system

Abstract

The system according to the embodiment aims to provide personalized care services based on the elderly person's comments and wishes. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a generation unit. The collection unit collects utterances from elderly people. The analysis unit analyzes the utterances collected by the collection unit and generates advice. The provision unit provides the advice generated by the analysis unit to the elderly. The generation unit generates an image desired by the elderly. The provision unit provides the image generated by the generation unit to the elderly.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide personalized care services based on the elderly's voices and wishes, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized care services based on the elderly person's comments and wishes. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a generation unit. The collection unit collects utterances from the elderly. The analysis unit analyzes the utterances collected by the collection unit and generates advice. The provision unit provides the advice generated by the analysis unit to the elderly. The generation unit generates an image desired by the elderly. The provision unit provides the image generated by the generation unit to the elderly. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized care services based on the elderly person's statements and wishes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A care service system according to an embodiment of the present invention is a system that collects and analyzes statements from elderly people, provides appropriate advice, and generates and provides desired images. The care service system collects and analyzes statements from elderly people, provides appropriate advice, and generates and provides desired images, thereby providing personalized care services to elderly people. For example, the care service system collects statements from elderly people. For example, the care service system collects anxieties and worries felt by elderly people in their daily lives and provides appropriate advice and words of comfort. Next, the care service system analyzes the collected statements and generates appropriate advice. For example, the care service system provides appropriate exercise and diet advice based on the elderly's health condition and lifestyle habits. Next, the care service system provides the generated advice to the elderly. For example, the care service system provides the generated advice to the elderly to improve their quality of life. Next, the care service system generates desired images for the elderly. For example, the care service system generates photos of scenery or memories that the elderly would like to see and provides them to the elderly. Next, the care service system provides the generated images to the elderly. For example, the care service system provides the generated images to the elderly to provide visual satisfaction. This allows the nursing care service system to provide individually optimized nursing care services to the elderly, reducing the burden on caregivers. The nursing care service system can provide personalized nursing care services to the elderly by collecting and analyzing the elderly's comments, providing appropriate advice, and even generating and providing desired images. For example, this can be expected to help maintain the health and improve the quality of life of the elderly. It can also reduce the burden on caregivers.

[0029] A nursing care service system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a generation unit, and a provision unit. The collection unit collects utterances from elderly people. The utterances from elderly people include, but are not limited to, voice, text, and gestures. The collection unit collects, for example, anxieties and worries that elderly people experience in their daily lives. The collection unit can also collect utterances from elderly people in real time. For example, the collection unit collects what the elderly people say using a microphone and saves it as audio data. The collection unit can also collect text data entered by the elderly people. For example, the collection unit converts the text data entered by the elderly people into a format that is easy to analyze. The analysis unit analyzes the utterances collected by the collection unit and generates appropriate advice. The analysis is performed based on, for example, but is not limited to, an algorithm used and a purpose of the analysis. For example, the analysis unit uses natural language processing technology to analyze the utterances from elderly people and generates appropriate advice. The analysis unit can also analyze the utterances from elderly people using a machine learning algorithm. For example, the analysis unit analyzes the content and emotions of the utterances from elderly people and generates appropriate advice. The providing unit provides the elderly person with the advice generated by the analysis unit. The advice may be provided verbally, in writing, digitally, or in any other form, but is not limited to these examples. For example, the providing unit verbally conveys the generated advice to the elderly person. The providing unit may also send the generated advice to the elderly person as a text message. For example, the providing unit provides the elderly person with the generated advice through a smartphone app. The generating unit generates an image desired by the elderly person. The generation may be performed in the form of, for example, a landscape image, a family photo, or the like, but is not limited to these examples. For example, the generating unit may generate a landscape or a memorable photo that the elderly person wants to see. The generating unit may also generate an image desired by the elderly person using a generation AI. For example, the generating unit may input a prompt to the generation AI saying, "Please generate a landscape that the elderly person wants to see," and provide the generated image to the elderly person. The providing unit provides the elderly person with the image generated by the generating unit. The advice may be provided digitally, for example, but is not limited to these examples. For example, the providing unit may send the generated image to the elderly person's smartphone.The providing unit can also print the generated image and provide it to the elderly. For example, the providing unit prints the generated image using a printer and hands it over to the elderly. This allows the nursing care service system according to the embodiment to collect and analyze the elderly's comments, provide appropriate advice, and further generate and provide the desired image. This allows the nursing care service system to provide personalized nursing care services to the elderly and reduce the burden on caregivers.

[0030] The nursing care service system includes a management unit that manages the collaboration between the generation AI and the image generation AI. The management unit manages the collaboration between the generation AI and the image generation AI. Management is performed, for example, by adjusting the frequency and content of collaboration, but is not limited to such examples. For example, the management unit strengthens the collaboration between the generation AI and the image generation AI to provide more effective nursing care services. The management unit can also optimize the collaboration between the generation AI and the image generation AI to improve the quality of services. For example, the management unit adjusts the collaboration between the generation AI and the image generation AI to provide optimal services to the elderly. In this way, by managing the collaboration between the generation AI and the image generation AI, more effective nursing care services can be provided.

[0031] The collection unit can analyze the elderly person's past speech history and select a collection method. For example, the collection unit prioritizes the use of collection methods (voice, text, etc.) that the elderly person has previously preferred. The collection unit can also obtain more information from the elderly person's speech history by collecting information at specific time periods. The collection unit can also analyze the elderly person's speech history and select the most effective collection method. In this way, by analyzing the past speech history, the optimal collection method can be selected and information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the elderly person's speech history data into the generation AI and have the generation AI select the optimal collection method.

[0032] When collecting utterances, the collection unit can filter them based on the elderly person's current health condition and living situation. For example, if the elderly person's health condition is good, the collection unit can collect detailed utterances. Furthermore, if the elderly person's health condition is deteriorating, the collection unit can collect only brief utterances. Furthermore, the collection unit can adjust the content of the utterances to be collected according to the elderly person's living situation. In this way, more appropriate information can be collected by filtering utterances according to the elderly person's health condition and living situation. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the elderly person's health condition data into the generation AI and have the generation AI perform utterance filtering.

[0033] When collecting utterances, the collection unit can select a collection means according to the input method of the elderly person. For example, if the elderly person prefers voice input, the collection unit can prioritize collecting voice utterances. Furthermore, if the elderly person prefers text input, the collection unit can also prioritize collecting text utterances. Furthermore, if the elderly person prefers image input, the collection unit can also prioritize collecting image utterances. This allows information to be collected efficiently by selecting the optimal collection means according to the elderly person's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the elderly person's input method data into the generation AI and cause the generation AI to select the optimal collection means.

[0034] When collecting utterances, the collection unit can prioritize collecting highly relevant utterances by taking into account the geographical location information of the elderly person. For example, when the elderly person is in a specific location, the collection unit prioritizes collecting utterances related to that location. Furthermore, when the elderly person is traveling, the collection unit can prioritize collecting utterances related to the travel destination. Furthermore, when the elderly person is at home, the collection unit can prioritize collecting utterances related to the home. In this way, by taking the geographical location information into account, highly relevant utterances can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the geographical location information data of the elderly person to the generation AI and cause the generation AI to collect highly relevant utterances.

[0035] The collection unit can analyze the social media activity of the elderly person and collect related comments when collecting comments. For example, the collection unit collects related comments based on content posted by the elderly person on social media. The collection unit can also collect related comments by referring to the activities of the elderly person's friends on social media. The collection unit can also collect related comments based on the elderly person's check-in information on social media. In this way, related comments can be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the elderly person's social media activity data into the generation AI and cause the generation AI to collect related comments.

[0036] When collecting utterances, the collection unit can customize the collection method by reflecting the elderly's past feedback. For example, the collection unit preferentially uses a collection method that the elderly has previously preferred. The collection unit can also improve the collection method based on the elderly's past feedback. The collection unit can also adjust the collection timing by referring to the elderly's past feedback. In this way, by reflecting the past feedback, the collection method can be optimized and information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the elderly's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0037] During analysis, the analysis unit can adjust the level of detail of the advice based on the importance of the utterance. For example, the analysis unit provides detailed advice for an important utterance. The analysis unit can also provide concise advice for a less important utterance. The analysis unit can also adjust the level of detail of the advice according to the importance of the utterance. This allows advice to be provided efficiently by adjusting the level of detail of the advice based on the importance of the utterance. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input utterance data of the elderly person to a generation AI and cause the generation AI to adjust the level of detail of the advice.

[0038] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the utterance. For example, the analysis unit can apply a health-related analysis algorithm to utterances related to health. The analysis unit can also apply a lifestyle-related analysis algorithm to utterances related to lifestyle. The analysis unit can also apply an emotion analysis algorithm to utterances related to emotions. This enables more accurate analysis by applying an analysis algorithm depending on the category of the utterance. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input utterance data of elderly people into the generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the elderly person's past advice results. The analysis unit improves the accuracy of the analysis, for example, based on the results of advice the elderly person received in the past. The analysis unit can also analyze the elderly person's past advice results and select the optimal analysis method. The analysis unit can also improve the analysis algorithm by referring to the elderly person's past advice results. In this way, the accuracy of the analysis can be improved by referring to the past advice results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the elderly person's advice result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0040] During analysis, the analysis unit can determine the priority of advice based on the time when the utterance was submitted. For example, the analysis unit can prioritize providing advice for recent utterances. The analysis unit can also lower the priority of advice provided for older utterances. The analysis unit can also adjust the priority of advice depending on the time when the utterance was submitted. This allows for efficient provision of advice by determining the priority of advice based on the time when the utterance was submitted. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input utterance data of elderly people into a generation AI and have the generation AI determine the priority of advice.

[0041] During analysis, the analysis unit can adjust the order of advice based on the relevance of the utterances. For example, the analysis unit can provide advice preferentially for highly relevant utterances. The analysis unit can also provide advice later for less relevant utterances. The analysis unit can also adjust the order of advice based on the relevance of the utterances. This allows advice to be provided efficiently by adjusting the order of advice based on the relevance of the utterances. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input utterance data of the elderly person into a generation AI and cause the generation AI to adjust the order of advice.

[0042] During analysis, the analysis unit can adjust the use of technical terms in the advice according to the elderly person's level of expertise. For example, if the elderly person has technical expertise, the analysis unit can provide advice using technical terms. Furthermore, if the elderly person does not have technical expertise, the analysis unit can also provide advice in simple language. Furthermore, the analysis unit can adjust the use of technical terms in the advice according to the elderly person's level of expertise. In this way, by adjusting the use of technical terms in the advice according to the elderly person's level of expertise, it is possible to provide advice that is easier to understand. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the elderly person's level of expertise data into the generation AI and cause the generation AI to use technical terms in the advice.

[0043] The providing unit can improve the providing method by reflecting the elderly person's past feedback when providing advice. For example, the providing unit preferentially uses a providing method that the elderly person has previously preferred. The providing unit can also improve the providing method based on the elderly person's past feedback. The providing unit can also adjust the timing of providing advice by referring to the elderly person's past feedback. In this way, by reflecting the past feedback, the providing method can be optimized and advice can be provided efficiently. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the elderly person's feedback data into the generating AI and cause the generating AI to improve the providing method.

[0044] The providing unit can customize the means for providing advice based on the elderly person's current living situation when providing the advice. For example, if the elderly person is at home, the providing unit can provide advice that can be implemented at home. Furthermore, if the elderly person is out, the providing unit can also provide advice that can be implemented while out. The providing unit can also customize the means for providing advice according to the elderly person's living situation. In this way, by customizing the means for providing advice according to the elderly person's living situation, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the elderly person's living situation data into the generating AI and cause the generating AI to customize the means for providing advice.

[0045] The providing unit can adjust the content of the advice taking into account the health condition of the elderly person when providing the advice. For example, if the health condition of the elderly person is good, the providing unit can provide proactive advice. Furthermore, if the health condition of the elderly person is deteriorating, the providing unit can also provide reasonable advice. Furthermore, the providing unit can adjust the content of the advice according to the health condition of the elderly person. In this way, more appropriate advice can be provided by adjusting the content of the advice according to the health condition of the elderly person. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the health condition data of the elderly person to the generating AI and cause the generating AI to adjust the content of the advice.

[0046] The providing unit can provide advice taking into account the geographical location information of the elderly person when providing the advice. For example, if the elderly person is in a specific location, the providing unit can provide advice related to that location. Furthermore, if the elderly person is traveling, the providing unit can also provide advice related to the travel destination. Furthermore, if the elderly person is at home, the providing unit can also provide advice related to the home. In this way, highly relevant advice can be provided by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the elderly person to the generation AI and cause the generation AI to provide the advice.

[0047] At the time of providing, the providing unit can analyze the elderly person's social media activity and provide relevant advice. The providing unit can provide relevant advice based on, for example, content posted by the elderly person on social media. The providing unit can also provide relevant advice by referring to the activities of the elderly person's friends on social media. The providing unit can also provide relevant advice based on the elderly person's check-in information on social media. In this way, relevant advice can be efficiently provided by analyzing social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without using AI. For example, the providing unit can input the elderly person's social media activity data into the generation AI and cause the generation AI to provide relevant advice.

[0048] The providing unit can customize the content of advice by reflecting the elderly person's past feedback when providing the advice. For example, the providing unit can prioritize providing advice content that the elderly person has previously preferred. The providing unit can also improve the content of advice based on the elderly person's past feedback. The providing unit can also customize the content of advice by referring to the elderly person's past feedback. In this way, by reflecting past feedback, the content of advice can be optimized and provided efficiently. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the elderly person's feedback data into the generating AI and cause the generating AI to customize the content of the advice.

[0049] During generation, the generation unit can generate an optimal image by referring to the elderly person's past desired image history. For example, the generation unit generates a similar image based on an image previously desired by the elderly person. The generation unit can also analyze the elderly person's past desired image history and generate the most preferred image. The generation unit can also customize the content of the image by referring to the elderly person's past desired image history. In this way, the optimal image can be generated and provided by referring to the elderly person's past desired image history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the elderly person's desired image history data into the generation AI and cause the generation AI to generate the optimal image.

[0050] The generation unit can analyze the elderly person's current living situation and interests at the time of generation and generate an optimal image. The generation unit can, for example, generate a relevant image according to the elderly person's current living situation. The generation unit can also analyze the elderly person's interests and generate an image related to the interests. The generation unit can also customize the content of the image based on the elderly person's living situation and interests. This makes it possible to provide more appropriate images by generating images based on the elderly person's living situation and interests. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the elderly person's living situation and interest data into the generation AI and cause the generation AI to generate an optimal image.

[0051] The generation unit can improve the generation algorithm by reflecting the elderly's feedback during generation. The generation unit improves the generation algorithm based on, for example, the elderly's feedback. The generation unit can also optimize the generation algorithm by referring to the elderly's past feedback. The generation unit can also customize the generation algorithm by reflecting the elderly's feedback. In this way, by reflecting the feedback, the generation algorithm can be improved and a more appropriate image can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the elderly's feedback data into the generation AI and cause the generation AI to improve the generation algorithm.

[0052] The generation unit can generate an optimal image by taking into account the geographical location information of the elderly person at the time of generation. For example, if the elderly person is in a specific location, the generation unit generates an image related to that location. Furthermore, if the elderly person is traveling, the generation unit can generate an image related to the travel destination. Furthermore, if the elderly person is at home, the generation unit can generate an image related to the home. In this way, by taking the geographical location information into consideration, highly relevant images can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the geographical location information data of the elderly person into the generation AI and cause the generation AI to generate an optimal image.

[0053] At the time of generation, the generation unit can generate related images by analyzing the social media activity of the elderly person. The generation unit can generate related images based on, for example, content posted by the elderly person on social media. The generation unit can also generate related images by referring to the activities of the elderly person's friends on social media. The generation unit can also generate related images based on the elderly person's check-in information on social media. This makes it possible to efficiently provide related images by analyzing social media activity. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the elderly person's social media activity data into the generation AI and cause the generation AI to generate related images.

[0054] The generation unit can customize the content of the image to be generated by reflecting the elderly person's past feedback during generation. For example, the generation unit generates image content that the elderly person has previously preferred, with priority. The generation unit can also improve the content of the image based on the elderly person's past feedback. The generation unit can also customize the content of the image by referring to the elderly person's past feedback. In this way, the content of the image can be optimized and efficiently provided by reflecting the past feedback. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the elderly person's feedback data into the generation AI and cause the generation AI to customize the content of the image.

[0055] The providing unit can improve the providing method by reflecting the elderly person's past feedback when providing images. For example, the providing unit preferentially uses a providing method that the elderly person has previously preferred. The providing unit can also improve the providing method based on the elderly person's past feedback. The providing unit can also adjust the timing of providing images by referring to the elderly person's past feedback. In this way, by reflecting the past feedback, the providing method can be optimized and images can be provided efficiently. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the elderly person's feedback data into the generating AI and cause the generating AI to improve the providing method.

[0056] The providing unit can customize the image providing means based on the elderly person's current living situation at the time of providing. For example, if the elderly person is at home, the providing unit can provide images that can be executed at home. Furthermore, if the elderly person is out, the providing unit can also provide images that can be executed while out. The providing unit can also customize the image providing means according to the elderly person's living situation. In this way, by customizing the image providing means according to the elderly person's living situation, more appropriate images can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the elderly person's living situation data into the generating AI and cause the generating AI to customize the image providing means.

[0057] The providing unit can adjust the content of the image taking into consideration the health condition of the elderly person when providing the image. For example, if the health condition of the elderly person is good, the providing unit can provide a positive image. Furthermore, if the health condition of the elderly person is deteriorating, the providing unit can also provide a relaxed image. Furthermore, the providing unit can adjust the content of the image according to the health condition of the elderly person. In this way, by adjusting the content of the image according to the health condition of the elderly person, more appropriate images can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the health condition data of the elderly person to the generating AI and cause the generating AI to adjust the content of the image.

[0058] The providing unit can provide the optimal image by taking into consideration the geographical location information of the elderly person. For example, if the elderly person is in a specific location, the providing unit can provide an image related to that location. Furthermore, if the elderly person is traveling, the providing unit can also provide an image related to the travel destination. Furthermore, if the elderly person is at home, the providing unit can also provide an image related to the home. In this way, highly relevant images can be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the elderly person to the generation AI and cause the generation AI to provide the optimal image.

[0059] At the time of providing, the providing unit can analyze the elderly person's social media activity and provide related images. The providing unit can provide related images based on, for example, content posted by the elderly person on social media. The providing unit can also provide related images by referring to the activities of the elderly person's friends on social media. The providing unit can also provide related images based on the elderly person's check-in information on social media. In this way, related images can be efficiently provided by analyzing social media activity. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the elderly person's social media activity data into the generation AI and cause the generation AI to provide related images.

[0060] The providing unit can customize the content of the image by reflecting the elderly person's past feedback when providing the image. For example, the providing unit can preferentially provide image content that the elderly person has previously preferred. The providing unit can also improve the content of the image based on the elderly person's past feedback. The providing unit can also customize the content of the image by referring to the elderly person's past feedback. In this way, by reflecting the past feedback, the content of the image can be optimized and provided efficiently. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the elderly person's feedback data into the generating AI and cause the generating AI to customize the content of the image.

[0061] During management, the management unit can select the optimal collaboration method by referring to the past collaboration history between the generation AI and the image generation AI. For example, the management unit analyzes the past collaboration history between the generation AI and the image generation AI and selects the most effective collaboration method. The management unit can also select a method that improves the success rate of collaboration based on the past collaboration history. The management unit can also optimize the collaboration method by referring to the past collaboration history between the generation AI and the image generation AI. This makes it possible to select the optimal collaboration method by referring to the past collaboration history and provide services efficiently. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI or may be performed without using AI. For example, the management unit can input collaboration history data between the generation AI and the image generation AI into the generation AI and have the generation AI select the optimal collaboration method.

[0062] During management, the management unit can customize the means of collaboration based on the elderly person's current living situation. For example, the management unit adjusts the means of collaboration between the generation AI and the image generation AI according to the elderly person's current living situation. Furthermore, when the elderly person is at home, the management unit can provide a means of collaboration that can be executed at home. Furthermore, when the elderly person is out, the management unit can provide a means of collaboration that can be executed while away from home. This allows for customizing the means of collaboration according to the elderly person's living situation to provide more appropriate services. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input the elderly person's living situation data into the generation AI and have the generation AI customize the means of collaboration.

[0063] During management, the management unit can improve the collaboration method by reflecting feedback from the elderly. For example, the management unit improves the collaboration method between the generation AI and the image generation AI based on the elderly's feedback. The management unit can also optimize the collaboration method by referring to the elderly's past feedback. The management unit can also customize the collaboration method by reflecting the elderly's feedback. In this way, by reflecting the feedback, the collaboration method can be improved and more appropriate services can be provided. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input the elderly's feedback data into the generation AI and have the generation AI improve the collaboration method.

[0064] During management, the management unit can select a collaboration method taking into account the geographical location information of the elderly person. For example, if the elderly person is in a specific location, the management unit selects a collaboration method related to that location. Furthermore, if the elderly person is traveling, the management unit can select a collaboration method related to the travel destination. Furthermore, if the elderly person is at home, the management unit can select a collaboration method related to the home. In this way, by taking the geographical location information into consideration, a highly relevant collaboration method can be selected and services can be provided efficiently. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI or without AI. For example, the management unit can input the elderly person's geographical location information data into the generation AI and cause the generation AI to select the optimal collaboration method.

[0065] During management, the management unit can analyze the social media activity of the elderly person and suggest means of collaboration. For example, the management unit can suggest relevant means of collaboration based on content posted by the elderly person on social media. The management unit can also suggest relevant means of collaboration based on the activities of the elderly person's friends on social media. The management unit can also suggest relevant means of collaboration based on the elderly person's check-in information on social media. In this way, relevant means of collaboration can be efficiently suggested by analyzing social media activity. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the elderly person's social media activity data into a generation AI and have the generation AI suggest means of collaboration.

[0066] During management, the management unit can customize the collaboration method by reflecting the elderly person's past feedback. For example, the management unit can prioritize the use of collaboration methods that the elderly person has previously preferred. The management unit can also improve the collaboration method based on the elderly person's past feedback. The management unit can also customize the collaboration method by referring to the elderly person's past feedback. In this way, by reflecting past feedback, the collaboration method can be optimized and services can be provided efficiently. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input the elderly person's feedback data into the generation AI and have the generation AI customize the collaboration method.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The nursing care service system can further provide customized advice based on the hobbies and interests of the elderly. For example, the collection unit can collect information about the hobbies and interests of the elderly, and the analysis unit can suggest hobby activities and events suitable for the elderly based on that information. In addition, the provision unit can provide advice related to the elderly's hobbies, and the generation unit can generate and provide images and videos related to the hobbies. This can improve the quality of life of the elderly.

[0069] The nursing care service system can further include functions to strengthen the social connections of the elderly. For example, the collection unit can collect the elderly's communication history with friends and family, and the analysis unit can use that information to suggest communication methods and timing that are appropriate for the elderly. In addition, the provision unit can provide the elderly with advice to encourage communication with friends and family, and the generation unit can generate and provide images and messages to facilitate communication. This can reduce the elderly's sense of loneliness and strengthen their social connections.

[0070] The nursing care service system can also collect and analyze health data of the elderly and provide health management advice. For example, the collection unit can collect health data of the elderly (e.g., blood pressure, heart rate, body temperature, etc.), and the analysis unit can generate health management advice appropriate for the elderly based on that data. In addition, the provision unit can provide health management advice to the elderly, and the generation unit can generate and provide health information and graphs. This makes it possible to monitor the health status of the elderly and support appropriate health management.

[0071] The nursing care service system can also monitor the elderly's living environment and provide advice appropriate to the environment. For example, the collection unit can monitor the elderly's living environment (e.g., room temperature, humidity, lighting, etc.), and the analysis unit can generate advice on adjusting the environment to suit the elderly based on that data. In addition, the provision unit can provide advice on adjusting the environment to the elderly, and the generation unit can generate and provide information and graphs related to the environment. This makes it possible to optimize the elderly's living environment and support a comfortable life.

[0072] The care service system can also collect and analyze exercise data from elderly people and provide them with exercise advice. For example, the collection unit can collect exercise data from elderly people (e.g., number of steps, exercise time, calories burned, etc.), and the analysis unit can generate exercise advice suitable for elderly people based on that data. The provision unit can also provide exercise advice to elderly people, and the generation unit can generate and provide information and graphs related to exercise. This can support elderly people's exercise habits and promote health maintenance.

[0073] The nursing care service system can further provide customized event information based on the hobbies and interests of the elderly. For example, the collection unit can collect information about the hobbies and interests of the elderly, and the analysis unit can generate event information suitable for the elderly based on that information. The provision unit can also provide the event information to the elderly, and the generation unit can generate and provide images and videos related to the event. This can improve the quality of life of the elderly and promote social activities.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The collection unit collects what the elderly say. The elderly's speech includes voice, text, gestures, etc. The collection unit collects the anxieties and worries that the elderly feel in their daily lives, and can also collect this information in real time. For example, the collection unit can use a microphone to collect what the elderly say and save it as audio data. It can also collect text data entered by the elderly and convert it into a format that is easy to analyze. Step 2: The analysis unit analyzes the comments collected by the collection unit and generates appropriate advice. The analysis is performed based on the algorithm used and the purpose of the analysis. For example, the analysis unit uses natural language processing technology or machine learning algorithms to analyze the content and emotions of the elderly person's comments and generates appropriate advice. Step 3: The providing unit provides the advice generated by the analysis unit to the elderly. The advice may be provided orally, in writing, digitally, or in any other format. For example, the providing unit may provide the generated advice to the elderly orally, by sending it as a text message, or through a smartphone app. Step 4: The generator generates an image desired by the elderly person. The image can be in the form of a landscape image, a family photo, or other similar format. For example, the generator uses a generation AI to generate an image desired by the elderly person, and generates the image by inputting a prompt such as, "Please generate a landscape that the elderly person would like to see." Step 5: The providing unit provides the image generated by the generating unit to the elderly person. The image is provided in a digital or printed format. For example, the providing unit sends the generated image to the elderly person's smartphone or prints it out on a printer and hands it over to the elderly person.

[0076] (Example 2) A care service system according to an embodiment of the present invention is a system that collects and analyzes statements from elderly people, provides appropriate advice, and generates and provides desired images. The care service system collects and analyzes statements from elderly people, provides appropriate advice, and generates and provides desired images, thereby providing personalized care services to elderly people. For example, the care service system collects statements from elderly people. For example, the care service system collects anxieties and worries felt by elderly people in their daily lives and provides appropriate advice and words of comfort. Next, the care service system analyzes the collected statements and generates appropriate advice. For example, the care service system provides appropriate exercise and diet advice based on the elderly's health condition and lifestyle habits. Next, the care service system provides the generated advice to the elderly. For example, the care service system provides the generated advice to the elderly to improve their quality of life. Next, the care service system generates desired images for the elderly. For example, the care service system generates photos of scenery or memories that the elderly would like to see and provides them to the elderly. Next, the care service system provides the generated images to the elderly. For example, the care service system provides the generated images to the elderly to provide visual satisfaction. This allows the nursing care service system to provide individually optimized nursing care services to the elderly, reducing the burden on caregivers. The nursing care service system can provide personalized nursing care services to the elderly by collecting and analyzing the elderly's comments, providing appropriate advice, and even generating and providing desired images. For example, this can be expected to help maintain the health and improve the quality of life of the elderly. It can also reduce the burden on caregivers.

[0077] A nursing care service system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a generation unit, and a provision unit. The collection unit collects utterances from elderly people. The utterances from elderly people include, but are not limited to, voice, text, and gestures. The collection unit collects, for example, anxieties and worries that elderly people experience in their daily lives. The collection unit can also collect utterances from elderly people in real time. For example, the collection unit collects what the elderly people say using a microphone and saves it as audio data. The collection unit can also collect text data entered by the elderly people. For example, the collection unit converts the text data entered by the elderly people into a format that is easy to analyze. The analysis unit analyzes the utterances collected by the collection unit and generates appropriate advice. The analysis is performed based on, for example, but is not limited to, an algorithm used and a purpose of the analysis. For example, the analysis unit uses natural language processing technology to analyze the utterances from elderly people and generates appropriate advice. The analysis unit can also analyze the utterances from elderly people using a machine learning algorithm. For example, the analysis unit analyzes the content and emotions of the utterances from elderly people and generates appropriate advice. The providing unit provides the elderly person with the advice generated by the analysis unit. The advice may be provided verbally, in writing, digitally, or in any other form, but is not limited to these examples. For example, the providing unit verbally conveys the generated advice to the elderly person. The providing unit may also send the generated advice to the elderly person as a text message. For example, the providing unit provides the elderly person with the generated advice through a smartphone app. The generating unit generates an image desired by the elderly person. The generation may be performed in the form of, for example, a landscape image, a family photo, or the like, but is not limited to these examples. For example, the generating unit may generate a landscape or a memorable photo that the elderly person wants to see. The generating unit may also generate an image desired by the elderly person using a generation AI. For example, the generating unit may input a prompt to the generation AI saying, "Please generate a landscape that the elderly person wants to see," and provide the generated image to the elderly person. The providing unit provides the elderly person with the image generated by the generating unit. The advice may be provided digitally, for example, but is not limited to these examples. For example, the providing unit may send the generated image to the elderly person's smartphone.The providing unit can also print the generated image and provide it to the elderly. For example, the providing unit prints the generated image using a printer and hands it over to the elderly. This allows the nursing care service system according to the embodiment to collect and analyze the elderly's comments, provide appropriate advice, and further generate and provide the desired image. This allows the nursing care service system to provide personalized nursing care services to the elderly and reduce the burden on caregivers.

[0078] The nursing care service system includes a management unit that manages the collaboration between the generation AI and the image generation AI. The management unit manages the collaboration between the generation AI and the image generation AI. Management is performed, for example, by adjusting the frequency and content of collaboration, but is not limited to such examples. For example, the management unit strengthens the collaboration between the generation AI and the image generation AI to provide more effective nursing care services. The management unit can also optimize the collaboration between the generation AI and the image generation AI to improve the quality of services. For example, the management unit adjusts the collaboration between the generation AI and the image generation AI to provide optimal services to the elderly. In this way, by managing the collaboration between the generation AI and the image generation AI, more effective nursing care services can be provided.

[0079] The collection unit can estimate the elderly person's emotions and adjust the timing of utterance collection based on the estimated elderly person's emotions. For example, when the elderly person is relaxed, the collection unit can collect utterances frequently to obtain detailed information. Furthermore, when the elderly person is stressed, the collection unit can reduce the collection of utterances to reduce the burden on the elderly person. Furthermore, when the elderly person is excited, the collection unit can temporarily suspend utterance collection and resume it after the elderly person has calmed down. This allows for more appropriate information to be collected by adjusting the timing of utterance collection according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input facial expression data of the elderly person into the generation AI and have the generation AI perform emotion estimation.

[0080] The collection unit can analyze the elderly person's past speech history and select a collection method. For example, the collection unit prioritizes the use of collection methods (voice, text, etc.) that the elderly person has previously preferred. The collection unit can also obtain more information from the elderly person's speech history by collecting information at specific time periods. The collection unit can also analyze the elderly person's speech history and select the most effective collection method. In this way, by analyzing the past speech history, the optimal collection method can be selected and information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the elderly person's speech history data into the generation AI and have the generation AI select the optimal collection method.

[0081] When collecting utterances, the collection unit can filter them based on the elderly person's current health condition and living situation. For example, if the elderly person's health condition is good, the collection unit can collect detailed utterances. Furthermore, if the elderly person's health condition is deteriorating, the collection unit can collect only brief utterances. Furthermore, the collection unit can adjust the content of the utterances to be collected according to the elderly person's living situation. In this way, more appropriate information can be collected by filtering utterances according to the elderly person's health condition and living situation. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the elderly person's health condition data into the generation AI and have the generation AI perform utterance filtering.

[0082] When collecting utterances, the collection unit can select a collection means according to the input method of the elderly person. For example, if the elderly person prefers voice input, the collection unit can prioritize collecting voice utterances. Furthermore, if the elderly person prefers text input, the collection unit can also prioritize collecting text utterances. Furthermore, if the elderly person prefers image input, the collection unit can also prioritize collecting image utterances. This allows information to be collected efficiently by selecting the optimal collection means according to the elderly person's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the elderly person's input method data into the generation AI and cause the generation AI to select the optimal collection means.

[0083] The collection unit can estimate the elderly person's emotions and determine the priority of utterances to be collected based on the estimated elderly person's emotions. For example, if the elderly person is feeling anxious, the collection unit can prioritize collecting reassuring utterances. Furthermore, if the elderly person is happy, the collection unit can prioritize collecting positive utterances. Furthermore, if the elderly person is sad, the collection unit can prioritize collecting comforting words. Thus, by prioritizing utterances based on the elderly person's emotions, important information can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input facial expression data of the elderly person into the generation AI and have the generation AI perform emotion estimation.

[0084] When collecting utterances, the collection unit can prioritize collecting highly relevant utterances by taking into account the geographical location information of the elderly person. For example, when the elderly person is in a specific location, the collection unit prioritizes collecting utterances related to that location. Furthermore, when the elderly person is traveling, the collection unit can prioritize collecting utterances related to the travel destination. Furthermore, when the elderly person is at home, the collection unit can prioritize collecting utterances related to the home. In this way, by taking the geographical location information into account, highly relevant utterances can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the geographical location information data of the elderly person to the generation AI and cause the generation AI to collect highly relevant utterances.

[0085] The collection unit can analyze the social media activity of the elderly person and collect related comments when collecting comments. For example, the collection unit collects related comments based on content posted by the elderly person on social media. The collection unit can also collect related comments by referring to the activities of the elderly person's friends on social media. The collection unit can also collect related comments based on the elderly person's check-in information on social media. In this way, related comments can be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the elderly person's social media activity data into the generation AI and cause the generation AI to collect related comments.

[0086] When collecting utterances, the collection unit can customize the collection method by reflecting the elderly's past feedback. For example, the collection unit preferentially uses a collection method that the elderly has previously preferred. The collection unit can also improve the collection method based on the elderly's past feedback. The collection unit can also adjust the collection timing by referring to the elderly's past feedback. In this way, by reflecting the past feedback, the collection method can be optimized and information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the elderly's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0087] The analysis unit can estimate the elderly person's emotions and adjust the way advice is expressed based on the estimated elderly person's emotions. For example, if the elderly person is feeling anxious, the analysis unit can use gentle expressions to reassure them. If the elderly person is happy, the analysis unit can also use positive expressions. If the elderly person is sad, the analysis unit can also use comforting words. This allows for adjusting the way advice is expressed based on the elderly person's emotions, making it possible to provide more appropriate advice. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input facial expression data of the elderly person into the generation AI and cause the generation AI to estimate emotions.

[0088] During analysis, the analysis unit can adjust the level of detail of the advice based on the importance of the utterance. For example, the analysis unit provides detailed advice for an important utterance. The analysis unit can also provide concise advice for a less important utterance. The analysis unit can also adjust the level of detail of the advice according to the importance of the utterance. This allows advice to be provided efficiently by adjusting the level of detail of the advice based on the importance of the utterance. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input utterance data of the elderly person to a generation AI and cause the generation AI to adjust the level of detail of the advice.

[0089] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the utterance. For example, the analysis unit can apply a health-related analysis algorithm to utterances related to health. The analysis unit can also apply a lifestyle-related analysis algorithm to utterances related to lifestyle. The analysis unit can also apply an emotion analysis algorithm to utterances related to emotions. This enables more accurate analysis by applying an analysis algorithm depending on the category of the utterance. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input utterance data of elderly people into the generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0090] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the elderly person's past advice results. The analysis unit improves the accuracy of the analysis, for example, based on the results of advice the elderly person received in the past. The analysis unit can also analyze the elderly person's past advice results and select the optimal analysis method. The analysis unit can also improve the analysis algorithm by referring to the elderly person's past advice results. In this way, the accuracy of the analysis can be improved by referring to the past advice results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the elderly person's advice result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0091] The analysis unit can estimate the elderly person's emotions and adjust the length of the advice based on the estimated elderly person's emotions. For example, if the elderly person is in a hurry, the analysis unit can provide short, to-the-point advice. If the elderly person is relaxed, the analysis unit can provide longer advice with detailed explanations. If the elderly person is excited, the analysis unit can provide advice with visually stimulating effects. By adjusting the length of the advice based on the elderly person's emotions, more appropriate advice can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input facial expression data of the elderly person into the generation AI and cause the generation AI to estimate the emotion.

[0092] During analysis, the analysis unit can determine the priority of advice based on the time when the utterance was submitted. For example, the analysis unit can prioritize providing advice for recent utterances. The analysis unit can also lower the priority of advice provided for older utterances. The analysis unit can also adjust the priority of advice depending on the time when the utterance was submitted. This allows for efficient provision of advice by determining the priority of advice based on the time when the utterance was submitted. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input utterance data of elderly people into a generation AI and have the generation AI determine the priority of advice.

[0093] During analysis, the analysis unit can adjust the order of advice based on the relevance of the utterances. For example, the analysis unit can provide advice preferentially for highly relevant utterances. The analysis unit can also provide advice later for less relevant utterances. The analysis unit can also adjust the order of advice based on the relevance of the utterances. This allows advice to be provided efficiently by adjusting the order of advice based on the relevance of the utterances. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input utterance data of the elderly person into a generation AI and cause the generation AI to adjust the order of advice.

[0094] During analysis, the analysis unit can adjust the use of technical terms in the advice according to the elderly person's level of expertise. For example, if the elderly person has technical expertise, the analysis unit can provide advice using technical terms. Furthermore, if the elderly person does not have technical expertise, the analysis unit can also provide advice in simple language. Furthermore, the analysis unit can adjust the use of technical terms in the advice according to the elderly person's level of expertise. In this way, by adjusting the use of technical terms in the advice according to the elderly person's level of expertise, it is possible to provide advice that is easier to understand. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the elderly person's level of expertise data into the generation AI and cause the generation AI to use technical terms in the advice.

[0095] The providing unit can estimate the elderly person's emotions and adjust the way in which advice is provided based on the estimated elderly person's emotions. For example, if the elderly person is feeling anxious, the providing unit can provide advice using gentle words to reassure the elderly person. Furthermore, if the elderly person is happy, the providing unit can also provide advice using positive words. Furthermore, if the elderly person is sad, the providing unit can also provide advice using comforting words. By adjusting the way in which advice is provided based on the elderly person's emotions, more appropriate advice can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input facial expression data of the elderly person into the generation AI and cause the generation AI to estimate the emotions.

[0096] The providing unit can improve the providing method by reflecting the elderly person's past feedback when providing advice. For example, the providing unit preferentially uses a providing method that the elderly person has previously preferred. The providing unit can also improve the providing method based on the elderly person's past feedback. The providing unit can also adjust the timing of providing advice by referring to the elderly person's past feedback. In this way, by reflecting the past feedback, the providing method can be optimized and advice can be provided efficiently. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the elderly person's feedback data into the generating AI and cause the generating AI to improve the providing method.

[0097] The providing unit can customize the means for providing advice based on the elderly person's current living situation when providing the advice. For example, if the elderly person is at home, the providing unit can provide advice that can be implemented at home. Furthermore, if the elderly person is out, the providing unit can also provide advice that can be implemented while out. The providing unit can also customize the means for providing advice according to the elderly person's living situation. In this way, by customizing the means for providing advice according to the elderly person's living situation, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the elderly person's living situation data into the generating AI and cause the generating AI to customize the means for providing advice.

[0098] The providing unit can adjust the content of the advice taking into account the health condition of the elderly person when providing the advice. For example, if the health condition of the elderly person is good, the providing unit can provide proactive advice. Furthermore, if the health condition of the elderly person is deteriorating, the providing unit can also provide reasonable advice. Furthermore, the providing unit can adjust the content of the advice according to the health condition of the elderly person. In this way, more appropriate advice can be provided by adjusting the content of the advice according to the health condition of the elderly person. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the health condition data of the elderly person to the generating AI and cause the generating AI to adjust the content of the advice.

[0099] The providing unit can estimate the elderly person's emotions and determine the priority of advice based on the estimated elderly person's emotions. For example, if the elderly person is feeling anxious, the providing unit can prioritize providing reassuring advice. Furthermore, if the elderly person is happy, the providing unit can prioritize providing positive advice. Furthermore, if the elderly person is sad, the providing unit can prioritize providing comforting advice. In this way, by determining the priority of advice based on the elderly person's emotions, important advice can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input facial expression data of the elderly person to the generation AI and cause the generation AI to estimate the emotions.

[0100] The providing unit can provide advice taking into account the geographical location information of the elderly person when providing the advice. For example, if the elderly person is in a specific location, the providing unit can provide advice related to that location. Furthermore, if the elderly person is traveling, the providing unit can also provide advice related to the travel destination. Furthermore, if the elderly person is at home, the providing unit can also provide advice related to the home. In this way, highly relevant advice can be provided by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the elderly person to the generation AI and cause the generation AI to provide the advice.

[0101] At the time of providing, the providing unit can analyze the elderly person's social media activity and provide relevant advice. The providing unit can provide relevant advice based on, for example, content posted by the elderly person on social media. The providing unit can also provide relevant advice by referring to the activities of the elderly person's friends on social media. The providing unit can also provide relevant advice based on the elderly person's check-in information on social media. In this way, relevant advice can be efficiently provided by analyzing social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without using AI. For example, the providing unit can input the elderly person's social media activity data into the generation AI and cause the generation AI to provide relevant advice.

[0102] The providing unit can customize the content of advice by reflecting the elderly person's past feedback when providing the advice. For example, the providing unit can prioritize providing advice content that the elderly person has previously preferred. The providing unit can also improve the content of advice based on the elderly person's past feedback. The providing unit can also customize the content of advice by referring to the elderly person's past feedback. In this way, by reflecting past feedback, the content of advice can be optimized and provided efficiently. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the elderly person's feedback data into the generating AI and cause the generating AI to customize the content of the advice.

[0103] The generation unit can estimate the elderly person's emotions and adjust the content of the generated image based on the estimated elderly person's emotions. For example, if the elderly person is relaxed, the generation unit can generate an image of a calm landscape. If the elderly person is excited, the generation unit can also generate an image of a lively landscape. If the elderly person is sad, the generation unit can also generate a comforting image. By adjusting the content of the image based on the elderly person's emotions, more appropriate images can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be 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-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input facial expression data of the elderly person into the generation AI and cause the generation AI to estimate the emotions.

[0104] During generation, the generation unit can generate an optimal image by referring to the elderly person's past desired image history. For example, the generation unit generates a similar image based on an image previously desired by the elderly person. The generation unit can also analyze the elderly person's past desired image history and generate the most preferred image. The generation unit can also customize the content of the image by referring to the elderly person's past desired image history. In this way, the optimal image can be generated and provided by referring to the elderly person's past desired image history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the elderly person's desired image history data into the generation AI and cause the generation AI to generate the optimal image.

[0105] The generation unit can analyze the elderly person's current living situation and interests at the time of generation and generate an optimal image. The generation unit can, for example, generate a relevant image according to the elderly person's current living situation. The generation unit can also analyze the elderly person's interests and generate an image related to the interests. The generation unit can also customize the content of the image based on the elderly person's living situation and interests. This makes it possible to provide more appropriate images by generating images based on the elderly person's living situation and interests. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the elderly person's living situation and interest data into the generation AI and cause the generation AI to generate an optimal image.

[0106] The generation unit can improve the generation algorithm by reflecting the elderly's feedback during generation. The generation unit improves the generation algorithm based on, for example, the elderly's feedback. The generation unit can also optimize the generation algorithm by referring to the elderly's past feedback. The generation unit can also customize the generation algorithm by reflecting the elderly's feedback. In this way, by reflecting the feedback, the generation algorithm can be improved and a more appropriate image can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the elderly's feedback data into the generation AI and cause the generation AI to improve the generation algorithm.

[0107] The generation unit can estimate the elderly person's emotions and determine the priority of images to be generated based on the estimated elderly person's emotions. For example, if the elderly person is feeling anxious, the generation unit can prioritize generating images that reassure them. Furthermore, if the elderly person is happy, the generation unit can prioritize generating positive images. Furthermore, if the elderly person is sad, the generation unit can prioritize generating comforting images. Thus, by determining the priority of images based on the elderly person's emotions, important images can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input facial expression data of the elderly person into the generation AI and cause the generation AI to estimate the emotions.

[0108] The generation unit can generate an optimal image by taking into account the geographical location information of the elderly person at the time of generation. For example, if the elderly person is in a specific location, the generation unit generates an image related to that location. Furthermore, if the elderly person is traveling, the generation unit can generate an image related to the travel destination. Furthermore, if the elderly person is at home, the generation unit can generate an image related to the home. In this way, by taking the geographical location information into consideration, highly relevant images can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the geographical location information data of the elderly person into the generation AI and cause the generation AI to generate an optimal image.

[0109] At the time of generation, the generation unit can generate related images by analyzing the social media activity of the elderly person. The generation unit can generate related images based on, for example, content posted by the elderly person on social media. The generation unit can also generate related images by referring to the activities of the elderly person's friends on social media. The generation unit can also generate related images based on the elderly person's check-in information on social media. This makes it possible to efficiently provide related images by analyzing social media activity. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the elderly person's social media activity data into the generation AI and cause the generation AI to generate related images.

[0110] The generation unit can customize the content of the image to be generated by reflecting the elderly person's past feedback during generation. For example, the generation unit generates image content that the elderly person has previously preferred, with priority. The generation unit can also improve the content of the image based on the elderly person's past feedback. The generation unit can also customize the content of the image by referring to the elderly person's past feedback. In this way, the content of the image can be optimized and efficiently provided by reflecting the past feedback. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the elderly person's feedback data into the generation AI and cause the generation AI to customize the content of the image.

[0111] The providing unit can estimate the elderly person's emotions and adjust the image providing method based on the estimated elderly person's emotions. For example, if the elderly person is feeling anxious, the providing unit can provide images with kind words to reassure them. Furthermore, if the elderly person is happy, the providing unit can provide images with positive words. Furthermore, if the elderly person is sad, the providing unit can provide images with comforting words. By adjusting the image providing method based on the elderly person's emotions, more appropriate images can be provided. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be 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-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input facial expression data of the elderly person into the generation AI and cause the generation AI to estimate the emotion.

[0112] The providing unit can improve the providing method by reflecting the elderly person's past feedback when providing images. For example, the providing unit preferentially uses a providing method that the elderly person has previously preferred. The providing unit can also improve the providing method based on the elderly person's past feedback. The providing unit can also adjust the timing of providing images by referring to the elderly person's past feedback. In this way, by reflecting the past feedback, the providing method can be optimized and images can be provided efficiently. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the elderly person's feedback data into the generating AI and cause the generating AI to improve the providing method.

[0113] The providing unit can customize the image providing means based on the elderly person's current living situation at the time of providing. For example, if the elderly person is at home, the providing unit can provide images that can be executed at home. Furthermore, if the elderly person is out, the providing unit can also provide images that can be executed while out. The providing unit can also customize the image providing means according to the elderly person's living situation. In this way, by customizing the image providing means according to the elderly person's living situation, more appropriate images can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the elderly person's living situation data into the generating AI and cause the generating AI to customize the image providing means.

[0114] The providing unit can adjust the content of the image taking into consideration the health condition of the elderly person when providing the image. For example, if the health condition of the elderly person is good, the providing unit can provide a positive image. Furthermore, if the health condition of the elderly person is deteriorating, the providing unit can also provide a relaxed image. Furthermore, the providing unit can adjust the content of the image according to the health condition of the elderly person. In this way, by adjusting the content of the image according to the health condition of the elderly person, more appropriate images can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the health condition data of the elderly person to the generating AI and cause the generating AI to adjust the content of the image.

[0115] The providing unit can estimate the elderly person's emotions and determine the priority of images based on the estimated elderly person's emotions. For example, if the elderly person is feeling anxious, the providing unit can prioritize providing reassuring images. Furthermore, if the elderly person is happy, the providing unit can prioritize providing positive images. Furthermore, if the elderly person is sad, the providing unit can prioritize providing comforting images. Thus, by determining the priority of images based on the elderly person's emotions, important images can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can input facial expression data of the elderly person into the generation AI and cause the generation AI to estimate the emotions.

[0116] The providing unit can provide the optimal image by taking into consideration the geographical location information of the elderly person. For example, if the elderly person is in a specific location, the providing unit can provide an image related to that location. Furthermore, if the elderly person is traveling, the providing unit can also provide an image related to the travel destination. Furthermore, if the elderly person is at home, the providing unit can also provide an image related to the home. In this way, highly relevant images can be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the elderly person to the generation AI and cause the generation AI to provide the optimal image.

[0117] At the time of providing, the providing unit can analyze the elderly person's social media activity and provide related images. The providing unit can provide related images based on, for example, content posted by the elderly person on social media. The providing unit can also provide related images by referring to the activities of the elderly person's friends on social media. The providing unit can also provide related images based on the elderly person's check-in information on social media. In this way, related images can be efficiently provided by analyzing social media activity. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the elderly person's social media activity data into the generation AI and cause the generation AI to provide related images.

[0118] The providing unit can customize the content of the image by reflecting the elderly person's past feedback when providing the image. For example, the providing unit can preferentially provide image content that the elderly person has previously preferred. The providing unit can also improve the content of the image based on the elderly person's past feedback. The providing unit can also customize the content of the image by referring to the elderly person's past feedback. In this way, by reflecting the past feedback, the content of the image can be optimized and provided efficiently. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the elderly person's feedback data into the generating AI and cause the generating AI to customize the content of the image.

[0119] The management unit can estimate the elderly person's emotions and adjust the collaboration between the generation AI and the image generation AI based on the estimated elderly person's emotions. For example, if the elderly person is feeling anxious, the management unit can strengthen the collaboration between the generation AI and the image generation AI to provide a reassuring service. Furthermore, if the elderly person is happy, the management unit can optimize the collaboration between the generation AI and the image generation AI to provide a positive service. Furthermore, if the elderly person is sad, the management unit can adjust the collaboration between the generation AI and the image generation AI to provide a comforting service. This allows for more appropriate services to be provided by adjusting the collaboration based on the elderly person's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the management unit may be performed using AI, or without AI. For example, the management unit can input facial expression data of the elderly person into the generation AI and have the generation AI perform emotion estimation.

[0120] During management, the management unit can select the optimal collaboration method by referring to the past collaboration history between the generation AI and the image generation AI. For example, the management unit analyzes the past collaboration history between the generation AI and the image generation AI and selects the most effective collaboration method. The management unit can also select a method that improves the success rate of collaboration based on the past collaboration history. The management unit can also optimize the collaboration method by referring to the past collaboration history between the generation AI and the image generation AI. This makes it possible to select the optimal collaboration method by referring to the past collaboration history and provide services efficiently. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI or may be performed without using AI. For example, the management unit can input collaboration history data between the generation AI and the image generation AI into the generation AI and have the generation AI select the optimal collaboration method.

[0121] During management, the management unit can customize the means of collaboration based on the elderly person's current living situation. For example, the management unit adjusts the means of collaboration between the generation AI and the image generation AI according to the elderly person's current living situation. Furthermore, when the elderly person is at home, the management unit can provide a means of collaboration that can be executed at home. Furthermore, when the elderly person is out, the management unit can provide a means of collaboration that can be executed while away from home. This allows for customizing the means of collaboration according to the elderly person's living situation to provide more appropriate services. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input the elderly person's living situation data into the generation AI and have the generation AI customize the means of collaboration.

[0122] During management, the management unit can improve the collaboration method by reflecting feedback from the elderly. For example, the management unit improves the collaboration method between the generation AI and the image generation AI based on the elderly's feedback. The management unit can also optimize the collaboration method by referring to the elderly's past feedback. The management unit can also customize the collaboration method by reflecting the elderly's feedback. In this way, by reflecting the feedback, the collaboration method can be improved and more appropriate services can be provided. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input the elderly's feedback data into the generation AI and have the generation AI improve the collaboration method.

[0123] The management unit can estimate the elderly person's emotions and determine the priority of collaboration based on the estimated elderly person's emotions. For example, if the elderly person is feeling anxious, the management unit can prioritize collaboration to reassure them. Furthermore, if the elderly person is happy, the management unit can prioritize positive collaboration. Furthermore, if the elderly person is sad, the management unit can prioritize collaboration to comfort them. Thus, by determining collaboration priorities based on the elderly person's emotions, important collaborations can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the management unit can be performed using AI, for example, or without AI. For example, the management unit can input facial expression data of the elderly person into the generation AI and cause the generation AI to estimate emotions.

[0124] During management, the management unit can select a collaboration method taking into account the geographical location information of the elderly person. For example, if the elderly person is in a specific location, the management unit selects a collaboration method related to that location. Furthermore, if the elderly person is traveling, the management unit can select a collaboration method related to the travel destination. Furthermore, if the elderly person is at home, the management unit can select a collaboration method related to the home. In this way, by taking the geographical location information into consideration, a highly relevant collaboration method can be selected and services can be provided efficiently. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI or without AI. For example, the management unit can input the elderly person's geographical location information data into the generation AI and cause the generation AI to select the optimal collaboration method.

[0125] During management, the management unit can analyze the social media activity of the elderly person and suggest means of collaboration. For example, the management unit can suggest relevant means of collaboration based on content posted by the elderly person on social media. The management unit can also suggest relevant means of collaboration based on the activities of the elderly person's friends on social media. The management unit can also suggest relevant means of collaboration based on the elderly person's check-in information on social media. In this way, relevant means of collaboration can be efficiently suggested by analyzing social media activity. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the elderly person's social media activity data into a generation AI and have the generation AI suggest means of collaboration.

[0126] During management, the management unit can customize the collaboration method by reflecting the elderly person's past feedback. For example, the management unit can prioritize the use of collaboration methods that the elderly person has previously preferred. The management unit can also improve the collaboration method based on the elderly person's past feedback. The management unit can also customize the collaboration method by referring to the elderly person's past feedback. In this way, by reflecting past feedback, the collaboration method can be optimized and services can be provided efficiently. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input the elderly person's feedback data into the generation AI and have the generation AI customize the collaboration method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, generation unit, and management unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects utterances of the elderly person using the microphone 38B or camera 42 of the smart device 14 and transmits them to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected utterances and generates appropriate advice. The provision unit, for example, by the output device 40 of the smart device 14, provides the generated advice to the elderly person. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates an image desired by the elderly person. The management unit, realized, for example, by the specific processing unit 290 of the data processing device 12, manages the cooperation between the generation AI and the image generation AI. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, generation unit, and management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects utterances of the elderly person using the microphone 238 and camera 42 of the smart glasses 214 and transmits them to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected utterances and generates appropriate advice. The provision unit, for example, by the speaker 240 of the smart glasses 214, provides the generated advice to the elderly person. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates an image desired by the elderly person. The management unit, realized, for example, by the specific processing unit 290 of the data processing device 12, manages the cooperation between the generation AI and the image generation AI. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, generation unit, and management unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects utterances of the elderly person using the microphone 238 and camera 42 of the headset-type terminal 314 and transmits them to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected utterances and generates appropriate advice. The provision unit provides the generated advice to the elderly person using, for example, the speaker 240 of the headset-type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an image desired by the elderly person. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and manages the cooperation between the generation AI and the image generation AI. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, generation unit, and management unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects utterances of the elderly person using the microphone 238 and camera 42 of the robot 414 and transmits them to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected utterances and generates appropriate advice. The provision unit provides the generated advice to the elderly person using, for example, the speaker 240 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an image desired by the elderly person. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and manages cooperation between the generation AI and the image generation AI.

[0127] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0128] The nursing care service system can further provide customized advice based on the hobbies and interests of the elderly. For example, the collection unit can collect information about the hobbies and interests of the elderly, and the analysis unit can suggest hobby activities and events suitable for the elderly based on that information. In addition, the provision unit can provide advice related to the elderly's hobbies, and the generation unit can generate and provide images and videos related to the hobbies. This can improve the quality of life of the elderly.

[0129] The nursing care service system can further include functions to strengthen the social connections of the elderly. For example, the collection unit can collect the elderly's communication history with friends and family, and the analysis unit can use that information to suggest communication methods and timing that are appropriate for the elderly. In addition, the provision unit can provide the elderly with advice to encourage communication with friends and family, and the generation unit can generate and provide images and messages to facilitate communication. This can reduce the elderly's sense of loneliness and strengthen their social connections.

[0130] The nursing care service system can further estimate the emotions of the elderly person and suggest relaxation methods based on the estimated emotions. For example, the collection unit can estimate the emotions of the elderly person, and the analysis unit can suggest relaxation methods suitable for the elderly person (e.g., deep breathing, meditation, listening to music, etc.) based on that information. In addition, the provision unit can provide the elderly person with advice on how to practice the relaxation method, and the generation unit can generate and provide images and music suitable for relaxation. This can reduce stress for the elderly person and maintain their physical and mental health.

[0131] The nursing care service system can also collect and analyze health data of the elderly and provide health management advice. For example, the collection unit can collect health data of the elderly (e.g., blood pressure, heart rate, body temperature, etc.), and the analysis unit can generate health management advice appropriate for the elderly based on that data. In addition, the provision unit can provide health management advice to the elderly, and the generation unit can generate and provide health information and graphs. This makes it possible to monitor the health status of the elderly and support appropriate health management.

[0132] The nursing care service system can further estimate the emotions of the elderly and provide entertainment content based on the estimated emotions. For example, the collection unit can estimate the emotions of the elderly, and the analysis unit can suggest entertainment content (e.g., movies, music, games, etc.) suitable for the elderly based on that information. The provision unit can provide entertainment content to the elderly, and the generation unit can generate and provide customized content according to the emotions of the elderly. This can improve the mood of the elderly and provide enjoyment.

[0133] The nursing care service system can also monitor the elderly's living environment and provide advice appropriate to the environment. For example, the collection unit can monitor the elderly's living environment (e.g., room temperature, humidity, lighting, etc.), and the analysis unit can generate advice on adjusting the environment to suit the elderly based on that data. In addition, the provision unit can provide advice on adjusting the environment to the elderly, and the generation unit can generate and provide information and graphs related to the environment. This makes it possible to optimize the elderly's living environment and support a comfortable life.

[0134] The nursing care service system can further estimate the emotions of the elderly person and make meal suggestions based on the estimated emotions. For example, the collection unit can estimate the emotions of the elderly person, and the analysis unit can suggest meal menus suitable for the elderly person based on that information. In addition, the provision unit can provide meal suggestions to the elderly person, and the generation unit can generate and provide recipes and images related to meals. This makes it possible to suggest meals that suit the emotions of the elderly person and support a healthy diet.

[0135] The care service system can also collect and analyze exercise data from elderly people and provide them with exercise advice. For example, the collection unit can collect exercise data from elderly people (e.g., number of steps, exercise time, calories burned, etc.), and the analysis unit can generate exercise advice suitable for elderly people based on that data. The provision unit can also provide exercise advice to elderly people, and the generation unit can generate and provide information and graphs related to exercise. This can support elderly people's exercise habits and promote health maintenance.

[0136] The nursing care service system can further estimate the emotions of the elderly person and provide sleep advice based on the estimated emotions. For example, the collection unit can estimate the emotions of the elderly person, and the analysis unit can generate sleep advice suitable for the elderly person based on that information. In addition, the provision unit can provide sleep advice to the elderly person, and the generation unit can generate and provide information related to sleep and music with a relaxing effect. This can improve the quality of sleep of the elderly person and support a healthy lifestyle.

[0137] The nursing care service system can further provide customized event information based on the hobbies and interests of the elderly. For example, the collection unit can collect information about the hobbies and interests of the elderly, and the analysis unit can generate event information suitable for the elderly based on that information. The provision unit can also provide the event information to the elderly, and the generation unit can generate and provide images and videos related to the event. This can improve the quality of life of the elderly and promote social activities.

[0138] The processing flow of the second embodiment will be briefly explained below.

[0139] Step 1: The collection unit collects what the elderly say. The elderly's speech includes voice, text, gestures, etc. The collection unit collects the anxieties and worries that the elderly feel in their daily lives, and can also collect this information in real time. For example, the collection unit can use a microphone to collect what the elderly say and save it as audio data. It can also collect text data entered by the elderly and convert it into a format that is easy to analyze. Step 2: The analysis unit analyzes the comments collected by the collection unit and generates appropriate advice. The analysis is performed based on the algorithm used and the purpose of the analysis. For example, the analysis unit uses natural language processing technology or machine learning algorithms to analyze the content and emotions of the elderly person's comments and generates appropriate advice. Step 3: The providing unit provides the advice generated by the analysis unit to the elderly. The advice may be provided orally, in writing, digitally, or in any other format. For example, the providing unit may provide the generated advice to the elderly orally, by sending it as a text message, or through a smartphone app. Step 4: The generator generates an image desired by the elderly person. The image can be in the form of a landscape image, a family photo, or other format. For example, the generator uses a generation AI to generate an image desired by the elderly person, and generates the image by inputting a prompt such as, "Please generate a landscape that the elderly person would like to see." Step 5: The providing unit provides the image generated by the generating unit to the elderly person. The image is provided in a digital or printed format. For example, the providing unit sends the generated image to the elderly person's smartphone or prints it out on a printer and hands it over to the elderly person.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0145] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0161] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0167] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0177] 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.

[0178] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0179] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0180] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0181] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0182] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0183] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0184] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0185] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0186] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0187] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0188] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0189] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0190] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0191] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0194] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0195] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0196] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0197] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0198] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0199] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0200] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0201] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0202] 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.

[0203] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0204] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0205] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0206] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0207] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0208] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0209] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0210] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0211] [Explanation of symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects comments from elderly people; an analysis unit that analyzes the comments collected by the collection unit and generates advice; a providing unit that provides the advice generated by the analysis unit to the elderly; a generation unit that generates an image desired by the elderly person; a providing unit that provides the image generated by the generating unit to an elderly person; Equipped with A system characterized by:

2. Equipped with a management unit that manages the collaboration between the generation AI and the image generation AI 2. The system of claim 1.

3. The collecting unit Estimate the emotions of the elderly and adjust the timing of collecting utterances based on the estimated emotions of the elderly.

2. The system of claim 1.

4. The collecting unit Analyze the elderly person's past speech history and select a collection method 2. The system of claim 1.

5. The collecting unit When collecting comments, filtering is performed based on the elderly person's current health and living situation.

2. The system of claim 1.

6. The collecting unit When collecting utterances, select the collection method according to the elderly's input method.

2. The system of claim 1.

7. The collecting unit Estimate the emotions of the elderly and prioritize the utterances to be collected based on the estimated emotions of the elderly.

2. The system of claim 1.

8. The collecting unit When collecting utterances, the system prioritizes collecting relevant utterances by taking into account the elderly person's geographic location information.

2. The system of claim 1.

9. The collecting unit When collecting comments, analyze the social media activity of seniors and collect relevant comments.

2. The system of claim 1.

Images