preference data. The upload / download unit uploads and downloads the preference data stored by the preference data storage unit. The installation unit installs the preference data downloaded by the upload / download unit into a generation API. [Effects of the Invention]

[0007] The system according to the embodiment can allow users to easily install preference data into the generation API. [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 generation API system according to an embodiment of the present invention is a system that stores, uploads, downloads, and installs user preference data in the generation API, allowing the generation API system to obtain the desired answers without requiring the user to take the time to learn.

[0029] A generation API system according to an embodiment includes a preference data storage unit, an upload / download unit, and an installation unit. The preference data storage unit stores preference data. For example, the preference data storage unit stores preference data obtained by a user repeatedly interacting with the generation API in a dedicated data format. The preference data storage unit can also store data reflecting the user's preferences and tendencies. For example, if a user is interested in a particular topic, the generation API learns to prioritize providing information about that topic. The upload / download unit uploads and downloads the preference data stored by the preference data storage unit. For example, the upload / download unit allows users to freely upload and download preference data through a dedicated website. The upload / download unit also allows consistent preference data to be used across different devices and environments. For example, even if a user purchases a new device, the preference data learned on the previous device can be downloaded and used. The installation unit installs the preference data downloaded by the upload / download unit into the generation API. For example, the installation unit installs the preference data downloaded by the user into the generation API, allowing the generation API to generate answers based on the preference data. In addition, if a user is interested in a particular movie genre, the installation unit operates the generation API to prioritize providing information about that genre. As a result, the generation API system according to the embodiment allows users to obtain the answers they want without the effort of learning. For example, a user can use the generation API to quickly and accurately obtain information about a specific topic. Furthermore, the generation API system can improve user convenience by saving, uploading, downloading, and installing user preference data in the generation API.

[0030] The preference data storage unit can store preference data along a time axis, enabling changes in preferences to be tracked. For example, when a user uses the generation API to have a conversation, the preference data storage unit builds a system that stores the content of the conversation along a time axis. For example, it records the frequency and time that the user talks about a specific topic, and tracks changes in preferences based on that data. The preference data storage unit can also understand changes in the user's preferences by analyzing the data stored along a time axis. For example, it performs analysis of time-series data and trend analysis to track changes in the user's preferences. This makes it possible to track changes in preferences.

[0031] The preference data storage unit can simultaneously store multimodal information, including audio data or image data, and provide more detailed preference data to the generation AI. For example, the preference data storage unit builds a system that simultaneously stores audio and image data when a user uses the generation API to engage in a dialogue. For example, it stores audio data and related image data when a user talks about a specific topic, and provides more detailed preference data to the generation AI. In addition, by storing multimodal information, including audio data and image data, the generation AI can perform more accurate analysis. For example, the audio file format or image file format can be specified and saved, and the generation AI will analyze the data. This allows more detailed preference data to be provided to the generation AI.

[0032] The preference data storage unit can add a function to share preference data between different users and match users with common preferences. The preference data storage unit adds a function to share preference data when users use the generation API to have a conversation, for example. For example, it can match users who are interested in a common topic and promote the conversation. The preference data storage unit can also promote interaction between users by matching users with common preferences. For example, it can use similarity calculations and recommendation algorithms to identify and match users with common preferences. This makes it possible to match users with common preferences.

[0033] The upload / download unit enables the generation AI to automatically check the integrity of the data and correct any errors when uploading preference data. For example, the upload / download unit creates a system where, when a user uploads preference data through a dedicated website, the generation AI automatically checks the integrity of the data and corrects any errors. For example, it detects and automatically corrects inconsistencies in data formats and missing data. The upload / download unit also enables the generation AI to check the integrity of the data, ensuring that users upload accurate data. For example, it evaluates the consistency and completeness of the data and corrects any errors. This allows the generation AI to automatically check the integrity of the data and correct any errors.

[0034] The uploading and downloading unit allows the generation AI to evaluate the usefulness of preference data when a user downloads it and present recommended data. The uploading and downloading unit, for example, builds a system in which, when a user downloads preference data through a dedicated website, the generation AI evaluates the usefulness of the data and presents recommended data. For example, the unit recommends optimal preference data based on the user's past interaction history and interests. The uploading and downloading unit also allows the generation AI to evaluate the usefulness of data, enabling the user to download optimal data. For example, the unit evaluates the usefulness of data based on user feedback and frequency of use and presents recommended data. This allows the usefulness of data to be evaluated and recommended data to be presented.

[0035] The upload / download unit can automatically translate preference data into different languages ​​when it is uploaded or downloaded, making it possible to accommodate international users. For example, when a user uploads or downloads preference data through a dedicated website, the upload / download unit creates a system in which the generation AI automatically translates the data into different languages. For example, it supports multiple languages ​​such as English, Japanese, and French. The upload / download unit can also automatically translate into different languages ​​to accommodate international users. For example, it can use machine translation or neural network translation technology to make preference data multilingual. This allows it to be automatically translated into different languages, making it possible to accommodate international users.

[0036] The upload / download unit can convert preference data into visual notes or mind maps to make it easier to understand visually. For example, when a user uploads or downloads preference data through a dedicated website, the upload / download unit creates a system in which the generation AI converts the data into visual notes. For example, the key points of the preference data can be indicated with diagrams or icons to make it easier to understand visually. The upload / download unit can also convert preference data into mind maps to help users intuitively understand the data. For example, a node and edge structure can be used to visually represent the hierarchy and relationships of topics. This makes it easier to understand preference data visually.

[0037] The installation unit allows the generation AI to automatically adjust the scope of application of the data when installing preference data into the generation API and provide the optimal answer. For example, the installation unit builds a system in which the generation AI automatically adjusts the scope of application of the data when installing preference data downloaded by a user into the generation API. For example, it provides the optimal answer based on the user's interaction history and interests. The installation unit also allows the generation AI to adjust the scope of application of the data, allowing the user to obtain an answer that is more satisfactory to the user. For example, it sets the scope of target users and applicable scenarios and optimizes the scope of application of the data. This allows the data scope to be automatically adjusted and the optimal answer to be provided.

[0038] The installation unit allows the generation AI to refer to the user's past dialogue history during installation and improve the accuracy of applying the preference data. For example, the installation unit builds a system in which the generation AI refers to the user's past dialogue history when installing preference data downloaded by the user into the generation API. For example, the installation unit improves the accuracy of applying the preference data based on the content of the past dialogue. Furthermore, the installation unit allows the generation AI to provide answers that are more suited to the user's preferences by referring to the past dialogue history. For example, the installation unit analyzes chat logs and voice recordings to improve the accuracy of applying the preference data. This makes it possible to refer to the past dialogue history and improve the accuracy of applying the preference data.

[0039] The installation unit automatically synchronizes data between different devices when installing preference data, thereby providing consistent preference data. For example, the installation unit builds a system that automatically synchronizes data between different devices when installing preference data downloaded by a user into a generation API. For example, even if a user purchases a new device, the preference data learned on the previous device can be synchronized and used. The installation unit also synchronizes data, allowing the user to use consistent preference data across different devices. For example, data is synchronized between multiple devices, such as smartphones, tablets, and PCs. This allows data synchronization between different devices to be automatically performed, providing consistent preference data.

[0040] At the time of installation, the installation unit allows the generation AI to compare the preference data with that of other users and propose the optimal data application method. For example, when installing preference data downloaded by a user into the generation API, the installation unit builds a system in which the generation AI compares it with the preference data of other users. For example, it proposes the optimal data application method based on data from users who share common preferences. The installation unit also allows the generation AI to propose the optimal data application method for the user by comparing it with the preference data of other users. For example, it proposes the optimal data application method using similarity calculations or recommendation algorithms. This makes it possible to compare it with the preference data of other users and propose the optimal data application method.

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

[0042] The API system can also collect the user's health data and combine it with their preference data to make health-conscious suggestions. For example, it can collect the user's dietary history and exercise data to suggest healthy meals and exercise plans. It can also collect the user's sleep data and provide advice on improving sleep quality. It can also monitor the user's stress level and suggest relaxation methods. This allows for comprehensive management of the user's health status and supports a healthier lifestyle.

[0043] The generation API system can also use user preference data to suggest travel plans. For example, if a user likes nature, it can suggest tourist spots rich in nature, and if the user is interested in history, it can suggest travel plans that include historical sites. It can also recommend places that the user has not yet visited but may be interested in based on the user's past travel history. It can also automatically generate optimal travel plans that fit the user's budget and schedule. This makes it possible to provide travel plans that match the user's preferences.

[0044] The generation API system can also use user preference data to provide personalized study plans. For example, if a user is interested in a particular subject, it can prioritize providing learning resources related to that subject and provide materials to reinforce areas in which the user is weak. It can also monitor the user's learning progress and suggest review or additional learning resources at appropriate times. It can also automatically generate customized study plans tailored to the user's learning style, thereby improving the user's learning efficiency.

[0045] The generation API system can also use user preference data to provide personalized cooking recipes. For example, if a user prefers a particular ingredient, it can suggest recipes using that ingredient, and if the user is interested in a particular cooking genre, it can suggest recipes related to that genre. It can also suggest recipes that take into account the user's dietary restrictions and allergy information. It can also suggest new recipes based on the user's past cooking history. This makes it possible to provide cooking recipes that match the user's preferences.

[0046] The generation API system can also use the user's preference data to suggest personalized fashion coordination. For example, if a user has a preference for a particular style, it will suggest coordination that matches that style, and if the user is interested in a particular brand, it will provide coordination that includes products from that brand. It can also suggest coordination that matches the user's body type and the season. It can also suggest new coordinations based on the user's past fashion history. This makes it possible to provide fashion coordination that matches the user's preferences.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The preference data storage unit stores preference data. For example, preference data obtained by a user repeatedly interacting with the generation API is stored in a dedicated data format. Data reflecting the user's preferences and tendencies can also be stored. For example, if a user is interested in a particular topic, the generation API learns to provide information about that topic preferentially. Step 2: The upload / download unit uploads and downloads the preference data stored by the preference data storage unit. For example, users can freely upload and download preference data through a dedicated website. It also allows users to use consistent preference data across different devices and environments. For example, even if a user purchases a new device, the preference data learned on the previous device can be downloaded and used. Step 3: The installation unit installs the preference data downloaded by the upload / download unit into the generation API. For example, if a user installs the downloaded preference data into the generation API, the generation API generates answers based on that preference data. Also, if a user is interested in a particular movie genre, the generation API operates to provide information about that genre preferentially.

[0049] (Example 2) The generation API system according to an embodiment of the present invention is a system that stores, uploads, downloads, and installs user preference data in the generation API, allowing the generation API system to obtain the desired answers without requiring the user to take the time to learn.

[0050] A generation API system according to an embodiment includes a preference data storage unit, an upload / download unit, and an installation unit. The preference data storage unit stores preference data. For example, the preference data storage unit stores preference data obtained by a user repeatedly interacting with the generation API in a dedicated data format. The preference data storage unit can also store data reflecting the user's preferences and tendencies. For example, if a user is interested in a particular topic, the generation API learns to prioritize providing information about that topic. The upload / download unit uploads and downloads the preference data stored by the preference data storage unit. For example, the upload / download unit allows users to freely upload and download preference data through a dedicated website. The upload / download unit also allows consistent preference data to be used across different devices and environments. For example, even if a user purchases a new device, the preference data learned on the previous device can be downloaded and used. The installation unit installs the preference data downloaded by the upload / download unit into the generation API. For example, the installation unit installs the preference data downloaded by the user into the generation API, allowing the generation API to generate answers based on the preference data. In addition, if a user is interested in a particular movie genre, the installation unit operates the generation API to prioritize providing information about that genre. As a result, the generation API system according to the embodiment allows users to obtain the answers they want without the effort of learning. For example, a user can use the generation API to quickly and accurately obtain information about a specific topic. Furthermore, the generation API system can improve user convenience by saving, uploading, downloading, and installing user preference data in the generation API.

[0051] The preference data storage unit uses the generation AI to perform emotion analysis and classify and store data based on the intensity and type of emotion. For example, when a user uses the generation API to engage in a conversation, the generation AI performs real-time emotion analysis of the conversation content and quantifies the intensity and type of emotion. For example, when a user talks about a specific topic, the preference data storage unit classifies and stores preference data related to that topic based on the emotion score. The generation AI also classifies and stores data based on the intensity and type of emotion, enabling data management based on user emotions. For example, the generation AI performs emotion analysis using deep learning and natural language processing technology to evaluate the intensity and type of emotion. This makes it possible to classify and store data based on emotion.

[0052] The preference data storage unit can store preference data along a time axis, enabling changes in preferences to be tracked. For example, when a user uses the generation API to have a conversation, the preference data storage unit builds a system that stores the content of the conversation along a time axis. For example, it records the frequency and time that the user talks about a specific topic, and tracks changes in preferences based on that data. The preference data storage unit can also understand changes in the user's preferences by analyzing the data stored along a time axis. For example, it performs analysis of time-series data and trend analysis to track changes in the user's preferences. This makes it possible to track changes in preferences.

[0053] The preference data storage unit can use the emotion estimation function to analyze the user's emotions in real time when saving the preference data, and prioritize saving data that elicits positive emotions. For example, when a user uses the generation API to engage in a dialogue, the preference data storage unit uses the generation AI to perform real-time emotion analysis of the dialogue content and prioritize saving data that elicits positive emotions. For example, based on the degree of joy or excitement felt when the user talks about a particular topic, the preference data storage unit prioritizes saving preference data related to that topic. The preference data storage unit can also improve user satisfaction by using the emotion estimation function to estimate the user's emotional state and prioritize saving data that elicits positive emotions. For example, the preference data storage unit can perform emotion analysis using a machine learning model or emotion recognition algorithm to evaluate the emotional state in real time. This allows data that elicits positive emotions to be prioritized and saved.

[0054] The preference data storage unit can simultaneously store multimodal information, including audio data or image data, and provide more detailed preference data to the generation AI. For example, the preference data storage unit builds a system that simultaneously stores audio and image data when a user uses the generation API to engage in a dialogue. For example, it stores audio data and related image data when a user talks about a specific topic, and provides more detailed preference data to the generation AI. In addition, by storing multimodal information, including audio data and image data, the generation AI can perform more accurate analysis. For example, the audio file format or image file format can be specified and saved, and the generation AI will analyze the data. This allows more detailed preference data to be provided to the generation AI.

[0055] The preference data storage unit can add a function to share preference data between different users and match users with common preferences. The preference data storage unit adds a function to share preference data when users use the generation API to have a conversation, for example. For example, it can match users who are interested in a common topic and promote the conversation. The preference data storage unit can also promote interaction between users by matching users with common preferences. For example, it can use similarity calculations and recommendation algorithms to identify and match users with common preferences. This makes it possible to match users with common preferences.

[0056] The preference data storage unit uses an emotion estimation function to analyze the user's emotions in real time when saving preference data, and can optimize data storage based on emotions. For example, when a user uses the generation API to engage in a dialogue, the generation AI performs real-time emotion analysis of the dialogue content and optimizes data storage based on emotions. For example, based on the emotion score when the user talks about a specific topic, the preference data storage unit optimizes and stores preference data related to that topic. The preference data storage unit also uses an emotion estimation function to analyze the user's emotional state in real time and optimize data storage based on emotions, thereby improving user satisfaction. For example, emotion analysis is performed using a machine learning model or an emotion recognition algorithm to evaluate the emotional state in real time. This enables optimization of data storage based on emotions.

[0057] The upload / download unit enables the generation AI to automatically check the integrity of the data and correct any errors when uploading preference data. For example, the upload / download unit creates a system where, when a user uploads preference data through a dedicated website, the generation AI automatically checks the integrity of the data and corrects any errors. For example, it detects and automatically corrects inconsistencies in data formats and missing data. The upload / download unit also enables the generation AI to check the integrity of the data, ensuring that users upload accurate data. For example, it evaluates the consistency and completeness of the data and corrects any errors. This allows the generation AI to automatically check the integrity of the data and correct any errors.

[0058] The uploading and downloading unit allows the generation AI to evaluate the usefulness of preference data when a user downloads it and present recommended data. The uploading and downloading unit, for example, builds a system in which, when a user downloads preference data through a dedicated website, the generation AI evaluates the usefulness of the data and presents recommended data. For example, the unit recommends optimal preference data based on the user's past interaction history and interests. The uploading and downloading unit also allows the generation AI to evaluate the usefulness of data, enabling the user to download optimal data. For example, the unit evaluates the usefulness of data based on user feedback and frequency of use and presents recommended data. This allows the usefulness of data to be evaluated and recommended data to be presented.

[0059] The upload / download unit can estimate the user's emotional state during upload / download and provide an interface for eliciting positive emotions. For example, when a user uploads or downloads preference data through a dedicated website, the generation AI performs real-time emotional analysis of the conversation content and provides an interface for eliciting positive emotions. For example, a message for eliciting positive emotions can be displayed based on the emotion score when the user talks about a specific topic. The upload / download unit can also improve user satisfaction by using an emotion estimation function to estimate the user's emotional state and provide an interface for eliciting positive emotions. For example, the emotional state can be evaluated using facial expression analysis or voice analysis and the interface can be optimized. This can provide an interface for eliciting positive emotions.

[0060] The upload / download unit can automatically translate preference data into different languages ​​when it is uploaded or downloaded, making it possible to accommodate international users. For example, when a user uploads or downloads preference data through a dedicated website, the upload / download unit creates a system in which the generation AI automatically translates the data into different languages. For example, it supports multiple languages ​​such as English, Japanese, and French. The upload / download unit can also automatically translate into different languages ​​to accommodate international users. For example, it can use machine translation or neural network translation technology to make preference data multilingual. This allows it to be automatically translated into different languages, making it possible to accommodate international users.

[0061] The upload / download unit can convert preference data into visual notes or mind maps to make it easier to understand visually. For example, when a user uploads or downloads preference data through a dedicated website, the upload / download unit creates a system in which the generation AI converts the data into visual notes. For example, the key points of the preference data can be indicated with diagrams or icons to make it easier to understand visually. The upload / download unit can also convert preference data into mind maps to help users intuitively understand the data. For example, a node and edge structure can be used to visually represent the hierarchy and relationships of topics. This makes it easier to understand preference data visually.

[0062] The upload / download unit uses the emotion estimation function to collect users' emotional responses during upload / download, which can be used to improve the interface. For example, when a user uploads or downloads preference data through a dedicated website, the upload / download unit constructs a system in which the generation AI analyzes the emotions of the conversation in real time and collects emotional responses. For example, the upload / download unit identifies areas for improvement in the interface based on the emotion score when the user talks about a specific topic. The upload / download unit also uses the emotion estimation function to collect users' emotional responses and use this information to improve the interface, thereby increasing user satisfaction. For example, the unit measures emotional responses through questionnaires or sensors to optimize the interface. This allows the collection of emotional responses to be used to improve the interface.

[0063] The installation unit allows the generation AI to automatically adjust the scope of application of the data when installing preference data into the generation API and provide the optimal answer. For example, the installation unit builds a system in which the generation AI automatically adjusts the scope of application of the data when installing preference data downloaded by a user into the generation API. For example, it provides the optimal answer based on the user's interaction history and interests. The installation unit also allows the generation AI to adjust the scope of application of the data, allowing the user to obtain an answer that is more satisfactory to the user. For example, it sets the scope of target users and applicable scenarios and optimizes the scope of application of the data. This allows the data scope to be automatically adjusted and the optimal answer to be provided.

[0064] The installation unit allows the generation AI to refer to the user's past dialogue history during installation and improve the accuracy of applying the preference data. For example, the installation unit builds a system in which the generation AI refers to the user's past dialogue history when installing preference data downloaded by the user into the generation API. For example, the installation unit improves the accuracy of applying the preference data based on the content of the past dialogue. Furthermore, the installation unit allows the generation AI to provide answers that are more suited to the user's preferences by referring to the past dialogue history. For example, the installation unit analyzes chat logs and voice recordings to improve the accuracy of applying the preference data. This makes it possible to refer to the past dialogue history and improve the accuracy of applying the preference data.

[0065] The installation unit can estimate the user's emotional state during installation and apply data to elicit positive emotions. For example, when the installation unit installs the preference data downloaded by the user into the generation API, the generation AI analyzes the emotions of the conversation content in real time and builds a system that applies data to elicit positive emotions. For example, the installation unit applies data to elicit positive emotions based on the emotion score when the user talks about a specific topic. The installation unit can also improve user satisfaction by using the emotion estimation function to estimate the user's emotional state and apply data to elicit positive emotions. For example, the installation unit sets data selection criteria and application processes and applies data based on emotions. This makes it possible to apply data to elicit positive emotions.

[0066] The installation unit automatically synchronizes data between different devices when installing preference data, thereby providing consistent preference data. For example, the installation unit builds a system that automatically synchronizes data between different devices when installing preference data downloaded by a user into a generation API. For example, even if a user purchases a new device, the preference data learned on the previous device can be synchronized and used. The installation unit also synchronizes data, allowing the user to use consistent preference data across different devices. For example, data is synchronized between multiple devices, such as smartphones, tablets, and PCs. This allows data synchronization between different devices to be automatically performed, providing consistent preference data.

[0067] At the time of installation, the installation unit allows the generation AI to compare the preference data with that of other users and propose the optimal data application method. For example, when installing preference data downloaded by a user into the generation API, the installation unit builds a system in which the generation AI compares it with the preference data of other users. For example, it proposes the optimal data application method based on data from users who share common preferences. The installation unit also allows the generation AI to propose the optimal data application method for the user by comparing it with the preference data of other users. For example, it proposes the optimal data application method using similarity calculations or recommendation algorithms. This makes it possible to compare it with the preference data of other users and propose the optimal data application method.

[0068] The installation unit uses the emotion estimation function to monitor the user's emotional reactions in real time at the time of installation and continuously apply optimal data. For example, when the installation unit installs the preference data downloaded by the user into the generation API, the generation AI performs real-time emotional analysis of the dialogue content and builds a system to monitor the emotional reactions. For example, the installation unit continuously applies optimal data based on the emotion score when the user talks about a specific topic. The installation unit also uses the emotion estimation function to monitor the user's emotional reactions in real time and continuously apply optimal data, thereby improving user satisfaction. For example, the installation unit performs regular updates and real-time monitoring to optimize data application based on emotions. This makes it possible to monitor emotional reactions in real time and continuously apply optimal data.

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

[0070] The API system can also collect the user's health data and combine it with their preference data to make health-conscious suggestions. For example, it can collect the user's dietary history and exercise data to suggest healthy meals and exercise plans. It can also collect the user's sleep data and provide advice on improving sleep quality. It can also monitor the user's stress level and suggest relaxation methods. This allows for comprehensive management of the user's health status and supports a healthier lifestyle.

[0071] The generation API system can also estimate the user's emotions and recommend appropriate music based on the estimated emotions. For example, if the user is feeling stressed, it can recommend relaxing music, and if the user is feeling happy, it can recommend music that will further enhance those emotions. It can also recommend music that will improve concentration when the user wants to concentrate. It can also switch music in real time according to changes in the user's emotions. This allows for a music experience that is tailored to the user's emotions.

[0072] The generation API system can also use user preference data to suggest travel plans. For example, if a user likes nature, it can suggest tourist spots rich in nature, and if the user is interested in history, it can suggest travel plans that include historical sites. It can also recommend places that the user has not yet visited but may be interested in based on the user's past travel history. It can also automatically generate optimal travel plans that fit the user's budget and schedule. This makes it possible to provide travel plans that match the user's preferences.

[0073] The generative API system can also estimate the user's emotions and provide appropriate feedback based on the estimated emotions. For example, if the user is feeling anxious, it can display an encouraging message, and if the user feels a sense of accomplishment, it can display a message praising the user's efforts. It can also provide encouraging advice if the user is feeling down. Furthermore, it can adjust the content of the feedback in real time according to changes in the user's emotions. This allows it to provide feedback that is in line with the user's emotions.

[0074] The generation API system can also use user preference data to provide personalized study plans. For example, if a user is interested in a particular subject, it can prioritize providing learning resources related to that subject and provide materials to reinforce areas in which the user is weak. It can also monitor the user's learning progress and suggest review or additional learning resources at appropriate times. It can also automatically generate customized study plans tailored to the user's learning style, thereby improving the user's learning efficiency.

[0075] The generative API system can also estimate the user's emotions and suggest appropriate exercises based on the estimated emotions. For example, it can suggest relaxation exercises if the user is feeling stressed, high-intensity exercises if the user is feeling energetic, and light stretching if the user is tired. Furthermore, it can adjust the exercise content in real time according to changes in the user's emotions. This allows for an exercise experience that is tailored to the user's emotions.

[0076] The generation API system can also use user preference data to provide personalized cooking recipes. For example, if a user prefers a particular ingredient, it can suggest recipes using that ingredient, and if the user is interested in a particular cooking genre, it can suggest recipes related to that genre. It can also suggest recipes that take into account the user's dietary restrictions and allergy information. It can also suggest new recipes based on the user's past cooking history. This makes it possible to provide cooking recipes that match the user's preferences.

[0077] The generation API system can also estimate the user's emotions and suggest appropriate relaxation methods based on the estimated emotions. For example, if the user is feeling stressed, it can suggest meditation or deep breathing techniques, and if the user wants to relax, it can suggest aromatherapy or music therapy. It can also suggest relaxation methods to improve concentration when the user wants to concentrate. Furthermore, it can adjust relaxation methods in real time according to changes in the user's emotions. This makes it possible to provide a relaxation experience that is tailored to the user's emotions.

[0078] The generation API system can also use the user's preference data to suggest personalized fashion coordination. For example, if a user has a preference for a particular style, it will suggest coordination that matches that style, and if the user is interested in a particular brand, it will provide coordination that includes products from that brand. It can also suggest coordination that matches the user's body type and the season. It can also suggest new coordinations based on the user's past fashion history. This makes it possible to provide fashion coordination that matches the user's preferences.

[0079] The generative API system can further estimate the user's emotions and suggest appropriate reading lists based on the estimated emotions. For example, if the user wants to relax, it can suggest relaxing books, and if the user wants to be stimulated, it can suggest thrilling books. It can also suggest educational books when the user wants to learn. Furthermore, it can adjust the reading list in real time according to changes in the user's emotions. This allows for a reading experience that is tailored to the user's emotions.

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

[0081] Step 1: The preference data storage unit stores preference data. For example, preference data obtained by a user repeatedly interacting with the generation API is stored in a dedicated data format. Data reflecting the user's preferences and tendencies can also be stored. For example, if a user is interested in a particular topic, the generation API learns to provide information about that topic preferentially. Step 2: The upload / download unit uploads and downloads the preference data stored by the preference data storage unit. For example, users can freely upload and download preference data through a dedicated website. It also allows users to use consistent preference data across different devices and environments. For example, even if a user purchases a new device, the preference data learned on the previous device can be downloaded and used. Step 3: The installation unit installs the preference data downloaded by the upload / download unit into the generation API. For example, if a user installs the downloaded preference data into the generation API, the generation API generates answers based on that preference data. Also, if a user is interested in a particular movie genre, the generation API operates to provide information about that genre preferentially.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] 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.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0116] 7, 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] 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.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

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

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

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

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a preference data storage unit that stores preference data; an upload / download unit that uploads and downloads the preference data stored by the preference data storage unit; an installation unit that installs the preference data downloaded by the upload / download unit into a generation API; A system characterized by:

2. The preference data storage unit Multimodal information, including audio data or image data, is simultaneously stored, and more detailed preference data is provided to the generation AI.

2. The system of claim 1.

3. The upload / download unit When uploading the preference data, the generating AI automatically checks the integrity of the data and corrects errors.

2. The system of claim 1.

4. The installation unit When the preference data is installed in the generation API, the generation AI automatically adjusts the scope of the data to provide the optimal answer.

2. The system of claim 1.

5. The preference data storage unit The generative AI is used to perform emotion analysis, and data is classified and saved based on the intensity and type of emotion.

2. The system of claim 1.

6. The upload / download unit Provides an interface to estimate the user's emotional state and elicit positive emotions during uploading and downloading.

2. The system of claim 1.

7. The installation unit Upon installation, the app estimates the user's emotional state and applies data to elicit positive emotions.

2. The system of claim 1.

8. The preference data storage unit When saving the preference data, the user's emotions are analyzed in real time, and data that elicits positive emotions is preferentially saved.

2. The system of claim 1.

Images