system

Abstract

The system according to this embodiment aims to provide conversational assistance to individuals with insufficient language function and to reduce the burden on caregivers and childcare providers. [Solution] The system according to the embodiment comprises an input unit, a storage unit, an analysis unit, and a provision unit. The input unit inputs basic data. The storage unit stores conversation data. The analysis unit analyzes the data input and stored by the input unit and the storage unit and generates conversation support information. The provision unit provides the conversation support information generated by the analysis unit.

Description

Technical Field

[0002] , , , , , , ,

[0004] , ,

[0006] , , , , , , , ,

[0005] , , ,

[0003] , , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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] [[ID=IS=21]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that conversation assistance for those with insufficient language functions is not sufficiently provided, and the burden on assistants and caregivers is large.

[0005] The system according to the embodiment aims to provide conversation assistance for those with insufficient language functions and reduce the burden on assistants and caregivers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an input unit, a storage unit, an analysis unit, and a provision unit. The input unit receives basic data. The storage unit stores conversation data. The analysis unit analyzes the data input and stored by the input unit and the storage unit and generates conversation support information. The provision unit provides the conversation support information generated by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide conversational assistance to individuals with insufficient language function, thereby reducing the burden on caregivers and childcare providers. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The conversation assistance system according to an embodiment of the present invention is a system that provides conversation assistance functions for people with insufficient language functions, such as the elderly, people with disabilities, and infants, with the aim of reducing the burden on caregivers and childcare providers. This conversation assistance system also provides a useful function to supplement past memories for the person and their family. Specifically, the following functions are realized using a tablet or smartphone application. First, basic data is entered and set. This is done by voice input in a Q&A format or by batch input of text. Next, conversation data is accumulated. Everyday conversation information is accumulated by voice input and used as a reference for prompts of the generating AI. Conversation assistance functions for caregivers are also provided. By inputting basic data and conversation data as a reference for prompts of the generating AI, for example, if an elderly person says "that person," a candidate can be selected, or if a child says "sing that song," a candidate song can be found. Furthermore, memory assistance functions for the person and their family are also provided. For example, a parent can use the generating AI to check "What did you do today?" from a record of conversations at their child's nursery school, or it can be used to help the elderly recall their past memories. This service is expected to have the following effects in nursing homes and care facilities: Even people meeting for the first time can use the AI-powered conversation assistance function, which generates conversations based on basic data and past conversation data, to reduce the burden of communication. It can also reduce the burden of daily reporting and information sharing with family members. Furthermore, elderly individuals and their families can also benefit from the accumulation of data. In this way, the conversation assistance system can provide conversation assistance to people with insufficient language skills, such as the elderly, people with disabilities, and infants, and reduce the burden on caregivers and childcare providers.

[0029] The conversation assistance system according to the embodiment comprises an input unit, a storage unit, an analysis unit, and a provision unit. The input unit inputs basic data. The input unit can, for example, perform voice input in a Q&A format or batch input in text. For example, the input unit collects basic data by performing voice input in the form of the user answering questions. The input unit can also allow the user to input in batch in text format. For example, the input unit provides an interface for the user to input multiple data at once. The storage unit stores conversation data. For example, the storage unit can store everyday conversation information via voice input. For example, the storage unit collects and saves conversations that the user has on a daily basis as voice data. The storage unit can also store conversation data as text data. For example, the storage unit converts voice data into text data and saves it. The analysis unit analyzes the data input and stored by the input unit and the storage unit and generates conversation assistance information. The analysis unit analyzes basic data and conversation data, for example, using a generation AI. The generation AI generates conversation assistance information, for example, using a text generation AI (e.g., LLM). The analysis unit, for example, uses a generating AI to analyze basic data and conversation data and generate conversational support information suitable for the user. The provision unit provides the conversational support information generated by the analysis unit. The provision unit provides the conversational support information generated by the generating AI to caregivers, the user, or family members. The provision unit provides the conversational support information generated by the generating AI in voice or text format. The provision unit can also provide the conversational support information generated by the generating AI in real time. For example, the provision unit provides the conversational support information generated by the generating AI immediately while the user is having a conversation. As a result, the conversational support system according to this embodiment can provide conversational support functions to people with insufficient language skills, such as the elderly, people with disabilities, and infants, and reduce the burden on caregivers and childcare providers. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit can provide conversational support information using an AI model that takes conversational support information generated by the generating AI as input and outputs conversational support information.

[0030] The input unit is used to input basic data. The input unit can, for example, handle voice input in a Q&A format or batch text input. Specifically, it collects basic data by having the user answer questions via voice input. During this process, speech recognition technology is used to convert the user's speech into text data, which is then imported into the system. The input unit also allows users to input data in text format in batches. For example, it provides an interface for users to input multiple data items at once, enabling them to efficiently input necessary information into the system. Furthermore, the input unit enhances user convenience by supporting both voice and text input. For instance, in environments or situations where voice input is difficult, text input can be selected; conversely, when hands are busy or visual burden is reduced, voice input can be used. This allows the input unit to meet diverse user needs and achieve flexible data input.

[0031] The storage unit stores conversation data. For example, the storage unit can store everyday conversation information via voice input. Specifically, it collects and saves the conversations that users have on a daily basis as voice data. This allows for detailed recording of the user's conversation patterns and characteristics. The storage unit can also store conversation data as text data. For example, converting voice data to text data and saving it makes subsequent analysis and searching easier. Furthermore, the storage unit allows for flexible configuration of the data storage format and location. For example, by using cloud storage to store data, large amounts of data can be managed efficiently and accessed quickly as needed. Data backup and security measures are also considered to ensure the reliability and safety of the data. As a result, the storage unit can efficiently and securely manage user conversation data, improving the overall performance and reliability of the system.

[0032] The analysis unit analyzes the data input and stored by the input and storage units to generate conversational support information. The analysis unit analyzes basic data and conversational data, for example, using a generation AI. Specifically, the generation AI uses a text generation AI (e.g., LLM) to generate conversational support information. The generation AI analyzes the user's basic data and past conversational data to generate conversational support information tailored to the user. For example, it generates appropriate responses and advice based on the user's speech patterns, preferences, and past conversation content. Furthermore, the generation AI utilizes natural language processing technology to understand the user's intentions and emotions, providing more natural and appropriate conversational support information. The analysis unit also evaluates the conversational support information generated by the generation AI and makes corrections and improvements as needed. For example, if the generated information does not meet the user's needs or may be misleading, the analysis unit automatically corrects it to provide more appropriate information. This allows the analysis unit to provide high-quality conversational support information to the user, improving the overall reliability and usefulness of the system.

[0033] The service provider provides conversational support information generated by the analysis unit. For example, the service provider provides conversational support information generated by the generation AI to caregivers, the user, and family members. Specifically, it provides conversational support information generated by the generation AI in voice or text format. For example, by providing conversational support information generated by the generation AI immediately while the user is having a conversation, the user can receive appropriate advice and responses in real time. The service provider can also provide conversational support information generated by the generation AI in real time. For example, by providing conversational support information generated by the generation AI immediately while the user is having a conversation, the user can receive appropriate advice and responses in real time. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and usefulness of the information provided. For example, by allowing users to evaluate and comment on the information provided, the system improves the quality of the information based on that feedback. The service provider can also provide information through multiple devices and platforms. For example, users can choose a device that is convenient for them to use, such as a smartphone, tablet, or PC, to receive the information. This allows the service provider to provide conversational support information to users flexibly and quickly, improving the overall convenience and usefulness of the system.

[0034] The input unit can perform voice input in a Q&A format or batch input in text format. For example, the input unit collects basic data by taking voice input in the form of the user answering questions. For example, the input unit can input the user's name by having the user answer the question, "What is your name?" The input unit can also allow the user to input information in batch format in text format. For example, the input unit provides an interface for the user to input multiple data items at once. For example, the input unit can allow the user to input "name, age, and address" all at once. This makes it easy for the user to input basic data. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, the input unit can input the user's voice data into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0035] The storage unit can store everyday conversation information via voice input. For example, the storage unit collects and saves the conversations that the user has on a daily basis as voice data. For example, when the user says, "The weather is nice today," the storage unit stores that voice data. The storage unit can also store conversation data as text data. For example, the storage unit converts voice data into text data and saves it. For example, the storage unit converts the voice data "The weather is nice today" into text data and saves it. This allows for the efficient storage of everyday conversation information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's voice data into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0036] The analysis unit can analyze basic data and conversation data using a generation AI and generate conversational support information. For example, the analysis unit uses a generation AI to analyze basic data and conversation data. The generation AI uses a text generation AI (e.g., LLM) to generate conversational support information. For example, the analysis unit has the generation AI analyze basic data and conversation data and generates conversational support information suitable for the user. For example, the analysis unit has the generation AI generate conversational support information such as "select candidates when the elderly person says 'that person...' in their conversation." This improves the accuracy of conversational support information generation by using a generation AI. Some or all of the above processing in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit can input basic data and conversation data into the generation AI and have the generation AI generate conversational support information.

[0037] The service provider can provide conversational support information generated by the generation AI to caregivers, the individual, and their family. For example, the service provider can provide the conversational support information generated by the generation AI in audio or text format. For example, the service provider can play the conversational support information generated by the generation AI as audio and provide it to caregivers, the individual, and their family. The service provider can also display the conversational support information generated by the generation AI in text format and provide it to caregivers, the individual, and their family. For example, the service provider can display the conversational support information generated by the generation AI as text and provide it to caregivers, the individual, and their family. This ensures that the conversational support information generated by the generation AI is provided appropriately. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide conversational support information using an AI model that takes conversational support information generated by the generation AI as input and outputs conversational support information.

[0038] The service provider can provide the memory support information generated by the generating AI to the individual or their family. For example, the service provider can provide the memory support information generated by the generating AI in audio or text format. For example, the service provider can play the memory support information generated by the generating AI as audio and provide it to the individual or their family. The service provider can also display the memory support information generated by the generating AI in text format and provide it to the individual or their family. For example, the service provider can display the memory support information generated by the generating AI as text and provide it to the individual or their family. This ensures that the memory support information generated by the generating AI is provided appropriately. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide the memory support information using an AI model that takes the memory support information generated by the generating AI as input and outputs the memory support information.

[0039] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit can automatically display basic data that the user has frequently entered in the past as candidates. For example, the input unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest basic data to be used at specific times of day based on the user's past input history. For example, the input unit can distinguish between data that the user enters in the morning and data that the user enters at night and suggest the optimal input method for each. This allows the input unit to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input data into a generating AI and have the generating AI suggest the optimal input method.

[0040] The input unit can filter input content based on the user's current situation and environment when basic data is entered. For example, if the user is out, the input unit can provide concise input options to allow for quick input. For example, if the user is at home, the input unit can provide detailed input options to allow for the input of more information. The input unit can also provide privacy-conscious input options if the user is in a public place. For example, when the user is entering data in a public place, the input unit can avoid voice input and prioritize text input. This allows for filtering of input content according to the user's situation and environment. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, the input unit can input the user's current situation data into a generating AI and have the generating AI perform the filtering of input content.

[0041] The input unit can automatically suggest candidate locations by referring to the user's past travel history when basic data is entered. For example, the input unit can automatically display as candidate locations places that the user has frequently visited in the past. For example, the input unit can predict places the user will visit on specific days of the week or times of day and suggest them as candidate locations. The input unit can also analyze the user's past travel patterns and suggest the most suitable candidate locations. For example, the input unit can suggest places that the user is likely to visit next based on data of places they have visited in the past. This enables the automatic suggestion of candidate locations based on the user's past travel history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past travel data into a generating AI and have the generating AI perform the candidate location suggestion.

[0042] The input unit can make schedule-based suggestions by referring to the user's calendar information when basic data is entered. For example, the input unit can refer to the schedule registered in the user's calendar and automatically set the basic data. For example, when the user enters "tomorrow's schedule," the input unit can automatically display candidates based on the calendar information. The input unit can also suggest locations related to a specific event as candidate locations based on the user's calendar information. For example, when the user enters "meeting location," the input unit can suggest the most suitable location based on the calendar information. In this way, schedule-based suggestions can be made based on the user's calendar information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's calendar information into a generating AI and have the generating AI execute schedule-based suggestions.

[0043] The input unit can adjust the input content when basic data is entered, taking into account the user's health condition. For example, if the user is tired, the input unit can provide concise input options to allow for quick input. For example, if the user is healthy, the input unit can provide detailed input options to allow for the input of more information. The input unit can also provide options to minimize the input work if the user is unwell. For example, when the user enters "name, age, and address," the input unit provides a concise interface, which allows for adjustment of the input content according to the user's health condition. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, the input unit can input the user's health data into a generating AI and have the generating AI perform adjustments to the input content based on the health condition.

[0044] The storage unit can adjust the level of detail in the stored conversation data based on its importance. For example, the storage unit can store important conversation data in detail and general conversation data in a simplified manner. For example, the storage unit can adjust the amount of data stored according to the importance of the conversation data. The storage unit can also store important conversation data in both audio and text formats, and general conversation data in text format only. For example, the storage unit can save important conversation data as audio data and also save its text data. This allows the level of detail in the stored conversation data to be adjusted according to its importance. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the importance of the conversation data into a generating AI and have the generating AI perform the adjustment of the level of detail in the stored conversation data.

[0045] The storage unit can apply different storage algorithms depending on the category of the conversation data during storage. For example, the storage unit can store medical-related conversation data in detail and general conversation data in a simplified manner. For example, the storage unit can store education-related conversation data using a specific algorithm and other conversation data using a different algorithm. The storage unit can also adjust the format of the data to be stored depending on the category of the conversation data. For example, the storage unit can store medical-related conversation data as audio data and education-related conversation data as text data. This allows different storage algorithms to be applied depending on the category of the conversation data. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the categories of the conversation data into a generating AI and have the generating AI execute the application of the storage algorithm.

[0046] The storage unit can determine the priority of storage based on the submission date of the conversation data during storage. For example, the storage unit may prioritize storing the most recent conversation data and postpone older data. For example, the storage unit may prioritize storing conversation data that has been submitted recently. The storage unit can also adjust the amount of data stored based on the submission date. For example, the storage unit may store data that has been submitted recently in detail and store data that has been submitted later in a simplified manner. This allows the storage unit to determine the priority of storage based on the submission date of the conversation data. Some or all of the above processing in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input the submission date of the conversation data into a generating AI and have the generating AI perform the determination of the storage priority.

[0047] The storage unit can adjust the storage order based on the relevance of the conversation data during storage. For example, the storage unit prioritizes storing highly relevant conversation data. For example, it postpones storing less relevant conversation data. The storage unit can also adjust the format of the data to be stored based on the relevance of the conversation data. For example, it can store highly relevant data as audio data and less relevant data as text data. This allows the storage order to be adjusted based on the relevance of the conversation data. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the relevance of the conversation data into a generating AI and have the generating AI perform the adjustment of the storage order.

[0048] The storage unit can adjust the storage method based on the privacy settings of the conversation data during storage. For example, the storage unit may encrypt and store conversation data with high privacy settings. For example, the storage unit may store conversation data with low privacy settings in the normal way. The storage unit can also adjust the format of the data to be stored based on the privacy settings. For example, the storage unit may store data with high privacy settings as audio data and data with low privacy settings as text data. This allows the storage method to be adjusted based on the privacy settings of the conversation data. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit may input the privacy settings of the conversation data into a generating AI and have the generating AI perform the adjustment of the storage method.

[0049] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of conversation data during the analysis process. For example, the analysis unit can analyze the interrelationships of conversation data and prioritize the analysis of highly relevant data. For example, the analysis unit can improve the accuracy of its analysis based on the interrelationships of conversation data. The analysis unit can also adjust the analysis results by considering the interrelationships of conversation data. For example, the analysis unit can analyze the co-occurrence relationships of conversation data and prioritize the analysis of highly relevant data. This improves the accuracy of the analysis by considering the interrelationships of conversation data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships of conversation data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0050] The analysis unit can perform analysis while considering the attribute information of the person submitting the conversation data. For example, the analysis unit can adjust the analysis method based on the submitter's age and gender. For example, the analysis unit can adjust the analysis method based on the submitter's occupation and role. The analysis unit can also adjust the analysis results by considering the submitter's attribute information. For example, the analysis unit can adjust the analysis results based on the submitter's age and gender. This improves the accuracy of the analysis by considering the submitter's attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the submitter's attribute information into a generating AI and have the generating AI perform the adjustment of the analysis.

[0051] The analysis unit can perform analysis while considering the geographical distribution of conversation data. For example, the analysis unit can analyze the geographical distribution of conversation data and perform the analysis while considering the characteristics of each region. For example, the analysis unit can adjust the analysis results based on the geographical distribution of conversation data. The analysis unit can also adjust how the analysis results are displayed while considering the geographical distribution. For example, the analysis unit can display the analysis results while considering the characteristics of each region. This improves the accuracy of the analysis by considering the geographical distribution of conversation data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the geographical distribution of conversation data into a generating AI and have the generating AI perform the adjustment of the analysis.

[0052] The analysis unit can improve the accuracy of its analysis by referring to relevant literature for the conversation data during the analysis. For example, the analysis unit can improve the accuracy of the conversation data analysis by referring to relevant literature. For example, the analysis unit can adjust the analysis results based on relevant literature. The analysis unit can also optimize the analysis method by considering relevant literature. For example, the analysis unit can improve the accuracy of the conversation data analysis by referring to relevant literature. As a result, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0053] The information provider can adjust the level of detail provided based on the importance of the conversational aids at the time of provision. For example, the provider can provide important conversational aids in detail and general conversational aids in a simplified manner. For example, the provider can adjust the amount of information provided according to the importance of the conversational aids. The provider can also provide important conversational aids in both audio and text, and general conversational aids in text only. For example, the provider can provide important conversational aids as audio data and also provide the text data thereof. This allows the level of detail provided to be adjusted according to the importance of the conversational aids. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the importance of the conversational aids into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0054] The service provider can apply different service algorithms depending on the category of conversational aid information at the time of service provision. For example, the service provider may provide detailed medical-related conversational aid information and simplified general conversational aid information. For example, the service provider may provide educational-related conversational aid information using a specific algorithm and other conversational aid information using a different algorithm. The service provider can also adjust the format of the information provided depending on the category of conversational aid information. For example, the service provider may provide medical-related conversational aid information as audio data and educational-related conversational aid information as text data. This allows for the application of different service algorithms depending on the category of conversational aid information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the categories of conversational aid information into a generating AI and have the generating AI execute the application of the service algorithm.

[0055] The information provider can determine the priority of information provision based on the timing of its submission. For example, the provider may prioritize providing the most recent information and postpone older information. For example, the provider may prioritize providing information that is close to its submission date. The provider can also adjust the amount of information provided based on its submission date. For example, the provider may provide detailed information that is close to its submission date and simplified information that is far off. This allows the provider to determine the priority of information provision based on the timing of its submission. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the submission dates of the conversational information into a generating AI and have the generating AI determine the priority of information provision.

[0056] The information provider can adjust the order of provision based on the relevance of the conversational aids at the time of provision. For example, the provider can prioritize providing highly relevant conversational aids. For example, the provider can postpone providing less relevant conversational aids. The provider can also adjust the format of the information provided based on the relevance of the conversational aids. For example, the provider can provide highly relevant information as audio data and less relevant information as text data. This allows the order of provision to be adjusted based on the relevance of the conversational aids. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the relevance of the conversational aids into a generating AI and have the generating AI adjust the order of provision.

[0057] The service provider can adjust the method of providing conversational aids information based on its privacy settings at the time of provision. For example, the service provider may provide highly privacy-oriented conversational aids information in an encrypted form. For example, the service provider may provide less privacy-oriented conversational aids information in the usual way. The service provider can also adjust the format of the information provided based on its privacy settings. For example, the service provider may provide highly privacy-oriented information as audio data and less privacy-oriented information as text data. This allows the service provider to adjust the method of provision based on the privacy settings of the conversational aids information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the privacy settings of the conversational aids information into a generating AI and have the generating AI perform the adjustment of the method of provision.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The input unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display basic data that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest basic data to be used during specific time periods based on the user's past input history. This allows the system to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the input unit may be performed using AI, or it may be performed without AI.

[0060] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of conversation data during the analysis process. For example, it can analyze the interrelationships of conversation data and prioritize the analysis of highly relevant data. It can also adjust the analysis results based on the interrelationships of conversation data. Furthermore, it can analyze the co-occurrence relationships of conversation data and prioritize the analysis of highly relevant data. In this way, the accuracy of the analysis is improved by considering the interrelationships of conversation data. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0061] The input unit can filter the input content based on the user's current situation and environment when entering basic data. For example, if the user is out, it can provide concise input options to allow for quick data entry. If the user is at home, it can provide detailed input options to allow for the input of more information. Furthermore, if the user is in a public place, it can provide input options that take privacy into consideration. This allows for filtering of input content according to the user's situation and environment. Some or all of the above processing in the input unit may be performed using AI or not.

[0062] The storage unit can adjust the level of detail in the storage of conversation data based on its importance. For example, important conversation data can be stored in detail, while general conversation data can be stored in a simplified form. It is also possible to adjust the amount of data stored according to the importance of the conversation data. Furthermore, important conversation data can be stored in both audio and text, while general conversation data can be stored in text only. This allows for adjustment of the level of detail in the storage unit according to the importance of the conversation data. Some or all of the above processing in the storage unit may be performed using AI or not.

[0063] The information provider can adjust the level of detail provided based on the importance of the conversational aids at the time of provision. For example, important conversational aids can be provided in detail, while general conversational aids can be provided in a simplified manner. It is also possible to adjust the amount of information provided according to the importance of the conversational aids. Furthermore, important conversational aids can be provided in both audio and text, while general conversational aids can be provided in text only. This allows for adjustment of the level of detail provided according to the importance of the conversational aids. Some or all of the above processing in the information provider may be performed using AI or not.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The input section is used to input basic data. The input section can, for example, handle voice input in a Q&A format or batch text input. The system collects basic data by having the user answer questions via voice input. Users can also input data in a batch text format. For example, it provides an interface for users to input multiple data items at once. Step 2: The storage unit stores conversation data. The storage unit can store everyday conversation information via voice input, for example. It collects and saves the conversations that the user has on a daily basis as voice data. The storage unit can also store conversation data as text data. For example, it converts voice data into text data and saves it. Step 3: The analysis unit analyzes the data input and stored by the input and storage units and generates conversational support information. The analysis unit analyzes basic data and conversational data, for example, using a generation AI. The generation AI generates conversational support information, for example, using a text generation AI (e.g., LLM). The generation AI analyzes basic data and conversational data and generates conversational support information suitable for the user. Step 4: The service provider provides conversational support information generated by the analysis unit. For example, the service provider provides conversational support information generated by the generation AI to caregivers, the user, or family members. The service provider provides the conversational support information generated by the generation AI in audio or text format. The service provider can also provide conversational support information generated by the generation AI in real time. For example, it can instantly provide conversational support information generated by the generation AI while the user is having a conversation.

[0066] (Example of form 2) The conversation assistance system according to an embodiment of the present invention is a system that provides conversation assistance functions for people with insufficient language functions, such as the elderly, people with disabilities, and infants, with the aim of reducing the burden on caregivers and childcare providers. This conversation assistance system also provides a useful function to supplement past memories for the person and their family. Specifically, the following functions are realized using a tablet or smartphone application. First, basic data is entered and set. This is done by voice input in a Q&A format or by batch input of text. Next, conversation data is accumulated. Everyday conversation information is accumulated by voice input and used as a reference for prompts of the generating AI. Conversation assistance functions for caregivers are also provided. By inputting basic data and conversation data as a reference for prompts of the generating AI, for example, if an elderly person says "that person," a candidate can be selected, or if a child says "sing that song," a candidate song can be found. Furthermore, memory assistance functions for the person and their family are also provided. For example, a parent can use the generating AI to check "What did you do today?" from a record of conversations at their child's nursery school, or it can be used to help the elderly recall their past memories. This service is expected to have the following effects in nursing homes and care facilities: Even people meeting for the first time can use the AI-powered conversation assistance function, which generates conversations based on basic data and past conversation data, to reduce the burden of communication. It can also reduce the burden of daily reporting and information sharing with family members. Furthermore, elderly individuals and their families can also benefit from the accumulation of data. In this way, the conversation assistance system can provide conversation assistance to people with insufficient language skills, such as the elderly, people with disabilities, and infants, and reduce the burden on caregivers and childcare providers.

[0067] The conversation assistance system according to the embodiment comprises an input unit, a storage unit, an analysis unit, and a provision unit. The input unit inputs basic data. The input unit can, for example, perform voice input in a Q&A format or batch input in text. For example, the input unit collects basic data by performing voice input in the form of the user answering questions. The input unit can also allow the user to input in batch in text format. For example, the input unit provides an interface for the user to input multiple data at once. The storage unit stores conversation data. For example, the storage unit can store everyday conversation information via voice input. For example, the storage unit collects and saves conversations that the user has on a daily basis as voice data. The storage unit can also store conversation data as text data. For example, the storage unit converts voice data into text data and saves it. The analysis unit analyzes the data input and stored by the input unit and the storage unit and generates conversation assistance information. The analysis unit analyzes basic data and conversation data, for example, using a generation AI. The generation AI generates conversation assistance information, for example, using a text generation AI (e.g., LLM). The analysis unit, for example, uses a generating AI to analyze basic data and conversation data and generate conversational support information suitable for the user. The provision unit provides the conversational support information generated by the analysis unit. The provision unit provides the conversational support information generated by the generating AI to caregivers, the user, or family members. The provision unit provides the conversational support information generated by the generating AI in voice or text format. The provision unit can also provide the conversational support information generated by the generating AI in real time. For example, the provision unit provides the conversational support information generated by the generating AI immediately while the user is having a conversation. As a result, the conversational support system according to this embodiment can provide conversational support functions to people with insufficient language skills, such as the elderly, people with disabilities, and infants, and reduce the burden on caregivers and childcare providers. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit can provide conversational support information using an AI model that takes conversational support information generated by the generating AI as input and outputs conversational support information.

[0068] The input unit is used to input basic data. The input unit can, for example, handle voice input in a Q&A format or batch text input. Specifically, it collects basic data by having the user answer questions via voice input. During this process, speech recognition technology is used to convert the user's speech into text data, which is then imported into the system. The input unit also allows users to input data in text format in batches. For example, it provides an interface for users to input multiple data items at once, enabling them to efficiently input necessary information into the system. Furthermore, the input unit enhances user convenience by supporting both voice and text input. For instance, in environments or situations where voice input is difficult, text input can be selected; conversely, when hands are busy or visual burden is reduced, voice input can be used. This allows the input unit to meet diverse user needs and achieve flexible data input.

[0069] The storage unit stores conversation data. For example, the storage unit can store everyday conversation information via voice input. Specifically, it collects and saves the conversations that users have on a daily basis as voice data. This allows for detailed recording of the user's conversation patterns and characteristics. The storage unit can also store conversation data as text data. For example, converting voice data to text data and saving it makes subsequent analysis and searching easier. Furthermore, the storage unit allows for flexible configuration of the data storage format and location. For example, by using cloud storage to store data, large amounts of data can be managed efficiently and accessed quickly as needed. Data backup and security measures are also considered to ensure the reliability and safety of the data. As a result, the storage unit can efficiently and securely manage user conversation data, improving the overall performance and reliability of the system.

[0070] The analysis unit analyzes the data input and stored by the input and storage units to generate conversational support information. The analysis unit analyzes basic data and conversational data, for example, using a generation AI. Specifically, the generation AI uses a text generation AI (e.g., LLM) to generate conversational support information. The generation AI analyzes the user's basic data and past conversational data to generate conversational support information tailored to the user. For example, it generates appropriate responses and advice based on the user's speech patterns, preferences, and past conversation content. Furthermore, the generation AI utilizes natural language processing technology to understand the user's intentions and emotions, providing more natural and appropriate conversational support information. The analysis unit also evaluates the conversational support information generated by the generation AI and makes corrections and improvements as needed. For example, if the generated information does not meet the user's needs or may be misleading, the analysis unit automatically corrects it to provide more appropriate information. This allows the analysis unit to provide high-quality conversational support information to the user, improving the overall reliability and usefulness of the system.

[0071] The service provider provides conversational support information generated by the analysis unit. For example, the service provider provides conversational support information generated by the generation AI to caregivers, the user, and family members. Specifically, it provides conversational support information generated by the generation AI in voice or text format. For example, by providing conversational support information generated by the generation AI immediately while the user is having a conversation, the user can receive appropriate advice and responses in real time. The service provider can also provide conversational support information generated by the generation AI in real time. For example, by providing conversational support information generated by the generation AI immediately while the user is having a conversation, the user can receive appropriate advice and responses in real time. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and usefulness of the information provided. For example, by allowing users to evaluate and comment on the information provided, the system improves the quality of the information based on that feedback. The service provider can also provide information through multiple devices and platforms. For example, users can choose a device that is convenient for them to use, such as a smartphone, tablet, or PC, to receive the information. This allows the service provider to provide conversational support information to users flexibly and quickly, improving the overall convenience and usefulness of the system.

[0072] The input unit can perform voice input in a Q&A format or batch input in text format. For example, the input unit collects basic data by taking voice input in the form of the user answering questions. For example, the input unit can input the user's name by having the user answer the question, "What is your name?" The input unit can also allow the user to input information in batch format in text format. For example, the input unit provides an interface for the user to input multiple data items at once. For example, the input unit can allow the user to input "name, age, and address" all at once. This makes it easy for the user to input basic data. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, the input unit can input the user's voice data into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0073] The storage unit can store everyday conversation information via voice input. For example, the storage unit collects and saves the conversations that the user has on a daily basis as voice data. For example, when the user says, "The weather is nice today," the storage unit stores that voice data. The storage unit can also store conversation data as text data. For example, the storage unit converts voice data into text data and saves it. For example, the storage unit converts the voice data "The weather is nice today" into text data and saves it. This allows for the efficient storage of everyday conversation information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the user's voice data into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0074] The analysis unit can analyze basic data and conversation data using a generation AI and generate conversational support information. For example, the analysis unit uses a generation AI to analyze basic data and conversation data. The generation AI uses a text generation AI (e.g., LLM) to generate conversational support information. For example, the analysis unit has the generation AI analyze basic data and conversation data and generates conversational support information suitable for the user. For example, the analysis unit has the generation AI generate conversational support information such as "select candidates when the elderly person says 'that person...' in their conversation." This improves the accuracy of conversational support information generation by using a generation AI. Some or all of the above processing in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit can input basic data and conversation data into the generation AI and have the generation AI generate conversational support information.

[0075] The service provider can provide conversational support information generated by the generation AI to caregivers, the individual, and their family. For example, the service provider can provide the conversational support information generated by the generation AI in audio or text format. For example, the service provider can play the conversational support information generated by the generation AI as audio and provide it to caregivers, the individual, and their family. The service provider can also display the conversational support information generated by the generation AI in text format and provide it to caregivers, the individual, and their family. For example, the service provider can display the conversational support information generated by the generation AI as text and provide it to caregivers, the individual, and their family. This ensures that the conversational support information generated by the generation AI is provided appropriately. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide conversational support information using an AI model that takes conversational support information generated by the generation AI as input and outputs conversational support information.

[0076] The service provider can provide the memory support information generated by the generating AI to the individual or their family. For example, the service provider can provide the memory support information generated by the generating AI in audio or text format. For example, the service provider can play the memory support information generated by the generating AI as audio and provide it to the individual or their family. The service provider can also display the memory support information generated by the generating AI in text format and provide it to the individual or their family. For example, the service provider can display the memory support information generated by the generating AI as text and provide it to the individual or their family. This ensures that the memory support information generated by the generating AI is provided appropriately. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide the memory support information using an AI model that takes the memory support information generated by the generating AI as input and outputs the memory support information.

[0077] The input unit can estimate the user's emotions and adjust the input method for basic data based on the estimated emotions. For example, if the user is stressed, the input unit can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the input unit can provide detailed input options and suggest a customizable input method. The input unit can also prioritize voice input if the user is in a hurry, allowing for quick input of basic data. For example, the input unit can allow the user to input "name, age, and address" by voice. This allows the input method for basic data to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can input the user's emotion data into the generative AI and have the generative AI adjust the input method based on the emotion.

[0078] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit can automatically display basic data that the user has frequently entered in the past as candidates. For example, the input unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest basic data to be used at specific times of day based on the user's past input history. For example, the input unit can distinguish between data that the user enters in the morning and data that the user enters at night and suggest the optimal input method for each. This allows the input unit to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input data into a generating AI and have the generating AI suggest the optimal input method.

[0079] The input unit can filter input content based on the user's current situation and environment when basic data is entered. For example, if the user is out, the input unit can provide concise input options to allow for quick input. For example, if the user is at home, the input unit can provide detailed input options to allow for the input of more information. The input unit can also provide privacy-conscious input options if the user is in a public place. For example, when the user is entering data in a public place, the input unit can avoid voice input and prioritize text input. This allows for filtering of input content according to the user's situation and environment. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, the input unit can input the user's current situation data into a generating AI and have the generating AI perform the filtering of input content.

[0080] The input unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the input unit can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the input unit can provide an interface with bright colors to make the input process enjoyable. Also, if the user is tired, the input unit can provide a simple and highly visible interface to facilitate the input process. For example, when the user enters "name, age, and address," the input unit uses a highly visible font. This allows the design of the input interface to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, the input unit can input user emotion data into the generative AI and have the generative AI perform emotion-based interface adjustments.

[0081] The input unit can automatically suggest candidate locations by referring to the user's past travel history when basic data is entered. For example, the input unit can automatically display as candidate locations places that the user has frequently visited in the past. For example, the input unit can predict places the user will visit on specific days of the week or times of day and suggest them as candidate locations. The input unit can also analyze the user's past travel patterns and suggest the most suitable candidate locations. For example, the input unit can suggest places that the user is likely to visit next based on data of places they have visited in the past. This enables the automatic suggestion of candidate locations based on the user's past travel history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past travel data into a generating AI and have the generating AI perform the candidate location suggestion.

[0082] The input unit can make schedule-based suggestions by referring to the user's calendar information when basic data is entered. For example, the input unit can refer to the schedule registered in the user's calendar and automatically set the basic data. For example, when the user enters "tomorrow's schedule," the input unit can automatically display candidates based on the calendar information. The input unit can also suggest locations related to a specific event as candidate locations based on the user's calendar information. For example, when the user enters "meeting location," the input unit can suggest the most suitable location based on the calendar information. In this way, schedule-based suggestions can be made based on the user's calendar information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's calendar information into a generating AI and have the generating AI execute schedule-based suggestions.

[0083] The input unit can adjust the input content when basic data is entered, taking into account the user's health condition. For example, if the user is tired, the input unit can provide concise input options to allow for quick input. For example, if the user is healthy, the input unit can provide detailed input options to allow for the input of more information. The input unit can also provide options to minimize the input work if the user is unwell. For example, when the user enters "name, age, and address," the input unit provides a concise interface, which allows for adjustment of the input content according to the user's health condition. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, the input unit can input the user's health data into a generating AI and have the generating AI perform adjustments to the input content based on the health condition.

[0084] The storage unit can estimate the user's emotions and adjust the storage method based on the estimated emotions. For example, if the user is relaxed, the storage unit will store detailed conversation data. For example, if the user is in a hurry, the storage unit will store only important conversation data. The storage unit can also record changes in emotion in detail if the user is excited. For example, if the user says, "I had fun today," the storage unit will record the change in emotion in detail. This allows the storage method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not using AI. For example, the storage unit can input the user's emotion data into the generative AI and have the generative AI perform adjustments to the storage method based on emotions.

[0085] The storage unit can adjust the level of detail in the stored conversation data based on its importance. For example, the storage unit can store important conversation data in detail and general conversation data in a simplified manner. For example, the storage unit can adjust the amount of data stored according to the importance of the conversation data. The storage unit can also store important conversation data in both audio and text formats, and general conversation data in text format only. For example, the storage unit can save important conversation data as audio data and also save its text data. This allows the level of detail in the stored conversation data to be adjusted according to its importance. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the importance of the conversation data into a generating AI and have the generating AI perform the adjustment of the level of detail in the stored conversation data.

[0086] The storage unit can apply different storage algorithms depending on the category of the conversation data during storage. For example, the storage unit can store medical-related conversation data in detail and general conversation data in a simplified manner. For example, the storage unit can store education-related conversation data using a specific algorithm and other conversation data using a different algorithm. The storage unit can also adjust the format of the data to be stored depending on the category of the conversation data. For example, the storage unit can store medical-related conversation data as audio data and education-related conversation data as text data. This allows different storage algorithms to be applied depending on the category of the conversation data. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the categories of the conversation data into a generating AI and have the generating AI execute the application of the storage algorithm.

[0087] The data storage unit can estimate the user's emotions and prioritize the stored data based on the estimated emotions. For example, if the user is relaxed, the storage unit will prioritize storing detailed conversation data. For example, if the user is in a hurry, the storage unit will prioritize storing only important conversation data. The storage unit can also prioritize recording changes in emotions if the user is excited. For example, if the storage unit says, "I had fun today," it will prioritize recording that change in emotion. This allows the storage unit to prioritize the stored data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input the user's emotion data into a generative AI and have the generative AI perform the determination of priority of stored data based on emotions.

[0088] The storage unit can determine the priority of storage based on the submission date of the conversation data during storage. For example, the storage unit may prioritize storing the most recent conversation data and postpone older data. For example, the storage unit may prioritize storing conversation data that has been submitted recently. The storage unit can also adjust the amount of data stored based on the submission date. For example, the storage unit may store data that has been submitted recently in detail and store data that has been submitted later in a simplified manner. This allows the storage unit to determine the priority of storage based on the submission date of the conversation data. Some or all of the above processing in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input the submission date of the conversation data into a generating AI and have the generating AI perform the determination of the storage priority.

[0089] The storage unit can adjust the storage order based on the relevance of the conversation data during storage. For example, the storage unit prioritizes storing highly relevant conversation data. For example, it postpones storing less relevant conversation data. The storage unit can also adjust the format of the data to be stored based on the relevance of the conversation data. For example, it can store highly relevant data as audio data and less relevant data as text data. This allows the storage order to be adjusted based on the relevance of the conversation data. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the relevance of the conversation data into a generating AI and have the generating AI perform the adjustment of the storage order.

[0090] The storage unit can adjust the storage method based on the privacy settings of the conversation data during storage. For example, the storage unit may encrypt and store conversation data with high privacy settings. For example, the storage unit may store conversation data with low privacy settings in the normal way. The storage unit can also adjust the format of the data to be stored based on the privacy settings. For example, the storage unit may store data with high privacy settings as audio data and data with low privacy settings as text data. This allows the storage method to be adjusted based on the privacy settings of the conversation data. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit may input the privacy settings of the conversation data into a generating AI and have the generating AI perform the adjustment of the storage method.

[0091] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis. For example, if the user is in a hurry, the analysis unit analyzes only the important data. The analysis unit can also analyze changes in emotion in detail if the user is excited. For example, if the user says, "I had fun today," the analysis unit will analyze the change in emotion in detail. This allows the analysis method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the analysis method based on emotions.

[0092] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of conversation data during the analysis process. For example, the analysis unit can analyze the interrelationships of conversation data and prioritize the analysis of highly relevant data. For example, the analysis unit can improve the accuracy of its analysis based on the interrelationships of conversation data. The analysis unit can also adjust the analysis results by considering the interrelationships of conversation data. For example, the analysis unit can analyze the co-occurrence relationships of conversation data and prioritize the analysis of highly relevant data. This improves the accuracy of the analysis by considering the interrelationships of conversation data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships of conversation data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0093] The analysis unit can perform analysis while considering the attribute information of the person submitting the conversation data. For example, the analysis unit can adjust the analysis method based on the submitter's age and gender. For example, the analysis unit can adjust the analysis method based on the submitter's occupation and role. The analysis unit can also adjust the analysis results by considering the submitter's attribute information. For example, the analysis unit can adjust the analysis results based on the submitter's age and gender. This improves the accuracy of the analysis by considering the submitter's attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the submitter's attribute information into a generating AI and have the generating AI perform the adjustment of the analysis.

[0094] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit will prioritize displaying detailed analysis results. For example, if the user is in a hurry, the analysis unit will prioritize displaying only important analysis results. The analysis unit can also prioritize displaying changes in emotions if the user is excited. For example, if the user says, "I had fun today," the analysis unit will prioritize displaying that change in emotion. This allows the display order of analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display order of analysis results based on emotions.

[0095] The analysis unit can perform analysis while considering the geographical distribution of conversation data. For example, the analysis unit can analyze the geographical distribution of conversation data and perform the analysis while considering the characteristics of each region. For example, the analysis unit can adjust the analysis results based on the geographical distribution of conversation data. The analysis unit can also adjust how the analysis results are displayed while considering the geographical distribution. For example, the analysis unit can display the analysis results while considering the characteristics of each region. This improves the accuracy of the analysis by considering the geographical distribution of conversation data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the geographical distribution of conversation data into a generating AI and have the generating AI perform the adjustment of the analysis.

[0096] The analysis unit can improve the accuracy of its analysis by referring to relevant literature for the conversation data during the analysis. For example, the analysis unit can improve the accuracy of the conversation data analysis by referring to relevant literature. For example, the analysis unit can adjust the analysis results based on relevant literature. The analysis unit can also optimize the analysis method by considering relevant literature. For example, the analysis unit can improve the accuracy of the conversation data analysis by referring to relevant literature. As a result, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0097] The service provider can estimate the user's emotions and adjust its delivery method based on the estimated emotions. For example, if the user is relaxed, the service provider will provide detailed information. For example, if the user is in a hurry, the service provider will provide only essential information. The service provider can also provide detailed information about changes in emotions if the user is excited. For example, if the service provider says, "I had a great day," it will provide detailed information about that change in emotion. This allows the service provider to adjust its delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the delivery method based on emotions.

[0098] The information provider can adjust the level of detail provided based on the importance of the conversational aids at the time of provision. For example, the provider can provide important conversational aids in detail and general conversational aids in a simplified manner. For example, the provider can adjust the amount of information provided according to the importance of the conversational aids. The provider can also provide important conversational aids in both audio and text, and general conversational aids in text only. For example, the provider can provide important conversational aids as audio data and also provide the text data thereof. This allows the level of detail provided to be adjusted according to the importance of the conversational aids. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the importance of the conversational aids into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0099] The service provider can apply different service algorithms depending on the category of conversational aid information at the time of service provision. For example, the service provider may provide detailed medical-related conversational aid information and simplified general conversational aid information. For example, the service provider may provide educational-related conversational aid information using a specific algorithm and other conversational aid information using a different algorithm. The service provider can also adjust the format of the information provided depending on the category of conversational aid information. For example, the service provider may provide medical-related conversational aid information as audio data and educational-related conversational aid information as text data. This allows for the application of different service algorithms depending on the category of conversational aid information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the categories of conversational aid information into a generating AI and have the generating AI execute the application of the service algorithm.

[0100] The service provider can estimate the user's emotions and prioritize the content offered based on those emotions. For example, if the user is relaxed, the service provider will prioritize providing detailed conversational support information. For example, if the user is in a hurry, the service provider will prioritize providing only essential conversational support information. The service provider can also prioritize providing emotional changes if the user is excited. For example, if the user says, "I had a great day," the service provider will prioritize providing information about that emotional change. This allows the service provider to prioritize the content offered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of content offered based on emotions.

[0101] The information provider can determine the priority of information provision based on the timing of its submission. For example, the provider may prioritize providing the most recent information and postpone older information. For example, the provider may prioritize providing information that is close to its submission date. The provider can also adjust the amount of information provided based on its submission date. For example, the provider may provide detailed information that is close to its submission date and simplified information that is far off. This allows the provider to determine the priority of information provision based on the timing of its submission. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the submission dates of the conversational information into a generating AI and have the generating AI determine the priority of information provision.

[0102] The information provider can adjust the order of provision based on the relevance of the conversational aids at the time of provision. For example, the provider can prioritize providing highly relevant conversational aids. For example, the provider can postpone providing less relevant conversational aids. The provider can also adjust the format of the information provided based on the relevance of the conversational aids. For example, the provider can provide highly relevant information as audio data and less relevant information as text data. This allows the order of provision to be adjusted based on the relevance of the conversational aids. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the relevance of the conversational aids into a generating AI and have the generating AI adjust the order of provision.

[0103] The service provider can adjust the method of providing conversational aids information based on its privacy settings at the time of provision. For example, the service provider may provide highly privacy-oriented conversational aids information in an encrypted form. For example, the service provider may provide less privacy-oriented conversational aids information in the usual way. The service provider can also adjust the format of the information provided based on its privacy settings. For example, the service provider may provide highly privacy-oriented information as audio data and less privacy-oriented information as text data. This allows the service provider to adjust the method of provision based on the privacy settings of the conversational aids information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the privacy settings of the conversational aids information into a generating AI and have the generating AI perform the adjustment of the method of provision.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The conversation assistance system can further estimate the user's emotions and adjust the tone and content of the conversation based on those estimated emotions. For example, if the user is sad, the system can offer words of comfort or encouraging messages. If the user is agitated, the system can offer conversation that encourages calmness. Furthermore, if the user is relaxed, the system can offer conversation that maintains a relaxed atmosphere. This enables appropriate conversation assistance tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0106] The input unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display basic data that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest basic data to be used during specific time periods based on the user's past input history. This allows the system to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the input unit may be performed using AI, or it may be performed without AI.

[0107] The data storage unit can estimate the user's emotions and adjust the storage method based on the estimated emotions. For example, if the user is relaxed, detailed conversation data can be stored. If the user is in a hurry, only important conversation data can be stored. Furthermore, if the user is excited, changes in emotions can be recorded in detail. This allows the storage method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0108] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of conversation data during the analysis process. For example, it can analyze the interrelationships of conversation data and prioritize the analysis of highly relevant data. It can also adjust the analysis results based on the interrelationships of conversation data. Furthermore, it can analyze the co-occurrence relationships of conversation data and prioritize the analysis of highly relevant data. In this way, the accuracy of the analysis is improved by considering the interrelationships of conversation data. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0109] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is relaxed, detailed information can be provided. If the user is in a hurry, only essential information can be provided. Furthermore, if the user is excited, changes in emotions can be provided in detail. This allows the delivery method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0110] The input unit can filter the input content based on the user's current situation and environment when entering basic data. For example, if the user is out, it can provide concise input options to allow for quick data entry. If the user is at home, it can provide detailed input options to allow for the input of more information. Furthermore, if the user is in a public place, it can provide input options that take privacy into consideration. This allows for filtering of input content according to the user's situation and environment. Some or all of the above processing in the input unit may be performed using AI or not.

[0111] The storage unit can adjust the level of detail in the storage of conversation data based on its importance. For example, important conversation data can be stored in detail, while general conversation data can be stored in a simplified form. It is also possible to adjust the amount of data stored according to the importance of the conversation data. Furthermore, important conversation data can be stored in both audio and text, while general conversation data can be stored in text only. This allows for adjustment of the level of detail in the storage unit according to the importance of the conversation data. Some or all of the above processing in the storage unit may be performed using AI or not.

[0112] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, it is also possible to analyze only the important data. Furthermore, if the user is excited, changes in emotions can be analyzed in detail. This allows the analysis method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0113] The information provider can adjust the level of detail provided based on the importance of the conversational aids at the time of provision. For example, important conversational aids can be provided in detail, while general conversational aids can be provided in a simplified manner. It is also possible to adjust the amount of information provided according to the importance of the conversational aids. Furthermore, important conversational aids can be provided in both audio and text, while general conversational aids can be provided in text only. This allows for adjustment of the level of detail provided according to the importance of the conversational aids. Some or all of the above processing in the information provider may be performed using AI or not.

[0114] The service provider can estimate the user's emotions and prioritize the content provided based on those emotions. For example, if the user is relaxed, detailed conversational support information can be prioritized. If the user is in a hurry, only essential conversational support information can be prioritized. Furthermore, if the user is excited, emotional changes can be prioritized. This allows the service provider to prioritize content according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The input section is used to input basic data. The input section can, for example, handle voice input in a Q&A format or batch text input. The system collects basic data by having the user answer questions via voice input. Users can also input data in a batch text format. For example, it provides an interface for users to input multiple data items at once. Step 2: The storage unit stores conversation data. The storage unit can store everyday conversation information via voice input, for example. It collects and saves the conversations that the user has on a daily basis as voice data. The storage unit can also store conversation data as text data. For example, it converts voice data into text data and saves it. Step 3: The analysis unit analyzes the data input and stored by the input and storage units and generates conversational support information. The analysis unit analyzes basic data and conversational data, for example, using a generation AI. The generation AI generates conversational support information, for example, using a text generation AI (e.g., LLM). The generation AI analyzes basic data and conversational data and generates conversational support information suitable for the user. Step 4: The service provider provides conversational support information generated by the analysis unit. For example, the service provider provides conversational support information generated by the generation AI to caregivers, the user, or family members. The service provider provides the conversational support information generated by the generation AI in audio or text format. The service provider can also provide conversational support information generated by the generation AI in real time. For example, it can instantly provide conversational support information generated by the generation AI while the user is having a conversation.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the input unit, storage unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the input unit can perform voice input or text input using the receiving device 38 of the smart device 14. The storage unit stores conversation data in the storage 32 of the data processing unit 12. The analysis unit analyzes the data using generated AI by the specific processing unit 290 of the data processing unit 12. The provision unit provides conversational support information using the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the input unit, storage unit, analysis unit, and provision unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit can perform voice input using the microphone 238 of the smart glasses 214. The storage unit stores conversation data in, for example, the storage 32 of the data processing unit 12. The analysis unit analyzes the data using AI generated by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides conversational support information using, for example, the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the input unit, storage unit, analysis unit, and provision unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit can perform voice input using the microphone 238 of the headset terminal 314. The storage unit stores conversation data in, for example, the storage 32 of the data processing unit 12. The analysis unit analyzes the data using AI generated by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides conversation support information using, for example, the speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the input unit, storage unit, analysis unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the input unit can perform voice input using the microphone 238 of the robot 414. The storage unit stores conversation data in, for example, the storage 32 of the data processing unit 12. The analysis unit analyzes the data using generated AI by, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides conversational support information using, for example, the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) An input section for entering basic data, A storage unit that stores conversation data, An analysis unit analyzes the data input and stored by the input unit and the storage unit and generates conversational support information, The system includes a providing unit that provides conversational support information generated by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned input unit is Voice input in Q&A format or batch text input. The system described in Appendix 1, characterized by the features described herein. (Note 3) The storage unit is Accumulate everyday conversation information using voice input. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The generation AI analyzes basic data and conversation data to generate conversational support information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The AI ​​generates conversational support information which is then provided to caregivers, the individual, and their family. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The AI ​​generates memory support information which is then provided to the individual and their family. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned input unit is The system estimates the user's emotions and adjusts the input method for basic data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned input unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned input unit is When entering basic data, the input content is filtered based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned input unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned input unit is When entering basic data, the system automatically suggests potential locations based on the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned input unit is When basic data is entered, the system references the user's calendar information to provide schedule-based suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned input unit is When entering basic data, the input content is adjusted to take the user's health condition into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 14) The storage unit is The system estimates the user's emotions and adjusts the data collection method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The storage unit is During storage, the level of detail in the storage is adjusted based on the importance of the conversation data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The storage unit is During storage, different storage algorithms are applied depending on the category of the conversation data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The storage unit is It estimates user emotions and prioritizes accumulated data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The storage unit is During data storage, the storage priority is determined based on when the conversation data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The storage unit is During storage, the storage order is adjusted based on the relevance of the conversation data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The storage unit is When data is stored, the storage method is adjusted based on the privacy settings of the conversation data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, the interrelationships between conversational data are taken into consideration to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the attribute information of the person submitting the conversation data will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, During analysis, the geographical distribution of conversation data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, During analysis, we refer to relevant literature on conversation data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, adjust the level of detail based on the importance of the conversational aids. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of conversational aid information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the content offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing information, the priority of provision will be determined based on when conversational aids were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing information, adjust the order of presentation based on the relevance of the conversational aids. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the information, the method of provision will be adjusted based on the privacy settings for conversation aids. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An input section for entering basic data, A storage unit that stores conversation data, An analysis unit analyzes the data input and stored by the input unit and the storage unit and generates conversational support information, The system includes a providing unit that provides conversational support information generated by the analysis unit. A system characterized by the following features.

2. The aforementioned input unit is Voice input in Q&A format or batch text input. The system according to feature 1.

3. The storage unit is Accumulate everyday conversation information using voice input. The system according to feature 1.

4. The aforementioned analysis unit, The generation AI analyzes basic data and conversation data to generate conversational support information. The system according to feature 1.

5. The aforementioned supply unit is, The AI ​​generates conversational support information which is then provided to caregivers, the individual, and their family. The system according to feature 1.

6. The aforementioned supply unit is, The AI ​​generates memory support information which is then provided to the individual and their family. The system according to feature 1.

7. The aforementioned input unit is The system estimates the user's emotions and adjusts the input method for basic data based on the estimated user emotions. The system according to feature 1.

8. The aforementioned input unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

Images