system

Abstract

The system according to the embodiment aims to generate an appropriate response based on the user's utterance. [Solution] A system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives user utterances. The analysis unit analyzes the utterances received by the receiving unit and determines the user's preferences and emotions. The generation unit generates a response based on the preferences and emotions determined by the analysis unit. The provision unit provides the response generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately generate appropriate responses based on user utterances, and there is room for improvement.

[0005] The system according to the embodiment aims to generate an appropriate response based on the user's utterance. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit receives a user's utterance. The analyzing unit analyzes the utterance received by the receiving unit and determines the user's preferences and emotions. The generating unit generates a response based on the preferences and emotions determined by the analyzing unit. The providing unit provides the response generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate an appropriate response based on the user's utterance. [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 dialogue system according to an embodiment of the present invention accepts and analyzes user utterances and generates and provides appropriate responses. This dialogue system analyzes the user's preferences and emotions and automatically determines, based on past conversations, whether the current conversation is about a happy or sad topic and how important the topic is to the user. Specifically, a conversation with the user is initiated, and a generation AI generates the conversation. Next, past conversation data is analyzed to determine the user's preferences and emotions. This allows the system to understand the topic of the current conversation and generate an appropriate response. This system allows the user to enjoy more natural and enjoyable dialogue. For example, if the user asks, "What's the weather like today?", the generation AI responds, "It's sunny today. Do you have any plans to go out?" In this way, a natural dialogue is conducted. Next, past conversation data is analyzed. For example, if the user previously said, "I like traveling," the system memorizes this information and reflects it in the current conversation. If the user asks, "What's the weather like today?", the system responds, "It's sunny today. Do you have any plans to go out?" In this way, a response that takes the user's preferences and emotions into account is generated. Furthermore, the system determines what topic the current conversation is about for the user. For example, if the user says, "Work was hard today," the system can respond based on this information with, "Thank you for your hard work. Would you like to do something fun to relax?" In this way, a response that is in line with the user's emotions is generated. This system allows the user to enjoy more natural and enjoyable conversations. For example, if the user asks, "What's the weather like today?", the system can respond with, "It's sunny today. Do you have any plans to go out?" If the user says, "Work was hard today," the system can respond with, "Thank you for your hard work. Would you like to do something fun to relax?" In this way, a natural conversation that takes the user's preferences and emotions into consideration is conducted. This allows the dialogue system to automatically analyze the user's utterances and generate and provide appropriate responses.

[0029] The dialogue system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user utterances. The user utterances include, but are not limited to, speech, text, and gestures. The reception unit converts the speech utterances into text using, for example, speech recognition technology. The reception unit can also directly accept text input. The reception unit can also analyze gesture utterances using gesture recognition technology. For example, the reception unit converts the user's speech utterances into text using speech recognition technology and transmits the text to the analysis unit for analysis. The text input is directly received as text entered by the user using a keyboard or touch screen. The gesture recognition technology analyzes the user's hand movements and facial expressions and recognizes them as utterances. The analysis unit analyzes the utterances received by the reception unit and determines the user's preferences and emotions. For example, natural language processing technology, emotion analysis technology, machine learning algorithms, and the like are used for the analysis, but are not limited to these examples. For example, the analysis unit analyzes the meaning of the user's utterance using natural language processing technology and determines the emotion of the utterance using emotion analysis technology. The analysis unit can also learn the user's preferences using a machine learning algorithm and determine the preferences based on the content of the utterance. The generation unit generates an appropriate response based on the preferences and emotions determined by the analysis unit. For example, a generation AI (e.g., a text generation AI or a multimodal generation AI) can be used for generation, but this is not limited to this example. For example, the generation unit generates a response to the user's utterance using a text generation AI. The generation unit can also use a multimodal generation AI to generate a response that includes not only text but also images and audio. The generation AI generates an appropriate response based on the content and emotions of the user's utterance. The provision unit provides the response generated by the generation unit to the user. Providing the response includes, for example, providing in real time, providing by batch processing, etc., but is not limited to these examples. For example, the provision unit provides the response generated in real time to the user. The provision unit can also provide a batch of responses generated by batch processing.As a result, the dialogue system according to the embodiment receives and analyzes user utterances, and generates and provides appropriate responses, thereby realizing natural dialogue.

[0030] The analysis unit can analyze past conversation data to determine the user's preferences and emotions. Past conversation data includes, but is not limited to, chat logs and voice recordings. For example, the analysis unit can analyze past chat logs to determine the user's preferences and emotions. The analysis unit can also analyze past voice recordings to determine the user's preferences and emotions. For example, the analysis unit can extract topics frequently discussed by the user from past chat logs to determine the user's preferences. The analysis unit can also analyze the tone and speed of the user's speech from past voice recordings to determine the user's emotions. In this way, the analysis of past conversation data can accurately determine the user's preferences and emotions. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input past conversation data into AI and have the AI ​​determine the user's preferences and emotions.

[0031] The generation unit can generate an appropriate response based on the user's preferences and emotions. Examples of appropriate responses include, but are not limited to, text messages, voice responses, and images. The generation unit can generate, for example, text messages based on the user's preferences and emotions. The generation unit can also generate voice responses based on the user's preferences and emotions. For example, if the generation unit determines that the user likes traveling, it can generate a response such as, "Do you have plans for your next trip?" If the generation unit determines that the user is tired, it can generate a response such as, "Thank you for your hard work. Would you like to do something fun to relax?" This enables more natural dialogue by generating responses based on the user's preferences and emotions. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the generation unit can generate responses using a generation AI model that receives the user's preferences and emotions as input and outputs an appropriate response.

[0032] The providing unit can provide the generated response to the user. Providing includes, for example, providing in real time, providing in batch processing, and the like, but is not limited to these examples. For example, the providing unit can provide the generated response to the user in real time. The providing unit can also provide responses generated by batch processing in bulk. For example, the providing unit can immediately provide the response generated in real time to the user. The providing unit can also provide responses generated by batch processing in bulk. As a result, the dialogue is completed by providing the generated response to the user. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the generated response to AI and cause the AI ​​to execute a method of providing the response to the user.

[0033] The reception unit can analyze the user's past speech history and select the optimal reception method. For example, the reception unit prioritizes reception of speech formats (voice, text, etc.) that the user has frequently used in the past. The reception unit can also select a reception method suitable for a specific time period based on the user's past speech history. Furthermore, the reception unit can analyze the content of the user's past speech and prioritize reception of speech on related topics. For example, the reception unit prioritizes reception of speech formats that the user has frequently used in the past and selects a reception method suitable for a specific time period. In this way, the optimal reception method can be selected by analyzing the user's past speech history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past speech history into AI and have the AI ​​select the optimal reception method.

[0034] The reception unit can filter utterances based on the user's current situation and areas of interest when receiving utterances. For example, when a user inputs their current situation, the reception unit prioritizes receiving utterances related to the situation. The reception unit can also filter and receive relevant utterances based on the user's areas of interest. Furthermore, the reception unit can filter and receive appropriate utterances according to the user's current activity (e.g., at work, on a break, etc.). For example, when a user inputs their current situation, the reception unit prioritizes receiving utterances related to the situation and filters and receives relevant utterances based on the user's areas of interest. This enables more relevant dialogue by filtering utterances based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input data on the user's current situation and areas of interest into AI and have the AI ​​perform utterance filtering.

[0035] When receiving a utterance, the reception unit can prioritize receiving highly relevant utterances by taking into consideration the user's geographical location information. For example, when the user is in a specific location, the reception unit can prioritize receiving utterances related to that location. The reception unit can also prioritize receiving utterances related to local news and events based on the user's geographical location information. Furthermore, when the user is traveling, the reception unit can prioritize receiving utterances related to the travel destination. For example, when the user is in a specific location, the reception unit prioritizes receiving utterances related to that location and prioritizes receiving utterances related to local news and events based on the geographical location information. This enables more appropriate dialogue by preferentially receiving highly relevant utterances based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI to determine the priority of highly relevant utterances.

[0036] The reception unit can analyze the user's social media activity when receiving a comment and receive related comments. The reception unit, for example, prioritizes receiving related comments based on content shared by the user on social media. The reception unit can also receive related comments based on the activities of the user's social media followers and friends. The reception unit can also receive related comments based on topics in which the user has shown interest on social media. For example, the reception unit prioritizes receiving related comments based on content shared by the user on social media, and receives related comments based on the activities of followers and friends. This allows the user's social media activity to be analyzed and related comments to be received preferentially. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​receive related comments.

[0037] When analyzing a utterance, the analysis unit can improve the accuracy of the analysis by referring to past conversation data. For example, the analysis unit can refer to content that the user has previously uttered to understand the context of the current utterance. The analysis unit can also extract the user's preferences and interests from the past conversation data and reflect them in the analysis. Furthermore, the analysis unit can analyze the user's utterance patterns based on the past conversation data to generate highly accurate responses. For example, the analysis unit can refer to content that the user has previously uttered to understand the context of the current utterance, extract the user's preferences and interests, and reflect them in the analysis. In this way, the accuracy of the analysis is improved by referring to the past conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input past conversation data into AI and have the AI ​​improve the accuracy of the analysis.

[0038] The analysis unit can take user attribute information into consideration when analyzing a comment. The analysis unit selects an appropriate analysis method based on, for example, the user's age and gender. The analysis unit can also reflect related information based on the user's occupation and hobbies in the analysis. Furthermore, the analysis unit can accurately analyze the meaning of a comment based on the user's cultural background. For example, the analysis unit selects an appropriate analysis method based on the user's age and gender, and reflects related information based on the user's occupation and hobbies in the analysis. This enables more appropriate analysis by taking user attribute information into consideration. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input user attribute information into AI and have the AI ​​analyze the comment.

[0039] The analysis unit can take into account the geographical distribution of users when analyzing utterances. For example, if a user is in a specific region, the analysis unit prioritizes analyzing information related to that region. The analysis unit can also reflect regional expressions and words in the analysis based on the user's geographical distribution. Furthermore, if a user is traveling, the analysis unit can also reflect information about the user's travel destination in the analysis. For example, if a user is in a specific region, the analysis unit prioritizes analyzing information related to that region and reflects regional expressions and words in the analysis based on the user's geographical distribution. This enables more appropriate analysis by taking the user's geographical distribution into consideration. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input user geographical distribution data into AI and have the AI ​​analyze the utterances.

[0040] When analyzing a statement, the analysis unit can improve the accuracy of the analysis by referring to related literature. The analysis unit, for example, refers to academic papers related to the content of the user's statement and reflects the results in the analysis. The analysis unit can also refer to news articles related to the content of the user's statement and reflect the results in the analysis. The analysis unit can also refer to books related to the content of the user's statement and reflect the results in the analysis. For example, the analysis unit can refer to academic papers related to the content of the user's statement and reflect the results in the analysis. The analysis unit can also refer to news articles related to the content of the user's statement and reflect the results in the analysis. By referring to related literature, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input related literature data into AI and have the AI ​​analyze the statement.

[0041] When generating a response, the generation unit can improve the accuracy of the generation by referring to past conversation data. The generation unit, for example, refers to content that the user has previously said and reflects it in the current response. The generation unit can also extract the user's preferences and interests from the past conversation data and reflect them in the response. The generation unit can also analyze the user's speech patterns based on the past conversation data and generate a highly accurate response. For example, the generation unit refers to content that the user has previously said and reflects it in the current response. The generation unit can also extract the user's preferences and interests from the past conversation data and reflect them in the response. In this way, the accuracy of the response is improved by referring to the past conversation data. Some or all of the above-mentioned 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 past conversation data to the generation AI and cause the generation AI to generate a response.

[0042] The generation unit can generate a response taking into account the user's attribute information. The generation unit generates an appropriate response based on, for example, the user's age and gender. The generation unit can also reflect related information in the response based on the user's occupation and hobbies. The generation unit can also generate an appropriate response based on the user's cultural background. For example, the generation unit generates an appropriate response based on the user's age and gender, and reflects related information in the response based on the user's occupation and hobbies. This allows a more appropriate response to be generated by taking the user's attribute information into consideration. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's attribute information into the generation AI and cause the generation AI to generate a response.

[0043] The generation unit can generate a response taking into account the user's geographical distribution. For example, if the user is in a specific region, the generation unit can reflect information related to that region in the response. The generation unit can also reflect expressions and words specific to the region in the response based on the user's geographical distribution. Furthermore, if the user is traveling, the generation unit can reflect information about the user's travel destination in the response. For example, if the user is in a specific region, the generation unit can reflect information related to that region in the response and reflect expressions and words specific to the region in the response based on the user's geographical distribution. This allows for the generation of a more appropriate response by taking the user's geographical distribution into consideration. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input user geographical distribution data into the generation AI and cause the generation AI to generate a response.

[0044] The generation unit can improve the accuracy of the generation by referring to related literature when generating a response. The generation unit, for example, refers to academic papers related to the content of the user's utterance and reflects them in the response. The generation unit can also refer to news articles related to the content of the user's utterance and reflect them in the response. The generation unit can also refer to books related to the content of the user's utterance and reflect them in the response. For example, the generation unit can refer to academic papers related to the content of the user's utterance and reflect them in the response. The generation unit can also refer to news articles related to the content of the user's utterance and reflect them in the response. By doing so, the accuracy of the response is improved by referring to related literature. Some or all of the above-mentioned 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 related literature data into the generation AI and cause the generation AI to generate a response.

[0045] When providing a response, the providing unit can improve the accuracy of the response by referring to past conversation data. The providing unit, for example, refers to content that the user has previously said and reflects it in the current response. The providing unit can also extract the user's preferences and interests from the past conversation data and reflect them in the response. The providing unit can also analyze the user's speech patterns based on the past conversation data and provide a highly accurate response. For example, the providing unit refers to content that the user has previously said and reflects it in the current response. The providing unit can also extract the user's preferences and interests from the past conversation data and reflect them in the response. In this way, the accuracy of the response is improved by referring to the past conversation data. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input past conversation data to AI and have the AI ​​provide a response.

[0046] The providing unit can provide a response taking into consideration the user's attribute information. The providing unit can provide an appropriate response based on, for example, the user's age and gender. The providing unit can also reflect related information in the response based on the user's occupation and hobbies. The providing unit can also provide an appropriate response based on the user's cultural background. For example, the providing unit can provide an appropriate response based on the user's age and gender, and reflect related information in the response based on the user's occupation and hobbies. This allows a more appropriate response to be provided by taking the user's attribute information into consideration. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's attribute information into AI and have the AI ​​provide a response.

[0047] The providing unit may provide a response taking into consideration the user's geographical distribution. For example, if the user is in a specific region, the providing unit may reflect information related to that region in the response. The providing unit may also reflect expressions and words specific to the region in the response based on the user's geographical distribution. Furthermore, if the user is traveling, the providing unit may reflect information about the user's travel destination in the response. For example, if the user is in a specific region, the providing unit may reflect information related to that region in the response and reflect expressions and words specific to the region in the response based on the user's geographical distribution. This allows for the provision of a more appropriate response by taking the user's geographical distribution into consideration. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's geographical distribution data into AI and have the AI ​​provide the response.

[0048] The providing unit can improve the accuracy of the response by referring to related literature when providing a response. The providing unit, for example, refers to academic papers related to the content of the user's statement and reflects them in the response. The providing unit can also refer to news articles related to the content of the user's statement and reflect them in the response. The providing unit can also refer to books related to the content of the user's statement and reflect them in the response. For example, the providing unit can refer to academic papers related to the content of the user's statement and reflect them in the response. The providing unit can also refer to news articles related to the content of the user's statement and reflect them in the response. By doing so, the accuracy of the response is improved by referring to related literature. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input related literature data into AI and have the AI ​​provide a response.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] When receiving a user's utterances, the dialogue system can also generate responses that take into account the user's current health condition. For example, if the user is connected to a health management app, the system can obtain the user's heart rate and sleep data and adjust responses based on this. If the user is tired, the system can generate a response such as "Take it easy and rest today." If the user has just exercised, the system can generate a response such as "Thank you for your hard work. Don't forget to stay hydrated." Furthermore, if the user is feeling stressed, the system can generate a response suggesting ways to relax. This enables more personalized dialogue based on the user's health condition.

[0051] The analysis unit can also take into account the user's social media activity when analyzing a user's comments. For example, the analysis can reflect the content the user recently shared on social media and topics the user showed interest in. If the user frequently posts about a particular event, the system can prioritize analyzing topics related to that event. Also, if the user frequently uses a particular hashtag, the system can reflect information related to that hashtag in the analysis. Furthermore, the activity of the user's followers and friends can be taken into account and related topics can be reflected in the analysis. This enables more relevant analysis based on the user's social media activity.

[0052] The generator can also take into account the user's current geographic location information when generating a response to a user's utterance. For example, if the user is in a specific location, information related to that location can be reflected in the response. If the user is traveling, a response including tourist information and weather information for the travel destination can be generated. Also, if the user is participating in a specific event, information related to the event can be reflected in the response. Furthermore, if the user is at home, a response including local news and event information can be generated. This enables more personalized responses based on the user's geographic location information.

[0053] The providing unit may also take into account the user's device environment when providing the generated response to the user. For example, if the user is using a smartphone, the response may be provided via a short text message or push notification. If the user is using a smart speaker, the response may be provided by voice. Furthermore, if the user is using a smart watch, the response may be provided via vibration or a simple text message. This allows for more appropriate response provision based on the user's device environment.

[0054] The reception unit can also take the user's past purchasing history into consideration when receiving a user's utterances. For example, it can preferentially receive topics related to products and services the user has purchased in the past. If the user frequently purchases products from a specific brand, it can preferentially receive topics related to that brand. Also, if the user purchases many products from a specific category, it can preferentially receive topics related to that category. Furthermore, it can preferentially receive topics related to products recently purchased by the user. This enables more relevant dialogue based on the user's purchasing history.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The reception unit receives user utterances. The user utterances include voice, text, gestures, etc. The reception unit converts voice utterances into text using voice recognition technology, directly accepts text input, and analyzes gesture utterances using gesture recognition technology. Step 2: The analysis unit analyzes the comments received by the reception unit and determines the user's preferences and emotions. The analysis uses natural language processing technology, sentiment analysis technology, machine learning algorithms, etc. Step 3: The generator generates an appropriate response based on the preferences and emotions determined by the analyzer. This is done using generative AI (e.g., text generation AI or multimodal generation AI). Step 4: The providing unit provides the response generated by the generating unit to the user, including providing in real time or by batch processing.

[0057] (Example 2) A dialogue system according to an embodiment of the present invention accepts and analyzes user utterances and generates and provides appropriate responses. This dialogue system analyzes the user's preferences and emotions and automatically determines, based on past conversations, whether the current conversation is about a happy or sad topic and how important the topic is to the user. Specifically, a conversation with the user is initiated, and a generation AI generates the conversation. Next, past conversation data is analyzed to determine the user's preferences and emotions. This allows the system to understand the topic of the current conversation and generate an appropriate response. This system allows the user to enjoy more natural and enjoyable dialogue. For example, if the user asks, "What's the weather like today?", the generation AI responds, "It's sunny today. Do you have any plans to go out?" In this way, a natural dialogue is conducted. Next, past conversation data is analyzed. For example, if the user previously said, "I like traveling," the system memorizes this information and reflects it in the current conversation. If the user asks, "What's the weather like today?", the system responds, "It's sunny today. Do you have any plans to go out?" In this way, a response that takes the user's preferences and emotions into account is generated. Furthermore, the system determines what topic the current conversation is about for the user. For example, if the user says, "Work was hard today," the system can respond based on this information with, "Thank you for your hard work. Would you like to do something fun to relax?" In this way, a response that is in line with the user's emotions is generated. This system allows the user to enjoy more natural and enjoyable conversations. For example, if the user asks, "What's the weather like today?", the system can respond with, "It's sunny today. Do you have any plans to go out?" If the user says, "Work was hard today," the system can respond with, "Thank you for your hard work. Would you like to do something fun to relax?" In this way, a natural conversation that takes the user's preferences and emotions into consideration is conducted. This allows the dialogue system to automatically analyze the user's utterances and generate and provide appropriate responses.

[0058] The dialogue system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user utterances. The user utterances include, but are not limited to, speech, text, and gestures. The reception unit converts the speech utterances into text using, for example, speech recognition technology. The reception unit can also directly accept text input. The reception unit can also analyze gesture utterances using gesture recognition technology. For example, the reception unit converts the user's speech utterances into text using speech recognition technology and transmits the text to the analysis unit for analysis. The text input is directly received as text entered by the user using a keyboard or touch screen. The gesture recognition technology analyzes the user's hand movements and facial expressions and recognizes them as utterances. The analysis unit analyzes the utterances received by the reception unit and determines the user's preferences and emotions. For example, natural language processing technology, emotion analysis technology, machine learning algorithms, and the like are used for the analysis, but are not limited to these examples. For example, the analysis unit analyzes the meaning of the user's utterance using natural language processing technology and determines the emotion of the utterance using emotion analysis technology. The analysis unit can also learn the user's preferences using a machine learning algorithm and determine the preferences based on the content of the utterance. The generation unit generates an appropriate response based on the preferences and emotions determined by the analysis unit. For example, a generation AI (e.g., a text generation AI or a multimodal generation AI) can be used for generation, but this is not limited to this example. For example, the generation unit generates a response to the user's utterance using a text generation AI. The generation unit can also use a multimodal generation AI to generate a response that includes not only text but also images and audio. The generation AI generates an appropriate response based on the content and emotions of the user's utterance. The provision unit provides the response generated by the generation unit to the user. Providing the response includes, for example, providing in real time, providing by batch processing, etc., but is not limited to these examples. For example, the provision unit provides the response generated in real time to the user. The provision unit can also provide a batch of responses generated by batch processing.As a result, the dialogue system according to the embodiment receives and analyzes user utterances, and generates and provides appropriate responses, thereby realizing natural dialogue.

[0059] The analysis unit can analyze past conversation data to determine the user's preferences and emotions. Past conversation data includes, but is not limited to, chat logs and voice recordings. For example, the analysis unit can analyze past chat logs to determine the user's preferences and emotions. The analysis unit can also analyze past voice recordings to determine the user's preferences and emotions. For example, the analysis unit can extract topics frequently discussed by the user from past chat logs to determine the user's preferences. The analysis unit can also analyze the tone and speed of the user's speech from past voice recordings to determine the user's emotions. In this way, the analysis of past conversation data can accurately determine the user's preferences and emotions. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input past conversation data into AI and have the AI ​​determine the user's preferences and emotions.

[0060] The generation unit can generate an appropriate response based on the user's preferences and emotions. Examples of appropriate responses include, but are not limited to, text messages, voice responses, and images. The generation unit can generate, for example, text messages based on the user's preferences and emotions. The generation unit can also generate voice responses based on the user's preferences and emotions. For example, if the generation unit determines that the user likes traveling, it can generate a response such as, "Do you have plans for your next trip?" If the generation unit determines that the user is tired, it can generate a response such as, "Thank you for your hard work. Would you like to do something fun to relax?" This enables more natural dialogue by generating responses based on the user's preferences and emotions. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the generation unit can generate responses using a generation AI model that receives the user's preferences and emotions as input and outputs an appropriate response.

[0061] The providing unit can provide the generated response to the user. Providing includes, for example, providing in real time, providing in batch processing, and the like, but is not limited to these examples. For example, the providing unit can provide the generated response to the user in real time. The providing unit can also provide responses generated by batch processing in bulk. For example, the providing unit can immediately provide the response generated in real time to the user. The providing unit can also provide responses generated by batch processing in bulk. As a result, the dialogue is completed by providing the generated response to the user. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the generated response to AI and cause the AI ​​to execute a method of providing the response to the user.

[0062] The reception unit can estimate the user's emotions and adjust the timing of utterance reception based on the estimated user emotions. For example, if the user is excited, the reception unit can accelerate the timing of utterance reception and return a quick response. Furthermore, if the user is relaxed, the reception unit can delay the timing of utterance reception and provide a relaxed dialogue. Furthermore, if the user is feeling stressed, the reception unit can adjust the timing of utterance reception and wait until the user calms down. For example, the reception unit can estimate the user's emotions and return a quick response if the user is excited, and provide a relaxed dialogue if the user is relaxed. This allows for more appropriate dialogue by adjusting the timing of utterance reception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the timing of receiving utterances.

[0063] The reception unit can analyze the user's past speech history and select the optimal reception method. For example, the reception unit prioritizes reception of speech formats (voice, text, etc.) that the user has frequently used in the past. The reception unit can also select a reception method suitable for a specific time period based on the user's past speech history. Furthermore, the reception unit can analyze the content of the user's past speech and prioritize reception of speech on related topics. For example, the reception unit prioritizes reception of speech formats that the user has frequently used in the past and selects a reception method suitable for a specific time period. In this way, the optimal reception method can be selected by analyzing the user's past speech history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past speech history into AI and have the AI ​​select the optimal reception method.

[0064] The reception unit can filter utterances based on the user's current situation and areas of interest when receiving utterances. For example, when a user inputs their current situation, the reception unit prioritizes receiving utterances related to the situation. The reception unit can also filter and receive relevant utterances based on the user's areas of interest. Furthermore, the reception unit can filter and receive appropriate utterances according to the user's current activity (e.g., at work, on a break, etc.). For example, when a user inputs their current situation, the reception unit prioritizes receiving utterances related to the situation and filters and receives relevant utterances based on the user's areas of interest. This enables more relevant dialogue by filtering utterances based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input data on the user's current situation and areas of interest into AI and have the AI ​​perform utterance filtering.

[0065] The reception unit can estimate the user's emotions and determine the priority of utterances to be received based on the estimated user's emotions. For example, when the user is excited, the reception unit can prioritize receiving important utterances. Furthermore, when the user is relaxed, the reception unit can prioritize receiving light-hearted utterances. Furthermore, when the user is stressed, the reception unit can prioritize receiving comforting or encouraging utterances. For example, the reception unit can estimate the user's emotions and prioritize receiving important utterances when the user is excited, and prioritize receiving light-hearted utterances when the user is relaxed. This enables more appropriate dialogue by determining the priority of utterances based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of comments.

[0066] When receiving a utterance, the reception unit can prioritize receiving highly relevant utterances by taking into consideration the user's geographical location information. For example, when the user is in a specific location, the reception unit can prioritize receiving utterances related to that location. The reception unit can also prioritize receiving utterances related to local news and events based on the user's geographical location information. Furthermore, when the user is traveling, the reception unit can prioritize receiving utterances related to the travel destination. For example, when the user is in a specific location, the reception unit prioritizes receiving utterances related to that location and prioritizes receiving utterances related to local news and events based on the geographical location information. This enables more appropriate dialogue by preferentially receiving highly relevant utterances based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI to determine the priority of highly relevant utterances.

[0067] The reception unit can analyze the user's social media activity when receiving a comment and receive related comments. The reception unit, for example, prioritizes receiving related comments based on content shared by the user on social media. The reception unit can also receive related comments based on the activities of the user's social media followers and friends. The reception unit can also receive related comments based on topics in which the user has shown interest on social media. For example, the reception unit prioritizes receiving related comments based on content shared by the user on social media, and receives related comments based on the activities of followers and friends. This allows the user's social media activity to be analyzed and related comments to be received preferentially. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​receive related comments.

[0068] The analysis unit can estimate the user's emotions and adjust the analysis method of the utterances based on the estimated user emotions. For example, if the user is excited, the analysis unit can quickly analyze the utterances and generate a response immediately. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis and provide a deep dialogue. Furthermore, if the user is feeling stressed, the analysis unit can perform an analysis that is sensitive to the user's emotions and generate a comforting or encouraging response. For example, the analysis unit can estimate the user's emotions and quickly analyze the utterances if the user is excited, and perform a detailed analysis if the user is relaxed. This allows for more appropriate analysis by adjusting the analysis method of the utterances based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input user emotional data into the generation AI and have the generation AI adjust the method of analyzing utterances.

[0069] When analyzing a utterance, the analysis unit can improve the accuracy of the analysis by referring to past conversation data. For example, the analysis unit can refer to content that the user has previously uttered to understand the context of the current utterance. The analysis unit can also extract the user's preferences and interests from the past conversation data and reflect them in the analysis. Furthermore, the analysis unit can analyze the user's utterance patterns based on the past conversation data to generate highly accurate responses. For example, the analysis unit can refer to content that the user has previously uttered to understand the context of the current utterance, extract the user's preferences and interests, and reflect them in the analysis. In this way, the accuracy of the analysis is improved by referring to the past conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input past conversation data into AI and have the AI ​​improve the accuracy of the analysis.

[0070] The analysis unit can take user attribute information into consideration when analyzing a comment. The analysis unit selects an appropriate analysis method based on, for example, the user's age and gender. The analysis unit can also reflect related information based on the user's occupation and hobbies in the analysis. Furthermore, the analysis unit can accurately analyze the meaning of a comment based on the user's cultural background. For example, the analysis unit selects an appropriate analysis method based on the user's age and gender, and reflects related information based on the user's occupation and hobbies in the analysis. This enables more appropriate analysis by taking user attribute information into consideration. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input user attribute information into AI and have the AI ​​analyze the comment.

[0071] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit can provide a visually stimulating display method. Furthermore, if the user is relaxed, the analysis unit can provide a calming display method. Furthermore, if the user is stressed, the analysis unit can provide a simple, highly visible display method. For example, the analysis unit can estimate the user's emotions and provide a visually stimulating display method if the user is excited, and a calming display method if the user is relaxed. This allows for more appropriate display by adjusting the display method of the analysis results based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0072] The analysis unit can take into account the geographical distribution of users when analyzing utterances. For example, if a user is in a specific region, the analysis unit prioritizes analyzing information related to that region. The analysis unit can also reflect regional expressions and words in the analysis based on the user's geographical distribution. Furthermore, if a user is traveling, the analysis unit can also reflect information about the user's travel destination in the analysis. For example, if a user is in a specific region, the analysis unit prioritizes analyzing information related to that region and reflects regional expressions and words in the analysis based on the user's geographical distribution. This enables more appropriate analysis by taking the user's geographical distribution into consideration. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input user geographical distribution data into AI and have the AI ​​analyze the utterances.

[0073] When analyzing a statement, the analysis unit can improve the accuracy of the analysis by referring to related literature. The analysis unit, for example, refers to academic papers related to the content of the user's statement and reflects the results in the analysis. The analysis unit can also refer to news articles related to the content of the user's statement and reflect the results in the analysis. The analysis unit can also refer to books related to the content of the user's statement and reflect the results in the analysis. For example, the analysis unit can refer to academic papers related to the content of the user's statement and reflect the results in the analysis. The analysis unit can also refer to news articles related to the content of the user's statement and reflect the results in the analysis. By referring to related literature, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input related literature data into AI and have the AI ​​analyze the statement.

[0074] The generation unit can estimate the user's emotions and adjust the response generation method based on the estimated user's emotions. For example, the generation unit generates a quick and concise response when the user is excited. The generation unit can also generate a detailed and polite response when the user is relaxed. Furthermore, the generation unit can generate a comforting or encouraging response when the user is stressed. For example, the generation unit estimates the user's emotions and generates a quick and concise response when the user is excited, and a detailed and polite response when the user is relaxed. This allows for the generation of a more appropriate response by adjusting the response generation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the response generation method.

[0075] When generating a response, the generation unit can improve the accuracy of the generation by referring to past conversation data. The generation unit, for example, refers to content that the user has previously said and reflects it in the current response. The generation unit can also extract the user's preferences and interests from the past conversation data and reflect them in the response. The generation unit can also analyze the user's speech patterns based on the past conversation data and generate a highly accurate response. For example, the generation unit refers to content that the user has previously said and reflects it in the current response. The generation unit can also extract the user's preferences and interests from the past conversation data and reflect them in the response. In this way, the accuracy of the response is improved by referring to the past conversation data. Some or all of the above-mentioned 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 past conversation data to the generation AI and cause the generation AI to generate a response.

[0076] The generation unit can generate a response taking into account the user's attribute information. The generation unit generates an appropriate response based on, for example, the user's age and gender. The generation unit can also reflect related information in the response based on the user's occupation and hobbies. The generation unit can also generate an appropriate response based on the user's cultural background. For example, the generation unit generates an appropriate response based on the user's age and gender, and reflects related information in the response based on the user's occupation and hobbies. This allows a more appropriate response to be generated by taking the user's attribute information into consideration. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's attribute information into the generation AI and cause the generation AI to generate a response.

[0077] The generation unit can estimate the user's emotion and adjust the response display method based on the estimated user's emotion. For example, if the user is excited, the generation unit can provide a visually stimulating display method. Furthermore, if the user is relaxed, the generation unit can provide a calming display method. Furthermore, if the user is stressed, the generation unit can provide a simple, highly visible display method. For example, the generation unit can estimate the user's emotion and provide a visually stimulating display method if the user is excited, and a calming display method if the user is relaxed. This allows for a more appropriate display by adjusting the response display method based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the response display method.

[0078] The generation unit can generate a response taking into account the user's geographical distribution. For example, if the user is in a specific region, the generation unit can reflect information related to that region in the response. The generation unit can also reflect expressions and words specific to the region in the response based on the user's geographical distribution. Furthermore, if the user is traveling, the generation unit can reflect information about the user's travel destination in the response. For example, if the user is in a specific region, the generation unit can reflect information related to that region in the response and reflect expressions and words specific to the region in the response based on the user's geographical distribution. This allows for the generation of a more appropriate response by taking the user's geographical distribution into consideration. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input user geographical distribution data into the generation AI and cause the generation AI to generate a response.

[0079] The generation unit can improve the accuracy of the generation by referring to related literature when generating a response. The generation unit, for example, refers to academic papers related to the content of the user's utterance and reflects them in the response. The generation unit can also refer to news articles related to the content of the user's utterance and reflect them in the response. The generation unit can also refer to books related to the content of the user's utterance and reflect them in the response. For example, the generation unit can refer to academic papers related to the content of the user's utterance and reflect them in the response. The generation unit can also refer to news articles related to the content of the user's utterance and reflect them in the response. By doing so, the accuracy of the response is improved by referring to related literature. Some or all of the above-mentioned 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 related literature data into the generation AI and cause the generation AI to generate a response.

[0080] The providing unit can estimate the user's emotions and adjust the response provision method based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide a quick response. Furthermore, if the user is relaxed, the providing unit can provide a response at a leisurely pace. Furthermore, if the user is feeling stressed, the providing unit can provide a response that is in tune with the user's emotions. For example, the providing unit can estimate the user's emotions and provide a quick response if the user is excited, and provide a response at a leisurely pace if the user is relaxed. This allows for adjusting the response provision method based on the user's emotions, thereby providing a more appropriate response. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the response provision method.

[0081] When providing a response, the providing unit can improve the accuracy of the response by referring to past conversation data. The providing unit, for example, refers to content that the user has previously said and reflects it in the current response. The providing unit can also extract the user's preferences and interests from the past conversation data and reflect them in the response. The providing unit can also analyze the user's speech patterns based on the past conversation data and provide a highly accurate response. For example, the providing unit refers to content that the user has previously said and reflects it in the current response. The providing unit can also extract the user's preferences and interests from the past conversation data and reflect them in the response. In this way, the accuracy of the response is improved by referring to the past conversation data. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input past conversation data to AI and have the AI ​​provide a response.

[0082] The providing unit can provide a response taking into consideration the user's attribute information. The providing unit can provide an appropriate response based on, for example, the user's age and gender. The providing unit can also reflect related information in the response based on the user's occupation and hobbies. The providing unit can also provide an appropriate response based on the user's cultural background. For example, the providing unit can provide an appropriate response based on the user's age and gender, and reflect related information in the response based on the user's occupation and hobbies. This allows a more appropriate response to be provided by taking the user's attribute information into consideration. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's attribute information into AI and have the AI ​​provide a response.

[0083] The providing unit can estimate the user's emotions and adjust the order in which responses are provided based on the estimated user's emotions. For example, if the user is excited, the providing unit can prioritize providing important responses. Furthermore, if the user is relaxed, the providing unit can prioritize providing light-hearted responses. Furthermore, if the user is stressed, the providing unit can prioritize providing comforting or encouraging responses. For example, the providing unit can estimate the user's emotions and prioritize providing important responses when the user is excited, and prioritize providing light-hearted responses when the user is relaxed. This allows for adjusting the order in which responses are provided based on the user's emotions, thereby providing more appropriate responses. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the order in which responses are provided.

[0084] The providing unit may provide a response taking into consideration the user's geographical distribution. For example, if the user is in a specific region, the providing unit may reflect information related to that region in the response. The providing unit may also reflect expressions and words specific to the region in the response based on the user's geographical distribution. Furthermore, if the user is traveling, the providing unit may reflect information about the user's travel destination in the response. For example, if the user is in a specific region, the providing unit may reflect information related to that region in the response and reflect expressions and words specific to the region in the response based on the user's geographical distribution. This allows for the provision of a more appropriate response by taking the user's geographical distribution into consideration. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's geographical distribution data into AI and have the AI ​​provide the response.

[0085] The providing unit can improve the accuracy of the response by referring to related literature when providing a response. The providing unit, for example, refers to academic papers related to the content of the user's statement and reflects them in the response. The providing unit can also refer to news articles related to the content of the user's statement and reflect them in the response. The providing unit can also refer to books related to the content of the user's statement and reflect them in the response. For example, the providing unit can refer to academic papers related to the content of the user's statement and reflect them in the response. The providing unit can also refer to news articles related to the content of the user's statement and reflect them in the response. By doing so, the accuracy of the response is improved by referring to related literature. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input related literature data into AI and have the AI ​​provide a response. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a user's utterance using the microphone 38B or touch panel 38A of the smart device 14, and converts the speech into text using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's utterance using natural language processing technology or sentiment analysis technology. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate response using a generation AI. The provision unit provides the generated response to the user using the display 40A or speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's utterance using the microphone 238 of the smart glasses 214 and converts the speech into text using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's utterance using natural language processing technology and sentiment analysis technology. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate response using a generation AI. The provision unit provides the generated response to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives a user's utterance using the microphone 238 of the headset-type terminal 314 and converts the speech into text using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's utterance using natural language processing technology and sentiment analysis technology. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate response using a generation AI. The provision unit provides the generated response to the user using the display 343 or speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a user's utterance using the microphone 238 of the robot 414 and converts the speech into text by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's utterance using natural language processing technology and sentiment analysis technology. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate response using a generation AI. The provision unit provides the generated response to the user using the speaker 240 or display device of the robot 414.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] When receiving a user's utterances, the dialogue system can also generate responses that take into account the user's current health condition. For example, if the user is connected to a health management app, the system can obtain the user's heart rate and sleep data and adjust responses based on this. If the user is tired, the system can generate a response such as "Take it easy and rest today." If the user has just exercised, the system can generate a response such as "Thank you for your hard work. Don't forget to stay hydrated." Furthermore, if the user is feeling stressed, the system can generate a response suggesting ways to relax. This enables more personalized dialogue based on the user's health condition.

[0088] The analysis unit can also take into account the user's social media activity when analyzing a user's comments. For example, the analysis can reflect the content the user recently shared on social media and topics the user showed interest in. If the user frequently posts about a particular event, the system can prioritize analyzing topics related to that event. Also, if the user frequently uses a particular hashtag, the system can reflect information related to that hashtag in the analysis. Furthermore, the activity of the user's followers and friends can be taken into account and related topics can be reflected in the analysis. This enables more relevant analysis based on the user's social media activity.

[0089] The generator can also take into account the user's current geographic location information when generating a response to a user's utterance. For example, if the user is in a specific location, information related to that location can be reflected in the response. If the user is traveling, a response including tourist information and weather information for the travel destination can be generated. Also, if the user is participating in a specific event, information related to the event can be reflected in the response. Furthermore, if the user is at home, a response including local news and event information can be generated. This enables more personalized responses based on the user's geographic location information.

[0090] The providing unit may also take into account the user's device environment when providing the generated response to the user. For example, if the user is using a smartphone, the response may be provided via a short text message or push notification. If the user is using a smart speaker, the response may be provided by voice. Furthermore, if the user is using a smart watch, the response may be provided via vibration or a simple text message. This allows for more appropriate response provision based on the user's device environment.

[0091] The reception unit can also take the user's past purchasing history into consideration when receiving a user's utterances. For example, it can preferentially receive topics related to products and services the user has purchased in the past. If the user frequently purchases products from a specific brand, it can preferentially receive topics related to that brand. Also, if the user purchases many products from a specific category, it can preferentially receive topics related to that category. Furthermore, it can preferentially receive topics related to products recently purchased by the user. This enables more relevant dialogue based on the user's purchasing history.

[0092] The reception unit can estimate the user's emotions and adjust the method of receiving utterances based on the estimated user emotions. For example, if the user is excited, voice input can be preferentially received and a response can be returned quickly. Also, if the user is relaxed, text input can be preferentially received and a relaxed dialogue can be provided. Furthermore, if the user is stressed, gesture input can be preferentially received and a wait can be made until the user calms down. In this way, by adjusting the method of receiving utterances according to the user's emotions, more appropriate dialogue can be achieved.

[0093] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is excited, important statements can be analyzed with priority and a response can be generated quickly. If the user is relaxed, a detailed analysis can be performed to provide an in-depth dialogue. Furthermore, if the user is feeling stressed, an analysis that is sensitive to the user's emotions can be performed to generate a comforting or encouraging response. This allows for more appropriate analysis by determining the priority of analysis based on the user's emotions.

[0094] The generation unit can estimate the user's emotion and adjust the tone of the response based on the estimated user's emotion. For example, if the user is excited, a response with a lively and cheerful tone can be generated. If the user is relaxed, a response with a calm and soothing tone can be generated. Furthermore, if the user is stressed, a response with a gentle and comforting tone can be generated. This allows for more appropriate dialogue by adjusting the tone of the response based on the user's emotion.

[0095] The providing unit can estimate the user's emotions and adjust the timing of providing a response based on the estimated user's emotions. For example, if the user is excited, the response can be provided quickly. If the user is relaxed, the response can be provided at a leisurely pace. Furthermore, if the user is feeling stressed, the response can be provided at a timing that is in line with the user's emotions. In this way, by adjusting the timing of providing a response based on the user's emotions, a more appropriate response can be provided.

[0096] The providing unit can estimate the user's emotion and adjust the format of the response based on the estimated user's emotion. For example, if the user is excited, the response can be provided in a visually stimulating format. If the user is relaxed, the response can be provided in a calm format. Furthermore, if the user is stressed, the response can be provided in a simple, highly visible format. In this way, by adjusting the format of the response based on the user's emotion, a more appropriate response can be provided.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The reception unit receives user utterances. The user utterances include voice, text, gestures, etc. The reception unit converts voice utterances into text using voice recognition technology, directly accepts text input, and analyzes gesture utterances using gesture recognition technology. Step 2: The analysis unit analyzes the comments received by the reception unit and determines the user's preferences and emotions. The analysis uses natural language processing technology, sentiment analysis technology, machine learning algorithms, etc. Step 3: The generator generates an appropriate response based on the preferences and emotions determined by the analyzer. This is done using generative AI (e.g., text generation AI or multimodal generation AI). Step 4: The providing unit provides the response generated by the generating unit to the user, including providing in real time or by batch processing.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] 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.

[0170] [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives user comments; an analysis unit that analyzes the comments received by the reception unit and determines the preferences and emotions of the user; a generator for generating a response based on the preferences and emotions determined by the analyzer; a providing unit that provides the response generated by the generating unit. A system characterized by:

2. The analysis unit Analyze past conversation data to determine user preferences and emotions 2. The system of claim 1.

3. The generation unit Generate appropriate responses based on user preferences and emotions 2. The system of claim 1.

4. The providing unit Providing the generated response to the user 2. The system of claim 1.

5. The reception unit Estimates the user's emotions and adjusts the timing of speech acceptance based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past speech history and select the reception method 2. The system of claim 1.

7. The reception unit When accepting comments, filter them based on the user's current situation and areas of interest.

2. The system of claim 1.

8. The reception unit Estimate the user's emotions and prioritize the comments to be accepted based on the estimated user emotions.

2. The system of claim 1.

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