system

Abstract

The system according to the embodiment aims to analyze what the user says and provide appropriate responses and health advice. [Solution] A system according to an embodiment includes an analysis unit, a reaction unit, a collection unit, an advice unit, and a conversation unit. The analysis unit analyzes what a user says. The reaction unit generates a reaction based on the content analyzed by the analysis unit. The collection unit collects health data from the user. The advice unit analyzes the data collected by the collection unit and provides health advice. The conversation unit continues a conversation based on the user's hobbies or interests.

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 technologies are not sufficient in understanding what users say and providing appropriate responses and health advice, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze what the user says and provide appropriate responses and health advice. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a reaction unit, a collection unit, an advice unit, and a conversation unit. The analysis unit analyzes what the user says. The reaction unit generates a reaction based on the content analyzed by the analysis unit. The collection unit collects health data of the user. The advice unit analyzes the data collected by the collection unit and provides health advice. The conversation unit continues a conversation based on the user's hobbies or interests. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's speech and provide appropriate responses and health advice. [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) An AI robot system according to an embodiment of the present invention listens to a user's complaints, provides health advice, and alleviates feelings of loneliness. When a user complains to the AI ​​robot, the AI ​​robot system analyzes the content of the complaint and provides an appropriate response. The AI ​​robot then monitors the user's health and provides health advice. Furthermore, the AI ​​robot can engage in everyday conversations to alleviate the user's feelings of loneliness. For example, when a user complains to the AI ​​robot, the AI ​​robot analyzes the content of the complaint and provides an appropriate response. The AI ​​robot then monitors the user's health and provides health advice. Furthermore, the AI ​​robot can engage in everyday conversations to alleviate the user's feelings of loneliness. In this way, the AI ​​robot acts like a personal butler, listening to the user's complaints, providing health advice, and alleviating feelings of loneliness, thereby supporting the user's daily life. This allows the AI ​​robot system to listen to the user's complaints, provide health advice, and alleviate feelings of loneliness.

[0029] The AI ​​robot system according to the embodiment includes an analysis unit, a response unit, a collection unit, an advice unit, and a conversation unit. The analysis unit analyzes a user's speech. The user's speech may include, but is not limited to, complaints and everyday events. The analysis unit converts the user's speech into text data using, for example, voice analysis technology and analyzes the content. The analysis unit can also understand the emotions and content of the user's speech using text analysis technology. For example, the analysis unit converts the user's speech into text data using voice recognition technology and estimates emotions using an emotion analysis algorithm. The response unit generates a response based on the content analyzed by the analysis unit. The response may include, but is not limited to, a voice response or a text message. The response unit may generate a response based on the user's emotions. The response unit can also generate an action based on the situation. For example, if the user is sad, the response unit may respond with kind words. The collection unit collects the user's health data using a sensor. The health data may include, but is not limited to, heart rate, blood pressure, body temperature, etc. The collection unit, for example, collects health data in real time using a wearable device. The collection unit can also collect health data periodically. For example, the collection unit records daily health data and monitors long-term health status. The advice unit analyzes the data collected by the collection unit and provides health advice. The advice includes, for example, but is not limited to, dietary suggestions, exercise recommendations, and lifestyle improvements. The advice unit, for example, provides personalized advice. The advice unit can also provide recommendations based on data. For example, the advice unit analyzes the user's health data and provides appropriate diet and exercise advice. The conversation unit continues a conversation based on the user's hobbies and interests. The conversation includes, for example, information about hobbies and daily events, for example, but is not limited to, the conversation unit customizes the conversation content based on the user's past statements and behavioral history. The conversation unit can also adjust the conversation content according to the user's current situation. For example, if the user is traveling, the conversation unit provides travel-related information.As a result, the AI ​​robot system according to the embodiment can listen to the user's complaints, provide health advice, and alleviate feelings of loneliness.

[0030] The analysis unit can analyze the text of the user's speech to understand the emotions and content. Text analysis includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis, for example. The analysis unit, for example, uses morphological analysis technology to break down the user's speech into words and uses grammatical analysis technology to analyze the sentence structure. The analysis unit can also understand the meaning of the user's speech using semantic analysis technology. For example, the analysis unit uses a sentiment analysis algorithm to estimate the emotions of the user's speech. This allows the analysis unit to understand the emotions and content by analyzing the text of the user's speech.

[0031] The reaction unit can generate an appropriate reaction based on the analysis result. Examples of appropriate reactions include, but are not limited to, voice responses, text messages, gestures, and the like. The reaction unit can generate a response according to the user's emotions. For example, if the user is sad, the reaction unit can respond with kind words. The reaction unit can also generate an action according to the situation. For example, if the user is angry, the reaction unit can respond in a calm and collected tone. This allows the reaction unit to generate an appropriate reaction based on the analysis result.

[0032] The collection unit can collect the user's health data in real time using a sensor. Real-time collection includes, but is not limited to, real-time data collection and periodic data collection. For example, the collection unit collects the health data in real time using a wearable device. The collection unit can also collect the health data periodically. For example, the collection unit records daily health data and monitors long-term health conditions. This allows the collection unit to collect the user's health data in real time using a sensor.

[0033] The advice unit can analyze the collected data and provide appropriate health advice. Examples of appropriate health advice include, but are not limited to, dietary suggestions, exercise recommendations, and lifestyle improvement. The advice unit can provide, for example, personalized advice. The advice unit can also provide recommendations based on the data. For example, the advice unit can analyze the user's health data and provide appropriate dietary and exercise advice. This allows the advice unit to analyze the collected data and provide appropriate health advice.

[0034] The conversation unit can provide information related to the user's topic and continue the conversation. Related information includes, but is not limited to, the user's past statements, behavioral history, and current situation, for example. The conversation unit customizes the conversation content based on the user's past statements and behavioral history, for example. The conversation unit can also adjust the conversation content according to the user's current situation. For example, if the user is traveling, the conversation unit provides travel-related information. This allows the conversation unit to provide information related to the user's topic and continue the conversation.

[0035] The analysis unit can analyze the user's past conversation history and select the optimal analysis method. For example, the analysis unit saves the user's past conversation history as log data and analyzes it using text mining technology. For example, the analysis unit customizes the analysis method based on topics that the user has frequently talked about in the past. The analysis unit can also extract specific emotion patterns from the user's past conversation history and adjust the analysis method. For example, the analysis unit analyzes the user's past conversation history and improves the analysis accuracy for specific keywords. This allows the analysis unit to select the optimal analysis method by analyzing the user's past conversation history.

[0036] During analysis, the analysis unit can perform filtering based on the user's current living situation and areas of interest. For example, when the user inputs their current living situation, the analysis unit filters the analysis content based on that information. For example, the analysis unit analyzes only relevant information based on the user's areas of interest. The analysis unit can also eliminate unnecessary information according to the user's living situation and areas of interest to improve the accuracy of the analysis. For example, the analysis unit customizes the analysis content based on the user's living situation and areas of interest. This allows the analysis unit to improve the accuracy of the analysis by filtering based on the user's current living situation and areas of interest.

[0037] During analysis, the analysis unit can prioritize analysis of highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit prioritizes analysis of information related to that region. For example, the analysis unit can analyze topics specific to the region based on the user's geographical location information. Furthermore, if the user is traveling, the analysis unit can prioritize analysis of tourist information and event information related to the user's current location. Furthermore, the analysis unit can analyze local health information and lifestyle information based on the user's geographical location information. This allows the analysis unit to prioritize analysis of highly relevant information by taking into account the user's geographical location information.

[0038] During the analysis, the analysis unit can analyze the user's social media activities and analyze related information. For example, the analysis unit analyzes related topics based on information shared by the user on social media. For example, the analysis unit analyzes the user's social media activity history and customizes the analysis content based on the user's interests. The analysis unit can also analyze information on accounts the user follows on social media. Furthermore, the analysis unit can analyze the content posted by the user on social media and analyze trend information. In this way, the analysis unit can analyze related information by analyzing the user's social media activities.

[0039] When generating a reaction, the reaction unit can adjust the level of detail of the reaction based on the importance of the conversation. The reaction unit evaluates the importance of the conversation based on, for example, the user's level of interest or urgency. For example, the reaction unit generates a detailed reaction when the user is having an important conversation. The reaction unit can also generate a concise reaction in the case of an everyday conversation. Furthermore, the reaction unit can also generate a detailed reaction for a topic in which the user is particularly interested. In this way, the reaction unit can provide a more appropriate reaction by adjusting the level of detail of the reaction based on the importance of the conversation.

[0040] The reaction unit can apply different reaction algorithms depending on the category of the conversation when generating a reaction. For example, the reaction unit classifies the category of the conversation into business, personal, hobby, etc. For example, the reaction unit applies a reaction algorithm that shows empathy to a complaint. The reaction unit can also apply a reaction algorithm based on expert knowledge to health advice. Furthermore, the reaction unit can apply a reaction algorithm with a light tone to everyday conversations. In this way, the reaction unit can provide a more appropriate reaction by applying different reaction algorithms depending on the category of the conversation.

[0041] When generating reactions, the reaction unit can determine the priority of reactions based on the time of submission of the conversation. For example, the reaction unit generates reactions preferentially to topics that the user has recently talked about. For example, the reaction unit also generates reactions to topics that the user has talked about in the past according to the importance of those topics. The reaction unit can also adjust the priority of reactions based on the content that the user spoke about during a specific time period. In this way, the reaction unit can provide more appropriate reactions by determining the priority of reactions based on the time of submission of the conversation.

[0042] The reaction unit can adjust the order of reactions based on the relevance of the conversation when generating reactions. The reaction unit determines the order of reactions based on, for example, the relevance of the content spoken by the user. For example, when the user speaks about multiple topics, the reaction unit generates reactions preferentially to the most relevant topic. The reaction unit can also dynamically adjust the order of reactions according to the flow of the user's speech. In this way, the reaction unit can provide more appropriate reactions by adjusting the order of reactions based on the relevance of the conversation.

[0043] The collection unit can analyze the user's past health data and select the optimal collection method. For example, the collection unit collects and analyzes the user's past health data as electronic medical records or data from wearable devices. For example, the collection unit selects the optimal sensor based on the user's past health data. The collection unit can also select a collection method for a specific time period based on the user's past health data. Furthermore, the collection unit can analyze the user's past health data and select the most efficient collection method. In this way, the collection unit can select the optimal collection method by analyzing the user's past health data.

[0044] The collection unit can filter the health data based on the user's current living situation and areas of interest when collecting the health data. For example, if the user inputs their current living situation, the collection unit filters the health data based on that information. For example, the collection unit collects only relevant health data based on the user's areas of interest. The collection unit can also eliminate unnecessary health data according to the user's living situation and areas of interest, thereby improving the accuracy of the collection. As a result, the collection unit can improve the accuracy of the collection by filtering the health data based on the user's current living situation and areas of interest when collecting the health data.

[0045] When collecting health data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting health data related to that area. For example, the collection unit targets area-specific health data for collection based on the user's geographical location information. In addition, when the user is traveling, the collection unit can also prioritize collecting health data related to the user's current location. Furthermore, the collection unit can target area health information and lifestyle information for collection based on the user's geographical location information. In this way, the collection unit can prioritize collecting highly relevant health data by taking into account the user's geographical location information.

[0046] When collecting health data, the collection unit can analyze the user's social media activities and collect related data. The collection unit collects related data, for example, based on health information shared by the user on social media. For example, the collection unit analyzes the user's social media activity history and collects health data based on their interests. The collection unit can also collect health information from accounts the user follows on social media. Furthermore, the collection unit can analyze the content of the user's social media posts and collect trend information. In this way, the collection unit can collect related health data by analyzing the user's social media activities.

[0047] When providing advice, the advice unit can adjust the level of detail of the advice based on the importance of the health data. The advice unit evaluates the importance based on, for example, the urgency or impact of the health data. For example, the advice unit provides detailed advice based on important health data. The advice unit can also provide concise advice based on everyday health data. Furthermore, the advice unit can provide detailed advice for health data in which the user is particularly interested. In this way, the advice unit can provide more appropriate advice by adjusting the level of detail of the advice based on the importance of the health data.

[0048] When providing advice, the advice unit can apply different advice algorithms depending on the category of health data. The advice unit, for example, classifies health data into categories such as nutrition, exercise, and sleep. For example, the advice unit applies a diet advice algorithm to data related to nutrition. The advice unit can also apply an exercise advice algorithm to data related to exercise. Furthermore, the advice unit can apply a sleep advice algorithm to data related to sleep. In this way, the advice unit can provide more appropriate advice by applying different advice algorithms depending on the category of health data.

[0049] When providing advice, the advice unit can determine the priority of advice based on the time of submission of health data. For example, the advice unit can provide advice preferentially based on health data recently collected by the user. For example, the advice unit can also provide advice for health data collected by the user in the past according to the importance. The advice unit can also adjust the priority of advice based on health data collected by the user during a specific time period. In this way, the advice unit can provide more appropriate advice by determining the priority of advice based on the time of submission of health data.

[0050] When providing advice, the advice unit can adjust the order of advice based on the relevance of health data. The advice unit determines the order of advice based, for example, on the relevance of health data collected by the user. For example, if the user collects multiple health data, the advice unit provides advice preferentially for the most relevant data. The advice unit can also dynamically adjust the order of advice according to the flow of the user's health data. This allows the advice unit to provide more appropriate advice by adjusting the order of advice based on the relevance of health data.

[0051] When providing a conversation, the conversation unit can refer to the user's past conversation history to provide the most appropriate conversation. For example, the conversation unit saves the user's past conversation history as log data and analyzes it using text mining technology. For example, the conversation unit can provide related conversations based on topics the user has previously discussed. The conversation unit can also extract specific emotional patterns from the user's past conversation history to provide the most appropriate conversation. Furthermore, the conversation unit can analyze the user's past conversation history and provide conversations related to specific keywords. This allows the conversation unit to provide the most appropriate conversation by referring to the user's past conversation history.

[0052] The conversation unit can customize the content of the conversation based on the user's current living situation when providing a conversation. For example, if the user inputs their current living situation, the conversation unit customizes the content of the conversation based on that information. For example, the conversation unit includes related topics in the conversation content based on the user's living situation. The conversation unit can also remove unnecessary information according to the user's living situation to improve the accuracy of the conversation. Furthermore, the conversation unit can adjust the tone and style of the conversation based on the user's living situation. In this way, the conversation unit can provide more appropriate conversation by customizing the content of the conversation based on the user's current living situation.

[0053] When providing a conversation, the conversation unit can provide the most appropriate conversation by taking into account the user's geographical location information. For example, if the user is in a specific region, the conversation unit includes information related to that region in the conversation content. For example, the conversation unit can include region-specific topics in the conversation content based on the user's geographical location information. Also, if the user is traveling, the conversation unit can include tourist information and event information related to the user's current location in the conversation content. Furthermore, the conversation unit can include information about local culture and customs in the conversation content based on the user's geographical location information. This allows the conversation unit to provide the most appropriate conversation by taking into account the user's geographical location information.

[0054] When providing a conversation, the conversation unit can analyze the user's social media activity and suggest conversation content. For example, the conversation unit includes related topics in the conversation content based on information shared by the user on social media. For example, the conversation unit analyzes the user's social media activity history and customizes the conversation content based on their interests. The conversation unit can also include information about accounts the user follows on social media in the conversation content. Furthermore, the conversation unit can analyze the content posted by the user on social media and include trend information in the conversation content. In this way, the conversation unit can suggest more appropriate conversation content by analyzing the user's social media activity.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The AI ​​robot system may further include an entertainment unit that provides entertainment content customized based on the user's hobbies and interests. The entertainment unit may provide content such as music, movies, and games that the user likes. The entertainment unit may also suggest personalized content based on the user's past viewing history and ratings. Furthermore, the entertainment unit may provide relaxing or uplifting content according to the user's current mood and emotions. In this way, the AI ​​robot system can enrich the user's life by providing entertainment content customized based on the user's hobbies and interests.

[0057] The collection unit can take into account the user's lifestyle habits and daily activity patterns when collecting the user's health data. For example, if the user exercises at the same time every day, the collection unit can pay particular attention to collecting data during that time period. The collection unit can also collect meal-related data based on the time period during which the user eats a particular meal. Furthermore, the collection unit can identify time periods or situations in which the user is likely to feel stressed and collect stress-related data at those times. In this way, the collection unit can collect more accurate health data by taking into account the user's lifestyle habits and daily activity patterns.

[0058] The advice unit can take into account the user's past health data and medical history when analyzing the collected data. For example, the advice unit can provide appropriate advice based on the user's past illness and injury history. The advice unit can also suggest preventive measures for high-risk health problems by taking into account the user's family history and genetic factors. Furthermore, the advice unit can analyze the user's past health data and grasp long-term health trends to predict future health risks and take early measures. This allows the advice unit to provide more personalized health advice by taking into account the user's past health data and medical history.

[0059] When analyzing a user's conversation, the analysis unit can select the optimal analysis method by referring to the user's past conversation history. For example, the analysis unit customizes the analysis method based on topics that the user has frequently discussed in the past. The analysis unit can also extract specific emotional patterns from the user's past conversation history and adjust the analysis method. Furthermore, the analysis unit can analyze the user's past conversation history to improve the analysis accuracy for specific keywords. This allows the analysis unit to perform more appropriate analysis by referring to the user's past conversation history.

[0060] When collecting a user's health data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting health data related to that area. Also, if the user is traveling, the collection unit can prioritize collecting health data related to the user's current location. Furthermore, the collection unit can target regional health information and lifestyle information for collection based on the user's geographical location information. This allows the collection unit to prioritize collecting highly relevant health data by taking into account the user's geographical location information.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The analysis unit analyzes the user's speech. The user's speech may include, but is not limited to, complaints and everyday events. The analysis unit converts the user's speech into text data using, for example, voice analysis technology and analyzes the content. The analysis unit can also use text analysis technology to understand the emotions and content of the user's speech. For example, the analysis unit converts the user's speech into text data using voice recognition technology and estimates emotions using an emotion analysis algorithm. Step 2: The reaction unit generates a reaction based on the content analyzed by the analysis unit. The reaction may include, but is not limited to, a voice response or a text message. The reaction unit generates a response according to, for example, the user's emotions. The reaction unit can also generate an action according to the situation. For example, if the user is sad, the reaction unit responds with kind words. Step 3: The collection unit collects health data of the user using a sensor. Examples of health data include, but are not limited to, heart rate, blood pressure, and body temperature. The collection unit may collect the health data in real time using, for example, a wearable device. The collection unit may also collect the health data periodically. For example, the collection unit may record daily health data and monitor long-term health conditions. Step 4: The advice unit analyzes the data collected by the collection unit and provides health advice. Examples of advice include, but are not limited to, dietary suggestions, exercise recommendations, and lifestyle improvements. The advice unit provides, for example, personalized advice. The advice unit can also provide recommendations based on the data. For example, the advice unit analyzes the user's health data and provides appropriate dietary and exercise advice. Step 5: The conversation unit continues the conversation based on the user's hobbies and interests. The conversation may include, but is not limited to, information about hobbies and daily events. The conversation unit customizes the conversation content based on, for example, the user's past statements and behavioral history. The conversation unit can also adjust the conversation content according to the user's current situation. For example, if the user is traveling, the conversation unit provides travel-related information.

[0063] (Example 2) An AI robot system according to an embodiment of the present invention listens to a user's complaints, provides health advice, and alleviates feelings of loneliness. When a user complains to the AI ​​robot, the AI ​​robot system analyzes the content of the complaint and provides an appropriate response. The AI ​​robot then monitors the user's health and provides health advice. Furthermore, the AI ​​robot can engage in everyday conversations to alleviate the user's feelings of loneliness. For example, when a user complains to the AI ​​robot, the AI ​​robot analyzes the content of the complaint and provides an appropriate response. The AI ​​robot then monitors the user's health and provides health advice. Furthermore, the AI ​​robot can engage in everyday conversations to alleviate the user's feelings of loneliness. In this way, the AI ​​robot acts like a personal butler, listening to the user's complaints, providing health advice, and alleviating feelings of loneliness, thereby supporting the user's daily life. This allows the AI ​​robot system to listen to the user's complaints, provide health advice, and alleviate feelings of loneliness.

[0064] The AI ​​robot system according to the embodiment includes an analysis unit, a response unit, a collection unit, an advice unit, and a conversation unit. The analysis unit analyzes a user's speech. The user's speech may include, but is not limited to, complaints and everyday events. The analysis unit converts the user's speech into text data using, for example, voice analysis technology and analyzes the content. The analysis unit can also understand the emotions and content of the user's speech using text analysis technology. For example, the analysis unit converts the user's speech into text data using voice recognition technology and estimates emotions using an emotion analysis algorithm. The response unit generates a response based on the content analyzed by the analysis unit. The response may include, but is not limited to, a voice response or a text message. The response unit may generate a response based on the user's emotions. The response unit can also generate an action based on the situation. For example, if the user is sad, the response unit may respond with kind words. The collection unit collects the user's health data using a sensor. The health data may include, but is not limited to, heart rate, blood pressure, body temperature, etc. The collection unit, for example, collects health data in real time using a wearable device. The collection unit can also collect health data periodically. For example, the collection unit records daily health data and monitors long-term health status. The advice unit analyzes the data collected by the collection unit and provides health advice. The advice includes, for example, but is not limited to, dietary suggestions, exercise recommendations, and lifestyle improvements. The advice unit, for example, provides personalized advice. The advice unit can also provide recommendations based on data. For example, the advice unit analyzes the user's health data and provides appropriate diet and exercise advice. The conversation unit continues a conversation based on the user's hobbies and interests. The conversation includes, for example, information about hobbies and daily events, for example, but is not limited to, the conversation unit customizes the conversation content based on the user's past statements and behavioral history. The conversation unit can also adjust the conversation content according to the user's current situation. For example, if the user is traveling, the conversation unit provides travel-related information.As a result, the AI ​​robot system according to the embodiment can listen to the user's complaints, provide health advice, and alleviate feelings of loneliness.

[0065] The analysis unit can analyze the text of the user's speech to understand the emotions and content. Text analysis includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis, for example. The analysis unit, for example, uses morphological analysis technology to break down the user's speech into words and uses grammatical analysis technology to analyze the sentence structure. The analysis unit can also understand the meaning of the user's speech using semantic analysis technology. For example, the analysis unit uses a sentiment analysis algorithm to estimate the emotions of the user's speech. This allows the analysis unit to understand the emotions and content by analyzing the text of the user's speech.

[0066] The reaction unit can generate an appropriate reaction based on the analysis result. Examples of appropriate reactions include, but are not limited to, voice responses, text messages, gestures, and the like. The reaction unit can generate a response according to the user's emotions. For example, if the user is sad, the reaction unit can respond with kind words. The reaction unit can also generate an action according to the situation. For example, if the user is angry, the reaction unit can respond in a calm and collected tone. This allows the reaction unit to generate an appropriate reaction based on the analysis result.

[0067] The collection unit can collect the user's health data in real time using a sensor. Real-time collection includes, but is not limited to, real-time data collection and periodic data collection. For example, the collection unit collects the health data in real time using a wearable device. The collection unit can also collect the health data periodically. For example, the collection unit records daily health data and monitors long-term health conditions. This allows the collection unit to collect the user's health data in real time using a sensor.

[0068] The advice unit can analyze the collected data and provide appropriate health advice. Examples of appropriate health advice include, but are not limited to, dietary suggestions, exercise recommendations, and lifestyle improvement. The advice unit can provide, for example, personalized advice. The advice unit can also provide recommendations based on the data. For example, the advice unit can analyze the user's health data and provide appropriate dietary and exercise advice. This allows the advice unit to analyze the collected data and provide appropriate health advice.

[0069] The conversation unit can provide information related to the user's topic and continue the conversation. Related information includes, but is not limited to, the user's past statements, behavioral history, and current situation, for example. The conversation unit customizes the conversation content based on the user's past statements and behavioral history, for example. The conversation unit can also adjust the conversation content according to the user's current situation. For example, if the user is traveling, the conversation unit provides travel-related information. This allows the conversation unit to provide information related to the user's topic and continue the conversation.

[0070] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. The analysis unit estimates the user's emotions using, for example, voice analysis technology, facial expression recognition technology, or text analysis technology. For example, the analysis unit converts the user's speech into text data using voice recognition technology and estimates the emotions using an emotion analysis algorithm. The analysis unit can also adjust the accuracy of the analysis based on the user's emotions. For example, if the user is angry, the analysis unit increases the accuracy of the analysis according to the intensity of the emotion and understands the detailed content. This allows the analysis unit to adjust the accuracy of the analysis based on the user's emotions, enabling more appropriate analysis.

[0071] The analysis unit can analyze the user's past conversation history and select the optimal analysis method. For example, the analysis unit saves the user's past conversation history as log data and analyzes it using text mining technology. For example, the analysis unit customizes the analysis method based on topics that the user has frequently talked about in the past. The analysis unit can also extract specific emotion patterns from the user's past conversation history and adjust the analysis method. For example, the analysis unit analyzes the user's past conversation history and improves the analysis accuracy for specific keywords. This allows the analysis unit to select the optimal analysis method by analyzing the user's past conversation history.

[0072] During analysis, the analysis unit can perform filtering based on the user's current living situation and areas of interest. For example, when the user inputs their current living situation, the analysis unit filters the analysis content based on that information. For example, the analysis unit analyzes only relevant information based on the user's areas of interest. The analysis unit can also eliminate unnecessary information according to the user's living situation and areas of interest to improve the accuracy of the analysis. For example, the analysis unit customizes the analysis content based on the user's living situation and areas of interest. This allows the analysis unit to improve the accuracy of the analysis by filtering based on the user's current living situation and areas of interest.

[0073] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user's emotions. The analysis unit estimates the user's emotions using, for example, voice analysis technology, facial expression recognition technology, or text analysis technology. For example, the analysis unit converts the user's speech into text data using voice recognition technology and estimates the emotions using an emotion analysis algorithm. The analysis unit can also prioritize the analysis results based on the user's emotions. For example, if the user expresses a strong emotion, the analysis unit prioritizes analysis results related to that emotion. If the user expresses multiple emotions, the analysis unit can also prioritize the analysis results based on the strongest emotion. Furthermore, the analysis unit can dynamically adjust the priority of the analysis results in response to changes in the user's emotions. As a result, the analysis unit can provide more appropriate analysis results by prioritizing the analysis results based on the user's emotions.

[0074] During analysis, the analysis unit can prioritize analysis of highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit prioritizes analysis of information related to that region. For example, the analysis unit can analyze topics specific to the region based on the user's geographical location information. Furthermore, if the user is traveling, the analysis unit can prioritize analysis of tourist information and event information related to the user's current location. Furthermore, the analysis unit can analyze local health information and lifestyle information based on the user's geographical location information. This allows the analysis unit to prioritize analysis of highly relevant information by taking into account the user's geographical location information.

[0075] During the analysis, the analysis unit can analyze the user's social media activities and analyze related information. For example, the analysis unit analyzes related topics based on information shared by the user on social media. For example, the analysis unit analyzes the user's social media activity history and customizes the analysis content based on the user's interests. The analysis unit can also analyze information on accounts the user follows on social media. Furthermore, the analysis unit can analyze the content posted by the user on social media and analyze trend information. In this way, the analysis unit can analyze related information by analyzing the user's social media activities.

[0076] The reaction unit can estimate the user's emotions and adjust the way a reaction is expressed based on the estimated user's emotions. The reaction unit estimates the user's emotions using, for example, voice analysis technology, facial expression recognition technology, or text analysis technology. For example, the reaction unit converts the user's speech into text data using voice recognition technology and estimates the emotion using an emotion analysis algorithm. The reaction unit can also adjust the way a reaction is expressed based on the user's emotions. For example, if the user is sad, the reaction unit can respond with kind words. If the user is angry, the reaction unit can respond with a calm and composed tone. If the user is happy, the reaction unit can respond with a bright and cheerful tone. In this way, the reaction unit can provide a more appropriate reaction by adjusting the way a reaction is expressed based on the user's emotions.

[0077] When generating a reaction, the reaction unit can adjust the level of detail of the reaction based on the importance of the conversation. The reaction unit evaluates the importance of the conversation based on, for example, the user's level of interest or urgency. For example, the reaction unit generates a detailed reaction when the user is having an important conversation. The reaction unit can also generate a concise reaction in the case of an everyday conversation. Furthermore, the reaction unit can also generate a detailed reaction for a topic in which the user is particularly interested. In this way, the reaction unit can provide a more appropriate reaction by adjusting the level of detail of the reaction based on the importance of the conversation.

[0078] The reaction unit can apply different reaction algorithms depending on the category of the conversation when generating a reaction. For example, the reaction unit classifies the category of the conversation into business, personal, hobby, etc. For example, the reaction unit applies a reaction algorithm that shows empathy to a complaint. The reaction unit can also apply a reaction algorithm based on expert knowledge to health advice. Furthermore, the reaction unit can apply a reaction algorithm with a light tone to everyday conversations. In this way, the reaction unit can provide a more appropriate reaction by applying different reaction algorithms depending on the category of the conversation.

[0079] The reaction unit can estimate the user's emotions and adjust the length of the reaction based on the estimated user's emotions. The reaction unit estimates the user's emotions using, for example, voice analysis technology, facial expression recognition technology, or text analysis technology. For example, the reaction unit converts the user's speech into text data using voice recognition technology and estimates the emotion using an emotion analysis algorithm. The reaction unit can also adjust the length of the reaction based on the user's emotions. For example, the reaction unit can generate a short and to-the-point reaction when the user is in a hurry. The reaction unit can also generate a longer reaction with detailed explanations when the user is relaxed. Furthermore, the reaction unit can generate a reaction with a visually stimulating effect when the user is excited. In this way, the reaction unit can provide a more appropriate reaction by adjusting the length of the reaction based on the user's emotions.

[0080] When generating reactions, the reaction unit can determine the priority of reactions based on the time of submission of the conversation. For example, the reaction unit generates reactions preferentially to topics that the user has recently talked about. For example, the reaction unit also generates reactions to topics that the user has talked about in the past according to the importance of those topics. The reaction unit can also adjust the priority of reactions based on the content that the user spoke about during a specific time period. In this way, the reaction unit can provide more appropriate reactions by determining the priority of reactions based on the time of submission of the conversation.

[0081] The reaction unit can adjust the order of reactions based on the relevance of the conversation when generating reactions. The reaction unit determines the order of reactions based on, for example, the relevance of the content spoken by the user. For example, when the user speaks about multiple topics, the reaction unit generates reactions preferentially to the most relevant topic. The reaction unit can also dynamically adjust the order of reactions according to the flow of the user's speech. In this way, the reaction unit can provide more appropriate reactions by adjusting the order of reactions based on the relevance of the conversation.

[0082] The collection unit can estimate the user's emotions and adjust the timing of collecting health data based on the estimated user's emotions. The collection unit estimates the user's emotions using, for example, voice analysis technology, facial expression recognition technology, or text analysis technology. For example, the collection unit converts the user's speech into text data using voice recognition technology and estimates the emotions using an emotion analysis algorithm. The collection unit can also adjust the timing of collecting health data based on the user's emotions. For example, if the user is feeling stressed, the collection unit collects health data when the user is relaxed. The collection unit can also collect detailed health data when the user is relaxed. Furthermore, the collection unit can collect simplified health data when the user is in a hurry. In this way, the collection unit can collect more appropriate health data by adjusting the timing of collecting health data based on the user's emotions.

[0083] The collection unit can analyze the user's past health data and select the optimal collection method. For example, the collection unit collects and analyzes the user's past health data as electronic medical records or data from wearable devices. For example, the collection unit selects the optimal sensor based on the user's past health data. The collection unit can also select a collection method for a specific time period based on the user's past health data. Furthermore, the collection unit can analyze the user's past health data and select the most efficient collection method. In this way, the collection unit can select the optimal collection method by analyzing the user's past health data.

[0084] The collection unit can filter the health data based on the user's current living situation and areas of interest when collecting the health data. For example, if the user inputs their current living situation, the collection unit filters the health data based on that information. For example, the collection unit collects only relevant health data based on the user's areas of interest. The collection unit can also eliminate unnecessary health data according to the user's living situation and areas of interest, thereby improving the accuracy of the collection. As a result, the collection unit can improve the accuracy of the collection by filtering the health data based on the user's current living situation and areas of interest when collecting the health data.

[0085] The collection unit can estimate the user's emotions and determine the priority of health data to be collected based on the estimated user's emotions. The collection unit can estimate the user's emotions using, for example, voice analysis technology, facial expression recognition technology, or text analysis technology. For example, the collection unit can convert the user's speech into text data using voice recognition technology and estimate the emotion using an emotion analysis algorithm. The collection unit can also determine the priority of health data to be collected based on the user's emotions. For example, the collection unit can prioritize collecting stress-related health data when the user is feeling stressed. The collection unit can also collect general health data when the user is relaxed. Furthermore, the collection unit can prioritize collecting only important health data when the user is in a hurry. In this way, the collection unit can prioritize collecting more important data by determining the priority of health data to be collected based on the user's emotions.

[0086] When collecting health data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting health data related to that area. For example, the collection unit targets area-specific health data for collection based on the user's geographical location information. In addition, when the user is traveling, the collection unit can also prioritize collecting health data related to the user's current location. Furthermore, the collection unit can target area health information and lifestyle information for collection based on the user's geographical location information. In this way, the collection unit can prioritize collecting highly relevant health data by taking into account the user's geographical location information.

[0087] When collecting health data, the collection unit can analyze the user's social media activities and collect related data. The collection unit collects related data, for example, based on health information shared by the user on social media. For example, the collection unit analyzes the user's social media activity history and collects health data based on their interests. The collection unit can also collect health information from accounts the user follows on social media. Furthermore, the collection unit can analyze the content of the user's social media posts and collect trend information. In this way, the collection unit can collect related health data by analyzing the user's social media activities.

[0088] The advice unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. The advice unit estimates the user's emotions using, for example, voice analysis technology, facial expression recognition technology, or text analysis technology. For example, the advice unit converts the user's speech into text data using voice recognition technology and estimates the emotions using an emotion analysis algorithm. The advice unit can also adjust the way in which advice is expressed based on the user's emotions. For example, the advice unit can provide advice in kind words if the user is sad. Furthermore, the advice unit can provide advice in a calm and subdued tone if the user is angry. Furthermore, the advice unit can provide advice in a bright and cheerful tone if the user is happy. In this way, the advice unit can provide more appropriate advice by adjusting the way in which advice is expressed based on the user's emotions.

[0089] When providing advice, the advice unit can adjust the level of detail of the advice based on the importance of the health data. The advice unit evaluates the importance based on, for example, the urgency or impact of the health data. For example, the advice unit provides detailed advice based on important health data. The advice unit can also provide concise advice based on everyday health data. Furthermore, the advice unit can provide detailed advice for health data in which the user is particularly interested. In this way, the advice unit can provide more appropriate advice by adjusting the level of detail of the advice based on the importance of the health data.

[0090] When providing advice, the advice unit can apply different advice algorithms depending on the category of health data. The advice unit, for example, classifies health data into categories such as nutrition, exercise, and sleep. For example, the advice unit applies a diet advice algorithm to data related to nutrition. The advice unit can also apply an exercise advice algorithm to data related to exercise. Furthermore, the advice unit can apply a sleep advice algorithm to data related to sleep. In this way, the advice unit can provide more appropriate advice by applying different advice algorithms depending on the category of health data.

[0091] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. The advice unit estimates the user's emotions using, for example, voice analysis technology, facial expression recognition technology, or text analysis technology. For example, the advice unit converts the user's speech into text data using voice recognition technology and estimates the emotions using an emotion analysis algorithm. The advice unit can also adjust the length of the advice based on the user's emotions. For example, the advice unit can provide short, to-the-point advice when the user is in a hurry. The advice unit can also provide longer advice with detailed explanations when the user is relaxed. Furthermore, the advice unit can provide advice with visually stimulating effects when the user is excited. In this way, the advice unit can provide more appropriate advice by adjusting the length of the advice based on the user's emotions.

[0092] When providing advice, the advice unit can determine the priority of advice based on the time of submission of health data. For example, the advice unit can provide advice preferentially based on health data recently collected by the user. For example, the advice unit can also provide advice for health data collected by the user in the past according to the importance. The advice unit can also adjust the priority of advice based on health data collected by the user during a specific time period. In this way, the advice unit can provide more appropriate advice by determining the priority of advice based on the time of submission of health data.

[0093] When providing advice, the advice unit can adjust the order of advice based on the relevance of health data. The advice unit determines the order of advice based, for example, on the relevance of health data collected by the user. For example, if the user collects multiple health data, the advice unit provides advice preferentially for the most relevant data. The advice unit can also dynamically adjust the order of advice according to the flow of the user's health data. This allows the advice unit to provide more appropriate advice by adjusting the order of advice based on the relevance of health data.

[0094] The conversation unit can estimate the user's emotions and adjust the way the conversation is expressed based on the estimated user's emotions. The conversation unit estimates the user's emotions using, for example, voice analysis technology, facial expression recognition technology, or text analysis technology. For example, the conversation unit converts the user's speech into text data using voice recognition technology and estimates the emotions using an emotion analysis algorithm. The conversation unit can also adjust the way the conversation is expressed based on the user's emotions. For example, if the user is sad, the conversation unit can use gentle words to advance the conversation. If the user is angry, the conversation unit can also use a calm and subdued tone to advance the conversation. Furthermore, if the user is happy, the conversation unit can use a bright and cheerful tone to advance the conversation. In this way, the conversation unit can provide a more appropriate conversation by adjusting the way the conversation is expressed based on the user's emotions.

[0095] When providing a conversation, the conversation unit can refer to the user's past conversation history to provide the most appropriate conversation. For example, the conversation unit saves the user's past conversation history as log data and analyzes it using text mining technology. For example, the conversation unit can provide related conversations based on topics the user has previously discussed. The conversation unit can also extract specific emotional patterns from the user's past conversation history to provide the most appropriate conversation. Furthermore, the conversation unit can analyze the user's past conversation history and provide conversations related to specific keywords. This allows the conversation unit to provide the most appropriate conversation by referring to the user's past conversation history.

[0096] The conversation unit can customize the content of the conversation based on the user's current living situation when providing a conversation. For example, if the user inputs their current living situation, the conversation unit customizes the content of the conversation based on that information. For example, the conversation unit includes related topics in the conversation content based on the user's living situation. The conversation unit can also remove unnecessary information according to the user's living situation to improve the accuracy of the conversation. Furthermore, the conversation unit can adjust the tone and style of the conversation based on the user's living situation. In this way, the conversation unit can provide more appropriate conversation by customizing the content of the conversation based on the user's current living situation.

[0097] The conversation unit can estimate a user's emotions and determine the priority of conversations based on the estimated user emotions. The conversation unit estimates the user's emotions using, for example, voice analysis technology, facial expression recognition technology, or text analysis technology. For example, the conversation unit converts the user's speech into text data using voice recognition technology and estimates the emotions using an emotion analysis algorithm. The conversation unit can also determine the priority of conversations based on the user's emotions. For example, if the user shows a strong emotion, the conversation unit can preferentially provide conversations related to that emotion. Furthermore, if the user shows multiple emotions, the conversation unit can determine the priority of conversations based on the strongest emotion. Furthermore, the conversation unit can dynamically adjust the priority of conversations in response to changes in the user's emotions. In this way, the conversation unit can provide more appropriate conversations by determining the priority of conversations based on the user's emotions.

[0098] When providing a conversation, the conversation unit can provide the most appropriate conversation by taking into account the user's geographical location information. For example, if the user is in a specific region, the conversation unit includes information related to that region in the conversation content. For example, the conversation unit can include region-specific topics in the conversation content based on the user's geographical location information. Also, if the user is traveling, the conversation unit can include tourist information and event information related to the user's current location in the conversation content. Furthermore, the conversation unit can include information about local culture and customs in the conversation content based on the user's geographical location information. This allows the conversation unit to provide the most appropriate conversation by taking into account the user's geographical location information.

[0099] When providing a conversation, the conversation unit can analyze the user's social media activity and suggest conversation content. For example, the conversation unit includes related topics in the conversation content based on information shared by the user on social media. For example, the conversation unit analyzes the user's social media activity history and customizes the conversation content based on their interests. The conversation unit can also include information about accounts the user follows on social media in the conversation content. Furthermore, the conversation unit can analyze the content posted by the user on social media and include trend information in the conversation content. In this way, the conversation unit can suggest more appropriate conversation content by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the analysis unit, reaction unit, collection unit, advice unit, and conversation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14, converting the user's speech into text data using voice analysis technology and analyzing the content. The reaction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generating a voice response or a text message based on the analyzed content. The collection unit is realized, for example, by a sensor of the smart device 14, and collects health data of the user. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to provide health advice. The conversation unit is realized, for example, by the control unit 46A of the smart device 14, and continues a conversation based on the user's hobbies and interests. === Hard Collateral 1-2 === Each of the multiple elements, including the analysis unit, reaction unit, collection unit, advice unit, and conversation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214, converting the user's speech into text data using voice analysis technology and analyzing the content. The reaction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generating a voice response or a text message based on the analyzed content. The collection unit is realized, for example, by a sensor of the smart glasses 214, and collects health data of the user. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and provides health advice. The conversation unit is realized, for example, by the control unit 46A of the smart glasses 214, and continues a conversation based on the user's hobbies and interests. === Hard Collateral 1-3 === Each of the multiple elements, including the analysis unit, response unit, collection unit, advice unit, and conversation unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset-type terminal 314, converting the user's speech into text data using voice analysis technology and analyzing the content. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generating a voice response or a text message based on the analyzed content. The collection unit is realized, for example, by a sensor of the headset-type terminal 314, and collects health data of the user. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to provide health advice. The conversation unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and continues a conversation based on the user's hobbies and interests. === Hard Collateral 1-4 === Each of the multiple elements, including the analysis unit, reaction unit, collection unit, advice unit, and conversation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 and converts the user's speech into text data using voice analysis technology and analyzes the content. The reaction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a voice response or a text message based on the analyzed content. The collection unit is realized, for example, by a sensor of the robot 414 and collects health data of the user. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to provide health advice. The conversation unit is realized, for example, by the control unit 46A of the robot 414 and continues a conversation based on the user's hobbies and interests.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The AI ​​robot system may further include an entertainment unit that provides entertainment content customized based on the user's hobbies and interests. The entertainment unit may provide content such as music, movies, and games that the user likes. The entertainment unit may also suggest personalized content based on the user's past viewing history and ratings. Furthermore, the entertainment unit may provide relaxing or uplifting content according to the user's current mood and emotions. In this way, the AI ​​robot system can enrich the user's life by providing entertainment content customized based on the user's hobbies and interests.

[0102] When analyzing a user's speech, the analysis unit can take into account speech characteristics such as the user's tone of voice, speaking speed, and pauses. For example, if the user speaks quickly, the analysis unit can determine that the user is nervous or excited and respond appropriately. If the user speaks slowly, the analysis unit can determine that the user is relaxed and generate a response in a relaxed tone. Furthermore, if the user's tone of voice is low, the analysis unit can determine that the user is sad or tired and generate a comforting response. In this way, the analysis unit can perform more appropriate analysis and response by taking into account the user's speech characteristics.

[0103] The reaction unit can estimate the user's emotion and adjust the way the reaction is expressed based on the estimated user's emotion. For example, if the user is sad, the reaction unit can respond in kind words. If the user is angry, the reaction unit can also respond in a calm and collected tone. If the user is happy, the reaction unit can also respond in a bright and cheerful tone. This allows the reaction unit to provide a more appropriate reaction by adjusting the way the reaction is expressed based on the user's emotion.

[0104] The collection unit can take into account the user's lifestyle habits and daily activity patterns when collecting the user's health data. For example, if the user exercises at the same time every day, the collection unit can pay particular attention to collecting data during that time period. The collection unit can also collect meal-related data based on the time period during which the user eats a particular meal. Furthermore, the collection unit can identify time periods or situations in which the user is likely to feel stressed and collect stress-related data at those times. In this way, the collection unit can collect more accurate health data by taking into account the user's lifestyle habits and daily activity patterns.

[0105] The advice unit can take into account the user's past health data and medical history when analyzing the collected data. For example, the advice unit can provide appropriate advice based on the user's past illness and injury history. The advice unit can also suggest preventive measures for high-risk health problems by taking into account the user's family history and genetic factors. Furthermore, the advice unit can analyze the user's past health data and grasp long-term health trends to predict future health risks and take early measures. This allows the advice unit to provide more personalized health advice by taking into account the user's past health data and medical history.

[0106] The conversation unit can estimate the user's emotions and adjust the way the conversation is expressed based on the estimated user's emotions. For example, if the user is sad, the conversation unit can use gentle words to advance the conversation. If the user is angry, the conversation unit can also use a calm and composed tone to advance the conversation. Furthermore, if the user is happy, the conversation unit can also use a bright and cheerful tone to advance the conversation. In this way, the conversation unit can provide a more appropriate conversation by adjusting the way the conversation is expressed based on the user's emotions.

[0107] When analyzing a user's conversation, the analysis unit can select the optimal analysis method by referring to the user's past conversation history. For example, the analysis unit customizes the analysis method based on topics that the user has frequently discussed in the past. The analysis unit can also extract specific emotional patterns from the user's past conversation history and adjust the analysis method. Furthermore, the analysis unit can analyze the user's past conversation history to improve the analysis accuracy for specific keywords. This allows the analysis unit to perform more appropriate analysis by referring to the user's past conversation history.

[0108] The reaction unit can estimate the user's emotions and adjust the length of the reaction based on the estimated user's emotions. For example, if the user is in a hurry, the reaction unit can generate a short and to-the-point reaction. If the user is relaxed, the reaction unit can generate a longer reaction with detailed explanations. Furthermore, if the user is excited, the reaction unit can generate a reaction with a visually stimulating effect. In this way, the reaction unit can provide a more appropriate reaction by adjusting the length of the reaction based on the user's emotions.

[0109] When collecting a user's health data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting health data related to that area. Also, if the user is traveling, the collection unit can prioritize collecting health data related to the user's current location. Furthermore, the collection unit can target regional health information and lifestyle information for collection based on the user's geographical location information. This allows the collection unit to prioritize collecting highly relevant health data by taking into account the user's geographical location information.

[0110] The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is sad, the advice unit can provide advice in kind words. If the user is angry, the advice unit can also provide advice in a calm and collected tone. If the user is happy, the advice unit can also provide advice in a bright and cheerful tone. In this way, the advice unit can provide more appropriate advice by adjusting the way the advice is expressed based on the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The analysis unit analyzes the user's speech. The user's speech may include, but is not limited to, complaints and everyday events. The analysis unit converts the user's speech into text data using, for example, voice analysis technology and analyzes the content. The analysis unit can also use text analysis technology to understand the emotions and content of the user's speech. For example, the analysis unit converts the user's speech into text data using voice recognition technology and estimates emotions using an emotion analysis algorithm. Step 2: The reaction unit generates a reaction based on the content analyzed by the analysis unit. The reaction may include, but is not limited to, a voice response or a text message. The reaction unit generates a response according to, for example, the user's emotions. The reaction unit can also generate an action according to the situation. For example, if the user is sad, the reaction unit responds with kind words. Step 3: The collection unit collects health data of the user using a sensor. Examples of health data include, but are not limited to, heart rate, blood pressure, and body temperature. The collection unit may collect the health data in real time using, for example, a wearable device. The collection unit may also collect the health data periodically. For example, the collection unit may record daily health data and monitor long-term health conditions. Step 4: The advice unit analyzes the data collected by the collection unit and provides health advice. Examples of advice include, but are not limited to, dietary suggestions, exercise recommendations, and lifestyle improvements. The advice unit provides, for example, personalized advice. The advice unit can also provide recommendations based on the data. For example, the advice unit analyzes the user's health data and provides appropriate dietary and exercise advice. Step 5: The conversation unit continues the conversation based on the user's hobbies and interests. The conversation may include, but is not limited to, information about hobbies and daily events. The conversation unit customizes the conversation content based on, for example, the user's past statements and behavioral history. The conversation unit can also adjust the conversation content according to the user's current situation. For example, if the user is traveling, the conversation unit provides travel-related information.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the user's speech; a reaction unit that generates a reaction based on the content analyzed by the analysis unit; a collection unit that collects health data of a user; an advice unit that analyzes the data collected by the collection unit and provides health advice; a conversation unit that continues a conversation based on the user's hobbies or interests; A system characterized by:

2. The analysis unit Analyze user text to understand emotions and content The system of claim 1 .

3. The reaction section is Generate appropriate responses based on analysis results The system of claim 1 .

4. The collecting unit Using sensors to instantly collect user health data The system of claim 1 .

5. The advice unit Analyze the collected data and provide appropriate health advice The system of claim 1 .

6. The conversation unit is Provide information relevant to the user's topic and keep the conversation going The system of claim 1 .

7. The analysis unit Estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. The system of claim 1 .

8. The analysis unit Analyze the user's past conversation history and select the optimal analysis method The system of claim 1 .

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