System

Abstract

An object of a system according to an embodiment is to detect a sign of a married fight and appropriately arbitrate.SOLUTION: A system according to an embodiment includes a conversation collection unit, a machine learning unit, a fight detection unit, and an arbitration voice generation unit. The conversation collection unit collects daily conversations. The machine learning unit performs machine learning on the basis of the conversation data collected by the conversation collection unit. The fight detector detects a sign of a married fight based on the data learned by the machine learning unit. The arbitration sound generator generates an arbitration sound based on the sign of the married fight detected by the fight detector.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 had the problem of making it difficult to detect early signs of marital conflict and mediate appropriately.

[0005] The system according to the embodiment aims to detect signs of marital conflict and mediate appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation collection unit, a machine learning unit, a fight detection unit, and an arbitration voice generation unit. The conversation collection unit collects everyday conversations. The machine learning unit performs machine learning based on the conversation data collected by the conversation collection unit. The fight detection unit detects signs of a marital fight based on the data learned by the machine learning unit. The arbitration voice generation unit generates arbitration voice based on the signs of a marital fight detected by the fight detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect signs of marital conflict and mediate appropriately. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The arbitration system according to an embodiment of the present invention collects everyday conversations, uses machine learning to detect signs of marital disputes, and generates arbitration audio. This allows the arbitration system to facilitate smooth communication between couples and prevent marital disputes.

[0029] An arbitration system according to an embodiment includes a conversation collection unit, a machine learning unit, a fight detection unit, and an arbitration audio generation unit. The conversation collection unit collects everyday conversations. For example, it constantly listens to and collects household conversations using a device such as Google Home. The conversation collection unit can also send the collected conversation data to the generation AI. The machine learning unit performs machine learning based on the conversation data collected by the conversation collection unit. For example, the generation AI can analyze a couple's conversation patterns and emotional changes using deep learning. The generation AI can also classify conversation data using a support vector machine. The generation AI can also learn what topics a couple regularly talks about and in what tone. The fight detection unit detects signs of a marital fight based on the data learned by the machine learning unit. For example, the generation AI can determine that a higher-pitched voice or rougher language is a sign of a marital fight. The generation AI can also analyze the frequency of occurrence of specific keywords and phrases to detect signs of a marital fight. The generation AI can also detect sudden changes in emotions and determine them as signs of marital quarrels. The arbitration voice generation unit generates arbitration voices based on the signs of marital quarrels detected by the quarrel detection unit. For example, the generation AI generates voices such as "Let's listen to each other's opinions calmly" or "Let's take a short break and continue the discussion." The generation AI can also generate voices to calm the couple's emotions. The generation AI can also generate individually customized arbitration voices based on the couple's past conversation data. This allows the arbitration system according to the embodiment to facilitate communication between couples and prevent marital quarrels. For example, the arbitration system can understand the couple's conversation patterns and generate arbitration voices at the appropriate time to resolve marital quarrels early. The arbitration system can also generate arbitration voices that correspond to the couple's emotional state, allowing for more effective arbitration.

[0030] The conversation collection unit can analyze background sounds and environmental sounds of a conversation to understand the context of the conversation. For example, the conversation collection unit uses a generation AI to analyze background sounds and environmental sounds of a conversation to understand the context of the conversation. For example, it analyzes background sounds such as the sound of a television or children's voices to understand the situation of the conversation. The conversation collection unit also analyzes background sounds of a conversation to identify the location and time of the conversation and understand the context of the conversation. For example, it can distinguish between conversations in the kitchen and conversations at night. The conversation collection unit also analyzes environmental sounds to understand the atmosphere and tension of the conversation. For example, it can understand the difference between conversations in a quiet environment and conversations in a noisy environment. In this way, by analyzing background sounds and environmental sounds of a conversation, the situation of the conversation can be understood more deeply.

[0031] The conversation collection unit can track specific keywords and phrases used in conversations to identify the couple's interests and stressors. The conversation collection unit, for example, tracks keywords and phrases frequently used in conversations to identify the couple's interests. For example, it analyzes keywords such as "work," "children," and "housework." The conversation collection unit also tracks specific phrases and word usage to identify the couple's stressors. For example, it analyzes phrases such as "tired," "busy," and "irritated." The conversation collection unit also analyzes the frequency of keyword appearance in conversations to identify the couple's interests and stressors. For example, if a specific keyword appears frequently, it determines that the topic is important. In this way, the couple's interests and stressors can be identified by tracking specific keywords and phrases used in conversations.

[0032] The conversation collection unit can also collect data from other devices in the home to build a more comprehensive data set. For example, the conversation collection unit can collect data from smartphones and tablets in the home and integrate it with the conversation data. For example, it can analyze conversation histories from messaging apps and calendar appointments. The conversation collection unit can also collect data from other devices and integrate it with the conversation data to build a more comprehensive data set. For example, it can analyze heart rate data and fitness data from a smart watch. The conversation collection unit can also collect data from multiple devices in the home and integrate it with the conversation data. For example, it can analyze usage history and voice commands of smart home devices. This allows it to build a more comprehensive data set by collecting data from other devices in the home.

[0033] The conversation collection unit collects conversation data from different languages ​​and cultural spheres, and can perform machine learning from a global perspective. The conversation collection unit, for example, collects conversation data from different languages ​​and performs machine learning from a global perspective. For example, conversation data from English, Spanish, Chinese, etc. is analyzed. The conversation collection unit also collects conversation data from different cultural spheres and performs machine learning that takes cultural differences into account. For example, conversation data from Asia, Europe, Africa, etc. The conversation collection unit also collects conversation data from different languages ​​and cultural spheres in order to perform machine learning from a global perspective. For example, an international dataset is constructed and cultural differences are learned. In this way, by collecting conversation data from different languages ​​and cultural spheres, machine learning from a global perspective becomes possible.

[0034] The fight detection unit analyzes changes in the volume and speed of conversations, enabling more accurate detection of signs of a fight. For example, the fight detection unit uses a generation AI to analyze changes in the volume of conversations and detect signs of a fight. For example, a sudden increase in volume is determined to be a sign of a fight. The fight detection unit also analyzes changes in the speed of conversations and detects signs of a fight. For example, a speed increase in conversation is determined to be a sign of a fight. The fight detection unit also analyzes changes in volume and speed simultaneously to detect signs of a fight with more accuracy. For example, a speed increase in conversation is determined to be a sign of a fight. In this way, by analyzing changes in the volume and speed of conversations, signs of a fight can be detected with more accuracy.

[0035] The fight detection unit can detect signs of a fight by analyzing not only the content of the conversation but also non-verbal elements. The fight detection unit detects signs of a fight by, for example, analyzing the length and frequency of silences in a conversation. For example, a long period of silence is determined to be a sign of a fight. The fight detection unit also analyzes non-verbal elements to detect signs of a fight. For example, a high frequency of silence is determined to be a sign of a fight. The fight detection unit also simultaneously analyzes the content of the conversation and non-verbal elements to detect signs of a fight. For example, a situation in which the language becomes rough and silence continues is determined to be a sign of a fight. In this way, by analyzing non-verbal elements, signs of a fight can be detected with greater accuracy.

[0036] The fight detection unit can also integrate data from other devices in the home when detecting signs of a fight. For example, the fight detection unit collects heart rate data from a smartwatch and integrates it when detecting signs of a fight. For example, a sudden increase in heart rate may be determined to be a sign of a fight. The fight detection unit also integrates data from other devices to detect signs of a fight. For example, it may analyze smartphone usage history and location information. The fight detection unit also integrates data from multiple devices in the home to detect signs of a fight. For example, it may analyze the usage status and voice commands of smart home devices. In this way, by integrating data from other devices in the home, signs of a fight can be detected with greater accuracy.

[0037] The fight detection unit can learn marital quarrel patterns in different cultural spheres and languages ​​and detect fights from a global perspective. The fight detection unit, for example, learns marital quarrel patterns in different cultural spheres and detects fights from a global perspective. For example, it analyzes quarrel patterns in Asia, Europe, Africa, etc. The fight detection unit also learns marital quarrel patterns in different languages ​​and detects fights. For example, it analyzes quarrel patterns in English, Spanish, Chinese, etc. The fight detection unit also learns marital quarrel patterns in different cultural spheres and languages ​​to detect fights from a global perspective. For example, it builds an international dataset and learns cultural differences. As a result, by learning marital quarrel patterns in different cultural spheres and languages, it becomes possible to detect fights from a global perspective.

[0038] The arbitration voice generation unit can generate individually customized arbitration voices based on the couple's past conversation data. For example, the arbitration voice generation unit generates individually customized arbitration voices by using a generation AI to analyze the couple's past conversation data. For example, it includes content related to the couple's interests and hobbies. The arbitration voice generation unit also generates arbitration voices that address the couple's specific problems and stressors based on the past conversation data. For example, it provides advice on specific topics. The arbitration voice generation unit also analyzes the couple's past conversation data to generate individually customized arbitration voices. For example, it generates voices that match the couple's conversation patterns and vocabulary. This makes it possible to generate individually customized arbitration voices based on the couple's past conversation data.

[0039] The arbitration audio generation unit associates the content of the arbitration audio with the interests and hobbies of the couple, enabling more effective arbitration. For example, the arbitration audio generation unit uses a generation AI to generate an arbitration audio that includes content related to the interests and hobbies of the couple. For example, it provides advice based on common hobbies and interests. The arbitration audio generation unit also customizes the content of the arbitration audio based on information related to the interests and hobbies of the couple. For example, it incorporates topics related to specific hobbies. The arbitration audio generation unit also generates arbitration audio associated with the interests and hobbies of the couple, enabling more effective arbitration. For example, it includes content that draws on common interests. This allows for more effective arbitration by associating the content of the arbitration audio with the interests and hobbies of the couple.

[0040] The arbitration audio generation unit can provide visual feedback in addition to the arbitration audio. For example, the arbitration audio generation unit displays visual feedback on a smartphone screen in addition to the arbitration audio. For example, it displays a message or advice to help the person stay calm. The arbitration audio generation unit also enhances the effectiveness of the arbitration audio by providing visual feedback. For example, it displays images or videos to help the person relax. The arbitration audio generation unit also provides a combination of the arbitration audio and visual feedback. For example, it displays related information on the screen at the same time as providing audio advice. In this way, the effectiveness of arbitration can be enhanced by providing visual feedback in addition to the arbitration audio.

[0041] The arbitration audio generation unit can generate arbitration audio that corresponds to different languages ​​and cultural spheres, enabling global use. The arbitration audio generation unit generates arbitration audio that corresponds to different languages, enabling global use. For example, it generates audio in English, Spanish, Chinese, etc. The arbitration audio generation unit also generates arbitration audio that corresponds to different cultural spheres, allowing arbitration that takes cultural differences into consideration. For example, it generates audio that suits the cultures of Asia, Europe, Africa, etc. The arbitration audio generation unit also generates arbitration audio that corresponds to different languages ​​and cultural spheres, enabling global use. For example, it generates audio based on an international dataset. This allows arbitration audio that corresponds to different languages ​​and cultural spheres to be generated, enabling global use.

[0042] In exchange for being provided free of charge, the system can collect anonymized data with the user's consent and use it for research and improvement. For example, the system may collect anonymized data with the user's consent and use it for research and improvement. For example, by analyzing conversation data and improving the accuracy of AI. In addition, the system may collect anonymized data in exchange for being provided free of charge and use it to improve the application. For example, by adding features based on user feedback. In addition, the system may collect anonymized data with the user's consent and use it for research and improvement. For example, by developing a new algorithm based on the data. In this way, in exchange for being provided free of charge, the system can collect anonymized data with the user's consent and use it for research and improvement, thereby improving the accuracy and functionality of the system.

[0043] The system can display advertisements in apps that are provided free of charge and operate based on the revenue generated. For example, the system displays advertisements in apps that are provided free of charge and operates based on the revenue generated. For example, the system displays banner advertisements or interstitial advertisements. The system also displays in-app advertisements and operates based on the revenue generated. For example, the system displays video advertisements or native advertisements. The system also displays advertisements in apps that are provided free of charge and operates based on the revenue generated. For example, the system adjusts the frequency or position of advertisement display. In this way, by displaying advertisements in apps that are provided free of charge and operating based on the revenue generated, it becomes possible to provide a sustainable service.

[0044] The system can add premium features to a free app and offer them for a fee. For example, the system can add premium features to a free app and offer them for a fee. For example, the system can provide ad-free functionality or additional functionality. The system can also add premium features to a free app and offer them for a fee, thereby increasing revenue from the app. For example, the system can provide exclusive content or customization functionality. The system can also add premium features to a free app and offer them for a fee. For example, the system can provide a monthly subscription or a one-time purchase option. In this way, the system can add premium features to a free app and offer them for a fee, thereby increasing revenue.

[0045] The system can provide versions compatible with different platforms. For example, the system provides versions compatible with different platforms. For example, apps are developed for iOS, Android, and smart speakers. The system also provides versions optimized for each platform. For example, apps are developed taking into consideration the UI / UX for each platform. The system also provides versions compatible with different platforms to improve user convenience. For example, a cross-platform data synchronization function is provided. This allows for improving user convenience by providing versions compatible with different platforms.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The arbitration system can further include a health data collection unit that collects the user's health data. For example, it can collect heart rate and sleep data from a smartwatch or fitness tracker to monitor the couple's health. The health data collection unit can also analyze the couple's stress levels and fatigue levels based on the collected data. For example, a high heart rate or persistent lack of sleep can be determined to be signs of stress. The health data collection unit can also suggest appropriate advice and relaxation methods based on the couple's health status. This enables arbitration that takes the couple's health status into consideration, which is expected to improve communication more effectively.

[0048] The arbitration system can further include a schedule collection unit that collects the user's schedule data. For example, schedule data can be collected from a calendar app or a task management app to understand the couple's schedule. The schedule collection unit can also analyze the couple's busyness and stress factors based on the collected data. For example, if the couple's schedule is packed or an important event is approaching, it can determine this as a sign of stress. Furthermore, the schedule collection unit can generate arbitration audio at an appropriate time based on the couple's schedule. This enables arbitration that takes the couple's schedules into consideration, which is expected to improve communication more effectively.

[0049] The arbitration system can further include a hobby data collection unit that collects data on the user's hobbies and interests. For example, data on hobbies and interests can be collected from social media and search history to understand the interests of the couple. The hobby data collection unit can also analyze the couple's common hobbies and interests based on the collected data. For example, if the couple has a common hobby, this can be used to improve communication. Furthermore, the hobby data collection unit can suggest appropriate activities and topics based on the couple's hobbies and interests. This enables arbitration that takes the couple's hobbies and interests into consideration, which is expected to improve communication more effectively.

[0050] The arbitration system can further include a purchase data collection unit that collects users' purchasing histories. For example, purchasing histories can be collected from online shopping sites or point card data to understand the couple's consumption trends. The purchase data collection unit can also analyze the couple's interests and stress factors based on the collected data. For example, if a particular product is frequently purchased, this can be determined to be an interest. Furthermore, the purchase data collection unit can provide appropriate advice and suggestions based on the couple's purchasing history. This enables arbitration that takes into account the couple's purchasing trends, which is expected to lead to more effective communication improvements.

[0051] The arbitration system can further include a location information collection unit that collects user location information. For example, location information can be collected using the GPS function of a smartphone to understand the couple's range of activities. The location information collection unit can also analyze the couple's behavioral patterns and stress factors based on the collected data. For example, if the couple frequently visits a particular location, this can be determined to be a concern. Furthermore, the location information collection unit can provide appropriate advice and suggestions based on the couple's location information. This enables arbitration that takes into account the couple's range of activities, which is expected to lead to more effective improvements in communication.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The conversation collection unit collects everyday conversations. For example, it can constantly listen to and collect conversations within the home using a device like Google Home. The conversation collection unit can also send the collected conversation data to the generation AI. Step 2: The machine learning unit performs machine learning based on the conversation data collected by the conversation collection unit. For example, the generation AI uses deep learning to analyze the couple's conversation patterns and emotional changes. The generation AI can also classify conversation data using a support vector machine. The generation AI also learns what topics the couple talks about on a daily basis and the tone of their speech. Step 3: The quarrel detection unit detects signs of marital quarrels based on the data learned by the machine learning unit. For example, the generation AI will determine that a higher tone of voice or rougher language is a sign of marital quarrels. The generation AI can also analyze the frequency of occurrence of specific keywords and phrases to detect signs of marital quarrels. The generation AI can also detect sudden changes in emotions and determine these as signs of marital quarrels. Step 4: The mediation voice generation unit generates a mediation voice based on the signs of a marital quarrel detected by the quarrel detection unit. For example, the generation AI generates voices such as "Listen to each other's opinions calmly" or "Let's take a short break and continue the discussion." The generation AI can also generate voices to calm the couple's emotions. The generation AI can also generate individually customized mediation voices based on the couple's past conversation data.

[0054] (Example 2) The arbitration system according to an embodiment of the present invention collects everyday conversations, uses machine learning to detect signs of marital disputes, and generates arbitration audio. This allows the arbitration system to facilitate smooth communication between couples and prevent marital disputes.

[0055] An arbitration system according to an embodiment includes a conversation collection unit, a machine learning unit, a fight detection unit, and an arbitration audio generation unit. The conversation collection unit collects everyday conversations. For example, it constantly listens to and collects household conversations using a device such as Google Home. The conversation collection unit can also send the collected conversation data to the generation AI. The machine learning unit performs machine learning based on the conversation data collected by the conversation collection unit. For example, the generation AI can analyze a couple's conversation patterns and emotional changes using deep learning. The generation AI can also classify conversation data using a support vector machine. The generation AI can also learn what topics a couple regularly talks about and in what tone. The fight detection unit detects signs of a marital fight based on the data learned by the machine learning unit. For example, the generation AI can determine that a higher-pitched voice or rougher language is a sign of a marital fight. The generation AI can also analyze the frequency of occurrence of specific keywords and phrases to detect signs of a marital fight. The generation AI can also detect sudden changes in emotions and determine them as signs of marital quarrels. The arbitration voice generation unit generates arbitration voices based on the signs of marital quarrels detected by the quarrel detection unit. For example, the generation AI generates voices such as "Let's listen to each other's opinions calmly" or "Let's take a short break and continue the discussion." The generation AI can also generate voices to calm the couple's emotions. The generation AI can also generate individually customized arbitration voices based on the couple's past conversation data. This allows the arbitration system according to the embodiment to facilitate communication between couples and prevent marital quarrels. For example, the arbitration system can understand the couple's conversation patterns and generate arbitration voices at the appropriate time to resolve marital quarrels early. The arbitration system can also generate arbitration voices that correspond to the couple's emotional state, allowing for more effective arbitration.

[0056] The conversation collection unit can analyze background sounds and environmental sounds of a conversation to understand the context of the conversation. For example, the conversation collection unit uses a generation AI to analyze background sounds and environmental sounds of a conversation to understand the context of the conversation. For example, it analyzes background sounds such as the sound of a television or children's voices to understand the situation of the conversation. The conversation collection unit also analyzes background sounds of a conversation to identify the location and time of the conversation and understand the context of the conversation. For example, it can distinguish between conversations in the kitchen and conversations at night. The conversation collection unit also analyzes environmental sounds to understand the atmosphere and tension of the conversation. For example, it can understand the difference between conversations in a quiet environment and conversations in a noisy environment. In this way, by analyzing background sounds and environmental sounds of a conversation, the situation of the conversation can be understood more deeply.

[0057] The conversation collection unit can track specific keywords and phrases used in conversations to identify the couple's interests and stressors. The conversation collection unit, for example, tracks keywords and phrases frequently used in conversations to identify the couple's interests. For example, it analyzes keywords such as "work," "children," and "housework." The conversation collection unit also tracks specific phrases and word usage to identify the couple's stressors. For example, it analyzes phrases such as "tired," "busy," and "irritated." The conversation collection unit also analyzes the frequency of keyword appearance in conversations to identify the couple's interests and stressors. For example, if a specific keyword appears frequently, it determines that the topic is important. In this way, the couple's interests and stressors can be identified by tracking specific keywords and phrases used in conversations.

[0058] The conversation collection unit can use the emotion estimation function to analyze changes in emotions during a conversation in real time and learn emotional ups and downs. The conversation collection unit, for example, uses the emotion estimation function to analyze changes in emotions during a conversation in real time. For example, it analyzes changes in tone and speed of voice to grasp emotional ups and downs. The conversation collection unit also analyzes changes in emotions during a conversation in real time and learns emotional ups and downs. For example, it detects changes from positive emotions to negative emotions. The conversation collection unit also uses the emotion estimation function to analyze changes in emotions during a conversation and learns emotional ups and downs. For example, it analyzes patterns of emotional change to grasp the emotional tendencies of a couple. In this way, it is possible to use the emotion estimation function to analyze changes in emotions during a conversation in real time and learn emotional ups and downs.

[0059] The conversation collection unit can also collect data from other devices in the home to build a more comprehensive data set. For example, the conversation collection unit can collect data from smartphones and tablets in the home and integrate it with the conversation data. For example, it can analyze conversation histories from messaging apps and calendar appointments. The conversation collection unit can also collect data from other devices and integrate it with the conversation data to build a more comprehensive data set. For example, it can analyze heart rate data and fitness data from a smart watch. The conversation collection unit can also collect data from multiple devices in the home and integrate it with the conversation data. For example, it can analyze usage history and voice commands of smart home devices. This allows it to build a more comprehensive data set by collecting data from other devices in the home.

[0060] The conversation collection unit collects conversation data from different languages ​​and cultural spheres, and can perform machine learning from a global perspective. The conversation collection unit, for example, collects conversation data from different languages ​​and performs machine learning from a global perspective. For example, conversation data from English, Spanish, Chinese, etc. is analyzed. The conversation collection unit also collects conversation data from different cultural spheres and performs machine learning that takes cultural differences into account. For example, conversation data from Asia, Europe, Africa, etc. The conversation collection unit also collects conversation data from different languages ​​and cultural spheres in order to perform machine learning from a global perspective. For example, an international dataset is constructed and cultural differences are learned. In this way, by collecting conversation data from different languages ​​and cultural spheres, machine learning from a global perspective becomes possible.

[0061] The conversation collection unit can use the emotion estimation function to analyze changes in emotions during a conversation and provide real-time feedback according to the changes in emotions. The conversation collection unit, for example, uses the emotion estimation function to analyze changes in emotions during a conversation and provide feedback in real time. For example, it displays a message urging the person to stay calm when emotions become heightened. The conversation collection unit also analyzes changes in emotions during a conversation in real time and provides feedback according to the changes in emotions. For example, it makes suggestions to strengthen positive emotions. The conversation collection unit also uses the emotion estimation function to analyze changes in emotions during a conversation and provide feedback in real time. For example, it suggests ways to relax when negative emotions become stronger. In this way, it is possible to use the emotion estimation function to analyze changes in emotions during a conversation and provide feedback in real time.

[0062] The fight detection unit analyzes changes in the volume and speed of conversations, enabling more accurate detection of signs of a fight. For example, the fight detection unit uses a generation AI to analyze changes in the volume of conversations and detect signs of a fight. For example, a sudden increase in volume is determined to be a sign of a fight. The fight detection unit also analyzes changes in the speed of conversations and detects signs of a fight. For example, a speed increase in conversation is determined to be a sign of a fight. The fight detection unit also analyzes changes in volume and speed simultaneously to detect signs of a fight with more accuracy. For example, a speed increase in conversation is determined to be a sign of a fight. In this way, by analyzing changes in the volume and speed of conversations, signs of a fight can be detected with more accuracy.

[0063] The fight detection unit can detect signs of a fight by analyzing not only the content of the conversation but also non-verbal elements. The fight detection unit detects signs of a fight by, for example, analyzing the length and frequency of silences in a conversation. For example, a long period of silence is determined to be a sign of a fight. The fight detection unit also analyzes non-verbal elements to detect signs of a fight. For example, a high frequency of silence is determined to be a sign of a fight. The fight detection unit also simultaneously analyzes the content of the conversation and non-verbal elements to detect signs of a fight. For example, a situation in which the language becomes rough and silence continues is determined to be a sign of a fight. In this way, by analyzing non-verbal elements, signs of a fight can be detected with greater accuracy.

[0064] The fight detection unit can use the emotion estimation function to detect a sudden change in emotion during a conversation and recognize it as a sign of a fight. The fight detection unit, for example, uses the emotion estimation function to detect a sudden change in emotion during a conversation. For example, it determines a sudden change from a positive emotion to a negative emotion as a sign of a fight. The fight detection unit also analyzes the sudden change in emotion during a conversation in real time and recognizes it as a sign of a fight. For example, it determines a sudden change in emotion as a sign of a fight. The fight detection unit also uses the emotion estimation function to detect a sudden change in emotion during a conversation and recognize it as a sign of a fight. For example, it analyzes a pattern of emotion changes and identifies a sign of a fight. In this way, it is possible to use the emotion estimation function to detect a sudden change in emotion during a conversation and recognize it as a sign of a fight.

[0065] The fight detection unit can also integrate data from other devices in the home when detecting signs of a fight. For example, the fight detection unit collects heart rate data from a smartwatch and integrates it when detecting signs of a fight. For example, a sudden increase in heart rate may be determined to be a sign of a fight. The fight detection unit also integrates data from other devices to detect signs of a fight. For example, it may analyze smartphone usage history and location information. The fight detection unit also integrates data from multiple devices in the home to detect signs of a fight. For example, it may analyze the usage status and voice commands of smart home devices. In this way, by integrating data from other devices in the home, signs of a fight can be detected with greater accuracy.

[0066] The fight detection unit can learn marital quarrel patterns in different cultural spheres and languages ​​and detect fights from a global perspective. The fight detection unit, for example, learns marital quarrel patterns in different cultural spheres and detects fights from a global perspective. For example, it analyzes quarrel patterns in Asia, Europe, Africa, etc. The fight detection unit also learns marital quarrel patterns in different languages ​​and detects fights. For example, it analyzes quarrel patterns in English, Spanish, Chinese, etc. The fight detection unit also learns marital quarrel patterns in different cultural spheres and languages ​​to detect fights from a global perspective. For example, it builds an international dataset and learns cultural differences. As a result, by learning marital quarrel patterns in different cultural spheres and languages, it becomes possible to detect fights from a global perspective.

[0067] The fight detection unit can use the emotion estimation function to monitor the user's emotional state in real time when signs of a fight are detected and suggest an appropriate response. The fight detection unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time when signs of a fight are detected. For example, an appropriate response is suggested based on an emotion score. The fight detection unit can also monitor the user's emotional state in real time when signs of a fight are detected and suggest an appropriate response. For example, it can suggest ways to relax or ways to stay calm. The fight detection unit can also use the emotion estimation function to monitor the user's emotional state when signs of a fight are detected and suggest an appropriate response. For example, it can provide advice according to changes in emotions. In this way, the emotion estimation function can be used to monitor the user's emotional state in real time when signs of a fight are detected and suggest an appropriate response.

[0068] The arbitration voice generation unit can generate individually customized arbitration voices based on the couple's past conversation data. For example, the arbitration voice generation unit generates individually customized arbitration voices by using a generation AI to analyze the couple's past conversation data. For example, it includes content related to the couple's interests and hobbies. The arbitration voice generation unit also generates arbitration voices that address the couple's specific problems and stressors based on the past conversation data. For example, it provides advice on specific topics. The arbitration voice generation unit also analyzes the couple's past conversation data to generate individually customized arbitration voices. For example, it generates voices that match the couple's conversation patterns and vocabulary. This makes it possible to generate individually customized arbitration voices based on the couple's past conversation data.

[0069] The arbitration audio generation unit associates the content of the arbitration audio with the interests and hobbies of the couple, enabling more effective arbitration. For example, the arbitration audio generation unit uses a generation AI to generate an arbitration audio that includes content related to the interests and hobbies of the couple. For example, it provides advice based on common hobbies and interests. The arbitration audio generation unit also customizes the content of the arbitration audio based on information related to the interests and hobbies of the couple. For example, it incorporates topics related to specific hobbies. The arbitration audio generation unit also generates arbitration audio associated with the interests and hobbies of the couple, enabling more effective arbitration. For example, it includes content that draws on common interests. This allows for more effective arbitration by associating the content of the arbitration audio with the interests and hobbies of the couple.

[0070] The arbitration voice generation unit uses the emotion estimation function to adjust the content of the arbitration voice in real time, thereby generating a voice that is optimal for the emotional state of the couple. The arbitration voice generation unit, for example, uses the emotion estimation function to adjust the content of the arbitration voice in real time. For example, it changes the tone and content of the voice depending on the emotional state of the couple. The arbitration voice generation unit also monitors the emotional state of the couple in real time and generates an optimal arbitration voice. For example, it generates a voice that urges the couple to stay calm when emotions become heightened. The arbitration voice generation unit also uses the emotion estimation function to adjust the content of the arbitration voice in real time, thereby generating a voice that is optimal for the emotional state of the couple. For example, it provides advice in accordance with changes in emotions. In this way, the emotion estimation function can be used to adjust the content of the arbitration voice in real time, thereby generating a voice that is optimal for the emotional state of the couple.

[0071] The arbitration audio generation unit can provide visual feedback in addition to the arbitration audio. For example, the arbitration audio generation unit displays visual feedback on a smartphone screen in addition to the arbitration audio. For example, it displays a message or advice to help the person stay calm. The arbitration audio generation unit also enhances the effectiveness of the arbitration audio by providing visual feedback. For example, it displays images or videos to help the person relax. The arbitration audio generation unit also provides a combination of the arbitration audio and visual feedback. For example, it displays related information on the screen at the same time as providing audio advice. In this way, the effectiveness of arbitration can be enhanced by providing visual feedback in addition to the arbitration audio.

[0072] The arbitration audio generation unit can generate arbitration audio that corresponds to different languages ​​and cultural spheres, enabling global use. The arbitration audio generation unit generates arbitration audio that corresponds to different languages, enabling global use. For example, it generates audio in English, Spanish, Chinese, etc. The arbitration audio generation unit also generates arbitration audio that corresponds to different cultural spheres, allowing arbitration that takes cultural differences into consideration. For example, it generates audio that suits the cultures of Asia, Europe, Africa, etc. The arbitration audio generation unit also generates arbitration audio that corresponds to different languages ​​and cultural spheres, enabling global use. For example, it generates audio based on an international dataset. This allows arbitration audio that corresponds to different languages ​​and cultural spheres to be generated, enabling global use.

[0073] The arbitration voice generation unit can use the emotion estimation function to monitor the effectiveness of the arbitration voice in real time and propose a different approach if the effectiveness is low. The arbitration voice generation unit, for example, uses the emotion estimation function to monitor the effectiveness of the arbitration voice in real time. For example, it evaluates the effectiveness of the voice based on an emotion score. Furthermore, the arbitration voice generation unit proposes a different approach if the effectiveness of the arbitration voice is low. For example, it generates voice with a different tone or content. Furthermore, the arbitration voice generation unit uses the emotion estimation function to monitor the effectiveness of the arbitration voice in real time and proposes a different approach if the effectiveness is low. For example, it proposes visual feedback or relaxation techniques. In this way, the effectiveness of arbitration can be maximized by using the emotion estimation function to monitor the effectiveness of the arbitration voice in real time and proposing a different approach if the effectiveness is low.

[0074] In exchange for being provided free of charge, the system can collect anonymized data with the user's consent and use it for research and improvement. For example, the system may collect anonymized data with the user's consent and use it for research and improvement. For example, by analyzing conversation data and improving the accuracy of AI. In addition, the system may collect anonymized data in exchange for being provided free of charge and use it to improve the application. For example, by adding features based on user feedback. In addition, the system may collect anonymized data with the user's consent and use it for research and improvement. For example, by developing a new algorithm based on the data. In this way, in exchange for being provided free of charge, the system can collect anonymized data with the user's consent and use it for research and improvement, thereby improving the accuracy and functionality of the system.

[0075] The system can display advertisements in apps that are provided free of charge and operate based on the revenue generated. For example, the system displays advertisements in apps that are provided free of charge and operates based on the revenue generated. For example, the system displays banner advertisements or interstitial advertisements. The system also displays in-app advertisements and operates based on the revenue generated. For example, the system displays video advertisements or native advertisements. The system also displays advertisements in apps that are provided free of charge and operates based on the revenue generated. For example, the system adjusts the frequency or position of advertisement display. In this way, by displaying advertisements in apps that are provided free of charge and operating based on the revenue generated, it becomes possible to provide a sustainable service.

[0076] The system uses the emotion estimation function to display advertisements according to the user's emotional state, thereby maximizing the effectiveness of the advertisements. For example, the system uses the emotion estimation function to display advertisements according to the user's emotional state. For example, relevant advertisements are displayed when the user is in a positive emotional state. The system also analyzes the user's emotional state in real time and displays optimal advertisements. For example, the content of the advertisements is adjusted based on the emotion score. The system also uses the emotion estimation function to display advertisements according to the user's emotional state, thereby maximizing the effectiveness of the advertisements. For example, advertisements are displayed according to changes in emotions. In this way, the emotion estimation function can be used to display advertisements according to the user's emotional state, thereby maximizing the effectiveness of the advertisements.

[0077] The system can add premium features to a free app and offer them for a fee. For example, the system can add premium features to a free app and offer them for a fee. For example, the system can provide ad-free functionality or additional functionality. The system can also add premium features to a free app and offer them for a fee, thereby increasing revenue from the app. For example, the system can provide exclusive content or customization functionality. The system can also add premium features to a free app and offer them for a fee. For example, the system can provide a monthly subscription or a one-time purchase option. In this way, the system can add premium features to a free app and offer them for a fee, thereby increasing revenue.

[0078] The system can provide versions compatible with different platforms. For example, the system provides versions compatible with different platforms. For example, apps are developed for iOS, Android, and smart speakers. The system also provides versions optimized for each platform. For example, apps are developed taking into consideration the UI / UX for each platform. The system also provides versions compatible with different platforms to improve user convenience. For example, a cross-platform data synchronization function is provided. This allows for improving user convenience by providing versions compatible with different platforms.

[0079] The system can use the emotion estimation function to suggest premium features according to the user's emotional state and promote usage. For example, the system uses the emotion estimation function to suggest premium features according to the user's emotional state. For example, a relaxation function is suggested when stress is high. The system also analyzes the user's emotional state in real time and suggests optimal premium features. For example, features are suggested based on an emotion score. The system also uses the emotion estimation function to suggest premium features according to the user's emotional state and promote usage. For example, features are suggested according to changes in emotion. In this way, usage can be promoted by using the emotion estimation function to suggest premium features according to the user's emotional state.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The arbitration system can further include a health data collection unit that collects the user's health data. For example, it can collect heart rate and sleep data from a smartwatch or fitness tracker to monitor the couple's health. The health data collection unit can also analyze the couple's stress levels and fatigue levels based on the collected data. For example, a high heart rate or persistent lack of sleep can be determined to be signs of stress. The health data collection unit can also suggest appropriate advice and relaxation methods based on the couple's health status. This enables arbitration that takes the couple's health status into consideration, which is expected to improve communication more effectively.

[0082] The arbitration system can further include a schedule collection unit that collects the user's schedule data. For example, schedule data can be collected from a calendar app or a task management app to understand the couple's schedule. The schedule collection unit can also analyze the couple's busyness and stress factors based on the collected data. For example, if the couple's schedule is packed or an important event is approaching, it can determine this as a sign of stress. Furthermore, the schedule collection unit can generate arbitration audio at an appropriate time based on the couple's schedule. This enables arbitration that takes the couple's schedules into consideration, which is expected to improve communication more effectively.

[0083] The arbitration system can further include a hobby data collection unit that collects data on the user's hobbies and interests. For example, data on hobbies and interests can be collected from social media and search history to understand the interests of the couple. The hobby data collection unit can also analyze the couple's common hobbies and interests based on the collected data. For example, if the couple has a common hobby, this can be used to improve communication. Furthermore, the hobby data collection unit can suggest appropriate activities and topics based on the couple's hobbies and interests. This enables arbitration that takes the couple's hobbies and interests into consideration, which is expected to improve communication more effectively.

[0084] The arbitration system can further include a purchase data collection unit that collects users' purchasing histories. For example, purchasing histories can be collected from online shopping sites or point card data to understand the couple's consumption trends. The purchase data collection unit can also analyze the couple's interests and stress factors based on the collected data. For example, if a particular product is frequently purchased, this can be determined to be an interest. Furthermore, the purchase data collection unit can provide appropriate advice and suggestions based on the couple's purchasing history. This enables arbitration that takes into account the couple's purchasing trends, which is expected to lead to more effective communication improvements.

[0085] The arbitration system can further include a location information collection unit that collects user location information. For example, location information can be collected using the GPS function of a smartphone to understand the couple's range of activities. The location information collection unit can also analyze the couple's behavioral patterns and stress factors based on the collected data. For example, if the couple frequently visits a particular location, this can be determined to be a concern. Furthermore, the location information collection unit can provide appropriate advice and suggestions based on the couple's location information. This enables arbitration that takes into account the couple's range of activities, which is expected to lead to more effective improvements in communication.

[0086] The arbitration system can also estimate the user's emotional state and monitor the couple's stress level based on the estimated emotions. For example, the emotion estimation function can be used to analyze the couple's emotional state in real time and evaluate their stress level. The emotion estimation function can also be used to monitor changes in the couple's emotions and provide appropriate advice when stress increases, such as suggesting ways to relax or activities to relieve stress. Furthermore, the emotion estimation function can also be used to adjust the content of the arbitration audio based on the couple's emotional state. This enables arbitration that takes the couple's emotional state into consideration, which is expected to improve communication more effectively.

[0087] The arbitration system can also estimate the user's emotional state and monitor the couple's interests and hobbies based on the estimated emotions. For example, the emotion estimation function can be used to analyze the couple's emotional state in real time and evaluate their interests and hobbies. The emotion estimation function can also be used to monitor changes in the couple's emotions and suggest activities based on their interests and hobbies. For example, it can suggest events or activities related to shared hobbies. Furthermore, the emotion estimation function can also be used to adjust the content of the arbitration audio based on the couple's emotional state. This enables arbitration that takes the couple's emotional state into consideration, which is expected to improve communication more effectively.

[0088] The arbitration system can also estimate the user's emotional state and monitor the couple's purchasing trends based on the estimated emotions. For example, the emotion estimation function can be used to analyze the couple's emotional state in real time and evaluate their purchasing trends. The emotion estimation function can also be used to monitor changes in the couple's emotions and provide advice based on their purchasing trends. For example, it can suggest relaxation products when stress levels rise. Furthermore, the emotion estimation function can also be used to adjust the content of the arbitration audio based on the couple's emotional state. This enables arbitration that takes the couple's emotional state into consideration, which is expected to improve communication more effectively.

[0089] The arbitration system can also estimate the user's emotional state and monitor the couple's behavioral patterns based on the estimated emotions. For example, the emotion estimation function can be used to analyze the couple's emotional state in real time and evaluate their behavioral patterns. The emotion estimation function can also be used to monitor changes in the couple's emotions and provide advice based on their behavioral patterns. For example, the emotion estimation function can suggest a place where the couple can relax when stress levels rise. Furthermore, the emotion estimation function can also be used to adjust the content of the arbitration audio based on the couple's emotional state. This enables arbitration that takes the couple's emotional state into consideration, which is expected to lead to more effective communication improvements.

[0090] The arbitration system can also estimate the user's emotional state and monitor the couple's health status based on the estimated emotions. For example, the emotion estimation function can be used to analyze the couple's emotional state in real time and evaluate their health status. The emotion estimation function can also be used to monitor changes in the couple's emotions and provide advice based on their health status. For example, it can suggest relaxation methods or healthy meals when stress levels rise. Furthermore, the emotion estimation function can also be used to adjust the content of the arbitration audio based on the couple's emotional state. This enables arbitration that takes the couple's emotional state into consideration, which is expected to improve communication more effectively.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The conversation collection unit collects everyday conversations. For example, it can constantly listen to and collect conversations within the home using a device like Google Home. The conversation collection unit can also send the collected conversation data to the generation AI. Step 2: The machine learning unit performs machine learning based on the conversation data collected by the conversation collection unit. For example, the generation AI uses deep learning to analyze the couple's conversation patterns and emotional changes. The generation AI can also classify conversation data using a support vector machine. The generation AI also learns what topics the couple talks about on a daily basis and the tone of their speech. Step 3: The quarrel detection unit detects signs of marital quarrels based on the data learned by the machine learning unit. For example, the generation AI will determine that a higher tone of voice or rougher language is a sign of marital quarrels. The generation AI can also analyze the frequency of occurrence of specific keywords and phrases to detect signs of marital quarrels. The generation AI can also detect sudden changes in emotions and determine these as signs of marital quarrels. Step 4: The mediation voice generation unit generates a mediation voice based on the signs of a marital quarrel detected by the quarrel detection unit. For example, the generation AI generates voices such as "Listen to each other's opinions calmly" or "Let's take a short break and continue the discussion." The generation AI can also generate voices to calm the couple's emotions. The generation AI can also generate individually customized mediation voices based on the couple's past conversation data.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a conversation collection unit that collects everyday conversations; a machine learning unit that performs machine learning based on the conversation data collected by the conversation collection unit; a quarrel detection unit that detects signs of marital quarrels based on data learned by the machine learning unit; an arbitration voice generation unit that generates an arbitration voice based on the signs of a marital quarrel detected by the quarrel detection unit; A system characterized by:

2. The conversation collection unit Analyze background and environmental sounds to understand the context of a conversation 2. The system of claim 1.

3. The conversation collection unit Track specific keywords and phrases used in conversations to identify couples' concerns and stressors 2. The system of claim 1.

4. The conversation collection unit Analyzes emotional changes during conversations in real time and learns emotional fluctuations 2. The system of claim 1.

5. The conversation collection unit Collect data from other devices in the home to build a more comprehensive dataset 2. The system of claim 1.

6. The conversation collection unit Collecting conversation data from different languages ​​and cultures to conduct machine learning from a global perspective 2. The system of claim 1.

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