system

Abstract

The system according to this embodiment aims to analyze voice input and generate and provide an appropriate response. [Solution] A system according to an embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit receives a voice input from a user. The analyzing unit analyzes the voice input received by the receiving unit. The generating unit generates a response based on the analysis result by the analyzing unit. The providing unit provides the response generated by the generating unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not provide a sufficient streamlined process for analyzing voice input and generating appropriate responses, leaving room for improvement.

[0005] The system according to this embodiment aims to analyze voice input and generate and provide an appropriate response. [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 voice input from a user. The analyzing unit analyzes the voice input received by the receiving unit. The generating unit generates a response based on the analysis result by the analyzing unit. The providing unit provides the response generated by the generating unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can analyze voice input and generate and provide an appropriate response. [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 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 system according to an embodiment of the present invention aims to improve marketing efficiency and leverage a customer base by developing accessory devices that utilize generative AI under a common brand. This system develops accessory devices that utilize generative AI and deploys them under a common brand. Next, data collected through the common brand is analyzed to optimize marketing strategies. Furthermore, generative AI is used to provide personalized services to users, thereby improving user satisfaction and enhancing brand credibility. For example, specific functions of accessory devices that utilize generative AI include earphones that use generative AI to provide voice assistant functionality or automatically generate playlists based on the user's music preferences. Generative AI can also be used to analyze user behavior patterns and provide timely notifications and reminders. Furthermore, developing "generative AI-ready" accessory devices under a common brand is expected to improve marketing efficiency and leverage a customer base. By developing a common brand, users can enjoy a consistent brand experience and enhance brand credibility. Furthermore, utilizing data collected through the common brand can optimize marketing strategies and expand the customer base. Specifically, accessory devices that utilize generative AI are developed and deployed under a common brand. Next, the data collected through the shared brand is analyzed to optimize marketing strategies. Furthermore, generative AI is utilized to provide personalized services to users. This increases user satisfaction and strengthens brand credibility. As a result, the system can improve marketing efficiency and utilize its customer base by developing accessory devices that utilize generative AI under a shared brand.

[0029] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's voice input. For example, the reception unit can receive the voice input by the user speaking. The reception unit can also collect voice using a microphone and convert the voice input into digital data. The reception unit can also receive natural language or command format voice input. The analysis unit analyzes the voice input received by the reception unit. For example, the analysis unit can convert voice data into text data using voice recognition technology. The analysis unit can also analyze the meaning of the voice input using natural language processing technology. The analysis unit can also extract information for generating an appropriate response based on the content of the voice input. The generation unit generates a response based on the results of the analysis by the analysis unit. For example, the generation unit can generate an appropriate response using text generation AI. The generation unit can also convert text data into voice data using voice synthesis technology. The generation unit can also generate images or videos in response to a user request. The provision unit provides the response generated by the generation unit to the user. For example, the provision unit can provide the voice response using a speaker. The providing unit can also display text and images using a display. Furthermore, the providing unit can send notifications to the user's device. This allows the system according to the embodiment to analyze the user's voice input, generate and provide a response, and provide personalized services to the user.

[0030] The analysis unit can analyze the user's behavioral patterns. The analysis unit analyzes, for example, the user's daily behavioral patterns. For example, the analysis unit can analyze the user's schedule and calendar information to understand the user's daily behavioral patterns. The analysis unit can also analyze the user's location information to understand the user's behavioral patterns in specific locations. Furthermore, the analysis unit can analyze the user's device usage history to understand the user's behavioral patterns in specific time periods and situations. This allows the analysis unit to understand the user's behavioral patterns in detail and generate more appropriate responses.

[0031] The providing unit can provide notifications and reminders based on the analysis results. The providing unit, for example, transmits notifications to the user based on the analysis results. For example, the providing unit can transmit reminders based on the user's schedule. The providing unit can also transmit notifications at appropriate times based on the user's behavioral patterns. Furthermore, the providing unit can transmit notifications at specific locations based on the user's location information. In this way, the providing unit can improve user convenience by providing notifications and reminders based on the analysis results.

[0032] The reception unit can monitor the user's health condition. The reception unit monitors, for example, the user's heart rate. For example, the reception unit can measure the user's heart rate in real time using a heart rate sensor. The reception unit can also monitor the user's blood pressure. For example, the reception unit can measure the user's blood pressure using a blood pressure monitor and collect data. The reception unit can also monitor the user's body temperature. For example, the reception unit can measure the user's body temperature using a thermometer and collect data. In this way, the reception unit can monitor the user's health condition and provide information useful for health management.

[0033] The analysis unit can analyze the monitored health data. The analysis unit, for example, analyzes the user's heart rate data. For example, the analysis unit can analyze heart rate fluctuations and detect abnormal patterns. The analysis unit can also analyze the user's blood pressure data. For example, the analysis unit can analyze blood pressure fluctuations and detect abnormal patterns. The analysis unit can also analyze the user's body temperature data. For example, the analysis unit can analyze body temperature fluctuations and detect abnormal patterns. In this way, the analysis unit can generate a response based on the user's health condition by analyzing the monitored health data.

[0034] The providing unit can provide health management advice based on the analyzed health data. The providing unit can provide health management advice based on, for example, analyzed heart rate data. For example, the providing unit can suggest a way to relax when the heart rate is high. The providing unit can also provide health management advice based on analyzed blood pressure data. For example, the providing unit can suggest a dietary improvement when blood pressure is high. The providing unit can also provide health management advice based on analyzed body temperature data. For example, the providing unit can suggest rest when the body temperature is high. In this way, the providing unit can support the user's health management by providing health management advice based on the analyzed health data.

[0035] The reception unit can analyze the user's past voice input history and select the optimal reception method. The reception unit, for example, analyzes the user's past voice commands. For example, the reception unit can preferentially receive voice commands that the user has frequently used in the past. The reception unit can also predict and suggest commands to be used in a specific time period based on the user's past voice input history. Furthermore, the reception unit can also preferentially suggest voice input methods (voice, text, etc.) that the user has used in the past. In this way, the reception unit can provide the user with the optimal reception method by analyzing the user's past voice input history and selecting the optimal reception method.

[0036] The reception unit may filter the user's current environmental sound to remove noise when receiving a voice input. For example, when the user is in a noisy environment, the reception unit may filter the environmental sound to improve the accuracy of the voice input. For example, the reception unit may use noise canceling technology to remove background noise. Also, when the user is in a quiet environment, the reception unit may minimize the environmental sound and receive the voice input. Furthermore, when the user is performing a voice input while moving, the reception unit may filter wind noise and vehicle noise to remove noise. As a result, the reception unit may filter the user's current environmental sound to remove noise when receiving a voice input, thereby improving the accuracy of the voice input.

[0037] The reception unit can, when receiving a voice input, prioritize receiving a highly relevant voice input in consideration of the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes receiving a voice input related to the location. For example, when the user is in an office, the reception unit can prioritize receiving a voice input related to work. Furthermore, when the user is traveling, the reception unit can also prioritize receiving a voice input related to the destination. For example, when the user is driving a car, the reception unit can prioritize receiving a voice input related to driving. Furthermore, when the user is at home, the reception unit can prioritize receiving a voice input related to the home. For example, when the user is doing housework, the reception unit can prioritize receiving a voice input related to housework. As a result, the reception unit, when receiving a voice input, prioritizes receiving a highly relevant voice input in consideration of the user's geographical location information, thereby providing an optimal reception method for the user.

[0038] The reception unit can analyze the user's social media activities when receiving a voice input and receive related voice input. The reception unit, for example, prioritizes reception of the voice input based on keywords frequently used by the user on social media. For example, if the user frequently uses the keyword "lunch" on social media, the reception unit can prioritize reception of voice input related to lunch. The reception unit can also prioritize reception of voice input related to a specific topic from the user's social media activities. For example, if the user posts many times on social media related to the topic "travel," the reception unit can prioritize reception of voice input related to travel. Furthermore, the reception unit can prioritize reception of related voice input based on information shared by the user on social media. For example, if the user shares information that "I went to a new restaurant" on social media, the reception unit can prioritize reception of voice input related to the restaurant. In this way, the reception unit can analyze the user's social media activities when receiving a voice input and prioritize reception of related voice input, thereby providing an optimal reception method for the user.

[0039] When analyzing voice input, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral patterns. The analysis unit can improve the accuracy of the analysis, for example, based on commands frequently used by the user in the past. For example, the analysis unit can analyze the user's past voice input history and prioritize analysis of frequently used commands. The analysis unit can also predict commands to be used in specific time periods based on the user's past behavioral patterns, thereby improving the accuracy of the analysis. For example, the analysis unit can predict and analyze commands to be used in specific time periods by referring to the user's schedule or calendar information. Furthermore, the analysis unit can analyze the user's past behavioral patterns and select the most appropriate analysis method. For example, the analysis unit can analyze the user's device usage history and select the optimal analysis method to analyze the voice input. In this way, when analyzing voice input, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral patterns.

[0040] The analysis unit can take into account the user's health condition data when analyzing the voice input. For example, if the user is tired, the analysis unit analyzes the voice input using a simple analysis method. For example, the analysis unit can refer to the user's heart rate and body temperature data and analyze the voice input using the simple analysis method if it determines that the user is tired. The analysis unit can also analyze the voice input using a detailed analysis method if the user is healthy. For example, the analysis unit can refer to the user's blood pressure data and analyze the voice input using the detailed analysis method if it determines that the user is healthy. Furthermore, the analysis unit can select an optimal analysis method based on the user's health condition data. For example, the analysis unit can refer to the user's fitness data and select an optimal analysis method to analyze the voice input. This allows the analysis unit to perform a more appropriate analysis by taking the user's health condition data into account when analyzing the voice input.

[0041] The analysis unit can perform analysis taking into account the user's geographical location information when analyzing the voice input. For example, when the user is in a specific location, the analysis unit performs analysis based on information related to the location. For example, when the user is in an office, the analysis unit can perform analysis based on information related to work. Furthermore, when the user is traveling, the analysis unit can perform analysis based on information related to the destination. For example, when the user is driving a car, the analysis unit can perform analysis based on information related to driving. Furthermore, when the user is at home, the analysis unit can perform analysis based on information related to the home. For example, when the user is doing housework, the analysis unit can perform analysis based on information related to the housework. This allows the analysis unit to perform a more appropriate analysis by performing analysis taking into account the user's geographical location information when analyzing the voice input.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the user's social media activities when analyzing the voice input. The analysis unit improves the accuracy of the analysis, for example, based on keywords frequently used by the user on social media. For example, if the user frequently uses the keyword "lunch" on social media, the analysis unit can perform analysis based on information related to lunch. The analysis unit can also improve the accuracy of the analysis based on information related to a specific topic from the user's social media activities. For example, if the user frequently posts related to the topic "travel" on social media, the analysis unit can perform analysis based on information related to travel. The analysis unit can also improve the accuracy of the analysis based on information shared by the user on social media. For example, if the user shares information on social media that "I went to a new restaurant," the analysis unit can perform analysis based on information related to the restaurant. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activities when analyzing the voice input, thereby enabling more appropriate analysis.

[0043] When generating a response, the generation unit can generate an optimal response by referring to the user's past response history. The generation unit generates a response based on, for example, a response style that the user has previously preferred. For example, the generation unit can analyze the user's past response history and generate a frequently used response style with priority. The generation unit can also generate a response appropriate for a specific situation from the user's past response history. For example, the generation unit can generate a response appropriate for a specific time period or situation by referring to the user's past response history. Furthermore, the generation unit can analyze the user's past response history and generate the most appropriate response. For example, the generation unit can analyze the user's device usage history and generate an optimal response. As a result, the generation unit can provide the user with an optimal response by generating an optimal response by referring to the user's past response history when generating a response.

[0044] The generation unit can generate a response by taking into account the user's health condition data when generating a response. For example, if the user is tired, the generation unit generates a simple and easy-to-understand response. For example, the generation unit can refer to the user's heart rate and body temperature data and generate a simple response when it determines that the user is tired. The generation unit can also generate a detailed and informative response when the user is healthy. For example, the generation unit can refer to the user's blood pressure data and generate a detailed response when it determines that the user is healthy. Furthermore, the generation unit can generate an optimal response based on the user's health condition data. For example, the generation unit can refer to the user's fitness data and generate an optimal response. In this way, the generation unit can provide a more appropriate response by generating a response by taking into account the user's health condition data when generating a response.

[0045] The generation unit can generate a response by taking into account the user's geographical location information. For example, when the user is in a specific location, the generation unit generates a response based on information related to the location. For example, when the user is in an office, the generation unit can generate a response based on information related to work. Furthermore, when the user is traveling, the generation unit can generate a response based on information related to the destination. For example, when the user is driving a car, the generation unit can generate a response based on information related to driving. Furthermore, when the user is at home, the generation unit can generate a response based on information related to the home. For example, when the user is doing housework, the generation unit can generate a response based on information related to the housework. In this way, the generation unit can provide a more appropriate response by generating a response by taking into account the user's geographical location information.

[0046] The generation unit may improve the accuracy of a response by referring to the user's social media activity when generating a response. The generation unit may generate a response based on, for example, keywords frequently used by the user on social media. For example, if the user frequently uses the keyword "lunch" on social media, the generation unit may generate a response based on information related to lunch. The generation unit may also generate a response based on information related to a specific topic from the user's social media activity. For example, if the user frequently posts on social media about the topic "travel," the generation unit may generate a response based on information related to travel. The generation unit may also improve the accuracy of a response based on information shared by the user on social media. For example, if the user shares information that "I went to a new restaurant" on social media, the generation unit may generate a response based on information related to the restaurant. In this way, the generation unit may improve the accuracy of a response by referring to the user's social media activity when generating a response, thereby providing a more appropriate response.

[0047] When providing a response, the providing unit can select the optimal response method by referring to the user's past response history. The providing unit provides a response based on, for example, a response style that the user has previously preferred. For example, the providing unit can analyze the user's past response history and provide a frequently used response style preferentially. The providing unit can also provide a response appropriate for a specific situation from the user's past response history. For example, the providing unit can refer to the user's past response history and provide a response appropriate for a specific time period or situation. Furthermore, the providing unit can analyze the user's past response history and provide the most appropriate response. For example, the providing unit can analyze the user's device usage history and provide the optimal response. As a result, the providing unit can provide the optimal response to the user by selecting the optimal response method by referring to the user's past response history when providing a response.

[0048] The providing unit can provide a response by taking into consideration the user's health condition data. For example, if the user is tired, the providing unit can provide a simple and easy-to-understand response. For example, the providing unit can refer to the user's heart rate and body temperature data and provide a simple response if it determines that the user is tired. The providing unit can also provide a detailed and informative response if the user is healthy. For example, the providing unit can refer to the user's blood pressure data and provide a detailed response if it determines that the user is healthy. Furthermore, the providing unit can provide an optimal response based on the user's health condition data. For example, the providing unit can refer to the user's fitness data and provide an optimal response. As a result, the providing unit can provide a more appropriate response by taking into consideration the user's health condition data when providing a response.

[0049] When providing a response, the providing unit can select the optimal delivery method by taking into consideration the user's geographical location information. For example, when the user is in a specific location, the providing unit provides a response based on information related to the location. For example, when the user is in an office, the providing unit can provide a response based on information related to work. Furthermore, when the user is traveling, the providing unit can also provide a response based on information related to the destination. For example, when the user is driving a car, the providing unit can provide a response based on information related to driving. Furthermore, when the user is at home, the providing unit can provide a response based on information related to the home. For example, when the user is doing housework, the providing unit can provide a response based on information related to the housework. In this way, the providing unit can provide a more appropriate response by selecting the optimal delivery method by taking into consideration the user's geographical location information when providing a response.

[0050] The providing unit may adjust the method of providing a response by referring to the user's social media activity when providing a response. The providing unit may provide a response based on, for example, keywords frequently used by the user on social media. For example, if the user frequently uses the keyword "lunch" on social media, the providing unit may provide a response based on information related to lunch. The providing unit may also provide a response based on information related to a specific topic from the user's social media activity. For example, if the user frequently posts on social media about the topic "travel," the providing unit may provide a response based on information related to travel. The providing unit may also adjust the method of providing a response based on information shared by the user on social media. For example, if the user shares information that "I went to a new restaurant" on social media, the providing unit may provide a response based on information related to the restaurant. In this way, the providing unit may adjust the method of providing a response by referring to the user's social media activity when providing a response, thereby providing a more appropriate response.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit can not only accept voice input from the user, but also accept gesture input from the user. For example, the reception unit can detect the user's hand movements using a camera and recognize commands corresponding to specific gestures. The reception unit can also detect the direction of the user's face and their line of sight and accept gaze input. Furthermore, the reception unit can also accept touch input from the user. For example, the reception unit can detect the user's touch operation using a touch screen and recognize the corresponding command. This allows the reception unit to provide the user with a more diverse interface by accepting gesture, gaze, and touch input in addition to voice input.

[0053] The analysis unit can analyze not only the user's behavioral patterns but also the user's hobbies and interests. For example, the analysis unit can analyze the content of the user's social media posts to understand the user's hobbies and interests. The analysis unit can also analyze the user's web browsing history to understand the websites and search keywords the user frequently visits. Furthermore, the analysis unit can analyze the user's purchasing history to understand trends in the products and services the user purchases. In this way, the analysis unit can provide more personalized services by analyzing the user's hobbies and interests in addition to the user's behavioral patterns.

[0054] The providing unit can not only provide notifications and reminders based on the analysis results, but also automatically add tasks based on the user's schedule. For example, the providing unit can analyze the user's calendar information and send reminders before important events or meetings. The providing unit can also automatically add periodic tasks to the schedule based on the user's behavior patterns. Furthermore, the providing unit can automatically add tasks for specific locations based on the user's location information. In this way, the providing unit can improve user convenience by not only providing notifications and reminders based on the analysis results, but also automatically adding tasks based on the user's schedule.

[0055] The reception unit can monitor not only the user's health condition but also the user's sleep condition. For example, the reception unit can detect the user's movements while sleeping and evaluate the quality of sleep. The reception unit can also monitor the user's heart rate and breathing rate and detect abnormalities during sleep. Furthermore, the reception unit can analyze the user's sleep patterns and suggest optimal sleep durations and wake-up times. In this way, the reception unit can more effectively support the user's health management by monitoring the user's sleep condition in addition to the user's health condition.

[0056] The analysis unit can not only analyze the monitored health data, but also analyze the user's dietary data. For example, the analysis unit can analyze the contents of the meals the user has eaten and evaluate the nutritional balance. The analysis unit can also analyze the user's dietary history and understand eating patterns and trends. Furthermore, the analysis unit can analyze the user's health data and dietary data in combination to evaluate the impact of diet on the health condition. In this way, the analysis unit can provide more comprehensive support for the user's health management by analyzing the dietary data in addition to the monitored health data.

[0057] The reception unit can analyze the user's past voice input history and select the optimal reception method, as well as analyze the user's past behavioral patterns to provide predictive input. For example, the reception unit can predict and suggest commands that the user will frequently use during a specific time period. The reception unit can also predict and suggest commands that the user will frequently use in a specific location. Furthermore, the reception unit can predict and suggest commands that will be used in a specific situation based on the user's past behavioral patterns. In this way, the reception unit can provide more appropriate predictive input by analyzing the user's behavioral patterns in addition to the user's past voice input history.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives voice input from the user. For example, the reception unit can receive voice input by the user speaking. The reception unit can also collect voice using a microphone and convert it into digital data. Furthermore, the reception unit can receive voice input in the form of natural language or commands. Step 2: The analysis unit analyzes the voice input received by the reception unit. For example, the analysis unit converts the voice data into text data using voice recognition technology. The analysis unit can also analyze the meaning of the voice input using natural language processing technology. Furthermore, the analysis unit can extract information for generating an appropriate response based on the content of the voice input. Step 3: The generation unit generates a response based on the results of the analysis by the analysis unit. For example, the generation unit generates an appropriate response using text generation AI. The generation unit can also convert text data into voice data using voice synthesis technology. Furthermore, the generation unit can generate images and videos in response to user requests. Step 4: The providing unit provides the response generated by the generating unit to the user. For example, the providing unit may provide a voice response using a speaker. The providing unit may also display text or an image using a display. Furthermore, the providing unit may also send a notification to the user's device.

[0060] (Example 2) A system according to an embodiment of the present invention aims to improve marketing efficiency and leverage a customer base by developing accessory devices that utilize generative AI under a common brand. This system develops accessory devices that utilize generative AI and deploys them under a common brand. Next, data collected through the common brand is analyzed to optimize marketing strategies. Furthermore, generative AI is used to provide personalized services to users, thereby improving user satisfaction and enhancing brand credibility. For example, specific functions of accessory devices that utilize generative AI include earphones that use generative AI to provide voice assistant functionality or automatically generate playlists based on the user's music preferences. Generative AI can also be used to analyze user behavior patterns and provide timely notifications and reminders. Furthermore, developing "generative AI-ready" accessory devices under a common brand is expected to improve marketing efficiency and leverage a customer base. By developing a common brand, users can enjoy a consistent brand experience and enhance brand credibility. Furthermore, utilizing data collected through the common brand can optimize marketing strategies and expand the customer base. Specifically, accessory devices that utilize generative AI are developed and deployed under a common brand. Next, the data collected through the shared brand is analyzed to optimize marketing strategies. Furthermore, generative AI is utilized to provide personalized services to users. This increases user satisfaction and strengthens brand credibility. As a result, the system can improve marketing efficiency and utilize its customer base by developing accessory devices that utilize generative AI under a shared brand.

[0061] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's voice input. For example, the reception unit can receive the voice input by the user speaking. The reception unit can also collect voice using a microphone and convert the voice input into digital data. The reception unit can also receive natural language or command format voice input. The analysis unit analyzes the voice input received by the reception unit. For example, the analysis unit can convert voice data into text data using voice recognition technology. The analysis unit can also analyze the meaning of the voice input using natural language processing technology. The analysis unit can also extract information for generating an appropriate response based on the content of the voice input. The generation unit generates a response based on the results of the analysis by the analysis unit. For example, the generation unit can generate an appropriate response using text generation AI. The generation unit can also convert text data into voice data using voice synthesis technology. The generation unit can also generate images or videos in response to a user request. The provision unit provides the response generated by the generation unit to the user. For example, the provision unit can provide the voice response using a speaker. The providing unit can also display text and images using a display. Furthermore, the providing unit can send notifications to the user's device. This allows the system according to the embodiment to analyze the user's voice input, generate and provide a response, and provide personalized services to the user.

[0062] The analysis unit can analyze the user's behavioral patterns. The analysis unit analyzes, for example, the user's daily behavioral patterns. For example, the analysis unit can analyze the user's schedule and calendar information to understand the user's daily behavioral patterns. The analysis unit can also analyze the user's location information to understand the user's behavioral patterns in specific locations. Furthermore, the analysis unit can analyze the user's device usage history to understand the user's behavioral patterns in specific time periods and situations. This allows the analysis unit to understand the user's behavioral patterns in detail and generate more appropriate responses.

[0063] The providing unit can provide notifications and reminders based on the analysis results. The providing unit, for example, transmits notifications to the user based on the analysis results. For example, the providing unit can transmit reminders based on the user's schedule. The providing unit can also transmit notifications at appropriate times based on the user's behavioral patterns. Furthermore, the providing unit can transmit notifications at specific locations based on the user's location information. In this way, the providing unit can improve user convenience by providing notifications and reminders based on the analysis results.

[0064] The reception unit can monitor the user's health condition. The reception unit monitors, for example, the user's heart rate. For example, the reception unit can measure the user's heart rate in real time using a heart rate sensor. The reception unit can also monitor the user's blood pressure. For example, the reception unit can measure the user's blood pressure using a blood pressure monitor and collect data. The reception unit can also monitor the user's body temperature. For example, the reception unit can measure the user's body temperature using a thermometer and collect data. In this way, the reception unit can monitor the user's health condition and provide information useful for health management.

[0065] The analysis unit can analyze the monitored health data. The analysis unit, for example, analyzes the user's heart rate data. For example, the analysis unit can analyze heart rate fluctuations and detect abnormal patterns. The analysis unit can also analyze the user's blood pressure data. For example, the analysis unit can analyze blood pressure fluctuations and detect abnormal patterns. The analysis unit can also analyze the user's body temperature data. For example, the analysis unit can analyze body temperature fluctuations and detect abnormal patterns. In this way, the analysis unit can generate a response based on the user's health condition by analyzing the monitored health data.

[0066] The providing unit can provide health management advice based on the analyzed health data. The providing unit can provide health management advice based on, for example, analyzed heart rate data. For example, the providing unit can suggest a way to relax when the heart rate is high. The providing unit can also provide health management advice based on analyzed blood pressure data. For example, the providing unit can suggest a dietary improvement when blood pressure is high. The providing unit can also provide health management advice based on analyzed body temperature data. For example, the providing unit can suggest rest when the body temperature is high. In this way, the providing unit can support the user's health management by providing health management advice based on the analyzed health data.

[0067] The reception unit can estimate the user's emotion and adjust the method of receiving voice input based on the estimated user emotion. The reception unit, for example, analyzes the user's facial expression to estimate the emotion. For example, the reception unit can capture the user's facial expression using a camera and estimate the emotion using an emotion estimation algorithm. The reception unit can also analyze the user's voice to estimate the emotion. For example, the reception unit can analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the reception unit can analyze the user's biometric data to estimate the emotion. For example, the reception unit can analyze the heart rate or electrodermal activity to estimate the emotion. In this way, the reception unit can estimate the user's emotion and adjust the method of receiving voice input based on the estimated emotion, thereby providing a more appropriate interface. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] The reception unit can analyze the user's past voice input history and select the optimal reception method. The reception unit, for example, analyzes the user's past voice commands. For example, the reception unit can preferentially receive voice commands that the user has frequently used in the past. The reception unit can also predict and suggest commands to be used in a specific time period based on the user's past voice input history. Furthermore, the reception unit can also preferentially suggest voice input methods (voice, text, etc.) that the user has used in the past. In this way, the reception unit can provide the user with the optimal reception method by analyzing the user's past voice input history and selecting the optimal reception method.

[0069] The reception unit may filter the user's current environmental sound to remove noise when receiving a voice input. For example, when the user is in a noisy environment, the reception unit may filter the environmental sound to improve the accuracy of the voice input. For example, the reception unit may use noise canceling technology to remove background noise. Also, when the user is in a quiet environment, the reception unit may minimize the environmental sound and receive the voice input. Furthermore, when the user is performing a voice input while moving, the reception unit may filter wind noise and vehicle noise to remove noise. As a result, the reception unit may filter the user's current environmental sound to remove noise when receiving a voice input, thereby improving the accuracy of the voice input.

[0070] The reception unit can estimate the user's emotions and determine the priority of voice inputs to be received based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize receiving important voice inputs. For example, the reception unit can analyze the tone and speed of the user's voice and, if it determines that the user is nervous, prioritize receiving urgent voice inputs. The reception unit can also prioritize receiving normal voice inputs if the user is relaxed. For example, the reception unit can analyze the user's facial expression and, if it determines that the user is relaxed, prioritize receiving normal voice inputs. Furthermore, the reception unit can prioritize receiving urgent voice inputs if the user is in a hurry. For example, the reception unit can analyze the user's behavioral patterns and, if it determines that the user is in a hurry, prioritize receiving urgent voice inputs. In this way, the reception unit can prioritize receiving important voice inputs by determining the priority of voice inputs based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0071] The reception unit can, when receiving a voice input, prioritize receiving a highly relevant voice input in consideration of the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes receiving a voice input related to the location. For example, when the user is in an office, the reception unit can prioritize receiving a voice input related to work. Furthermore, when the user is traveling, the reception unit can also prioritize receiving a voice input related to the destination. For example, when the user is driving a car, the reception unit can prioritize receiving a voice input related to driving. Furthermore, when the user is at home, the reception unit can prioritize receiving a voice input related to the home. For example, when the user is doing housework, the reception unit can prioritize receiving a voice input related to housework. As a result, the reception unit, when receiving a voice input, prioritizes receiving a highly relevant voice input in consideration of the user's geographical location information, thereby providing an optimal reception method for the user.

[0072] The reception unit can analyze the user's social media activities when receiving a voice input and receive related voice input. The reception unit, for example, prioritizes reception of the voice input based on keywords frequently used by the user on social media. For example, if the user frequently uses the keyword "lunch" on social media, the reception unit can prioritize reception of voice input related to lunch. The reception unit can also prioritize reception of voice input related to a specific topic from the user's social media activities. For example, if the user posts many times on social media related to the topic "travel," the reception unit can prioritize reception of voice input related to travel. Furthermore, the reception unit can prioritize reception of related voice input based on information shared by the user on social media. For example, if the user shares information that "I went to a new restaurant" on social media, the reception unit can prioritize reception of voice input related to the restaurant. In this way, the reception unit can analyze the user's social media activities when receiving a voice input and prioritize reception of related voice input, thereby providing an optimal reception method for the user.

[0073] The analysis unit can estimate the user's emotions and adjust the analysis method of the voice input based on the estimated user's emotions. For example, if the user is nervous, the analysis unit analyzes the voice input using a simple analysis method. For example, the analysis unit can analyze the tone and speed of the user's voice and, if it determines that the user is nervous, analyze the voice input using a simple analysis method. The analysis unit can also analyze the voice input using a detailed analysis method if the user is relaxed. For example, the analysis unit can analyze the user's facial expression and, if it determines that the user is relaxed, analyze the voice input using a detailed analysis method. Furthermore, the analysis unit can analyze the voice input using a rapid analysis method if the user is in a hurry. For example, the analysis unit can analyze the user's behavioral patterns and, if it determines that the user is in a hurry, analyze the voice input using a rapid analysis method. This allows the analysis unit to adjust the analysis method of the voice input based on the user's emotions, enabling more appropriate analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0074] When analyzing voice input, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral patterns. The analysis unit can improve the accuracy of the analysis, for example, based on commands frequently used by the user in the past. For example, the analysis unit can analyze the user's past voice input history and prioritize analysis of frequently used commands. The analysis unit can also predict commands to be used in specific time periods based on the user's past behavioral patterns, thereby improving the accuracy of the analysis. For example, the analysis unit can predict and analyze commands to be used in specific time periods by referring to the user's schedule or calendar information. Furthermore, the analysis unit can analyze the user's past behavioral patterns and select the most appropriate analysis method. For example, the analysis unit can analyze the user's device usage history and select the optimal analysis method to analyze the voice input. In this way, when analyzing voice input, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral patterns.

[0075] The analysis unit can take into account the user's health condition data when analyzing the voice input. For example, if the user is tired, the analysis unit analyzes the voice input using a simple analysis method. For example, the analysis unit can refer to the user's heart rate and body temperature data and analyze the voice input using the simple analysis method if it determines that the user is tired. The analysis unit can also analyze the voice input using a detailed analysis method if the user is healthy. For example, the analysis unit can refer to the user's blood pressure data and analyze the voice input using the detailed analysis method if it determines that the user is healthy. Furthermore, the analysis unit can select an optimal analysis method based on the user's health condition data. For example, the analysis unit can refer to the user's fitness data and select an optimal analysis method to analyze the voice input. This allows the analysis unit to perform a more appropriate analysis by taking the user's health condition data into account when analyzing the voice input.

[0076] The analysis unit can estimate the user's emotions and prioritize analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize providing important analysis results. For example, the analysis unit can analyze the user's tone and speed of voice and prioritize providing important analysis results if it determines that the user is nervous. The analysis unit can also prioritize providing normal analysis results if the user is relaxed. For example, the analysis unit can analyze the user's facial expressions and prioritize providing normal analysis results if it determines that the user is relaxed. Furthermore, the analysis unit can prioritize providing urgent analysis results if the user is in a hurry. For example, the analysis unit can analyze the user's behavioral patterns and prioritize providing urgent analysis results if it determines that the user is in a hurry. In this way, the analysis unit can prioritize providing important analysis results by prioritizing analysis results 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The analysis unit can perform analysis taking into account the user's geographical location information when analyzing the voice input. For example, when the user is in a specific location, the analysis unit performs analysis based on information related to the location. For example, when the user is in an office, the analysis unit can perform analysis based on information related to work. Furthermore, when the user is traveling, the analysis unit can perform analysis based on information related to the destination. For example, when the user is driving a car, the analysis unit can perform analysis based on information related to driving. Furthermore, when the user is at home, the analysis unit can perform analysis based on information related to the home. For example, when the user is doing housework, the analysis unit can perform analysis based on information related to the housework. This allows the analysis unit to perform a more appropriate analysis by performing analysis taking into account the user's geographical location information when analyzing the voice input.

[0078] The analysis unit can improve the accuracy of the analysis by referring to the user's social media activities when analyzing the voice input. The analysis unit improves the accuracy of the analysis, for example, based on keywords frequently used by the user on social media. For example, if the user frequently uses the keyword "lunch" on social media, the analysis unit can perform analysis based on information related to lunch. The analysis unit can also improve the accuracy of the analysis based on information related to a specific topic from the user's social media activities. For example, if the user frequently posts related to the topic "travel" on social media, the analysis unit can perform analysis based on information related to travel. The analysis unit can also improve the accuracy of the analysis based on information shared by the user on social media. For example, if the user shares information on social media that "I went to a new restaurant," the analysis unit can perform analysis based on information related to the restaurant. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activities when analyzing the voice input, thereby enabling more appropriate analysis.

[0079] The generation unit can estimate the user's emotions and adjust the response generation method based on the estimated user emotions. For example, if the user is nervous, the generation unit generates a simple, reassuring response. For example, the generation unit can analyze the user's tone and speed of voice and generate a simple response if it determines that the user is nervous. The generation unit can also generate a detailed, friendly response if the user is relaxed. For example, the generation unit can analyze the user's facial expression and generate a detailed response if it determines that the user is relaxed. Furthermore, the generation unit can generate a quick, concise response if the user is in a hurry. For example, the generation unit can analyze the user's behavioral patterns and generate a quick response if it determines that the user is in a hurry. This allows the generation unit to generate 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 generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] When generating a response, the generation unit can generate an optimal response by referring to the user's past response history. The generation unit generates a response based on, for example, a response style that the user has previously preferred. For example, the generation unit can analyze the user's past response history and generate a frequently used response style with priority. The generation unit can also generate a response appropriate for a specific situation from the user's past response history. For example, the generation unit can generate a response appropriate for a specific time period or situation by referring to the user's past response history. Furthermore, the generation unit can analyze the user's past response history and generate the most appropriate response. For example, the generation unit can analyze the user's device usage history and generate an optimal response. As a result, the generation unit can provide the user with an optimal response by generating an optimal response by referring to the user's past response history when generating a response.

[0081] The generation unit can generate a response by taking into account the user's health condition data when generating a response. For example, if the user is tired, the generation unit generates a simple and easy-to-understand response. For example, the generation unit can refer to the user's heart rate and body temperature data and generate a simple response when it determines that the user is tired. The generation unit can also generate a detailed and informative response when the user is healthy. For example, the generation unit can refer to the user's blood pressure data and generate a detailed response when it determines that the user is healthy. Furthermore, the generation unit can generate an optimal response based on the user's health condition data. For example, the generation unit can refer to the user's fitness data and generate an optimal response. In this way, the generation unit can provide a more appropriate response by generating a response by taking into account the user's health condition data when generating a response.

[0082] The generation unit can estimate the user's emotions and prioritize responses based on the estimated user emotions. For example, if the user is nervous, the generation unit can prioritize providing important responses. For example, the generation unit can analyze the user's tone and speed of voice and prioritize providing important responses if it determines that the user is nervous. The generation unit can also prioritize providing normal responses if the user is relaxed. For example, the generation unit can analyze the user's facial expression and prioritize providing normal responses if it determines that the user is relaxed. Furthermore, the generation unit can prioritize providing urgent responses if the user is in a hurry. For example, the generation unit can analyze the user's behavioral patterns and prioritize providing urgent responses if it determines that the user is in a hurry. In this way, the generation unit can prioritize responses based on the user's emotions and prioritize providing important responses. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The generation unit can generate a response by taking into account the user's geographical location information. For example, when the user is in a specific location, the generation unit generates a response based on information related to the location. For example, when the user is in an office, the generation unit can generate a response based on information related to work. Furthermore, when the user is traveling, the generation unit can generate a response based on information related to the destination. For example, when the user is driving a car, the generation unit can generate a response based on information related to driving. Furthermore, when the user is at home, the generation unit can generate a response based on information related to the home. For example, when the user is doing housework, the generation unit can generate a response based on information related to the housework. In this way, the generation unit can provide a more appropriate response by generating a response by taking into account the user's geographical location information.

[0084] The generation unit may improve the accuracy of a response by referring to the user's social media activity when generating a response. The generation unit may generate a response based on, for example, keywords frequently used by the user on social media. For example, if the user frequently uses the keyword "lunch" on social media, the generation unit may generate a response based on information related to lunch. The generation unit may also generate a response based on information related to a specific topic from the user's social media activity. For example, if the user frequently posts on social media about the topic "travel," the generation unit may generate a response based on information related to travel. The generation unit may also improve the accuracy of a response based on information shared by the user on social media. For example, if the user shares information that "I went to a new restaurant" on social media, the generation unit may generate a response based on information related to the restaurant. In this way, the generation unit may improve the accuracy of a response by referring to the user's social media activity when generating a response, thereby providing a more appropriate response.

[0085] 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 nervous, the providing unit can provide a response in a calm voice. For example, the providing unit can analyze the tone and speed of the user's voice and provide a response in a calm voice if it determines that the user is nervous. The providing unit can also provide a response in a cheerful voice if the user is relaxed. For example, the providing unit can analyze the user's facial expression and provide a response in a cheerful voice if it determines that the user is relaxed. Furthermore, the providing unit can also provide a quick and concise response if the user is in a hurry. For example, the providing unit can analyze the user's behavioral patterns and provide a quick and concise response if it determines that the user is in a hurry. This allows the providing unit to adjust the response provision method based on the user's emotions, thereby providing a more appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] When providing a response, the providing unit can select the optimal response method by referring to the user's past response history. The providing unit provides a response based on, for example, a response style that the user has previously preferred. For example, the providing unit can analyze the user's past response history and provide a frequently used response style preferentially. The providing unit can also provide a response appropriate for a specific situation from the user's past response history. For example, the providing unit can refer to the user's past response history and provide a response appropriate for a specific time period or situation. Furthermore, the providing unit can analyze the user's past response history and provide the most appropriate response. For example, the providing unit can analyze the user's device usage history and provide the optimal response. As a result, the providing unit can provide the optimal response to the user by selecting the optimal response method by referring to the user's past response history when providing a response.

[0087] The providing unit can provide a response by taking into consideration the user's health condition data. For example, if the user is tired, the providing unit can provide a simple and easy-to-understand response. For example, the providing unit can refer to the user's heart rate and body temperature data and provide a simple response if it determines that the user is tired. The providing unit can also provide a detailed and informative response if the user is healthy. For example, the providing unit can refer to the user's blood pressure data and provide a detailed response if it determines that the user is healthy. Furthermore, the providing unit can provide an optimal response based on the user's health condition data. For example, the providing unit can refer to the user's fitness data and provide an optimal response. As a result, the providing unit can provide a more appropriate response by taking into consideration the user's health condition data when providing a response.

[0088] The providing unit can estimate the user's emotions and determine the order in which responses are provided based on the estimated user emotions. For example, if the user is nervous, the providing unit can prioritize providing important responses. For example, the providing unit can analyze the tone and speed of the user's voice and, if it determines that the user is nervous, prioritize providing important responses. The providing unit can also prioritize providing normal responses if the user is relaxed. For example, the providing unit can analyze the user's facial expression and, if it determines that the user is relaxed, prioritize providing normal responses. Furthermore, the providing unit can prioritize providing urgent responses if the user is in a hurry. For example, the providing unit can analyze the user's behavioral patterns and, if it determines that the user is in a hurry, prioritize providing urgent responses. In this way, the providing unit can prioritize providing important responses by determining the order in which responses are provided 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] When providing a response, the providing unit can select the optimal delivery method by taking into consideration the user's geographical location information. For example, when the user is in a specific location, the providing unit provides a response based on information related to the location. For example, when the user is in an office, the providing unit can provide a response based on information related to work. Furthermore, when the user is traveling, the providing unit can also provide a response based on information related to the destination. For example, when the user is driving a car, the providing unit can provide a response based on information related to driving. Furthermore, when the user is at home, the providing unit can provide a response based on information related to the home. For example, when the user is doing housework, the providing unit can provide a response based on information related to the housework. In this way, the providing unit can provide a more appropriate response by selecting the optimal delivery method by taking into consideration the user's geographical location information when providing a response.

[0090] The providing unit may adjust the method of providing a response by referring to the user's social media activity when providing a response. The providing unit may provide a response based on, for example, keywords frequently used by the user on social media. For example, if the user frequently uses the keyword "lunch" on social media, the providing unit may provide a response based on information related to lunch. The providing unit may also provide a response based on information related to a specific topic from the user's social media activity. For example, if the user frequently posts on social media about the topic "travel," the providing unit may provide a response based on information related to travel. The providing unit may also adjust the method of providing a response based on information shared by the user on social media. For example, if the user shares information that "I went to a new restaurant" on social media, the providing unit may provide a response based on information related to the restaurant. In this way, the providing unit may adjust the method of providing a response by referring to the user's social media activity when providing a response, thereby providing a more appropriate 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 collects voice using the microphone 38B of the smart device 14 and converts the voice into digital data using the control unit 46A. For example, the analysis unit converts the voice data into text data using the specific processing unit 290 of the data processing device 12 and analyzes the meaning of the voice input using natural language processing technology. For example, the generation unit generates an appropriate response using text generation AI using the specific processing unit 290 of the data processing device 12 and converts it into voice data using voice synthesis technology. For example, the provision unit provides a voice response using the speaker 40B of the smart device 14 and displays text and images using the display 40A. === 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 collects voice using the microphone 238 of the smart glasses 214 and converts it into digital data using the control unit 46A. For example, the analysis unit converts the voice data into text data using the specific processing unit 290 of the data processing device 12 and analyzes the meaning of the voice input using natural language processing technology. For example, the generation unit generates an appropriate response using text generation AI using the specific processing unit 290 of the data processing device 12 and converts it into voice data using voice synthesis technology. For example, the provision unit provides a voice response using the speaker 240 of the smart glasses 214 and displays text and images using the display. === 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 collects voice using the microphone 238 of the headset-type terminal 314 and converts it into digital data by the control unit 46A. For example, the analysis unit converts the voice data into text data by the specific processing unit 290 of the data processing device 12 and analyzes the meaning of the voice input using natural language processing technology. For example, the generation unit generates an appropriate response using a text generation AI by the specific processing unit 290 of the data processing device 12 and converts it into voice data using voice synthesis technology. For example, the provision unit provides a voice response using the speaker 240 of the headset-type terminal 314 and displays text and images using the display 343. === 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 collects voice using the microphone 238 of the robot 414 and converts it into digital data by the control unit 46A. For example, the analysis unit converts the voice data into text data by the specific processing unit 290 of the data processing device 12 and analyzes the meaning of the voice input using natural language processing technology. For example, the generation unit generates an appropriate response using a text generation AI by the specific processing unit 290 of the data processing device 12 and converts it into voice data using voice synthesis technology. For example, the provision unit provides a voice response using the speaker 240 of the robot 414 and displays text and images on the display.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The reception unit can not only accept voice input from the user, but also accept gesture input from the user. For example, the reception unit can detect the user's hand movements using a camera and recognize commands corresponding to specific gestures. The reception unit can also detect the direction of the user's face and their line of sight and accept gaze input. Furthermore, the reception unit can also accept touch input from the user. For example, the reception unit can detect the user's touch operation using a touch screen and recognize the corresponding command. This allows the reception unit to provide the user with a more diverse interface by accepting gesture, gaze, and touch input in addition to voice input.

[0093] The analysis unit can analyze not only the user's behavioral patterns but also the user's hobbies and interests. For example, the analysis unit can analyze the content of the user's social media posts to understand the user's hobbies and interests. The analysis unit can also analyze the user's web browsing history to understand the websites and search keywords the user frequently visits. Furthermore, the analysis unit can analyze the user's purchasing history to understand trends in the products and services the user purchases. In this way, the analysis unit can provide more personalized services by analyzing the user's hobbies and interests in addition to the user's behavioral patterns.

[0094] The providing unit can not only provide notifications and reminders based on the analysis results, but also automatically add tasks based on the user's schedule. For example, the providing unit can analyze the user's calendar information and send reminders before important events or meetings. The providing unit can also automatically add periodic tasks to the schedule based on the user's behavior patterns. Furthermore, the providing unit can automatically add tasks for specific locations based on the user's location information. In this way, the providing unit can improve user convenience by not only providing notifications and reminders based on the analysis results, but also automatically adding tasks based on the user's schedule.

[0095] The reception unit can monitor not only the user's health condition but also the user's sleep condition. For example, the reception unit can detect the user's movements while sleeping and evaluate the quality of sleep. The reception unit can also monitor the user's heart rate and breathing rate and detect abnormalities during sleep. Furthermore, the reception unit can analyze the user's sleep patterns and suggest optimal sleep durations and wake-up times. In this way, the reception unit can more effectively support the user's health management by monitoring the user's sleep condition in addition to the user's health condition.

[0096] The analysis unit can not only analyze the monitored health data, but also analyze the user's dietary data. For example, the analysis unit can analyze the contents of the meals the user has eaten and evaluate the nutritional balance. The analysis unit can also analyze the user's dietary history and understand eating patterns and trends. Furthermore, the analysis unit can analyze the user's health data and dietary data in combination to evaluate the impact of diet on the health condition. In this way, the analysis unit can provide more comprehensive support for the user's health management by analyzing the dietary data in addition to the monitored health data.

[0097] The providing unit can not only provide health management advice based on the analyzed health data, but also adjust the content of the advice based on the user's emotional state. For example, if the user is feeling stressed, the providing unit can suggest ways to relax or activities for relieving stress. Also, if the user is feeling down, the providing unit can provide positive activities or messages to lift the user's mood. Furthermore, if the user is excited, the providing unit can suggest ways to calm down or activities for relaxation. In this way, the providing unit can support more effective health management by not only providing health management advice based on the analyzed health data, but also adjusting the content of the advice based on the user's emotional state.

[0098] The reception unit can estimate the user's emotion and adjust the method of receiving voice input based on the estimated user's emotion, as well as change the interface design according to the user's emotion. For example, if the user is nervous, the reception unit can display an interface with calming colors. If the user is relaxed, the reception unit can also display an interface with bright colors. Furthermore, if the user is excited, the reception unit can display a simple, visually calming interface. In this way, the reception unit can provide a more comfortable user experience by changing the interface design based on the user's emotion.

[0099] The reception unit can analyze the user's past voice input history and select the optimal reception method, as well as analyze the user's past behavioral patterns to provide predictive input. For example, the reception unit can predict and suggest commands that the user will frequently use during a specific time period. The reception unit can also predict and suggest commands that the user will frequently use in a specific location. Furthermore, the reception unit can predict and suggest commands that will be used in a specific situation based on the user's past behavioral patterns. In this way, the reception unit can provide more appropriate predictive input by analyzing the user's behavioral patterns in addition to the user's past voice input history.

[0100] When receiving a voice input, the reception unit not only filters the user's current environmental sound to remove noise, but also analyzes the tone and speed of the user's voice input to estimate the user's emotion. For example, the reception unit can estimate that the user is excited if the user's voice tone is high. Also, the reception unit can estimate that the user is nervous if the user's voice speed is fast. Furthermore, the reception unit can analyze changes in the user's voice tone and speed to estimate changes in emotion in real time. As a result, when receiving a voice input, the reception unit not only filters the user's current environmental sound to remove noise, but also analyzes the tone and speed of the voice input to estimate the user's emotion, thereby providing a more appropriate interface.

[0101] The reception unit can estimate the user's emotions and prioritize the voice inputs to be received based on the estimated user's emotions, as well as adjust the content of the response according to the user's emotions. For example, if the user is nervous, the reception unit can provide a concise and reassuring response. If the user is relaxed, the reception unit can also provide a detailed and friendly response. Furthermore, if the user is in a hurry, the reception unit can also provide a quick and concise response. As a result, the reception unit can provide a more appropriate response by adjusting the content of the response based on the user's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit receives voice input from the user. For example, the reception unit can receive voice input by the user speaking. The reception unit can also collect voice using a microphone and convert it into digital data. Furthermore, the reception unit can receive voice input in the form of natural language or commands. Step 2: The analysis unit analyzes the voice input received by the reception unit. For example, the analysis unit converts the voice data into text data using voice recognition technology. The analysis unit can also analyze the meaning of the voice input using natural language processing technology. Furthermore, the analysis unit can extract information for generating an appropriate response based on the content of the voice input. Step 3: The generation unit generates a response based on the results of the analysis by the analysis unit. For example, the generation unit generates an appropriate response using text generation AI. The generation unit can also convert text data into voice data using voice synthesis technology. Furthermore, the generation unit can generate images and videos in response to user requests. Step 4: The providing unit provides the response generated by the generating unit to the user. For example, the providing unit may provide a voice response using a speaker. The providing unit may also display text or an image using a display. Furthermore, the providing unit may also send a notification to the user's device.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 voice input from a user; an analysis unit that analyzes the voice input received by the reception unit; a generation unit that generates a response based on the result of the analysis by the analysis unit; a providing unit that provides the response generated by the generating unit to a user; Equipped with A system characterized by:

2. The analysis unit Analyzing user behavior patterns 2. The system of claim 1.

3. The providing unit Providing notifications and reminders based on analytics results 2. The system of claim 1.

4. The reception unit Monitor the user's health 2. The system of claim 1.

5. The analysis unit Analyzing monitored health data 2. The system of claim 1.

6. The providing unit Providing health management advice based on analyzed health data 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the method of accepting voice input based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past voice input history and select the optimal reception method 2. The system of claim 1.

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