system

Abstract

The system according to the embodiment aims to reduce the psychological burden of interactions on a matching app. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a transmission unit. The reception unit receives a message from a user. The analysis unit analyzes the message received by the reception unit. The generation unit generates a reply based on the message analyzed by the analysis unit. The transmission unit transmits the reply generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that users feel psychologically burdened when interacting directly with people on dating apps.

[0005] The system according to the embodiment aims to reduce the psychological burden of interactions on a matching app. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a transmission unit. The reception unit receives a message from a user. The analysis unit analyzes the message received by the reception unit. The generation unit generates a reply based on the message analyzed by the analysis unit. The transmission unit transmits the reply generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the psychological burden of interactions on a matching app. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A matching app system according to an embodiment of the present invention reduces the psychological burden on users by allowing them to send messages via AI rather than directly. In this system, users input messages and send them to AI. The AI ​​then analyzes the user's message and generates an appropriate reply. The generated reply is then sent to the other user. This allows users to communicate via AI without sending messages directly. For example, if a user inputs "Hello," the AI ​​generates a reply such as "Hello, how are you?" and sends it to the other user. This mechanism reduces the psychological burden on users and allows them to enjoy communication more casually. Furthermore, the AI ​​has the ability to understand the user's emotions and intentions, enabling more natural communication. Furthermore, improving the accuracy of the AI's learning data and algorithms ensures the appropriateness of the generated replies. The interface used by users to send messages via AI is also designed to be easy to use. This allows the matching app system to reduce the psychological burden on users and enable more natural and appropriate communication.

[0029] A matching app system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a transmission unit. The reception unit receives a user's message. The user's message may include, but is not limited to, a text message, a voice message, or an image message. The reception unit transmits the message input by the user to an AI. The analysis unit analyzes the message received by the reception unit. The analysis may be performed using, for example, natural language processing technology or emotion analysis technology, but is not limited to, the examples. The analysis unit may also include, for example, an emotion analysis unit that analyzes the user's emotions and intentions. The generation unit generates a reply based on the message analyzed by the analysis unit. The reply may be generated by, for example, template-based generation or generation using a machine learning model, but is not limited to, the examples. The generation unit may also include, for example, a data management unit that manages learning data for the AI. The transmission unit transmits the reply generated by the generation unit. The transmission may be performed by, for example, real-time transmission or batch transmission, but is not limited to, the examples. The transmission unit may also include, for example, an interface unit that provides a user interface. As a result, the matching app system according to the embodiment processes the user's messages via AI, thereby reducing the psychological burden.

[0030] The generation unit may include a data management unit that manages the AI's learning data. The data management unit manages learning data such as text data, audio data, and image data. For example, the data management unit periodically updates the AI's learning data and performs learning based on the latest data. The data management unit may also manage the quality of the learning data and select appropriate data. Furthermore, the data management unit may ensure diversity in the learning data and improve the AI's learning accuracy. This allows the data management unit to manage the AI's learning data and ensure the appropriateness of the generated replies. Some or all of the above-described processing in the data management unit may be performed using, or without, AI. For example, the data management unit may input learning data to the generation AI and have the generation AI manage the data.

[0031] The transmission unit may include an interface unit that provides a user interface. The interface unit provides a user interface such as a web interface or a mobile application. For example, the interface unit allows a user to check the results of the generated AI and provide feedback. The interface unit may also design an easy-to-use interface to make it easy for the user to input messages. Furthermore, the interface unit may also customize the interface based on the user's operation history. This allows the interface unit to allow the user to check the results of the generated AI and provide feedback. Some or all of the above-described processing in the interface unit may be performed using AI, for example, or may be performed without using AI. For example, the interface unit may input the user's operation history into the generated AI and cause the generated AI to customize the interface.

[0032] The reception unit can send messages entered by the user to the AI. The reception unit, for example, sends messages entered by the user to the AI ​​via an API. For example, the reception unit sends text messages entered by the user to the AI ​​via the API. The reception unit can also send voice messages entered by the user to the AI ​​using a message queue. Furthermore, the reception unit can also send image messages entered by the user to the AI ​​in real time. This allows the reception unit to send messages entered by the user to the AI, making it easier for the AI ​​to analyze the messages. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, an AI. For example, the reception unit can input messages entered by the user to a generation AI and have the generation AI send the messages.

[0033] The generation unit can send a reply generated by the AI ​​to the other user. The generation unit sends the reply generated by the AI ​​to the other user, for example, by email or via a chat app. For example, the generation unit sends a text reply generated by the AI ​​to the other user by email. The generation unit can also send a voice reply generated by the AI ​​to the other user via a chat app. Furthermore, the generation unit can also send an image reply generated by the AI ​​to the other user in real time. This allows the generation unit to send the reply generated by the AI ​​to the other user, thereby reducing the psychological burden on the user. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input a reply generated by the AI ​​into the generation AI and cause the generation AI to send the reply.

[0034] The reception unit can analyze the user's past message history and select an appropriate reception method. For example, the reception unit prioritizes reception of message formats that the user has frequently used in the past. For example, the reception unit receives messages during a specific time period based on the user's past message history. The reception unit can also analyze the user's past message history and suggest an optimal message reception method. This allows the reception unit to analyze the user's past message history and select an optimal reception method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past message history into a generation AI and have the generation AI select a message reception method.

[0035] When receiving messages, the reception unit can prioritize receiving highly relevant messages by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving messages related to that area. For example, when the user is traveling, the reception unit prioritizes receiving messages related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving messages related to information about the area around the user's home. This allows the reception unit to prioritize receiving highly relevant messages based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant messages.

[0036] The reception unit can analyze the user's social media activity when receiving a message and receive related messages. For example, if the user frequently posts about a specific topic on social media, the reception unit can prioritize receiving messages related to that topic. For example, if the user is participating in a specific event, the reception unit can prioritize receiving messages related to that event. Furthermore, if the user belongs to a specific group, the reception unit can also prioritize receiving messages related to that group. This allows the reception unit to receive related messages based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to select related messages.

[0037] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the message. For example, the analysis unit analyzes messages of high importance in detail. For example, the analysis unit analyzes messages of low importance briefly. The analysis unit can also analyze messages of medium importance moderately. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the message and analyze important messages in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0038] During analysis, the analysis unit can apply different analysis algorithms depending on the message category. For example, the analysis unit applies a business analysis algorithm to business-related messages. For example, the analysis unit applies a private analysis algorithm to private messages. The analysis unit can also apply a rapid analysis algorithm to urgent messages. This allows the analysis unit to apply different analysis algorithms depending on the message category, allowing for more appropriate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input message category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0039] During analysis, the analysis unit can adjust the order of analysis based on the submission time of the message. For example, the analysis unit prioritizes analysis of recently submitted messages. For example, the analysis unit analyzes messages that were submitted recently later. The analysis unit can also moderately analyze messages that were submitted recently. This allows the analysis unit to adjust the order of analysis based on the submission time of the message and prioritize analysis of the most recent message. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message submission time data to the generation AI and have the generation AI adjust the order of analysis.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the messages. For example, the analysis unit prioritizes analyzing messages with high relevance. For example, the analysis unit analyzes messages with low relevance later. The analysis unit can also moderately analyze messages with medium relevance. This allows the analysis unit to adjust the order of analysis based on the relevance of the messages and prioritize analyzing highly relevant messages. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0041] The generation unit can adjust the level of detail of the reply based on the importance of the message during generation. For example, the generation unit generates a detailed reply for a message of high importance. For example, the generation unit generates a concise reply for a message of low importance. The generation unit can also generate a reply with appropriate detail for a message of medium importance. In this way, the generation unit can adjust the level of detail of the reply based on the importance of the message and generate an appropriate reply for an important message. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input message importance data to the generation AI and cause the generation AI to adjust the level of detail of the reply.

[0042] The generation unit can apply different generation algorithms depending on the message category during generation. For example, the generation unit applies a business generation algorithm to business-related messages. For example, the generation unit applies a private generation algorithm to private messages. The generation unit can also apply a quick generation algorithm to urgent messages. This allows the generation unit to apply different generation algorithms depending on the message category and generate a more appropriate reply. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input message category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0043] The generation unit can determine the priority of replies based on the submission time of the message at the time of generation. For example, the generation unit generates replies with priority to recently submitted messages. For example, the generation unit generates replies later for messages that were submitted recently. The generation unit can also generate replies appropriately for messages that were submitted recently. This allows the generation unit to determine the priority of replies based on the submission time of the message and quickly reply to the most recent message. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input message submission time data into the generation AI and have the generation AI determine the priority of replies.

[0044] The generation unit can adjust the order of replies based on the relevance of the messages during generation. For example, the generation unit generates replies preferentially for messages with high relevance. For example, the generation unit generates replies later for messages with low relevance. The generation unit can also generate replies appropriately for messages with medium relevance. This allows the generation unit to adjust the order of replies based on the relevance of the messages and prioritize replies to highly relevant messages. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input message relevance data into the generation AI and cause the generation AI to adjust the order of replies.

[0045] The transmitting unit can determine the priority of transmission based on the importance of the message at the time of transmission. For example, the transmitting unit prioritizes transmission of messages with high importance. For example, the transmitting unit transmits messages with low importance later. The transmitting unit can also moderately transmit messages with medium importance. This allows the transmitting unit to determine the priority of transmission based on the importance of the message and transmit important messages with priority. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input message importance data to a generating AI and have the generating AI determine the priority of transmission.

[0046] The sending unit can apply different sending methods depending on the message category when sending. For example, the sending unit applies a business sending method to a business-related message. For example, the sending unit applies a private sending method to a private message. The sending unit can also apply a rapid sending method to a highly urgent message. This allows the sending unit to apply different sending methods depending on the message category, allowing for more appropriate sending. Some or all of the above-mentioned processing in the sending unit may be performed using AI, for example, or may be performed without using AI. For example, the sending unit can input message category data to a generation AI and have the generation AI apply the sending method.

[0047] The sending unit can determine the priority of sending based on the submission time of the message at the time of sending. For example, the sending unit prioritizes sending recently submitted messages. For example, the sending unit postpones sending messages that were submitted recently. The sending unit can also moderately send messages that were submitted recently. This allows the sending unit to determine the priority of sending based on the submission time of the message and prioritize sending the most recent message. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input message submission time data to the generation AI and have the generation AI determine the priority of sending.

[0048] The transmitting unit can adjust the order of transmission based on the relevance of the messages at the time of transmission. For example, the transmitting unit prioritizes sending messages with high relevance. For example, the transmitting unit postpones sending messages with low relevance. The transmitting unit can also moderately send messages with medium relevance. This allows the transmitting unit to adjust the order of transmission based on the relevance of the messages and prioritize sending highly relevant messages. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input message relevance data to a generating AI and cause the generating AI to adjust the order of transmission.

[0049] During emotion analysis, the emotion analysis unit can weight the analysis data based on the time the message was submitted. For example, the emotion analysis unit may assign a high weight to the emotion data of a recently submitted message. For example, the emotion analysis unit may assign a low weight to the emotion data of a recently submitted message. The emotion analysis unit can also assign a moderate weight to the emotion data of a recently submitted message. This allows the emotion analysis unit to weight the analysis data based on the time the message was submitted, thereby performing analysis that emphasizes the most recent emotion data. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the emotion analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the emotion analysis unit may input message submission time data to the generation AI and have the generation AI perform weighting of the analysis data.

[0050] During data management, the data management unit can optimize the data management algorithm by referring to past data. The data management unit, for example, adjusts the current data management algorithm based on past data. For example, the data management unit improves the efficiency of data management by referring to past data. The data management unit can also improve the accuracy of the data management algorithm by using past data. This allows the data management unit to optimize the current data management algorithm by referring to past data. Some or all of the above-mentioned processing in the data management unit may be performed using, for example, AI, or may be performed without using AI. For example, the data management unit can input past data into a generation AI and have the generation AI optimize the data management algorithm.

[0051] During data management, the data management unit can weight data based on the time the message was submitted. For example, the data management unit assigns a high weight to data of recently submitted messages. For example, the data management unit assigns a low weight to data of messages submitted a long time ago. The data management unit can also assign a moderate weight to data of messages submitted a medium time ago. This allows the data management unit to weight data based on the time the message was submitted, and manage data with an emphasis on the most recent data. Some or all of the above-described processing in the data management unit may be performed using, for example, AI, or may be performed without using AI. For example, the data management unit can input message submission time data into a generation AI and have the generation AI perform data weighting.

[0052] When displaying an interface, the interface unit can select the optimal display method by referring to the user's past operation history. For example, the interface unit can preferentially display interface designs that the user has used favorably in the past. For example, the interface unit can suggest the most efficient display method based on the user's past operation history. The interface unit can also provide a customized interface based on the user's past operation history. This allows the interface unit to select the optimal display method by referring to the user's past operation history. Some or all of the above-mentioned processing in the interface unit may be performed using, or without, AI, for example. For example, the interface unit can input the user's operation history data into a generation AI and have the generation AI select a display method.

[0053] When displaying the interface, the interface unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. For example, if the user is using a tablet, the interface unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the interface unit can also provide a simple and highly visible display method. This allows the interface unit to select the optimal display method based on the user's device information and provide a more appropriate display. Some or all of the above-described processing in the interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the interface unit can input the user's device information to a generation AI and have the generation AI select a display method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When accepting a user's message, the acceptance unit can analyze the user's past message history and select an appropriate acceptance method. For example, the acceptance unit can prioritize message formats that the user has frequently used in the past. It can also accept messages during specific time periods based on the user's past message history. Furthermore, it can analyze the user's past message history and suggest the optimal message acceptance method. This allows the acceptance unit to analyze the user's past message history and select the optimal acceptance method.

[0056] The generation unit can include a data management unit that manages the AI's learning data. The data management unit manages learning data such as text data, audio data, and image data. For example, the data management unit periodically updates the AI's learning data and performs learning based on the latest data. The data management unit can also manage the quality of the learning data and select appropriate data. Furthermore, the data management unit can ensure diversity in the learning data and improve the AI's learning accuracy. In this way, the data management unit can manage the AI's learning data and ensure the appropriateness of the replies generated.

[0057] The transmission unit can include an interface unit that provides a user interface. The interface unit provides a user interface such as a web interface or a mobile application. For example, the interface unit allows a user to check the results of the generated AI and provide feedback. The interface unit can also design an easy-to-use interface to make it easy for the user to input messages. Furthermore, the interface unit can customize the interface based on the user's operation history. In this way, the interface unit can allow a user to check the results of the generated AI and provide feedback.

[0058] The reception unit can send messages entered by the user to the AI. The reception unit sends messages entered by the user to the AI, for example, via an API. For example, the reception unit sends text messages entered by the user to the AI ​​via the API. The reception unit can also send voice messages entered by the user to the AI ​​using a message queue. Furthermore, the reception unit can also send image messages entered by the user to the AI ​​in real time. This allows the reception unit to send messages entered by the user to the AI, making it easier for the AI ​​to analyze the messages.

[0059] The generation unit can send a reply generated by the AI ​​to the other user. The generation unit sends the reply generated by the AI ​​to the other user, for example, by email or via a chat app. For example, the generation unit sends a text reply generated by the AI ​​to the other user by email. The generation unit can also send a voice reply generated by the AI ​​to the other user via a chat app. Furthermore, the generation unit can also send an image reply generated by the AI ​​to the other user in real time. In this way, the generation unit can send the reply generated by the AI ​​to the other user, reducing the psychological burden on the user.

[0060] The reception unit can analyze the user's past message history and select an appropriate reception method. For example, the reception unit prioritizes reception of message formats that the user has frequently used in the past. For example, the reception unit may receive messages during a specific time period based on the user's past message history. The reception unit can also analyze the user's past message history and suggest the optimal message reception method. This allows the reception unit to analyze the user's past message history and select the optimal reception method.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives the user's message. The user's message can be a text message, a voice message, or an image message. The reception unit sends the message entered by the user to the AI. Step 2: The analysis unit analyzes the message received by the reception unit. The analysis is performed using natural language processing technology and emotion analysis technology. The analysis unit may also include an emotion analysis unit that analyzes the user's emotions and intentions. Step 3: The generation unit generates a reply based on the message analyzed by the analysis unit. The reply is generated using a method such as template-based generation or machine learning model generation. The generation unit may also include a data management unit that manages the AI's learning data. Step 4: The sending unit sends the reply generated by the generating unit. The sending is performed by a method such as real-time sending or batch sending. The sending unit may also include an interface unit that provides a user interface.

[0063] (Example 2) A matching app system according to an embodiment of the present invention reduces the psychological burden on users by allowing them to send messages via AI rather than directly. In this system, users input messages and send them to AI. The AI ​​then analyzes the user's message and generates an appropriate reply. The generated reply is then sent to the other user. This allows users to communicate via AI without sending messages directly. For example, if a user inputs "Hello," the AI ​​generates a reply such as "Hello, how are you?" and sends it to the other user. This mechanism reduces the psychological burden on users and allows them to enjoy communication more casually. Furthermore, the AI ​​has the ability to understand the user's emotions and intentions, enabling more natural communication. Furthermore, improving the accuracy of the AI's learning data and algorithms ensures the appropriateness of the generated replies. The interface used by users to send messages via AI is also designed to be easy to use. This allows the matching app system to reduce the psychological burden on users and enable more natural and appropriate communication.

[0064] A matching app system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a transmission unit. The reception unit receives a user's message. The user's message may include, but is not limited to, a text message, a voice message, or an image message. The reception unit transmits the message input by the user to an AI. The analysis unit analyzes the message received by the reception unit. The analysis may be performed using, for example, natural language processing technology or emotion analysis technology, but is not limited to, the examples. The analysis unit may also include, for example, an emotion analysis unit that analyzes the user's emotions and intentions. The generation unit generates a reply based on the message analyzed by the analysis unit. The reply may be generated by, for example, template-based generation or generation using a machine learning model, but is not limited to, the examples. The generation unit may also include, for example, a data management unit that manages learning data for the AI. The transmission unit transmits the reply generated by the generation unit. The transmission may be performed by, for example, real-time transmission or batch transmission, but is not limited to, the examples. The transmission unit may also include, for example, an interface unit that provides a user interface. As a result, the matching app system according to the embodiment processes the user's messages via AI, thereby reducing the psychological burden.

[0065] The analysis unit may include an emotion analysis unit that analyzes a user's emotions and intentions. The emotion analysis unit analyzes a user's emotions and intentions using, for example, an emotion analysis algorithm or intent recognition technology. For example, the emotion analysis unit can estimate emotions from a user's text message. The emotion analysis unit can also estimate emotions by analyzing the tone and speed of voice from a voice message. Furthermore, the emotion analysis unit can also estimate emotions by analyzing facial expressions from an image message. This allows the emotion analysis unit to understand a user's emotions and intentions and achieve more natural communication. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emotion analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the emotion analysis unit may input a user's text message to the generation AI and have the generation AI perform emotion estimation.

[0066] The generation unit may include a data management unit that manages the AI's learning data. The data management unit manages learning data such as text data, audio data, and image data. For example, the data management unit periodically updates the AI's learning data and performs learning based on the latest data. The data management unit may also manage the quality of the learning data and select appropriate data. Furthermore, the data management unit may ensure diversity in the learning data and improve the AI's learning accuracy. This allows the data management unit to manage the AI's learning data and ensure the appropriateness of the generated replies. Some or all of the above-described processing in the data management unit may be performed using, or without, AI. For example, the data management unit may input learning data to the generation AI and have the generation AI manage the data.

[0067] The transmission unit may include an interface unit that provides a user interface. The interface unit provides a user interface such as a web interface or a mobile application. For example, the interface unit allows a user to check the results of the generated AI and provide feedback. The interface unit may also design an easy-to-use interface to make it easy for the user to input messages. Furthermore, the interface unit may also customize the interface based on the user's operation history. This allows the interface unit to allow the user to check the results of the generated AI and provide feedback. Some or all of the above-described processing in the interface unit may be performed using AI, for example, or may be performed without using AI. For example, the interface unit may input the user's operation history into the generated AI and cause the generated AI to customize the interface.

[0068] The reception unit can send messages entered by the user to the AI. The reception unit, for example, sends messages entered by the user to the AI ​​via an API. For example, the reception unit sends text messages entered by the user to the AI ​​via the API. The reception unit can also send voice messages entered by the user to the AI ​​using a message queue. Furthermore, the reception unit can also send image messages entered by the user to the AI ​​in real time. This allows the reception unit to send messages entered by the user to the AI, making it easier for the AI ​​to analyze the messages. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, an AI. For example, the reception unit can input messages entered by the user to a generation AI and have the generation AI send the messages.

[0069] The generation unit can send a reply generated by the AI ​​to the other user. The generation unit sends the reply generated by the AI ​​to the other user, for example, by email or via a chat app. For example, the generation unit sends a text reply generated by the AI ​​to the other user by email. The generation unit can also send a voice reply generated by the AI ​​to the other user via a chat app. Furthermore, the generation unit can also send an image reply generated by the AI ​​to the other user in real time. This allows the generation unit to send the reply generated by the AI ​​to the other user, thereby reducing the psychological burden on the user. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input a reply generated by the AI ​​into the generation AI and cause the generation AI to send the reply.

[0070] The reception unit can estimate the user's emotions and adjust the timing of message reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit temporarily delays message reception to provide time for the user to relax. For example, if the user is relaxed, the reception unit can immediately receive messages to promote smooth communication. Furthermore, if the user is in a hurry, the reception unit can prioritize message reception and provide a prompt response. This allows the reception unit to adjust the timing of message reception according to the user's emotions and receive messages at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0071] The reception unit can analyze the user's past message history and select an appropriate reception method. For example, the reception unit prioritizes reception of message formats that the user has frequently used in the past. For example, the reception unit receives messages during a specific time period based on the user's past message history. The reception unit can also analyze the user's past message history and suggest an optimal message reception method. This allows the reception unit to analyze the user's past message history and select an optimal reception method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past message history into a generation AI and have the generation AI select a message reception method.

[0072] The reception unit can filter messages based on the user's current psychological state when receiving them. For example, if the user is feeling stressed, the reception unit can receive only positive messages. For example, if the user is relaxed, the reception unit can receive all messages. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving only important messages. This allows the reception unit to filter messages according to the user's psychological state and receive more appropriate messages. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's psychological state data into the generation AI and have the generation AI perform psychological state estimation.

[0073] The reception unit can estimate the user's emotions and determine the priority of messages to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit prioritizes messages of high importance. For example, when the user is relaxed, the reception unit equally accepts all messages. Furthermore, when the user is in a hurry, the reception unit can prioritize messages of high urgency. This allows the reception unit to prioritize messages according to the user's emotions and prioritize important messages. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0074] When receiving messages, the reception unit can prioritize receiving highly relevant messages by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving messages related to that area. For example, when the user is traveling, the reception unit prioritizes receiving messages related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving messages related to information about the area around the user's home. This allows the reception unit to prioritize receiving highly relevant messages based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant messages.

[0075] The reception unit can analyze the user's social media activity when receiving a message and receive related messages. For example, if the user frequently posts about a specific topic on social media, the reception unit can prioritize receiving messages related to that topic. For example, if the user is participating in a specific event, the reception unit can prioritize receiving messages related to that event. Furthermore, if the user belongs to a specific group, the reception unit can also prioritize receiving messages related to that group. This allows the reception unit to receive related messages based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to select related messages.

[0076] The analysis unit can estimate the user's emotions and adjust the message analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit performs a concise and clear analysis. For example, if the user is relaxed, the analysis unit performs a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis. This allows the analysis unit to adjust the message analysis method according to the user's emotions and perform a more appropriate analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the message. For example, the analysis unit analyzes messages of high importance in detail. For example, the analysis unit analyzes messages of low importance briefly. The analysis unit can also analyze messages of medium importance moderately. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the message and analyze important messages in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the message category. For example, the analysis unit applies a business analysis algorithm to business-related messages. For example, the analysis unit applies a private analysis algorithm to private messages. The analysis unit can also apply a rapid analysis algorithm to urgent messages. This allows the analysis unit to apply different analysis algorithms depending on the message category, allowing for more appropriate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input message category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0079] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit prioritizes analyzing messages with high importance. For example, if the user is relaxed, the analysis unit analyzes all messages equally. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing messages with high urgency. This allows the analysis unit to determine the analysis priority according to the user's emotions and prioritize analyzing important messages. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0080] During analysis, the analysis unit can adjust the order of analysis based on the submission time of the message. For example, the analysis unit prioritizes analysis of recently submitted messages. For example, the analysis unit analyzes messages that were submitted recently later. The analysis unit can also moderately analyze messages that were submitted recently. This allows the analysis unit to adjust the order of analysis based on the submission time of the message and prioritize analysis of the most recent message. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message submission time data to the generation AI and have the generation AI adjust the order of analysis.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the messages. For example, the analysis unit prioritizes analyzing messages with high relevance. For example, the analysis unit analyzes messages with low relevance later. The analysis unit can also moderately analyze messages with medium relevance. This allows the analysis unit to adjust the order of analysis based on the relevance of the messages and prioritize analyzing highly relevant messages. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0082] The generation unit can estimate the user's emotions and adjust the reply generation method based on the estimated user emotions. For example, if the user is stressed, the generation unit generates a concise and clear reply. For example, if the user is relaxed, the generation unit generates a detailed reply. Furthermore, if the user is in a hurry, the generation unit can also generate a quick reply. This allows the generation unit to adjust the reply generation method according to the user's emotions and generate a more appropriate reply. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0083] The generation unit can adjust the level of detail of the reply based on the importance of the message during generation. For example, the generation unit generates a detailed reply for a message of high importance. For example, the generation unit generates a concise reply for a message of low importance. The generation unit can also generate a reply with appropriate detail for a message of medium importance. In this way, the generation unit can adjust the level of detail of the reply based on the importance of the message and generate an appropriate reply for an important message. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input message importance data to the generation AI and cause the generation AI to adjust the level of detail of the reply.

[0084] The generation unit can apply different generation algorithms depending on the message category during generation. For example, the generation unit applies a business generation algorithm to business-related messages. For example, the generation unit applies a private generation algorithm to private messages. The generation unit can also apply a quick generation algorithm to urgent messages. This allows the generation unit to apply different generation algorithms depending on the message category and generate a more appropriate reply. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input message category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0085] The generation unit can estimate the user's emotions and adjust the length of the reply based on the estimated user emotions. For example, if the user is stressed, the generation unit generates a short, to-the-point reply. For example, if the user is relaxed, the generation unit generates a longer reply with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can also generate a quick, concise reply. This allows the generation unit to adjust the length of the reply according to the user's emotions and generate a more appropriate reply. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0086] The generation unit can determine the priority of replies based on the submission time of the message at the time of generation. For example, the generation unit generates replies with priority to recently submitted messages. For example, the generation unit generates replies later for messages that were submitted recently. The generation unit can also generate replies appropriately for messages that were submitted recently. This allows the generation unit to determine the priority of replies based on the submission time of the message and quickly reply to the most recent message. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input message submission time data into the generation AI and have the generation AI determine the priority of replies.

[0087] The generation unit can adjust the order of replies based on the relevance of the messages during generation. For example, the generation unit generates replies preferentially for messages with high relevance. For example, the generation unit generates replies later for messages with low relevance. The generation unit can also generate replies appropriately for messages with medium relevance. This allows the generation unit to adjust the order of replies based on the relevance of the messages and prioritize replies to highly relevant messages. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input message relevance data into the generation AI and cause the generation AI to adjust the order of replies.

[0088] The transmission unit can estimate the user's emotions and adjust the timing of sending a reply based on the estimated user's emotions. For example, if the user is feeling stressed, the transmission unit temporarily delays sending a reply to provide time for the user to relax. For example, if the user is relaxed, the transmission unit can immediately send a reply to promote smooth communication. Furthermore, if the user is in a hurry, the transmission unit can prioritize sending a reply to provide a quick response. This allows the transmission unit to adjust the timing of sending a reply according to the user's emotions and send a reply at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or without AI. For example, the transmission unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0089] The transmitting unit can determine the priority of transmission based on the importance of the message at the time of transmission. For example, the transmitting unit prioritizes transmission of messages with high importance. For example, the transmitting unit transmits messages with low importance later. The transmitting unit can also moderately transmit messages with medium importance. This allows the transmitting unit to determine the priority of transmission based on the importance of the message and transmit important messages with priority. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input message importance data to a generating AI and have the generating AI determine the priority of transmission.

[0090] The sending unit can apply different sending methods depending on the message category when sending. For example, the sending unit applies a business sending method to a business-related message. For example, the sending unit applies a private sending method to a private message. The sending unit can also apply a rapid sending method to a highly urgent message. This allows the sending unit to apply different sending methods depending on the message category, allowing for more appropriate sending. Some or all of the above-mentioned processing in the sending unit may be performed using AI, for example, or may be performed without using AI. For example, the sending unit can input message category data to a generation AI and have the generation AI apply the sending method.

[0091] The transmission unit can estimate the user's emotions and adjust the order of transmission based on the estimated user emotions. For example, if the user is feeling stressed, the transmission unit prioritizes sending messages of high importance. For example, if the user is relaxed, the transmission unit transmits all messages equally. Furthermore, if the user is in a hurry, the transmission unit can prioritize sending messages of high urgency. This allows the transmission unit to adjust the order of transmission according to the user's emotions and prioritize sending important messages. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0092] The sending unit can determine the priority of sending based on the submission time of the message at the time of sending. For example, the sending unit prioritizes sending recently submitted messages. For example, the sending unit postpones sending messages that were submitted recently. The sending unit can also moderately send messages that were submitted recently. This allows the sending unit to determine the priority of sending based on the submission time of the message and prioritize sending the most recent message. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input message submission time data to the generation AI and have the generation AI determine the priority of sending.

[0093] The transmitting unit can adjust the order of transmission based on the relevance of the messages at the time of transmission. For example, the transmitting unit prioritizes sending messages with high relevance. For example, the transmitting unit postpones sending messages with low relevance. The transmitting unit can also moderately send messages with medium relevance. This allows the transmitting unit to adjust the order of transmission based on the relevance of the messages and prioritize sending highly relevant messages. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input message relevance data to a generating AI and cause the generating AI to adjust the order of transmission.

[0094] The emotion analysis unit can estimate the user's emotions and improve the accuracy of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the emotion analysis unit collects additional data to improve the accuracy of the emotion analysis. For example, if the user is relaxed, the emotion analysis unit performs normal emotion analysis. Alternatively, if the user is in a hurry, the emotion analysis unit can perform quick emotion analysis. This allows the emotion analysis unit to improve the accuracy of the analysis based on the user's emotions and perform more accurate analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the emotion analysis unit can be performed using, for example, an AI, or without an AI. For example, the emotion analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0095] During emotion analysis, the emotion analysis unit can optimize the analysis algorithm by referring to the user's past emotion data. The emotion analysis unit, for example, more accurately estimates the user's current emotion based on the user's past emotion data. For example, the emotion analysis unit adjusts the emotion analysis algorithm by referring to the user's past emotion data. The emotion analysis unit can also improve the accuracy of emotion analysis by using the user's past emotion data. This allows the emotion analysis unit to more accurately estimate the user's current emotion by referring to the user's past emotion data. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the emotion analysis unit may be performed using an AI, or may be performed without an AI. For example, the emotion analysis unit can input the user's past emotion data into the generation AI and have the generation AI perform emotion estimation.

[0096] The emotion analysis unit can estimate the user's emotion and adjust the frequency of analysis based on the estimated user emotion. For example, the emotion analysis unit can increase the frequency of emotion analysis when the user is stressed. For example, the emotion analysis unit can maintain a normal emotion analysis frequency when the user is relaxed. The emotion analysis unit can also decrease the frequency of emotion analysis when the user is in a hurry. This allows the emotion analysis unit to adjust the analysis frequency according to the user's emotion and perform analysis at a more appropriate frequency. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the emotion analysis unit can be performed using, for example, AI, or without AI. For example, the emotion analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0097] During emotion analysis, the emotion analysis unit can weight the analysis data based on the time the message was submitted. For example, the emotion analysis unit may assign a high weight to the emotion data of a recently submitted message. For example, the emotion analysis unit may assign a low weight to the emotion data of a recently submitted message. The emotion analysis unit can also assign a moderate weight to the emotion data of a recently submitted message. This allows the emotion analysis unit to weight the analysis data based on the time the message was submitted, thereby performing analysis that emphasizes the most recent emotion data. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the emotion analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the emotion analysis unit may input message submission time data to the generation AI and have the generation AI perform weighting of the analysis data.

[0098] The data management unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is feeling stressed, the data management unit prioritizes positive emotion data as training data. For example, if the user is relaxed, the data management unit selects all emotion data equally as training data. Furthermore, if the user is in a hurry, the data management unit can quickly select training data. This allows the data management unit to select training data based on the user's emotions and select more appropriate training data. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the data management unit may be performed using AI, or may be performed without AI. For example, the data management unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0099] During data management, the data management unit can optimize the data management algorithm by referring to past data. The data management unit, for example, adjusts the current data management algorithm based on past data. For example, the data management unit improves the efficiency of data management by referring to past data. The data management unit can also improve the accuracy of the data management algorithm by using past data. This allows the data management unit to optimize the current data management algorithm by referring to past data. Some or all of the above-mentioned processing in the data management unit may be performed using, for example, AI, or may be performed without using AI. For example, the data management unit can input past data into a generation AI and have the generation AI optimize the data management algorithm.

[0100] The data management unit can estimate the user's emotions and adjust the frequency of data management based on the estimated user emotions. For example, the data management unit can increase the frequency of data management when the user is feeling stressed. For example, the data management unit can maintain a normal data management frequency when the user is relaxed. The data management unit can also decrease the frequency of data management when the user is in a hurry. This allows the data management unit to adjust the frequency of data management according to the user's emotions and manage data at a more appropriate frequency. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the data management unit can be performed using, for example, AI, or without AI. For example, the data management unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0101] During data management, the data management unit can weight data based on the time the message was submitted. For example, the data management unit assigns a high weight to data of recently submitted messages. For example, the data management unit assigns a low weight to data of messages submitted a long time ago. The data management unit can also assign a moderate weight to data of messages submitted a medium time ago. This allows the data management unit to weight data based on the time the message was submitted, and manage data with an emphasis on the most recent data. Some or all of the above-described processing in the data management unit may be performed using, for example, AI, or may be performed without using AI. For example, the data management unit can input message submission time data into a generation AI and have the generation AI perform data weighting.

[0102] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is feeling stressed, the interface unit provides an interface with subdued colors. For example, if the user is relaxed, the interface unit provides an interface with bright colors. Furthermore, if the user is in a hurry, the interface unit can also provide a simple, highly visible interface. This allows the interface unit to adjust the interface display method based on the user's emotions and provide a more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0103] When displaying an interface, the interface unit can select the optimal display method by referring to the user's past operation history. For example, the interface unit can preferentially display interface designs that the user has used favorably in the past. For example, the interface unit can suggest the most efficient display method based on the user's past operation history. The interface unit can also provide a customized interface based on the user's past operation history. This allows the interface unit to select the optimal display method by referring to the user's past operation history. Some or all of the above-mentioned processing in the interface unit may be performed using, or without, AI, for example. For example, the interface unit can input the user's operation history data into a generation AI and have the generation AI select a display method.

[0104] The interface unit can estimate the user's emotions and adjust the interface operation procedures based on the estimated user emotions. For example, the interface unit simplifies the operation procedures when the user is feeling stressed. For example, the interface unit provides detailed operation procedures when the user is relaxed. The interface unit can also provide procedures that allow the user to operate quickly when the user is in a hurry. This allows the interface unit to adjust the interface operation procedures based on the user's emotions and perform more appropriate operations. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the interface unit can be performed using AI, for example, or without AI. For example, the interface unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0105] When displaying the interface, the interface unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. For example, if the user is using a tablet, the interface unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the interface unit can also provide a simple and highly visible display method. This allows the interface unit to select the optimal display method based on the user's device information and provide a more appropriate display. Some or all of the above-described processing in the interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the interface unit can input the user's device information to a generation AI and have the generation AI select a display method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and transmission unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives a user's message. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received message. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a reply based on the analysis result. The transmission unit is realized by the communication I / F 44 of the smart device 14 and transmits the generated reply. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and transmission unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a user's message. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received message. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a reply based on the analysis result. The transmission unit is realized, for example, by the communication I / F 44 of the smart glasses 214 and transmits the generated reply. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and transmission unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives a user's message. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received message. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a reply based on the analysis result. The transmission unit is realized, for example, by the communication I / F 44 of the headset type terminal 314 and transmits the generated reply. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and transmission unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives a message from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received message. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a reply based on the analysis result. The transmission unit is realized, for example, by the communication I / F 44 of the robot 414 and transmits the generated reply.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When accepting a user's message, the acceptance unit can analyze the user's past message history and select an appropriate acceptance method. For example, the acceptance unit can prioritize message formats that the user has frequently used in the past. It can also accept messages during specific time periods based on the user's past message history. Furthermore, it can analyze the user's past message history and suggest the optimal message acceptance method. This allows the acceptance unit to analyze the user's past message history and select the optimal acceptance method.

[0108] The analysis unit may include an emotion analysis unit that analyzes the user's emotions and intentions. The emotion analysis unit analyzes the user's emotions and intentions using, for example, an emotion analysis algorithm or an intention recognition technology. For example, the emotion analysis unit can infer emotions from a user's text message. The emotion analysis unit can also infer emotions from a voice message by analyzing the tone and speed of the voice. Furthermore, the emotion analysis unit can infer emotions from an image message by analyzing facial expressions. This allows the emotion analysis unit to understand the user's emotions and intentions, enabling more natural communication.

[0109] The generation unit can include a data management unit that manages the AI's learning data. The data management unit manages learning data such as text data, audio data, and image data. For example, the data management unit periodically updates the AI's learning data and performs learning based on the latest data. The data management unit can also manage the quality of the learning data and select appropriate data. Furthermore, the data management unit can ensure diversity in the learning data and improve the AI's learning accuracy. In this way, the data management unit can manage the AI's learning data and ensure the appropriateness of the replies generated.

[0110] The transmission unit can include an interface unit that provides a user interface. The interface unit provides a user interface such as a web interface or a mobile application. For example, the interface unit allows a user to check the results of the generated AI and provide feedback. The interface unit can also design an easy-to-use interface to make it easy for the user to input messages. Furthermore, the interface unit can customize the interface based on the user's operation history. In this way, the interface unit can allow a user to check the results of the generated AI and provide feedback.

[0111] The reception unit can send messages entered by the user to the AI. The reception unit sends messages entered by the user to the AI, for example, via an API. For example, the reception unit sends text messages entered by the user to the AI ​​via the API. The reception unit can also send voice messages entered by the user to the AI ​​using a message queue. Furthermore, the reception unit can also send image messages entered by the user to the AI ​​in real time. This allows the reception unit to send messages entered by the user to the AI, making it easier for the AI ​​to analyze the messages.

[0112] The generation unit can send a reply generated by the AI ​​to the other user. The generation unit sends the reply generated by the AI ​​to the other user, for example, by email or via a chat app. For example, the generation unit sends a text reply generated by the AI ​​to the other user by email. The generation unit can also send a voice reply generated by the AI ​​to the other user via a chat app. Furthermore, the generation unit can also send an image reply generated by the AI ​​to the other user in real time. In this way, the generation unit can send the reply generated by the AI ​​to the other user, reducing the psychological burden on the user.

[0113] The reception unit can estimate the user's emotions and adjust the timing of message reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit temporarily delays message reception to provide time for the user to relax. For example, if the user is relaxed, the reception unit immediately receives messages to promote smooth communication. Furthermore, if the user is in a hurry, the reception unit can prioritize message reception and provide a prompt response. This allows the reception unit to adjust the timing of message reception according to the user's emotions and receive messages at more appropriate times.

[0114] The reception unit can analyze the user's past message history and select an appropriate reception method. For example, the reception unit prioritizes reception of message formats that the user has frequently used in the past. For example, the reception unit may receive messages during a specific time period based on the user's past message history. The reception unit can also analyze the user's past message history and suggest the optimal message reception method. This allows the reception unit to analyze the user's past message history and select the optimal reception method.

[0115] The reception unit can filter messages based on the user's current psychological state when receiving the messages. For example, if the user is feeling stressed, the reception unit receives only positive messages. For example, if the user is relaxed, the reception unit receives all messages. Furthermore, if the user is in a hurry, the reception unit can also prioritize receiving only important messages. This allows the reception unit to filter messages according to the user's psychological state and receive more appropriate messages.

[0116] The reception unit can estimate the user's emotions and determine the priority of messages to be received based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit prioritizes receiving messages of high importance. For example, when the user is relaxed, the reception unit equally receives all messages. Furthermore, when the user is in a hurry, the reception unit can also prioritize receiving messages of high urgency. In this way, the reception unit can determine the priority of messages according to the user's emotions and prioritize receiving important messages.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives the user's message. The user's message can be a text message, a voice message, or an image message. The reception unit sends the message entered by the user to the AI. Step 2: The analysis unit analyzes the message received by the reception unit. The analysis is performed using natural language processing technology and emotion analysis technology. The analysis unit may also include an emotion analysis unit that analyzes the user's emotions and intentions. Step 3: The generation unit generates a reply based on the message analyzed by the analysis unit. The reply is generated using a method such as template-based generation or machine learning model generation. The generation unit may also include a data management unit that manages the AI's learning data. Step 4: The sending unit sends the reply generated by the generating unit. The sending is performed by a method such as real-time sending or batch sending. The sending unit may also include an interface unit that provides a user interface.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives messages from users; an analysis unit that analyzes the message accepted by the acceptance unit; a generation unit that generates a reply based on the message analyzed by the analysis unit; a transmission unit that transmits the reply generated by the generation unit; Equipped with A system characterized by:

2. The analysis unit Equipped with an emotion analysis unit that analyzes the user's emotions and intentions The system of claim 1 .

3. The generation unit Equipped with a data management unit that manages AI learning data The system of claim 1 .

4. The transmission unit An interface unit that provides a user interface is provided. The system of claim 1 .

5. The reception unit Send the message entered by the user to the AI The system of claim 1 .

6. The generation unit Send the AI-generated reply to the other user The system of claim 1 .

7. The reception unit Estimates user emotions and adjusts message acceptance timing based on the estimated user emotions. The system of claim 1 .

8. The reception unit Analyze the user's past message history and select the appropriate reception method The system of claim 1 .

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