system

Abstract

The system according to this embodiment aims to provide appropriate conversation partners to people who are feeling lonely. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, an interjection unit, a repeat unit, and a provision unit. The reception unit receives the user's speech. The analysis unit analyzes the speech received by the reception unit and interjects at predetermined timings. The interjection unit interjects based on the speech analyzed by the analysis unit. After the interjection unit interjects, the repeat unit repeats words that indicate emotion. The provision unit provides the results repeated by the repeat unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. <00000l0>

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to provide a suitable conversation partner for people feeling lonely.

[0005] The system according to the embodiment aims to provide a suitable conversation partner for people feeling lonely.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an interjection unit, a repeat unit, and a provision unit. The reception unit receives the user's speech. The analysis unit analyzes the speech received by the reception unit and interjects at predetermined timings. The interjection unit interjects based on the speech analyzed by the analysis unit. After the interjection unit interjects, the repeat unit repeats words indicating emotion. The provision unit provides the results repeated by the repeat unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide a suitable conversation partner to people who are feeling lonely. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied 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 partner system according to an embodiment of the present invention is a system that automatically receives a user's speech, analyzes it with a generating AI, interjects at appropriate times, repeats emotions, and provides results. When a user starts talking, the generating AI analyzes the speech and interjects at appropriate times with phrases such as "Oh, really?", "Wow," and "That's amazing." Furthermore, if the user expresses an emotion, the generating AI repeats it in a way that shows empathy, such as "That was fun," "That's sad," or "That's interesting." This system is specialized in listening to the user's speech and can provide support to people who feel lonely. For example, various people such as elderly people living alone or young people with few friends can use this system to alleviate their feelings of loneliness. For example, if a user says, "I had fun today," the generating AI will repeat, "You had fun." Also, if a user says, "I'm sad," the generating AI will empathize by saying, "That's sad." This system is specialized in listening to the user's speech and can provide support to people who feel lonely. For example, various people such as elderly people living alone or young people with few friends can use this system to alleviate their feelings of loneliness. This allows the conversational system to automatically receive, analyze, respond to, and convey the user's words, thereby alleviating feelings of loneliness.

[0029] The conversation partner system according to this embodiment comprises a reception unit, an analysis unit, an acknowledgment unit, a repeating unit, and a delivery unit. The reception unit receives the user's speech. The user's speech may include, for example, everyday events or expressions of emotion, but is not limited to such examples. The reception unit receives the user's speech using, for example, voice input. The reception unit may also receive the user's speech using text input. Furthermore, the reception unit may record the user's speech and save it for later analysis. For example, the reception unit converts the user's speech into text data using speech recognition technology. Text input allows the user to input their speech using a keyboard or touchscreen. The recording function records the user's speech in high quality and saves it for later analysis. The analysis unit analyzes the speech received by the reception unit using a generation AI. The analysis is performed based on, for example, the content of the speech and expressions of emotion, but is not limited to such examples. For example, the generation AI analyzes the content of the speech using a text generation AI (e.g., LLM). The analysis unit may also analyze the content of the speech using a multimodal generation AI. Furthermore, the analysis unit can use generative AI to extract and analyze important parts of a conversation. For example, text generation AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. Multimodal generation AI can handle multiple modals, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from the conversation and performs analysis based on it. The interjection unit interjects based on the conversation analyzed by the analysis unit. Interjections are made with words such as "Oh really?", "Wow," and "That's amazing," but are not limited to these examples. The interjection unit can, for example, use generative AI to select appropriate interjections according to the content of the conversation. The interjection unit can also adjust the timing of interjections according to the user's emotions. For example, the generative AI analyzes the content of the conversation and interjects at the appropriate time. The repeat unit repeats words that express emotions after the interjection unit has interjected. Repeats are made with words such as "That was fun," "That's sad," and "That's interesting," but are not limited to these examples. The repeat function, for example, uses a generation AI to select appropriate repetitions based on the content of the conversation.Furthermore, the repeating unit can adjust the way it repeats according to the user's emotions. For example, the generating AI analyzes the content of the conversation and performs appropriate repetition. The providing unit provides the results repeated by the repeating unit. The provision is done, for example, by voice or text, but is not limited to these examples. The providing unit can, for example, use the generating AI to provide the repeated results in voice. The providing unit can also provide the repeated results in text. For example, the generating AI can provide the repeated results in voice using speech synthesis technology. Text provision displays the repeated results as text data. As a result, the conversation partner system according to the embodiment can automatically receive, analyze, respond to, and repeat the emotions of the user, and provide the results.

[0030] The reception desk can analyze the content of a user's past conversations and select an appropriate reception method. For example, the reception desk can prioritize receiving conversations on relevant topics based on what the user has said in the past. It can also analyze themes that the user has preferred to talk about in the past and prioritize receiving conversations on those themes. Furthermore, the reception desk can analyze patterns in the user's past conversations and suggest the optimal reception method. For example, the reception desk can store the content of past conversations in a database and use an analysis algorithm to extract highly relevant topics. The database stores the content and themes that the user has talked about in the past, and the analysis algorithm identifies highly relevant topics based on this data. This can improve user satisfaction by providing the optimal reception method based on the content of past conversations. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on past conversations into a generating AI and have the generating AI select the optimal reception method.

[0031] The reception desk can filter incoming messages based on the user's current living situation and areas of interest. For example, the reception desk can consider the user's current living situation and prioritize accepting relevant topics. It can also prioritize accepting topics that might interest the user based on their areas of interest. Furthermore, the reception desk can analyze the user's current living situation and areas of interest and suggest the most appropriate topics. For example, the reception desk can collect data on the user's living situation and areas of interest and use an analysis algorithm to identify highly relevant topics. This data includes the user's work situation, family situation, hobbies, and topics of interest. The analysis algorithm extracts highly relevant topics based on this data and prioritizes accepting them. This allows for more appropriate responses by providing topics based on the user's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on living situation and areas of interest into a generating AI and have the generating AI suggest the most appropriate topics.

[0032] The reception desk can prioritize receiving conversations based on the user's geographical location. For example, it can prioritize topics related to the user's current location. It can also prioritize conversations related to local news and events based on the user's geographical location. Furthermore, the reception desk can analyze the user's geographical location and suggest the most relevant topics. For example, it can use GPS data and address information to identify the user's current location and extract topics related to that location. GPS data is used to pinpoint the user's current location with high accuracy. Address information is used to identify relevant topics based on the address entered by the user. This allows for more appropriate responses by providing topics based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input geographical location data into a generating AI and have the generating AI extract highly relevant topics.

[0033] The reception desk can analyze a user's social media activity when receiving a message and accept relevant messages. For example, the reception desk can prioritize receiving relevant topics based on the content of the user's social media posts. It can also analyze the user's social media activity history and prioritize receiving topics that might be of interest. Furthermore, the reception desk can suggest the most suitable topics based on the user's areas of interest on social media. For example, the reception desk can store social media posts and the number of likes in a database and use an analysis algorithm to extract highly relevant topics. The database stores the user's social media activity history and areas of interest, and the analysis algorithm identifies highly relevant topics based on this data. This allows for more appropriate responses by providing topics based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input social media data into a generating AI and have the generating AI extract highly relevant topics.

[0034] The analysis unit can adjust the level of detail of its analysis based on the importance of the topic. For example, the analysis unit performs a detailed analysis on important topics. It can also perform a concise analysis on general topics. Furthermore, the analysis unit can adjust the level of detail of its analysis based on the user's level of interest. For example, the analysis unit evaluates the content of the topic using an analysis algorithm and adjusts the level of detail of its analysis according to its importance. The analysis algorithm evaluates importance based on the depth and impact of the topic's content, performing a detailed analysis when necessary and a concise analysis when appropriate. This allows for a more appropriate response by providing an analysis level that matches the importance of the topic. 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 content data of the topic into a generating AI and have the generating AI perform the importance evaluation and adjustment of the level of detail of its analysis.

[0035] The analysis unit can apply different analysis algorithms depending on the category of the conversation during analysis. For example, the analysis unit can apply an emotion analysis algorithm to emotional topics. It can also apply an information analysis algorithm to fact-based topics. Furthermore, the analysis unit can select the optimal analysis algorithm according to the category of the user's conversation. For example, the analysis unit can classify the content of the conversation using an analysis algorithm and apply the appropriate analysis algorithm according to the category. The analysis algorithm performs emotion analysis for emotional topics and information analysis for fact-based topics. This allows for a more appropriate response by providing an analysis algorithm tailored to the category of the conversation. 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 the content data of the conversation into a generating AI and have the generating AI perform category classification and application of the analysis algorithm.

[0036] The analysis unit can determine the priority of analysis based on when the stories were submitted. For example, the analysis unit can prioritize the analysis of recent topics. It can also postpone the analysis of older topics. Furthermore, the analysis unit can determine the optimal analysis order based on when the user's stories were submitted. For example, the analysis unit can store the submission dates of stories in a database and use an analysis algorithm to determine the priority. The database stores the submission date, time, and order of stories, and the analysis algorithm identifies the priority based on this data. This allows for more appropriate responses by providing analysis priorities according to the submission dates of stories. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date data of stories into a generating AI and have the generating AI perform the priority determination.

[0037] The analysis unit can adjust the order of analysis based on the relevance of the conversation during analysis. For example, the analysis unit prioritizes the analysis of highly relevant topics. It can also postpone the analysis of less relevant topics. Furthermore, the analysis unit can determine the optimal analysis order based on the relevance of the user's conversation. For example, the analysis unit evaluates the content of the conversation using an analysis algorithm and adjusts the analysis order according to relevance. The analysis algorithm evaluates the relevance of the conversation content, prioritizing the analysis of highly relevant topics and postponing the analysis of less relevant topics. This allows for a more appropriate response by providing an analysis order that corresponds to the relevance of the conversation. 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 the conversation content data into a generating AI and have the generating AI perform the evaluation of relevance and the adjustment of the analysis order.

[0038] The interjection unit can select the type of interjection based on the content of the conversation. For example, it will select an emotional interjection for emotional topics. It can also select an informational interjection for factual topics. Furthermore, the interjection unit can select the most appropriate type of interjection depending on the content of the user's conversation. For example, the interjection unit can evaluate the content of the conversation using an analysis algorithm and select an appropriate interjection according to the content. The analysis algorithm evaluates based on the depth and topic of the conversation, selecting an emotional interjection for emotional topics and an informational interjection for factual topics. This allows for more appropriate responses by providing interjections that match the content of the conversation. Some or all of the above processing in the interjection unit may be performed using AI, for example, or without AI. For example, the interjection unit can input data on the content of the conversation into a generating AI and have the generating AI select the type of interjection.

[0039] The response unit can select an appropriate response by referring to the user's past responses when responding. For example, the response unit can prioritize selecting responses that the user has preferred in the past. The response unit can also analyze the user's past responses and select the optimal response. Furthermore, the response unit can select the optimal type of response based on the user's past responses. For example, the response unit can store the user's past responses to responses in a database and extract the optimal response using an analysis algorithm. The database stores responses and responses that the user has preferred in the past, and the analysis algorithm identifies the optimal response based on this data. This enables more appropriate responses by providing responses based on the user's past responses. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input data on past responses into a generating AI and have the generating AI select the optimal response.

[0040] The response unit can select appropriate responses by considering the user's geographical location information when responding. For example, the response unit can select appropriate responses to topics related to the user's current location. It can also select responses related to local news or events based on the user's geographical location information. Furthermore, the response unit can analyze the user's geographical location information and select the optimal response. For example, the response unit can identify the user's current location using GPS data or address information and extract responses related to that location. GPS data is used to identify the user's current location with high accuracy. Address information is used to identify relevant responses based on the address entered by the user. This allows for more appropriate responses by providing responses based on the user's geographical location information. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input geographical location data into a generating AI and have the generating AI select appropriate responses.

[0041] The response function can analyze the user's social media activity and select the appropriate response when responding. For example, the response function can select a relevant response based on the user's social media posts. It can also analyze the user's social media activity history and select an interesting response. Furthermore, the response function can select the most appropriate response based on the user's areas of interest on social media. For example, the response function can store social media posts and the number of likes in a database and use an analysis algorithm to extract highly relevant responses. The database stores the user's social media activity history and areas of interest, and the analysis algorithm identifies the most appropriate response based on this data. This enables more appropriate responses by providing responses based on the user's social media activity. Some or all of the above processing in the response function may be performed using AI, for example, or without AI. For example, the response function can input social media data into a generating AI and have the generating AI select the type of response.

[0042] The repeat function can adjust the level of detail of its repetition based on the content of the conversation. For example, it can perform detailed repetition for important topics, and concise repetition for general topics. Furthermore, it can adjust the level of detail of its repetition based on the user's level of interest. For example, it can evaluate the content of the conversation using an analysis algorithm and adjust the level of detail of its repetition accordingly. The analysis algorithm evaluates the content based on its depth and topic, performing detailed repetition for important topics and concise repetition for general topics. This allows for more appropriate responses by providing a level of detail of repetition that matches the content of the conversation. Some or all of the above processing in the repeat function may be performed using AI, for example, or without AI. For example, the repeat function can input the content data of the conversation into a generating AI and have the generating AI adjust the level of detail of the repetition.

[0043] The repeat function can apply different repeat algorithms depending on the category of the conversation during repetition. For example, the repeat function can apply an emotional repeat algorithm to emotional topics. It can also apply an informational repeat algorithm to factual topics. Furthermore, the repeat function can select the optimal repeat algorithm depending on the category of the user's conversation. For example, the repeat function can classify the content of the conversation using an analysis algorithm and apply an appropriate repeat algorithm according to the category. The analysis algorithm performs emotional repeats for emotional topics and informational repeats for factual topics. This allows for more appropriate responses by providing repeat algorithms according to the category of the conversation. Some or all of the above processing in the repeat function may be performed using AI, for example, or without AI. For example, the repeat function can input conversation data into a generating AI and have the generating AI perform category classification and apply the repeat algorithm.

[0044] The repeat function can determine the priority of repeats based on when the story was submitted. For example, the repeat function can prioritize repeating recent topics. It can also postpone repeating older topics. Furthermore, the repeat function can determine the optimal repeat order based on when the user submitted the story. For example, the repeat function can store the story submission dates in a database and use an analysis algorithm to determine the priority. The database stores the date, time, and order of story submissions, and the analysis algorithm identifies the priority based on this data. This allows for more appropriate responses by providing repeat priority according to the story submission date. Some or all of the above processing in the repeat function may be performed using AI, for example, or without AI. For example, the repeat function can input the story submission date data into a generating AI and have the generating AI perform the priority determination.

[0045] The repeat function can adjust the order of repetition based on the relevance of the conversation. For example, the repeat function prioritizes repeating highly relevant topics. It can also postpone repeating less relevant topics. Furthermore, the repeat function can determine the optimal repetition order based on the relevance of the user's conversation. For example, the repeat function can evaluate the content of the conversation using an analysis algorithm and adjust the repetition order according to relevance. The analysis algorithm evaluates the relevance of the conversation content, prioritizing the repetition of highly relevant topics and postponing less relevant topics. This allows for a more appropriate response by providing a repetition order that corresponds to the relevance of the conversation. Some or all of the above processing in the repeat function may be performed using AI, for example, or without AI. For example, the repeat function can input conversation data into a generating AI and have the generating AI perform the relevance evaluation and adjustment of the repetition order.

[0046] The service provider can adjust the level of detail provided based on the importance of the repeated results. For example, the service provider can provide detailed information for important repeated results. It can also provide concise information for general repeated results. Furthermore, the service provider can adjust the level of detail based on the user's level of interest. For example, the service provider can evaluate the repeated results using an analysis algorithm and adjust the level of detail according to their importance. The analysis algorithm evaluates the depth and impact of the repeated results, providing detailed information for important results and concise information for general results. This allows for a more appropriate response by providing a level of detail according to the importance of the repeated results. 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 data of the repeated results into a generating AI and have the generating AI perform the importance evaluation and adjustment of the level of detail of the provision.

[0047] The service provider can apply different service algorithms depending on the category of the repeated results at the time of delivery. For example, the service provider can apply an emotional service algorithm to emotionally charged repeated results. It can also apply an informational service algorithm to factually charged repeated results. Furthermore, the service provider can select the optimal service algorithm depending on the category of the user's repeated results. For example, the service provider can classify the repeated results using an analysis algorithm and apply an appropriate service algorithm according to the category. The analysis algorithm provides emotional service for emotionally charged repeated results and informational service for factually charged repeated results. This allows for more appropriate responses by providing service algorithms tailored to the category of the repeated results. 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 data of the repeated results into a generating AI and have the generating AI perform category classification and application of the service algorithm.

[0048] The service provider can determine the priority of deliveries based on the submission timing of repeated results. For example, the service provider can prioritize the delivery of recent repeated results. It can also postpone the delivery of past repeated results. Furthermore, the service provider can determine the optimal delivery order based on the submission timing of the user's repeated results. For example, the service provider can store the submission timing of repeated results in a database and use an analysis algorithm to determine the priority. The database stores the submission date, time, and order of repeated results, and the analysis algorithm identifies the priority based on this data. This allows for more appropriate responses by providing a priority for deliveries according to the submission timing of repeated results. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input data on the submission timing of repeated results into a generating AI and have the generating AI perform the priority determination.

[0049] The service provider can adjust the order of delivery based on the relevance of the repeated results. For example, the service provider can prioritize the delivery of highly relevant repeated results. It can also postpone the delivery of less relevant repeated results. Furthermore, the service provider can determine the optimal delivery order based on the relevance of the user's repeated results. For example, the service provider can evaluate the repeated results using an analysis algorithm and adjust the delivery order according to their relevance. The analysis algorithm evaluates the relevance of the content of the repeated results, prioritizing the delivery of highly relevant results and postponing the delivery of less relevant results. This allows for a more appropriate response by providing a delivery order that corresponds to the relevance of the repeated results. 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 data of the repeated results into a generating AI and have the generating AI perform the relevance evaluation and adjustment of the delivery order.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The conversational system can also be equipped with a health management unit that monitors the user's health status. This unit acquires vital data such as the user's heart rate, blood pressure, and body temperature, and evaluates the user's health based on this data. For example, if the user is stressed, the health management unit can detect an increase in heart rate and provide advice for relaxation. Furthermore, if the user complains of feeling unwell, the health management unit can analyze body temperature and blood pressure data and suggest appropriate actions. In addition, the health management unit can monitor the user's health data over the long term and track changes in their health status. This supports the user's health management and provides them with a better quality of life.

[0052] The conversational partner system can also be equipped with a hobby learning unit that learns the user's hobbies and interests. This unit analyzes the user's conversations, search history, and social media activity to identify their hobbies and interests. For example, if a user frequently talks about movies, the hobby learning unit will determine the user is interested in movies and provide the latest movie information and recommendations. Similarly, if a user is interested in cooking, the hobby learning unit can suggest recipes and cooking tips. Furthermore, the hobby learning unit can adapt to changes in the user's interests and provide information based on those new hobbies and interests. This can enrich the user's life.

[0053] The conversation partner system can also include a schedule management unit to manage the user's schedule. This unit can register the user's appointments and set reminders. For example, if a user enters a meeting schedule, the schedule management unit will send a reminder at the meeting's start time. It can also provide advance notifications to ensure the user doesn't forget important events. Furthermore, the schedule management unit can provide optimal time management advice based on the user's schedule. This allows the user to manage their time efficiently and avoid forgetting appointments.

[0054] The conversational system can also include a learning support unit to assist the user's learning. The learning support unit creates a learning plan based on the user's input of what they want to learn. For example, if a user wants to learn a new language, the learning support unit suggests appropriate materials and a study schedule. If a user is studying for a specific exam, the learning support unit can provide exam preparation advice and practice tests. Furthermore, the learning support unit can track the user's learning progress and adjust the learning plan as needed. This allows the user to learn effectively.

[0055] The conversational system can also include a feedback collection unit to gather user feedback. This unit provides an interface where users can input their experience with the system and suggest improvements. For example, if a user is dissatisfied with the system's response speed, the feedback collection unit can collect this feedback and use it to improve the system. Furthermore, if a user suggests a new feature, the feedback collection unit can record the suggestion and share it with the development team. Additionally, the feedback collection unit can analyze user feedback to identify common problems and requests. This can improve system quality and increase user satisfaction.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives the user's story. The user's story may include everyday events and expressions of emotions. The reception desk can receive the user's story using voice input or text input. It can also record the user's story and save it for later analysis. For example, speech recognition technology can be used to convert the user's story into text data. Step 2: The analysis unit analyzes the story received by the reception unit. The analysis is based on the content of the story and the expression of emotions. Using a generation AI, the content of the story can be analyzed and important parts can be extracted. For example, the analysis can be performed using a text generation AI or a multimodal generation AI. Step 3: The interjection unit interjects based on the conversation analyzed by the analysis unit. Interjections are made using words such as "Oh really?", "Wow," and "That's amazing." Using a generation AI, the system selects appropriate interjections according to the content of the conversation and adjusts the timing of the interjections according to the user's emotions. Step 4: The repeating section repeats words expressing emotion after the nodding section provides verbal acknowledgments. The repetition includes words such as "That was fun," "That's sad," and "That's interesting." Using a generation AI, the appropriate repetition is selected according to the content of the conversation, and the way the repetition is expressed is adjusted according to the user's emotions. Step 5: The delivery unit provides the results repeated by the repeat unit. The delivery is done via voice or text. Using a generation AI, the repeated results are provided via voice using speech synthesis technology. The repeated results can also be displayed as text data.

[0058] (Example of form 2) The conversation partner system according to an embodiment of the present invention is a system that automatically receives a user's speech, analyzes it with a generating AI, interjects at appropriate times, repeats emotions, and provides results. When a user starts talking, the generating AI analyzes the speech and interjects at appropriate times with phrases such as "Oh, really?", "Wow," and "That's amazing." Furthermore, if the user expresses an emotion, the generating AI repeats it in a way that shows empathy, such as "That was fun," "That's sad," or "That's interesting." This system is specialized in listening to the user's speech and can provide support to people who feel lonely. For example, various people such as elderly people living alone or young people with few friends can use this system to alleviate their feelings of loneliness. For example, if a user says, "I had fun today," the generating AI will repeat, "You had fun." Also, if a user says, "I'm sad," the generating AI will empathize by saying, "That's sad." This system is specialized in listening to the user's speech and can provide support to people who feel lonely. For example, various people such as elderly people living alone or young people with few friends can use this system to alleviate their feelings of loneliness. This allows the conversational system to automatically receive, analyze, respond to, and convey the user's words, thereby alleviating feelings of loneliness.

[0059] The conversation partner system according to this embodiment comprises a reception unit, an analysis unit, an acknowledgment unit, a repeating unit, and a delivery unit. The reception unit receives the user's speech. The user's speech may include, for example, everyday events or expressions of emotion, but is not limited to such examples. The reception unit receives the user's speech using, for example, voice input. The reception unit may also receive the user's speech using text input. Furthermore, the reception unit may record the user's speech and save it for later analysis. For example, the reception unit converts the user's speech into text data using speech recognition technology. Text input allows the user to input their speech using a keyboard or touchscreen. The recording function records the user's speech in high quality and saves it for later analysis. The analysis unit analyzes the speech received by the reception unit using a generation AI. The analysis is performed based on, for example, the content of the speech and expressions of emotion, but is not limited to such examples. For example, the generation AI analyzes the content of the speech using a text generation AI (e.g., LLM). The analysis unit may also analyze the content of the speech using a multimodal generation AI. Furthermore, the analysis unit can use generative AI to extract and analyze important parts of a conversation. For example, text generation AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. Multimodal generation AI can handle multiple modals, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from the conversation and performs analysis based on it. The interjection unit interjects based on the conversation analyzed by the analysis unit. Interjections are made with words such as "Oh really?", "Wow," and "That's amazing," but are not limited to these examples. The interjection unit can, for example, use generative AI to select appropriate interjections according to the content of the conversation. The interjection unit can also adjust the timing of interjections according to the user's emotions. For example, the generative AI analyzes the content of the conversation and interjects at the appropriate time. The repeat unit repeats words that express emotions after the interjection unit has interjected. Repeats are made with words such as "That was fun," "That's sad," and "That's interesting," but are not limited to these examples. The repeat function, for example, uses a generation AI to select appropriate repetitions based on the content of the conversation.Furthermore, the repeating unit can adjust the way it repeats according to the user's emotions. For example, the generating AI analyzes the content of the conversation and performs appropriate repetition. The providing unit provides the results repeated by the repeating unit. The provision is done, for example, by voice or text, but is not limited to these examples. The providing unit can, for example, use the generating AI to provide the repeated results in voice. The providing unit can also provide the repeated results in text. For example, the generating AI can provide the repeated results in voice using speech synthesis technology. Text provision displays the repeated results as text data. As a result, the conversation partner system according to the embodiment can automatically receive, analyze, respond to, and repeat the emotions of the user, and provide the results.

[0060] The reception unit can estimate the user's emotions and adjust how it receives the conversation based on the estimated emotions. For example, if the user is sad, the reception unit can provide an interface that receives the conversation in a gentle tone. It can also provide an interface that receives the conversation in an energetic tone if the user is excited. Furthermore, if the user is tired, the reception unit can provide a simple and intuitive interface to facilitate conversation. For example, the reception unit can estimate the user's emotions using voice analysis technology and adjust the interface tone based on the results. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows for a more appropriate response by providing a reception method that matches the user's emotions. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's voice data into a generating AI and have the generating AI perform emotion estimation.

[0061] The reception desk can analyze the content of a user's past conversations and select an appropriate reception method. For example, the reception desk can prioritize receiving conversations on relevant topics based on what the user has said in the past. It can also analyze themes that the user has preferred to talk about in the past and prioritize receiving conversations on those themes. Furthermore, the reception desk can analyze patterns in the user's past conversations and suggest the optimal reception method. For example, the reception desk can store the content of past conversations in a database and use an analysis algorithm to extract highly relevant topics. The database stores the content and themes that the user has talked about in the past, and the analysis algorithm identifies highly relevant topics based on this data. This can improve user satisfaction by providing the optimal reception method based on the content of past conversations. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on past conversations into a generating AI and have the generating AI select the optimal reception method.

[0062] The reception desk can filter incoming messages based on the user's current living situation and areas of interest. For example, the reception desk can consider the user's current living situation and prioritize accepting relevant topics. It can also prioritize accepting topics that might interest the user based on their areas of interest. Furthermore, the reception desk can analyze the user's current living situation and areas of interest and suggest the most appropriate topics. For example, the reception desk can collect data on the user's living situation and areas of interest and use an analysis algorithm to identify highly relevant topics. This data includes the user's work situation, family situation, hobbies, and topics of interest. The analysis algorithm extracts highly relevant topics based on this data and prioritizes accepting them. This allows for more appropriate responses by providing topics based on the user's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on living situation and areas of interest into a generating AI and have the generating AI suggest the most appropriate topics.

[0063] The reception desk can estimate the user's emotions and determine the priority of conversations to accept based on the estimated emotions. For example, if the user is sad, the reception desk will prioritize conversations that require emotional support. Similarly, if the user is excited, the reception desk may prioritize interesting topics. Furthermore, if the user is tired, the reception desk may prioritize relaxing topics. For example, the reception desk may use voice analysis technology to estimate the user's emotions and determine conversation priorities based on the results. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows for more appropriate responses by prioritizing conversations according to the user's emotions. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's voice data into a generating AI and have the generating AI perform emotion estimation.

[0064] The reception desk can prioritize receiving conversations based on the user's geographical location. For example, it can prioritize topics related to the user's current location. It can also prioritize conversations related to local news and events based on the user's geographical location. Furthermore, the reception desk can analyze the user's geographical location and suggest the most relevant topics. For example, it can use GPS data and address information to identify the user's current location and extract topics related to that location. GPS data is used to pinpoint the user's current location with high accuracy. Address information is used to identify relevant topics based on the address entered by the user. This allows for more appropriate responses by providing topics based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input geographical location data into a generating AI and have the generating AI extract highly relevant topics.

[0065] The reception desk can analyze a user's social media activity when receiving a message and accept relevant messages. For example, the reception desk can prioritize receiving relevant topics based on the content of the user's social media posts. It can also analyze the user's social media activity history and prioritize receiving topics that might be of interest. Furthermore, the reception desk can suggest the most suitable topics based on the user's areas of interest on social media. For example, the reception desk can store social media posts and the number of likes in a database and use an analysis algorithm to extract highly relevant topics. The database stores the user's social media activity history and areas of interest, and the analysis algorithm identifies highly relevant topics based on this data. This allows for more appropriate responses by providing topics based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input social media data into a generating AI and have the generating AI extract highly relevant topics.

[0066] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is sad, the analysis unit will present the results in a gentle tone. If the user is excited, the analysis unit can present the results in a lively tone. Furthermore, if the user is tired, the analysis unit can provide a simple and intuitive presentation. For example, the analysis unit can estimate the user's emotions using voice analysis technology and adjust the way the analysis is presented based on the results. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows for a more appropriate response by providing an analysis presentation that matches the user's emotions. 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 user's voice data into a generating AI and have the generating AI perform emotion estimation.

[0067] The analysis unit can adjust the level of detail of its analysis based on the importance of the topic. For example, the analysis unit performs a detailed analysis on important topics. It can also perform a concise analysis on general topics. Furthermore, the analysis unit can adjust the level of detail of its analysis based on the user's level of interest. For example, the analysis unit evaluates the content of the topic using an analysis algorithm and adjusts the level of detail of its analysis according to its importance. The analysis algorithm evaluates importance based on the depth and impact of the topic's content, performing a detailed analysis when necessary and a concise analysis when appropriate. This allows for a more appropriate response by providing an analysis level that matches the importance of the topic. 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 content data of the topic into a generating AI and have the generating AI perform the importance evaluation and adjustment of the level of detail of its analysis.

[0068] The analysis unit can apply different analysis algorithms depending on the category of the conversation during analysis. For example, the analysis unit can apply an emotion analysis algorithm to emotional topics. It can also apply an information analysis algorithm to fact-based topics. Furthermore, the analysis unit can select the optimal analysis algorithm according to the category of the user's conversation. For example, the analysis unit can classify the content of the conversation using an analysis algorithm and apply the appropriate analysis algorithm according to the category. The analysis algorithm performs emotion analysis for emotional topics and information analysis for fact-based topics. This allows for a more appropriate response by providing an analysis algorithm tailored to the category of the conversation. 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 the content data of the conversation into a generating AI and have the generating AI perform category classification and application of the analysis algorithm.

[0069] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will perform a short, concise analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. For example, the analysis unit can estimate the user's emotions using voice analysis technology and adjust the length of the analysis based on the results. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows for a more appropriate response by providing an analysis length that matches the user's emotions. 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 the user's voice data into a generating AI and have the generating AI perform emotion estimation and adjustment of the analysis length.

[0070] The analysis unit can determine the priority of analysis based on when the stories were submitted. For example, the analysis unit can prioritize the analysis of recent topics. It can also postpone the analysis of older topics. Furthermore, the analysis unit can determine the optimal analysis order based on when the user's stories were submitted. For example, the analysis unit can store the submission dates of stories in a database and use an analysis algorithm to determine the priority. The database stores the submission date, time, and order of stories, and the analysis algorithm identifies the priority based on this data. This allows for more appropriate responses by providing analysis priorities according to the submission dates of stories. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date data of stories into a generating AI and have the generating AI perform the priority determination.

[0071] The analysis unit can adjust the order of analysis based on the relevance of the conversation during analysis. For example, the analysis unit prioritizes the analysis of highly relevant topics. It can also postpone the analysis of less relevant topics. Furthermore, the analysis unit can determine the optimal analysis order based on the relevance of the user's conversation. For example, the analysis unit evaluates the content of the conversation using an analysis algorithm and adjusts the analysis order according to relevance. The analysis algorithm evaluates the relevance of the conversation content, prioritizing the analysis of highly relevant topics and postponing the analysis of less relevant topics. This allows for a more appropriate response by providing an analysis order that corresponds to the relevance of the conversation. 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 the conversation content data into a generating AI and have the generating AI perform the evaluation of relevance and the adjustment of the analysis order.

[0072] The interjection unit can estimate the user's emotions and adjust the timing of its interjections based on those emotions. For example, if the user is sad, the interjection unit will interject gently. If the user is excited, the interjection unit can interject energetically. Furthermore, if the user is tired, the interjection unit can interject simply and intuitively. For example, the interjection unit can estimate the user's emotions using voice analysis technology and adjust the timing of its interjections based on the results. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows for more appropriate responses by providing interjection timing that matches the user's emotions. Some or all of the above processing in the interjection unit may be performed using AI, for example, or without AI. For example, the interjection unit can input the user's voice data into a generating AI and have the generating AI perform emotion estimation and adjustment of interjection timing.

[0073] The interjection unit can select the type of interjection based on the content of the conversation. For example, it will select an emotional interjection for emotional topics. It can also select an informational interjection for factual topics. Furthermore, the interjection unit can select the most appropriate type of interjection depending on the content of the user's conversation. For example, the interjection unit can evaluate the content of the conversation using an analysis algorithm and select an appropriate interjection according to the content. The analysis algorithm evaluates based on the depth and topic of the conversation, selecting an emotional interjection for emotional topics and an informational interjection for factual topics. This allows for more appropriate responses by providing interjections that match the content of the conversation. Some or all of the above processing in the interjection unit may be performed using AI, for example, or without AI. For example, the interjection unit can input data on the content of the conversation into a generating AI and have the generating AI select the type of interjection.

[0074] The response unit can select an appropriate response by referring to the user's past responses when responding. For example, the response unit can prioritize selecting responses that the user has preferred in the past. The response unit can also analyze the user's past responses and select the optimal response. Furthermore, the response unit can select the optimal type of response based on the user's past responses. For example, the response unit can store the user's past responses to responses in a database and extract the optimal response using an analysis algorithm. The database stores responses and responses that the user has preferred in the past, and the analysis algorithm identifies the optimal response based on this data. This enables more appropriate responses by providing responses based on the user's past responses. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input data on past responses into a generating AI and have the generating AI select the optimal response.

[0075] The voice-over function can estimate the user's emotions and adjust the strength of its responses based on those emotions. For example, if the user is sad, the voice-over function will respond with a gentle response. If the user is excited, the voice-over function can also respond with a stronger response. Furthermore, if the user is tired, the voice-over function can respond with a simple and intuitive response. For example, the voice-over function can estimate the user's emotions using voice analysis technology and adjust the strength of its responses based on the results. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows for more appropriate responses by providing responses that match the user's emotions. Some or all of the above processing in the voice-over function may be performed using AI, for example, or without AI. For example, the voice-over function can input the user's voice data into a generating AI and have the generating AI perform emotion estimation and adjustment of response strength.

[0076] The response unit can select appropriate responses by considering the user's geographical location information when responding. For example, the response unit can select appropriate responses to topics related to the user's current location. It can also select responses related to local news or events based on the user's geographical location information. Furthermore, the response unit can analyze the user's geographical location information and select the optimal response. For example, the response unit can identify the user's current location using GPS data or address information and extract responses related to that location. GPS data is used to identify the user's current location with high accuracy. Address information is used to identify relevant responses based on the address entered by the user. This allows for more appropriate responses by providing responses based on the user's geographical location information. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input geographical location data into a generating AI and have the generating AI select appropriate responses.

[0077] The response function can analyze the user's social media activity and select the appropriate response when responding. For example, the response function can select a relevant response based on the user's social media posts. It can also analyze the user's social media activity history and select an interesting response. Furthermore, the response function can select the most appropriate response based on the user's areas of interest on social media. For example, the response function can store social media posts and the number of likes in a database and use an analysis algorithm to extract highly relevant responses. The database stores the user's social media activity history and areas of interest, and the analysis algorithm identifies the most appropriate response based on this data. This enables more appropriate responses by providing responses based on the user's social media activity. Some or all of the above processing in the response function may be performed using AI, for example, or without AI. For example, the response function can input social media data into a generating AI and have the generating AI select the type of response.

[0078] The repeat function can estimate the user's emotions and adjust the way it repeats based on those emotions. For example, if the user is sad, the repeat function will repeat in a gentle tone. If the user is excited, the repeat function can also repeat in a lively tone. Furthermore, if the user is tired, the repeat function can repeat in a simple and intuitive way. For example, the repeat function can use voice analysis technology to estimate the user's emotions and adjust the way it repeats based on the results. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows for a more appropriate response by providing a way of repeating that matches the user's emotions. Some or all of the above processing in the repeat function may be performed using AI, for example, or without AI. For example, the repeat function can input the user's voice data into a generating AI and have the generating AI perform emotion estimation and adjustment of the repeat expression.

[0079] The repeat function can adjust the level of detail of its repetition based on the content of the conversation. For example, it can perform detailed repetition for important topics, and concise repetition for general topics. Furthermore, it can adjust the level of detail of its repetition based on the user's level of interest. For example, it can evaluate the content of the conversation using an analysis algorithm and adjust the level of detail of its repetition accordingly. The analysis algorithm evaluates the content based on its depth and topic, performing detailed repetition for important topics and concise repetition for general topics. This allows for more appropriate responses by providing a level of detail of repetition that matches the content of the conversation. Some or all of the above processing in the repeat function may be performed using AI, for example, or without AI. For example, the repeat function can input the content data of the conversation into a generating AI and have the generating AI adjust the level of detail of the repetition.

[0080] The repeat function can apply different repeat algorithms depending on the category of the conversation during repetition. For example, the repeat function can apply an emotional repeat algorithm to emotional topics. It can also apply an informational repeat algorithm to factual topics. Furthermore, the repeat function can select the optimal repeat algorithm depending on the category of the user's conversation. For example, the repeat function can classify the content of the conversation using an analysis algorithm and apply an appropriate repeat algorithm according to the category. The analysis algorithm performs emotional repeats for emotional topics and informational repeats for factual topics. This allows for more appropriate responses by providing repeat algorithms according to the category of the conversation. Some or all of the above processing in the repeat function may be performed using AI, for example, or without AI. For example, the repeat function can input conversation data into a generating AI and have the generating AI perform category classification and apply the repeat algorithm.

[0081] The repeat function can estimate the user's emotions and adjust the length of the repeat based on those emotions. For example, if the user is in a hurry, the repeat function will perform a short, concise repetition. If the user is relaxed, the repeat function can perform a more detailed repetition. Furthermore, if the user is excited, the repeat function can perform a visually stimulating repetition. For example, the repeat function can use voice analysis technology to estimate the user's emotions and adjust the length of the repeat based on the results. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows for a more appropriate response by providing a repeat length that matches the user's emotions. Some or all of the above processing in the repeat function may be performed using AI, for example, or without AI. For example, the repeat function can input the user's voice data into a generating AI and have the generating AI perform emotion estimation and repeat length adjustment.

[0082] The repeat function can determine the priority of repeats based on when the story was submitted. For example, the repeat function can prioritize repeating recent topics. It can also postpone repeating older topics. Furthermore, the repeat function can determine the optimal repeat order based on when the user submitted the story. For example, the repeat function can store the story submission dates in a database and use an analysis algorithm to determine the priority. The database stores the date, time, and order of story submissions, and the analysis algorithm identifies the priority based on this data. This allows for more appropriate responses by providing repeat priority according to the story submission date. Some or all of the above processing in the repeat function may be performed using AI, for example, or without AI. For example, the repeat function can input the story submission date data into a generating AI and have the generating AI perform the priority determination.

[0083] The repeat function can adjust the order of repetition based on the relevance of the conversation. For example, the repeat function prioritizes repeating highly relevant topics. It can also postpone repeating less relevant topics. Furthermore, the repeat function can determine the optimal repetition order based on the relevance of the user's conversation. For example, the repeat function can evaluate the content of the conversation using an analysis algorithm and adjust the repetition order according to relevance. The analysis algorithm evaluates the relevance of the conversation content, prioritizing the repetition of highly relevant topics and postponing less relevant topics. This allows for a more appropriate response by providing a repetition order that corresponds to the relevance of the conversation. Some or all of the above processing in the repeat function may be performed using AI, for example, or without AI. For example, the repeat function can input conversation data into a generating AI and have the generating AI perform the relevance evaluation and adjustment of the repetition order.

[0084] 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 sad, the delivery unit will deliver in a gentle tone. It can also deliver in an energetic tone if the user is excited. Furthermore, if the user is tired, the delivery unit can deliver in a simple and intuitive way. For example, the delivery unit can estimate the user's emotions using voice analysis technology and adjust the delivery method based on the results. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows for a more appropriate response by providing a delivery method that matches the user's emotions. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's voice data into a generating AI and have the generating AI perform emotion estimation and adjustment of the delivery method.

[0085] The service provider can adjust the level of detail provided based on the importance of the repeated results. For example, the service provider can provide detailed information for important repeated results. It can also provide concise information for general repeated results. Furthermore, the service provider can adjust the level of detail based on the user's level of interest. For example, the service provider can evaluate the repeated results using an analysis algorithm and adjust the level of detail according to their importance. The analysis algorithm evaluates the depth and impact of the repeated results, providing detailed information for important results and concise information for general results. This allows for a more appropriate response by providing a level of detail according to the importance of the repeated results. 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 data of the repeated results into a generating AI and have the generating AI perform the importance evaluation and adjustment of the level of detail of the provision.

[0086] The service provider can apply different service algorithms depending on the category of the repeated results at the time of delivery. For example, the service provider can apply an emotional service algorithm to emotionally charged repeated results. It can also apply an informational service algorithm to factually charged repeated results. Furthermore, the service provider can select the optimal service algorithm depending on the category of the user's repeated results. For example, the service provider can classify the repeated results using an analysis algorithm and apply an appropriate service algorithm according to the category. The analysis algorithm provides emotional service for emotionally charged repeated results and informational service for factually charged repeated results. This allows for more appropriate responses by providing service algorithms tailored to the category of the repeated results. 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 data of the repeated results into a generating AI and have the generating AI perform category classification and application of the service algorithm.

[0087] The delivery unit can estimate the user's emotions and adjust the length of the delivery based on the estimated emotions. For example, if the user is in a hurry, the delivery unit will provide a short, concise delivery. If the user is relaxed, the delivery unit can provide a detailed delivery. Furthermore, if the user is excited, the delivery unit can provide a visually stimulating delivery. For example, the delivery unit can use voice analysis technology to estimate the user's emotions and adjust the length of the delivery based on the results. Voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. This allows for a more appropriate response by providing a delivery length that matches the user's emotions. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's voice data into a generating AI and have the generating AI perform emotion estimation and delivery length adjustment.

[0088] The service provider can determine the priority of deliveries based on the submission timing of repeated results. For example, the service provider can prioritize the delivery of recent repeated results. It can also postpone the delivery of past repeated results. Furthermore, the service provider can determine the optimal delivery order based on the submission timing of the user's repeated results. For example, the service provider can store the submission timing of repeated results in a database and use an analysis algorithm to determine the priority. The database stores the submission date, time, and order of repeated results, and the analysis algorithm identifies the priority based on this data. This allows for more appropriate responses by providing a priority for deliveries according to the submission timing of repeated results. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input data on the submission timing of repeated results into a generating AI and have the generating AI perform the priority determination.

[0089] The service provider can adjust the order of delivery based on the relevance of the repeated results. For example, the service provider can prioritize the delivery of highly relevant repeated results. It can also postpone the delivery of less relevant repeated results. Furthermore, the service provider can determine the optimal delivery order based on the relevance of the user's repeated results. For example, the service provider can evaluate the repeated results using an analysis algorithm and adjust the delivery order according to their relevance. The analysis algorithm evaluates the relevance of the content of the repeated results, prioritizing the delivery of highly relevant results and postponing the delivery of less relevant results. This allows for a more appropriate response by providing a delivery order that corresponds to the relevance of the repeated results. 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 data of the repeated results into a generating AI and have the generating AI perform the relevance evaluation and adjustment of the delivery order.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The conversational system can also be equipped with a health management unit that monitors the user's health status. This unit acquires vital data such as the user's heart rate, blood pressure, and body temperature, and evaluates the user's health based on this data. For example, if the user is stressed, the health management unit can detect an increase in heart rate and provide advice for relaxation. Furthermore, if the user complains of feeling unwell, the health management unit can analyze body temperature and blood pressure data and suggest appropriate actions. In addition, the health management unit can monitor the user's health data over the long term and track changes in their health status. This supports the user's health management and provides them with a better quality of life.

[0092] The conversational partner system can also be equipped with a hobby learning unit that learns the user's hobbies and interests. This unit analyzes the user's conversations, search history, and social media activity to identify their hobbies and interests. For example, if a user frequently talks about movies, the hobby learning unit will determine the user is interested in movies and provide the latest movie information and recommendations. Similarly, if a user is interested in cooking, the hobby learning unit can suggest recipes and cooking tips. Furthermore, the hobby learning unit can adapt to changes in the user's interests and provide information based on those new hobbies and interests. This can enrich the user's life.

[0093] The conversation partner system can also include a schedule management unit to manage the user's schedule. This unit can register the user's appointments and set reminders. For example, if a user enters a meeting schedule, the schedule management unit will send a reminder at the meeting's start time. It can also provide advance notifications to ensure the user doesn't forget important events. Furthermore, the schedule management unit can provide optimal time management advice based on the user's schedule. This allows the user to manage their time efficiently and avoid forgetting appointments.

[0094] The conversational system can also include a learning support unit to assist the user's learning. The learning support unit creates a learning plan based on the user's input of what they want to learn. For example, if a user wants to learn a new language, the learning support unit suggests appropriate materials and a study schedule. If a user is studying for a specific exam, the learning support unit can provide exam preparation advice and practice tests. Furthermore, the learning support unit can track the user's learning progress and adjust the learning plan as needed. This allows the user to learn effectively.

[0095] The conversational system can also include a feedback collection unit to gather user feedback. This unit provides an interface where users can input their experience with the system and suggest improvements. For example, if a user is dissatisfied with the system's response speed, the feedback collection unit can collect this feedback and use it to improve the system. Furthermore, if a user suggests a new feature, the feedback collection unit can record the suggestion and share it with the development team. Additionally, the feedback collection unit can analyze user feedback to identify common problems and requests. This can improve system quality and increase user satisfaction.

[0096] The conversation partner system may also include a music provider that estimates the user's emotions and provides appropriate music based on those emotions. The music provider analyzes the user's emotions and selects music that matches those emotions. For example, if the user is sad, the music provider will provide relaxing music. If the user is excited, it can also provide energetic music. Furthermore, the music provider can switch music according to the user's changing emotions. This allows the user to enjoy music that matches their emotions and refresh their mood.

[0097] The conversational system can also include a relaxation suggestion unit that estimates the user's emotions and proposes relaxation methods based on those emotions. The relaxation suggestion unit analyzes the user's emotions and suggests relaxation methods appropriate to those emotions. For example, if the user is feeling stressed, the relaxation suggestion unit might suggest deep breathing or meditation. If the user is tired, the unit might suggest light stretching or taking a break. Furthermore, the relaxation suggestion unit can adjust the relaxation methods according to changes in the user's emotions. This allows the user to relax effectively and reduce stress.

[0098] The conversational system can also include an exercise suggestion unit that estimates the user's emotions and proposes appropriate exercises based on those emotions. The exercise suggestion unit analyzes the user's emotions and selects exercises that match those emotions. For example, if the user is feeling stressed, the exercise suggestion unit might suggest yoga or light jogging. If the user is feeling energetic, the exercise suggestion unit might also suggest high-intensity training. Furthermore, the exercise suggestion unit can adjust the exercise according to changes in the user's emotions. This allows the user to perform exercises that match their emotions and maintain their health.

[0099] The conversational system can also include a reading suggestion unit that estimates the user's emotions and suggests appropriate reading based on those emotions. The reading suggestion unit analyzes the user's emotions and selects a book that matches those emotions. For example, if the user is sad, the reading suggestion unit suggests a book with a heartwarming story. If the user is excited, it can also suggest a thrilling adventure novel. Furthermore, the reading suggestion unit can adjust its reading suggestions according to changes in the user's emotions. This allows the user to enjoy reading that matches their emotions and refresh their mood.

[0100] The conversational system can also include a movie suggestion unit that estimates the user's emotions and proposes an appropriate movie based on those emotions. The movie suggestion unit analyzes the user's emotions and selects a movie that matches those emotions. For example, if the user is sad, the movie suggestion unit will suggest an emotional movie. If the user is excited, it may also suggest an action movie. Furthermore, the movie suggestion unit can adjust its movie suggestions according to changes in the user's emotions. This allows the user to enjoy a movie that matches their emotions and refresh their mood.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk receives the user's story. The user's story may include everyday events and expressions of emotions. The reception desk can receive the user's story using voice input or text input. It can also record the user's story and save it for later analysis. For example, speech recognition technology can be used to convert the user's story into text data. Step 2: The analysis unit analyzes the story received by the reception unit. The analysis is based on the content of the story and the expression of emotions. Using a generation AI, the content of the story can be analyzed and important parts can be extracted. For example, the analysis can be performed using a text generation AI or a multimodal generation AI. Step 3: The interjection unit interjects based on the conversation analyzed by the analysis unit. Interjections are made using words such as "Oh really?", "Wow," and "That's amazing." Using a generation AI, the system selects appropriate interjections according to the content of the conversation and adjusts the timing of the interjections according to the user's emotions. Step 4: The repeating section repeats words expressing emotion after the nodding section provides verbal acknowledgments. The repetition includes words such as "That was fun," "That's sad," and "That's interesting." Using a generation AI, the appropriate repetition is selected according to the content of the conversation, and the way the repetition is expressed is adjusted according to the user's emotions. Step 5: The delivery unit provides the results repeated by the repeat unit. The delivery is done via voice or text. Using a generation AI, the repeated results are provided via voice using speech synthesis technology. The repeated results can also be displayed as text data.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the reception unit, analysis unit, interjection unit, repeat unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives the user's speech using the microphone 38B or touch panel 38A of the smart device 14. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the content of the speech using a generation AI. The interjection unit is implemented, for example, by the control unit 46A of the smart device 14, and provides appropriate interjections based on the analyzed speech. The repeat unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and repeats words that express emotion. The provision unit provides the repeated results using, for example, the speaker 40B or display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the reception unit, analysis unit, interjection unit, repeat unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives the user's speech using the microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the content of the speech using a generation AI. The interjection unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides appropriate interjections based on the analyzed speech. The repeat unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and repeats words that express emotion. The provision unit provides the repeated results 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.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, analysis unit, interjection unit, repeat unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives the user's speech using the microphone 238 of the headset terminal 314. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the content of the speech using a generation AI. The interjection unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides appropriate interjections based on the analyzed speech. The repeat unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and repeats words that express emotion. The provision unit provides the repeated results 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.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, analysis unit, interjection unit, repeat unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives the user's speech using the microphone 238 of the robot 414. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the content of the speech using a generation AI. The interjection unit is implemented, for example, in the control unit 46A of the robot 414, and provides appropriate interjections based on the analyzed speech. The repeat unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and repeats words that express emotion. The provision unit provides the repeated results 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 various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) A reception desk to receive user inquiries, An analysis unit analyzes the conversation received by the reception unit and interjects with responses at predetermined timings, The interjection unit provides interjections based on the analysis performed by the aforementioned analysis unit, After the aforementioned nodding section provides an affirmative response, the repeating section repeats words that express emotion, The system comprises a providing unit that provides the result repeated by the repeating unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts how it responds to conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past conversations and select the appropriate method of communication. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When receiving a message, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of conversations to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving a message, the system prioritizes receiving messages that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving a story, the system analyzes the user's social media activity and accepts relevant stories. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of each topic. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the discussion. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the presentations were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the discussion. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned hammer part is, It estimates the user's emotions and adjusts the timing of responses based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned hammer part is, When giving verbal cues, select the type of cues based on the content of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned hammer part is, When acknowledging a user's response, the system selects the appropriate response by referring to the user's past reactions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned hammer part is, It estimates the user's emotions and adjusts the intensity of the nods based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned hammer part is, When acknowledging a conversation, the system selects an appropriate response considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned hammer part is, When responding to a conversation, the system analyzes the user's social media activity to select the appropriate type of response. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned repeat section is, It estimates the user's emotions and adjusts the way repetition is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned repeat section is, When repeating, adjust the level of detail based on the content of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned repeat section is, When repeating, different repeat algorithms are applied depending on the category of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned repeat section is, It estimates the user's emotions and adjusts the length of repetition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned repeat section is, When repeating a request, the priority of repeat requests will be determined based on when the story was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned repeat section is, When repeating, adjust the order of repetition based on the relevance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 26) 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 27) The aforementioned supply unit is, When providing the service, adjust the level of detail based on the importance of the repeat results. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, a different service algorithm is applied depending on the category of the repeat result. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the service based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we will prioritize the delivery based on the timing of the submission of repeat results. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, adjust the order of delivery based on the relevance of the repeated results. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk to receive user inquiries, An analysis unit analyzes the conversation received by the reception unit and interjects with responses at predetermined timings, The interjection unit provides interjections based on the analysis performed by the aforementioned analysis unit, After the aforementioned nodding section provides an affirmative response, the repeating section repeats words that express emotion, The system comprises a providing unit that provides the result repeated by the repeating unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts how it responds to conversations based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past conversations and select the appropriate method of communication. The system according to feature 1.

4. The aforementioned reception unit is When receiving a message, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of conversations to accept based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving a message, the system prioritizes receiving messages that are highly relevant based on the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When receiving a story, the system analyzes the user's social media activity and accepts relevant stories. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of each topic. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the discussion. The system according to feature 1.

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