system

Abstract

The system according to this embodiment aims to detect the possibility of dementia early based on the lifestyle patterns and conversations of seniors, and to take appropriate action. [Solution] The system according to the embodiment comprises a conversation analysis unit, a pattern learning unit, a notification / advice unit, and an anomaly detection unit. The conversation analysis unit analyzes conversations and detects the possibility of dementia. The pattern learning unit learns the lifestyle patterns of seniors. The notification / advice unit detects behavioral anomalies early based on the lifestyle patterns learned by the pattern learning unit and provides notifications and advice. The anomaly detection unit detects anomalies from GPS information.

Description

Technical Field

[0006] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the possibility of dementia has not been sufficiently detected early from the life patterns and conversations of seniors, and appropriate measures have not been taken.

[0005] The system according to an embodiment aims to detect the possibility of dementia early from the life patterns and conversations of seniors and take appropriate measures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a conversation analysis unit, a pattern learning unit, a notification / advice unit, and an anomaly detection unit. The conversation analysis unit analyzes conversations and detects the possibility of dementia. The pattern learning unit learns the lifestyle patterns of seniors. The notification / advice unit detects behavioral anomalies early on based on the lifestyle patterns learned by the pattern learning unit and provides notifications and advice. The anomaly detection unit detects anomalies from GPS information. [Effects of the Invention]

[0007] The system according to this embodiment can detect the possibility of dementia early based on the lifestyle patterns and conversations of seniors, and take appropriate action. [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 numbered 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 applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the 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 device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 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 system according to an embodiment of the present invention is an AI+α service unique to telecommunications companies, designed to address the expanding lifestyles of seniors. This system has the function of detecting the possibility of dementia from normal conversations, learning the lifestyle patterns of seniors, and detecting and notifying / advising on behavioral abnormalities at an early stage. Furthermore, if dementia is suspected or if an abnormality is detected from GPS information, various services of the telecommunications company are utilized to support the senior. In this way, the system can ensure the safety of seniors by learning their lifestyle patterns, detecting abnormalities at an early stage, and providing notification and advice. For example, the system analyzes the daily conversations of seniors to detect signs of dementia. The system collects data on the daily lives of seniors and learns their lifestyle patterns. The system notifies and provides appropriate advice if abnormalities are observed, such as seniors not eating meals at the usual time or not going for walks. The system supports seniors by utilizing various services of the telecommunications company if dementia is suspected or if an abnormality is detected from GPS information. In this way, the system can provide an environment in which seniors can live with peace of mind. For example, even if a senior is living alone, the system can ensure their safety by monitoring them and notifying and advising on any abnormalities. Furthermore, by utilizing the various services offered by telecommunications companies, it is possible to support the lives of seniors and provide them with an environment in which they can live with peace of mind.

[0029] The system according to this embodiment comprises a conversation analysis unit, a pattern learning unit, a notification / advice unit, and an anomaly detection unit. The conversation analysis unit analyzes the daily conversations of seniors and detects the possibility of dementia. For example, the conversation analysis unit converts the seniors' conversations into text data using speech recognition technology and detects signs of dementia using a language model. The conversation analysis unit can also analyze the frequency of use of specific keywords and phrases in the seniors' conversations to detect signs of dementia. Furthermore, the conversation analysis unit can also analyze the flow and consistency of the seniors' conversations to detect signs of dementia. The pattern learning unit collects data on the seniors' daily lives and learns their lifestyle patterns. For example, the pattern learning unit collects daily behavioral data such as the seniors' meal times and walking times, and learns lifestyle patterns using a learning algorithm. The pattern learning unit can also learn lifestyle patterns considering the seniors' living environment and health condition. Furthermore, the pattern learning unit can optimize the learning algorithm by referring to the seniors' past lifestyle data. The notification / advice unit detects behavioral anomalies early based on the lifestyle patterns learned by the pattern learning unit and provides notifications and advice. The notification and advice unit notifies users and provides appropriate advice when abnormalities are observed, such as when a senior citizen does not eat meals or go for walks at their usual times. The notification and advice unit can also estimate the senior citizen's emotions and adjust the content of the notification and advice based on these estimates. Furthermore, the notification and advice unit can provide optimal advice by referring to the senior citizen's past behavioral history. The anomaly detection unit detects anomalies from GPS information and takes appropriate action based on the senior citizen's location. For example, the anomaly detection unit monitors the senior citizen's location in real time and takes appropriate action when an anomaly is detected. The anomaly detection unit can also estimate the senior citizen's emotions and adjust the anomaly detection criteria based on these estimates. Furthermore, the anomaly detection unit can optimize the anomaly detection algorithm by referring to the senior citizen's past behavioral data. As a result, the system according to this embodiment can learn the senior citizen's lifestyle patterns, detect anomalies early, and provide notifications and advice, thereby ensuring the senior citizen's safety.

[0030] The conversation analysis unit analyzes the daily conversations of seniors to detect the possibility of dementia. Specifically, it uses speech recognition technology to convert seniors' conversations into text data and inputs that text data into a language model. The language model utilizes natural language processing technology to detect specific patterns and anomalies in seniors' conversations that indicate signs of dementia. For example, if the frequency of use of certain keywords or phrases in a conversation is unusually high, or if the flow of the conversation is inconsistent, these can be considered early signs of dementia. The conversation analysis unit also analyzes the temporal changes in seniors' conversations, the frequency of topic changes, and word choices, and evaluates whether these elements are related to signs of dementia. Furthermore, the conversation analysis unit analyzes the emotional tone and changes in voice tone in seniors' conversations, and evaluates whether these are related to signs of dementia. In this way, the conversation analysis unit can detect signs of dementia from seniors' daily conversations from multiple angles and provide information for early intervention.

[0031] The pattern learning unit collects data on seniors' daily lives and learns their lifestyle patterns. Specifically, it collects daily behavioral data such as meal times, walking times, sleep times, and medication times, and learns lifestyle patterns based on this data. The learning algorithm analyzes this data and models the seniors' daily rhythms and behavioral patterns. For example, if a senior has a habit of eating at the same time every day, an anomaly such as not eating at that time will be detected. The pattern learning unit can also learn lifestyle patterns while considering the senior's living environment and health condition. For example, if a senior has a specific health condition, it learns a lifestyle pattern appropriate to that condition and adjusts the criteria for detecting anomalies. Furthermore, the pattern learning unit can optimize the learning algorithm by referring to the senior's past lifestyle data. As a result, the pattern learning unit can learn seniors' lifestyle patterns with high accuracy and provide a foundation for early detection of anomalies.

[0032] The notification and advice unit detects behavioral abnormalities early based on lifestyle patterns learned by the pattern learning unit and provides notifications and advice. Specifically, if an abnormality is observed, such as a senior not eating meals at the usual time or not going for a walk, the unit will notify the senior, their family, or caregiver. Notifications are made via smartphone apps, email, or voice calls. The notification and advice unit can also estimate the senior's emotions and adjust the content of the notifications and advice based on those emotions. For example, if the senior is feeling stressed, the unit will send a notification using gentle language that takes those emotions into consideration. The notification and advice unit can also provide optimal advice by referring to the senior's past behavioral history. For example, it can provide more effective advice by referring to how similar abnormalities were handled in the past. In this way, the notification and advice unit can provide appropriate support to improve the senior's quality of life.

[0033] The anomaly detection unit detects anomalies from GPS information and takes appropriate action based on the senior's location. Specifically, it monitors the senior's location in real time and detects anomalies if they deviate from their usual range of activity or enter a specific danger area. If an anomaly is detected, it notifies the senior, their family, or caregiver to encourage a quick response. The anomaly detection unit can also estimate the senior's emotions and adjust the anomaly detection criteria based on those emotions. For example, if the senior is feeling anxious, the anomaly detection criteria are relaxed to accommodate those emotions. Furthermore, the anomaly detection unit can optimize its anomaly detection algorithm by referring to the senior's past behavioral data. As a result, the anomaly detection unit can perform highly accurate anomaly detection based on the senior's location information and support a quick and appropriate response.

[0034] The notification and advice unit can notify seniors and provide appropriate advice when an anomaly is detected. For example, the notification and advice unit can notify seniors and provide appropriate advice if an anomaly is observed, such as when a senior does not eat or go for a walk at the usual time. The notification and advice unit can also estimate the senior's emotions and adjust the content of the notification and advice based on the estimated emotions. Furthermore, the notification and advice unit can provide optimal advice by referring to the senior's past behavioral history. In this way, the safety of seniors can be ensured by notifying them and providing appropriate advice when an anomaly is detected. Some or all of the above processing in the notification and advice unit may be performed using AI, for example, or without AI. For example, the notification and advice unit can provide notifications and advice using an AI model that notifies seniors and provides appropriate advice when an anomaly is detected.

[0035] The anomaly detection unit can detect anomalies from GPS information and take appropriate action based on the senior's location information. For example, the anomaly detection unit can monitor the senior's location information in real time and take appropriate action when an anomaly is detected. The anomaly detection unit can also estimate the senior's emotions and adjust the anomaly detection criteria based on the estimated emotions. Furthermore, the anomaly detection unit can optimize the anomaly detection algorithm by referring to the senior's past behavioral data. This ensures the safety of seniors by detecting anomalies from GPS information and taking appropriate action based on their location information. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can detect anomalies using an AI model that takes GPS information as input and outputs anomalies.

[0036] The conversation analysis unit can analyze the daily conversations of seniors and detect signs of dementia. For example, the conversation analysis unit can convert seniors' conversations into text data using speech recognition technology and detect signs of dementia using a language model. The conversation analysis unit can also analyze the frequency of use of specific keywords and phrases in seniors' conversations and detect signs of dementia. Furthermore, the conversation analysis unit can analyze the flow and consistency of seniors' conversations and detect signs of dementia. In this way, by analyzing seniors' daily conversations and detecting signs of dementia, the possibility of dementia can be detected at an early stage. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can detect signs of dementia using an AI model that takes seniors' daily conversations as input and outputs signs of dementia.

[0037] The pattern learning unit can collect data on the daily lives of seniors and learn their lifestyle patterns. For example, the pattern learning unit collects data on seniors' daily activities, such as meal times and walking times, and learns lifestyle patterns using a learning algorithm. The pattern learning unit can also learn lifestyle patterns while considering the seniors' living environment and health status. Furthermore, the pattern learning unit can optimize its learning algorithm by referring to the seniors' past lifestyle data. This allows for the early detection of abnormalities by collecting data on seniors' daily lives and learning their lifestyle patterns. Some or all of the above-described processes in the pattern learning unit may be performed using AI, for example, or without AI. For example, the pattern learning unit can learn lifestyle patterns using an AI model that takes data on seniors' daily lives as input and outputs lifestyle patterns.

[0038] The notification and advice unit can notify seniors if they exhibit abnormal behavior, such as not eating meals or going for walks at their usual times, and provide appropriate advice. The notification and advice unit can also estimate the senior's emotions and adjust the content of the notification and advice based on those emotions. Furthermore, the notification and advice unit can refer to the senior's past behavioral history to provide optimal advice. This ensures the safety of seniors by notifying them and providing appropriate advice when abnormal behavior, such as not eating meals or going for walks, is observed. Some or all of the above processing in the notification and advice unit may be performed using AI, or not. For example, the notification and advice unit can use an AI model to notify seniors and provide appropriate advice when abnormal behavior, such as not eating meals or going for walks, is observed.

[0039] The anomaly detection unit can contact the senior from the call center to confirm the situation if dementia is suspected. For example, based on data from the conversation analysis unit and pattern learning unit, the anomaly detection unit notifies the call center if dementia is suspected, and the call center operator contacts the senior. The anomaly detection unit can also notify the call center if an anomaly is detected based on the senior's location information, and appropriate action can be taken. This ensures the safety of the senior by allowing the call center to contact the senior and confirm the situation if dementia is suspected. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can use an AI model to notify the call center when dementia is suspected, and the call center operator contacts the senior.

[0040] The conversation analysis unit can estimate the senior's emotions and adjust the accuracy of the conversation analysis based on the estimated emotions. For example, if the senior is stressed, the conversation analysis unit can adjust the tone and speed of the conversation to improve the accuracy of the analysis. Also, if the senior is relaxed, the conversation analysis unit can analyze the content of the conversation in detail and more accurately detect signs of dementia. Furthermore, if the senior is agitated, the conversation analysis unit can emphasize certain keywords in the conversation to improve the accuracy of the analysis. In this way, by adjusting the accuracy of the conversation analysis based on the senior's emotions, signs of dementia can be detected more accurately. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can adjust the accuracy of the conversation analysis using an AI model that takes senior's emotion data as input and adjusts the accuracy of the conversation analysis.

[0041] The conversation analysis unit can more accurately detect signs of dementia by referring to the senior's past conversation history during conversation analysis. For example, the conversation analysis unit analyzes the senior's past conversation history to detect changes in word choice and flow of conversation. The conversation analysis unit can also analyze the frequency of use of specific phrases and words from the senior's past conversation history to detect signs of dementia. Furthermore, the conversation analysis unit can detect changes in conversation consistency and logic based on the senior's past conversation history. As a result, by referring to the senior's past conversation history, signs of dementia can be detected more accurately. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can detect signs of dementia using an AI model that takes the senior's past conversation history as input and outputs signs of dementia.

[0042] The conversation analysis unit can learn the language usage patterns of seniors during conversation analysis and detect anomalies early. For example, the conversation analysis unit can learn the language usage patterns of seniors and detect abnormal word choices and conversation flow. The conversation analysis unit can also detect changes in the frequency of use of specific phrases or words based on the language usage patterns of seniors. Furthermore, the conversation analysis unit can analyze the language usage patterns of seniors and detect changes in the consistency and logic of the conversation. In this way, by learning the language usage patterns of seniors, anomalies can be detected early. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can detect anomalies using an AI model that takes the language usage patterns of seniors as input and outputs anomalies.

[0043] The conversation analysis unit can estimate the emotions of seniors and determine the priority of conversation analysis based on the estimated emotions. For example, if a senior is stressed, the conversation analysis unit can increase the priority of the conversation analysis and perform the analysis quickly. Conversely, if a senior is relaxed, the conversation analysis unit can lower the priority of the conversation analysis and perform a more detailed analysis. Furthermore, if a senior is agitated, the conversation analysis unit can adjust the priority of the conversation analysis and emphasize specific keywords during the analysis. In this way, by determining the priority of conversation analysis based on the emotions of seniors, important conversations can be analyzed preferentially. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can take senior emotion data as input and use an AI model to determine the priority of conversation analysis.

[0044] The conversation analysis unit can perform analysis based on the geographical and cultural backgrounds of seniors during conversation analysis. For example, the conversation analysis unit can consider the geographical background of seniors and reflect region-specific words and phrases in the analysis. It can also consider the cultural background of seniors and reflect culture-specific words and phrases in the analysis. Furthermore, the conversation analysis unit can analyze the coherence and logic of the conversation based on the geographical and cultural backgrounds of seniors. This allows for more accurate analysis by performing analysis based on the geographical and cultural backgrounds of seniors. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can perform analysis using an AI model that takes the geographical and cultural backgrounds of seniors as input and outputs analysis results.

[0045] The conversation analysis unit can analyze seniors' social media activities and acquire relevant conversation data during conversation analysis. For example, the conversation analysis unit can analyze seniors' social media activities and reflect the conversation topics and word choices in the analysis. The conversation analysis unit can also analyze the frequency of use of specific phrases and words from seniors' social media activities. Furthermore, the conversation analysis unit can analyze the consistency and logic of conversations based on seniors' social media activities. In this way, by analyzing seniors' social media activities, relevant conversation data can be acquired and the accuracy of the analysis can be improved. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can acquire conversation data using an AI model that takes seniors' social media activities as input and outputs relevant conversation data.

[0046] The pattern learning unit can estimate the emotions of seniors and adjust the learning method of lifestyle patterns based on the estimated emotions of the seniors. For example, if a senior is feeling stressed, the pattern learning unit can adjust the learning method of lifestyle patterns to prioritize learning patterns that reduce stress. Also, if a senior is relaxed, the pattern learning unit can adjust the learning method of lifestyle patterns to prioritize learning patterns that maintain relaxation. Furthermore, if a senior is agitated, the pattern learning unit can adjust the learning method of lifestyle patterns to prioritize learning patterns that suppress agitation. In this way, by adjusting the learning method of lifestyle patterns based on the emotions of seniors, more appropriate lifestyle patterns can be learned. Some or all of the above processing in the pattern learning unit may be performed using AI, for example, or without AI. For example, the pattern learning unit can take senior emotional data as input and adjust the learning method using an AI model that adjusts the learning method of lifestyle patterns.

[0047] The pattern learning unit can optimize its learning algorithm by referring to the senior's past life data during pattern learning. For example, the pattern learning unit can analyze the senior's past life data and select the optimal learning algorithm. It can also extract specific patterns from the senior's past life data and optimize the learning algorithm. Furthermore, the pattern learning unit can adjust the parameters of the learning algorithm based on the senior's past life data. This allows for optimization of the learning algorithm by referring to the senior's past life data, enabling more accurate learning. Some or all of the above processes in the pattern learning unit may be performed using AI, for example, or without AI. For example, the pattern learning unit can use the senior's past life data as input and optimize the learning algorithm using an AI model that optimizes learning algorithms.

[0048] The pattern learning unit can learn patterns while considering the senior's living environment and health condition. For example, the pattern learning unit can learn lifestyle patterns that are appropriate for the senior's living environment. It can also learn lifestyle patterns that maintain the senior's health, taking their health condition into consideration. Furthermore, the pattern learning unit can learn the optimal lifestyle pattern based on the senior's living environment and health condition. This allows for the learning of more appropriate lifestyle patterns by considering the senior's living environment and health condition. Some or all of the above processing in the pattern learning unit may be performed using AI, for example, or without AI. For example, the pattern learning unit can learn using an AI model that takes the senior's living environment and health condition as input and outputs lifestyle patterns.

[0049] The pattern learning unit can estimate the emotions of seniors and determine the priority of lifestyle patterns to learn based on the estimated emotions of the seniors. For example, if a senior is feeling stressed, the pattern learning unit will prioritize learning lifestyle patterns that reduce stress. It can also prioritize learning lifestyle patterns that maintain relaxation if the senior is relaxed. Furthermore, if the senior is excited, the pattern learning unit can prioritize learning lifestyle patterns that suppress excitement. This allows for the priority learning of important lifestyle patterns by determining the priority of lifestyle patterns based on the senior's emotions. Some or all of the above processing in the pattern learning unit may be performed using AI, for example, or without AI. For example, the pattern learning unit can use senior emotion data as input and determine priorities using an AI model that determines the priority of lifestyle patterns.

[0050] The pattern learning unit can learn based on the geographical and cultural backgrounds of seniors during pattern learning. For example, the pattern learning unit can learn region-specific lifestyle patterns by considering the geographical background of seniors. It can also learn culture-specific lifestyle patterns by considering the cultural background of seniors. Furthermore, the pattern learning unit can learn the optimal lifestyle pattern based on the geographical and cultural backgrounds of seniors. This allows for the learning of more appropriate lifestyle patterns by learning based on the geographical and cultural backgrounds of seniors. Some or all of the above processing in the pattern learning unit may be performed using AI, for example, or without AI. For example, the pattern learning unit can learn using an AI model that takes the geographical and cultural backgrounds of seniors as input and outputs lifestyle patterns.

[0051] The pattern learning unit can analyze seniors' social media activities and collect relevant lifestyle data during pattern learning. For example, the pattern learning unit can analyze seniors' social media activities and detect changes in lifestyle patterns. The pattern learning unit can also extract specific behavioral patterns from seniors' social media activities and reflect them in the learning process. Furthermore, the pattern learning unit can learn the consistency and changes in lifestyle patterns based on seniors' social media activities. This allows for the collection of relevant lifestyle data and improvement of learning accuracy by analyzing seniors' social media activities. Some or all of the above processing in the pattern learning unit may be performed using AI, for example, or without AI. For example, the pattern learning unit can collect lifestyle data using an AI model that takes seniors' social media activities as input and outputs relevant lifestyle data.

[0052] The notification and advice unit can estimate the senior's emotions and adjust the content of notifications and advice based on the estimated emotions. For example, if the senior is feeling stressed, the notification and advice unit can provide advice to reduce stress. It can also provide advice to maintain relaxation if the senior is relaxed. Furthermore, if the senior is excited, the notification and advice unit can provide advice to calm the excitement. In this way, by adjusting the content of notifications and advice based on the senior's emotions, more appropriate advice can be provided. Some or all of the above processing in the notification and advice unit may be performed using AI, for example, or without AI. For example, the notification and advice unit can take senior's emotion data as input and adjust the content of notifications and advice using an AI model.

[0053] The notification and advice unit can provide optimal advice by referring to the senior's past behavioral history when providing notifications and advice. For example, the notification and advice unit can analyze the senior's past behavioral history and provide optimal advice. It can also extract specific behavioral patterns from the senior's past behavioral history and reflect them in the advice. Furthermore, the notification and advice unit can provide advice that takes into account the consistency and changes in the senior's behavior based on their past behavioral history. In this way, it can provide optimal advice by referring to the senior's past behavioral history. Some or all of the above processing in the notification and advice unit may be performed using AI, for example, or without AI. For example, the notification and advice unit can provide advice using an AI model that takes the senior's past behavioral history as input and outputs optimal advice.

[0054] The notification and advice unit can customize advice by considering the senior's health condition and living environment when providing notifications and advice. For example, the notification and advice unit can consider the senior's health condition and provide advice to maintain their health. It can also consider the senior's living environment and provide advice appropriate to that environment. Furthermore, the notification and advice unit can provide optimal advice based on the senior's health condition and living environment. This allows for the provision of more appropriate advice by considering the senior's health condition and living environment. Some or all of the above processing in the notification and advice unit may be performed using AI, for example, or without AI. For example, the notification and advice unit can use an AI model that takes the senior's health condition and living environment as input and customizes the advice to provide it.

[0055] The notification and advice unit can estimate the senior's emotions and determine the priority of notifications and advice based on the estimated emotions. For example, if the senior is feeling stressed, the notification and advice unit will prioritize providing advice to reduce stress. It can also prioritize providing advice to maintain relaxation if the senior is relaxed. Furthermore, if the senior is agitated, the notification and advice unit will prioritize providing advice to calm the agitation. This allows for the priority of important advice by determining the priority of notifications and advice based on the senior's emotions. Some or all of the above processing in the notification and advice unit may be performed using AI, for example, or without AI. For example, the notification and advice unit can use senior emotion data as input and determine the priority of notifications and advice using an AI model.

[0056] The notification and advice unit can provide advice based on the senior's geographical and cultural background when providing notifications and advice. For example, the notification and advice unit can consider the senior's geographical background and provide region-specific advice. It can also consider the senior's cultural background and provide culture-specific advice. Furthermore, the notification and advice unit can provide optimal advice based on the senior's geographical and cultural background. This allows for the provision of more appropriate advice by providing advice based on the senior's geographical and cultural background. Some or all of the above processing in the notification and advice unit may be performed using AI, for example, or not using AI. For example, the notification and advice unit can provide advice using an AI model that takes the senior's geographical and cultural background as input and provides advice.

[0057] The notification and advice unit can analyze seniors' social media activities and provide relevant advice when providing notifications and advice. For example, the notification and advice unit can analyze seniors' social media activities and provide advice based on changes in their lifestyle patterns. It can also extract specific behavioral patterns from seniors' social media activities and reflect them in the advice. Furthermore, the notification and advice unit can provide advice that takes into account the consistency and changes in lifestyle patterns based on seniors' social media activities. In this way, by analyzing seniors' social media activities, it is possible to provide relevant advice and improve the accuracy of the advice. Some or all of the above processing in the notification and advice unit may be performed using AI, for example, or not using AI. For example, the notification and advice unit can provide advice using an AI model that takes seniors' social media activities as input and outputs relevant advice.

[0058] The anomaly detection unit can estimate the senior's emotions and adjust the anomaly detection criteria based on the estimated emotions. For example, if the senior is stressed, the anomaly detection unit can relax the anomaly detection criteria and prioritize actions to reduce stress. Conversely, if the senior is relaxed, the anomaly detection unit can tighten the anomaly detection criteria and detect more detailed anomalies. Furthermore, if the senior is agitated, the anomaly detection unit can adjust the anomaly detection criteria and prioritize actions to calm the agitation. By adjusting the anomaly detection criteria based on the senior's emotions, more appropriate anomaly detection becomes possible. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can take senior's emotional data as input and adjust the criteria using an AI model that adjusts the anomaly detection criteria.

[0059] The anomaly detection unit can optimize its anomaly detection algorithm by referring to the senior's past behavioral data when an anomaly is detected. For example, the anomaly detection unit can analyze the senior's past behavioral data and select the optimal anomaly detection algorithm. The anomaly detection unit can also extract specific anomaly patterns from the senior's past behavioral data and optimize the anomaly detection algorithm. Furthermore, the anomaly detection unit can adjust the parameters of the anomaly detection algorithm based on the senior's past behavioral data. This optimizes the anomaly detection algorithm by referring to the senior's past behavioral data, enabling more accurate anomaly detection. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can optimize the algorithm using an AI model that optimizes the anomaly detection algorithm with the senior's past behavioral data as input.

[0060] The anomaly detection unit can detect anomalies by considering the health status and living environment of seniors when an anomaly is detected. For example, the anomaly detection unit can consider the health status of seniors and detect anomalies that are necessary to maintain their health. The anomaly detection unit can also consider the living environment of seniors and detect anomalies appropriate to that environment. Furthermore, the anomaly detection unit can detect the most appropriate anomaly based on the health status and living environment of seniors. This makes it possible to detect anomalies more appropriately by considering the health status and living environment of seniors. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without using AI. For example, the anomaly detection unit can take the health status and living environment of seniors as input and use an AI model that detects anomalies to detect them.

[0061] The anomaly detection unit can estimate the senior's emotions and determine the priority of anomaly detection based on the estimated emotions. For example, if the senior is stressed, the anomaly detection unit will prioritize anomaly detection to reduce stress. It can also prioritize anomaly detection to maintain relaxation if the senior is relaxed. Furthermore, if the senior is excited, the anomaly detection unit will prioritize anomaly detection to suppress excitement. This allows for the priority detection of important anomalies based on the senior's emotions. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can use senior emotion data as input and determine the priority of anomaly detection using an AI model.

[0062] The anomaly detection unit can detect anomalies based on the geographical and cultural backgrounds of seniors when an anomaly is detected. For example, the anomaly detection unit can detect region-specific anomalies by considering the geographical background of seniors. It can also detect culture-specific anomalies by considering the cultural background of seniors. Furthermore, the anomaly detection unit can detect the most appropriate anomaly based on the geographical and cultural backgrounds of seniors. This makes it possible to detect anomalies more appropriately by detecting them based on the geographical and cultural backgrounds of seniors. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can detect anomalies using an AI model that takes the geographical and cultural backgrounds of seniors as input.

[0063] The anomaly detection unit can analyze the social media activities of seniors when an anomaly is detected and detect related anomalies. For example, the anomaly detection unit can analyze the social media activities of seniors and detect anomalies based on changes in lifestyle patterns. The anomaly detection unit can also extract specific behavioral patterns from the social media activities of seniors and reflect them in the anomaly detection. Furthermore, the anomaly detection unit can detect anomalies that take into account the consistency and changes in lifestyle patterns based on the social media activities of seniors. In this way, by analyzing the social media activities of seniors, related anomalies can be detected and the accuracy of anomaly detection can be improved. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without using AI. For example, the anomaly detection unit can detect anomalies using an AI model that takes the social media activities of seniors as input and outputs related anomalies.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The system not only learns the lifestyle patterns of seniors but also understands their hobbies and interests and can suggest daily activities based on that. For example, if a senior is interested in gardening, the system can refer to the weather forecast and suggest a suitable day for gardening. If a senior enjoys reading, it can also provide information on new book releases. Furthermore, if a senior enjoys music, the system can recommend music that suits their taste. This is expected to enrich and increase the activity level of seniors' lives.

[0066] The system can monitor the health status of seniors and automatically contact medical institutions if an abnormality is detected. For example, if a senior's heart rate or blood pressure shows abnormal values, the system will immediately notify a medical institution. Also, if a senior falls, the system can notify emergency contacts to encourage a quick response. Furthermore, it can send reminders to seniors to undergo regular health checkups. This is expected to lead to more effective health management for seniors.

[0067] The system not only learns the lifestyle patterns of seniors but can also monitor their eating habits and suggest nutritionally balanced meals. For example, if a senior is deficient in a particular nutrient, the system will suggest recipes using ingredients that contain that nutrient. Furthermore, if a senior tends to forget meals, the system can remind them of meal times. In addition, if a senior has an allergy to a particular ingredient, the system can suggest recipes that avoid that ingredient. This is expected to lead to healthier eating habits for seniors.

[0068] The system can not only learn the lifestyle patterns of seniors but also provide event information to promote their social activities. For example, it can send notifications encouraging seniors to participate in local community events. It can also provide information on hobby clubs and circles that seniors may be interested in. Furthermore, it can provide opportunities for seniors to participate in volunteer activities. This is expected to strengthen seniors' social connections and reduce feelings of isolation.

[0069] The system not only learns the lifestyle patterns of seniors but also understands their hobbies and interests and can suggest daily activities based on that. For example, if a senior is interested in gardening, the system can refer to the weather forecast and suggest a suitable day for gardening. If a senior enjoys reading, it can also provide information on new book releases. Furthermore, if a senior enjoys music, the system can recommend music that suits their taste. This is expected to enrich and increase the activity level of seniors' lives.

[0070] The system can monitor the health status of seniors and automatically contact medical institutions if an abnormality is detected. For example, if a senior's heart rate or blood pressure shows abnormal values, the system will immediately notify a medical institution. Also, if a senior falls, the system can notify emergency contacts to encourage a quick response. Furthermore, it can send reminders to seniors to undergo regular health checkups. This is expected to lead to more effective health management for seniors.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The conversation analysis unit analyzes the daily conversations of seniors to detect the possibility of dementia. Specifically, it uses speech recognition technology to convert seniors' conversations into text data and uses a language model to detect signs of dementia. It also detects signs of dementia by analyzing the frequency of use of specific keywords and phrases, as well as the flow and consistency of the conversation. Step 2: The pattern learning unit collects data on the seniors' daily lives and learns their lifestyle patterns. Specifically, it collects data on the seniors' daily activities, such as meal times and walking times, and learns their lifestyle patterns using a learning algorithm. It also optimizes the learning algorithm by considering the seniors' living environment and health status and referring to past lifestyle data. Step 3: The notification and advice unit detects behavioral abnormalities early based on the lifestyle patterns learned by the pattern learning unit and provides notifications and advice. Specifically, it notifies and provides appropriate advice when abnormalities are observed, such as when a senior does not eat meals or go for walks at the usual time. It also estimates the senior's emotions and provides optimal advice by referring to past behavioral history. Step 4: The anomaly detection unit detects anomalies from GPS information and takes appropriate action based on the senior's location information. Specifically, it monitors the senior's location information in real time and takes appropriate action when an anomaly is detected. It also estimates the senior's emotions and optimizes the anomaly detection algorithm by referring to past behavioral data.

[0073] (Example of form 2) The system according to an embodiment of the present invention is an AI+α service unique to telecommunications companies, designed to address the expanding lifestyles of seniors. This system has the function of detecting the possibility of dementia from normal conversations, learning the lifestyle patterns of seniors, and detecting and notifying / advising on behavioral abnormalities at an early stage. Furthermore, if dementia is suspected or if an abnormality is detected from GPS information, various services of the telecommunications company are utilized to support the senior. In this way, the system can ensure the safety of seniors by learning their lifestyle patterns, detecting abnormalities at an early stage, and providing notification and advice. For example, the system analyzes the daily conversations of seniors to detect signs of dementia. The system collects data on the daily lives of seniors and learns their lifestyle patterns. The system notifies and provides appropriate advice if abnormalities are observed, such as seniors not eating meals at the usual time or not going for walks. The system supports seniors by utilizing various services of the telecommunications company if dementia is suspected or if an abnormality is detected from GPS information. In this way, the system can provide an environment in which seniors can live with peace of mind. For example, even if a senior is living alone, the system can ensure their safety by monitoring them and notifying and advising on any abnormalities. Furthermore, by utilizing the various services offered by telecommunications companies, it is possible to support the lives of seniors and provide them with an environment in which they can live with peace of mind.

[0074] The system according to this embodiment comprises a conversation analysis unit, a pattern learning unit, a notification / advice unit, and an anomaly detection unit. The conversation analysis unit analyzes the daily conversations of seniors and detects the possibility of dementia. For example, the conversation analysis unit converts the seniors' conversations into text data using speech recognition technology and detects signs of dementia using a language model. The conversation analysis unit can also analyze the frequency of use of specific keywords and phrases in the seniors' conversations to detect signs of dementia. Furthermore, the conversation analysis unit can also analyze the flow and consistency of the seniors' conversations to detect signs of dementia. The pattern learning unit collects data on the seniors' daily lives and learns their lifestyle patterns. For example, the pattern learning unit collects daily behavioral data such as the seniors' meal times and walking times, and learns lifestyle patterns using a learning algorithm. The pattern learning unit can also learn lifestyle patterns considering the seniors' living environment and health condition. Furthermore, the pattern learning unit can optimize the learning algorithm by referring to the seniors' past lifestyle data. The notification / advice unit detects behavioral anomalies early based on the lifestyle patterns learned by the pattern learning unit and provides notifications and advice. The notification and advice unit notifies users and provides appropriate advice when abnormalities are observed, such as when a senior citizen does not eat meals or go for walks at their usual times. The notification and advice unit can also estimate the senior citizen's emotions and adjust the content of the notification and advice based on these estimates. Furthermore, the notification and advice unit can provide optimal advice by referring to the senior citizen's past behavioral history. The anomaly detection unit detects anomalies from GPS information and takes appropriate action based on the senior citizen's location. For example, the anomaly detection unit monitors the senior citizen's location in real time and takes appropriate action when an anomaly is detected. The anomaly detection unit can also estimate the senior citizen's emotions and adjust the anomaly detection criteria based on these estimates. Furthermore, the anomaly detection unit can optimize the anomaly detection algorithm by referring to the senior citizen's past behavioral data. As a result, the system according to this embodiment can learn the senior citizen's lifestyle patterns, detect anomalies early, and provide notifications and advice, thereby ensuring the senior citizen's safety.

[0075] The conversation analysis unit analyzes the daily conversations of seniors to detect the possibility of dementia. Specifically, it uses speech recognition technology to convert seniors' conversations into text data and inputs that text data into a language model. The language model utilizes natural language processing technology to detect specific patterns and anomalies in seniors' conversations that indicate signs of dementia. For example, if the frequency of use of certain keywords or phrases in a conversation is unusually high, or if the flow of the conversation is inconsistent, these can be considered early signs of dementia. The conversation analysis unit also analyzes the temporal changes in seniors' conversations, the frequency of topic changes, and word choices, and evaluates whether these elements are related to signs of dementia. Furthermore, the conversation analysis unit analyzes the emotional tone and changes in voice tone in seniors' conversations, and evaluates whether these are related to signs of dementia. In this way, the conversation analysis unit can detect signs of dementia from seniors' daily conversations from multiple angles and provide information for early intervention.

[0076] The pattern learning unit collects data on seniors' daily lives and learns their lifestyle patterns. Specifically, it collects daily behavioral data such as meal times, walking times, sleep times, and medication times, and learns lifestyle patterns based on this data. The learning algorithm analyzes this data and models the seniors' daily rhythms and behavioral patterns. For example, if a senior has a habit of eating at the same time every day, an anomaly such as not eating at that time will be detected. The pattern learning unit can also learn lifestyle patterns while considering the senior's living environment and health condition. For example, if a senior has a specific health condition, it learns a lifestyle pattern appropriate to that condition and adjusts the criteria for detecting anomalies. Furthermore, the pattern learning unit can optimize the learning algorithm by referring to the senior's past lifestyle data. As a result, the pattern learning unit can learn seniors' lifestyle patterns with high accuracy and provide a foundation for early detection of anomalies.

[0077] The notification and advice unit detects behavioral abnormalities early based on lifestyle patterns learned by the pattern learning unit and provides notifications and advice. Specifically, if an abnormality is observed, such as a senior not eating meals at the usual time or not going for a walk, the unit will notify the senior, their family, or caregiver. Notifications are made via smartphone apps, email, or voice calls. The notification and advice unit can also estimate the senior's emotions and adjust the content of the notifications and advice based on those emotions. For example, if the senior is feeling stressed, the unit will send a notification using gentle language that takes those emotions into consideration. The notification and advice unit can also provide optimal advice by referring to the senior's past behavioral history. For example, it can provide more effective advice by referring to how similar abnormalities were handled in the past. In this way, the notification and advice unit can provide appropriate support to improve the senior's quality of life.

[0078] The anomaly detection unit detects anomalies from GPS information and takes appropriate action based on the senior's location. Specifically, it monitors the senior's location in real time and detects anomalies if they deviate from their usual range of activity or enter a specific danger area. If an anomaly is detected, it notifies the senior, their family, or caregiver to encourage a quick response. The anomaly detection unit can also estimate the senior's emotions and adjust the anomaly detection criteria based on those emotions. For example, if the senior is feeling anxious, the anomaly detection criteria are relaxed to accommodate those emotions. Furthermore, the anomaly detection unit can optimize its anomaly detection algorithm by referring to the senior's past behavioral data. As a result, the anomaly detection unit can perform highly accurate anomaly detection based on the senior's location information and support a quick and appropriate response.

[0079] The notification and advice unit can notify seniors and provide appropriate advice when an anomaly is detected. For example, the notification and advice unit can notify seniors and provide appropriate advice if an anomaly is observed, such as when a senior does not eat or go for a walk at the usual time. The notification and advice unit can also estimate the senior's emotions and adjust the content of the notification and advice based on the estimated emotions. Furthermore, the notification and advice unit can provide optimal advice by referring to the senior's past behavioral history. In this way, the safety of seniors can be ensured by notifying them and providing appropriate advice when an anomaly is detected. Some or all of the above processing in the notification and advice unit may be performed using AI, for example, or without AI. For example, the notification and advice unit can provide notifications and advice using an AI model that notifies seniors and provides appropriate advice when an anomaly is detected.

[0080] The anomaly detection unit can detect anomalies from GPS information and take appropriate action based on the senior's location information. For example, the anomaly detection unit can monitor the senior's location information in real time and take appropriate action when an anomaly is detected. The anomaly detection unit can also estimate the senior's emotions and adjust the anomaly detection criteria based on the estimated emotions. Furthermore, the anomaly detection unit can optimize the anomaly detection algorithm by referring to the senior's past behavioral data. This ensures the safety of seniors by detecting anomalies from GPS information and taking appropriate action based on their location information. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can detect anomalies using an AI model that takes GPS information as input and outputs anomalies.

[0081] The conversation analysis unit can analyze the daily conversations of seniors and detect signs of dementia. For example, the conversation analysis unit can convert seniors' conversations into text data using speech recognition technology and detect signs of dementia using a language model. The conversation analysis unit can also analyze the frequency of use of specific keywords and phrases in seniors' conversations and detect signs of dementia. Furthermore, the conversation analysis unit can analyze the flow and consistency of seniors' conversations and detect signs of dementia. In this way, by analyzing seniors' daily conversations and detecting signs of dementia, the possibility of dementia can be detected at an early stage. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can detect signs of dementia using an AI model that takes seniors' daily conversations as input and outputs signs of dementia.

[0082] The pattern learning unit can collect data on the daily lives of seniors and learn their lifestyle patterns. For example, the pattern learning unit collects data on seniors' daily activities, such as meal times and walking times, and learns lifestyle patterns using a learning algorithm. The pattern learning unit can also learn lifestyle patterns while considering the seniors' living environment and health status. Furthermore, the pattern learning unit can optimize its learning algorithm by referring to the seniors' past lifestyle data. This allows for the early detection of abnormalities by collecting data on seniors' daily lives and learning their lifestyle patterns. Some or all of the above-described processes in the pattern learning unit may be performed using AI, for example, or without AI. For example, the pattern learning unit can learn lifestyle patterns using an AI model that takes data on seniors' daily lives as input and outputs lifestyle patterns.

[0083] The notification and advice unit can notify seniors if they exhibit abnormal behavior, such as not eating meals or going for walks at their usual times, and provide appropriate advice. The notification and advice unit can also estimate the senior's emotions and adjust the content of the notification and advice based on those emotions. Furthermore, the notification and advice unit can refer to the senior's past behavioral history to provide optimal advice. This ensures the safety of seniors by notifying them and providing appropriate advice when abnormal behavior, such as not eating meals or going for walks, is observed. Some or all of the above processing in the notification and advice unit may be performed using AI, or not. For example, the notification and advice unit can use an AI model to notify seniors and provide appropriate advice when abnormal behavior, such as not eating meals or going for walks, is observed.

[0084] The anomaly detection unit can contact the senior from the call center to confirm the situation if dementia is suspected. For example, based on data from the conversation analysis unit and pattern learning unit, the anomaly detection unit notifies the call center if dementia is suspected, and the call center operator contacts the senior. The anomaly detection unit can also notify the call center if an anomaly is detected based on the senior's location information, and appropriate action can be taken. This ensures the safety of the senior by allowing the call center to contact the senior and confirm the situation if dementia is suspected. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can use an AI model to notify the call center when dementia is suspected, and the call center operator contacts the senior.

[0085] The conversation analysis unit can estimate the senior's emotions and adjust the accuracy of the conversation analysis based on the estimated emotions. For example, if the senior is stressed, the conversation analysis unit can adjust the tone and speed of the conversation to improve the accuracy of the analysis. Also, if the senior is relaxed, the conversation analysis unit can analyze the content of the conversation in detail and more accurately detect signs of dementia. Furthermore, if the senior is agitated, the conversation analysis unit can emphasize certain keywords in the conversation to improve the accuracy of the analysis. In this way, by adjusting the accuracy of the conversation analysis based on the senior's emotions, signs of dementia can be detected more accurately. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can adjust the accuracy of the conversation analysis using an AI model that takes senior's emotion data as input and adjusts the accuracy of the conversation analysis.

[0086] The conversation analysis unit can more accurately detect signs of dementia by referring to the senior's past conversation history during conversation analysis. For example, the conversation analysis unit analyzes the senior's past conversation history to detect changes in word choice and flow of conversation. The conversation analysis unit can also analyze the frequency of use of specific phrases and words from the senior's past conversation history to detect signs of dementia. Furthermore, the conversation analysis unit can detect changes in conversation consistency and logic based on the senior's past conversation history. As a result, by referring to the senior's past conversation history, signs of dementia can be detected more accurately. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can detect signs of dementia using an AI model that takes the senior's past conversation history as input and outputs signs of dementia.

[0087] The conversation analysis unit can learn the language usage patterns of seniors during conversation analysis and detect anomalies early. For example, the conversation analysis unit can learn the language usage patterns of seniors and detect abnormal word choices and conversation flow. The conversation analysis unit can also detect changes in the frequency of use of specific phrases or words based on the language usage patterns of seniors. Furthermore, the conversation analysis unit can analyze the language usage patterns of seniors and detect changes in the consistency and logic of the conversation. In this way, by learning the language usage patterns of seniors, anomalies can be detected early. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can detect anomalies using an AI model that takes the language usage patterns of seniors as input and outputs anomalies.

[0088] The conversation analysis unit can estimate the emotions of seniors and determine the priority of conversation analysis based on the estimated emotions. For example, if a senior is stressed, the conversation analysis unit can increase the priority of the conversation analysis and perform the analysis quickly. Conversely, if a senior is relaxed, the conversation analysis unit can lower the priority of the conversation analysis and perform a more detailed analysis. Furthermore, if a senior is agitated, the conversation analysis unit can adjust the priority of the conversation analysis and emphasize specific keywords during the analysis. In this way, by determining the priority of conversation analysis based on the emotions of seniors, important conversations can be analyzed preferentially. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can take senior emotion data as input and use an AI model to determine the priority of conversation analysis.

[0089] The conversation analysis unit can perform analysis based on the geographical and cultural backgrounds of seniors during conversation analysis. For example, the conversation analysis unit can consider the geographical background of seniors and reflect region-specific words and phrases in the analysis. It can also consider the cultural background of seniors and reflect culture-specific words and phrases in the analysis. Furthermore, the conversation analysis unit can analyze the coherence and logic of the conversation based on the geographical and cultural backgrounds of seniors. This allows for more accurate analysis by performing analysis based on the geographical and cultural backgrounds of seniors. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can perform analysis using an AI model that takes the geographical and cultural backgrounds of seniors as input and outputs analysis results.

[0090] The conversation analysis unit can analyze seniors' social media activities and acquire relevant conversation data during conversation analysis. For example, the conversation analysis unit can analyze seniors' social media activities and reflect the conversation topics and word choices in the analysis. The conversation analysis unit can also analyze the frequency of use of specific phrases and words from seniors' social media activities. Furthermore, the conversation analysis unit can analyze the consistency and logic of conversations based on seniors' social media activities. In this way, by analyzing seniors' social media activities, relevant conversation data can be acquired and the accuracy of the analysis can be improved. Some or all of the above processing in the conversation analysis unit may be performed using AI, for example, or without AI. For example, the conversation analysis unit can acquire conversation data using an AI model that takes seniors' social media activities as input and outputs relevant conversation data.

[0091] The pattern learning unit can estimate the emotions of seniors and adjust the learning method of lifestyle patterns based on the estimated emotions of the seniors. For example, if a senior is feeling stressed, the pattern learning unit can adjust the learning method of lifestyle patterns to prioritize learning patterns that reduce stress. Also, if a senior is relaxed, the pattern learning unit can adjust the learning method of lifestyle patterns to prioritize learning patterns that maintain relaxation. Furthermore, if a senior is agitated, the pattern learning unit can adjust the learning method of lifestyle patterns to prioritize learning patterns that suppress agitation. In this way, by adjusting the learning method of lifestyle patterns based on the emotions of seniors, more appropriate lifestyle patterns can be learned. Some or all of the above processing in the pattern learning unit may be performed using AI, for example, or without AI. For example, the pattern learning unit can take senior emotional data as input and adjust the learning method using an AI model that adjusts the learning method of lifestyle patterns.

[0092] The pattern learning unit can optimize its learning algorithm by referring to the senior's past life data during pattern learning. For example, the pattern learning unit can analyze the senior's past life data and select the optimal learning algorithm. It can also extract specific patterns from the senior's past life data and optimize the learning algorithm. Furthermore, the pattern learning unit can adjust the parameters of the learning algorithm based on the senior's past life data. This allows for optimization of the learning algorithm by referring to the senior's past life data, enabling more accurate learning. Some or all of the above processes in the pattern learning unit may be performed using AI, for example, or without AI. For example, the pattern learning unit can use the senior's past life data as input and optimize the learning algorithm using an AI model that optimizes learning algorithms.

[0093] The pattern learning unit can learn patterns while considering the senior's living environment and health condition. For example, the pattern learning unit can learn lifestyle patterns that are appropriate for the senior's living environment. It can also learn lifestyle patterns that maintain the senior's health, taking their health condition into consideration. Furthermore, the pattern learning unit can learn the optimal lifestyle pattern based on the senior's living environment and health condition. This allows for the learning of more appropriate lifestyle patterns by considering the senior's living environment and health condition. Some or all of the above processing in the pattern learning unit may be performed using AI, for example, or without AI. For example, the pattern learning unit can learn using an AI model that takes the senior's living environment and health condition as input and outputs lifestyle patterns.

[0094] The pattern learning unit can estimate the emotions of seniors and determine the priority of lifestyle patterns to learn based on the estimated emotions of the seniors. For example, if a senior is feeling stressed, the pattern learning unit will prioritize learning lifestyle patterns that reduce stress. It can also prioritize learning lifestyle patterns that maintain relaxation if the senior is relaxed. Furthermore, if the senior is excited, the pattern learning unit can prioritize learning lifestyle patterns that suppress excitement. This allows for the priority learning of important lifestyle patterns by determining the priority of lifestyle patterns based on the senior's emotions. Some or all of the above processing in the pattern learning unit may be performed using AI, for example, or without AI. For example, the pattern learning unit can use senior emotion data as input and determine priorities using an AI model that determines the priority of lifestyle patterns.

[0095] The pattern learning unit can learn based on the geographical and cultural backgrounds of seniors during pattern learning. For example, the pattern learning unit can learn region-specific lifestyle patterns by considering the geographical background of seniors. It can also learn culture-specific lifestyle patterns by considering the cultural background of seniors. Furthermore, the pattern learning unit can learn the optimal lifestyle pattern based on the geographical and cultural backgrounds of seniors. This allows for the learning of more appropriate lifestyle patterns by learning based on the geographical and cultural backgrounds of seniors. Some or all of the above processing in the pattern learning unit may be performed using AI, for example, or without AI. For example, the pattern learning unit can learn using an AI model that takes the geographical and cultural backgrounds of seniors as input and outputs lifestyle patterns.

[0096] The pattern learning unit can analyze seniors' social media activities and collect relevant lifestyle data during pattern learning. For example, the pattern learning unit can analyze seniors' social media activities and detect changes in lifestyle patterns. The pattern learning unit can also extract specific behavioral patterns from seniors' social media activities and reflect them in the learning process. Furthermore, the pattern learning unit can learn the consistency and changes in lifestyle patterns based on seniors' social media activities. This allows for the collection of relevant lifestyle data and improvement of learning accuracy by analyzing seniors' social media activities. Some or all of the above processing in the pattern learning unit may be performed using AI, for example, or without AI. For example, the pattern learning unit can collect lifestyle data using an AI model that takes seniors' social media activities as input and outputs relevant lifestyle data.

[0097] The notification and advice unit can estimate the senior's emotions and adjust the content of notifications and advice based on the estimated emotions. For example, if the senior is feeling stressed, the notification and advice unit can provide advice to reduce stress. It can also provide advice to maintain relaxation if the senior is relaxed. Furthermore, if the senior is excited, the notification and advice unit can provide advice to calm the excitement. In this way, by adjusting the content of notifications and advice based on the senior's emotions, more appropriate advice can be provided. Some or all of the above processing in the notification and advice unit may be performed using AI, for example, or without AI. For example, the notification and advice unit can take senior's emotion data as input and adjust the content of notifications and advice using an AI model.

[0098] The notification and advice unit can provide optimal advice by referring to the senior's past behavioral history when providing notifications and advice. For example, the notification and advice unit can analyze the senior's past behavioral history and provide optimal advice. It can also extract specific behavioral patterns from the senior's past behavioral history and reflect them in the advice. Furthermore, the notification and advice unit can provide advice that takes into account the consistency and changes in the senior's behavior based on their past behavioral history. In this way, it can provide optimal advice by referring to the senior's past behavioral history. Some or all of the above processing in the notification and advice unit may be performed using AI, for example, or without AI. For example, the notification and advice unit can provide advice using an AI model that takes the senior's past behavioral history as input and outputs optimal advice.

[0099] The notification and advice unit can customize advice by considering the senior's health condition and living environment when providing notifications and advice. For example, the notification and advice unit can consider the senior's health condition and provide advice to maintain their health. It can also consider the senior's living environment and provide advice appropriate to that environment. Furthermore, the notification and advice unit can provide optimal advice based on the senior's health condition and living environment. This allows for the provision of more appropriate advice by considering the senior's health condition and living environment. Some or all of the above processing in the notification and advice unit may be performed using AI, for example, or without AI. For example, the notification and advice unit can use an AI model that takes the senior's health condition and living environment as input and customizes the advice to provide it.

[0100] The notification and advice unit can estimate the senior's emotions and determine the priority of notifications and advice based on the estimated emotions. For example, if the senior is feeling stressed, the notification and advice unit will prioritize providing advice to reduce stress. It can also prioritize providing advice to maintain relaxation if the senior is relaxed. Furthermore, if the senior is agitated, the notification and advice unit will prioritize providing advice to calm the agitation. This allows for the priority of important advice by determining the priority of notifications and advice based on the senior's emotions. Some or all of the above processing in the notification and advice unit may be performed using AI, for example, or without AI. For example, the notification and advice unit can use senior emotion data as input and determine the priority of notifications and advice using an AI model.

[0101] The notification and advice unit can provide advice based on the senior's geographical and cultural background when providing notifications and advice. For example, the notification and advice unit can consider the senior's geographical background and provide region-specific advice. It can also consider the senior's cultural background and provide culture-specific advice. Furthermore, the notification and advice unit can provide optimal advice based on the senior's geographical and cultural background. This allows for the provision of more appropriate advice by providing advice based on the senior's geographical and cultural background. Some or all of the above processing in the notification and advice unit may be performed using AI, for example, or not using AI. For example, the notification and advice unit can provide advice using an AI model that takes the senior's geographical and cultural background as input and provides advice.

[0102] The notification and advice unit can analyze seniors' social media activities and provide relevant advice when providing notifications and advice. For example, the notification and advice unit can analyze seniors' social media activities and provide advice based on changes in their lifestyle patterns. It can also extract specific behavioral patterns from seniors' social media activities and reflect them in the advice. Furthermore, the notification and advice unit can provide advice that takes into account the consistency and changes in lifestyle patterns based on seniors' social media activities. In this way, by analyzing seniors' social media activities, it is possible to provide relevant advice and improve the accuracy of the advice. Some or all of the above processing in the notification and advice unit may be performed using AI, for example, or not using AI. For example, the notification and advice unit can provide advice using an AI model that takes seniors' social media activities as input and outputs relevant advice.

[0103] The anomaly detection unit can estimate the senior's emotions and adjust the anomaly detection criteria based on the estimated emotions. For example, if the senior is stressed, the anomaly detection unit can relax the anomaly detection criteria and prioritize actions to reduce stress. Conversely, if the senior is relaxed, the anomaly detection unit can tighten the anomaly detection criteria and detect more detailed anomalies. Furthermore, if the senior is agitated, the anomaly detection unit can adjust the anomaly detection criteria and prioritize actions to calm the agitation. By adjusting the anomaly detection criteria based on the senior's emotions, more appropriate anomaly detection becomes possible. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can take senior's emotional data as input and adjust the criteria using an AI model that adjusts the anomaly detection criteria.

[0104] The anomaly detection unit can optimize its anomaly detection algorithm by referring to the senior's past behavioral data when an anomaly is detected. For example, the anomaly detection unit can analyze the senior's past behavioral data and select the optimal anomaly detection algorithm. The anomaly detection unit can also extract specific anomaly patterns from the senior's past behavioral data and optimize the anomaly detection algorithm. Furthermore, the anomaly detection unit can adjust the parameters of the anomaly detection algorithm based on the senior's past behavioral data. This optimizes the anomaly detection algorithm by referring to the senior's past behavioral data, enabling more accurate anomaly detection. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can optimize the algorithm using an AI model that optimizes the anomaly detection algorithm with the senior's past behavioral data as input.

[0105] The anomaly detection unit can detect anomalies by considering the health status and living environment of seniors when an anomaly is detected. For example, the anomaly detection unit can consider the health status of seniors and detect anomalies that are necessary to maintain their health. The anomaly detection unit can also consider the living environment of seniors and detect anomalies appropriate to that environment. Furthermore, the anomaly detection unit can detect the most appropriate anomaly based on the health status and living environment of seniors. This makes it possible to detect anomalies more appropriately by considering the health status and living environment of seniors. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without using AI. For example, the anomaly detection unit can take the health status and living environment of seniors as input and use an AI model that detects anomalies to detect them.

[0106] The anomaly detection unit can estimate the senior's emotions and determine the priority of anomaly detection based on the estimated emotions. For example, if the senior is stressed, the anomaly detection unit will prioritize anomaly detection to reduce stress. It can also prioritize anomaly detection to maintain relaxation if the senior is relaxed. Furthermore, if the senior is excited, the anomaly detection unit will prioritize anomaly detection to suppress excitement. This allows for the priority detection of important anomalies based on the senior's emotions. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can use senior emotion data as input and determine the priority of anomaly detection using an AI model.

[0107] The anomaly detection unit can detect anomalies based on the geographical and cultural backgrounds of seniors when an anomaly is detected. For example, the anomaly detection unit can detect region-specific anomalies by considering the geographical background of seniors. It can also detect culture-specific anomalies by considering the cultural background of seniors. Furthermore, the anomaly detection unit can detect the most appropriate anomaly based on the geographical and cultural backgrounds of seniors. This makes it possible to detect anomalies more appropriately by detecting them based on the geographical and cultural backgrounds of seniors. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can detect anomalies using an AI model that takes the geographical and cultural backgrounds of seniors as input.

[0108] The anomaly detection unit can analyze the social media activities of seniors when an anomaly is detected and detect related anomalies. For example, the anomaly detection unit can analyze the social media activities of seniors and detect anomalies based on changes in lifestyle patterns. The anomaly detection unit can also extract specific behavioral patterns from the social media activities of seniors and reflect them in the anomaly detection. Furthermore, the anomaly detection unit can detect anomalies that take into account the consistency and changes in lifestyle patterns based on the social media activities of seniors. In this way, by analyzing the social media activities of seniors, related anomalies can be detected and the accuracy of anomaly detection can be improved. Some or all of the above processing in the anomaly detection unit may be performed using AI, for example, or without using AI. For example, the anomaly detection unit can detect anomalies using an AI model that takes the social media activities of seniors as input and outputs related anomalies.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The system not only learns the lifestyle patterns of seniors but also understands their hobbies and interests and can suggest daily activities based on that. For example, if a senior is interested in gardening, the system can refer to the weather forecast and suggest a suitable day for gardening. If a senior enjoys reading, it can also provide information on new book releases. Furthermore, if a senior enjoys music, the system can recommend music that suits their taste. This is expected to enrich and increase the activity level of seniors' lives.

[0111] The system can monitor the health status of seniors and automatically contact medical institutions if an abnormality is detected. For example, if a senior's heart rate or blood pressure shows abnormal values, the system will immediately notify a medical institution. Also, if a senior falls, the system can notify emergency contacts to encourage a quick response. Furthermore, it can send reminders to seniors to undergo regular health checkups. This is expected to lead to more effective health management for seniors.

[0112] The system can estimate a senior's emotions, assess their stress level based on those emotions, and provide advice for stress reduction. For example, if a senior is feeling stressed, the system can suggest breathing exercises or simple exercises to help them relax. If a senior is feeling lonely, the system can also send messages to encourage communication with friends and family. Furthermore, if a senior is feeling anxious, the system can provide relaxing music or meditation guidance. This is expected to support the mental health of seniors.

[0113] The system not only learns the lifestyle patterns of seniors but can also monitor their eating habits and suggest nutritionally balanced meals. For example, if a senior is deficient in a particular nutrient, the system will suggest recipes using ingredients that contain that nutrient. Furthermore, if a senior tends to forget meals, the system can remind them of meal times. In addition, if a senior has an allergy to a particular ingredient, the system can suggest recipes that avoid that ingredient. This is expected to lead to healthier eating habits for seniors.

[0114] The system can estimate a senior's emotions, evaluate their sleep patterns based on those emotions, and provide advice for improvement. For example, if a senior complains of insomnia, the system can provide guidance on relaxing music or meditation. If a senior frequently wakes up at night, the system can also suggest ways to improve their sleep environment. Furthermore, if a senior feels excessively sleepy during the day, the system can advise adjusting nap times. This is expected to improve the quality of sleep and overall health of seniors.

[0115] The system can not only learn the lifestyle patterns of seniors but also provide event information to promote their social activities. For example, it can send notifications encouraging seniors to participate in local community events. It can also provide information on hobby clubs and circles that seniors may be interested in. Furthermore, it can provide opportunities for seniors to participate in volunteer activities. This is expected to strengthen seniors' social connections and reduce feelings of isolation.

[0116] The system can estimate a senior's emotions, evaluate their exercise habits based on those emotions, and suggest an appropriate exercise program. For example, if a senior is feeling stressed, the system can suggest relaxing yoga or stretching programs. If a senior is feeling energetic, the system can suggest walking or light jogging programs. Furthermore, if a senior is feeling fatigued, the system can suggest light exercise or relaxation programs. This is expected to improve the senior's exercise habits and overall health.

[0117] The system not only learns the lifestyle patterns of seniors but also understands their hobbies and interests and can suggest daily activities based on that. For example, if a senior is interested in gardening, the system can refer to the weather forecast and suggest a suitable day for gardening. If a senior enjoys reading, it can also provide information on new book releases. Furthermore, if a senior enjoys music, the system can recommend music that suits their taste. This is expected to enrich and increase the activity level of seniors' lives.

[0118] The system can estimate a senior's emotions, assess their stress level based on those emotions, and provide advice for stress reduction. For example, if a senior is feeling stressed, the system can suggest breathing exercises or simple exercises to help them relax. If a senior is feeling lonely, the system can also send messages to encourage communication with friends and family. Furthermore, if a senior is feeling anxious, the system can provide relaxing music or meditation guidance. This is expected to support the mental health of seniors.

[0119] The system can monitor the health status of seniors and automatically contact medical institutions if an abnormality is detected. For example, if a senior's heart rate or blood pressure shows abnormal values, the system will immediately notify a medical institution. Also, if a senior falls, the system can notify emergency contacts to encourage a quick response. Furthermore, it can send reminders to seniors to undergo regular health checkups. This is expected to lead to more effective health management for seniors.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The conversation analysis unit analyzes the daily conversations of seniors to detect the possibility of dementia. Specifically, it uses speech recognition technology to convert seniors' conversations into text data and uses a language model to detect signs of dementia. It also detects signs of dementia by analyzing the frequency of use of specific keywords and phrases, as well as the flow and consistency of the conversation. Step 2: The pattern learning unit collects data on the seniors' daily lives and learns their lifestyle patterns. Specifically, it collects data on the seniors' daily activities, such as meal times and walking times, and learns their lifestyle patterns using a learning algorithm. It also optimizes the learning algorithm by considering the seniors' living environment and health status and referring to past lifestyle data. Step 3: The notification and advice unit detects behavioral abnormalities early based on the lifestyle patterns learned by the pattern learning unit and provides notifications and advice. Specifically, it notifies and provides appropriate advice when abnormalities are observed, such as when a senior does not eat meals or go for walks at the usual time. It also estimates the senior's emotions and provides optimal advice by referring to past behavioral history. Step 4: The anomaly detection unit detects anomalies from GPS information and takes appropriate action based on the senior's location information. Specifically, it monitors the senior's location information in real time and takes appropriate action when an anomaly is detected. It also estimates the senior's emotions and optimizes the anomaly detection algorithm by referring to past behavioral data.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the conversation analysis unit, pattern learning unit, notification / advice unit, and anomaly detection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the conversation analysis unit collects the senior's daily conversations using the microphone 38B of the smart device 14 and analyzes them using the specific processing unit 290 of the data processing unit 12. The pattern learning unit collects data on the senior's daily life using the specific processing unit 290 of the data processing unit 12 and learns their lifestyle patterns. The notification / advice unit notifies the senior using the control unit 46A of the smart device 14 and provides appropriate advice. The anomaly detection unit monitors the senior's location information using the GPS function of the smart device 14 and detects anomalies using the specific processing unit 290 of the data processing unit 12. 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.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the conversation analysis unit, pattern learning unit, notification / advice unit, and anomaly detection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the conversation analysis unit collects the senior's daily conversations using the microphone 238 of the smart glasses 214 and analyzes them using the identification processing unit 290 of the data processing unit 12. The pattern learning unit collects data on the senior's daily life using the identification processing unit 290 of the data processing unit 12 and learns their lifestyle patterns. The notification / advice unit notifies the senior using the control unit 46A of the smart glasses 214 and provides appropriate advice. The anomaly detection unit monitors the senior's location information using the GPS function of the smart glasses 214 and detects anomalies using the identification processing unit 290 of the data processing unit 12. 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.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the conversation analysis unit, pattern learning unit, notification / advice unit, and anomaly detection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the conversation analysis unit collects the senior's daily conversations using the microphone 238 of the headset terminal 314 and analyzes them using the specific processing unit 290 of the data processing unit 12. The pattern learning unit collects data on the senior's daily life using the specific processing unit 290 of the data processing unit 12 and learns their lifestyle patterns. The notification / advice unit notifies the senior using the control unit 46A of the headset terminal 314 and provides appropriate advice. The anomaly detection unit monitors the senior's location information using the GPS function of the headset terminal 314 and detects anomalies using the specific processing unit 290 of the data processing unit 12. 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.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the conversation analysis unit, pattern learning unit, notification / advice unit, and anomaly detection unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the conversation analysis unit collects the senior's daily conversations using the microphone 238 of the robot 414 and analyzes them using the specific processing unit 290 of the data processing unit 12. The pattern learning unit collects data on the senior's daily life using the specific processing unit 290 of the data processing unit 12 and learns their lifestyle patterns. The notification / advice unit notifies the senior and provides appropriate advice using, for example, the control unit 46A of the robot 414. The anomaly detection unit monitors the senior's location information using the GPS function of the robot 414 and detects anomalies using the specific processing unit 290 of the data processing unit 12. 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A conversation analysis unit that analyzes conversations and detects the possibility of dementia, The pattern learning section learns about the lifestyle patterns of seniors, A notification and advice unit that detects abnormal behavior early and provides notification and advice based on the lifestyle patterns learned by the aforementioned pattern learning unit, It includes an anomaly detection unit that detects anomalies from GPS information. A system characterized by the following features. (Note 2) The aforementioned notification and advice department, If an anomaly is detected, the system will notify the senior and provide appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned abnormality detection unit, The system detects anomalies using GPS information and takes appropriate action based on the senior's location. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned conversation analysis unit, Analyzing the daily conversations of seniors to detect signs of dementia. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned pattern learning unit, Collect data on seniors' daily lives and learn their lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification and advice department, If a senior citizen exhibits unusual behavior, such as not eating meals or going for walks at their usual times, the system will notify the user and provide appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned abnormality detection unit, If dementia is suspected, the call center will contact the senior to check on their condition. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned conversation analysis unit, It estimates the emotions of seniors and adjusts the accuracy of conversation analysis based on the estimated emotions of seniors. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned conversation analysis unit, During conversation analysis, the system references the senior's past conversation history to more accurately detect signs of dementia. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned conversation analysis unit, During conversation analysis, the system learns the language usage patterns of seniors and detects anomalies early. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned conversation analysis unit, The system estimates the emotions of seniors and determines the priority of conversation analysis based on the estimated emotions of seniors. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned conversation analysis unit, During conversation analysis, the analysis is conducted based on the geographical and cultural backgrounds of the seniors. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned conversation analysis unit, During conversation analysis, we analyze seniors' social media activity and obtain relevant conversation data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned pattern learning unit, It estimates the emotions of seniors and adjusts the learning method of lifestyle patterns based on the estimated emotions of seniors. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned pattern learning unit, During pattern learning, the learning algorithm is optimized by referencing past lifestyle data of seniors. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned pattern learning unit, During pattern learning, the learning process takes into account the living environment and health condition of seniors. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned pattern learning unit, It estimates the emotions of seniors and prioritizes learning lifestyle patterns based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned pattern learning unit, When learning patterns, the learning process is based on the geographical and cultural backgrounds of seniors. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned pattern learning unit, During pattern learning, we analyze seniors' social media activities and collect relevant lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification and advice department, The system estimates the emotions of seniors and adjusts the content of notifications and advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification and advice department, When providing notifications and advice, the system refers to the senior's past behavioral history to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification and advice department, When sending notifications and advice, the advice is customized to take into account the senior's health condition and living environment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification and advice department, It estimates the emotions of seniors and prioritizes notifications and advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification and advice department, When providing notifications and advice, we offer advice based on the senior's geographical and cultural background. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification and advice department, When providing notifications and advice, we analyze seniors' social media activity and offer relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned abnormality detection unit, The system estimates the emotions of seniors and adjusts the criteria for detecting anomalies based on the estimated emotions of seniors. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned abnormality detection unit, When an anomaly is detected, the anomaly detection algorithm is optimized by referring to the senior's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned abnormality detection unit, When an anomaly is detected, the system takes into account the senior's health condition and living environment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned abnormality detection unit, The system estimates the emotions of seniors and determines the priority of anomaly detection based on the estimated emotions of seniors. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned abnormality detection unit, When an anomaly is detected, the system will use the geographical and cultural background of the senior citizen to detect the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned abnormality detection unit, When an anomaly is detected, the system analyzes the social media activity of seniors to identify related anomalies. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 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 conversation analysis unit that analyzes conversations and detects the possibility of dementia, The pattern learning section learns about the lifestyle patterns of seniors, A notification and advice unit that detects abnormal behavior early and provides notification and advice based on the lifestyle patterns learned by the aforementioned pattern learning unit, It includes an anomaly detection unit that detects anomalies from GPS information. A system characterized by the following features.

2. The aforementioned notification and advice department, If an anomaly is detected, the system will notify the senior and provide appropriate advice. The system according to feature 1.

3. The aforementioned abnormality detection unit, The system detects anomalies using GPS information and takes appropriate action based on the senior's location. The system according to feature 1.

4. The aforementioned conversation analysis unit, Analyzing the daily conversations of seniors to detect signs of dementia. The system according to feature 1.

5. The aforementioned pattern learning unit, Collect data on seniors' daily lives and learn their lifestyle patterns. The system according to feature 1.

6. The aforementioned notification and advice department, If a senior citizen exhibits unusual behavior, such as not eating meals or going for walks at their usual times, the system will notify the user and provide appropriate advice. The system according to feature 1.

7. The aforementioned abnormality detection unit, If dementia is suspected, the call center will contact the senior to check on their condition. The system according to feature 1.

8. The aforementioned conversation analysis unit, It estimates the emotions of seniors and adjusts the accuracy of conversation analysis based on the estimated emotions of seniors. The system according to feature 1.

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