system

Abstract

The system according to the embodiment aims to accurately grasp the emotional state of the user and provide an appropriate response accordingly. [Solution] A system according to an embodiment includes an analysis unit, a generation unit, and a dialogue unit. The analysis unit analyzes the content of a user's speech, tone of voice, and facial expression to estimate an emotional state. The generation unit generates a response based on the emotional state estimated by the analysis unit. The dialogue unit provides the response generated by the generation unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to accurately grasp a user's emotional state and provide an appropriate response accordingly.

[0005] The system according to the embodiment aims to accurately grasp the emotional state of the user and provide an appropriate response accordingly. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and a dialogue unit. The analysis unit analyzes the content of a user's speech, tone of voice, and facial expression to estimate an emotional state. The generation unit generates a response based on the emotional state estimated by the analysis unit. The dialogue unit provides the response generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp the emotional state of the user and provide an appropriate response accordingly. [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 personal assistant system according to an embodiment of the present invention combines a generative AI and an emotion engine to provide a personal assistant that is empathetic to people's hearts. When a user consults or converses with the system in natural language, the system uses the emotion engine to understand the user's emotions and intentions, and the generative AI generates an appropriate response. For example, if the user expresses sadness, the generative AI can offer words of comfort. Furthermore, the system is compatible with multiple devices, such as glasses, earphones, and smartphones, and can communicate with the user through voice and text interfaces. This allows users to interact with the personal assistant anytime, anywhere. A partner generation AI platform has also been developed, allowing users to create their own partner. This platform is free to use, while rights holders and creators can publish content for a fee. This creates an ecosystem that allows users to choose from a variety of partners. For example, if a user prefers the voice of a particular character or voice actor, they can create a personal assistant with that character or voice actor's voice. This allows users to interact with a more familiar partner, thereby reducing their sense of loneliness. Thus, a personal assistant that combines a generative AI and an emotion engine is an effective means of empathizing with users' hearts and reducing their sense of loneliness. This allows the personal assistant system to generate and provide appropriate responses based on the user's emotional state.

[0029] A personal assistant system according to an embodiment includes an analysis unit, a generation unit, and a dialogue unit. The analysis unit analyzes a user's utterance content, tone of voice, and facial expression to estimate an emotional state. The user's utterance content includes, but is not limited to, text, voice, and gestures. The analysis unit analyzes, for example, tone of voice, such as voice frequency, volume, and pitch. The analysis unit can also analyze facial expressions using facial feature point detection and a facial expression recognition algorithm. For example, the analysis unit analyzes a user's utterance content using text analysis technology to estimate an emotional state. The analysis unit can also analyze tone of voice using voice analysis technology to estimate an emotional state. The analysis unit can also detect facial feature points and analyze facial expressions using a facial expression recognition algorithm to estimate an emotional state. The generation unit generates a response based on the emotional state estimated by the analysis unit. The generation unit, for example, uses a generation AI to conduct a natural conversation while taking into account the user's emotions and intentions. The generation AI can generate a response using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit generates comforting words when the user expresses sadness. The generation unit can also generate a response in a calm and collected tone when the user is angry. The generation unit can also generate a response in a bright and empathetic tone when the user is happy. The dialogue unit provides the response generated by the generation unit to the user. The dialogue unit is compatible with multiple devices, such as glasses, earphones, and a smartphone, and communicates with the user through a voice or text interface. For example, the dialogue unit provides the response to the user using a voice interface. The dialogue unit can also provide the response to the user using a text interface. The dialogue unit can also provide the response to the user through a device such as glasses or earphones. This allows the personal assistant system according to the embodiment to generate and provide an appropriate response based on the user's emotional state.

[0030] The dialogue unit can be compatible with multiple devices, including glasses, earphones, and a smartphone. The dialogue unit provides a response to the user using, for example, glasses. For example, the dialogue unit displays text on the display of the glasses and provides the response to the user. The dialogue unit can also provide a response to the user using earphones. For example, the dialogue unit provides a response by voice through the earphones. The dialogue unit can also provide a response to the user using a smartphone. For example, the dialogue unit displays text on the screen of the smartphone and provides the response to the user. This allows the user to interact with the personal assistant through various devices. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input text to be displayed on the display of the glasses to a generation AI and have the generation AI generate the text.

[0031] The generation unit can conduct a conversation while taking into account the user's emotions and intentions. For example, if the user expresses sadness, the generation unit generates comforting words. For example, if the user says, "I'm so sad today," the generation unit responds, "Are you okay? Is there anything I can help you with?" The generation unit can also generate a response in a calm and collected tone if the user is angry. For example, if the user says, "Why did this happen?", the generation unit can respond, "Please calm down. Tell me what happened." The generation unit can also generate a response in a bright and empathetic tone if the user is happy. For example, if the user says, "Something very happy happened today!", the generation unit can respond, "That's great! What happened?" This allows the generation unit to conduct a natural conversation based on the user's emotions and intentions. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's utterances into the generation AI and have the generation AI generate a response.

[0032] When generating a response, the generation unit can improve the accuracy of the response by referring to the user's past dialogue history. For example, the generation unit generates a response that reflects specific preferences and interests from the user's past dialogue history. For example, if the user has previously said, "I like movies," the generation unit generates a response that includes topics related to movies. The generation unit can also generate a response that deepens knowledge about a specific topic based on the user's past dialogue history. For example, if the user has previously said, "I like traveling," the generation unit generates a response that includes information related to travel. Furthermore, the generation unit can also generate consistent responses by referring to the user's past dialogue history. For example, if the user has previously said, "I like music," the generation unit generates a response that includes topics related to music. Thus, the generation unit improves the accuracy of the response by referring to the past dialogue history. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's past dialogue history into the generation AI and have the generation AI generate a response.

[0033] The generation unit can generate a response based on the user's current situation when generating a response. For example, if the user is interacting at night, the generation unit generates a response in a relaxed tone. For example, if the user says, "I'm tired today," the generation unit generates a response in a relaxed tone, such as, "Thank you for your hard work. Please get plenty of rest." The generation unit can also generate a response in a professional tone when the user is at work. For example, if the user says, "I'm busy at work," the generation unit can generate a response in a professional tone, such as, "Good luck. Is there anything I can help you with?" The generation unit can also generate a response including travel-related information when the user is traveling. For example, if the user says, "I'm traveling," the generation unit can generate a response including travel-related information, such as, "That's great! Where are you going?" This allows the generation unit to provide a more appropriate response by taking the current situation into consideration. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's current situation data into the generation AI and have the generation AI generate a response.

[0034] When generating a response, the generation unit can determine the priority of the response based on the time of the user's submission. For example, if the user asks an urgent question, the generation unit can prioritize generating a response. For example, if the user says, "I'm in a hurry," the generation unit can prioritize generating a response such as, "I'll answer right away." The generation unit can also prioritize generating answers to questions previously submitted by the user. For example, if the user says, "I've asked this question before," the generation unit can prioritize generating a response such as, "I'll provide you with information related to your previous question." The generation unit can also generate responses at an appropriate time for questions submitted by the user during a specific time period. For example, if the user says, "I asked the question at night," the generation unit can appropriately generate a response such as, "I'll provide you with information about nighttime." This allows the generation unit to prioritize responses based on the time of submission, thereby providing responses at a more appropriate time. Some or all of the above-described processing by the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's submission time data into the generation AI and have the generation AI determine the priority of the responses.

[0035] When generating a response, the generation unit can refer to the user's relevant past behavioral data to generate the response. The generation unit, for example, generates a response that reflects a specific pattern from the user's past behavioral data. For example, if the user previously said, "I jog every morning," the generation unit generates the response, "How was your jog today?" The generation unit can also generate a response that deepens knowledge about a specific topic based on the user's past behavioral data. For example, if the user previously said, "I like cooking," the generation unit can generate the response, "What dish have you made recently?" The generation unit can also refer to the user's past behavioral data to generate a consistent response. For example, if the user previously said, "My hobby is reading," the generation unit can generate the response, "What book have you read recently?" By referring to the past behavioral data, the generation unit can improve the accuracy of the response. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's past behavioral data into the generation AI and cause the generation AI to generate a response.

[0036] During a dialogue, the dialogue unit can select an optimal dialogue method by referring to the user's past dialogue history. For example, the dialogue unit selects a dialogue method that reflects specific preferences and interests from the user's past dialogue history. For example, if the user has previously said, "I like movies," the dialogue unit selects a dialogue method that includes topics related to movies. The dialogue unit can also select a dialogue method that deepens knowledge about a specific topic based on the user's past dialogue history. For example, if the user has previously said, "I like travel," the dialogue unit selects a dialogue method that includes information related to travel. Furthermore, the dialogue unit can select a consistent dialogue method by referring to the user's past dialogue history. For example, if the user has previously said, "I like music," the dialogue unit selects a dialogue method that includes topics related to music. In this way, the dialogue unit can select an optimal dialogue method by referring to the past dialogue history. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit inputs the user's past dialogue history into a generation AI and has the generation AI select an optimal dialogue method.

[0037] The dialogue unit can conduct dialogue based on the user's current device status during dialogue. For example, if the user's device battery is low, the dialogue unit shortens the dialogue to reduce battery consumption. For example, if the user says, "My battery is low," the dialogue unit may respond with a short dialogue, "Okay, I'll just give you the gist." The dialogue unit can also adjust the dialogue so that it does not interrupt if the user's device connection is unstable. For example, if the user says, "My connection is unstable," the dialogue unit may respond with, "Please wait until the connection stabilizes." Furthermore, the dialogue unit can select an optimal dialogue method based on the user's device settings. For example, if the user says, "I've changed my device settings," the dialogue unit may select an optimal dialogue method, such as, "I will continue the dialogue based on the new settings." This allows the dialogue unit to provide more appropriate dialogue by taking the device status into consideration. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit may input the user's device status data into the generation AI and have the generation AI adjust the dialogue method.

[0038] The dialogue unit can select a dialogue method based on the user's geographical location information during a dialogue. For example, if the user is at home, the dialogue unit may conduct the dialogue in a relaxed tone. For example, if the user says, "I'm at home," the dialogue unit may conduct the dialogue in a relaxed tone, saying, "Please relax." The dialogue unit can also conduct the dialogue in a professional tone if the user is at work. For example, if the user says, "I'm at work," the dialogue unit may conduct the dialogue in a professional tone, saying, "Good luck with your work." Furthermore, if the user is traveling, the dialogue unit can conduct a dialogue including travel-related information. For example, if the user says, "I'm traveling," the dialogue unit may conduct a dialogue including travel-related information, saying, "That's great! Where are you going?" This allows the dialogue unit to provide a more appropriate dialogue by taking the geographical location information into consideration. Some or all of the above-described processing in the dialogue unit may be performed using AI, for example, or may be performed without AI. For example, the dialogue unit may input the user's geographical location information to a generation AI and have the generation AI select a dialogue method.

[0039] The dialogue unit can adjust the content of the dialogue by analyzing the user's social media activity during the dialogue. For example, the dialogue unit analyzes the content of the user's social media posts and adjusts the content of the dialogue. For example, if the user says, "I recently posted this on social media," the dialogue unit adjusts the content of the dialogue by saying, "Tell me more about that post." The dialogue unit can also analyze the user's social media responses (likes, comments, etc.) and adjust the content of the dialogue. For example, if the user says, "This post got a lot of likes," the dialogue unit adjusts the content of the dialogue by saying, "That's great! What was it about?" The dialogue unit can also adjust the content of the dialogue by taking into account the frequency of the user's social media activity. For example, if the user says, "I've been more active on social media lately," the dialogue unit adjusts the content of the dialogue by saying, "What are you interested in?" In this way, the dialogue unit analyzes social media activity and improves the accuracy of the content of the dialogue. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's social media activity data into the generation AI and have the generation AI adjust the content of the dialogue.

[0040] When generating a partner, the partner generation AI platform can generate a partner by referencing the user's past partner selection history. The partner generation AI platform, for example, generates an optimal partner based on the characteristics of partners previously selected by the user. For example, if the user previously selected a "partner with a kind personality," the partner generation AI platform generates a partner with similar characteristics. The partner generation AI platform can also generate a partner that reflects specific preferences and interests based on the user's past partner selection history. For example, if the user previously selected a "partner who likes music," the partner generation AI platform generates a partner who talks about music. The partner generation AI platform can also analyze the user's past partner selection history to generate a consistent partner. For example, if the user previously selected a "partner who likes travel," the partner generation AI platform generates a partner who talks about travel. In this way, the partner generation AI platform can generate an optimal partner by referencing the past partner selection history. Some or all of the above-described processing in the partner generation AI platform may be performed using, or without, the generation AI. For example, the partner generation AI platform can input the user's past partner selection history into the generation AI and have the generation AI generate a partner.

[0041] When generating a partner, the partner generation AI platform can tailor the partner based on the user's current interests. For example, the partner generation AI platform generates a partner related to a topic that the user is currently interested in. For example, if the user says, "I've been interested in cooking lately," the partner generation AI platform generates a partner with cooking-related topics. The partner generation AI platform can also generate a partner that reflects the user's current interests. For example, if the user says, "I've been interested in sports lately," the partner generation AI platform generates a partner with sports-related topics. The partner generation AI platform can also customize a partner based on the user's current hobbies and activities. For example, if the user says, "I've recently taken up reading," the partner generation AI platform generates a partner with reading-related topics. This allows the partner generation AI platform to provide a more appropriate partner by taking current interests and interests into consideration. Some or all of the above-described processing in the partner generation AI platform may be performed using, or without, the generation AI. For example, the partner generation AI platform can input the user's current interest data into the generation AI and have the generation AI customize the partner.

[0042] The partner generation AI platform can generate a partner based on the user's geographic location information when generating a partner. For example, if the user is at home, the partner generation AI platform generates a partner with a relaxed tone. For example, if the user says, "I'm at home," the partner generation AI platform generates a partner with a relaxed tone, saying, "Please relax." The partner generation AI platform can also generate a partner with a professional tone when the user is at work. For example, if the user says, "I'm at work," the partner generation AI platform generates a partner with a professional tone, saying, "Good luck with your work." The partner generation AI platform can also generate a partner including travel-related information when the user is traveling. For example, if the user says, "I'm traveling," the partner generation AI platform generates a partner including travel-related information, saying, "That's great! Where are you going?" This allows the partner generation AI platform to provide a more appropriate partner by taking geographic location information into consideration. Some or all of the above-described processing in the partner generation AI platform may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the partner generation AI platform can input the user's geographical location information into the generation AI and have the generation AI generate a partner.

[0043] When generating a partner, the partner generation AI platform can analyze a user's social media activity and tailor the partner. For example, the partner generation AI platform analyzes the content of a user's social media posts to customize the partner. For example, if a user says, "I recently posted this on social media," the partner generation AI platform customizes the partner by asking, "Tell me more about that post." The partner generation AI platform can also analyze a user's social media responses (likes, comments, etc.) to customize the partner. For example, if a user says, "This post got a lot of likes," the partner generation AI platform customizes the partner by asking, "That's great! What was it about?" The partner generation AI platform can also customize the partner by taking into account the frequency of the user's social media activity. For example, if a user says, "I've been more active on social media lately," the partner generation AI platform customizes the partner by asking, "What are you interested in?" This allows the partner generation AI platform to analyze social media activity and provide a more appropriate partner. Some or all of the above-described processing in the partner generation AI platform may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, a partner generation AI platform can input a user's social media activity data into the generation AI, allowing the generation AI to customize a partner.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The analysis unit can analyze the user's past statement history and infer the user's preferences and interests. For example, if the user has previously said, "I like movies," the analysis unit can provide information related to movies. If the user has previously said, "I like traveling," the analysis unit can also provide information related to traveling. Furthermore, if the user has previously said, "I like music," the analysis unit can also provide information related to music. In this way, the analysis unit can infer the user's preferences and interests by analyzing the user's past statement history and provide appropriate information.

[0046] The generation unit can generate a response based on the user's preferences and interests by referring to the user's past dialogue history. For example, if the user has previously said, "I like movies," the generation unit can generate a response including a topic related to movies. If the user has previously said, "I like traveling," the generation unit can also generate a response including a topic related to traveling. Furthermore, if the user has previously said, "I like music," the generation unit can also generate a response including a topic related to music. In this way, the generation unit can generate a response based on the user's preferences and interests by referring to the past dialogue history.

[0047] The dialogue unit can adjust the dialogue format based on the user's current device status. For example, if the user's device has low battery, the dialogue unit can shorten the dialogue to reduce battery consumption. Also, if the user's device connection status is unstable, the dialogue unit can adjust so that the dialogue is not interrupted. Furthermore, the dialogue unit can select the optimal dialogue method based on the user's device settings. This allows the dialogue unit to provide more appropriate dialogue by taking the device status into consideration.

[0048] The dialogue unit can adjust the content of the dialogue based on the user's geographic location information. For example, if the user is at home, the dialogue unit can conduct the dialogue in a relaxed tone. If the user is at work, the dialogue unit can conduct the dialogue in a professional tone. Furthermore, if the user is traveling, the dialogue unit can conduct the dialogue including travel-related information. In this way, the dialogue unit can provide a more appropriate dialogue by taking the geographic location information into consideration.

[0049] The dialogue unit can analyze the user's social media activity and adjust the content of the dialogue based on the user's interests. For example, if the user posts on social media that "I've been interested in cooking lately," the dialogue unit can provide a dialogue including topics related to cooking. Also, if the user posts on social media that "I've been interested in sports lately," the dialogue unit can provide a dialogue including topics related to sports. Furthermore, if the user posts on social media that "I've recently taken up reading as a hobby," the dialogue unit can provide a dialogue including topics related to reading. In this way, the dialogue unit can provide a dialogue based on the user's interests by analyzing social media activity.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The analysis unit analyzes the user's speech content, tone of voice, and facial expressions to estimate their emotional state. The user's speech content includes text, voice, gestures, etc. The analysis unit analyzes the tone of voice, such as voice frequency, volume, and pitch, and analyzes facial expressions using facial feature detection and facial expression recognition algorithms. For example, the analysis unit uses text analysis technology to analyze the speech content, voice analysis technology to analyze the tone of voice, and facial feature detection and facial expression recognition algorithms to analyze the facial expressions. Step 2: The generator generates a response based on the emotional state estimated by the analyzer. The generator uses AI to conduct natural conversations while taking into account the user's emotions and intentions. For example, if the user expresses sadness, the generator generates comforting words; if the user is angry, the generator generates a response in a calm and collected tone; and if the user is happy, the generator generates a response in a bright and empathetic tone. Step 3: The dialogue unit provides the user with the response generated by the generation unit. The dialogue unit is compatible with multiple devices such as glasses, earphones, and smartphones, and communicates with the user through a voice or text interface. For example, the dialogue unit can provide a response to the user using a voice or text interface, or through a device such as glasses or earphones.

[0052] (Example 2) A personal assistant system according to an embodiment of the present invention combines a generative AI and an emotion engine to provide a personal assistant that is empathetic to people's hearts. When a user consults or converses with the system in natural language, the system uses the emotion engine to understand the user's emotions and intentions, and the generative AI generates an appropriate response. For example, if the user expresses sadness, the generative AI can offer words of comfort. Furthermore, the system is compatible with multiple devices, such as glasses, earphones, and smartphones, and can communicate with the user through voice and text interfaces. This allows users to interact with the personal assistant anytime, anywhere. A partner generation AI platform has also been developed, allowing users to create their own partner. This platform is free to use, while rights holders and creators can publish content for a fee. This creates an ecosystem that allows users to choose from a variety of partners. For example, if a user prefers the voice of a particular character or voice actor, they can create a personal assistant with that character or voice actor's voice. This allows users to interact with a more familiar partner, thereby reducing their sense of loneliness. Thus, a personal assistant that combines a generative AI and an emotion engine is an effective means of empathizing with users' hearts and reducing their sense of loneliness. This allows the personal assistant system to generate and provide appropriate responses based on the user's emotional state.

[0053] A personal assistant system according to an embodiment includes an analysis unit, a generation unit, and a dialogue unit. The analysis unit analyzes a user's utterance content, tone of voice, and facial expression to estimate an emotional state. The user's utterance content includes, but is not limited to, text, voice, and gestures. The analysis unit analyzes, for example, tone of voice, such as voice frequency, volume, and pitch. The analysis unit can also analyze facial expressions using facial feature point detection and a facial expression recognition algorithm. For example, the analysis unit analyzes a user's utterance content using text analysis technology to estimate an emotional state. The analysis unit can also analyze tone of voice using voice analysis technology to estimate an emotional state. The analysis unit can also detect facial feature points and analyze facial expressions using a facial expression recognition algorithm to estimate an emotional state. The generation unit generates a response based on the emotional state estimated by the analysis unit. The generation unit, for example, uses a generation AI to conduct a natural conversation while taking into account the user's emotions and intentions. The generation AI can generate a response using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit generates comforting words when the user expresses sadness. The generation unit can also generate a response in a calm and collected tone when the user is angry. The generation unit can also generate a response in a bright and empathetic tone when the user is happy. The dialogue unit provides the response generated by the generation unit to the user. The dialogue unit is compatible with multiple devices, such as glasses, earphones, and a smartphone, and communicates with the user through a voice or text interface. For example, the dialogue unit provides the response to the user using a voice interface. The dialogue unit can also provide the response to the user using a text interface. The dialogue unit can also provide the response to the user through a device such as glasses or earphones. This allows the personal assistant system according to the embodiment to generate and provide an appropriate response based on the user's emotional state.

[0054] The dialogue unit can be compatible with multiple devices, including glasses, earphones, and a smartphone. The dialogue unit provides a response to the user using, for example, glasses. For example, the dialogue unit displays text on the display of the glasses and provides the response to the user. The dialogue unit can also provide a response to the user using earphones. For example, the dialogue unit provides a response by voice through the earphones. The dialogue unit can also provide a response to the user using a smartphone. For example, the dialogue unit displays text on the screen of the smartphone and provides the response to the user. This allows the user to interact with the personal assistant through various devices. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input text to be displayed on the display of the glasses to a generation AI and have the generation AI generate the text.

[0055] The generation unit can conduct a conversation while taking into account the user's emotions and intentions. For example, if the user expresses sadness, the generation unit generates comforting words. For example, if the user says, "I'm so sad today," the generation unit responds, "Are you okay? Is there anything I can help you with?" The generation unit can also generate a response in a calm and collected tone if the user is angry. For example, if the user says, "Why did this happen?", the generation unit can respond, "Please calm down. Tell me what happened." The generation unit can also generate a response in a bright and empathetic tone if the user is happy. For example, if the user says, "Something very happy happened today!", the generation unit can respond, "That's great! What happened?" This allows the generation unit to conduct a natural conversation based on the user's emotions and intentions. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's utterances into the generation AI and have the generation AI generate a response.

[0056] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, when the user is feeling stressed, the analysis unit increases the accuracy of the analysis to grasp the user's emotional state in more detail. For example, the analysis unit analyzes the user's tone of voice and facial expression in detail to estimate the level of stress. Furthermore, when the user is relaxed, the analysis unit can appropriately adjust the accuracy of the analysis to maintain a natural conversation. For example, the analysis unit analyzes the user's tone of voice and facial expression to estimate the relaxed state. Furthermore, when the user is excited, the analysis unit can increase the accuracy of the analysis to quickly capture changes in emotions. For example, the analysis unit analyzes the user's tone of voice and facial expression in detail to estimate the excited state. In this way, the analysis unit can adjust the accuracy of the analysis according to the user's emotions to grasp the user's emotional state more accurately. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's tone of voice and facial expression into the generation AI, allowing the generation AI to estimate the user's emotional state.

[0057] The analysis unit can analyze the user's past utterance history to improve the accuracy of the estimation of the emotional state. For example, the analysis unit extracts specific emotional patterns from the user's past utterance history to improve the estimation accuracy. For example, the analysis unit analyzes the tone of voice and facial expression when the user previously said "I'm tired" and, if a similar pattern appears, estimates fatigue. The analysis unit can also predict emotional changes based on the user's past utterance history to improve the estimation accuracy. For example, the analysis unit analyzes the tone of voice and facial expression when the user previously said "I'm happy" and, if a similar pattern appears, estimates joy. Furthermore, the analysis unit can analyze the user's past utterance history to learn emotional responses to specific trigger words. For example, the analysis unit analyzes the tone of voice and facial expression when the user previously said "I'm stressed" and, if a similar pattern appears, estimates stress. In this way, the analysis unit improves the estimation accuracy of the emotional state by analyzing the past utterance history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's past utterance history into the generation AI and have the generation AI estimate the user's emotional state.

[0058] The analysis unit can estimate the emotional state by using the user's biometric information in combination with the analysis. The analysis unit, for example, monitors the user's heart rate in real time to estimate the emotional state. For example, the analysis unit analyzes heart rate fluctuations to estimate stress or excitement. The analysis unit can also analyze the user's electrodermal response to estimate stress or excitement. For example, the analysis unit analyzes electrodermal response fluctuations to estimate stress or excitement. The analysis unit can also analyze the user's breathing pattern to estimate a relaxed or tense state. For example, the analysis unit analyzes breathing pattern fluctuations to estimate a relaxed or tense state. This improves the accuracy of the analysis unit's estimation of the emotional state by using biometric information in combination. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's heart rate and electrodermal response data into the generation AI and cause the generation AI to estimate the emotional state.

[0059] The analysis unit can estimate the user's emotions and adjust the display format of the analysis results based on the estimated user emotions. For example, if the user is sad, the analysis unit displays the analysis results in a gentle tone and adds comforting words. For example, if the user says, "I'm very sad today," the analysis unit displays the analysis results in a gentle tone, asking, "Are you okay? Is there anything I can help you with?". If the user is angry, the analysis unit can display the analysis results in a calm tone to calm the user. For example, if the user says, "Why did this happen?", the analysis unit can display the analysis results in a calm tone, asking, "Please calm down. Tell me what happened." If the user is happy, the analysis unit can display the analysis results in a bright tone to show empathy. For example, if the user says, "Something very happy happened today!", the analysis unit can display the analysis results in a bright tone, asking, "That's great! What happened?". This allows the analysis unit to provide more appropriate feedback by adjusting the display method of the analysis results according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0060] During analysis, the analysis unit can estimate the emotional state by taking the user's geographical location information into consideration. For example, when the user is at home, the analysis unit estimates that the user is relaxed. For example, the analysis unit estimates a relaxed state when the user is at home based on the user's geographical location information. The analysis unit can also estimate that the user is feeling stressed when the user is at work. For example, the analysis unit estimates a stressed state when the user is at work based on the user's geographical location information. Furthermore, the analysis unit can also estimate that the user is feeling excited or happy when the user is traveling. For example, the analysis unit estimates an excited state when the user is traveling based on the user's geographical location information. In this way, the analysis unit improves the accuracy of estimating the emotional state by taking the geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to a generation AI and cause the generation AI to estimate the emotional state.

[0061] During analysis, the analysis unit can analyze the user's social media activity and estimate the emotional state. The analysis unit, for example, analyzes the content of the user's social media posts and estimates the emotional state. For example, the analysis unit may analyze the content of the user's posts using text analysis technology and estimate the emotional state. The analysis unit can also analyze the user's social media reactions (likes, comments, etc.) and estimate the emotional state. For example, the analysis unit may analyze reactions to the user's posts and estimate the emotional state. Furthermore, the analysis unit can estimate the emotional state by taking into account the frequency of the user's social media activities. For example, the analysis unit may analyze the user's posting frequency and the number of reactions to estimate the emotional state. In this way, the analysis unit can analyze the social media activity and improve the accuracy of the emotional state estimation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input the user's social media activity data into a generation AI and have the generation AI estimate the emotional state.

[0062] The generation unit can estimate the user's emotions and adjust the expression style of the response based on the estimated user's emotions. For example, if the user is sad, the generation unit generates comforting words in a gentle tone. For example, if the user says, "I'm so sad today," the generation unit generates a response in a gentle tone, such as, "Are you okay? Is there anything I can help you with?" The generation unit can also generate a response in a calm and collected tone if the user is angry. For example, if the user says, "Why did this happen?" the generation unit can generate a response in a calm and collected tone, such as, "Please calm down. Tell me what happened." The generation unit can also generate a response in a bright and empathetic tone if the user is happy. For example, if the user says, "Something very happy happened today!" the generation unit can generate a response in a bright tone, such as, "That's great! What happened?" This allows the generation unit to provide a more appropriate response by adjusting the expression style of the response according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the response is expressed.

[0063] When generating a response, the generation unit can improve the accuracy of the response by referring to the user's past dialogue history. For example, the generation unit generates a response that reflects specific preferences and interests from the user's past dialogue history. For example, if the user has previously said, "I like movies," the generation unit generates a response that includes topics related to movies. The generation unit can also generate a response that deepens knowledge about a specific topic based on the user's past dialogue history. For example, if the user has previously said, "I like traveling," the generation unit generates a response that includes information related to travel. Furthermore, the generation unit can also generate consistent responses by referring to the user's past dialogue history. For example, if the user has previously said, "I like music," the generation unit generates a response that includes topics related to music. Thus, the generation unit improves the accuracy of the response by referring to the past dialogue history. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's past dialogue history into the generation AI and have the generation AI generate a response.

[0064] The generation unit can generate a response based on the user's current situation when generating a response. For example, if the user is interacting at night, the generation unit generates a response in a relaxed tone. For example, if the user says, "I'm tired today," the generation unit generates a response in a relaxed tone, such as, "Thank you for your hard work. Please get plenty of rest." The generation unit can also generate a response in a professional tone when the user is at work. For example, if the user says, "I'm busy at work," the generation unit can generate a response in a professional tone, such as, "Good luck. Is there anything I can help you with?" The generation unit can also generate a response including travel-related information when the user is traveling. For example, if the user says, "I'm traveling," the generation unit can generate a response including travel-related information, such as, "That's great! Where are you going?" This allows the generation unit to provide a more appropriate response by taking the current situation into consideration. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's current situation data into the generation AI and have the generation AI generate a response.

[0065] The generation unit can estimate the user's emotions and change the length of the response based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short, to-the-point response. For example, if the user says, "I'm in a hurry," the generation unit generates a short response such as, "Okay, I'll just give you the gist." The generation unit can also generate a longer response with more detailed explanations if the user is relaxed. For example, if the user says, "I have time," the generation unit can generate a longer response such as, "Okay, I'll explain in detail." Furthermore, if the user is excited, the generation unit can generate a response with a visually stimulating effect. For example, if the user says, "I'm very excited," the generation unit can generate a response with a visually stimulating effect such as, "That's great!" This allows the generation unit to provide a more appropriate response by adjusting the length of the response according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the response.

[0066] When generating a response, the generation unit can determine the priority of the response based on the time of the user's submission. For example, if the user asks an urgent question, the generation unit can prioritize generating a response. For example, if the user says, "I'm in a hurry," the generation unit can prioritize generating a response such as, "I'll answer right away." The generation unit can also prioritize generating answers to questions previously submitted by the user. For example, if the user says, "I've asked this question before," the generation unit can prioritize generating a response such as, "I'll provide you with information related to your previous question." The generation unit can also generate responses at an appropriate time for questions submitted by the user during a specific time period. For example, if the user says, "I asked the question at night," the generation unit can appropriately generate a response such as, "I'll provide you with information about nighttime." This allows the generation unit to prioritize responses based on the time of submission, thereby providing responses at a more appropriate time. Some or all of the above-described processing by the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's submission time data into the generation AI and have the generation AI determine the priority of the responses.

[0067] When generating a response, the generation unit can refer to the user's relevant past behavioral data to generate the response. The generation unit, for example, generates a response that reflects a specific pattern from the user's past behavioral data. For example, if the user previously said, "I jog every morning," the generation unit generates the response, "How was your jog today?" The generation unit can also generate a response that deepens knowledge about a specific topic based on the user's past behavioral data. For example, if the user previously said, "I like cooking," the generation unit can generate the response, "What dish have you made recently?" The generation unit can also refer to the user's past behavioral data to generate a consistent response. For example, if the user previously said, "My hobby is reading," the generation unit can generate the response, "What book have you read recently?" By referring to the past behavioral data, the generation unit can improve the accuracy of the response. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's past behavioral data into the generation AI and cause the generation AI to generate a response.

[0068] The dialogue unit can estimate the user's emotions and adjust the dialogue progression style based on the estimated user emotions. For example, if the user is sad, the dialogue unit can proceed slowly and include many comforting words. For example, if the user says, "I'm very sad today," the dialogue unit can proceed with the dialogue in a slow tone, asking, "Are you okay? Is there anything I can help you with?" The dialogue unit can also proceed with a calm and collected dialogue if the user is angry. For example, if the user says, "Why did this happen?" the dialogue unit can proceed with a calm tone, asking, "Please calm down. Tell me what happened." The dialogue unit can also proceed with a bright and empathetic tone if the user is happy. For example, if the user says, "Something very happy happened today!" the dialogue unit can proceed with a bright tone, asking, "That's great! What happened?" This allows the dialogue unit to provide a more appropriate dialogue by adjusting the dialogue progression style according to the user's emotions. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit may input user emotion data to the generation AI and have the generation AI adjust the way the dialogue proceeds.

[0069] During a dialogue, the dialogue unit can select an optimal dialogue method by referring to the user's past dialogue history. For example, the dialogue unit selects a dialogue method that reflects specific preferences and interests from the user's past dialogue history. For example, if the user has previously said, "I like movies," the dialogue unit selects a dialogue method that includes topics related to movies. The dialogue unit can also select a dialogue method that deepens knowledge about a specific topic based on the user's past dialogue history. For example, if the user has previously said, "I like travel," the dialogue unit selects a dialogue method that includes information related to travel. Furthermore, the dialogue unit can select a consistent dialogue method by referring to the user's past dialogue history. For example, if the user has previously said, "I like music," the dialogue unit selects a dialogue method that includes topics related to music. In this way, the dialogue unit can select an optimal dialogue method by referring to the past dialogue history. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit inputs the user's past dialogue history into a generation AI and has the generation AI select an optimal dialogue method.

[0070] The dialogue unit can conduct dialogue based on the user's current device status during dialogue. For example, if the user's device battery is low, the dialogue unit shortens the dialogue to reduce battery consumption. For example, if the user says, "My battery is low," the dialogue unit may respond with a short dialogue, "Okay, I'll just give you the gist." The dialogue unit can also adjust the dialogue so that it does not interrupt if the user's device connection is unstable. For example, if the user says, "My connection is unstable," the dialogue unit may respond with, "Please wait until the connection stabilizes." Furthermore, the dialogue unit can select an optimal dialogue method based on the user's device settings. For example, if the user says, "I've changed my device settings," the dialogue unit may select an optimal dialogue method, such as, "I will continue the dialogue based on the new settings." This allows the dialogue unit to provide more appropriate dialogue by taking the device status into consideration. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit may input the user's device status data into the generation AI and have the generation AI adjust the dialogue method.

[0071] The dialogue unit can estimate the user's emotions and determine the priority of the dialogue based on the estimated user emotions. For example, if the user is sad, the dialogue unit can prioritize the dialogue and include many comforting words. For example, if the user says, "I'm very sad today," the dialogue unit can prioritize the dialogue by asking, "Are you okay? Is there anything I can help you with?" Furthermore, if the user is angry, the dialogue unit can prioritize the dialogue in a calm and collected tone. For example, if the user says, "Why did this happen?", the dialogue unit can prioritize the dialogue by asking, "Please calm down. Tell me what happened." Furthermore, if the user is happy, the dialogue unit can prioritize the dialogue by asking, "That's great! What happened?" This allows the dialogue unit to prioritize the dialogue based on the user's emotions, thereby providing a more appropriate dialogue. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without AI. For example, the dialogue unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of the dialogue.

[0072] The dialogue unit can select a dialogue method based on the user's geographical location information during a dialogue. For example, if the user is at home, the dialogue unit may conduct the dialogue in a relaxed tone. For example, if the user says, "I'm at home," the dialogue unit may conduct the dialogue in a relaxed tone, saying, "Please relax." The dialogue unit can also conduct the dialogue in a professional tone if the user is at work. For example, if the user says, "I'm at work," the dialogue unit may conduct the dialogue in a professional tone, saying, "Good luck with your work." Furthermore, if the user is traveling, the dialogue unit can conduct a dialogue including travel-related information. For example, if the user says, "I'm traveling," the dialogue unit may conduct a dialogue including travel-related information, saying, "That's great! Where are you going?" This allows the dialogue unit to provide a more appropriate dialogue by taking the geographical location information into consideration. Some or all of the above-described processing in the dialogue unit may be performed using AI, for example, or may be performed without AI. For example, the dialogue unit may input the user's geographical location information to a generation AI and have the generation AI select a dialogue method.

[0073] The dialogue unit can adjust the content of the dialogue by analyzing the user's social media activity during the dialogue. For example, the dialogue unit analyzes the content of the user's social media posts and adjusts the content of the dialogue. For example, if the user says, "I recently posted this on social media," the dialogue unit adjusts the content of the dialogue by saying, "Tell me more about that post." The dialogue unit can also analyze the user's social media responses (likes, comments, etc.) and adjust the content of the dialogue. For example, if the user says, "This post got a lot of likes," the dialogue unit adjusts the content of the dialogue by saying, "That's great! What was it about?" The dialogue unit can also adjust the content of the dialogue by taking into account the frequency of the user's social media activity. For example, if the user says, "I've been more active on social media lately," the dialogue unit adjusts the content of the dialogue by saying, "What are you interested in?" In this way, the dialogue unit analyzes social media activity and improves the accuracy of the content of the dialogue. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's social media activity data into the generation AI and have the generation AI adjust the content of the dialogue.

[0074] The partner generation AI platform can estimate the user's emotions and adjust the partner generation format based on the estimated user emotions. For example, if the user is sad, the partner generation AI platform generates a partner that includes many comforting words. For example, if the user says, "I'm so sad today," the partner generation AI platform generates a partner that includes many comforting words, such as, "Are you okay? Is there anything I can help you with?". The partner generation AI platform can also generate a partner with a calm and collected tone if the user is angry. For example, if the user says, "Why did this happen?", the partner generation AI platform can generate a partner with a calm tone, such as, "Please calm down. Tell me what happened." Furthermore, the partner generation AI platform can generate a partner with a bright and empathetic tone if the user is happy. For example, if the user says, "Something very happy happened today!", the partner generation AI platform can generate a partner with a bright tone, such as, "That's great! What happened?". This allows the partner generation AI platform to provide a more appropriate partner by adjusting the partner generation method according to the user's emotions. Some or all of the above-described processing in the partner generation AI platform may be performed using the generation AI, or may be performed without using the generation AI. For example, the partner generation AI platform may input user emotion data into the generation AI and have the generation AI adjust the partner generation method.

[0075] When generating a partner, the partner generation AI platform can generate a partner by referencing the user's past partner selection history. The partner generation AI platform, for example, generates an optimal partner based on the characteristics of partners previously selected by the user. For example, if the user previously selected a "partner with a kind personality," the partner generation AI platform generates a partner with similar characteristics. The partner generation AI platform can also generate a partner that reflects specific preferences and interests based on the user's past partner selection history. For example, if the user previously selected a "partner who likes music," the partner generation AI platform generates a partner who talks about music. The partner generation AI platform can also analyze the user's past partner selection history to generate a consistent partner. For example, if the user previously selected a "partner who likes travel," the partner generation AI platform generates a partner who talks about travel. In this way, the partner generation AI platform can generate an optimal partner by referencing the past partner selection history. Some or all of the above-described processing in the partner generation AI platform may be performed using, or without, the generation AI. For example, the partner generation AI platform can input the user's past partner selection history into the generation AI and have the generation AI generate a partner.

[0076] When generating a partner, the partner generation AI platform can tailor the partner based on the user's current interests. For example, the partner generation AI platform generates a partner related to a topic that the user is currently interested in. For example, if the user says, "I've been interested in cooking lately," the partner generation AI platform generates a partner with cooking-related topics. The partner generation AI platform can also generate a partner that reflects the user's current interests. For example, if the user says, "I've been interested in sports lately," the partner generation AI platform generates a partner with sports-related topics. The partner generation AI platform can also customize a partner based on the user's current hobbies and activities. For example, if the user says, "I've recently taken up reading," the partner generation AI platform generates a partner with reading-related topics. This allows the partner generation AI platform to provide a more appropriate partner by taking current interests and interests into consideration. Some or all of the above-described processing in the partner generation AI platform may be performed using, or without, the generation AI. For example, the partner generation AI platform can input the user's current interest data into the generation AI and have the generation AI customize the partner.

[0077] The partner generation AI platform can estimate the user's emotions and adjust the partner's display style based on the estimated user's emotions. For example, if the user is sad, the partner generation AI platform provides a display style that includes many comforting words. For example, if the user says, "I'm so sad today," the partner generation AI platform provides a display style that includes many comforting words, such as, "Are you okay? Is there anything I can help you with?". Furthermore, if the user is angry, the partner generation AI platform can provide a display style that uses a calm and collected tone. For example, if the user says, "Why did this happen?", the partner generation AI platform can provide a display style that uses a calm tone, such as, "Please calm down. Tell me what happened." Furthermore, if the user is happy, the partner generation AI platform can provide a display style that uses a bright and empathetic tone. For example, if the user says, "Something so happy happened today!", the partner generation AI platform can provide a display style that uses a bright tone, such as, "That's great! What happened?" This allows the partner generation AI platform to provide more appropriate display by adjusting the partner's display style according to the user's emotions. Some or all of the above-described processing in the partner generation AI platform may be performed using the generation AI, or may be performed without using the generation AI. For example, the partner generation AI platform may input user emotion data into the generation AI and have the generation AI adjust how the partner is displayed.

[0078] The partner generation AI platform can generate a partner based on the user's geographic location information when generating a partner. For example, if the user is at home, the partner generation AI platform generates a partner with a relaxed tone. For example, if the user says, "I'm at home," the partner generation AI platform generates a partner with a relaxed tone, saying, "Please relax." The partner generation AI platform can also generate a partner with a professional tone when the user is at work. For example, if the user says, "I'm at work," the partner generation AI platform generates a partner with a professional tone, saying, "Good luck with your work." The partner generation AI platform can also generate a partner including travel-related information when the user is traveling. For example, if the user says, "I'm traveling," the partner generation AI platform generates a partner including travel-related information, saying, "That's great! Where are you going?" This allows the partner generation AI platform to provide a more appropriate partner by taking geographic location information into consideration. Some or all of the above-described processing in the partner generation AI platform may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the partner generation AI platform can input the user's geographical location information into the generation AI and have the generation AI generate a partner.

[0079] When generating a partner, the partner generation AI platform can analyze a user's social media activity and tailor the partner. For example, the partner generation AI platform analyzes the content of a user's social media posts to customize the partner. For example, if a user says, "I recently posted this on social media," the partner generation AI platform customizes the partner by asking, "Tell me more about that post." The partner generation AI platform can also analyze a user's social media responses (likes, comments, etc.) to customize the partner. For example, if a user says, "This post got a lot of likes," the partner generation AI platform customizes the partner by asking, "That's great! What was it about?" The partner generation AI platform can also customize the partner by taking into account the frequency of the user's social media activity. For example, if a user says, "I've been more active on social media lately," the partner generation AI platform customizes the partner by asking, "What are you interested in?" This allows the partner generation AI platform to analyze social media activity and provide a more appropriate partner. Some or all of the above-described processing in the partner generation AI platform may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, a partner generation AI platform can input a user's social media activity data into the generation AI, allowing the generation AI to customize a partner. === Hard Collateral 1-1 === Each of the multiple elements of the personal assistant system described above, including the analysis unit, generation unit, and dialogue unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit detects the user's facial expressions and speech using the camera 42 and microphone 38B of the smart device 14, and estimates the user's emotional state using the control unit 46A. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and generates a response based on the user's emotion using the emotion identification model 59. The dialogue unit is realized, for example, by the control unit 46A of the smart device 14, and provides a response to the user through a voice or text interface. === Hard Collateral 1-2 === Each of the multiple elements of the personal assistant system described above, including the analysis unit, generation unit, and dialogue unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit detects the user's facial expressions and speech using the camera 42 and microphone 238 of the smart glasses 214, and estimates the user's emotional state using the control unit 46A. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and generates a response based on the user's emotion using the emotion identification model 59. The dialogue unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides a response to the user through a voice or text interface. === Hard Collateral 1-3 === Each of the multiple elements of the personal assistant system described above, including the analysis unit, generation unit, and dialogue unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit detects the user's facial expressions and speech using the camera 42 and microphone 238 of the headset terminal 314, and estimates the emotional state using the control unit 46A. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and generates a response based on the user's emotion using an emotion identification model 59. The dialogue unit is realized, for example, by the control unit 46A of the headset terminal 314, and provides a response to the user through a voice or text interface. === Hard Collateral 1-4 === Each of the multiple elements of the personal assistant system described above, including the analysis unit, generation unit, and dialogue unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit detects the user's facial expressions and speech using the camera 42 and microphone 238 of the robot 414, and estimates the emotional state using the control unit 46A. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and generates a response based on the user's emotion using an emotion identification model 59. The dialogue unit is realized, for example, by the control unit 46A of the robot 414, and provides a response to the user through a voice or text interface.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The analysis unit can also estimate the user's emotions and evaluate the user's health state based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can evaluate the stress level and suggest an appropriate relaxation method. Also, if the user is feeling tired, the analysis unit can evaluate the need for rest and suggest taking a rest. Furthermore, if the user is excited, the analysis unit can evaluate the level of excitement and suggest an appropriate activity. In this way, the analysis unit can evaluate the user's health state based on the user's emotions and provide appropriate advice.

[0082] The dialogue unit can also estimate the user's emotions and select a dialogue topic based on the estimated emotions. For example, if the user is sad, the dialogue unit can select a topic of comfort or encouragement. If the user is angry, the dialogue unit can select a topic of calm listening. If the user is happy, the dialogue unit can select a topic of empathy or congratulations. This allows the dialogue unit to select an appropriate topic according to the user's emotions and provide a more effective dialogue.

[0083] The generation unit can also estimate the user's emotions and adjust the style of the response based on the estimated emotions. For example, if the user is sad, the generation unit can generate a response using gentle language. If the user is angry, the generation unit can generate a response using calm and collected language. Furthermore, if the user is happy, the generation unit can generate a response using cheerful and empathetic language. This allows the generation unit to adjust the style of the response according to the user's emotions and provide a more appropriate response.

[0084] The analysis unit can also estimate the user's emotions and predict the user's behavioral patterns based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can predict actions to relieve stress and provide appropriate advice. Also, if the user is feeling tired, the analysis unit can predict actions to take a rest and encourage the user to rest. Furthermore, if the user is excited, the analysis unit can predict actions to reduce the excitement and suggest appropriate activities. In this way, the analysis unit can predict behavioral patterns based on the user's emotions and provide appropriate advice.

[0085] The analysis unit can also estimate the user's emotions and evaluate the user's communication style based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can evaluate the impact of stress on communication and suggest appropriate measures. Also, if the user is feeling tired, the analysis unit can evaluate the impact of fatigue on communication and suggest taking a rest. Furthermore, if the user is excited, the analysis unit can evaluate the impact of excitement on communication and suggest an appropriate activity. In this way, the analysis unit can evaluate the user's communication style based on the user's emotions and provide appropriate advice.

[0086] The analysis unit can analyze the user's past statement history and infer the user's preferences and interests. For example, if the user has previously said, "I like movies," the analysis unit can provide information related to movies. If the user has previously said, "I like traveling," the analysis unit can also provide information related to traveling. Furthermore, if the user has previously said, "I like music," the analysis unit can also provide information related to music. In this way, the analysis unit can infer the user's preferences and interests by analyzing the user's past statement history and provide appropriate information.

[0087] The generation unit can generate a response based on the user's preferences and interests by referring to the user's past dialogue history. For example, if the user has previously said, "I like movies," the generation unit can generate a response including a topic related to movies. If the user has previously said, "I like traveling," the generation unit can also generate a response including a topic related to traveling. Furthermore, if the user has previously said, "I like music," the generation unit can also generate a response including a topic related to music. In this way, the generation unit can generate a response based on the user's preferences and interests by referring to the past dialogue history.

[0088] The dialogue unit can adjust the dialogue format based on the user's current device status. For example, if the user's device has low battery, the dialogue unit can shorten the dialogue to reduce battery consumption. Also, if the user's device connection status is unstable, the dialogue unit can adjust so that the dialogue is not interrupted. Furthermore, the dialogue unit can select the optimal dialogue method based on the user's device settings. This allows the dialogue unit to provide more appropriate dialogue by taking the device status into consideration.

[0089] The dialogue unit can adjust the content of the dialogue based on the user's geographic location information. For example, if the user is at home, the dialogue unit can conduct the dialogue in a relaxed tone. If the user is at work, the dialogue unit can conduct the dialogue in a professional tone. Furthermore, if the user is traveling, the dialogue unit can conduct the dialogue including travel-related information. In this way, the dialogue unit can provide a more appropriate dialogue by taking the geographic location information into consideration.

[0090] The dialogue unit can analyze the user's social media activity and adjust the content of the dialogue based on the user's interests. For example, if the user posts on social media that "I've been interested in cooking lately," the dialogue unit can provide a dialogue including topics related to cooking. Also, if the user posts on social media that "I've been interested in sports lately," the dialogue unit can provide a dialogue including topics related to sports. Furthermore, if the user posts on social media that "I've recently taken up reading as a hobby," the dialogue unit can provide a dialogue including topics related to reading. In this way, the dialogue unit can provide a dialogue based on the user's interests by analyzing social media activity.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The analysis unit analyzes the user's speech content, tone of voice, and facial expressions to estimate their emotional state. The user's speech content includes text, voice, gestures, etc. The analysis unit analyzes the tone of voice, such as voice frequency, volume, and pitch, and analyzes facial expressions using facial feature detection and facial expression recognition algorithms. For example, the analysis unit uses text analysis technology to analyze the speech content, voice analysis technology to analyze the tone of voice, and facial feature detection and facial expression recognition algorithms to analyze the facial expressions. Step 2: The generator generates a response based on the emotional state estimated by the analyzer. The generator uses AI to conduct natural conversations while taking into account the user's emotions and intentions. For example, if the user expresses sadness, the generator generates comforting words; if the user is angry, the generator generates a response in a calm and collected tone; and if the user is happy, the generator generates a response in a bright and empathetic tone. Step 3: The dialogue unit provides the user with the response generated by the generation unit. The dialogue unit is compatible with multiple devices such as glasses, earphones, and smartphones, and communicates with the user through a voice or text interface. For example, the dialogue unit can provide a response to the user using a voice or text interface, or through a device such as glasses or earphones.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the content of a user's speech, tone of voice, and facial expression to estimate the user's emotional state; a generation unit that generates a response based on the emotional state estimated by the analysis unit; a dialogue unit that provides the response generated by the generation unit to the user; A system characterized by:

2. The dialogue unit Compatible with multiple devices including glasses, earphones, and smartphones 2. The system of claim 1.

3. The generation unit Conduct conversations while taking into account the user's emotions and intentions 2. The system of claim 1.

4. The analysis unit Estimate the user's emotions and adjust the accuracy of analysis based on the estimated user emotions.

2. The system of claim 1.

5. The analysis unit Analyzing the user's past speech history to improve the accuracy of emotional state estimation 2. The system of claim 1.

6. The analysis unit During analysis, the user's biological information is also used to estimate their emotional state.

2. The system of claim 1.

7. The analysis unit Estimate the user's emotions and adjust the display format of the analysis results based on the estimated user emotions.

2. The system of claim 1.

8. The analysis unit During analysis, the emotional state is estimated based on the user's geographic location.

2. The system of claim 1.

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