System

Abstract

An object of a system according to an embodiment is to realize a secure and safe environment by smoothing communication between residents in a housing complex.SOLUTION: A system includes an input unit, an analysis unit, and a proposal unit. The input unit inputs information from the resident. The analysis unit analyzes the information input by the input unit. The proposal unit makes a proposal based on the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not allow smooth communication between residents in apartment complexes, posing challenges in creating a safe and secure environment.

[0005] The system according to the embodiment aims to facilitate communication between residents in an apartment complex and to realize a safe and secure environment. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, and a proposal unit. The input unit inputs information from a resident. The analysis unit analyzes the information input by the input unit. The proposal unit makes a proposal based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can facilitate communication between residents in an apartment complex and realize a safe and secure environment. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​intermediary app according to an embodiment of the present invention is a system that inputs, analyzes, and makes suggestions from residents. The AI ​​intermediary app allows residents to share information and seek advice anonymously through a smartphone app. AI analyzes information such as residents' pet sounds, specialized skills, and elderly residents' anxieties, and makes suggestions to promote communication between residents. For example, if a resident finds their pet's barking too loud, the AI ​​intermediary app can alert other residents by inputting that information. Residents with specialized skills can also share their skills with other residents. The AI ​​intermediary app then analyzes the information input by residents using AI. For example, it can suggest pet training methods to a resident who finds their pet's barking too loud. It can also suggest ways for a resident with specialized skills to share their skills with other residents. Furthermore, the AI ​​intermediary app makes suggestions to promote mutual assistance among residents. For example, if an elderly person is feeling anxious, it can provide support to other residents to alleviate that anxiety. This allows the AI ​​intermediary app to promote mutual assistance among residents, creating a safe and comfortable living environment. This allows the AI ​​intermediary app to efficiently input, analyze, and make suggestions about residents. For example, residents can input information such as pet sounds, special skills, or elderly people's concerns, and the AI ​​can analyze it and make appropriate suggestions, facilitating smooth communication between residents. This maximizes residents' happiness.

[0029] The AI ​​intermediary app according to the embodiment includes an input unit, an analysis unit, and a suggestion unit. The input unit inputs information from the resident. The information from the resident includes, but is not limited to, text, audio, and images. The input unit allows the resident to input information such as pet sounds, special skills, and elderly anxiety via a smartphone app. The input unit also allows the resident to anonymously consult with the help of the helper. The analysis unit uses AI to analyze the information input by the input unit. The analysis may be performed using, but is not limited to, methods such as data mining, natural language processing, and image analysis. For example, the analysis unit may analyze the sounds of a pet and provide information to alleviate the resident's dissatisfaction. The analysis unit may also detect elderly anxiety and provide information to provide appropriate support. The suggestion unit makes suggestions based on the information analyzed by the analysis unit. The suggestions may include, but are not limited to, methods for training pets, methods for sharing special skills with other residents, and hosting cooking classes. For example, the suggestion unit can suggest pet training methods to a resident who finds their pet's barking too noisy. The suggestion unit can also suggest ways for a resident with a special skill to share that skill with other residents. This allows the AI ​​intermediary app according to the embodiment to efficiently input, analyze, and suggest information about residents. For example, residents can input information such as the sounds of their pets, their special skills, or the anxieties of elderly people, and the AI ​​can analyze this information and make appropriate suggestions, facilitating smooth communication between residents. This maximizes the happiness of residents.

[0030] The analysis unit can analyze pet sounds. Examples of pet sounds include, but are not limited to, dog barks and cat meows. The analysis unit analyzes pet sounds using audio analysis technology. For example, the analysis unit can analyze the frequency and volume of a dog's bark to identify the cause of the bark. The analysis unit can also analyze the pattern of a cat's meow to interpret the meaning of the meow. The analysis unit can also analyze pet sounds and provide information to reduce resident dissatisfaction. For example, the analysis unit can suggest pet training methods to residents who find their pet sounds too loud. In this way, analyzing pet sounds can reduce resident dissatisfaction. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input audio data of a pet's bark into a generation AI and have the generation AI analyze the pet sound.

[0031] The analysis unit can detect anxiety in the elderly. Examples of anxiety in the elderly include, but are not limited to, health concerns, loneliness, and inconveniences in daily life. The analysis unit can detect anxiety in the elderly using, for example, voice analysis technology. For example, the analysis unit can analyze the tone and speed of the elderly's voice to detect signs of anxiety. The analysis unit can also analyze the elderly's facial expressions to detect signs of anxiety. The analysis unit can also detect anxiety in the elderly and provide information for providing appropriate support. For example, if the elderly is feeling anxious, the analysis unit can suggest support to relieve the anxiety. In this way, by detecting anxiety in the elderly, appropriate support can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the elderly's voice data and facial expression data into the generation AI and cause the generation AI to detect anxiety.

[0032] The suggestion unit can suggest a pet training method. Examples of pet training methods include, but are not limited to, training methods and behavior modification methods. The suggestion unit can suggest, for example, a pet training method. For example, the suggestion unit can suggest a basic command training method as a dog training method. The suggestion unit can also suggest a toilet training method as a cat training method. By suggesting a pet training method, the suggestion unit can reduce the resident's dissatisfaction. For example, the suggestion unit can suggest a pet training method to a resident who finds the barking of their pet too loud. In this way, by suggesting a pet training method, the resident's dissatisfaction can be reduced. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information on a pet training method to a generation AI and cause the generation AI to execute the proposed training method.

[0033] The suggestion unit can suggest a method for providing a specialized skill to other residents. Specialized skills include, but are not limited to, cooking, handicrafts, and technical support. For example, the suggestion unit can suggest a method for providing a specialized skill to other residents. For example, the suggestion unit can suggest to a resident who is good at cooking that they hold a cooking class. Furthermore, the suggestion unit can suggest to a resident who is good at handicrafts that they hold a handicraft class. Furthermore, by suggesting a method for providing a specialized skill to other residents, the suggestion unit can encourage mutual assistance among residents. For example, the suggestion unit can suggest to a resident who is good at technical support that they provide technical support to other residents. In this way, by suggesting a method for providing a specialized skill to other residents, it is possible to encourage mutual assistance among residents. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the specialized skill to a generation AI and cause the generation AI to execute a suggestion of a skill provision method.

[0034] The suggestion unit can suggest holding a cooking class. Cooking classes include, but are not limited to, Japanese cuisine, Western cuisine, and Chinese cuisine. The suggestion unit can suggest holding a cooking class, for example. For example, the suggestion unit can suggest holding a Japanese cuisine cooking class. The suggestion unit can also suggest holding a Western cuisine cooking class. By suggesting holding a cooking class, the suggestion unit can promote interaction between residents. For example, the suggestion unit can suggest holding a Chinese cuisine cooking class. In this way, by suggesting holding a cooking class, it is possible to promote interaction between residents. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about a cooking class into the generation AI and suggest holding a cooking class.

[0035] The input unit can analyze the resident's past input history and select the optimal input method. Optimal input methods include, but are not limited to, voice input, text input, and image input. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the resident has frequently used in the past. The input unit can also predict and suggest an input method to be used during a specific time period based on the resident's past input history. The input unit can also automatically complete related input fields based on information previously entered by the resident. This makes it possible to provide the optimal input method by analyzing the resident's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the resident's past input history data into a generation AI and have the generation AI select the optimal input method.

[0036] When inputting information, the input unit can filter the information based on the resident's current living situation and areas of interest. Examples of living situation include, but are not limited to, family composition and health status. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, if the resident has a pet, the input unit can prioritize displaying information input related to the pet. Furthermore, if the resident is elderly, the input unit can also emphasize information input items for elderly people. Furthermore, if the resident has a specific hobby, the input unit can prioritize displaying information input related to that hobby. In this way, by filtering information based on the resident's living situation and areas of interest, it is possible to provide highly relevant information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input data on the resident's living situation and areas of interest to the generation AI and cause the generation AI to filter the information.

[0037] When inputting information, the input unit can select the optimal input means depending on the resident's input method (voice, text, image, etc.). Input methods include, but are not limited to, voice input, text input, and image input. For example, if the resident prefers voice input, the input unit can prioritize voice input. Also, if the resident prefers text input, the input unit can prioritize text input. Also, if the resident prefers image input, the input unit can prioritize image input. This allows for smooth information input by providing the optimal input means depending on the resident's input method. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input data on the resident's input method into the generation AI and cause the generation AI to select the optimal input means.

[0038] When inputting information, the input unit can prioritize inputting highly relevant information by taking into account the resident's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, if the resident lives in a specific area, the input unit can prioritize inputting information related to that area. Furthermore, if the resident is traveling, the input unit can prioritize inputting information related to the travel destination. Furthermore, if the resident is planning to move, the input unit can prioritize inputting information related to the new residence. In this way, highly relevant information can be provided by taking the resident's geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input data on the resident's geographical location information to the generation AI and cause the generation AI to input highly relevant information.

[0039] The input unit can analyze the resident's social media activity and input relevant information when inputting information. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. The input unit can, for example, suggest relevant input items based on information shared by the resident on social media. The input unit can also analyze the resident's social media activity and automatically input relevant information. The input unit can also input relevant information based on the activity of the resident's friends on social media. In this way, relevant information can be provided by analyzing the resident's social media activity. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input data on the resident's social media activity to the generation AI and cause the generation AI to input relevant information.

[0040] When inputting information, the input unit can customize the input method by reflecting the resident's past feedback. Past feedback includes, but is not limited to, survey results, comments, etc. The input unit can improve the input method, for example, based on feedback provided by the resident in the past. The input unit can also suggest the optimal input means based on the resident's past feedback. The input unit can also customize the input interface by reflecting the resident's feedback. In this way, the optimal input method can be provided by reflecting the resident's past feedback. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the resident's past feedback data into the generation AI and have the generation AI customize the input method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The importance of the information includes, but is not limited to, for example, impact and urgency. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. Information categories include, but are not limited to, text data and image data. For example, the analysis unit can apply a voice analysis algorithm to a pet's cry. The analysis unit can also apply an emotion analysis algorithm to the anxiety of elderly people. The analysis unit can also apply a skill matching algorithm to specialized skills. This enables highly accurate analysis by applying different analysis algorithms depending on the category of information. 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 information category data to the generation AI and have the generation AI apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the resident's past analysis results. Past analysis results include, but are not limited to, past data sets and analysis reports. For example, the analysis unit adjusts the analysis algorithm based on the resident's past analysis results. The analysis unit can also extract patterns for improving the analysis accuracy from the resident's past analysis results. The analysis unit can also improve the analysis accuracy by referring to the resident's past analysis results. In this way, the analysis accuracy is improved by referring to the resident's past analysis results. Some or all of the above-described 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 resident's past analysis result data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The time of submission of information includes, but is not limited to, for example, the submission date and the submission time. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of information that was submitted earlier. The analysis unit can also determine the priority of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. 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 information submission time data to the generation AI and have the generation AI determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. Examples of the relevance of information include, but are not limited to, common keywords and related topics. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of information. This enables efficient analysis by adjusting the order of analysis based on the relevance of information. 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 information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the resident's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the resident has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Alternatively, if the resident does not have technical expertise, the analysis unit can provide analysis results that avoid technical terminology. The analysis unit can also adjust the use of technical terminology in the analysis according to the resident's level of expertise. This allows for the provision of analysis results that are easy to understand by adjusting the use of technical terminology in the analysis according to the resident's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the resident's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0047] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the information. The level of detail of the proposal includes, but is not limited to, a detailed explanation, a concise explanation, and the like. For example, the suggestion unit makes a detailed proposal for information with high importance. The suggestion unit can also make a simplified proposal for information with low importance. The suggestion unit can also determine the priority of the proposal according to the importance of the information. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of information. Examples of suggestion algorithms include, but are not limited to, recommendation algorithms and optimization algorithms. For example, the suggestion unit can apply an algorithm that suggests training methods to a pet's cries. The suggestion unit can also apply an algorithm that suggests support methods to address the anxiety of elderly people. The suggestion unit can also apply an algorithm that suggests skill provision methods to specialized skills. By applying different suggestion algorithms depending on the category of information, highly accurate suggestions can be made. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input information category data into a generation AI and cause the generation AI to apply a suggestion algorithm.

[0049] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the resident's past proposal results. Past proposal results include, but are not limited to, past proposal history, feedback, etc. The suggestion unit, for example, adjusts the proposal algorithm based on the resident's past proposal results. The suggestion unit can also extract patterns for improving the accuracy of the proposal from the resident's past proposal results. The suggestion unit can also improve the accuracy of the proposal by referring to the resident's past proposal results. In this way, the accuracy of the proposal is improved by referring to the resident's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the resident's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0050] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the information. The time of submission of the information includes, but is not limited to, for example, the submission date and the submission time. For example, the suggestion unit can prioritize the proposal for the most recent information. The suggestion unit can also postpone the proposal for information that was submitted earlier. The suggestion unit can also determine the priority of the proposal based on the time of submission. This enables efficient proposals by determining the priority of the proposal based on the time of submission of the information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information submission time data into the generation AI and cause the generation AI to determine the priority of the proposals.

[0051] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the information. Examples of the relevance of the information include, but are not limited to, common keywords and related topics. For example, the suggestion unit prioritizes proposals for highly relevant information. The suggestion unit can also postpone proposals for less relevant information. The suggestion unit can also adjust the order of proposals based on the relevance of the information. This enables efficient proposals by adjusting the order of proposals based on the relevance of the information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input information relevance data to a generation AI and cause the generation AI to adjust the order of proposals.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the resident's level of expertise. Examples of expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the resident has specialized knowledge, the suggestion unit may make a proposal that uses a lot of technical terminology. Alternatively, if the resident does not have specialized knowledge, the suggestion unit may make a proposal that avoids technical terminology. The suggestion unit can also adjust the use of technical terminology in the proposal depending on the resident's level of expertise. This allows for the provision of easy-to-understand proposals by adjusting the use of technical terminology in the proposal depending on the resident's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the resident's level of expertise data into a generation AI and cause the generation AI to use technical terminology.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analysis unit can analyze the resident's health data and make appropriate suggestions based on their health condition. For example, it can analyze the resident's heart rate and sleep data, and if their health condition worsens, it can suggest that they visit a medical institution. The analysis unit can also analyze the resident's exercise data and suggest an appropriate exercise program if it detects a lack of exercise. Furthermore, the analysis unit can analyze the resident's dietary data and suggest a balanced meal menu if their nutritional balance is unbalanced. This makes it possible to support the resident's health by comprehensively analyzing their health condition and making appropriate suggestions.

[0055] The suggestion unit can suggest community events based on the hobbies and interests of residents. For example, if a resident is interested in gardening, it can suggest holding a gardening workshop. If a resident is interested in music, it can suggest holding a music event or concert. Furthermore, if a resident is interested in cooking, it can suggest holding a cooking class or cooking contest. In this way, by suggesting community events based on the hobbies and interests of residents, it is possible to promote interaction between residents and increase a sense of unity in the community.

[0056] The analysis unit can analyze the resident's purchasing history and make suggestions based on purchasing patterns. For example, by analyzing the products that the resident frequently purchases, if the products are on sale, the analysis unit can suggest sale information. Also, if the resident prefers to purchase products from a specific brand, the analysis unit can suggest new product information from that brand. Furthermore, if the resident frequently purchases health foods, the analysis unit can suggest health information and recipes. In this way, by analyzing the resident's purchasing history, suggestions can be made based on purchasing patterns, improving the resident's purchasing experience.

[0057] The analysis unit can analyze residents' social media activities and provide relevant information. For example, it can provide relevant news and event information based on information shared by residents on social media. It can also analyze the activities of accounts that residents follow on social media and suggest related content. It can also analyze the activities of groups that residents participate in on social media and suggest related community events. In this way, by analyzing residents' social media activities, it is possible to provide relevant information and make suggestions based on the residents' interests and concerns.

[0058] The analysis unit can analyze the resident's lifestyle rhythm and make suggestions based on that lifestyle rhythm. For example, it can analyze the resident's sleep pattern and suggest appropriate sleep times and ways to improve the sleep environment. It can also analyze the resident's eating pattern and suggest balanced meal menus and meal timings. It can also analyze the resident's exercise pattern and suggest appropriate exercise programs and exercise timings. In this way, by analyzing the resident's lifestyle rhythm, it is possible to make suggestions to support a healthy lifestyle.

[0059] The analysis unit can analyze the resident's past behavioral data and make suggestions based on the behavioral patterns. For example, it can analyze places the resident has frequently visited in the past and suggest events and services related to those places. It can also analyze data on events the resident has previously attended and suggest similar events. It can also analyze data on products the resident has previously purchased and suggest related products. In this way, by analyzing the resident's past behavioral data, it is possible to make suggestions based on behavioral patterns and provide information that matches the resident's interests and concerns.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The input unit inputs information from residents. Information from residents includes text, audio, and images. For example, residents can input information such as the sounds of their pets, their special skills, and the concerns of elderly people through a smartphone app. Residents can also provide advice anonymously. Step 2: The analysis unit uses AI to analyze the information entered by the input unit. The analysis is performed using methods such as data mining, natural language processing, and image analysis. For example, it can analyze the cries of pets and provide information to alleviate residents' dissatisfaction. It can also detect anxiety in elderly people and provide information to provide appropriate support. Step 3: The suggestion unit makes suggestions based on the information analyzed by the analysis unit. Suggestions include how to train a pet, how to offer a special skill to other residents, and holding cooking classes. For example, if a resident finds their pet's barking too noisy, the suggestion unit can suggest ways to train the pet. It can also suggest ways for a resident with a special skill to offer that skill to other residents.

[0062] (Example 2) The AI ​​intermediary app according to an embodiment of the present invention is a system that inputs, analyzes, and makes suggestions from residents. The AI ​​intermediary app allows residents to share information and seek advice anonymously through a smartphone app. AI analyzes information such as residents' pet sounds, specialized skills, and elderly residents' anxieties, and makes suggestions to promote communication between residents. For example, if a resident finds their pet's barking too loud, the AI ​​intermediary app can alert other residents by inputting that information. Residents with specialized skills can also share their skills with other residents. The AI ​​intermediary app then analyzes the information input by residents using AI. For example, it can suggest pet training methods to a resident who finds their pet's barking too loud. It can also suggest ways for a resident with specialized skills to share their skills with other residents. Furthermore, the AI ​​intermediary app makes suggestions to promote mutual assistance among residents. For example, if an elderly person is feeling anxious, it can provide support to other residents to alleviate that anxiety. This allows the AI ​​intermediary app to promote mutual assistance among residents, creating a safe and comfortable living environment. This allows the AI ​​intermediary app to efficiently input, analyze, and make suggestions about residents. For example, residents can input information such as pet sounds, special skills, or elderly people's concerns, and the AI ​​can analyze it and make appropriate suggestions, facilitating smooth communication between residents. This maximizes residents' happiness.

[0063] The AI ​​intermediary app according to the embodiment includes an input unit, an analysis unit, and a suggestion unit. The input unit inputs information from the resident. The information from the resident includes, but is not limited to, text, audio, and images. The input unit allows the resident to input information such as pet sounds, special skills, and elderly anxiety via a smartphone app. The input unit also allows the resident to anonymously consult with the help of the helper. The analysis unit uses AI to analyze the information input by the input unit. The analysis may be performed using, but is not limited to, methods such as data mining, natural language processing, and image analysis. For example, the analysis unit may analyze the sounds of a pet and provide information to alleviate the resident's dissatisfaction. The analysis unit may also detect elderly anxiety and provide information to provide appropriate support. The suggestion unit makes suggestions based on the information analyzed by the analysis unit. The suggestions may include, but are not limited to, methods for training pets, methods for sharing special skills with other residents, and hosting cooking classes. For example, the suggestion unit can suggest pet training methods to a resident who finds their pet's barking too noisy. The suggestion unit can also suggest ways for a resident with a special skill to share that skill with other residents. This allows the AI ​​intermediary app according to the embodiment to efficiently input, analyze, and suggest information about residents. For example, residents can input information such as the sounds of their pets, their special skills, or the anxieties of elderly people, and the AI ​​can analyze this information and make appropriate suggestions, facilitating smooth communication between residents. This maximizes the happiness of residents.

[0064] The analysis unit can analyze pet sounds. Examples of pet sounds include, but are not limited to, dog barks and cat meows. The analysis unit analyzes pet sounds using audio analysis technology. For example, the analysis unit can analyze the frequency and volume of a dog's bark to identify the cause of the bark. The analysis unit can also analyze the pattern of a cat's meow to interpret the meaning of the meow. The analysis unit can also analyze pet sounds and provide information to reduce resident dissatisfaction. For example, the analysis unit can suggest pet training methods to residents who find their pet sounds too loud. In this way, analyzing pet sounds can reduce resident dissatisfaction. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input audio data of a pet's bark into a generation AI and have the generation AI analyze the pet sound.

[0065] The analysis unit can detect anxiety in the elderly. Examples of anxiety in the elderly include, but are not limited to, health concerns, loneliness, and inconveniences in daily life. The analysis unit can detect anxiety in the elderly using, for example, voice analysis technology. For example, the analysis unit can analyze the tone and speed of the elderly's voice to detect signs of anxiety. The analysis unit can also analyze the elderly's facial expressions to detect signs of anxiety. The analysis unit can also detect anxiety in the elderly and provide information for providing appropriate support. For example, if the elderly is feeling anxious, the analysis unit can suggest support to relieve the anxiety. In this way, by detecting anxiety in the elderly, appropriate support can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the elderly's voice data and facial expression data into the generation AI and cause the generation AI to detect anxiety.

[0066] The suggestion unit can suggest a pet training method. Examples of pet training methods include, but are not limited to, training methods and behavior modification methods. The suggestion unit can suggest, for example, a pet training method. For example, the suggestion unit can suggest a basic command training method as a dog training method. The suggestion unit can also suggest a toilet training method as a cat training method. By suggesting a pet training method, the suggestion unit can reduce the resident's dissatisfaction. For example, the suggestion unit can suggest a pet training method to a resident who finds the barking of their pet too loud. In this way, by suggesting a pet training method, the resident's dissatisfaction can be reduced. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information on a pet training method to a generation AI and cause the generation AI to execute the proposed training method.

[0067] The suggestion unit can suggest a method for providing a specialized skill to other residents. Specialized skills include, but are not limited to, cooking, handicrafts, and technical support. For example, the suggestion unit can suggest a method for providing a specialized skill to other residents. For example, the suggestion unit can suggest to a resident who is good at cooking that they hold a cooking class. Furthermore, the suggestion unit can suggest to a resident who is good at handicrafts that they hold a handicraft class. Furthermore, by suggesting a method for providing a specialized skill to other residents, the suggestion unit can encourage mutual assistance among residents. For example, the suggestion unit can suggest to a resident who is good at technical support that they provide technical support to other residents. In this way, by suggesting a method for providing a specialized skill to other residents, it is possible to encourage mutual assistance among residents. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the specialized skill to a generation AI and cause the generation AI to execute a suggestion of a skill provision method.

[0068] The suggestion unit can suggest holding a cooking class. Cooking classes include, but are not limited to, Japanese cuisine, Western cuisine, and Chinese cuisine. The suggestion unit can suggest holding a cooking class, for example. For example, the suggestion unit can suggest holding a Japanese cuisine cooking class. The suggestion unit can also suggest holding a Western cuisine cooking class. By suggesting holding a cooking class, the suggestion unit can promote interaction between residents. For example, the suggestion unit can suggest holding a Chinese cuisine cooking class. In this way, by suggesting holding a cooking class, it is possible to promote interaction between residents. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about a cooking class into the generation AI and suggest holding a cooking class.

[0069] The input unit can estimate the resident's emotions and adjust the timing of information input based on the estimated resident's emotions. Examples of resident emotions include, but are not limited to, stress, relaxation, and hurry. For example, if the resident is feeling stressed, the input unit can delay the input timing to allow the resident to input in a relaxed state. Furthermore, if the resident is relaxed, the input unit can prompt the resident to input information immediately, thereby realizing smooth information sharing. Furthermore, if the resident is in a hurry, the input unit can provide a simplified input form to allow the resident to input information quickly. This can reduce stress by adjusting the timing of information input according to the resident's emotions. The emotion estimation is realized 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 input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the resident's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0070] The input unit can analyze the resident's past input history and select the optimal input method. Optimal input methods include, but are not limited to, voice input, text input, and image input. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the resident has frequently used in the past. The input unit can also predict and suggest an input method to be used during a specific time period based on the resident's past input history. The input unit can also automatically complete related input fields based on information previously entered by the resident. This makes it possible to provide the optimal input method by analyzing the resident's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the resident's past input history data into a generation AI and have the generation AI select the optimal input method.

[0071] When inputting information, the input unit can filter the information based on the resident's current living situation and areas of interest. Examples of living situation include, but are not limited to, family composition and health status. Examples of areas of interest include, but are not limited to, hobbies and topics of interest. For example, if the resident has a pet, the input unit can prioritize displaying information input related to the pet. Furthermore, if the resident is elderly, the input unit can also emphasize information input items for elderly people. Furthermore, if the resident has a specific hobby, the input unit can prioritize displaying information input related to that hobby. In this way, by filtering information based on the resident's living situation and areas of interest, it is possible to provide highly relevant information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input data on the resident's living situation and areas of interest to the generation AI and cause the generation AI to filter the information.

[0072] When inputting information, the input unit can select the optimal input means depending on the resident's input method (voice, text, image, etc.). Input methods include, but are not limited to, voice input, text input, and image input. For example, if the resident prefers voice input, the input unit can prioritize voice input. Also, if the resident prefers text input, the input unit can prioritize text input. Also, if the resident prefers image input, the input unit can prioritize image input. This allows for smooth information input by providing the optimal input means depending on the resident's input method. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input data on the resident's input method into the generation AI and cause the generation AI to select the optimal input means.

[0073] The input unit can estimate the resident's emotions and determine the priority of information to be input based on the estimated resident's emotions. Information priority can include, but is not limited to, importance and urgency. For example, if the resident is stressed, the input unit postpones less important information. Furthermore, if the resident is relaxed, the input unit can prioritize input of more important information. Furthermore, if the resident is in a hurry, the input unit can prioritize input of the most important information first. Thus, by determining the priority of information according to the resident's emotions, important information can be input 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 input unit can be performed using, for example, AI, or without AI. For example, the input unit can input the resident's emotion data into the generation AI and have the generation AI determine the priority of the information.

[0074] When inputting information, the input unit can prioritize inputting highly relevant information by taking into account the resident's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, if the resident lives in a specific area, the input unit can prioritize inputting information related to that area. Furthermore, if the resident is traveling, the input unit can prioritize inputting information related to the travel destination. Furthermore, if the resident is planning to move, the input unit can prioritize inputting information related to the new residence. In this way, highly relevant information can be provided by taking the resident's geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input data on the resident's geographical location information to the generation AI and cause the generation AI to input highly relevant information.

[0075] The input unit can analyze the resident's social media activity and input relevant information when inputting information. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. The input unit can, for example, suggest relevant input items based on information shared by the resident on social media. The input unit can also analyze the resident's social media activity and automatically input relevant information. The input unit can also input relevant information based on the activity of the resident's friends on social media. In this way, relevant information can be provided by analyzing the resident's social media activity. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input data on the resident's social media activity to the generation AI and cause the generation AI to input relevant information.

[0076] When inputting information, the input unit can customize the input method by reflecting the resident's past feedback. Past feedback includes, but is not limited to, survey results, comments, etc. The input unit can improve the input method, for example, based on feedback provided by the resident in the past. The input unit can also suggest the optimal input means based on the resident's past feedback. The input unit can also customize the input interface by reflecting the resident's feedback. In this way, the optimal input method can be provided by reflecting the resident's past feedback. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the resident's past feedback data into the generation AI and have the generation AI customize the input method.

[0077] The analysis unit can estimate the resident's emotions and adjust the presentation method of the analysis based on the estimated resident's emotions. Examples of presentation methods for the analysis include, but are not limited to, graph display and text display. For example, if the resident is stressed, the analysis unit can provide simple, highly visible analysis results. For example, if the resident is relaxed, the analysis unit can provide detailed analysis results. For example, if the resident is in a hurry, the analysis unit can provide analysis results that focus on the main points. This allows stress to be reduced by adjusting the presentation method of the analysis according to the resident'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 these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input the resident's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The importance of the information includes, but is not limited to, for example, impact and urgency. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. Information categories include, but are not limited to, text data and image data. For example, the analysis unit can apply a voice analysis algorithm to a pet's cry. The analysis unit can also apply an emotion analysis algorithm to the anxiety of elderly people. The analysis unit can also apply a skill matching algorithm to specialized skills. This enables highly accurate analysis by applying different analysis algorithms depending on the category of information. 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 information category data to the generation AI and have the generation AI apply the analysis algorithm.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the resident's past analysis results. Past analysis results include, but are not limited to, past data sets and analysis reports. For example, the analysis unit adjusts the analysis algorithm based on the resident's past analysis results. The analysis unit can also extract patterns for improving the analysis accuracy from the resident's past analysis results. The analysis unit can also improve the analysis accuracy by referring to the resident's past analysis results. In this way, the analysis accuracy is improved by referring to the resident's past analysis results. Some or all of the above-described 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 resident's past analysis result data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0081] The analysis unit can estimate the resident's emotions and adjust the length of the analysis based on the estimated resident's emotions. Examples of the length of the analysis include, but are not limited to, the analysis time and data volume. For example, if the resident is stressed, the analysis unit can provide a short and concise analysis result. If the resident is relaxed, the analysis unit can also provide a detailed analysis result. If the resident is in a hurry, the analysis unit can also provide a concise analysis result. This allows stress to be reduced by adjusting the length of the analysis according to the resident'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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the resident's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0082] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The time of submission of information includes, but is not limited to, for example, the submission date and the submission time. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of information that was submitted earlier. The analysis unit can also determine the priority of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. 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 information submission time data to the generation AI and have the generation AI determine the priority of analysis.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. Examples of the relevance of information include, but are not limited to, common keywords and related topics. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of information. This enables efficient analysis by adjusting the order of analysis based on the relevance of information. 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 information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0084] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the resident's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the resident has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Alternatively, if the resident does not have technical expertise, the analysis unit can provide analysis results that avoid technical terminology. The analysis unit can also adjust the use of technical terminology in the analysis according to the resident's level of expertise. This allows for the provision of analysis results that are easy to understand by adjusting the use of technical terminology in the analysis according to the resident's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the resident's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0085] The suggestion unit can estimate the resident's emotions and adjust the way the suggestions are expressed based on the estimated resident's emotions. Examples of ways to express the suggestions include, but are not limited to, text suggestions and visual suggestions. For example, if the resident is feeling stressed, the suggestion unit can provide simple, highly visible suggestions. For example, if the resident is relaxed, the suggestion unit can provide detailed suggestions. For example, if the resident is in a hurry, the suggestion unit can provide suggestions that focus on the main points. This allows the resident to reduce stress by adjusting the way the suggestions are expressed based on their 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 these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI. For example, the suggestion unit can input the resident's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0086] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the information. The level of detail of the proposal includes, but is not limited to, a detailed explanation, a concise explanation, and the like. For example, the suggestion unit makes a detailed proposal for information with high importance. The suggestion unit can also make a simplified proposal for information with low importance. The suggestion unit can also determine the priority of the proposal according to the importance of the information. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0087] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of information. Examples of suggestion algorithms include, but are not limited to, recommendation algorithms and optimization algorithms. For example, the suggestion unit can apply an algorithm that suggests training methods to a pet's cries. The suggestion unit can also apply an algorithm that suggests support methods to address the anxiety of elderly people. The suggestion unit can also apply an algorithm that suggests skill provision methods to specialized skills. By applying different suggestion algorithms depending on the category of information, highly accurate suggestions can be made. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input information category data into a generation AI and cause the generation AI to apply a suggestion algorithm.

[0088] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the resident's past proposal results. Past proposal results include, but are not limited to, past proposal history, feedback, etc. The suggestion unit, for example, adjusts the proposal algorithm based on the resident's past proposal results. The suggestion unit can also extract patterns for improving the accuracy of the proposal from the resident's past proposal results. The suggestion unit can also improve the accuracy of the proposal by referring to the resident's past proposal results. In this way, the accuracy of the proposal is improved by referring to the resident's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the resident's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0089] The suggestion unit can estimate the resident's emotions and adjust the length of the suggestions based on the estimated resident's emotions. Examples of the length of the suggestions include, but are not limited to, the amount of text in the suggestions and the duration of the suggestions. For example, if the resident is stressed, the suggestion unit can provide short, concise suggestions. If the resident is relaxed, the suggestion unit can also provide detailed suggestions. If the resident is in a hurry, the suggestion unit can also provide concise suggestions. This allows stress to be reduced by adjusting the length of the suggestions according to the resident'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 these examples. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the resident's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0090] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the information. The time of submission of the information includes, but is not limited to, for example, the submission date and the submission time. For example, the suggestion unit can prioritize the proposal for the most recent information. The suggestion unit can also postpone the proposal for information that was submitted earlier. The suggestion unit can also determine the priority of the proposal based on the time of submission. This enables efficient proposals by determining the priority of the proposal based on the time of submission of the information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information submission time data into the generation AI and cause the generation AI to determine the priority of the proposals.

[0091] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the information. Examples of the relevance of the information include, but are not limited to, common keywords and related topics. For example, the suggestion unit prioritizes proposals for highly relevant information. The suggestion unit can also postpone proposals for less relevant information. The suggestion unit can also adjust the order of proposals based on the relevance of the information. This enables efficient proposals by adjusting the order of proposals based on the relevance of the information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input information relevance data to a generation AI and cause the generation AI to adjust the order of proposals.

[0092] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the resident's level of expertise. Examples of expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the resident has specialized knowledge, the suggestion unit may make a proposal that uses a lot of technical terminology. Alternatively, if the resident does not have specialized knowledge, the suggestion unit may make a proposal that avoids technical terminology. The suggestion unit can also adjust the use of technical terminology in the proposal depending on the resident's level of expertise. This allows for the provision of easy-to-understand proposals by adjusting the use of technical terminology in the proposal depending on the resident's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the resident's level of expertise data into a generation AI and cause the generation AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the above-described input unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input information from the resident through the reception device 38 of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes appropriate suggestions based on the analysis results. For example, the suggestion unit may be realized by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described input unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can input information from the resident through the microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes appropriate suggestions based on the analysis results. For example, the suggestion unit may be realized by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, and suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the input unit can input information from the resident through the microphone 238 of the headset type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes appropriate suggestions based on the analysis results. For example, the suggestion unit may be realized by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can input information from the resident through the microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes appropriate suggestions based on the analysis results. For example, the suggestion unit may be realized by the control unit 46A of the robot 414.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The analysis unit can analyze the resident's health data and make appropriate suggestions based on their health condition. For example, it can analyze the resident's heart rate and sleep data, and if their health condition worsens, it can suggest that they visit a medical institution. The analysis unit can also analyze the resident's exercise data and suggest an appropriate exercise program if it detects a lack of exercise. Furthermore, the analysis unit can analyze the resident's dietary data and suggest a balanced meal menu if their nutritional balance is unbalanced. This makes it possible to support the resident's health by comprehensively analyzing their health condition and making appropriate suggestions.

[0095] The suggestion unit can suggest community events based on the hobbies and interests of residents. For example, if a resident is interested in gardening, it can suggest holding a gardening workshop. If a resident is interested in music, it can suggest holding a music event or concert. Furthermore, if a resident is interested in cooking, it can suggest holding a cooking class or cooking contest. In this way, by suggesting community events based on the hobbies and interests of residents, it is possible to promote interaction between residents and increase a sense of unity in the community.

[0096] The input unit can analyze the resident's tone of voice and facial expression to infer their emotions. For example, if a resident is feeling stressed, their voice tone may become higher and their facial expression may become stern. The input unit can analyze this information and infer that the resident is feeling stressed. Also, if a resident is relaxed, their voice tone may become lower and their facial expression may become calmer. The input unit can analyze this information and infer that the resident is relaxed. Furthermore, if a resident is in a hurry, their voice speed may increase and their facial expression may show impatience. The input unit can analyze this information and infer that the resident is in a hurry. In this way, by analyzing the resident's tone of voice and facial expression, it is possible to infer their emotions and take appropriate action.

[0097] The analysis unit can analyze the resident's purchasing history and make suggestions based on purchasing patterns. For example, by analyzing the products that the resident frequently purchases, if the products are on sale, the analysis unit can suggest sale information. Also, if the resident prefers to purchase products from a specific brand, the analysis unit can suggest new product information from that brand. Furthermore, if the resident frequently purchases health foods, the analysis unit can suggest health information and recipes. In this way, by analyzing the resident's purchasing history, suggestions can be made based on purchasing patterns, improving the resident's purchasing experience.

[0098] The suggestion unit can estimate the resident's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the resident is feeling stressed, the suggestion unit can delay the timing of suggestions to make suggestions in a relaxed state. Also, if the resident is relaxed, the suggestion unit can make suggestions immediately to ensure smooth information provision. Furthermore, if the resident is in a hurry, the suggestion unit can make simplified suggestions to provide information quickly. In this way, by adjusting the timing of suggestions according to the resident's emotions, stress can be reduced and effective suggestions can be made.

[0099] The analysis unit can analyze residents' social media activities and provide relevant information. For example, it can provide relevant news and event information based on information shared by residents on social media. It can also analyze the activities of accounts that residents follow on social media and suggest related content. It can also analyze the activities of groups that residents participate in on social media and suggest related community events. In this way, by analyzing residents' social media activities, it is possible to provide relevant information and make suggestions based on the residents' interests and concerns.

[0100] The suggestion unit can estimate the resident's emotions and customize the content of the suggestions based on the estimated emotions. For example, if the resident is feeling stressed, the suggestion unit can suggest relaxing activities or stress relief methods. If the resident is feeling relaxed, the suggestion unit can make suggestions about new challenges or hobbies. Furthermore, if the resident is in a hurry, the suggestion unit can suggest ways to save time or efficient task management. In this way, by customizing the content of the suggestions according to the resident's emotions, more effective suggestions can be made.

[0101] The analysis unit can analyze the resident's lifestyle rhythm and make suggestions based on that lifestyle rhythm. For example, it can analyze the resident's sleep pattern and suggest appropriate sleep times and ways to improve the sleep environment. It can also analyze the resident's eating pattern and suggest balanced meal menus and meal timings. It can also analyze the resident's exercise pattern and suggest appropriate exercise programs and exercise timings. In this way, by analyzing the resident's lifestyle rhythm, it is possible to make suggestions to support a healthy lifestyle.

[0102] The suggestion unit can estimate the resident's emotions and adjust the format of the suggestions based on the estimated emotions. For example, if the resident is feeling stressed, the suggestion unit can provide visually easy-to-understand graphs and diagrams. If the resident is relaxed, the suggestion unit can provide detailed text-based suggestions. If the resident is in a hurry, the suggestion unit can provide bullet-point suggestions that focus on the main points. By adjusting the format of the suggestions according to the resident's emotions, stress can be reduced and information can be provided effectively.

[0103] The analysis unit can analyze the resident's past behavioral data and make suggestions based on the behavioral patterns. For example, it can analyze places the resident has frequently visited in the past and suggest events and services related to those places. It can also analyze data on events the resident has previously attended and suggest similar events. It can also analyze data on products the resident has previously purchased and suggest related products. In this way, by analyzing the resident's past behavioral data, it is possible to make suggestions based on behavioral patterns and provide information that matches the resident's interests and concerns.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The input unit inputs information from residents. Information from residents includes text, audio, and images. For example, residents can input information such as the sounds of their pets, their special skills, and the concerns of elderly people through a smartphone app. Residents can also provide advice anonymously. Step 2: The analysis unit uses AI to analyze the information entered by the input unit. The analysis is performed using methods such as data mining, natural language processing, and image analysis. For example, it can analyze the cries of pets and provide information to alleviate residents' dissatisfaction. It can also detect anxiety in elderly people and provide information to provide appropriate support. Step 3: The suggestion unit makes suggestions based on the information analyzed by the analysis unit. Suggestions include how to train a pet, how to offer a special skill to other residents, and holding cooking classes. For example, if a resident finds their pet's barking too noisy, the suggestion unit can suggest ways to train the pet. It can also suggest ways for a resident with a special skill to offer that skill to other residents.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 AI 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 AI 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 AI 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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, in order to avoid confusion and to 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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. an input section for inputting information from residents; an analysis unit that analyzes the information input by the input unit; a proposal unit that makes a proposal based on the information analyzed by the analysis unit. system.

2. The analysis unit Analyzing pet sounds 2. The system of claim 1.

3. The analysis unit Detecting Anxiety in the Elderly 2. The system of claim 1.

4. The proposal unit Suggest ways to train your pet 2. The system of claim 1.

5. The proposal unit Suggest ways to share your special skills with other Residents 2. The system of claim 1.

6. The proposal unit Propose a cooking class 2. The system of claim 1.

7. The input unit Estimate the resident's emotions and adjust the timing of information input based on the estimated resident's emotions 2. The system of claim 1.

8. The input unit Analyze the resident's past input history and select the optimal input method 2. The system of claim 1.

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