system

Abstract

The system according to this embodiment aims to appropriately respond to phone calls from people with dementia and reduce the burden on their families. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a summarization unit, and a provision unit. The reception unit receives calls from people with dementia. The generation unit generates audio based on the calls received by the reception unit. The summarization unit summarizes the conversation based on the audio generated by the generation unit. The provision unit provides information based on the conversation summarized by the summarization unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method 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 as a 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 burden on family members in dealing with frequent calls from people with dementia is large and appropriate support is insufficient.

[0005] The system according to the embodiment aims to appropriately respond to calls from people with dementia and reduce the burden on family members.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a summarization unit, and a provision unit. The reception unit receives phone calls from people with dementia. The generation unit generates audio based on the phone calls received by the reception unit. The summarization unit summarizes the conversation content based on the audio generated by the generation unit. The provision unit provides information based on the conversation content summarized by the summarization unit. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately respond to phone calls from people with dementia and reduce the burden on their families. [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 controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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 telephone answering system according to an embodiment of the present invention is a system that takes calls and converses on behalf of a person with dementia who frequently makes such calls. The purpose of this telephone answering system is to alleviate the anxiety of the person with dementia and reduce the burden on their family. The telephone answering system has the function of taking calls and conversing on behalf of a person with dementia. In this function, it basically acts as a listener, responding with nods and reactions according to what is being said. The telephone answering system has the function of sampling the voice of a family member and conversing in a voice that resembles that voice. This allows the person with dementia to feel as if they are talking to a family member. The telephone answering system has the function of taking information about the person's surroundings and upcoming plans and providing simple answers. For example, it can answer questions such as the date of the next hospital appointment. The telephone answering system has the function of summarizing and reporting to the family when and how many calls were made and what was discussed. This allows the family to understand the situation of the person with dementia. The telephone answering system has the function of researching and disseminating correct knowledge about dementia and information that the family wants to know to the family. This allows the family to deepen their understanding of dementia. The telephone answering service provides a 24 / 7 conversation partner for people with dementia and acts as a supporter for their families, helping them cope with the challenges of dementia and reducing feelings of isolation. For example, even if a person with dementia makes negative statements such as "I want to die," the telephone answering service will respond appropriately, reducing the emotional burden on the family. Furthermore, if a family member is at work and unable to answer the phone, the service will handle it, reducing their burden. The telephone answering service is even more effective when combined with government and private caregiving burden reduction services. For instance, even if a person with dementia is living in a care facility and separated from their family, the telephone answering service can facilitate communication with family and reduce feelings of isolation. The telephone answering service is an extremely useful new support tool for people with dementia and their families. By providing a safe and secure environment for people with dementia and reducing the burden on families, it can improve the quality of dementia care.This means that the telephone answering system can alleviate anxiety for people with dementia and reduce the burden on their families.

[0029] The telephone answering system according to this embodiment comprises a reception unit, a generation unit, a summarization unit, and a provision unit. The reception unit receives calls from people with dementia. For example, when a person with dementia makes a call, the reception unit automatically answers and starts a conversation. When a person with dementia makes a call, the reception unit analyzes the content of the call using speech recognition technology and takes appropriate action. The reception unit can also use AI to estimate the emotions of the person making the call and take appropriate action. The generation unit generates audio based on the calls received by the reception unit. For example, the generation unit samples the voice of a family member and generates audio that resembles that voice. The generation unit generates natural-sounding audio using speech synthesis technology. The generation unit can also use AI to generate audio that corresponds to the emotions of the person with dementia. The summarization unit summarizes the content of the conversation based on the audio generated by the generation unit. For example, the summarization unit extracts important parts of the conversation and summarizes them concisely. The summarization unit uses AI to automatically summarize the content of the conversation. The summarization unit can also analyze the content of the conversation and adjust the level of detail in the summary according to its importance. The information provider unit provides information based on the conversation content summarized by the summarization unit. For example, the information provider unit researches and transmits accurate information about dementia and things that families want to know. The information provider unit inputs information about the person's surroundings and upcoming schedules and provides simple responses. The information provider unit can also use AI to provide information that is tailored to the emotions of the person with dementia. As a result, the telephone answering system according to this embodiment can reduce anxiety for people with dementia and lessen the burden on their families.

[0030] The reception desk receives calls from people with dementia. For example, when a person with dementia calls, the reception desk automatically answers and starts a conversation. Specifically, the reception desk uses speech recognition technology to analyze the content of the call in real time and provide an appropriate response. The speech recognition technology converts the speech of the person with dementia into text data and analyzes its content. Furthermore, it can use AI to estimate the emotions of the speech and respond accordingly. For example, if a person with dementia is feeling anxious, the reception desk will choose reassuring words to respond. The reception desk can also provide information related to specific keywords if the person with dementia utters them. For example, if keywords such as "medicine" or "hospital" are included, it will provide appropriate advice and information. In addition, the reception desk has a function to record the content of calls so that they can be analyzed and reviewed later. This allows family members and medical professionals to review the call content and take necessary actions. Each time a person with dementia calls, the reception desk refers to past call history and learns to provide more appropriate responses. This allows the reception desk to provide consistent responses to people with dementia and give them a sense of security.

[0031] The generation unit generates speech based on phone calls received by the reception unit. For example, the generation unit samples the voices of family members and generates speech that resembles them. Specifically, it uses speech synthesis technology to generate natural-sounding speech. Speech synthesis technology converts text data into speech data, achieving natural speech. The generation unit can also use AI to generate speech that responds to the emotions of people with dementia. For example, if a person with dementia is feeling anxious, the generation unit will generate calm and reassuring speech. The generation unit can also understand the context of a conversation and generate appropriate responses. For example, if a person with dementia asks, "What day is it today?", the generation unit will provide specific information such as, "Today is the day of XX." Furthermore, the generation unit can adjust the tone and speed of speech to make it easier for people with dementia to understand. For example, speaking in a slow tone makes it easier for people with dementia to understand the content. When sampling family members' voices, the generation unit collects multiple voice samples to build a database for generating more natural speech. This allows the generation unit to generate speech that is very close to the voices of family members, providing reassurance to people with dementia.

[0032] The summarization unit summarizes the conversation content based on the audio generated by the generation unit. For example, the summarization unit extracts important parts of the conversation and summarizes them concisely. Specifically, it uses AI to automatically summarize the conversation content. The AI ​​uses natural language processing technology to analyze the conversation content and extract important information. For example, if a person with dementia says, "I have an appointment to go to the hospital today," the summarization unit extracts and summarizes the important information, such as "I have an appointment to go to the hospital." Furthermore, the summarization unit can analyze the content of the conversation and adjust the level of detail in the summary according to its importance. For example, if there is a lot of important information, it will provide a detailed summary, and if there is little important information, it will provide a concise summary. The summarization unit can also understand the context of the conversation and integrate relevant information. For example, if a person with dementia says, "I also went to the hospital yesterday," the summarization unit will integrate the information in the form of "I have an appointment to go to the hospital yesterday and today." The summarization unit also has a function to save the summarized information as text data so that family members and medical professionals can review it later. This allows the summarization unit to efficiently manage conversation content and quickly provide necessary information.

[0033] The information provider unit provides information based on the conversation content summarized by the summarization unit. For example, the unit researches and disseminates accurate information about dementia and answers questions that families want to know. Specifically, it takes input such as information about the person's surroundings and upcoming plans and provides simple responses. The information provider unit can also use AI to provide information that is tailored to the emotions of a person with dementia. For example, if a person with dementia is feeling anxious, the unit will provide information that will make them feel at ease. The information provider unit can also quickly provide specific information if a person with dementia requests it. For example, if asked, "When is my next hospital appointment?", the unit will provide specific information such as, "Your next hospital appointment is on [date]." Furthermore, the information provider unit can continuously improve the accuracy and content of the information it provides based on feedback from families and medical professionals. For example, based on feedback from families, it will prioritize providing information that is of particular interest to people with dementia. The information provider unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only voice calls but also text messages and emails. This allows the information provider unit to provide people with dementia with timely and appropriate information and a sense of security.

[0034] The generation unit can sample the voices of family members and generate voices that resemble those voices. For example, the generation unit can record the voices of family members, analyze the audio data, and extract features. Using speech synthesis technology, the generation unit generates voices that resemble the family members' voices based on the extracted features. The generation unit can also use AI to learn the features of the family members' voices and generate more natural-sounding voices. As a result, by generating voices that resemble the family members' voices, people with dementia can feel as if they are talking to their family members. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the audio data of the family members' voices into a generation AI and have the generation AI generate voices that resemble the family members' voices.

[0035] The information provider can research and disseminate accurate information about dementia and answers questions that families want to know. For example, the information provider can collect the latest research findings and information about dementia from reliable sources on the internet. The information provider can organize the collected information and provide it in a format that families can easily understand. The information provider can also use AI to automatically search for and provide the information that families want to know. This allows for a deeper understanding of dementia by disseminating accurate information and answers that families want to know. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the information provider can input the information that families want to know into a generative AI and have the generative AI perform the information search and provision.

[0036] The reception department can receive calls from people with dementia 24 hours a day. The reception department can, for example, build a system that operates 24 hours a day so that people with dementia can make calls at any time. The reception department monitors the system's operation and performs maintenance as needed. The reception department can also use AI to handle calls 24 hours a day. This allows them to provide someone to talk to at any time by receiving calls from people with dementia 24 hours a day. Some or all of the above processes in the reception department may be performed using, for example, generative AI, or not using generative AI. For example, the reception department can have generative AI design a system for handling calls 24 hours a day.

[0037] The summarization unit can summarize when and how many calls were made, and what was discussed, and report this to the family. For example, the summarization unit can record the number and duration of calls and save them as a log. The summarization unit can analyze the conversation content using speech recognition technology, extract important parts, and summarize them. The summarization unit can also use AI to automatically summarize the conversation content and report it to the family. This allows the family to understand the situation of the person with dementia. Some or all of the above processing in the summarization unit may be performed using, for example, generative AI, or without generative AI. For example, the summarization unit can have generative AI perform the summarization of the conversation content and report it to the family.

[0038] The information provider can input information about the user's surroundings and upcoming schedules, and provide simple responses. For example, the information provider can input information about the next hospital appointment date or daily life and respond to a person with dementia. The information provider can also provide information about upcoming schedules based on calendar information. The information provider can also use AI to provide appropriate responses to questions from a person with dementia. This allows the information provider to provide information about the user's surroundings and upcoming schedules to a person with dementia. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can have a generative AI perform the responses to questions from a person with dementia.

[0039] The reception department can analyze past call history and select the optimal timing for receiving calls. For example, if the reception department finds from past call history that a person with dementia tends to call during certain times, it will prioritize receiving calls during those times. The reception department will analyze past call history to identify the days and times when calls are made most frequently and respond accordingly. If the reception department finds from past call history that many calls are related to a particular event or situation, it will respond accordingly. This allows the reception department to receive calls at the optimal time based on past call history. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can input past call history data into an AI and have the AI ​​select the optimal timing for receiving calls.

[0040] The reception desk can filter incoming calls based on the current living situation and interests of people with dementia. For example, if a person with dementia is interested in a particular topic based on their current living situation, the reception desk will prioritize calls related to that topic. If a person with dementia has a particular hobby or interest, the reception desk will prioritize calls related to that hobby or interest. If a person with dementia has a particular health condition, the reception desk will prioritize calls related to that health condition. This allows for priority reception of calls that are tailored to the living situation and interests of people with dementia. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the living situation and interests of people with dementia into the AI ​​and have the AI ​​perform the filtering.

[0041] The reception desk can prioritize receiving calls that are highly relevant to the person with dementia, taking into account their geographical location. For example, if the person with dementia lives in a specific area, the reception desk will prioritize receiving calls that provide information related to that area. If the person with dementia is in a specific facility, the reception desk will prioritize receiving calls that provide information related to that facility. If the person with dementia is participating in a specific event, the reception desk will prioritize receiving calls that provide information related to that event. This allows the reception desk to prioritize receiving calls that are highly relevant based on the geographical location of the person with dementia. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the geographical location of the person with dementia into the AI ​​and have the AI ​​select the most relevant calls.

[0042] The reception desk can analyze the social media activity of people with dementia when answering calls and prioritize relevant calls. For example, if a person with dementia frequently posts about a particular topic on social media, the reception desk will prioritize calls related to that topic. If a person with dementia participates in a particular group on social media, the reception desk will prioritize calls related to that group. If a person with dementia participates in a particular event on social media, the reception desk will prioritize calls related to that event. This allows the reception desk to prioritize relevant calls based on the social media activity of people with dementia. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input social media activity data of people with dementia into an AI and have the AI ​​select relevant calls.

[0043] The generation unit can adjust the level of detail in the speech based on the importance of the conversation during speech generation. For example, in the case of an important conversation, the generation unit generates speech that includes detailed explanations. In the case of a general conversation, the generation unit generates speech that includes concise explanations. In the case of an urgent conversation, the generation unit generates concise speech for quick response. This allows the level of detail in the speech to be adjusted according to the importance of the conversation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input conversation importance data into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the speech.

[0044] The generation unit can apply different speech generation algorithms depending on the category of the conversation during speech generation. For example, in the case of a medical conversation, the generation unit generates speech containing specialized terminology. In the case of a conversation about daily life, the generation unit generates speech containing familiar language. In the case of a conversation about an emergency, the generation unit generates concise speech for quick response. This allows the appropriate speech generation algorithm to be applied according to the category of the conversation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input conversation category data into the AI ​​and have the AI ​​perform the application of the speech generation algorithm.

[0045] The generation unit can determine the priority of speech based on when the conversation occurred during speech generation. For example, the generation unit generates speech with the highest priority in the case of an urgent conversation. For important conversations, it generates speech with the next highest priority. For general conversations, it generates speech last. This allows the generation unit to determine the priority of speech based on when the conversation occurred. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input conversation occurrence data into AI and have the AI ​​perform the determination of speech priority.

[0046] The generation unit can adjust the order of speech based on the relevance of the conversation during speech generation. For example, the generation unit generates speech first in conversations that contain important information. The generation unit generates speech next in conversations that contain highly relevant information. The generation unit generates speech last in conversations that contain general information. This allows the order of speech to be adjusted based on the relevance of the conversation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input conversation relevance data into the AI ​​and have the AI ​​perform the adjustment of the speech order.

[0047] The summarization unit can adjust the level of detail in the summary based on the importance of the conversation during summary generation. For example, the summarization unit generates a detailed summary for important conversations. For general conversations, it generates a concise summary. For urgent conversations, it generates a concise summary for quick response. This allows the level of detail in the summary to be adjusted according to the importance of the conversation. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input conversation importance data into AI and have the AI ​​perform the adjustment of the level of detail in the summary.

[0048] The summarization unit can apply different summarization algorithms depending on the category of the conversation when generating summaries. For example, in the case of a medical conversation, the summarization unit generates a summary that includes specialized terminology. In the case of a conversation about daily life, the summarization unit generates a summary that includes familiar language. In the case of a conversation about an emergency, the summarization unit generates a concise summary for quick response. This allows the application of an appropriate summarization algorithm depending on the category of the conversation. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input conversation category data into AI and have the AI ​​perform the application of the summarization algorithm.

[0049] The summarization unit can determine the priority of summaries based on when the conversation occurred during summary generation. For example, the summarization unit generates the highest priority summary for urgent conversations. For important conversations, it generates the next highest priority summary. For general conversations, it generates the last summary. This allows the summarization unit to determine the priority of summaries based on when the conversation occurred. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input conversation occurrence data into AI and have the AI ​​perform the determination of summary priority.

[0050] The summarization unit can adjust the order of summaries based on the relevance of the conversation during summary generation. For example, the summarization unit generates a summary first for conversations containing important information. For conversations containing highly relevant information, it generates a summary next. For conversations containing general information, it generates a summary last. This allows the order of summaries to be adjusted based on the relevance of the conversation. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input conversation relevance data into AI and have the AI ​​perform the adjustment of the summary order.

[0051] The information delivery unit can select the optimal information delivery method by referring to past delivery history when providing information. For example, the information delivery unit can select an information delivery method preferred by a person with dementia from past delivery history. The information delivery unit can analyze past delivery history and select an information delivery method that is easy for a person with dementia to understand. The information delivery unit can select an information delivery method that elicited the strongest response from a person with dementia from past delivery history. This allows the optimal information delivery method to be selected based on past delivery history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input past delivery history data into AI and have the AI ​​select the optimal information delivery method.

[0052] The information provider can customize the means of providing information based on the current living situation of a person with dementia. For example, the provider may prioritize a specific means of providing information based on the current living situation of a person with dementia. If a person with dementia has a specific health condition, the provider may select a means of providing information that is appropriate for that health condition. If a person with dementia is in a specific living environment, the provider may select a means of providing information that is appropriate for that environment. This allows the provider to select a means of providing information that is appropriate for the living situation of a person with dementia. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI. For example, the provider may input data on the living situation of a person with dementia into AI and have AI perform the customization of the means of providing information.

[0053] The information provider can select the most appropriate method of information delivery by considering the geographical location of the person with dementia when providing information. For example, if the person with dementia lives in a specific area, the provider will provide information related to that area. If the person with dementia is in a specific facility, the provider will provide information related to that facility. If the person with dementia is participating in a specific event, the provider will provide information related to that event. This allows the provider to select the most appropriate method of information delivery based on the geographical location of the person with dementia. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the geographical location of the person with dementia into the AI ​​and have the AI ​​select the most appropriate method of information delivery.

[0054] The information provider can analyze the social media activity of a person with dementia and propose methods for providing information. For example, if a person with dementia frequently posts about a particular topic on social media, the provider will provide information related to that topic. If a person with dementia participates in a particular group on social media, the provider will provide information related to that group. If a person with dementia participates in a particular event on social media, the provider will provide information related to that event. This allows the provider to propose the most suitable method of providing information based on the social media activity of a person with dementia. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input social media activity data of a person with dementia into AI and have the AI ​​propose methods of providing information.

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

[0056] Telephone answering systems can also be equipped with a reminder function. This function sends regular reminders to help people with dementia remember important appointments and tasks. For example, it can notify users of medication times or medical appointments. The reminder function can also notify users of specific events and tasks set by family members. This helps support people with dementia in remembering important things in their daily lives. The reminder function can send notifications in multiple formats, including voice, text messages, and even visual alerts. This allows people with dementia to receive reminders in the format that is most easily understood by them.

[0057] The telephone answering system can also be equipped with a health monitoring function. This function periodically checks the health status of individuals with dementia and notifies family members or medical institutions if any abnormalities are detected. For example, it can monitor vital signs such as heart rate, blood pressure, and body temperature. Furthermore, the health monitoring function can record the daily exercise and dietary habits of individuals with dementia. This allows for comprehensive management of the health status of individuals with dementia and enables early detection of abnormalities. The health monitoring function can collect and analyze data in conjunction with wearable devices and smartphone apps.

[0058] Telephone answering systems can also be equipped with video call functionality. This video call feature allows people with dementia to converse with family and friends while seeing their faces. For example, regular video calls with family members living far away can alleviate feelings of loneliness in people with dementia. Furthermore, the video call function can be used for remote medical consultations with healthcare institutions. This allows people with dementia to receive medical examinations from doctors while at home. The video call function can be accessed through devices such as smartphones, tablets, and personal computers.

[0059] The telephone answering system can also be equipped with a gaming function. This gaming function provides games designed to help people with dementia maintain and improve their cognitive function while having fun. For example, it can offer puzzle games or memory training games. Furthermore, the gaming function can provide games customized to the interests and skill levels of people with dementia. This allows people with dementia to maintain and improve their cognitive function while having fun. The gaming function can be accessed through devices such as smartphones, tablets, and personal computers.

[0060] Telephone answering systems can also be equipped with virtual assistant functionality. This virtual assistant function can provide voice-activated instructions to support the daily lives of people with dementia. For example, it can guide a person with dementia through recipes while they are cooking. It can also provide directions and traffic information when they are going out. This allows people with dementia to live their daily lives without difficulty. The virtual assistant function can be accessed through smart speakers or smartphones.

[0061] Telephone answering systems can also incorporate community features. These community features provide a platform where people with dementia and their families can interact online. For example, people with dementia can reduce feelings of isolation by connecting with others in similar situations. Families can also share knowledge and experiences regarding dementia care by exchanging information with other families. Community features can be provided in the form of forums, chat rooms, and video conferencing. This allows people with dementia and their families to support each other and solve problems together.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives calls from people with dementia. For example, when a person with dementia calls, the system automatically answers and starts a conversation. The reception desk uses voice recognition technology to analyze the content of the call and respond appropriately. It can also use AI to estimate emotions and respond accordingly. Step 2: The generation unit generates voice based on the phone call received by the reception unit. For example, it can sample a family member's voice and generate a voice that sounds similar to it. The generation unit can generate natural-sounding voices using speech synthesis technology, and can also use AI to generate voices that respond to the emotions of a person with dementia. Step 3: The summarizing unit summarizes the conversation based on the audio generated by the generation unit. For example, it extracts the important parts of the conversation and summarizes them concisely. The summarizing unit can also use AI to automatically summarize the conversation and analyze the conversation content to adjust the level of detail in the summary according to its importance. Step 4: The information provider provides information based on the conversation summarized by the summarization unit. For example, it researches and disseminates accurate information about dementia and things that families want to know. The information provider inputs information about the person's surroundings and upcoming schedules and provides simple responses. It can also use AI to provide information that is tailored to the emotions of the person with dementia.

[0064] (Example of form 2) The telephone answering system according to an embodiment of the present invention is a system that takes calls and converses on behalf of a person with dementia who frequently makes such calls. The purpose of this telephone answering system is to alleviate the anxiety of the person with dementia and reduce the burden on their family. The telephone answering system has the function of taking calls and conversing on behalf of a person with dementia. In this function, it basically acts as a listener, responding with nods and reactions according to what is being said. The telephone answering system has the function of sampling the voice of a family member and conversing in a voice that resembles that voice. This allows the person with dementia to feel as if they are talking to a family member. The telephone answering system has the function of taking information about the person's surroundings and upcoming plans and providing simple answers. For example, it can answer questions such as the date of the next hospital appointment. The telephone answering system has the function of summarizing and reporting to the family when and how many calls were made and what was discussed. This allows the family to understand the situation of the person with dementia. The telephone answering system has the function of researching and disseminating correct knowledge about dementia and information that the family wants to know to the family. This allows the family to deepen their understanding of dementia. The telephone answering service provides a 24 / 7 conversation partner for people with dementia and acts as a supporter for their families, helping them cope with the challenges of dementia and reducing feelings of isolation. For example, even if a person with dementia makes negative statements such as "I want to die," the telephone answering service will respond appropriately, reducing the emotional burden on the family. Furthermore, if a family member is at work and unable to answer the phone, the service will handle it, reducing their burden. The telephone answering service is even more effective when combined with government and private caregiving burden reduction services. For instance, even if a person with dementia is living in a care facility and separated from their family, the telephone answering service can facilitate communication with family and reduce feelings of isolation. The telephone answering service is an extremely useful new support tool for people with dementia and their families. By providing a safe and secure environment for people with dementia and reducing the burden on families, it can improve the quality of dementia care.This means that the telephone answering system can alleviate anxiety for people with dementia and reduce the burden on their families.

[0065] The telephone answering system according to this embodiment comprises a reception unit, a generation unit, a summarization unit, and a provision unit. The reception unit receives calls from people with dementia. For example, when a person with dementia makes a call, the reception unit automatically answers and starts a conversation. When a person with dementia makes a call, the reception unit analyzes the content of the call using speech recognition technology and takes appropriate action. The reception unit can also use AI to estimate the emotions of the person making the call and take appropriate action. The generation unit generates audio based on the calls received by the reception unit. For example, the generation unit samples the voice of a family member and generates audio that resembles that voice. The generation unit generates natural-sounding audio using speech synthesis technology. The generation unit can also use AI to generate audio that corresponds to the emotions of the person with dementia. The summarization unit summarizes the content of the conversation based on the audio generated by the generation unit. For example, the summarization unit extracts important parts of the conversation and summarizes them concisely. The summarization unit uses AI to automatically summarize the content of the conversation. The summarization unit can also analyze the content of the conversation and adjust the level of detail in the summary according to its importance. The information provider unit provides information based on the conversation content summarized by the summarization unit. For example, the information provider unit researches and transmits accurate information about dementia and things that families want to know. The information provider unit inputs information about the person's surroundings and upcoming schedules and provides simple responses. The information provider unit can also use AI to provide information that is tailored to the emotions of the person with dementia. As a result, the telephone answering system according to this embodiment can reduce anxiety for people with dementia and lessen the burden on their families.

[0066] The reception desk receives calls from people with dementia. For example, when a person with dementia calls, the reception desk automatically answers and starts a conversation. Specifically, the reception desk uses speech recognition technology to analyze the content of the call in real time and provide an appropriate response. The speech recognition technology converts the speech of the person with dementia into text data and analyzes its content. Furthermore, it can use AI to estimate the emotions of the speech and respond accordingly. For example, if a person with dementia is feeling anxious, the reception desk will choose reassuring words to respond. The reception desk can also provide information related to specific keywords if the person with dementia utters them. For example, if keywords such as "medicine" or "hospital" are included, it will provide appropriate advice and information. In addition, the reception desk has a function to record the content of calls so that they can be analyzed and reviewed later. This allows family members and medical professionals to review the call content and take necessary actions. Each time a person with dementia calls, the reception desk refers to past call history and learns to provide more appropriate responses. This allows the reception desk to provide consistent responses to people with dementia and give them a sense of security.

[0067] The generation unit generates speech based on phone calls received by the reception unit. For example, the generation unit samples the voices of family members and generates speech that resembles them. Specifically, it uses speech synthesis technology to generate natural-sounding speech. Speech synthesis technology converts text data into speech data, achieving natural speech. The generation unit can also use AI to generate speech that responds to the emotions of people with dementia. For example, if a person with dementia is feeling anxious, the generation unit will generate calm and reassuring speech. The generation unit can also understand the context of a conversation and generate appropriate responses. For example, if a person with dementia asks, "What day is it today?", the generation unit will provide specific information such as, "Today is the day of XX." Furthermore, the generation unit can adjust the tone and speed of speech to make it easier for people with dementia to understand. For example, speaking in a slow tone makes it easier for people with dementia to understand the content. When sampling family members' voices, the generation unit collects multiple voice samples to build a database for generating more natural speech. This allows the generation unit to generate speech that is very close to the voices of family members, providing reassurance to people with dementia.

[0068] The summarization unit summarizes the conversation content based on the audio generated by the generation unit. For example, the summarization unit extracts important parts of the conversation and summarizes them concisely. Specifically, it uses AI to automatically summarize the conversation content. The AI ​​uses natural language processing technology to analyze the conversation content and extract important information. For example, if a person with dementia says, "I have an appointment to go to the hospital today," the summarization unit extracts and summarizes the important information, such as "I have an appointment to go to the hospital." Furthermore, the summarization unit can analyze the content of the conversation and adjust the level of detail in the summary according to its importance. For example, if there is a lot of important information, it will provide a detailed summary, and if there is little important information, it will provide a concise summary. The summarization unit can also understand the context of the conversation and integrate relevant information. For example, if a person with dementia says, "I also went to the hospital yesterday," the summarization unit will integrate the information in the form of "I have an appointment to go to the hospital yesterday and today." The summarization unit also has a function to save the summarized information as text data so that family members and medical professionals can review it later. This allows the summarization unit to efficiently manage conversation content and quickly provide necessary information.

[0069] The information provider unit provides information based on the conversation content summarized by the summarization unit. For example, the unit researches and disseminates accurate information about dementia and answers questions that families want to know. Specifically, it takes input such as information about the person's surroundings and upcoming plans and provides simple responses. The information provider unit can also use AI to provide information that is tailored to the emotions of a person with dementia. For example, if a person with dementia is feeling anxious, the unit will provide information that will make them feel at ease. The information provider unit can also quickly provide specific information if a person with dementia requests it. For example, if asked, "When is my next hospital appointment?", the unit will provide specific information such as, "Your next hospital appointment is on [date]." Furthermore, the information provider unit can continuously improve the accuracy and content of the information it provides based on feedback from families and medical professionals. For example, based on feedback from families, it will prioritize providing information that is of particular interest to people with dementia. The information provider unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only voice calls but also text messages and emails. This allows the information provider unit to provide people with dementia with timely and appropriate information and a sense of security.

[0070] The generation unit can sample the voices of family members and generate voices that resemble those voices. For example, the generation unit can record the voices of family members, analyze the audio data, and extract features. Using speech synthesis technology, the generation unit generates voices that resemble the family members' voices based on the extracted features. The generation unit can also use AI to learn the features of the family members' voices and generate more natural-sounding voices. As a result, by generating voices that resemble the family members' voices, people with dementia can feel as if they are talking to their family members. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the audio data of the family members' voices into a generation AI and have the generation AI generate voices that resemble the family members' voices.

[0071] The information provider can research and disseminate accurate information about dementia and answers questions that families want to know. For example, the information provider can collect the latest research findings and information about dementia from reliable sources on the internet. The information provider can organize the collected information and provide it in a format that families can easily understand. The information provider can also use AI to automatically search for and provide the information that families want to know. This allows for a deeper understanding of dementia by disseminating accurate information and answers that families want to know. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the information provider can input the information that families want to know into a generative AI and have the generative AI perform the information search and provision.

[0072] The reception department can receive calls from people with dementia 24 hours a day. The reception department can, for example, build a system that operates 24 hours a day so that people with dementia can make calls at any time. The reception department monitors the system's operation and performs maintenance as needed. The reception department can also use AI to handle calls 24 hours a day. This allows them to provide someone to talk to at any time by receiving calls from people with dementia 24 hours a day. Some or all of the above processes in the reception department may be performed using, for example, generative AI, or not using generative AI. For example, the reception department can have generative AI design a system for handling calls 24 hours a day.

[0073] The summarization unit can summarize when and how many calls were made, and what was discussed, and report this to the family. For example, the summarization unit can record the number and duration of calls and save them as a log. The summarization unit can analyze the conversation content using speech recognition technology, extract important parts, and summarize them. The summarization unit can also use AI to automatically summarize the conversation content and report it to the family. This allows the family to understand the situation of the person with dementia. Some or all of the above processing in the summarization unit may be performed using, for example, generative AI, or without generative AI. For example, the summarization unit can have generative AI perform the summarization of the conversation content and report it to the family.

[0074] The information provider can input information about the user's surroundings and upcoming schedules, and provide simple responses. For example, the information provider can input information about the next hospital appointment date or daily life and respond to a person with dementia. The information provider can also provide information about upcoming schedules based on calendar information. The information provider can also use AI to provide appropriate responses to questions from a person with dementia. This allows the information provider to provide information about the user's surroundings and upcoming schedules to a person with dementia. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can have a generative AI perform the responses to questions from a person with dementia.

[0075] The reception desk can estimate the emotions of a person with dementia and adjust the way it answers the phone based on the estimated emotions. For example, if a person with dementia is feeling anxious, the reception desk will respond in a gentle voice to provide reassurance. If a person with dementia is agitated, the reception desk will respond in a calm voice to help them regain their composure. If a person with dementia is sad, the reception desk will respond in an empathetic voice to acknowledge their feelings. This allows for appropriate responses according to the emotions of the person with dementia. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input emotional data of a person with dementia into a generative AI and have the generative AI perform emotion estimation and adjust the response method.

[0076] The reception department can analyze past call history and select the optimal timing for receiving calls. For example, if the reception department finds from past call history that a person with dementia tends to call during certain times, it will prioritize receiving calls during those times. The reception department will analyze past call history to identify the days and times when calls are made most frequently and respond accordingly. If the reception department finds from past call history that many calls are related to a particular event or situation, it will respond accordingly. This allows the reception department to receive calls at the optimal time based on past call history. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can input past call history data into an AI and have the AI ​​select the optimal timing for receiving calls.

[0077] The reception desk can filter incoming calls based on the current living situation and interests of people with dementia. For example, if a person with dementia is interested in a particular topic based on their current living situation, the reception desk will prioritize calls related to that topic. If a person with dementia has a particular hobby or interest, the reception desk will prioritize calls related to that hobby or interest. If a person with dementia has a particular health condition, the reception desk will prioritize calls related to that health condition. This allows for priority reception of calls that are tailored to the living situation and interests of people with dementia. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the living situation and interests of people with dementia into the AI ​​and have the AI ​​perform the filtering.

[0078] The reception desk can estimate the emotions of a person with dementia and determine the priority of incoming calls based on the estimated emotions. For example, if a person with dementia is feeling anxious, the reception desk will prioritize calls from family members to alleviate that anxiety. If a person with dementia is agitated, the reception desk will prioritize calls from friends with calm voices to calm them down. If a person with dementia is sad, the reception desk will prioritize calls from professionals with empathetic voices to ease their sadness. This allows the reception desk to prioritize calls according to the emotions of a person with dementia. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input emotional data of a person with dementia into a generative AI and have the generative AI perform emotion estimation and call priority determination.

[0079] The reception desk can prioritize receiving calls that are highly relevant to the person with dementia, taking into account their geographical location. For example, if the person with dementia lives in a specific area, the reception desk will prioritize receiving calls that provide information related to that area. If the person with dementia is in a specific facility, the reception desk will prioritize receiving calls that provide information related to that facility. If the person with dementia is participating in a specific event, the reception desk will prioritize receiving calls that provide information related to that event. This allows the reception desk to prioritize receiving calls that are highly relevant based on the geographical location of the person with dementia. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the geographical location of the person with dementia into the AI ​​and have the AI ​​select the most relevant calls.

[0080] The reception desk can analyze the social media activity of people with dementia when answering calls and prioritize relevant calls. For example, if a person with dementia frequently posts about a particular topic on social media, the reception desk will prioritize calls related to that topic. If a person with dementia participates in a particular group on social media, the reception desk will prioritize calls related to that group. If a person with dementia participates in a particular event on social media, the reception desk will prioritize calls related to that event. This allows the reception desk to prioritize relevant calls based on the social media activity of people with dementia. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input social media activity data of people with dementia into an AI and have the AI ​​select relevant calls.

[0081] The generation unit can estimate the emotions of a person with dementia and adjust the tone and expression of the generated voice based on the estimated emotions. For example, if a person with dementia is feeling anxious, the generation unit will generate a voice that speaks in a gentle tone. If a person with dementia is agitated, the generation unit will generate a voice that speaks in a calm tone. If a person with dementia is sad, the generation unit will generate a voice that speaks in an empathetic tone. This allows the generation of voice to be appropriate in tone and expression according to the emotions of the person with dementia. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input emotion data of a person with dementia into the generation AI and have the generation AI adjust the tone and expression of the voice.

[0082] The generation unit can adjust the level of detail in the speech based on the importance of the conversation during speech generation. For example, in the case of an important conversation, the generation unit generates speech that includes detailed explanations. In the case of a general conversation, the generation unit generates speech that includes concise explanations. In the case of an urgent conversation, the generation unit generates concise speech for quick response. This allows the level of detail in the speech to be adjusted according to the importance of the conversation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input conversation importance data into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the speech.

[0083] The generation unit can apply different speech generation algorithms depending on the category of the conversation during speech generation. For example, in the case of a medical conversation, the generation unit generates speech containing specialized terminology. In the case of a conversation about daily life, the generation unit generates speech containing familiar language. In the case of a conversation about an emergency, the generation unit generates concise speech for quick response. This allows the appropriate speech generation algorithm to be applied according to the category of the conversation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input conversation category data into the AI ​​and have the AI ​​perform the application of the speech generation algorithm.

[0084] The generation unit can estimate the emotions of a person with dementia and adjust the length of the generated speech based on the estimated emotions. For example, if a person with dementia is feeling anxious, the generation unit can provide reassurance with a longer speech. If a person with dementia is agitated, the generation unit can help them regain composure with a shorter speech. If a person with dementia is sad, the generation unit can show empathy with a speech of appropriate length. In this way, the length of the speech can be adjusted according to the emotions of the person with dementia. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input emotion data of a person with dementia into the generation AI and have the generation AI adjust the length of the speech.

[0085] The generation unit can determine the priority of speech based on when the conversation occurred during speech generation. For example, the generation unit generates speech with the highest priority in the case of an urgent conversation. For important conversations, it generates speech with the next highest priority. For general conversations, it generates speech last. This allows the generation unit to determine the priority of speech based on when the conversation occurred. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input conversation occurrence data into AI and have the AI ​​perform the determination of speech priority.

[0086] The generation unit can adjust the order of speech based on the relevance of the conversation during speech generation. For example, the generation unit generates speech first in conversations that contain important information. The generation unit generates speech next in conversations that contain highly relevant information. The generation unit generates speech last in conversations that contain general information. This allows the order of speech to be adjusted based on the relevance of the conversation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input conversation relevance data into the AI ​​and have the AI ​​perform the adjustment of the speech order.

[0087] The summarization unit can estimate the emotions of a person with dementia and adjust the way the summary is expressed based on the estimated emotions. For example, if the person with dementia is feeling anxious, the summarization unit will summarize in a way that provides reassurance. If the person with dementia is agitated, the summarization unit will summarize in a way that helps them regain their composure. If the person with dementia is sad, the summarization unit will summarize in an empathetic way. This allows the summarization unit to generate an appropriate expression according to the emotions of the person with dementia. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the emotional data of the person with dementia into the generative AI and have the generative AI adjust the way the summary is expressed.

[0088] The summarization unit can adjust the level of detail in the summary based on the importance of the conversation during summary generation. For example, the summarization unit generates a detailed summary for important conversations. For general conversations, it generates a concise summary. For urgent conversations, it generates a concise summary for quick response. This allows the level of detail in the summary to be adjusted according to the importance of the conversation. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input conversation importance data into AI and have the AI ​​perform the adjustment of the level of detail in the summary.

[0089] The summarization unit can apply different summarization algorithms depending on the category of the conversation when generating summaries. For example, in the case of a medical conversation, the summarization unit generates a summary that includes specialized terminology. In the case of a conversation about daily life, the summarization unit generates a summary that includes familiar language. In the case of a conversation about an emergency, the summarization unit generates a concise summary for quick response. This allows the application of an appropriate summarization algorithm depending on the category of the conversation. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input conversation category data into AI and have the AI ​​perform the application of the summarization algorithm.

[0090] The summarization unit can estimate the emotions of a person with dementia and adjust the length of the summary based on the estimated emotions. For example, if the person with dementia is feeling anxious, the summarization unit can provide a longer summary to give a sense of security. If the person with dementia is agitated, the summarization unit can help them regain their composure with a shorter summary. If the person with dementia is sad, the summarization unit can show empathy with a summary of appropriate length. In this way, the length of the summary can be adjusted according to the emotions of the person with dementia. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input the emotional data of the person with dementia into the generative AI and have the generative AI adjust the length of the summary.

[0091] The summarization unit can determine the priority of summaries based on when the conversation occurred during summary generation. For example, the summarization unit generates the highest priority summary for urgent conversations. For important conversations, it generates the next highest priority summary. For general conversations, it generates the last summary. This allows the summarization unit to determine the priority of summaries based on when the conversation occurred. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input conversation occurrence data into AI and have the AI ​​perform the determination of summary priority.

[0092] The summarization unit can adjust the order of summaries based on the relevance of the conversation during summary generation. For example, the summarization unit generates a summary first for conversations containing important information. For conversations containing highly relevant information, it generates a summary next. For conversations containing general information, it generates a summary last. This allows the order of summaries to be adjusted based on the relevance of the conversation. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input conversation relevance data into AI and have the AI ​​perform the adjustment of the summary order.

[0093] The information provider can estimate the emotions of a person with dementia and adjust the way the information is presented based on the estimated emotions. For example, if a person with dementia is feeling anxious, the information provider will present the information in a way that provides reassurance. If a person with dementia is agitated, the information provider will present the information in a way that helps them regain their composure. If a person with dementia is sad, the information provider will present the information in an empathetic way. This allows the information provider to present it in an appropriate way according to the emotions of the person with dementia. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input emotional data of a person with dementia into a generative AI and have the generative AI adjust the way the information is presented.

[0094] The information delivery unit can select the optimal information delivery method by referring to past delivery history when providing information. For example, the information delivery unit can select an information delivery method preferred by a person with dementia from past delivery history. The information delivery unit can analyze past delivery history and select an information delivery method that is easy for a person with dementia to understand. The information delivery unit can select an information delivery method that elicited the strongest response from a person with dementia from past delivery history. This allows the optimal information delivery method to be selected based on past delivery history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input past delivery history data into AI and have the AI ​​select the optimal information delivery method.

[0095] The information provider can customize the means of providing information based on the current living situation of a person with dementia. For example, the provider may prioritize a specific means of providing information based on the current living situation of a person with dementia. If a person with dementia has a specific health condition, the provider may select a means of providing information that is appropriate for that health condition. If a person with dementia is in a specific living environment, the provider may select a means of providing information that is appropriate for that environment. This allows the provider to select a means of providing information that is appropriate for the living situation of a person with dementia. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI. For example, the provider may input data on the living situation of a person with dementia into AI and have AI perform the customization of the means of providing information.

[0096] The information provider can estimate the emotions of a person with dementia and determine the priority of the information to be provided based on the estimated emotions. For example, if a person with dementia is feeling anxious, the information provider will prioritize providing information that provides a sense of security. If a person with dementia is agitated, the information provider will prioritize providing information that helps them regain their composure. If a person with dementia is sad, the information provider will prioritize providing empathetic information. In this way, the information priority can be determined according to the emotions of the person with dementia. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input emotional data of a person with dementia into a generative AI and have the generative AI perform the determination of information priority.

[0097] The information provider can select the most appropriate method of information delivery by considering the geographical location of the person with dementia when providing information. For example, if the person with dementia lives in a specific area, the provider will provide information related to that area. If the person with dementia is in a specific facility, the provider will provide information related to that facility. If the person with dementia is participating in a specific event, the provider will provide information related to that event. This allows the provider to select the most appropriate method of information delivery based on the geographical location of the person with dementia. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the geographical location of the person with dementia into the AI ​​and have the AI ​​select the most appropriate method of information delivery.

[0098] The information provider can analyze the social media activity of a person with dementia and propose methods for providing information. For example, if a person with dementia frequently posts about a particular topic on social media, the provider will provide information related to that topic. If a person with dementia participates in a particular group on social media, the provider will provide information related to that group. If a person with dementia participates in a particular event on social media, the provider will provide information related to that event. This allows the provider to propose the most suitable method of providing information based on the social media activity of a person with dementia. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input social media activity data of a person with dementia into AI and have the AI ​​propose methods of providing information.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] Telephone answering systems can also be equipped with a reminder function. This function sends regular reminders to help people with dementia remember important appointments and tasks. For example, it can notify users of medication times or medical appointments. The reminder function can also notify users of specific events and tasks set by family members. This helps support people with dementia in remembering important things in their daily lives. The reminder function can send notifications in multiple formats, including voice, text messages, and even visual alerts. This allows people with dementia to receive reminders in the format that is most easily understood by them.

[0101] The telephone answering system can also be equipped with a health monitoring function. This function periodically checks the health status of individuals with dementia and notifies family members or medical institutions if any abnormalities are detected. For example, it can monitor vital signs such as heart rate, blood pressure, and body temperature. Furthermore, the health monitoring function can record the daily exercise and dietary habits of individuals with dementia. This allows for comprehensive management of the health status of individuals with dementia and enables early detection of abnormalities. The health monitoring function can collect and analyze data in conjunction with wearable devices and smartphone apps.

[0102] The telephone answering system can also be equipped with a music therapy function. This function provides appropriate music to stabilize the emotions and behavior of people with dementia. For example, if a person with dementia is feeling anxious, relaxing music can be played. Similarly, if a person with dementia is agitated, calming music can be played. The music therapy function can select the most suitable music based on the preferences and past musical history of the person with dementia. This can stabilize their emotions and improve their quality of life. The music therapy function can deliver music through speakers or headphones.

[0103] Telephone answering systems can also be equipped with video call functionality. This video call feature allows people with dementia to converse with family and friends while seeing their faces. For example, regular video calls with family members living far away can alleviate feelings of loneliness in people with dementia. Furthermore, the video call function can be used for remote medical consultations with healthcare institutions. This allows people with dementia to receive medical examinations from doctors while at home. The video call function can be accessed through devices such as smartphones, tablets, and personal computers.

[0104] The telephone answering system can also be equipped with a gaming function. This gaming function provides games designed to help people with dementia maintain and improve their cognitive function while having fun. For example, it can offer puzzle games or memory training games. Furthermore, the gaming function can provide games customized to the interests and skill levels of people with dementia. This allows people with dementia to maintain and improve their cognitive function while having fun. The gaming function can be accessed through devices such as smartphones, tablets, and personal computers.

[0105] The telephone answering system can also be equipped with an emotional diary function. This function records changes in the emotions of a person with dementia and reports them to family members or medical institutions. For example, a person with dementia can record their daily feelings and experiences in voice or text. Furthermore, the emotional diary function can analyze the emotional tendencies of a person with dementia and identify the causes of stress and anxiety. This allows for a better understanding of the emotional changes of a person with dementia and enables appropriate responses. The emotional diary function can be accessed through devices such as smartphones, tablets, and personal computers.

[0106] Telephone answering systems can also be equipped with virtual assistant functionality. This virtual assistant function can provide voice-activated instructions to support the daily lives of people with dementia. For example, it can guide a person with dementia through recipes while they are cooking. It can also provide directions and traffic information when they are going out. This allows people with dementia to live their daily lives without difficulty. The virtual assistant function can be accessed through smart speakers or smartphones.

[0107] Telephone answering systems can also be equipped with emotion analysis capabilities. This emotion analysis function analyzes the emotions of a person with dementia based on their conversation content and tone of voice, and provides appropriate responses. For example, if a person with dementia is feeling anxious, the system can engage in conversation that provides reassurance. Similarly, if a person with dementia is agitated, the system can engage in conversation that calms them down. The emotion analysis function can grasp changes in a person with dementia's emotions in real time and respond appropriately. This allows for support tailored to the emotions of the person with dementia. The emotion analysis function is implemented using speech recognition technology and AI.

[0108] Telephone answering systems can also incorporate community features. These community features provide a platform where people with dementia and their families can interact online. For example, people with dementia can reduce feelings of isolation by connecting with others in similar situations. Families can also share knowledge and experiences regarding dementia care by exchanging information with other families. Community features can be provided in the form of forums, chat rooms, and video conferencing. This allows people with dementia and their families to support each other and solve problems together.

[0109] The telephone answering system can also be equipped with an emotional feedback function. This function provides real-time feedback on changes in the emotions of a person with dementia and notifies family members and medical institutions. For example, if a person with dementia suddenly feels anxious, this information can be sent to the family to encourage appropriate action. Similarly, if a person with dementia is saddened for an extended period, this information can be sent to medical institutions to receive professional support. The emotional feedback function continuously monitors changes in the emotions of a person with dementia and provides feedback at the appropriate time. This enables a rapid response tailored to the emotions of the person with dementia. The emotional feedback function is implemented using speech recognition technology and AI.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives calls from people with dementia. For example, when a person with dementia calls, the system automatically answers and starts a conversation. The reception desk uses voice recognition technology to analyze the content of the call and respond appropriately. It can also use AI to estimate emotions and respond accordingly. Step 2: The generation unit generates voice based on the phone call received by the reception unit. For example, it can sample a family member's voice and generate a voice that sounds similar to it. The generation unit can generate natural-sounding voices using speech synthesis technology, and can also use AI to generate voices that respond to the emotions of a person with dementia. Step 3: The summarizing unit summarizes the conversation based on the audio generated by the generation unit. For example, it extracts the important parts of the conversation and summarizes them concisely. The summarizing unit can also use AI to automatically summarize the conversation and analyze the conversation content to adjust the level of detail in the summary according to its importance. Step 4: The information provider provides information based on the conversation summarized by the summarization unit. For example, it researches and disseminates accurate information about dementia and things that families want to know. The information provider inputs information about the person's surroundings and upcoming schedules and provides simple responses. It can also use AI to provide information that is tailored to the emotions of the person with dementia.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, generation unit, summarization unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives calls from people with dementia. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and samples the voices of family members and generates voices that resemble those voices. The summarization unit is implemented by the specific processing unit 290 of the data processing unit 12 and summarizes the content of conversations. The provision unit is implemented by the control unit 46A of the smart device 14 and provides information about dementia and upcoming schedules. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, generation unit, summarization unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives calls from people with dementia. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and samples the voices of family members and generates voices that resemble those voices. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the content of conversations. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides information about dementia and upcoming schedules. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

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

[0135] The 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.

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

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

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

[0139] Figure 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.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0142] In the 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.

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

[0144] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0146] The data processing system 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.

[0147] Each of the multiple elements described above, including the reception unit, generation unit, summarization unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives calls from people with dementia. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and samples the voices of family members and generates voices that resemble those voices. The summarization unit is implemented by the specific processing unit 290 of the data processing unit 12 and summarizes the content of the conversation. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides information about dementia and upcoming schedules. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

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

[0151] The 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.

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

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, generation unit, summarization unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives calls from people with dementia. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and samples the voices of family members and generates voices that resemble those voices. The summarization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and summarizes the content of conversations. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides information about dementia and upcoming schedules. 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A reception desk that takes calls from people with dementia, A generation unit that generates audio based on a telephone call received by the reception unit, A summarizing unit that summarizes the content of a conversation based on the audio generated by the generation unit, The system includes a providing unit that provides information based on the conversation content summarized by the summarizing unit. A system characterized by the following features. (Note 2) The generating unit is It samples the voices of family members and generates voices that resemble those voices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We research and share accurate information about dementia and answers questions that families want to know. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is We accept calls from people with dementia 24 hours a day. The system described in Appendix 1, characterized by the features described herein. (Note 5) The summary section above is, Summarize when and how many calls were made, and what was discussed, and report it to your family. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Enter personal information and recent plans, and answer simple questions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the emotions of people with dementia and adjusts how phone calls are answered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze past call history to select the optimal time to receive calls. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When answering the phone, filtering is performed based on the current living situation and interests of people with dementia. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the emotions of people with dementia and prioritizes incoming calls based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When answering phone calls, the system prioritizes calls that are highly relevant, taking into account the geographical location of the person with dementia. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When answering phone calls, the system analyzes the social media activity of people with dementia and accepts calls related to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the emotions of people with dementia and adjusts the tone and expression of the voice generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating speech, adjust the level of detail in the speech based on the importance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating speech, different speech generation algorithms are applied depending on the category of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the emotions of people with dementia and adjusts the length of the generated audio based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating speech, the priority of speech is determined based on when the conversation occurred. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During speech generation, the order of speech is adjusted based on the relevance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The summary section above is, This process estimates the emotions of people with dementia and adjusts the way summaries are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The summary section above is, When generating a summary, adjust the level of detail in the summary based on the importance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The summary section above is, When generating summaries, different summarization algorithms are applied depending on the category of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The summary section above is, Estimate the emotions of a person with dementia and adjust the length of the summary based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The summary section above is, When generating summaries, prioritize summaries based on when the conversations occurred. The system described in Appendix 1, characterized by the features described herein. (Note 24) The summary section above is, When generating summaries, the order of the summaries is adjusted based on the relevance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, This system estimates the emotions of people with dementia and adjusts the way information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, the most suitable method of information provision is selected by referring to past provision history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, customize the method of information delivery based on the current living situation of the person with dementia. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, This system estimates the emotions of people with dementia and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, the most appropriate method of information delivery will be selected, taking into account the geographical location of the person with dementia. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, we analyze the social media activity of people with dementia and propose methods for providing that information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 reception desk that takes calls from people with dementia, A generation unit that generates audio based on a telephone call received by the reception unit, A summarizing unit that summarizes the content of a conversation based on the audio generated by the generation unit, The system includes a providing unit that provides information based on the conversation content summarized by the summarizing unit. A system characterized by the following features.

2. The generating unit is It samples the voices of family members and generates voices that resemble those voices. The system according to feature 1.

3. The aforementioned supply unit is, We research and share accurate information about dementia and answers questions that families want to know. The system according to feature 1.

4. The aforementioned reception unit is We accept calls from people with dementia 24 hours a day. The system according to feature 1.

5. The summary section above is, Summarize when and how many calls were made, and what was discussed, and report it to your family. The system according to feature 1.

6. The aforementioned supply unit is, Enter personal information and recent plans, and answer simple questions. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the emotions of people with dementia and adjusts how phone calls are answered based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze past call history to select the optimal time to receive calls. The system according to feature 1.

9. The aforementioned reception unit is When answering the phone, filtering is performed based on the current living situation and interests of people with dementia. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the emotions of people with dementia and prioritizes incoming calls based on those estimated emotions. The system according to feature 1.

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