system

Abstract

The system according to this embodiment aims to alleviate loneliness among the elderly and to provide appropriate responses in emergencies. [Solution] The system according to the embodiment comprises a language processing unit, a response unit, an output unit, a provision unit, a monitoring unit, and a notification unit. The language processing unit verbalizes the spoken content using speech recognition technology. The response unit generates a response based on the content verbalized by the language processing unit and responds in voice. The output unit outputs the response generated by the response unit in voice. The provision unit provides the voice output by the output unit to the elderly person. The monitoring unit monitors the health status of the elderly person based on the voice provided by the provision unit. The notification unit notifies a medical institution or family in an emergency based on the health status monitored by the monitoring unit.

Description

[[TECHNICAL FIELD]]

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

[0002] Patent Document 1 discloses a persona chatbot control method executed by at least one processor, the method comprising: receiving a user utterance; adding the user utterance to a prompt including an instruction associated with a description of a character of a chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance. [[PRIOR ART DOCUMENTS]] [[PATENT DOCUMENTS]]

[0003] [[Patent Document 1]] Japanese Patent Laid-Open No. 2022-180282 [[SUMMARY OF THE INVENTION]] [[PROBLEM TO BE SOLVED BY THE INVENTION]]

[0004] In the conventional technology, a system that alleviates the loneliness of elderly people and provides appropriate responses in emergencies has not been sufficiently provided, and there is room for improvement.

[0005] An object of the system according to an embodiment is to alleviate the loneliness of elderly people and provide appropriate responses in emergencies. [[MEANS FOR SOLVING THE PROBLEM]]

[0006] The system according to this embodiment comprises a language processing unit, a response unit, an output unit, a provision unit, a monitoring unit, and a notification unit. The language processing unit verbalizes the spoken content using speech recognition technology. The response unit generates a response based on the content verbalized by the language processing unit and responds in voice. The output unit outputs the response generated by the response unit in voice. The provision unit provides the voice output by the output unit to the elderly person. The monitoring unit monitors the health status of the elderly person based on the voice provided by the provision unit. The notification unit notifies a medical institution or family in an emergency based on the health status monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can alleviate loneliness among the elderly and provide appropriate responses in emergencies. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), Bluetooth (registered trademark), and the like.

[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. In addition, in the present specification, when three or more matters are expressed by connecting them with "and / or", the same concept as "A and / or B" applies.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing apparatus 12 and a smart device 14. A server is an example of the data processing apparatus 12.

[0018] The data processing apparatus 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, the RAM 30, and the 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 WAN (Wide Area Network) and / or LAN (Local Area Network), and the like.

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The family AI robot system according to an embodiment of the present invention is a system for elderly people living alone. This system has the function of verbalizing spoken content using speech recognition technology and responding in voice. This can alleviate feelings of loneliness in the elderly. It also has a function to notify medical institutions or family members in emergencies. The robot is cat-shaped and has a design that is easy for the elderly to understand. For example, when an elderly person speaks to the robot, the robot verbalizes what they said using speech recognition technology. Next, the robot generates an appropriate response based on the verbalized content and responds in voice. For example, if an elderly person says, "The weather is nice today," the robot will respond, "Yes, it's sunny today." Furthermore, the robot has a function to monitor the health status of the elderly person. For example, the robot periodically measures the elderly person's heart rate and body temperature, and if there is an abnormality, it notifies medical institutions or family members. The notification is made automatically by the robot, so the elderly person does not need to make a notification themselves. In an aging society, this robot can not only alleviate feelings of loneliness in the elderly but also improve the quality of life for the elderly by enabling emergency response. This will enable the family AI robot system to alleviate feelings of loneliness among the elderly and to respond in emergencies.

[0029] The family AI robot system according to this embodiment comprises a language processing unit, a response unit, an output unit, a provision unit, a monitoring unit, and a notification unit. The language processing unit verbalizes the spoken content using speech recognition technology. For example, the language processing unit can accurately verbalize the content spoken by the elderly person using deep learning-based speech recognition technology. The language processing unit can also use HMM (Hidden Markov Model)-based speech recognition technology. The response unit generates an appropriate response based on the content verbalized by the language processing unit. For example, the response unit can generate an appropriate response for the elderly person using natural language generation technology. The response unit can also use template-based response generation technology. The output unit outputs the response generated by the response unit as audio. For example, the output unit can output the generated response in high-quality audio using speech synthesis technology. The provision unit provides the audio output by the output unit to the elderly person. For example, the provision unit can provide the output audio at a volume that is easy for the elderly person to hear. The monitoring unit monitors the health status of the elderly person based on the audio provided by the provision unit. For example, the monitoring unit can periodically measure the heart rate and body temperature of elderly individuals to monitor their health status. The notification unit, based on the health status monitored by the monitoring unit, notifies medical institutions or family members in emergencies. For example, the notification unit can automatically notify medical institutions or family members if an abnormality is detected. As a result, the family AI robot system according to this embodiment can alleviate feelings of loneliness in elderly individuals and enable emergency response.

[0030] The monitoring unit can periodically measure the heart rate or body temperature of elderly individuals. For example, it can measure an elderly person's heart rate every hour. It can also measure an elderly person's body temperature daily. Furthermore, the monitoring unit can measure heart rate or body temperature during specific events. For example, it can measure an elderly person's heart rate after exercise. This allows for regular monitoring of the elderly person's health status.

[0031] The notification unit can notify a medical institution or family member when an abnormality is detected. For example, the notification unit can notify a medical institution if an elderly person's heart rate is abnormally high. It can also notify a family member if an elderly person's body temperature is abnormally high. Furthermore, the notification unit can automatically send notifications when an abnormality is detected. For example, it can notify a medical institution if the heart rate increases rapidly. This allows for a quick response when an abnormality is detected.

[0032] The language processing unit can verbalize what elderly people say using speech recognition technology. For example, the language processing unit can accurately verbalize what elderly people say using deep learning-based speech recognition technology. Alternatively, the language processing unit can also use HMM (Hidden Markov Model)-based speech recognition technology, which allows for accurate verbalization of what elderly people say.

[0033] The response unit can generate responses based on verbalized content. For example, the response unit can use natural language generation technology to generate appropriate responses for elderly individuals. Alternatively, the response unit can use template-based response generation technology, which also enables the generation of appropriate responses for elderly individuals.

[0034] The output unit can output responses in voice. For example, the output unit can use speech synthesis technology to output the generated responses in high-quality voice. This allows for providing voice responses to elderly individuals.

[0035] The delivery unit can provide audio to the elderly. For example, the delivery unit can provide the outputted audio at a volume easily audible to the elderly. This allows audio to be provided to the elderly.

[0036] The monitoring unit can monitor health status. For example, the monitoring unit can periodically measure the heart rate and body temperature of elderly individuals to monitor their health status. This allows for continuous monitoring of the health status of elderly individuals.

[0037] The notification unit can notify medical institutions or family members in emergencies. For example, if an abnormality is detected, the notification unit can automatically notify medical institutions or family members. This allows for a swift response in emergencies.

[0038] The verbalization unit can adjust the accuracy of verbalization according to the speaking speed and volume of the elderly person. For example, if the elderly person speaks slowly, the verbalization unit can slow down the verbalization speed. If the elderly person speaks loudly, the verbalization unit can improve the accuracy of verbalization according to the volume. Furthermore, if the elderly person speaks quickly, the verbalization unit can speed up the verbalization speed. This allows for verbalization that is appropriate to the speaking speed and volume of the elderly person.

[0039] The verbalization unit can improve the accuracy of verbalization by referring to the elderly person's past conversation history. For example, the verbalization unit can refer to what the elderly person has said in the past and perform verbalization on the same topic with high accuracy. In addition, the verbalization unit can learn the elderly person's past speaking patterns and improve the accuracy of verbalization. Furthermore, the verbalization unit can memorize words and phrases that the elderly person frequently uses and utilize them during verbalization. As a result, the accuracy of verbalization is improved by referring to past conversation history.

[0040] The language processing unit can apply region-specific speech recognition models to accommodate the dialects and accents of elderly people. For example, if an elderly person speaks Kansai dialect, the language processing unit can use a speech recognition model that corresponds to Kansai dialect. Similarly, if an elderly person speaks Tohoku dialect, the language processing unit can use a speech recognition model that corresponds to Tohoku dialect. Furthermore, if an elderly person speaks Okinawan dialect, the language processing unit can use a speech recognition model that corresponds to Okinawan dialect. This enables language processing that accommodates the dialects and accents of elderly people.

[0041] The language processing unit can emphasize specific keywords in response to what the elderly person is saying. For example, if the elderly person says, "I want to go to the hospital," the language processing unit can emphasize "hospital." Similarly, if the elderly person says, "I forgot to take my medicine," the language processing unit can emphasize "medicine." Furthermore, if the elderly person says, "I want to go for a walk," the language processing unit can emphasize "walk." This allows for language processing that is tailored to what the elderly person is saying.

[0042] The response unit can generate more personalized responses by referring to the elderly person's past conversation history. For example, it can refer to what the elderly person has said in the past and respond to the same topic. Furthermore, the response unit can learn the elderly person's past speaking patterns and generate personalized responses. In addition, the response unit can memorize words and phrases that the elderly person frequently uses and utilize them when responding. This allows for personalized responses by referring to past conversation history.

[0043] The response unit can generate responses that include appropriate advice and warnings depending on the health condition of the elderly person. For example, if the elderly person has a high heart rate, the response unit can advise them to calm down. If the elderly person has a high body temperature, the response unit can encourage them to drink fluids. Furthermore, if the elderly person's health condition is poor, the response unit can recommend that they visit a medical institution. This allows for responses tailored to the health condition of the elderly person.

[0044] The response unit can generate responses that include relevant information based on the hobbies and interests of elderly individuals. For example, if an elderly person is interested in gardening, the response unit can include information about seasonal flowers in its response. If an elderly person is interested in cooking, the response unit can include simple recipes in its response. Furthermore, if an elderly person is interested in travel, the response unit can include information about travel destinations in its response. This allows for responses tailored to the hobbies and interests of elderly individuals.

[0045] The response unit can adjust the order and priority of responses according to what the elderly person is saying. For example, if the elderly person brings up an urgent topic, the response unit can prioritize that response. Also, if the elderly person brings up multiple topics, the response unit can determine the order of responses according to their importance. Furthermore, if the elderly person brings up everyday topics, the response unit can respond in a relaxed order. In this way, responses can be given in an order and priority that is appropriate to what the elderly person is saying.

[0046] The output unit can adjust the volume and frequency of the output sound according to the hearing ability of the elderly person. For example, if the elderly person's hearing is impaired, the output unit can increase the volume of the sound. Conversely, if the elderly person's hearing is good, the output unit can output the sound at an appropriate volume. Furthermore, the output unit can adjust the frequency of the sound according to the elderly person's hearing ability. This allows the output of sound at a volume and frequency appropriate to the elderly person's hearing.

[0047] The output unit can optimize the content of the output voice by referring to the elderly person's past responses. For example, the output unit can refer to the voice tone that the elderly person previously preferred and output voice in the same tone. Furthermore, the output unit can analyze the elderly person's past responses and output voice with optimal content. In addition, the output unit can remember the content of the elderly person's past responses and reflect it in the output voice. This allows for the output of voice with optimal content by referring to past responses.

[0048] The output unit can detect ambient noises around the elderly person and automatically adjust the volume of the output sound. For example, if the elderly person's surroundings are noisy, the output unit can increase the volume of the output sound. Conversely, if the elderly person's surroundings are quiet, the output unit can output sound at an appropriate volume. Furthermore, the output unit can automatically adjust the volume of the output sound according to the ambient noise level of the elderly person. This allows the output sound to be at a volume appropriate to the ambient noise level.

[0049] The output unit can adjust the emphasis of the output voice according to what the elderly person is saying. For example, if the elderly person is talking about an important topic, the output unit can emphasize that part of the voice. If the elderly person is talking about everyday topics, the output unit can output the voice in a relaxed tone. Furthermore, if the elderly person is talking about an urgent topic, the output unit can quickly emphasize that part of the voice. In this way, the voice can be output with emphasis appropriate to the content being spoken.

[0050] The delivery unit can optimize the timing of audio delivery by referring to the elderly person's past responses. For example, the delivery unit can deliver audio at a time the elderly person preferred in the past. Furthermore, the delivery unit can analyze the elderly person's past responses and deliver audio at the optimal timing. In addition, the delivery unit can remember the timing of the elderly person's past responses and reflect this in the audio delivery. This allows the delivery of audio at the optimal timing by referring to past responses.

[0051] The service provider can customize the content of the audio messages they provide according to the health condition of the elderly person. For example, if an elderly person has a high heart rate, the service provider can provide an audio message advising them to calm down. If an elderly person has a high body temperature, the service provider can provide an audio message encouraging them to stay hydrated. Furthermore, if an elderly person is not in good health, the service provider can provide an audio message recommending that they visit a medical institution. This allows the service provider to deliver audio content tailored to the individual's health condition.

[0052] The audio provider can adjust the volume and tone of the audio they deliver according to the elderly person's environment. For example, if the elderly person's surroundings are noisy, the provider can increase the volume of the audio. Conversely, if the elderly person's surroundings are quiet, the provider can deliver the audio at an appropriate volume. Furthermore, the provider can adjust the tone of the audio according to the elderly person's environment. This allows the audio to be delivered at a volume and tone appropriate to the environment.

[0053] The audio provider can adjust the level of detail in the audio they provide according to what the elderly person is saying. For example, if the elderly person is talking about an important topic, the provider can provide detailed information. If the elderly person is talking about everyday topics, the provider can provide concise information. Furthermore, if the elderly person is talking about an urgent topic, the provider can quickly provide detailed information. This allows the audio to be provided with a level of detail appropriate to what is being said.

[0054] The monitoring unit can improve the accuracy of monitoring by referring to the elderly person's past health data. For example, the monitoring unit can improve the accuracy of monitoring by referring to the elderly person's past heart rate data. Furthermore, the monitoring unit can improve the accuracy of monitoring by referring to the elderly person's past body temperature data. In addition, the monitoring unit can improve the accuracy of monitoring by referring to the elderly person's past health status. Thus, by referring to past health data, the accuracy of monitoring is improved.

[0055] The monitoring unit can optimize the timing of monitoring according to the elderly person's lifestyle. For example, if the elderly person is a morning person, the monitoring unit can perform monitoring in the morning. Similarly, if the elderly person is a night owl, the monitoring unit can perform monitoring in the evening. Furthermore, the monitoring unit can perform monitoring at the optimal timing according to the elderly person's lifestyle. This allows for monitoring at a time that matches their lifestyle.

[0056] The monitoring unit can customize the monitoring content by referring to the elderly person's environmental data. For example, if the elderly person lives in a cold region, the monitoring unit can enhance room temperature monitoring. Similarly, if the elderly person lives in a hot and humid environment, the monitoring unit can enhance humidity monitoring. Furthermore, the monitoring unit can customize the monitoring items according to the elderly person's living environment. This allows for monitoring tailored to the environmental data.

[0057] The monitoring unit can adjust the monitoring items according to what the elderly person says. For example, if the elderly person complains of feeling unwell, the monitoring unit can strengthen the monitoring of their health status. Furthermore, if the elderly person complains of lack of exercise, the monitoring unit can strengthen the monitoring of their activity level. Additionally, if the elderly person complains of sleep deprivation, the monitoring unit can strengthen the monitoring of their sleep status. This allows for monitoring items tailored to the content of the conversation.

[0058] The notification system can improve the accuracy of its notifications by referring to the elderly person's past health data. For example, it can improve the accuracy of its notifications by referring to the elderly person's past heart rate data. It can also improve the accuracy of its notifications by referring to the elderly person's past body temperature data. Furthermore, it can improve the accuracy of its notifications by referring to the elderly person's past health status. In this way, the accuracy of notifications is improved by referring to past health data.

[0059] The notification system can optimize the timing of notifications according to the elderly person's daily routine. For example, if the elderly person is a morning person, the notification system can make a notification in the morning. Similarly, if the elderly person is a night owl, the notification system can make a notification in the evening. Furthermore, the notification system can make notifications at the optimal time according to the elderly person's daily routine. This ensures that notifications are made at a time that suits their lifestyle.

[0060] The notification unit can customize the content of notifications by referring to the elderly person's environmental data. For example, if the elderly person lives in a cold region, the notification unit can include information about room temperature in the notification. Similarly, if the elderly person lives in a hot and humid environment, the notification unit can include information about humidity in the notification. Furthermore, the notification unit can customize the content of notifications according to the elderly person's living environment. This allows notifications to be tailored to the environmental data.

[0061] The reporting unit can adjust the items of the report according to what the elderly person says. For example, if the elderly person complains of feeling unwell, the reporting unit can make a report regarding their health condition. Also, if the elderly person complains of not getting enough exercise, the reporting unit can make a report regarding their activity level. Furthermore, if the elderly person complains of not getting enough sleep, the reporting unit can make a report regarding their sleep condition. This allows the reporting unit to make a report that is tailored to what the elderly person says.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The data acquisition unit can measure the activity level of elderly individuals and record their daily exercise. For example, it can measure the distance and time an elderly person walks and record their daily exercise. It can also record the content and time spent on household chores performed by the elderly person. Furthermore, it can record the content and progress of rehabilitation exercises performed by the elderly person. This allows for an understanding of the elderly person's daily activity level and helps in health management.

[0064] The assessment unit can record the dietary content of elderly individuals and evaluate their nutritional balance. For example, it can record the contents of meals consumed by elderly individuals and evaluate the balance of calories and nutrients. It can also record the amount of fluids consumed by elderly individuals and evaluate whether adequate hydration is being provided. Furthermore, it can record the types and amounts of medications taken by elderly individuals, supporting medication management. This allows for centralized management of elderly individuals' diets, hydration, and medications.

[0065] The service provider can suggest daily activities based on the hobbies and interests of elderly individuals. For example, if an elderly person is interested in gardening, the service provider can suggest how to care for plants according to the season. Similarly, if an elderly person is interested in cooking, the service provider can suggest simple recipes. Furthermore, if an elderly person is interested in handicrafts, the service provider can suggest new handicraft projects. This allows for the suggestion of activities tailored to the hobbies and interests of elderly individuals, enriching their daily lives.

[0066] The data acquisition unit can monitor the sleep patterns of elderly individuals and evaluate their sleep quality. For example, it can record bedtime and wake-up time to understand their sleep rhythm. It can also record their movements during sleep to evaluate sleep depth and quality. Furthermore, it can record their snoring and breathing patterns to assess their risk of sleep apnea. This allows for the provision of information to improve the sleep quality of elderly individuals.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The verbalization unit verbalizes the spoken content using speech recognition technology. For example, deep learning-based speech recognition technology or HMM (Hidden Markov Model)-based speech recognition technology can be used to accurately verbalize what elderly people say. Step 2: The response unit generates an appropriate response based on the content verbalized by the verbalization unit. For example, natural language generation technology or template-based response generation technology can be used to generate an appropriate response for elderly people. Step 3: The output unit outputs the response generated by the response unit as audio. For example, the generated response can be output in high quality audio using speech synthesis technology. Step 4: The providing unit provides the elderly with the audio output by the output unit. For example, the output audio can be provided at a volume that is easy for the elderly to hear. Step 5: The monitoring unit monitors the health status of the elderly person based on the voice data provided by the service provider. For example, it can periodically measure the elderly person's heart rate and body temperature to monitor their health status. Step 6: The notification unit will notify a medical institution or family member in an emergency based on the health status monitored by the monitoring unit. For example, if an abnormality is detected, it can automatically notify a medical institution or family member.

[0069] (Example of form 2) The family AI robot system according to an embodiment of the present invention is a system for elderly people living alone. This system has the function of verbalizing spoken content using speech recognition technology and responding in voice. This can alleviate feelings of loneliness in the elderly. It also has a function to notify medical institutions or family members in emergencies. The robot is cat-shaped and has a design that is easy for the elderly to understand. For example, when an elderly person speaks to the robot, the robot verbalizes what they said using speech recognition technology. Next, the robot generates an appropriate response based on the verbalized content and responds in voice. For example, if an elderly person says, "The weather is nice today," the robot will respond, "Yes, it's sunny today." Furthermore, the robot has a function to monitor the health status of the elderly person. For example, the robot periodically measures the elderly person's heart rate and body temperature, and if there is an abnormality, it notifies medical institutions or family members. The notification is made automatically by the robot, so the elderly person does not need to make a notification themselves. In an aging society, this robot can not only alleviate feelings of loneliness in the elderly but also improve the quality of life for the elderly by enabling emergency response. This will enable the family AI robot system to alleviate feelings of loneliness among the elderly and to respond in emergencies.

[0070] The family AI robot system according to this embodiment comprises a language processing unit, a response unit, an output unit, a provision unit, a monitoring unit, and a notification unit. The language processing unit verbalizes the spoken content using speech recognition technology. For example, the language processing unit can accurately verbalize the content spoken by the elderly person using deep learning-based speech recognition technology. The language processing unit can also use HMM (Hidden Markov Model)-based speech recognition technology. The response unit generates an appropriate response based on the content verbalized by the language processing unit. For example, the response unit can generate an appropriate response for the elderly person using natural language generation technology. The response unit can also use template-based response generation technology. The output unit outputs the response generated by the response unit as audio. For example, the output unit can output the generated response in high-quality audio using speech synthesis technology. The provision unit provides the audio output by the output unit to the elderly person. For example, the provision unit can provide the output audio at a volume that is easy for the elderly person to hear. The monitoring unit monitors the health status of the elderly person based on the audio provided by the provision unit. For example, the monitoring unit can periodically measure the heart rate and body temperature of elderly individuals to monitor their health status. The notification unit, based on the health status monitored by the monitoring unit, notifies medical institutions or family members in emergencies. For example, the notification unit can automatically notify medical institutions or family members if an abnormality is detected. As a result, the family AI robot system according to this embodiment can alleviate feelings of loneliness in elderly individuals and enable emergency response.

[0071] The monitoring unit can periodically measure the heart rate or body temperature of elderly individuals. For example, it can measure an elderly person's heart rate every hour. It can also measure an elderly person's body temperature daily. Furthermore, the monitoring unit can measure heart rate or body temperature during specific events. For example, it can measure an elderly person's heart rate after exercise. This allows for regular monitoring of the elderly person's health status.

[0072] The notification unit can notify a medical institution or family member when an abnormality is detected. For example, the notification unit can notify a medical institution if an elderly person's heart rate is abnormally high. It can also notify a family member if an elderly person's body temperature is abnormally high. Furthermore, the notification unit can automatically send notifications when an abnormality is detected. For example, it can notify a medical institution if the heart rate increases rapidly. This allows for a quick response when an abnormality is detected.

[0073] The language processing unit can verbalize what elderly people say using speech recognition technology. For example, the language processing unit can accurately verbalize what elderly people say using deep learning-based speech recognition technology. Alternatively, the language processing unit can also use HMM (Hidden Markov Model)-based speech recognition technology, which allows for accurate verbalization of what elderly people say.

[0074] The response unit can generate responses based on verbalized content. For example, the response unit can use natural language generation technology to generate appropriate responses for elderly individuals. Alternatively, the response unit can use template-based response generation technology, which also enables the generation of appropriate responses for elderly individuals.

[0075] The output unit can output responses in voice. For example, the output unit can use speech synthesis technology to output the generated responses in high-quality voice. This allows for providing voice responses to elderly individuals.

[0076] The delivery unit can provide audio to the elderly. For example, the delivery unit can provide the outputted audio at a volume easily audible to the elderly. This allows audio to be provided to the elderly.

[0077] The monitoring unit can monitor health status. For example, the monitoring unit can periodically measure the heart rate and body temperature of elderly individuals to monitor their health status. This allows for continuous monitoring of the health status of elderly individuals.

[0078] The notification unit can notify medical institutions or family members in emergencies. For example, if an abnormality is detected, the notification unit can automatically notify medical institutions or family members. This allows for a swift response in emergencies.

[0079] The verbalization unit can estimate emotions and adjust the verbalization method based on the estimated emotion. For example, if an elderly person is sad, the verbalization unit can use gentle language. If an elderly person is agitated, the verbalization unit can use a calm tone. Furthermore, if an elderly person is tired, the verbalization unit can choose concise and easy-to-understand words. This allows for verbalization in a manner appropriate to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The verbalization unit can adjust the accuracy of verbalization according to the speaking speed and volume of the elderly person. For example, if the elderly person speaks slowly, the verbalization unit can slow down the verbalization speed. If the elderly person speaks loudly, the verbalization unit can improve the accuracy of verbalization according to the volume. Furthermore, if the elderly person speaks quickly, the verbalization unit can speed up the verbalization speed. This allows for verbalization that is appropriate to the speaking speed and volume of the elderly person.

[0081] The verbalization unit can improve the accuracy of verbalization by referring to the elderly person's past conversation history. For example, the verbalization unit can refer to what the elderly person has said in the past and perform verbalization on the same topic with high accuracy. In addition, the verbalization unit can learn the elderly person's past speaking patterns and improve the accuracy of verbalization. Furthermore, the verbalization unit can memorize words and phrases that the elderly person frequently uses and utilize them during verbalization. As a result, the accuracy of verbalization is improved by referring to past conversation history.

[0082] The verbalization unit can estimate the emotions of elderly individuals and adjust the timing of verbalization based on the estimated emotions. For example, if an elderly person is depressed, the verbalization unit can delay verbalization slightly. If an elderly person is excited, the verbalization unit can verbalize immediately. Furthermore, if an elderly person is tired, the verbalization unit can verbalize at a slower pace. This allows for verbalization at a timing appropriate to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The language processing unit can apply region-specific speech recognition models to accommodate the dialects and accents of elderly people. For example, if an elderly person speaks Kansai dialect, the language processing unit can use a speech recognition model that corresponds to Kansai dialect. Similarly, if an elderly person speaks Tohoku dialect, the language processing unit can use a speech recognition model that corresponds to Tohoku dialect. Furthermore, if an elderly person speaks Okinawan dialect, the language processing unit can use a speech recognition model that corresponds to Okinawan dialect. This enables language processing that accommodates the dialects and accents of elderly people.

[0084] The language processing unit can emphasize specific keywords in response to what the elderly person is saying. For example, if the elderly person says, "I want to go to the hospital," the language processing unit can emphasize "hospital." Similarly, if the elderly person says, "I forgot to take my medicine," the language processing unit can emphasize "medicine." Furthermore, if the elderly person says, "I want to go for a walk," the language processing unit can emphasize "walk." This allows for language processing that is tailored to what the elderly person is saying.

[0085] The response unit can estimate the emotions of elderly individuals and adjust the tone and content of its response based on the estimated emotions. For example, if an elderly person is sad, the response unit can respond with words of encouragement in a gentle tone. If an elderly person is agitated, the response unit can respond in a calm tone. Furthermore, if an elderly person is tired, the response unit can respond with concise and easy-to-understand content. This allows for responses to be tailored to the emotions of the elderly person. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The response unit can generate more personalized responses by referring to the elderly person's past conversation history. For example, it can refer to what the elderly person has said in the past and respond to the same topic. Furthermore, the response unit can learn the elderly person's past speaking patterns and generate personalized responses. In addition, the response unit can memorize words and phrases that the elderly person frequently uses and utilize them when responding. This allows for personalized responses by referring to past conversation history.

[0087] The response unit can generate responses that include appropriate advice and warnings depending on the health condition of the elderly person. For example, if the elderly person has a high heart rate, the response unit can advise them to calm down. If the elderly person has a high body temperature, the response unit can encourage them to drink fluids. Furthermore, if the elderly person's health condition is poor, the response unit can recommend that they visit a medical institution. This allows for responses tailored to the health condition of the elderly person.

[0088] The response unit can estimate the emotions of elderly individuals and adjust the length of its response based on the estimated emotions. For example, if an elderly person is depressed, the response unit can offer a longer, encouraging response. If an elderly person is agitated, the response unit can offer a shorter, calming response. Furthermore, if an elderly person is tired, the response unit can provide a concise response. This allows for responses of appropriate length to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The response unit can generate responses that include relevant information based on the hobbies and interests of elderly individuals. For example, if an elderly person is interested in gardening, the response unit can include information about seasonal flowers in its response. If an elderly person is interested in cooking, the response unit can include simple recipes in its response. Furthermore, if an elderly person is interested in travel, the response unit can include information about travel destinations in its response. This allows for responses tailored to the hobbies and interests of elderly individuals.

[0090] The response unit can adjust the order and priority of responses according to what the elderly person is saying. For example, if the elderly person brings up an urgent topic, the response unit can prioritize that response. Also, if the elderly person brings up multiple topics, the response unit can determine the order of responses according to their importance. Furthermore, if the elderly person brings up everyday topics, the response unit can respond in a relaxed order. In this way, responses can be given in an order and priority that is appropriate to what the elderly person is saying.

[0091] The output unit can estimate the emotions of elderly individuals and adjust the tone and speed of the output voice based on the estimated emotions. For example, if an elderly person is sad, the output unit can output voice in a gentle tone and at a slow speed. If an elderly person is excited, the output unit can output voice in a calm tone. Furthermore, if an elderly person is tired, the output unit can output voice in a concise and easy-to-understand tone. This allows for the output of voice in a tone and speed appropriate to the emotions of the elderly person. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The output unit can adjust the volume and frequency of the output sound according to the hearing ability of the elderly person. For example, if the elderly person's hearing is impaired, the output unit can increase the volume of the sound. Conversely, if the elderly person's hearing is good, the output unit can output the sound at an appropriate volume. Furthermore, the output unit can adjust the frequency of the sound according to the elderly person's hearing ability. This allows the output of sound at a volume and frequency appropriate to the elderly person's hearing.

[0093] The output unit can optimize the content of the output voice by referring to the elderly person's past responses. For example, the output unit can refer to the voice tone that the elderly person previously preferred and output voice in the same tone. Furthermore, the output unit can analyze the elderly person's past responses and output voice with optimal content. In addition, the output unit can remember the content of the elderly person's past responses and reflect it in the output voice. This allows for the output of voice with optimal content by referring to past responses.

[0094] The output unit can estimate the emotions of elderly individuals and adjust the timing of the output audio based on the estimated emotions. For example, if an elderly person is depressed, the output unit can output audio after a short delay. If an elderly person is excited, the output unit can output audio immediately. Furthermore, if an elderly person is tired, the output unit can output audio at a slower pace. This allows for audio output at a timing appropriate to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The output unit can detect ambient noises around the elderly person and automatically adjust the volume of the output sound. For example, if the elderly person's surroundings are noisy, the output unit can increase the volume of the output sound. Conversely, if the elderly person's surroundings are quiet, the output unit can output sound at an appropriate volume. Furthermore, the output unit can automatically adjust the volume of the output sound according to the ambient noise level of the elderly person. This allows the output sound to be at a volume appropriate to the ambient noise level.

[0096] The output unit can adjust the emphasis of the output voice according to what the elderly person is saying. For example, if the elderly person is talking about an important topic, the output unit can emphasize that part of the voice. If the elderly person is talking about everyday topics, the output unit can output the voice in a relaxed tone. Furthermore, if the elderly person is talking about an urgent topic, the output unit can quickly emphasize that part of the voice. In this way, the voice can be output with emphasis appropriate to the content being spoken.

[0097] The service provider can estimate the emotions of elderly individuals and adjust the content of the audio provided based on the estimated emotions. For example, if an elderly person is sad, the service provider can provide words of encouragement. If an elderly person is agitated, the service provider can provide calming words. Furthermore, if an elderly person is tired, the service provider can provide relaxing words. This allows the service provider to deliver audio content tailored to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The delivery unit can optimize the timing of audio delivery by referring to the elderly person's past responses. For example, the delivery unit can deliver audio at a time the elderly person preferred in the past. Furthermore, the delivery unit can analyze the elderly person's past responses and deliver audio at the optimal timing. In addition, the delivery unit can remember the timing of the elderly person's past responses and reflect this in the audio delivery. This allows the delivery of audio at the optimal timing by referring to past responses.

[0099] The service provider can customize the content of the audio messages they provide according to the health condition of the elderly person. For example, if an elderly person has a high heart rate, the service provider can provide an audio message advising them to calm down. If an elderly person has a high body temperature, the service provider can provide an audio message encouraging them to stay hydrated. Furthermore, if an elderly person is not in good health, the service provider can provide an audio message recommending that they visit a medical institution. This allows the service provider to deliver audio content tailored to the individual's health condition.

[0100] The delivery unit can estimate the emotions of elderly individuals and adjust the order of the audio delivered based on the estimated emotions. For example, if an elderly person is depressed, the delivery unit can deliver words of encouragement first. If an elderly person is agitated, the delivery unit can deliver calming words first. Furthermore, if an elderly person is tired, the delivery unit can deliver relaxing words first. This allows the audio to be delivered in an order appropriate to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The audio provider can adjust the volume and tone of the audio they deliver according to the elderly person's environment. For example, if the elderly person's surroundings are noisy, the provider can increase the volume of the audio. Conversely, if the elderly person's surroundings are quiet, the provider can deliver the audio at an appropriate volume. Furthermore, the provider can adjust the tone of the audio according to the elderly person's environment. This allows the audio to be delivered at a volume and tone appropriate to the environment.

[0102] The audio provider can adjust the level of detail in the audio they provide according to what the elderly person is saying. For example, if the elderly person is talking about an important topic, the provider can provide detailed information. If the elderly person is talking about everyday topics, the provider can provide concise information. Furthermore, if the elderly person is talking about an urgent topic, the provider can quickly provide detailed information. This allows the audio to be provided with a level of detail appropriate to what is being said.

[0103] The monitoring unit can estimate the emotions of elderly individuals and adjust the monitoring frequency based on the estimated emotions. For example, if an elderly individual is depressed, the monitoring unit can increase the monitoring frequency. Conversely, if an elderly individual is agitated, the monitoring unit can decrease the monitoring frequency. Furthermore, if an elderly individual is tired, the monitoring unit can monitor at an appropriate frequency. This allows for monitoring at a frequency appropriate to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The monitoring unit can improve the accuracy of monitoring by referring to the elderly person's past health data. For example, the monitoring unit can improve the accuracy of monitoring by referring to the elderly person's past heart rate data. Furthermore, the monitoring unit can improve the accuracy of monitoring by referring to the elderly person's past body temperature data. In addition, the monitoring unit can improve the accuracy of monitoring by referring to the elderly person's past health status. Thus, by referring to past health data, the accuracy of monitoring is improved.

[0105] The monitoring unit can optimize the timing of monitoring according to the elderly person's lifestyle. For example, if the elderly person is a morning person, the monitoring unit can perform monitoring in the morning. Similarly, if the elderly person is a night owl, the monitoring unit can perform monitoring in the evening. Furthermore, the monitoring unit can perform monitoring at the optimal timing according to the elderly person's lifestyle. This allows for monitoring at a time that matches their lifestyle.

[0106] The monitoring unit can estimate the emotions of elderly individuals and determine monitoring priorities based on the estimated emotions. For example, if an elderly individual is depressed, the monitoring unit can increase the monitoring priority. Conversely, if an elderly individual is agitated, the monitoring unit can decrease the monitoring priority. Furthermore, if an elderly individual is tired, the monitoring unit can perform monitoring with an appropriate priority. This allows for monitoring with priorities that correspond to the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The monitoring unit can customize the monitoring content by referring to the elderly person's environmental data. For example, if the elderly person lives in a cold region, the monitoring unit can enhance room temperature monitoring. Similarly, if the elderly person lives in a hot and humid environment, the monitoring unit can enhance humidity monitoring. Furthermore, the monitoring unit can customize the monitoring items according to the elderly person's living environment. This allows for monitoring tailored to the environmental data.

[0108] The monitoring unit can adjust the monitoring items according to what the elderly person says. For example, if the elderly person complains of feeling unwell, the monitoring unit can strengthen the monitoring of their health status. Furthermore, if the elderly person complains of lack of exercise, the monitoring unit can strengthen the monitoring of their activity level. Additionally, if the elderly person complains of sleep deprivation, the monitoring unit can strengthen the monitoring of their sleep status. This allows for monitoring items tailored to the content of the conversation.

[0109] The notification system can estimate the emotions of elderly individuals and adjust the content of the notification based on the estimated emotions. For example, if an elderly person is depressed, the notification system can send a notification to their family that includes a message of encouragement. If an elderly person is agitated, the notification system can send a notification that includes a message of calming them down. Furthermore, if an elderly person is tired, the notification system can send a notification that includes a message encouraging them to rest. This allows for notifications to be tailored to the individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The notification system can improve the accuracy of its notifications by referring to the elderly person's past health data. For example, it can improve the accuracy of its notifications by referring to the elderly person's past heart rate data. It can also improve the accuracy of its notifications by referring to the elderly person's past body temperature data. Furthermore, it can improve the accuracy of its notifications by referring to the elderly person's past health status. In this way, the accuracy of notifications is improved by referring to past health data.

[0111] The notification system can optimize the timing of notifications according to the elderly person's daily routine. For example, if the elderly person is a morning person, the notification system can make a notification in the morning. Similarly, if the elderly person is a night owl, the notification system can make a notification in the evening. Furthermore, the notification system can make notifications at the optimal time according to the elderly person's daily routine. This ensures that notifications are made at a time that suits their lifestyle.

[0112] The reporting system can estimate the emotions of elderly individuals and determine the priority of reporting based on the estimated emotions. For example, if an elderly person is depressed, the reporting system can increase the priority of reporting. Conversely, if an elderly person is agitated, the reporting system can decrease the priority of reporting. Furthermore, if an elderly person is tired, the reporting system can make a report with an appropriate priority. This allows for reporting with prioritization according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The notification unit can customize the content of notifications by referring to the elderly person's environmental data. For example, if the elderly person lives in a cold region, the notification unit can include information about room temperature in the notification. Similarly, if the elderly person lives in a hot and humid environment, the notification unit can include information about humidity in the notification. Furthermore, the notification unit can customize the content of notifications according to the elderly person's living environment. This allows notifications to be tailored to the environmental data.

[0114] The reporting unit can adjust the items of the report according to what the elderly person says. For example, if the elderly person complains of feeling unwell, the reporting unit can make a report regarding their health condition. Also, if the elderly person complains of not getting enough exercise, the reporting unit can make a report regarding their activity level. Furthermore, if the elderly person complains of not getting enough sleep, the reporting unit can make a report regarding their sleep condition. This allows the reporting unit to make a report that is tailored to what the elderly person says.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The data acquisition unit can measure the activity level of elderly individuals and record their daily exercise. For example, it can measure the distance and time an elderly person walks and record their daily exercise. It can also record the content and time spent on household chores performed by the elderly person. Furthermore, it can record the content and progress of rehabilitation exercises performed by the elderly person. This allows for an understanding of the elderly person's daily activity level and helps in health management.

[0117] The assessment unit can record the dietary content of elderly individuals and evaluate their nutritional balance. For example, it can record the contents of meals consumed by elderly individuals and evaluate the balance of calories and nutrients. It can also record the amount of fluids consumed by elderly individuals and evaluate whether adequate hydration is being provided. Furthermore, it can record the types and amounts of medications taken by elderly individuals, supporting medication management. This allows for centralized management of elderly individuals' diets, hydration, and medications.

[0118] The service provider can suggest daily activities based on the hobbies and interests of elderly individuals. For example, if an elderly person is interested in gardening, the service provider can suggest how to care for plants according to the season. Similarly, if an elderly person is interested in cooking, the service provider can suggest simple recipes. Furthermore, if an elderly person is interested in handicrafts, the service provider can suggest new handicraft projects. This allows for the suggestion of activities tailored to the hobbies and interests of elderly individuals, enriching their daily lives.

[0119] The data acquisition unit can monitor the sleep patterns of elderly individuals and evaluate their sleep quality. For example, it can record bedtime and wake-up time to understand their sleep rhythm. It can also record their movements during sleep to evaluate sleep depth and quality. Furthermore, it can record their snoring and breathing patterns to assess their risk of sleep apnea. This allows for the provision of information to improve the sleep quality of elderly individuals.

[0120] The system can estimate the emotions of elderly individuals and provide appropriate music based on those estimates. For example, if an elderly person is feeling depressed, the system can provide relaxing music. It can also provide calming music if an elderly person is feeling agitated. Furthermore, if an elderly person is feeling tired, the system can provide refreshing music. This allows the system to provide music tailored to the emotions of elderly individuals, thereby improving their mood.

[0121] The assessment unit can estimate the emotions of elderly individuals and suggest appropriate activities based on those estimates. For example, if an elderly individual is feeling depressed, the assessment unit can suggest a walk or light exercise. If an elderly individual is feeling agitated, the assessment unit can suggest relaxing activities such as yoga or meditation. Furthermore, if an elderly individual is feeling tired, the assessment unit can suggest rest or relaxing reading. This allows for the suggestion of activities tailored to the elderly individual's emotions, thereby improving their quality of life.

[0122] The reporting system can estimate the emotions of elderly individuals and adjust the content of the report based on those estimates. For example, if an elderly person is depressed, the system can send a report to their family that includes a message of encouragement. Similarly, if an elderly person is agitated, the system can send a report that includes a message of calm. Furthermore, if an elderly person is tired, the system can send a report that includes a message encouraging rest. This allows for reports to be tailored to the individual's emotional state.

[0123] The service provider can estimate the emotions of elderly individuals and adjust the content of the audio based on those estimated emotions. For example, if an elderly person is sad, the service provider can offer words of encouragement. Similarly, if an elderly person is agitated, the service provider can offer calming words. Furthermore, if an elderly person is tired, the service provider can offer relaxing words. This allows the service provider to deliver audio content tailored to the elderly person's emotions.

[0124] The monitoring unit can estimate the emotions of elderly individuals and adjust the monitoring frequency based on the estimated emotions. For example, if an elderly individual is depressed, the monitoring unit can increase the monitoring frequency. Conversely, if an elderly individual is agitated, the monitoring unit can decrease the monitoring frequency. Furthermore, if an elderly individual is tired, the monitoring unit can monitor at an appropriate frequency. This allows for monitoring at a frequency that matches the emotions of the elderly individual.

[0125] The audio provider can estimate the emotions of elderly individuals and adjust the order in which it delivers audio based on those estimated emotions. For example, if an elderly person is depressed, the provider can deliver encouraging words first. Similarly, if an elderly person is agitated, it can deliver calming words first. Furthermore, if an elderly person is tired, it can deliver relaxing words first. This allows the audio to be delivered in an order appropriate to the elderly person's emotions.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The verbalization unit verbalizes the spoken content using speech recognition technology. For example, deep learning-based speech recognition technology or HMM (Hidden Markov Model)-based speech recognition technology can be used to accurately verbalize what elderly people say. Step 2: The response unit generates an appropriate response based on the content verbalized by the verbalization unit. For example, natural language generation technology or template-based response generation technology can be used to generate an appropriate response for elderly people. Step 3: The output unit outputs the response generated by the response unit as audio. For example, the generated response can be output in high quality audio using speech synthesis technology. Step 4: The providing unit provides the elderly with the audio output by the output unit. For example, the output audio can be provided at a volume that is easy for the elderly to hear. Step 5: The monitoring unit monitors the health status of the elderly person based on the voice data provided by the service provider. For example, it can periodically measure the elderly person's heart rate and body temperature to monitor their health status. Step 6: The notification unit will notify a medical institution or family member in an emergency based on the health status monitored by the monitoring unit. For example, if an abnormality is detected, it can automatically notify a medical institution or family member.

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0129] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0130] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0131] Each of the multiple elements described above, including the language processing unit, response unit, output unit, provision unit, monitoring unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the language processing unit is implemented by the processor 46 of the smart device 14 and uses speech recognition technology to verbalize what the elderly person says. The response unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an appropriate response based on the verbalized content. The output unit is implemented by the processor 46 of the smart device 14 and outputs the generated response as voice. The provision unit is implemented by the output device 40 of the smart device 14 and provides the outputted voice to the elderly person. The monitoring unit monitors the health status of the elderly person using the camera 42 and sensors of the smart device 14. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12 and notifies medical institutions or family members in case of emergency. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] As shown in Figure 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.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the language processing unit, response unit, output unit, provision unit, monitoring unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the language processing unit is implemented by the processor 46 of the smart glasses 214 and uses speech recognition technology to verbalize what the elderly person says. The response unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an appropriate response based on the verbalized content. The output unit is implemented by the processor 46 of the smart glasses 214 and outputs the generated response as voice. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the outputted voice to the elderly person. The monitoring unit monitors the health status of the elderly person using the camera 42 and sensors of the smart glasses 214. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12 and notifies medical institutions or family members in case of emergency. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the language processing unit, response unit, output unit, provision unit, monitoring unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the language processing unit is implemented by the processor 46 of the headset terminal 314 and uses speech recognition technology to verbalize what the elderly person says. The response unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an appropriate response based on the verbalized content. The output unit is implemented by the processor 46 of the headset terminal 314 and outputs the generated response as voice. The provision unit is implemented by the speaker 240 of the headset terminal 314 and provides the outputted voice to the elderly person. The monitoring unit monitors the health status of the elderly person using the camera 42 and sensors of the headset terminal 314. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12 and notifies medical institutions or family members in case of emergency. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0168] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0170] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0171] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0172] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the language processing unit, response unit, output unit, provision unit, monitoring unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the language processing unit is implemented by the processor 46 of the robot 414 and uses speech recognition technology to verbalize what the elderly person says. The response unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an appropriate response based on the verbalized content. The output unit is implemented by the processor 46 of the robot 414 and outputs the generated response as voice. The provision unit is implemented by the speaker 240 of the robot 414 and provides the outputted voice to the elderly person. The monitoring unit monitors the health status of the elderly person using the camera 42 and sensors of the robot 414. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12 and notifies medical institutions or family members in case of emergency. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0181] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0183] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0184] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0185] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0189] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0190] 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.

[0191] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0192] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0194] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0196] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0197] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0198] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0199] (Note 1) A language processing unit that uses speech recognition technology to verbalize what is spoken, A response unit that generates a response based on the content verbalized by the verbalization unit and responds in voice, An output unit that outputs the response generated by the response unit as sound, A providing unit that provides the sound output by the output unit to the elderly, A monitoring unit that monitors the health status of elderly people based on the voice provided by the aforementioned provisioning unit, The system includes a notification unit that, in an emergency, notifies a medical institution or family member based on the health status monitored by the aforementioned monitoring unit. A system characterized by the following features. (Note 2) The monitoring unit, Regularly measure the heart rate or body temperature of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reporting unit, When an abnormality is detected, notify a medical institution or family member. The system described in Appendix 1, characterized by the features described herein. (Note 4) The language processing unit, Using speech recognition technology to verbalize what elderly people say. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned response section is, Generate a response based on the verbalized content. The system described in Appendix 1, characterized by the features described herein. (Note 6) The output unit is, Output the response as audio. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Providing audio to the elderly The system described in Appendix 1, characterized by the features described herein. (Note 8) The monitoring unit, Monitor your health status The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reporting unit, In case of emergency, contact a medical institution or family member. The system described in Appendix 1, characterized by the features described herein. (Note 10) The language processing unit, It estimates emotions and adjusts the way verbalization is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The language processing unit, The accuracy of verbalization is adjusted according to the speaking speed and volume of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 12) The language processing unit, Referencing the past conversation history of elderly individuals improves the accuracy of verbalization. The system described in Appendix 1, characterized by the features described herein. (Note 13) The language processing unit, The system estimates the emotions of elderly individuals and adjusts the timing of verbalization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The language processing unit, To accommodate the dialects and accents of the elderly, we apply region-specific speech recognition models. The system described in Appendix 1, characterized by the features described herein. (Note 15) The language processing unit, Depending on what the elderly person is saying, emphasize specific keywords in your speech. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned response section is, The system estimates the emotions of elderly individuals and adjusts the tone and content of responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned response section is, By referring to the elderly person's past conversation history, more personalized responses can be generated. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned response section is, The system generates responses that include appropriate advice and warnings based on the health status of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned response section is, The system estimates the emotions of elderly individuals and adjusts the length of responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned response section is, Based on the hobbies and interests of elderly people, the system generates responses that include relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned response section is, Adjust the order and priority of responses according to what the elderly person is saying. The system described in Appendix 1, characterized by the features described herein. (Note 22) The output unit is, It estimates the emotions of elderly people and adjusts the tone and speed of the output voice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The output unit is, The volume and frequency of the output sound are adjusted according to the hearing ability of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 24) The output unit is, The content of the output voice is optimized by referring to the elderly person's past responses. The system described in Appendix 1, characterized by the features described herein. (Note 25) The output unit is, It estimates the emotions of elderly people and adjusts the timing of the output voice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The output unit is, It detects ambient noises in the environment for elderly people and automatically adjusts the volume of the output audio. The system described in Appendix 1, characterized by the features described herein. (Note 27) The output unit is, The output voice is emphasized according to what the elderly person is saying. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the emotions of elderly people and adjusts the content of the audio provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, Referencing the elderly person's past responses optimizes the timing of the audio provided. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, The content of the audio provided will be customized according to the health condition of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, The system estimates the emotions of elderly individuals and adjusts the order of audio content provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, The volume and tone of the audio provided will be adjusted according to the environment of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, The level of detail in the audio provided will be adjusted according to what the elderly person is saying. The system described in Appendix 1, characterized by the features described herein. (Note 34) The monitoring unit, The system estimates the emotions of older adults and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The monitoring unit, Referencing past health data of elderly individuals improves the accuracy of monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 36) The monitoring unit, Optimize monitoring timing according to the elderly person's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 37) The monitoring unit, The system estimates the emotions of older adults and determines monitoring priorities based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The monitoring unit, Customize monitoring content by referring to environmental data of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 39) The monitoring unit, The monitoring items are adjusted according to what the elderly person says. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned reporting unit, The system estimates the emotions of elderly individuals and adjusts the content of the report based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned reporting unit, Referencing past health data of elderly individuals improves the accuracy of emergency notifications. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned reporting unit, Optimizing the timing of notifications according to the elderly person's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned reporting unit, The system estimates the emotions of elderly individuals and prioritizes reporting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned reporting unit, Customize the content of the notification by referring to environmental data of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned reporting unit, Adjust the reporting items according to what the elderly person is saying. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A system comprising at least one processor, a microphone, a speaker, and a sensor, The aforementioned at least one processor is The system includes a language processing unit that receives speech from an elderly person via the microphone and converts the speech into language using speech recognition technology, A response unit that generates a response based on the content verbalized by the verbalization unit, An output unit that outputs the response generated by the response unit as sound via the speaker, A providing unit that provides the sound output by the output unit to the elderly person, A monitoring unit that monitors the health status of the elderly person by periodically measuring the heart rate or body temperature of the elderly person using the aforementioned sensor, It functions as a notification unit that automatically notifies a medical institution or family member if the heart rate or body temperature measured by the monitoring unit shows an abnormal value, The at least one processor further estimates the emotions of the elderly person using an emotion identification model, The monitoring unit adjusts to increase the frequency of monitoring when the estimated emotion is depressed, and to decrease the frequency of monitoring when the estimated emotion is excited. A system characterized by the following features.

2. The monitoring unit, If the elderly person is a morning person, the monitoring will be performed in the morning; if the elderly person is a night owl, the monitoring will be performed in the evening; the timing of the monitoring will be optimized according to the elderly person's lifestyle. The system according to feature 1.

3. The aforementioned reporting unit, If the estimated emotion is depressed, a notification including an encouraging message will be sent to the family; if the estimated emotion is agitated, a notification including a calming message will be sent. The system according to feature 1.

4. The language processing unit, The speed of verbalization is adjusted according to the speaking speed of the elderly person, such as slowing down the verbalization speed when the elderly person speaks slowly and speeding up the verbalization speed when the elderly person speaks quickly. The system according to feature 1.

5. The aforementioned response section is, If the estimated emotion is sadness, the response is generated in a gentle tone and includes words of encouragement; if the estimated emotion is fatigue, the response is generated to be concise and easy to understand. The system according to feature 1.

6. The output section is, If the estimated emotion is sadness, the response is output aloud in a gentle tone and at a slow pace; if the estimated emotion is excitement, the response is output aloud in a calm tone. The system according to feature 1.

7. The aforementioned supply unit is, If the estimated emotion is sadness, audio containing words of encouragement will be prioritized for the elderly person; if the estimated emotion is fatigue, audio containing words of relaxation will be prioritized for the elderly person. The system according to feature 1.

8. The monitoring unit, If the elderly person lives in a cold region, monitoring of room temperature will be strengthened; if the elderly person lives in a hot and humid environment, monitoring of humidity will be strengthened. The monitoring items will be customized by referring to the elderly person's environmental data. The system according to feature 1.

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