system

Abstract

The system according to the embodiment aims to provide mental care through conversation with the user, detect abnormalities early, and contact a medical institution. [Solution] A system according to an embodiment includes a conversation unit, a linking unit, an analysis unit, a detection unit, and a contact unit. The conversation unit converses with a user. The linking unit links conversation data collected by the conversation unit to the cloud. The analysis unit analyzes the data linked to the cloud by the linking unit. The detection unit detects abnormalities (e.g., sudden changes in heart rate or abnormal speech patterns) based on the data analyzed by the analysis unit. The contact unit contacts a medical institution based on the abnormality detected by the detection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately provide mental care for lonely households or detect abnormalities early, so there is room for improvement.

[0005] The system according to the embodiment aims to provide mental care through conversation with the user, detect abnormalities early, and contact a medical institution. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation unit, a linking unit, an analysis unit, a detection unit, and a contact unit. The conversation unit converses with the user. The linking unit links conversation data collected by the conversation unit to the cloud. The analysis unit analyzes the data linked to the cloud by the linking unit. The detection unit detects abnormalities (e.g., sudden changes in heart rate or abnormal speech patterns) based on the data analyzed by the analysis unit. The contact unit contacts a medical institution based on the abnormality detected by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment provides mental care through conversation with the user, and can detect abnormalities early and contact a medical institution. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The nursing care robot system according to an embodiment of the present invention uses a cute, home-use robot to provide mental care through AI-assisted conversations. This system aims to address the growing number of lonely households due to the declining marriage rate. Specifically, the system comprises the following steps: First, the home-use robot converses with the user. Next, the conversation data is linked to the cloud, and if unusual conversation content is detected, a medical institution is contacted. This system provides mental care for the user and, if necessary, coordinates with a medical institution. First, the home-use robot converses with the user. For example, the robot asks, "How was your day today?" and the user replies, "I had fun meeting up with my friends today." This conversation is analyzed by AI to determine the user's mental state. Next, the conversation data is linked to the cloud. The data is stored in the cloud, and the AI ​​detects unusual conversation content. For example, if the user replies, "I didn't feel like doing anything today," the AI ​​detects the abnormality and contacts a medical institution. This system provides mental care for the user. Through conversations with the home-use robot, the user can reduce feelings of loneliness and receive mental support. Furthermore, if an abnormality is detected, appropriate action can be taken by quickly coordinating with a medical institution. For example, if a user is usually cheerful but suddenly becomes depressed, the AI ​​can detect this change and contact a medical institution, allowing the user to receive appropriate care promptly. In this way, mental care using a home robot is realized. This allows the nursing care robot system to provide mental care to the user and coordinate with a medical institution as necessary.

[0029] A nursing care robot system according to an embodiment includes a conversation unit, a collaboration unit, an analysis unit, a detection unit, and a communication unit. The conversation unit engages in conversation with a user. For example, the conversation unit asks the user, "How was your day today?" and the user replies, "I met up with my friends today and it was fun." This conversation is analyzed by AI to understand the user's mental state. The conversation unit can also estimate the user's emotions and select a conversation topic based on the estimated user emotions. For example, if the user is sad, a topic of encouragement or comfort can be selected. The collaboration unit links the conversation data collected by the conversation unit to the cloud. For example, the collaboration unit can transmit the conversation data to the cloud. The data is stored on the cloud, and the analysis unit detects conversation content that is different from usual. The analysis unit analyzes the conversation data on the cloud and detects conversation content that is different from usual. For example, the analysis unit can detect conversation content that deviates from a normal conversation pattern. The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit can detect, for example, a sudden change in heart rate or an abnormal speech pattern. The communication unit contacts a medical institution based on the abnormality detected by the detection unit. The communication unit can contact the medical institution by, for example, telephone, email, or messaging app. This allows the nursing care robot system according to the embodiment to provide mental care to the user and cooperate with a medical institution as necessary.

[0030] The conversation unit can have a conversation to understand the user's mental state. For example, the conversation unit asks the user, "How was your day today?" and the user replies, "I met up with my friends today and it was fun." This conversation is analyzed by AI to understand the user's mental state. The conversation unit can also estimate the user's emotions and select a conversation topic based on the estimated user emotions. For example, if the user is sad, it can select a topic of encouragement or comfort. This makes it possible to understand the user's mental state.

[0031] The linking unit can transmit the conversation data to the cloud. The linking unit can, for example, transmit the conversation data to the cloud. For transmission, protocols such as HTTP, HTTPS, and FTP can be used. This allows the conversation data to be transmitted to the cloud.

[0032] The analysis unit can analyze the conversation data on the cloud and detect conversation content that is different from usual. The analysis unit can, for example, analyze the conversation data on the cloud and detect conversation content that is different from usual. To detect conversation content that is different from usual, for example, an algorithm that detects deviations from normal conversation patterns can be used. This makes it possible to detect conversation content that is different from usual.

[0033] The detection unit can contact a medical institution when it detects an abnormality. The detection unit can contact a medical institution when it detects an abnormality, for example. To detect an abnormality, for example, an algorithm that detects a sudden change in heart rate or an abnormal speech pattern can be used. This makes it possible to contact a medical institution when an abnormality is detected.

[0034] The communication unit can contact a medical institution and support the user's mental care. The communication unit can, for example, contact a medical institution and support the user's mental care. For communication, for example, telephone, email, messaging app, etc. can be used. This makes it possible to support the user's mental care.

[0035] The conversation unit can analyze the user's past conversation history and select a method for progressing the conversation. The conversation unit can, for example, analyze the user's past conversation history and select a method for progressing the conversation. In selecting a method for progressing the conversation, for example, the conversation can be progressed based on topics that the user liked in the past. It can also progress the conversation while avoiding topics that the user avoided in the past. Furthermore, it can analyze the user's past conversation patterns and ask questions at appropriate times. In this way, it is possible to select an optimal method for progressing the conversation based on the user's past conversation history.

[0036] The conversation unit can perform filtering based on the user's current living situation and areas of interest. The conversation unit can perform filtering based on the user's current living situation and areas of interest, for example. For filtering, for example, topics related to hobbies that the user has recently become interested in can be selected. Topics related to the user's current living situation (work, family, etc.) can also be selected. Furthermore, topics can be selected based on books the user has recently read or movies the user has recently seen. This allows for appropriate conversation to be held based on the user's current living situation and areas of interest.

[0037] The conversation unit can prioritize selecting highly relevant topics based on the user's geographical location information. The conversation unit can prioritize selecting highly relevant topics based on, for example, the user's geographical location information. To acquire the geographical location information, for example, GPS data or location information services can be used. For example, topics related to news and events in the area where the user lives can be selected. Also, if the user is traveling, topics related to tourist information and recommended spots at the travel destination can be selected. Furthermore, if the user is in a specific location, topics related to the history and culture of that location can be selected. This makes it possible to select appropriate conversation topics based on the user's geographical location information.

[0038] The conversation unit can analyze the user's social media activity and select related topics. The conversation unit can, for example, analyze the user's social media activity and select related topics. When analyzing social media activity, for example, the content of posts and the number of likes can be used as evaluation criteria. For example, topics can be selected based on articles recently shared by the user on social media. Topics can also be selected based on the content of posts from accounts the user follows on social media. Furthermore, topics related to groups or communities the user participates in on social media can be selected. This makes it possible to select appropriate conversation topics based on the user's social media activity.

[0039] The linking unit can analyze the user's past data transmission history and select the optimal transmission method when transmitting data. For example, the linking unit can analyze the user's past data transmission history and select the optimal transmission method when transmitting data. When selecting the transmission method, for example, it can prioritize transmission methods (email, message, etc.) that the user has preferred in the past. It can also perform transmission while avoiding transmission methods that the user has avoided in the past. Furthermore, it can analyze the user's past transmission history and transmit data at an appropriate time. This makes it possible to select the optimal transmission method based on the user's past data transmission history.

[0040] The linking unit can perform filtering based on the user's current living situation and areas of interest when transmitting data. The linking unit can perform filtering based on the user's current living situation and areas of interest when transmitting data, for example. For filtering, for example, data related to topics in which the user is currently interested can be preferentially transmitted. Data related to the user's current living situation (work, home, etc.) can also be preferentially transmitted. Furthermore, data related to events in which the user recently participated can be preferentially transmitted. This makes it possible to transmit appropriate data based on the user's current living situation and areas of interest.

[0041] When transmitting data, the linking unit can prioritize transmitting highly relevant data in consideration of the user's geographical location information. For example, when transmitting data, the linking unit can prioritize transmitting highly relevant data in consideration of the user's geographical location information. To obtain the geographical location information, for example, GPS data or location information services can be used. For example, when the user is in a specific location, data related to that location can be prioritized. Furthermore, when the user is traveling, data related to tourist information and recommended spots for the travel destination can be prioritized. Furthermore, data related to news and events in the area where the user lives can be prioritized. This allows appropriate data to be transmitted based on the user's geographical location information.

[0042] The linking unit can analyze the user's social media activity and transmit related data when transmitting data. For example, the linking unit can analyze the user's social media activity and transmit related data when transmitting data. For example, the analysis of social media activity can use the content of posts and the number of likes as evaluation criteria. For example, data can be transmitted based on articles recently shared by the user on social media. Data can also be transmitted based on the content of posts from accounts the user follows on social media. Furthermore, data related to groups and communities the user participates in on social media can be transmitted. This makes it possible to transmit appropriate data based on the user's social media activity.

[0043] The analysis unit can improve the accuracy of the current analysis by referring to past analysis data during analysis. The analysis unit can improve the accuracy of the current analysis by referring to past analysis data, for example, during analysis. To improve the analysis accuracy, for example, the current analysis algorithm can be adjusted based on the past analysis data. The current analysis accuracy can also be improved by referring to past analysis results. Furthermore, the past analysis data can be analyzed and reflected in the current analysis. This makes it possible to improve the accuracy of the current analysis based on the past analysis data.

[0044] The analysis unit can apply different analysis methods to each category of conversation data during analysis. For example, the analysis unit can apply different analysis methods to each category of conversation data during analysis. Categories can be classified using criteria such as by topic or by emotion. For example, an emotion analysis algorithm can be applied to conversation data related to emotions. An algorithm for evaluating health status can be applied to conversation data related to health. Furthermore, an algorithm for analyzing lifestyle patterns can be applied to conversation data related to daily life. This makes it possible to apply an appropriate analysis method to each category of conversation data.

[0045] The analysis unit can determine the analysis priority based on the submission time of the conversation data during analysis. The analysis unit can, for example, determine the analysis priority based on the submission time of the conversation data during analysis. The submission time can be evaluated using, for example, the submission date and time or the submission frequency as criteria. For example, the most recent conversation data can be analyzed preferentially. Also, conversation data submitted during a specific time period can be analyzed preferentially. Furthermore, the analysis priority can be determined based on the user's schedule. This makes it possible to determine the analysis priority based on the submission time of the conversation data.

[0046] The analysis unit can improve the accuracy of the analysis by referring to literature related to the conversation data during analysis. The analysis unit can improve the accuracy of the analysis by referring to literature related to the conversation data, for example, during analysis. The related literature can be referenced, for example, to academic papers or technical reports. For example, the analysis can be performed by referring to the latest research papers related to the conversation data. The analysis can also be performed by referring to past research results related to the conversation data. Furthermore, the analysis can be performed by referring to specialized books related to the conversation data. In this way, the accuracy of the analysis can be improved by referring to literature related to the conversation data.

[0047] The detection unit can improve the current detection accuracy by referring to past abnormality data when detecting an anomaly. For example, the detection unit can improve the current detection accuracy by referring to past abnormality data when detecting an anomaly. For example, past abnormality cases or abnormality patterns can be used to refer to the abnormality data. For example, the current anomaly detection algorithm can be adjusted based on the past abnormality data. Also, the current detection accuracy can be improved by referring to past abnormality detection results. Furthermore, the past abnormality data can be analyzed and reflected in the current anomaly detection. In this way, the current detection accuracy can be improved based on the past abnormality data.

[0048] The detection unit can apply different detection methods to each category of conversation data when detecting an anomaly. For example, the detection unit can apply different detection methods to each category of conversation data when detecting an anomaly. Categories can be classified using criteria such as by topic or by emotion. For example, an emotion anomaly detection algorithm can be applied to conversation data related to emotions. A health anomaly detection algorithm can be applied to conversation data related to health. Furthermore, a lifestyle anomaly detection algorithm can be applied to conversation data related to daily life. This makes it possible to apply an appropriate detection method to each category of conversation data.

[0049] When detecting an anomaly, the detection unit can determine the detection priority based on the submission time of the conversation data. For example, when detecting an anomaly, the detection unit can determine the detection priority based on the submission time of the conversation data. For example, the submission date and time or the submission frequency can be used as a criterion for evaluating the submission time. For example, the most recent conversation data can be preferentially detected. Also, conversation data submitted during a specific time period can be preferentially detected. Furthermore, the detection priority can be determined based on the user's schedule. In this way, the detection priority can be determined based on the submission time of the conversation data.

[0050] The detection unit can improve the accuracy of detection by referring to literature related to the conversation data when detecting an anomaly. The detection unit can improve the accuracy of detection by referring to literature related to the conversation data when detecting an anomaly, for example. The related literature can be referenced, for example, to academic papers or technical reports. For example, detection can be performed by referring to the latest research papers related to the conversation data. Detection can also be performed by referring to past research results related to the conversation data. Furthermore, detection can be performed by referring to specialized books related to the conversation data. In this way, the accuracy of detection can be improved by referring to literature related to the conversation data.

[0051] The contact unit can select the optimal contact method by referring to past contact history when making contact. For example, the contact unit can select the optimal contact method by referring to past contact history when making contact. When selecting the contact method, for example, it can prioritize contact methods (email, telephone, etc.) that the user has preferred in the past. It can also contact the user while avoiding contact methods that the user has avoided in the past. Furthermore, it can analyze the user's past contact history and make contact at an appropriate time. This makes it possible to select the optimal contact method based on the past contact history.

[0052] The contact unit can perform filtering based on the user's current living situation and areas of interest when contacting the user. The contact unit can perform filtering based on the user's current living situation and areas of interest when contacting the user, for example. For example, filtering can prioritize contact of information related to topics in which the user is currently interested. It can also prioritize contact of information related to the user's current living situation (work, family, etc.). It can also prioritize contact of information related to events that the user recently participated in. This makes it possible to make appropriate contact based on the user's current living situation and areas of interest.

[0053] The contact unit can select a highly relevant contact method in consideration of the user's geographical location information when making contact. For example, the contact unit can select a highly relevant contact method in consideration of the user's geographical location information when making contact. For example, GPS data, location information services, etc. can be used to acquire the geographical location information. For example, if the user is in a specific location, information related to that location can be prioritized in contact. Also, if the user is traveling, tourist information about the travel destination and information about recommended spots can be prioritized in contact. Furthermore, information about news and events in the area where the user lives can be prioritized in contact. In this way, an appropriate contact method can be selected based on the user's geographical location information.

[0054] The contact unit can analyze the user's social media activity and select a relevant contact method when contacting the user. For example, the contact unit can analyze the user's social media activity and select a relevant contact method when contacting the user. When analyzing social media activity, for example, the content of posts and the number of likes can be used as evaluation criteria. For example, contact can be made based on articles recently shared by the user on social media. Contact can also be made based on the content of posts from accounts the user follows on social media. Furthermore, information related to groups and communities the user participates in on social media can be communicated. This makes it possible to select an appropriate contact method based on 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 care robot system may further include a health management unit that monitors the user's physical health condition. The health management unit may periodically measure the user's vital signs, such as heart rate, blood pressure, and body temperature, and contact a medical institution if an abnormality is detected. The health management unit may also record the user's exercise and dietary habits and provide advice to help improve the user's health condition. Furthermore, the health management unit may monitor the user's sleep patterns and make suggestions to improve the quality of sleep. This allows the system to comprehensively manage the user's physical health condition and provide comprehensive support, including mental care.

[0057] The care robot system may further include an entertainment unit that provides entertainment content customized based on the user's hobbies and interests. For example, the entertainment unit may recommend and play the user's favorite music or movies. It may also provide the latest news and articles related to the user's fields of interest. Furthermore, the entertainment unit may introduce online events and communities that the user can participate in, promoting social connections to reduce feelings of loneliness. This may improve the user's quality of life and provide enjoyment and a sense of fulfillment as part of mental care.

[0058] The care robot system can further include a reminder unit that monitors the user's daily rhythm and provides reminders at appropriate times. For example, the reminder unit can notify the user so that they do not forget to take their medicine or to make an appointment with a medical institution. It can also set reminders for the user to exercise regularly. Furthermore, it can provide reminders in cooperation with a calendar so that the user does not forget important appointments or tasks. This can support the user's daily rhythm and help them manage their daily life.

[0059] The care robot system may further include a communication support unit that promotes communication with the user's family and friends. For example, the communication support unit may provide support for the user to make video calls with family and friends. It may also suggest appropriate expressions and words when the user sends a message. Furthermore, if the user feels lonely, it may provide a reminder to encourage the user to contact family and friends. This may strengthen the user's social connections and reduce feelings of loneliness.

[0060] The care robot system can further include an environmental management unit that monitors the user's living environment and provides a comfortable environment. The environmental management unit can, for example, appropriately adjust room temperature and humidity to provide a comfortable living environment. It can also adjust the brightness and color of lighting to provide a lighting environment that matches the user's mood. Furthermore, it can link with home appliances such as air purifiers and humidifiers to improve indoor air quality. This can optimize the user's living environment and provide a comfortable living space.

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

[0062] Step 1: The conversation unit engages in a conversation with the user. For example, the user asks, "How was your day today?" and the user replies, "I met up with my friends today and had a great time." This conversation is analyzed by AI to understand the user's mental state. The conversation unit can also estimate the user's emotions and select a conversation topic based on the estimated user emotions. For example, if the user is sad, it can select a topic of encouragement or comfort. Step 2: The linking unit links the conversation data collected by the conversation unit to the cloud. For example, the conversation data is sent to the cloud, where it is stored. Step 3: The analysis unit analyzes the conversation data on the cloud and detects unusual conversation content, such as conversation content that deviates from normal conversation patterns. Step 4: The detection unit detects abnormalities based on the data analyzed by the analysis unit. For example, it can detect sudden changes in heart rate or abnormal speech patterns. Step 5: The communication unit contacts a medical institution based on the abnormality detected by the detection unit. For example, the medical institution can be contacted by phone, email, messaging app, etc.

[0063] (Example 2) The nursing care robot system according to an embodiment of the present invention uses a cute, home-use robot to provide mental care through AI-assisted conversations. This system aims to address the growing number of lonely households due to the declining marriage rate. Specifically, the system comprises the following steps: First, the home-use robot converses with the user. Next, the conversation data is linked to the cloud, and if unusual conversation content is detected, a medical institution is contacted. This system provides mental care for the user and, if necessary, coordinates with a medical institution. First, the home-use robot converses with the user. For example, the robot asks, "How was your day today?" and the user replies, "I had fun meeting up with my friends today." This conversation is analyzed by AI to determine the user's mental state. Next, the conversation data is linked to the cloud. The data is stored in the cloud, and the AI ​​detects unusual conversation content. For example, if the user replies, "I didn't feel like doing anything today," the AI ​​detects the abnormality and contacts a medical institution. This system provides mental care for the user. Through conversations with the home-use robot, the user can reduce feelings of loneliness and receive mental support. Furthermore, if an abnormality is detected, appropriate action can be taken by quickly coordinating with a medical institution. For example, if a user is usually cheerful but suddenly becomes depressed, the AI ​​can detect this change and contact a medical institution, allowing the user to receive appropriate care promptly. In this way, mental care using a home robot is realized. This allows the nursing care robot system to provide mental care to the user and coordinate with a medical institution as necessary.

[0064] A nursing care robot system according to an embodiment includes a conversation unit, a collaboration unit, an analysis unit, a detection unit, and a communication unit. The conversation unit engages in conversation with a user. For example, the conversation unit asks the user, "How was your day today?" and the user replies, "I met up with my friends today and it was fun." This conversation is analyzed by AI to understand the user's mental state. The conversation unit can also estimate the user's emotions and select a conversation topic based on the estimated user emotions. For example, if the user is sad, a topic of encouragement or comfort can be selected. The collaboration unit links the conversation data collected by the conversation unit to the cloud. For example, the collaboration unit can transmit the conversation data to the cloud. The data is stored on the cloud, and the analysis unit detects conversation content that is different from usual. The analysis unit analyzes the conversation data on the cloud and detects conversation content that is different from usual. For example, the analysis unit can detect conversation content that deviates from a normal conversation pattern. The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit can detect, for example, a sudden change in heart rate or an abnormal speech pattern. The communication unit contacts a medical institution based on the abnormality detected by the detection unit. The communication unit can contact the medical institution by, for example, telephone, email, or messaging app. This allows the nursing care robot system according to the embodiment to provide mental care to the user and cooperate with a medical institution as necessary.

[0065] The conversation unit can have a conversation to understand the user's mental state. For example, the conversation unit asks the user, "How was your day today?" and the user replies, "I met up with my friends today and it was fun." This conversation is analyzed by AI to understand the user's mental state. The conversation unit can also estimate the user's emotions and select a conversation topic based on the estimated user emotions. For example, if the user is sad, it can select a topic of encouragement or comfort. This makes it possible to understand the user's mental state.

[0066] The linking unit can transmit the conversation data to the cloud. The linking unit can, for example, transmit the conversation data to the cloud. For transmission, protocols such as HTTP, HTTPS, and FTP can be used. This allows the conversation data to be transmitted to the cloud.

[0067] The analysis unit can analyze the conversation data on the cloud and detect conversation content that is different from usual. The analysis unit can, for example, analyze the conversation data on the cloud and detect conversation content that is different from usual. To detect conversation content that is different from usual, for example, an algorithm that detects deviations from normal conversation patterns can be used. This makes it possible to detect conversation content that is different from usual.

[0068] The detection unit can contact a medical institution when it detects an abnormality. The detection unit can contact a medical institution when it detects an abnormality, for example. To detect an abnormality, for example, an algorithm that detects a sudden change in heart rate or an abnormal speech pattern can be used. This makes it possible to contact a medical institution when an abnormality is detected.

[0069] The communication unit can contact a medical institution and support the user's mental care. The communication unit can, for example, contact a medical institution and support the user's mental care. For communication, for example, telephone, email, messaging app, etc. can be used. This makes it possible to support the user's mental care.

[0070] The conversation unit can estimate the user's emotions and select a conversation topic based on the estimated user emotions. The conversation unit can, for example, estimate the user's emotions and select a conversation topic based on the estimated user emotions. Emotion estimation can use techniques such as facial expression analysis and voice analysis. For example, if the user is sad, a topic of encouragement or comfort can be selected. Also, if the user is happy, a topic of empathy or congratulations can be selected. Furthermore, if the user is anxious, a topic that gives a sense of security can be selected. In this way, an appropriate conversation topic can be selected based on the user's emotions.

[0071] The conversation unit can analyze the user's past conversation history and select a method for progressing the conversation. The conversation unit can, for example, analyze the user's past conversation history and select a method for progressing the conversation. In selecting a method for progressing the conversation, for example, the conversation can be progressed based on topics that the user liked in the past. It can also progress the conversation while avoiding topics that the user avoided in the past. Furthermore, it can analyze the user's past conversation patterns and ask questions at appropriate times. In this way, it is possible to select an optimal method for progressing the conversation based on the user's past conversation history.

[0072] The conversation unit can perform filtering based on the user's current living situation and areas of interest. The conversation unit can perform filtering based on the user's current living situation and areas of interest, for example. For filtering, for example, topics related to hobbies that the user has recently become interested in can be selected. Topics related to the user's current living situation (work, family, etc.) can also be selected. Furthermore, topics can be selected based on books the user has recently read or movies the user has recently seen. This allows for appropriate conversation to be held based on the user's current living situation and areas of interest.

[0073] The conversation unit can estimate the user's emotions and adjust the tempo of the conversation based on the estimated user's emotions. The conversation unit can, for example, estimate the user's emotions and adjust the tempo of the conversation based on the estimated user's emotions. For example, techniques such as facial expression analysis and voice analysis can be used to estimate emotions. For example, if the user is relaxed, the conversation can proceed at a slow tempo. Also, if the user is in a hurry, the conversation can proceed at a fast tempo. Furthermore, if the user is excited, the conversation can proceed at a lively tempo. In this way, the tempo of the conversation can be adjusted based on the user's emotions.

[0074] The conversation unit can prioritize selecting highly relevant topics based on the user's geographical location information. The conversation unit can prioritize selecting highly relevant topics based on, for example, the user's geographical location information. To acquire the geographical location information, for example, GPS data or location information services can be used. For example, topics related to news and events in the area where the user lives can be selected. Also, if the user is traveling, topics related to tourist information and recommended spots at the travel destination can be selected. Furthermore, if the user is in a specific location, topics related to the history and culture of that location can be selected. This makes it possible to select appropriate conversation topics based on the user's geographical location information.

[0075] The conversation unit can analyze the user's social media activity and select related topics. The conversation unit can, for example, analyze the user's social media activity and select related topics. When analyzing social media activity, for example, the content of posts and the number of likes can be used as evaluation criteria. For example, topics can be selected based on articles recently shared by the user on social media. Topics can also be selected based on the content of posts from accounts the user follows on social media. Furthermore, topics related to groups or communities the user participates in on social media can be selected. This makes it possible to select appropriate conversation topics based on the user's social media activity.

[0076] The linking unit can estimate the user's emotion and adjust the timing of data transmission based on the estimated user's emotion. The linking unit can, for example, estimate the user's emotion and adjust the timing of data transmission based on the estimated user's emotion. For example, techniques such as facial expression analysis and voice analysis can be used to estimate the emotion. For example, if the user is relaxed, data transmission can be delayed. Also, if the user is in a hurry, data transmission can be expedited. Furthermore, if the user is feeling anxious, data transmission can be refrained from. In this way, the timing of data transmission can be adjusted based on the user's emotion.

[0077] The linking unit can analyze the user's past data transmission history and select the optimal transmission method when transmitting data. For example, the linking unit can analyze the user's past data transmission history and select the optimal transmission method when transmitting data. When selecting the transmission method, for example, it can prioritize transmission methods (email, message, etc.) that the user has preferred in the past. It can also perform transmission while avoiding transmission methods that the user has avoided in the past. Furthermore, it can analyze the user's past transmission history and transmit data at an appropriate time. This makes it possible to select the optimal transmission method based on the user's past data transmission history.

[0078] The linking unit can perform filtering based on the user's current living situation and areas of interest when transmitting data. The linking unit can perform filtering based on the user's current living situation and areas of interest when transmitting data, for example. For filtering, for example, data related to topics in which the user is currently interested can be preferentially transmitted. Data related to the user's current living situation (work, home, etc.) can also be preferentially transmitted. Furthermore, data related to events in which the user recently participated can be preferentially transmitted. This makes it possible to transmit appropriate data based on the user's current living situation and areas of interest.

[0079] The linking unit can estimate the user's emotion and determine the priority of data to be transmitted based on the estimated user's emotion. The linking unit can, for example, estimate the user's emotion and determine the priority of data to be transmitted based on the estimated user's emotion. For example, techniques such as facial expression analysis and voice analysis can be used to estimate the emotion. For example, if the user is relaxed, data of low importance can be transmitted with priority. Also, if the user is in a hurry, data of high importance can be transmitted with priority. Furthermore, if the user is feeling anxious, data that gives a sense of security can be transmitted with priority. In this way, the priority of data to be transmitted can be determined based on the user's emotion.

[0080] When transmitting data, the linking unit can prioritize transmitting highly relevant data in consideration of the user's geographical location information. For example, when transmitting data, the linking unit can prioritize transmitting highly relevant data in consideration of the user's geographical location information. To obtain the geographical location information, for example, GPS data or location information services can be used. For example, when the user is in a specific location, data related to that location can be prioritized. Furthermore, when the user is traveling, data related to tourist information and recommended spots for the travel destination can be prioritized. Furthermore, data related to news and events in the area where the user lives can be prioritized. This allows appropriate data to be transmitted based on the user's geographical location information.

[0081] The linking unit can analyze the user's social media activity and transmit related data when transmitting data. For example, the linking unit can analyze the user's social media activity and transmit related data when transmitting data. For example, the analysis of social media activity can use the content of posts and the number of likes as evaluation criteria. For example, data can be transmitted based on articles recently shared by the user on social media. Data can also be transmitted based on the content of posts from accounts the user follows on social media. Furthermore, data related to groups and communities the user participates in on social media can be transmitted. This makes it possible to transmit appropriate data based on the user's social media activity.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. The analysis unit can, for example, estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. Technologies such as facial expression analysis and voice analysis can be used to estimate emotions. For example, if the user is relaxed, an algorithm that performs a detailed analysis can be applied. Also, if the user is in a hurry, an algorithm that performs a quick analysis can be applied. Furthermore, if the user is feeling anxious, an algorithm that provides an analysis result that gives a sense of security can be applied. This makes it possible to adjust the analysis algorithm based on the user's emotions.

[0083] The analysis unit can improve the accuracy of the current analysis by referring to past analysis data during analysis. The analysis unit can improve the accuracy of the current analysis by referring to past analysis data, for example, during analysis. To improve the analysis accuracy, for example, the current analysis algorithm can be adjusted based on the past analysis data. The current analysis accuracy can also be improved by referring to past analysis results. Furthermore, the past analysis data can be analyzed and reflected in the current analysis. This makes it possible to improve the accuracy of the current analysis based on the past analysis data.

[0084] The analysis unit can apply different analysis methods to each category of conversation data during analysis. For example, the analysis unit can apply different analysis methods to each category of conversation data during analysis. Categories can be classified using criteria such as by topic or by emotion. For example, an emotion analysis algorithm can be applied to conversation data related to emotions. An algorithm for evaluating health status can be applied to conversation data related to health. Furthermore, an algorithm for analyzing lifestyle patterns can be applied to conversation data related to daily life. This makes it possible to apply an appropriate analysis method to each category of conversation data.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can, for example, estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Technologies such as facial expression analysis and voice analysis can be used to estimate emotions. For example, if the user is relaxed, detailed analysis results can be displayed. Also, if the user is in a hurry, analysis results that focus on the main points can be displayed. Furthermore, if the user is feeling anxious, analysis results that give a sense of security can be displayed. In this way, the display method of the analysis results can be adjusted based on the user's emotions.

[0086] The analysis unit can determine the analysis priority based on the submission time of the conversation data during analysis. The analysis unit can, for example, determine the analysis priority based on the submission time of the conversation data during analysis. The submission time can be evaluated using, for example, the submission date and time or the submission frequency as criteria. For example, the most recent conversation data can be analyzed preferentially. Also, conversation data submitted during a specific time period can be analyzed preferentially. Furthermore, the analysis priority can be determined based on the user's schedule. This makes it possible to determine the analysis priority based on the submission time of the conversation data.

[0087] The analysis unit can improve the accuracy of the analysis by referring to literature related to the conversation data during analysis. The analysis unit can improve the accuracy of the analysis by referring to literature related to the conversation data, for example, during analysis. The related literature can be referenced, for example, to academic papers or technical reports. For example, the analysis can be performed by referring to the latest research papers related to the conversation data. The analysis can also be performed by referring to past research results related to the conversation data. Furthermore, the analysis can be performed by referring to specialized books related to the conversation data. In this way, the accuracy of the analysis can be improved by referring to literature related to the conversation data.

[0088] The detection unit can estimate the user's emotion and adjust the anomaly detection criteria based on the estimated user's emotion. The detection unit can, for example, estimate the user's emotion and adjust the anomaly detection criteria based on the estimated user's emotion. For example, techniques such as facial expression analysis and voice analysis can be used to estimate the emotion. For example, if the user is relaxed, the anomaly detection criteria can be relaxed. Also, if the user is in a hurry, the anomaly detection criteria can be tightened. Furthermore, if the user is feeling anxious, the anomaly detection criteria can be adjusted. In this way, the anomaly detection criteria can be adjusted based on the user's emotion.

[0089] The detection unit can improve the current detection accuracy by referring to past abnormality data when detecting an anomaly. For example, the detection unit can improve the current detection accuracy by referring to past abnormality data when detecting an anomaly. For example, past abnormality cases or abnormality patterns can be used to refer to the abnormality data. For example, the current anomaly detection algorithm can be adjusted based on the past abnormality data. Also, the current detection accuracy can be improved by referring to past abnormality detection results. Furthermore, the past abnormality data can be analyzed and reflected in the current anomaly detection. In this way, the current detection accuracy can be improved based on the past abnormality data.

[0090] The detection unit can apply different detection methods to each category of conversation data when detecting an anomaly. For example, the detection unit can apply different detection methods to each category of conversation data when detecting an anomaly. Categories can be classified using criteria such as by topic or by emotion. For example, an emotion anomaly detection algorithm can be applied to conversation data related to emotions. A health anomaly detection algorithm can be applied to conversation data related to health. Furthermore, a lifestyle anomaly detection algorithm can be applied to conversation data related to daily life. This makes it possible to apply an appropriate detection method to each category of conversation data.

[0091] The detection unit can estimate the user's emotion and adjust the display method of the anomaly detection result based on the estimated user's emotion. The detection unit can, for example, estimate the user's emotion and adjust the display method of the anomaly detection result based on the estimated user's emotion. For example, techniques such as facial expression analysis and voice analysis can be used to estimate the emotion. For example, if the user is relaxed, detailed anomaly detection results can be displayed. Also, if the user is in a hurry, anomaly detection results that focus on the main points can be displayed. Furthermore, if the user is feeling anxious, an anomaly detection result that gives a sense of security can be displayed. In this way, the display method of the anomaly detection result can be adjusted based on the user's emotion.

[0092] When detecting an anomaly, the detection unit can determine the detection priority based on the submission time of the conversation data. For example, when detecting an anomaly, the detection unit can determine the detection priority based on the submission time of the conversation data. For example, the submission date and time or the submission frequency can be used as a criterion for evaluating the submission time. For example, the most recent conversation data can be preferentially detected. Also, conversation data submitted during a specific time period can be preferentially detected. Furthermore, the detection priority can be determined based on the user's schedule. In this way, the detection priority can be determined based on the submission time of the conversation data.

[0093] The detection unit can improve the accuracy of detection by referring to literature related to the conversation data when detecting an anomaly. The detection unit can improve the accuracy of detection by referring to literature related to the conversation data when detecting an anomaly, for example. The related literature can be referenced, for example, to academic papers or technical reports. For example, detection can be performed by referring to the latest research papers related to the conversation data. Detection can also be performed by referring to past research results related to the conversation data. Furthermore, detection can be performed by referring to specialized books related to the conversation data. In this way, the accuracy of detection can be improved by referring to literature related to the conversation data.

[0094] The communication unit can estimate the user's emotions and adjust the method of communication based on the estimated user's emotions. The communication unit can, for example, estimate the user's emotions and adjust the method of communication based on the estimated user's emotions. For example, techniques such as facial expression analysis and voice analysis can be used to estimate emotions. For example, if the user is relaxed, the user can be contacted by email. Also, if the user is in a hurry, the user can be contacted by phone. Furthermore, if the user is feeling anxious, the user can be contacted in a way that gives the user a sense of security. In this way, the method of communication can be adjusted based on the user's emotions.

[0095] The contact unit can select the optimal contact method by referring to past contact history when making contact. For example, the contact unit can select the optimal contact method by referring to past contact history when making contact. When selecting the contact method, for example, it can prioritize contact methods (email, telephone, etc.) that the user has preferred in the past. It can also contact the user while avoiding contact methods that the user has avoided in the past. Furthermore, it can analyze the user's past contact history and make contact at an appropriate time. This makes it possible to select the optimal contact method based on the past contact history.

[0096] The contact unit can perform filtering based on the user's current living situation and areas of interest when contacting the user. The contact unit can perform filtering based on the user's current living situation and areas of interest when contacting the user, for example. For example, filtering can prioritize contact of information related to topics in which the user is currently interested. It can also prioritize contact of information related to the user's current living situation (work, family, etc.). It can also prioritize contact of information related to events that the user recently participated in. This makes it possible to make appropriate contact based on the user's current living situation and areas of interest.

[0097] The communication unit can estimate the user's emotions and determine the priority of communications based on the estimated user emotions. The communication unit can, for example, estimate the user's emotions and determine the priority of communications based on the estimated user emotions. For example, techniques such as facial expression analysis and voice analysis can be used to estimate emotions. For example, if the user is relaxed, communications of lower importance can be prioritized. Also, if the user is in a hurry, communications of higher importance can be prioritized. Furthermore, if the user is feeling anxious, communications that provide a sense of security can be prioritized. In this way, the priority of communications can be determined based on the user's emotions.

[0098] The contact unit can select a highly relevant contact method in consideration of the user's geographical location information when making contact. For example, the contact unit can select a highly relevant contact method in consideration of the user's geographical location information when making contact. For example, GPS data, location information services, etc. can be used to acquire the geographical location information. For example, if the user is in a specific location, information related to that location can be prioritized in contact. Also, if the user is traveling, tourist information about the travel destination and information about recommended spots can be prioritized in contact. Furthermore, information about news and events in the area where the user lives can be prioritized in contact. In this way, an appropriate contact method can be selected based on the user's geographical location information.

[0099] The contact unit can analyze the user's social media activity and select a relevant contact method when contacting the user. For example, the contact unit can analyze the user's social media activity and select a relevant contact method when contacting the user. When analyzing social media activity, for example, the content of posts and the number of likes can be used as evaluation criteria. For example, contact can be made based on articles recently shared by the user on social media. Contact can also be made based on the content of posts from accounts the user follows on social media. Furthermore, information related to groups and communities the user participates in on social media can be communicated. This makes it possible to select an appropriate contact method based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the conversation unit, collaboration unit, analysis unit, detection unit, and communication 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 conversation unit is realized by the control unit 46A of the smart device 14 and converses with the user. The collaboration unit transmits conversation data to the cloud via the communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the conversation data on the cloud. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects an abnormality based on the analyzed data. The communication unit contacts a medical institution via the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the conversation unit, collaboration unit, analysis unit, detection unit, and communication 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 conversation unit is realized by the control unit 46A of the smart glasses 214 and converses with the user. The collaboration unit, for example, transmits conversation data to the cloud via the communication I / F 44 of the smart glasses 214. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the conversation data on the cloud. The detection unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and detects abnormalities based on the analyzed data. The communication unit, for example, contacts a medical institution via the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the conversation unit, collaboration unit, analysis unit, detection unit, and communication 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 conversation unit is realized by the control unit 46A of the headset type terminal 314 and converses with the user. The collaboration unit transmits conversation data to the cloud via the communication I / F 44 of the headset type terminal 314, for example. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the conversation data on the cloud. The detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects an abnormality based on the analyzed data. The communication unit contacts a medical institution via the communication I / F 44 of the headset type terminal 314, for example. === Hard Collateral 1-4 === Each of the multiple elements including the conversation unit, collaboration unit, analysis unit, detection unit, and communication unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the robot 414 and converses with the user. The collaboration unit, for example, transmits conversation data to the cloud via the communication I / F 44 of the robot 414. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the conversation data on the cloud. The detection unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and detects an abnormality based on the analyzed data. The communication unit, for example, contacts a medical institution via the communication I / F 44 of the robot 414.

[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 care robot system may further include a health management unit that monitors the user's physical health condition. The health management unit may periodically measure the user's vital signs, such as heart rate, blood pressure, and body temperature, and contact a medical institution if an abnormality is detected. The health management unit may also record the user's exercise and dietary habits and provide advice to help improve the user's health condition. Furthermore, the health management unit may monitor the user's sleep patterns and make suggestions to improve the quality of sleep. This allows the system to comprehensively manage the user's physical health condition and provide comprehensive support, including mental care.

[0102] The care robot system may further include an entertainment unit that provides entertainment content customized based on the user's hobbies and interests. For example, the entertainment unit may recommend and play the user's favorite music or movies. It may also provide the latest news and articles related to the user's fields of interest. Furthermore, the entertainment unit may introduce online events and communities that the user can participate in, promoting social connections to reduce feelings of loneliness. This may improve the user's quality of life and provide enjoyment and a sense of fulfillment as part of mental care.

[0103] The care robot system can further include a relaxation unit that estimates the user's emotions and suggests appropriate relaxation methods based on the estimated emotions. For example, if the user is feeling stressed, the relaxation unit can provide deep breathing or meditation guidance. If the user feels like relaxing, the relaxation unit can play relaxing music or natural sounds. Furthermore, if the user is feeling anxious, the relaxation unit can provide positive messages or affirmations to give a sense of security. This makes it possible to suggest relaxation methods according to the user's emotions and support mental care.

[0104] The nursing care robot system can further include a fitness unit that estimates the user's emotions and suggests an appropriate exercise program based on the estimated emotions. For example, the fitness unit can suggest light exercises or stretching if the user is feeling energetic. Also, if the user feels like relaxing, it can suggest relaxation exercises such as yoga or Pilates. Furthermore, if the user is feeling stressed, it can provide an exercise program that is effective for relieving stress. In this way, an exercise program can be suggested based on the user's emotions, achieving both physical health and mental care.

[0105] The nursing care robot system can further include a nutrition management unit that estimates the user's emotions and proposes an appropriate meal plan based on the estimated emotions. For example, if the user is tired, the nutrition management unit can suggest a nutritionally balanced meal to replenish energy. If the user feels like relaxing, the nutrition management unit can also suggest relaxing herbal tea or light meals. Furthermore, if the user is feeling stressed, the nutrition management unit can provide recipes using ingredients that are effective in relieving stress. In this way, it is possible to propose a meal plan based on the user's emotions and support physical health and mental care.

[0106] The care robot system can further include a reminder unit that monitors the user's daily rhythm and provides reminders at appropriate times. For example, the reminder unit can notify the user so that they do not forget to take their medicine or to make an appointment with a medical institution. It can also set reminders for the user to exercise regularly. Furthermore, it can provide reminders in cooperation with a calendar so that the user does not forget important appointments or tasks. This can support the user's daily rhythm and help them manage their daily life.

[0107] The care robot system may further include a communication support unit that promotes communication with the user's family and friends. For example, the communication support unit may provide support for the user to make video calls with family and friends. It may also suggest appropriate expressions and words when the user sends a message. Furthermore, if the user feels lonely, it may provide a reminder to encourage the user to contact family and friends. This may strengthen the user's social connections and reduce feelings of loneliness.

[0108] The nursing care robot system can further include a learning support unit that estimates the user's emotions and provides appropriate learning content based on the estimated emotions. The learning support unit can, for example, recommend online courses or learning materials related to the user's fields of interest. If the user feels like relaxing, it can also provide learning content that has a relaxing effect. Furthermore, if the user is feeling stressed, it can provide learning content that helps relieve stress. This allows the system to provide learning content that corresponds to the user's emotions, thereby achieving both knowledge improvement and mental care.

[0109] The care robot system can further include an environmental management unit that monitors the user's living environment and provides a comfortable environment. The environmental management unit can, for example, appropriately adjust room temperature and humidity to provide a comfortable living environment. It can also adjust the brightness and color of lighting to provide a lighting environment that matches the user's mood. Furthermore, it can link with home appliances such as air purifiers and humidifiers to improve indoor air quality. This can optimize the user's living environment and provide a comfortable living space.

[0110] The care robot system may further include a hobby support unit that estimates the user's emotions and suggests appropriate hobby activities based on the estimated emotions. For example, if the user feels like relaxing, the hobby support unit may suggest a hobby activity with a relaxing effect, such as handicrafts or painting. If the user feels energetic, the hobby support unit may suggest outdoor activities or sports. Furthermore, if the user feels lonely, the hobby support unit may introduce online communities or group activities to promote social connections. This allows the system to suggest hobby activities that correspond to the user's emotions and improve the user's quality of life.

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

[0112] Step 1: The conversation unit engages in a conversation with the user. For example, the user asks, "How was your day today?" and the user replies, "I met up with my friends today and had a great time." This conversation is analyzed by AI to understand the user's mental state. The conversation unit can also estimate the user's emotions and select a conversation topic based on the estimated user emotions. For example, if the user is sad, it can select a topic of encouragement or comfort. Step 2: The linking unit links the conversation data collected by the conversation unit to the cloud. For example, the conversation data is sent to the cloud, where it is stored. Step 3: The analysis unit analyzes the conversation data on the cloud and detects unusual conversation content, such as conversation content that deviates from normal conversation patterns. Step 4: The detection unit detects abnormalities based on the data analyzed by the analysis unit. For example, it can detect sudden changes in heart rate or abnormal speech patterns. Step 5: The communication unit contacts a medical institution based on the abnormality detected by the detection unit. For example, the medical institution can be contacted by phone, email, messaging app, etc.

[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. a conversation unit that converses with a user; a linking unit that links the conversation data collected by the conversation unit to a cloud; an analysis unit that analyzes the data linked to the cloud by the link unit; a detection unit that detects an abnormality based on the data analyzed by the analysis unit; a contact unit that contacts a medical institution based on the abnormality detected by the detection unit. A system characterized by:

2. The conversation unit is Conduct conversations to understand the user's mental state 2. The system of claim 1.

3. The linking unit is Sending conversation data to the cloud 2. The system of claim 1.

4. The analysis unit Analyze conversation data on the cloud to detect unusual conversation content 2. The system of claim 1.

5. The detection unit If an abnormality is detected, contact a medical institution 2. The system of claim 1.

6. The communication unit Contacting medical institutions and providing mental health support to users 2. The system of claim 1.

7. The conversation unit is Estimate user emotions and select conversation topics based on the estimated user emotions 2. The system of claim 1.

8. The conversation unit is Analyze the user's past conversation history and decide how to proceed with the conversation 2. The system of claim 1.

Images