System

Abstract

An object of a system according to an embodiment is to provide a function that leads to a specific AI personality for each of telephone numbers.SOLUTION: In accordance with an embodiment, a system comprises a AI ledger. The AI address book is set so as to be connected to a specific AI personality for each of the telephone numbers. A generation AI sets a AI personality on the basis of telephone numbers transmitted by users. The generation AI performs a response according to the AI personality.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacked the ability to connect each phone number to a specific AI personality, making it difficult for users to obtain diverse AI responses.

[0005] The system of the embodiment aims to provide a function that connects each phone number to a specific AI personality. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI phone book. The AI ​​phone book is configured to connect each phone number to a specific AI personality. The generated AI sets an AI personality based on the phone number called by the user. The generated AI responds according to the AI ​​personality. [Effects of the Invention]

[0007] The system according to the embodiment can provide a function that connects each phone number to a specific AI personality. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The AI ​​phone book system according to an embodiment of the present invention is a system that connects each phone number to a specific AI personality. This allows a user to converse with a specific AI personality simply by making a call from a phone app. This allows the AI ​​phone book system to allow a user to converse with a specific AI personality for each phone number.

[0029] An AI phone book system according to an embodiment includes an AI phone book, a generation AI, a telephone number, and an AI personality. The AI ​​phone book is configured to connect each telephone number to a specific AI personality. For example, when a user calls a telephone number registered in the phone book as "AI Foreigner," the generation AI answers, allowing the user to practice English conversation. The generation AI sets a specific AI personality based on the telephone number the user called and responds according to that personality. For example, when a user who wants to practice English conversation calls "AI Foreigner," the generation AI responds in English and begins a dialogue with the user. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates a response based on the prompt. This allows the user to interact with a specific AI personality for each telephone number.

[0030] The AI ​​phone book can refer to the user's past conversation history to generate more personalized responses. For example, for each phone number, the generation AI refers to the user's past conversation history to generate more personalized responses. For example, if the user has a history of practicing English conversation in the past, appropriate topics and phrases can be suggested based on that history. This makes it possible to generate more personalized responses based on the user's past conversation history.

[0031] AI Phonebook can manage the user's schedule and tasks and send reminders at the appropriate time. For example, based on the phone numbers registered in the AI ​​Phonebook, the generated AI manages the user's schedule and sends reminders at the appropriate time. For example, if a user regularly practices English conversation, a reminder can be sent at that time. This makes it easier for users to manage their schedules and tasks.

[0032] The AI ​​phone book adds a voice recognition function, allowing users to connect to a specific AI personality simply by speaking a phone number. The AI ​​phone book adds a voice recognition function, for example, and builds a system that allows users to connect to a specific AI personality simply by speaking a phone number. For example, a user can connect to an AI foreigner simply by saying "English conversation practice." This allows users to connect to a specific AI personality simply by speaking a phone number.

[0033] The AI ​​phone book can be made accessible from different devices, making it compatible with multiple devices. For example, the AI ​​phone book can be made accessible from different devices, creating a system that makes it compatible with multiple devices. For example, you can call an AI foreigner from your smartwatch to practice English conversation. This allows you to access the AI ​​phone book from different devices.

[0034] The generation AI can automatically select themes and topics of conversation based on the phone number called by the user, and provide conversations that match the user's interests. For example, if a user wants to practice English conversation, the generation AI can select topics related to travel or business. This allows the generation AI to provide conversations that match the user's interests.

[0035] The generation AI can adjust the difficulty of the dialogue based on the phone number called by the user and respond according to the user's skill level. For example, the generation AI can adjust the difficulty of the dialogue based on the phone number called by the user and respond according to the user's skill level. For example, for a user who wants to practice English conversation, it can start with a simple conversation for beginners. This makes it possible to respond according to the user's skill level.

[0036] The generation AI can provide dialogue in different languages ​​based on the phone number called by the user, thereby realizing multilingual support. The generation AI can provide dialogue in different languages ​​based on the phone number called by the user, thereby realizing multilingual support. For example, if the user wants to practice French, the generation AI can respond in French. This makes it possible to have dialogue in different languages.

[0037] The generation AI can automatically record the content of a conversation based on the phone number called by the user, making it possible to refer to it later. The generation AI can, for example, build a system that automatically records the content of a conversation based on the phone number called by the user, making it possible to refer to it later. For example, it can record the content of a user's English conversation practice, making it possible to review it later. This allows the content of a conversation to be automatically recorded and referred to later.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The AI ​​phone book system can monitor the user's health condition and provide appropriate advice. For example, when a user makes a call, the system measures the user's heart rate and stress level and provides health advice. Furthermore, if the user has a specific health problem, it can also provide specialized advice for that problem. This makes it easier for users to manage their health.

[0040] The AI ​​phone book system can provide customized entertainment content based on the user's hobbies and interests. For example, if the user is a movie lover, it can provide the latest movie information and recommended movies. Furthermore, if the user is a music lover, it can provide the latest music trends and recommended playlists. This allows it to provide entertainment content that suits the user's hobbies and interests.

[0041] The AI ​​phone book system can refer to a user's learning history and provide appropriate learning content. For example, it can suggest the next topic to study based on what the user has learned in the past. Furthermore, if a user is interested in a particular field, it can provide learning content related to that field. This improves the user's learning efficiency.

[0042] The AI ​​phone book system can refer to a user's purchasing history and suggest appropriate products. For example, it can suggest related products based on products the user has previously purchased. Furthermore, if a user likes a particular brand, it can also suggest new products from that brand. This improves the user's purchasing experience.

[0043] The AI ​​phone book system can use the user's location information to suggest nearby events and activities. For example, if the user is in a specific area, it can suggest events being held in that area. Furthermore, if the user is interested in a specific activity, it can also suggest the location where that activity is taking place. This makes it possible to suggest events and activities based on the user's location information.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The AI ​​phone book is set up to connect each phone number to a specific AI personality. For example, when a user calls a phone number registered as an "AI foreigner" in the phone book, the generated AI will answer and they can practice English conversation. Step 2: The generated AI will set a specific AI personality based on the phone number the user called and respond according to that personality. For example, if a user who wants to practice English calls the "AI Foreigner," the generated AI will respond in English and begin a dialogue with the user.

[0046] (Example 2) The AI ​​phone book system according to an embodiment of the present invention is a system that connects each phone number to a specific AI personality. This allows a user to converse with a specific AI personality simply by making a call from a phone app. This allows the AI ​​phone book system to allow a user to converse with a specific AI personality for each phone number.

[0047] An AI phone book system according to an embodiment includes an AI phone book, a generation AI, a telephone number, and an AI personality. The AI ​​phone book is configured to connect each telephone number to a specific AI personality. For example, when a user calls a telephone number registered in the phone book as "AI Foreigner," the generation AI answers, allowing the user to practice English conversation. The generation AI sets a specific AI personality based on the telephone number the user called and responds according to that personality. For example, when a user who wants to practice English conversation calls "AI Foreigner," the generation AI responds in English and begins a dialogue with the user. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates a response based on the prompt. This allows the user to interact with a specific AI personality for each telephone number.

[0048] The AI ​​phone book can refer to the user's past conversation history to generate more personalized responses. For example, for each phone number, the generation AI refers to the user's past conversation history to generate more personalized responses. For example, if the user has a history of practicing English conversation in the past, appropriate topics and phrases can be suggested based on that history. This makes it possible to generate more personalized responses based on the user's past conversation history.

[0049] AI Phonebook can manage the user's schedule and tasks and send reminders at the appropriate time. For example, based on the phone numbers registered in the AI ​​Phonebook, the generated AI manages the user's schedule and sends reminders at the appropriate time. For example, if a user regularly practices English conversation, a reminder can be sent at that time. This makes it easier for users to manage their schedules and tasks.

[0050] The AI ​​phone book uses its emotion estimation function to analyze the emotional state of the user when making a call and can automatically select an AI personality that matches that emotion. For example, if the user is feeling stressed, the AI ​​phone book selects an AI personality that helps them relax. This allows the AI ​​phone book to automatically select an AI personality that matches the user's emotional state.

[0051] The AI ​​phone book adds a voice recognition function, allowing users to connect to a specific AI personality simply by speaking a phone number. The AI ​​phone book adds a voice recognition function, for example, and builds a system that allows users to connect to a specific AI personality simply by speaking a phone number. For example, a user can connect to an AI foreigner simply by saying "English conversation practice." This allows users to connect to a specific AI personality simply by speaking a phone number.

[0052] The AI ​​phone book can be made accessible from different devices, making it compatible with multiple devices. For example, the AI ​​phone book can be made accessible from different devices, creating a system that makes it compatible with multiple devices. For example, you can call an AI foreigner from your smartwatch to practice English conversation. This allows you to access the AI ​​phone book from different devices.

[0053] The AI ​​Phone Book uses its emotion estimation function to monitor the emotions of users when using the AI ​​Phone Book in real time and make suggestions to bring out positive emotions. For example, the AI ​​Phone Book uses its emotion estimation function to monitor the emotions of users when using the AI ​​Phone Book in real time and make suggestions to bring out positive emotions. For example, if the user is nervous, it will suggest conversations that will help them relax. This makes it possible to monitor the user's emotions in real time and make suggestions to bring out positive emotions.

[0054] The generation AI can automatically select themes and topics of conversation based on the phone number called by the user, and provide conversations that match the user's interests. For example, if a user wants to practice English conversation, the generation AI can select topics related to travel or business. This allows the generation AI to provide conversations that match the user's interests.

[0055] The generation AI can adjust the difficulty of the dialogue based on the phone number called by the user and respond according to the user's skill level. For example, the generation AI can adjust the difficulty of the dialogue based on the phone number called by the user and respond according to the user's skill level. For example, for a user who wants to practice English conversation, it can start with a simple conversation for beginners. This makes it possible to respond according to the user's skill level.

[0056] The generation AI can use the emotion estimation function to analyze the user's emotional state and adjust the tone and content of the dialogue according to that emotion. For example, the generation AI can use the emotion estimation function to analyze the user's emotional state and adjust the tone and content of the dialogue according to that emotion. For example, if the user is nervous, the generation AI can use a relaxing tone to engage in the dialogue. This makes it possible to adjust the tone and content of the dialogue according to the user's emotional state.

[0057] The generation AI can provide dialogue in different languages ​​based on the phone number called by the user, thereby realizing multilingual support. The generation AI can provide dialogue in different languages ​​based on the phone number called by the user, thereby realizing multilingual support. For example, if the user wants to practice French, the generation AI can respond in French. This makes it possible to have dialogue in different languages.

[0058] The generation AI can automatically record the content of a conversation based on the phone number called by the user, making it possible to refer to it later. The generation AI can, for example, build a system that automatically records the content of a conversation based on the phone number called by the user, making it possible to refer to it later. For example, it can record the content of a user's English conversation practice, making it possible to review it later. This allows the content of a conversation to be automatically recorded and referred to later.

[0059] The generation AI can use the emotion estimation function to monitor the content of the conversation in real time based on the phone number called by the user and provide feedback according to the user's emotions. For example, the generation AI can use the emotion estimation function to monitor the content of the conversation in real time based on the phone number called by the user and provide feedback according to the user's emotions. For example, if the user is feeling anxious, it can provide reassuring feedback. This makes it possible to provide feedback according to the user's emotions in real time.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The AI ​​phone book system can monitor the user's health condition and provide appropriate advice. For example, when a user makes a call, the system measures the user's heart rate and stress level and provides health advice. Furthermore, if the user has a specific health problem, it can also provide specialized advice for that problem. This makes it easier for users to manage their health.

[0062] The AI ​​phone book system can provide customized entertainment content based on the user's hobbies and interests. For example, if the user is a movie lover, it can provide the latest movie information and recommended movies. Furthermore, if the user is a music lover, it can provide the latest music trends and recommended playlists. This allows it to provide entertainment content that suits the user's hobbies and interests.

[0063] The AI ​​phone book system can estimate the user's emotions and suggest appropriate relaxation methods based on those emotions. For example, if the user is feeling stressed, it can suggest deep breathing or meditation. Furthermore, if the user is tired, it can suggest relaxing music or aromatherapy. In this way, it can suggest relaxation methods according to the user's emotional state.

[0064] The AI ​​phone book system can estimate the user's emotions and suggest appropriate fitness programs based on those emotions. For example, if the user is feeling energetic, it can suggest high-intensity training. Furthermore, if the user wants to relax, it can suggest yoga or stretching programs. This allows it to suggest fitness programs that correspond to the user's emotional state.

[0065] The AI ​​phone book system can estimate the user's emotions and suggest appropriate meal plans based on those emotions. For example, if the user is feeling stressed, it can suggest recipes using ingredients that have a relaxing effect. Furthermore, if the user is feeling energetic, it can suggest recipes using nutritious ingredients. This allows it to suggest meal plans that correspond to the user's emotional state.

[0066] The AI ​​phone book system can refer to a user's learning history and provide appropriate learning content. For example, it can suggest the next topic to study based on what the user has learned in the past. Furthermore, if a user is interested in a particular field, it can provide learning content related to that field. This improves the user's learning efficiency.

[0067] The AI ​​phone book system can estimate the user's emotions and suggest appropriate travel plans based on those emotions. For example, if the user wants to relax, it can suggest quiet resorts. Furthermore, if the user is seeking adventure, it can suggest travel destinations with plenty of activities. This allows it to suggest travel plans that correspond to the user's emotional state.

[0068] The AI ​​phone book system can refer to a user's purchasing history and suggest appropriate products. For example, it can suggest related products based on products the user has previously purchased. Furthermore, if a user likes a particular brand, it can also suggest new products from that brand. This improves the user's purchasing experience.

[0069] The AI ​​phone book system can estimate the user's emotions and suggest appropriate reading lists based on those emotions. For example, if the user wants to relax, it can suggest relaxing books. Furthermore, if the user is looking for excitement, it can suggest thrilling books. This allows it to suggest reading lists that correspond to the user's emotional state.

[0070] The AI ​​phone book system can use the user's location information to suggest nearby events and activities. For example, if the user is in a specific area, it can suggest events being held in that area. Furthermore, if the user is interested in a specific activity, it can also suggest the location where that activity is taking place. This makes it possible to suggest events and activities based on the user's location information.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The AI ​​phone book is set up to connect each phone number to a specific AI personality. For example, when a user calls a phone number registered as an "AI foreigner" in the phone book, the generated AI will answer and they can practice English conversation. Step 2: The generated AI will set a specific AI personality based on the phone number the user called and respond according to that personality. For example, if a user who wants to practice English calls the "AI Foreigner," the generated AI will respond in English and begin a dialogue with the user.

[0073] 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.

[0074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0086] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0087] 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.

[0088] 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.

[0089] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0099] 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.

[0100] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0139] 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. [Explanation of symbols]

[0140] 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. Equipped with an AI phone book, The AI ​​phone book is Each phone number is set to connect to a specific AI personality, The generation AI sets the AI ​​personality based on the phone number called by the user, Respond according to the AI ​​personality A system characterized by:

2. The AI ​​phone book is Referencing the user's past interactions to generate more personalized responses 2. The system of claim 1.

3. The AI ​​phone book is Add a voice recognition function, and users can connect to a specific AI personality simply by specifying the phone number by voice.

2. The system of claim 1.

4. The generated AI is Based on the telephone number called by the user, the theme or topic of the conversation is automatically selected, and a conversation tailored to the user's interests is provided.

2. The system of claim 1.

5. The AI ​​phone book is Using the emotion estimation function, the emotional state of the user when sending a message is analyzed, and the AI ​​personality is automatically selected according to that emotion.

2. The system of claim 1.

6. The AI ​​phone book is Enable access from different devices and achieve multi-device compatibility 2. The system of claim 1.

7. The generated AI is Emotion estimation capabilities are used to analyze the user's emotional state and adjust the tone and content of the dialogue accordingly.

2. The system of claim 1.

8. The generated AI is Based on the telephone number called by the user, dialogues are provided in different languages, realizing multilingual support.

2. The system of claim 1.

Images