System

Abstract

An object of the system according to the embodiment is to naturally acquire humor and conversation skills tailored to individual users.SOLUTION: A system includes a personality information collection unit, a content generation unit, and a training unit. The personality information collection unit collects information on the personality, hobbies, and interests of the user. The content generation unit generates jokes and episodes based on the information collected by the personality information collection unit. The training unit uses the jokes and episodes generated by the content generation unit to train the way and timing of a humorous conversation and the way of reading reactions.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 has had the problem of making it difficult for users to naturally acquire humor and conversational skills that are tailored to each individual user.

[0005] The system according to the embodiment aims to naturally acquire humor and conversation skills that are tailored to each individual user. [Means for solving the problem]

[0006] The system according to the embodiment includes a personality information collection unit, a content generation unit, and a training unit. The personality information collection unit collects information about a user's personality, hobbies, and interests. The content generation unit generates jokes and anecdotes based on the information collected by the personality information collection unit. The training unit uses the jokes and anecdotes generated by the content generation unit to train the user in how to engage in humorous conversation, the timing of such conversations, and how to read reactions. [Effects of the Invention]

[0007] The system according to the embodiment allows users to naturally acquire humor and conversation skills that are tailored to each individual user. [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 Smile Creator AI system according to an embodiment of the present invention is a system that allows users to acquire the skill of naturally making people smile in their daily lives. This system provides jokes and anecdotes based on the user's personality, hobbies, and interests, and trains the user in how to engage in humorous conversations, the timing of such conversations, and how to read reactions. In this way, the Smile Creator AI system allows users to acquire the skill of naturally making people smile in their daily lives.

[0029] A smile creator AI system according to an embodiment includes a personality information collection unit, a content generation unit, and a training unit. The personality information collection unit collects information about a user's personality, hobbies, and interests. For example, the personality information collection unit collects user survey responses and analyzes personality traits. The personality information collection unit can also analyze the user's past behavioral data to identify the user's hobbies and interests. The personality information collection unit can also analyze the user's social media posts to extract the user's interests. For example, the personality information collection unit identifies the user's hobbies based on topics frequently mentioned by the user. The content generation unit generates jokes and episodes based on the information collected by the personality information collection unit. For example, the content generation unit uses a generation AI to generate jokes related to the user's hobbies. The content generation unit can also use the generation AI to generate episodes based on the user's interests. The content generation unit can also use the generation AI to generate humor tailored to the user's personality. For example, the content generation unit generates appropriate jokes by using the generation AI to consider the user's personality traits. The training unit uses jokes and anecdotes generated by the content generation unit to train the user on how to engage in humorous conversations, the timing of such conversations, and how to read reactions. For example, the training unit teaches the user how to insert jokes at appropriate times. The training unit can also teach the user how to read the other person's reactions and continue the conversation. Furthermore, the training unit can train the user on how to engage in humorous conversations. For example, the training unit suggests to the user the timing to incorporate humor into a conversation. This allows the smile creator AI system according to the embodiment to help the user acquire the skill to make people smile naturally in their daily lives. For example, by incorporating humor into conversations with friends and family, the user can have more lively conversations and build better relationships. This also facilitates communication at work or school, contributing to stress reduction and improved teamwork.

[0030] The personality information collection unit can analyze a user's past SNS posts and message history to generate a more accurate profile of their personality, hobbies, and interests. For example, the personality information collection unit can analyze a user's past SNS posts and extract frequently mentioned topics and keywords. For example, if a user frequently posts about sports, the personality information collection unit can provide sports-related jokes and anecdotes based on that information. The personality information collection unit can also analyze a user's message history to identify trends in hobbies and interests. For example, related content can be provided based on topics the user frequently discusses in messages with friends. Furthermore, the personality information collection unit can analyze a user's personality traits based on SNS posts and message history. For example, the personality traits can be identified by analyzing the content of the user's posts and the tone of the messages. This improves the accuracy of providing content based on the user's personality, hobbies, and interests.

[0031] The personality information collection unit can collect real-time behavioral data of the user and provide content appropriate to the situation at that time. For example, the personality information collection unit can provide jokes and stories related to the location based on the user's location information. For example, if the user is in a park, jokes related to the park can be provided. The personality information collection unit can also collect a user's activity log and provide content appropriate to the situation at that time. For example, if the user is running, jokes related to running can be provided. Furthermore, the personality information collection unit can also provide content tailored to the user's interests and hobbies based on the real-time behavioral data. For example, if the user is at a movie theater, jokes related to movies can be provided. This makes it possible to provide content appropriate to the user's real-time situation.

[0032] The content generation unit can also provide content based on the user's personality, hobbies, and interests in audio or video format, allowing the user to enjoy the content visually or aurally. The content generation unit, for example, provides jokes or episodes generated based on the user's personality, hobbies, and interests in audio format. For example, the content generation unit reads jokes aloud through a voice assistant. The content generation unit can also provide jokes or episodes generated based on the user's personality, hobbies, and interests in video format. For example, the content generation unit visually expresses jokes as video content. Furthermore, the content generation unit can customize the content provided in audio or video format to suit the user's preferences. For example, the content can be provided using the user's favorite voice actor or actor. This allows the user to enjoy the content visually or aurally.

[0033] The content generation unit can generate content in multiple languages ​​to accommodate users from different cultural backgrounds or languages, and provide international humor. The content generation unit, for example, generates content in multiple languages ​​to accommodate users from different cultural backgrounds. For example, it provides jokes and stories in multiple languages, such as English, French, and Chinese. The content generation unit can also generate content taking into account humor from different cultural backgrounds. For example, it can provide jokes that take into account the cultural backgrounds of each country. Furthermore, the content generation unit can learn humor patterns from different cultural backgrounds in order to provide international humor. For example, it can analyze elements of laughter in each country and generate content based on that. This allows it to accommodate users from different cultural backgrounds and languages.

[0034] The training unit can analyze the user's past conversation data and extract specific conversation patterns and phrases to use in training. The training unit, for example, analyzes the user's past conversation data and extracts frequently used conversation patterns and phrases. For example, it identifies joke patterns that the user often uses and uses them for training. The training unit can also extract conversation patterns for specific scenarios based on the user's conversation data. For example, it can identify phrases that the user often uses in conversations at work and use them for training. Furthermore, the training unit can suggest optimal phrases for using humor based on the user's conversation data. For example, it can suggest new jokes based on phrases from jokes that the user has used successfully in the past. In this way, by training based on the user's past conversation data, more effective humor skills can be acquired.

[0035] The training unit can train the user in humorous conversation in a virtual reality environment, allowing the user to practice in a more realistic situation. For example, the training unit can train the user in humorous conversation in a VR environment, allowing the user to practice in a realistic situation. For example, the user can have a joke-filled conversation with a virtual conversation partner. The training unit can also use the VR environment to allow the user to practice humorous conversation in various situations. For example, conversations in different situations, such as at work, at school, or at a gathering with friends, can be simulated. Furthermore, the training unit can use the VR environment to provide real-time feedback on the user's conversation skills. For example, the training unit can suggest what kind of humor the user should use next based on the virtual conversation partner's reaction after the user tells a joke. This allows the user to train in humorous conversation in a realistic situation.

[0036] The training department can improve international social skills by conducting conversation simulations with people from different cultures and backgrounds. For example, the training department can improve a user's international social skills by conducting conversation simulations with people from different cultures and backgrounds. For example, it can conduct conversation simulations to deepen intercultural understanding. The training department can also teach a user appropriate ways to respond to different cultures through conversation simulations with people from different cultures and backgrounds. For example, it can learn jokes and humor patterns from different cultures. Furthermore, the training department can also teach a user international manners and etiquette through conversation simulations with people from different cultures and backgrounds. For example, it can learn conversation manners and etiquette from different cultures. This allows a user to improve their international social skills through conversation simulations with people from different cultures and backgrounds.

[0037] The training unit can analyze the user's conversation data in real time and provide specific advice on how to use humor at the appropriate time. The training unit, for example, analyzes the user's conversation data in real time and provides specific advice on how to use humor at the appropriate time. For example, it analyzes the flow of the conversation and suggests the optimal timing to insert a joke. The training unit can also suggest specific phrases for using humor based on the user's conversation data. For example, it can suggest joke phrases that can be used in the conversation. Furthermore, the training unit can suggest specific scenarios for using humor based on the user's conversation data. For example, it can suggest humorous scenarios that can be used in specific situations. This allows the user to receive specific advice on how to use humor at the appropriate time in real time.

[0038] The training unit can develop an advanced algorithm for analyzing the facial expressions and gestures of the user's conversation partner and reading their reactions. The training unit, for example, analyzes the facial expressions of the user's conversation partner and develops an advanced algorithm for reading their reactions. For example, it identifies the moment when the other person smiles. The training unit can also develop an advanced algorithm for analyzing the gestures of the user's conversation partner and reading their reactions. For example, it identifies the timing when the other person moves their hands. The training unit can also develop an advanced algorithm for reading their reactions by combining the facial expressions and gestures of the user's conversation partner. For example, it identifies the timing when the other person moves their hands while smiling. This allows the user to develop an advanced algorithm for analyzing the facial expressions and gestures of the user's conversation partner and reading their reactions.

[0039] The training unit can enable the user to utilize conversation timing and reaction reading in group discussions and presentations. For example, the training unit can utilize conversation timing and reaction reading in group discussions to enable the user to speak at an appropriate time. For example, the training unit can suggest the timing to use humor in a discussion. The training unit can also utilize conversation timing and reaction reading in presentations to enable the user to give an effective presentation. For example, the training unit can suggest the timing to use humor in a presentation. Furthermore, the training unit can utilize conversation timing and reaction reading for practical training in group discussions and presentations. For example, the user practices using humor in actual discussions and presentations. This enables the user to utilize conversation timing and reaction reading in group discussions and presentations.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The Smile Creator AI system can also include a health information collection unit that collects the user's health data and provides content tailored to the user's health condition. For example, based on data from the user's fitness tracker, the system can provide relaxing jokes to users after exercise. The health information collection unit can also analyze the user's sleep data and provide humor suitable for waking them up in the morning. Furthermore, the health information collection unit can provide stories to enjoy while eating based on the user's dietary data. This makes it possible to provide content tailored to the user's health condition.

[0042] The Smile Creator AI system can also include a music information collection unit that provides jokes and stories related to music based on the user's music preferences. For example, it can provide jokes related to the user's favorite artists or genres. The music information collection unit can also analyze the user's favorite playlists and provide stories related to those playlists. Furthermore, the music information collection unit can provide humor related to specific songs based on the user's music playback history. This makes it possible to provide content tailored to the user's music preferences.

[0043] The Smile Creator AI system can also include a reading information collection unit that analyzes a user's reading history and provides jokes and stories related to reading. For example, it can provide jokes related to the genre of books the user has read. The reading information collection unit can also analyze reviews of books the user has read and provide stories based on those reviews. Furthermore, the reading information collection unit can provide humor related to specific scenes based on the content of the books the user has read. This makes it possible to provide content that matches the user's reading history.

[0044] The Smile Creator AI system can also include a travel information collection unit that analyzes the user's travel history and provides travel-related jokes and stories. For example, it can provide jokes related to places the user has visited. The travel information collection unit can also analyze photos taken by the user and provide stories based on those photos. The travel information collection unit can also provide humor related to the culture and history of places the user has visited. This makes it possible to provide content tailored to the user's travel history.

[0045] The Smile Creator AI system may further include a pet information collection unit that collects information about the user's pet and provides jokes and stories related to the pet. For example, jokes related to the type and name of the user's pet may be provided. The pet information collection unit may also analyze photos taken by the user with the pet and provide stories based on those photos. Furthermore, the pet information collection unit may provide humor related to the behavior and habits of the user's pet. This makes it possible to provide content tailored to the information about the user's pet.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The personality information collection unit collects information about the user's personality, hobbies, and interests. For example, the personality information collection unit collects the user's questionnaire responses and analyzes their personality traits. The personality information collection unit can also analyze the user's past behavioral data to identify their hobbies and interests. Furthermore, the personality information collection unit can analyze the user's social media posts to extract their interests. For example, the personality information collection unit can identify their hobbies based on topics that the user frequently mentions. Step 2: The content generation unit generates jokes and episodes based on the information collected by the personality information collection unit. For example, the content generation unit uses a generation AI to generate jokes related to the user's hobbies. The content generation unit can also use the generation AI to generate episodes based on the user's interests. Furthermore, the content generation unit can also use the generation AI to generate humor that matches the user's personality. For example, the content generation unit generates appropriate jokes by using the generation AI to take into account the user's personality traits. Step 3: The training unit uses the jokes and episodes generated by the content generation unit to train the user on how to engage in humorous conversation, the timing of doing so, and how to read reactions. For example, the training unit teaches the user how to insert jokes at the appropriate time. The training unit can also teach the user how to read the other person's reactions and continue the conversation. Furthermore, the training unit can train the user on how to engage in humorous conversation. For example, the training unit suggests to the user the timing to incorporate humor into a conversation.

[0048] (Example 2) The Smile Creator AI system according to an embodiment of the present invention is a system that allows users to acquire the skill of naturally making people smile in their daily lives. This system provides jokes and anecdotes based on the user's personality, hobbies, and interests, and trains the user in how to engage in humorous conversations, the timing of such conversations, and how to read reactions. In this way, the Smile Creator AI system allows users to acquire the skill of naturally making people smile in their daily lives.

[0049] A smile creator AI system according to an embodiment includes a personality information collection unit, a content generation unit, and a training unit. The personality information collection unit collects information about a user's personality, hobbies, and interests. For example, the personality information collection unit collects user survey responses and analyzes personality traits. The personality information collection unit can also analyze the user's past behavioral data to identify the user's hobbies and interests. The personality information collection unit can also analyze the user's social media posts to extract the user's interests. For example, the personality information collection unit identifies the user's hobbies based on topics frequently mentioned by the user. The content generation unit generates jokes and episodes based on the information collected by the personality information collection unit. For example, the content generation unit uses a generation AI to generate jokes related to the user's hobbies. The content generation unit can also use the generation AI to generate episodes based on the user's interests. The content generation unit can also use the generation AI to generate humor tailored to the user's personality. For example, the content generation unit generates appropriate jokes by using the generation AI to consider the user's personality traits. The training unit uses jokes and anecdotes generated by the content generation unit to train the user on how to engage in humorous conversations, the timing of such conversations, and how to read reactions. For example, the training unit teaches the user how to insert jokes at appropriate times. The training unit can also teach the user how to read the other person's reactions and continue the conversation. Furthermore, the training unit can train the user on how to engage in humorous conversations. For example, the training unit suggests to the user the timing to incorporate humor into a conversation. This allows the smile creator AI system according to the embodiment to help the user acquire the skill to make people smile naturally in their daily lives. For example, by incorporating humor into conversations with friends and family, the user can have more lively conversations and build better relationships. This also facilitates communication at work or school, contributing to stress reduction and improved teamwork.

[0050] The personality information collection unit can analyze a user's past SNS posts and message history to generate a more accurate profile of their personality, hobbies, and interests. For example, the personality information collection unit can analyze a user's past SNS posts and extract frequently mentioned topics and keywords. For example, if a user frequently posts about sports, the personality information collection unit can provide sports-related jokes and anecdotes based on that information. The personality information collection unit can also analyze a user's message history to identify trends in hobbies and interests. For example, related content can be provided based on topics the user frequently discusses in messages with friends. Furthermore, the personality information collection unit can analyze a user's personality traits based on SNS posts and message history. For example, the personality traits can be identified by analyzing the content of the user's posts and the tone of the messages. This improves the accuracy of providing content based on the user's personality, hobbies, and interests.

[0051] The personality information collection unit can collect real-time behavioral data of the user and provide content appropriate to the situation at that time. For example, the personality information collection unit can provide jokes and stories related to the location based on the user's location information. For example, if the user is in a park, jokes related to the park can be provided. The personality information collection unit can also collect a user's activity log and provide content appropriate to the situation at that time. For example, if the user is running, jokes related to running can be provided. Furthermore, the personality information collection unit can also provide content tailored to the user's interests and hobbies based on the real-time behavioral data. For example, if the user is at a movie theater, jokes related to movies can be provided. This makes it possible to provide content appropriate to the user's real-time situation.

[0052] The content generation unit can use the emotion estimation function to predict what emotions a user will have toward specific content and preferentially provide content that elicits positive emotions. For example, the content generation unit can use the emotion estimation function to identify content to which the user has previously responded positively and preferentially provide similar content. For example, the content generation unit can generate a new joke based on a joke that the user laughed at in the past. The content generation unit can also use the emotion estimation function to predict whether the user will have positive emotions toward a specific episode. For example, the content generation unit can generate a new episode based on an episode that moved the user in the past. Furthermore, the content generation unit can use the emotion estimation function to monitor the user's emotional state in real time and provide content that elicits positive emotions. For example, the content generation unit can provide a joke when the user is relaxed. This allows content that elicits positive emotions to be preferentially provided to the user.

[0053] The content generation unit can also provide content based on the user's personality, hobbies, and interests in audio or video format, allowing the user to enjoy the content visually or aurally. The content generation unit, for example, provides jokes or episodes generated based on the user's personality, hobbies, and interests in audio format. For example, the content generation unit reads jokes aloud through a voice assistant. The content generation unit can also provide jokes or episodes generated based on the user's personality, hobbies, and interests in video format. For example, the content generation unit visually expresses jokes as video content. Furthermore, the content generation unit can customize the content provided in audio or video format to suit the user's preferences. For example, the content can be provided using the user's favorite voice actor or actor. This allows the user to enjoy the content visually or aurally.

[0054] The content generation unit can generate content in multiple languages ​​to accommodate users from different cultural backgrounds or languages, and provide international humor. The content generation unit, for example, generates content in multiple languages ​​to accommodate users from different cultural backgrounds. For example, it provides jokes and stories in multiple languages, such as English, French, and Chinese. The content generation unit can also generate content taking into account humor from different cultural backgrounds. For example, it can provide jokes that take into account the cultural backgrounds of each country. Furthermore, the content generation unit can learn humor patterns from different cultural backgrounds in order to provide international humor. For example, it can analyze elements of laughter in each country and generate content based on that. This allows it to accommodate users from different cultural backgrounds and languages.

[0055] The content generation unit can use the emotion estimation function to monitor the emotional state of the user when receiving content in real time and provide the content at the optimal timing. For example, the content generation unit can use the emotion estimation function to monitor the user's real-time emotional state and provide the content at the optimal timing. For example, the content generation unit can provide a joke when the user is relaxed. The content generation unit can also use the emotion estimation function to customize content according to the user's emotional state. For example, the content generation unit can provide a relaxing episode when the user is feeling stressed. Furthermore, the content generation unit can also use the emotion estimation function to predict the user's emotional state and provide the content at the optimal timing. For example, the content generation unit can identify the moment when the user is about to smile and provide a joke at that moment. This makes it possible to provide content at the optimal timing according to the user's emotional state.

[0056] The training unit can analyze the user's past conversation data and extract specific conversation patterns and phrases to use in training. The training unit, for example, analyzes the user's past conversation data and extracts frequently used conversation patterns and phrases. For example, it identifies joke patterns that the user often uses and uses them for training. The training unit can also extract conversation patterns for specific scenarios based on the user's conversation data. For example, it can identify phrases that the user often uses in conversations at work and use them for training. Furthermore, the training unit can suggest optimal phrases for using humor based on the user's conversation data. For example, it can suggest new jokes based on phrases from jokes that the user has used successfully in the past. In this way, by training based on the user's past conversation data, more effective humor skills can be acquired.

[0057] The training unit can use the emotion estimation function to predict the emotional reaction of the other party when the user is having a humorous conversation and provide appropriate feedback. For example, the training unit uses the emotion estimation function to predict the emotional reaction of the other party when the user is having a humorous conversation. For example, it identifies the timing when the other party will smile. The training unit can also use the emotion estimation function to monitor the emotional reaction of the other party in real time when the user is having a humorous conversation. For example, it can analyze the other party's facial expression and tone of voice to predict the emotional reaction. Furthermore, the training unit can also use the emotion estimation function to provide appropriate feedback to the user. For example, it can suggest what kind of humor the other party should use next based on the other party's reaction after the user tells a joke. In this way, the user can predict the emotional reaction of the other party when having a humorous conversation and receive appropriate feedback.

[0058] The training unit can train the user in humorous conversation in a virtual reality environment, allowing the user to practice in a more realistic situation. For example, the training unit can train the user in humorous conversation in a VR environment, allowing the user to practice in a realistic situation. For example, the user can have a joke-filled conversation with a virtual conversation partner. The training unit can also use the VR environment to allow the user to practice humorous conversation in various situations. For example, conversations in different situations, such as at work, at school, or at a gathering with friends, can be simulated. Furthermore, the training unit can use the VR environment to provide real-time feedback on the user's conversation skills. For example, the training unit can suggest what kind of humor the user should use next based on the virtual conversation partner's reaction after the user tells a joke. This allows the user to train in humorous conversation in a realistic situation.

[0059] The training department can improve international social skills by conducting conversation simulations with people from different cultures and backgrounds. For example, the training department can improve a user's international social skills by conducting conversation simulations with people from different cultures and backgrounds. For example, it can conduct conversation simulations to deepen intercultural understanding. The training department can also teach a user appropriate ways to respond to different cultures through conversation simulations with people from different cultures and backgrounds. For example, it can learn jokes and humor patterns from different cultures. Furthermore, the training department can also teach a user international manners and etiquette through conversation simulations with people from different cultures and backgrounds. For example, it can learn conversation manners and etiquette from different cultures. This allows a user to improve their international social skills through conversation simulations with people from different cultures and backgrounds.

[0060] The training unit can use the emotion estimation function to provide relaxation content for reducing stress and anxiety felt by the user during training. For example, the training unit can use the emotion estimation function to monitor the stress and anxiety felt by the user during training in real time and provide relaxation content. For example, the training unit can provide relaxing music or videos. The training unit can also use the emotion estimation function to suggest specific methods for reducing the stress and anxiety felt by the user during training. For example, the training unit can teach deep breathing or meditation techniques. Furthermore, the training unit can use the emotion estimation function to identify the cause of the user's stress or anxiety and provide relaxation content to address it. For example, the training unit can provide content for reducing stress felt by the user in a specific situation. This makes it possible to provide relaxation content for reducing the stress and anxiety felt by the user during training.

[0061] The training unit can analyze the user's conversation data in real time and provide specific advice on how to use humor at the appropriate time. The training unit, for example, analyzes the user's conversation data in real time and provides specific advice on how to use humor at the appropriate time. For example, it analyzes the flow of the conversation and suggests the optimal timing to insert a joke. The training unit can also suggest specific phrases for using humor based on the user's conversation data. For example, it can suggest joke phrases that can be used in the conversation. Furthermore, the training unit can suggest specific scenarios for using humor based on the user's conversation data. For example, it can suggest humorous scenarios that can be used in specific situations. This allows the user to receive specific advice on how to use humor at the appropriate time in real time.

[0062] The training unit can develop an advanced algorithm for analyzing the facial expressions and gestures of the user's conversation partner and reading their reactions. The training unit, for example, analyzes the facial expressions of the user's conversation partner and develops an advanced algorithm for reading their reactions. For example, it identifies the moment when the other person smiles. The training unit can also develop an advanced algorithm for analyzing the gestures of the user's conversation partner and reading their reactions. For example, it identifies the timing when the other person moves their hands. The training unit can also develop an advanced algorithm for reading their reactions by combining the facial expressions and gestures of the user's conversation partner. For example, it identifies the timing when the other person moves their hands while smiling. This allows the user to develop an advanced algorithm for analyzing the facial expressions and gestures of the user's conversation partner and reading their reactions.

[0063] The training unit can use the emotion estimation function to identify the moment in a conversation when the other person shows the most positive emotion and suggest a way to use humor at that timing. The training unit, for example, uses the emotion estimation function to identify the moment in a conversation when the other person shows the most positive emotion. For example, it identifies the moment when the other person smiles. The training unit can also use the emotion estimation function to predict the moment in a conversation when the other person shows the most positive emotion. For example, it identifies the moment when the other person makes a positive statement. Furthermore, the training unit can use the emotion estimation function to suggest a way to use humor at the moment when the other person shows the most positive emotion. For example, it suggests a way to tell a joke the moment the other person smiles. This allows the user to identify the moment in a conversation when the other person shows the most positive emotion and suggest a way to use humor at that timing.

[0064] The training unit can enable the user to utilize conversation timing and reaction reading in group discussions and presentations. For example, the training unit can utilize conversation timing and reaction reading in group discussions to enable the user to speak at an appropriate time. For example, the training unit can suggest the timing to use humor in a discussion. The training unit can also utilize conversation timing and reaction reading in presentations to enable the user to give an effective presentation. For example, the training unit can suggest the timing to use humor in a presentation. Furthermore, the training unit can utilize conversation timing and reaction reading for practical training in group discussions and presentations. For example, the user practices using humor in actual discussions and presentations. This enables the user to utilize conversation timing and reaction reading in group discussions and presentations.

[0065] The training unit can use the emotion estimation function to provide relaxation techniques for reducing tension and anxiety felt by the user during a conversation. For example, the training unit can use the emotion estimation function to monitor the tension and anxiety felt by the user during a conversation in real time and provide relaxation techniques. For example, the training unit can teach deep breathing or meditation techniques. The training unit can also use the emotion estimation function to suggest specific methods for reducing tension and anxiety felt by the user during a conversation. For example, the training unit can provide relaxing music or videos. Furthermore, the training unit can use the emotion estimation function to identify the cause of the user's tension or anxiety and provide relaxation techniques to address it. For example, the training unit can provide techniques for reducing tension felt by the user in a specific situation. This makes it possible to provide relaxation techniques for reducing tension and anxiety felt by the user during a conversation.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The Smile Creator AI system can also include a health information collection unit that collects the user's health data and provides content tailored to the user's health condition. For example, based on data from the user's fitness tracker, the system can provide relaxing jokes to users after exercise. The health information collection unit can also analyze the user's sleep data and provide humor suitable for waking them up in the morning. Furthermore, the health information collection unit can provide stories to enjoy while eating based on the user's dietary data. This makes it possible to provide content tailored to the user's health condition.

[0068] The Smile Creator AI system can also include a music information collection unit that provides jokes and stories related to music based on the user's music preferences. For example, it can provide jokes related to the user's favorite artists or genres. The music information collection unit can also analyze the user's favorite playlists and provide stories related to those playlists. Furthermore, the music information collection unit can provide humor related to specific songs based on the user's music playback history. This makes it possible to provide content tailored to the user's music preferences.

[0069] The Smile Creator AI system can also include a reading information collection unit that analyzes a user's reading history and provides jokes and stories related to reading. For example, it can provide jokes related to the genre of books the user has read. The reading information collection unit can also analyze reviews of books the user has read and provide stories based on those reviews. Furthermore, the reading information collection unit can provide humor related to specific scenes based on the content of the books the user has read. This makes it possible to provide content that matches the user's reading history.

[0070] The Smile Creator AI system can also include a travel information collection unit that analyzes the user's travel history and provides travel-related jokes and stories. For example, it can provide jokes related to places the user has visited. The travel information collection unit can also analyze photos taken by the user and provide stories based on those photos. The travel information collection unit can also provide humor related to the culture and history of places the user has visited. This makes it possible to provide content tailored to the user's travel history.

[0071] The Smile Creator AI system may further include a pet information collection unit that collects information about the user's pet and provides jokes and stories related to the pet. For example, jokes related to the type and name of the user's pet may be provided. The pet information collection unit may also analyze photos taken by the user with the pet and provide stories based on those photos. Furthermore, the pet information collection unit may provide humor related to the behavior and habits of the user's pet. This makes it possible to provide content tailored to the information about the user's pet.

[0072] The content generation unit can use the emotion estimation function to predict what emotions a user will have toward specific content and preferentially provide content that elicits positive emotions. For example, the emotion estimation function can be used to identify content to which a user has previously responded positively and preferentially provide similar content. For example, a new joke can be generated based on a joke that the user laughed at in the past. The emotion estimation function can also be used to predict whether a user will have positive emotions toward a specific episode. For example, a new episode can be generated based on an episode that moved the user in the past. Furthermore, the emotion estimation function can be used to monitor the user's emotional state in real time and provide content that elicits positive emotions. For example, a joke can be provided when the user is relaxed. This allows content that elicits positive emotions to be preferentially provided to the user.

[0073] The training unit can use the emotion estimation function to predict the emotional reaction of the other party when the user is having a humorous conversation and provide appropriate feedback. For example, the emotion estimation function can be used to predict the emotional reaction of the other party when the user is having a humorous conversation. For example, the timing when the other party will smile can be determined. The emotion estimation function can also be used to monitor the emotional reaction of the other party in real time when the user is having a humorous conversation. For example, the emotion estimation function can predict the emotional reaction by analyzing the other party's facial expression and tone of voice. Furthermore, the emotion estimation function can also be used to provide appropriate feedback to the user. For example, the emotion estimation function can suggest what kind of humor the other party should use next based on the other party's reaction after the user tells a joke. In this way, the user can predict the emotional reaction of the other party when having a humorous conversation and receive appropriate feedback.

[0074] The training unit can use the emotion estimation function to provide relaxation content to reduce stress and anxiety felt by the user during training. For example, the emotion estimation function can be used to monitor the stress and anxiety felt by the user during training in real time and provide relaxation content. For example, relaxing music or videos can be provided. The emotion estimation function can also be used to suggest specific methods for reducing stress and anxiety felt by the user during training. For example, deep breathing or meditation techniques can be taught. Furthermore, the emotion estimation function can be used to identify the cause of the user's stress or anxiety and provide relaxation content to address it. For example, content can be provided to reduce stress felt by the user in a specific situation. This makes it possible to provide relaxation content to reduce stress and anxiety felt by the user during training.

[0075] The training unit can use the emotion estimation function to identify the moment in a conversation when the other person shows the most positive emotion, and suggest a way to use humor at that timing. For example, the emotion estimation function can be used to identify the moment in a conversation when the other person shows the most positive emotion. For example, the moment when the other person smiles can be identified. The emotion estimation function can also be used to predict the moment in a conversation when the other person shows the most positive emotion. For example, the moment when the other person makes a positive statement can be identified. The emotion estimation function can also be used to suggest a way to use humor at the moment when the other person shows the most positive emotion. For example, a way to tell a joke the moment the other person smiles can be suggested. This allows the user to identify the moment in a conversation when the other person shows the most positive emotion, and suggest a way to use humor at that timing.

[0076] The training unit can use the emotion estimation function to provide relaxation techniques to reduce the tension and anxiety the user feels during a conversation. For example, the emotion estimation function can be used to monitor the tension and anxiety the user feels during a conversation in real time and provide relaxation techniques. For example, the training unit can teach deep breathing or meditation techniques. The emotion estimation function can also be used to suggest specific methods to reduce the tension and anxiety the user feels during a conversation. For example, the training unit can provide relaxing music or videos. Furthermore, the emotion estimation function can be used to identify the cause of the user's tension or anxiety and provide relaxation techniques to address it. For example, the training unit can provide techniques to reduce the tension the user feels in a specific situation. This makes it possible to provide relaxation techniques to reduce the tension and anxiety the user feels during a conversation.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The personality information collection unit collects information about the user's personality, hobbies, and interests. For example, the personality information collection unit collects the user's questionnaire responses and analyzes their personality traits. The personality information collection unit can also analyze the user's past behavioral data to identify their hobbies and interests. Furthermore, the personality information collection unit can analyze the user's social media posts to extract their interests. For example, the personality information collection unit can identify their hobbies based on topics that the user frequently mentions. Step 2: The content generation unit generates jokes and episodes based on the information collected by the personality information collection unit. For example, the content generation unit uses a generation AI to generate jokes related to the user's hobbies. The content generation unit can also use the generation AI to generate episodes based on the user's interests. Furthermore, the content generation unit can also use the generation AI to generate humor that matches the user's personality. For example, the content generation unit generates appropriate jokes by using the generation AI to take into account the user's personality traits. Step 3: The training unit uses the jokes and episodes generated by the content generation unit to train the user on how to engage in humorous conversation, the timing of doing so, and how to read reactions. For example, the training unit teaches the user how to insert jokes at the appropriate time. The training unit can also teach the user how to read the other person's reactions and continue the conversation. Furthermore, the training unit can train the user on how to engage in humorous conversation. For example, the training unit suggests to the user the timing to incorporate humor into a conversation.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The 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.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 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.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] 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.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] 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.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 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 personality information collection unit that collects information about the user's personality, hobbies, and interests; a content generation unit that generates jokes and episodes based on the information collected by the personality information collection unit; a training unit that uses jokes and episodes generated by the content generation unit to train how to have humorous conversations, the timing of such conversations, and how to read reactions. A system characterized by:

2. The personality information collection unit Analyzing the user's past SNS posts and message history to generate a more accurate profile of the user's personality, hobbies, and interests 2. The system of claim 1.

3. The content generation unit The content based on the user's personality, hobbies, and interests is also provided in audio or video format, allowing the user to enjoy the content visually or aurally.

2. The system of claim 1.

4. The training section Analyze the user's past conversation data, extract specific conversation patterns and phrases, and use them for training.

2. The system of claim 1.

5. The content generation unit Using an emotion estimation function, the system predicts how the user will feel about specific content and provides content that elicits positive emotions preferentially.

2. The system of claim 1.

6. The training section Using an emotion estimation function, the emotional response of the other person when the user engages in a humorous conversation is predicted and appropriate feedback is provided.

2. The system of claim 1.

7. The training section Analyzing the user's conversation data in real time and providing specific advice on how to use humor at the appropriate timing 2. The system of claim 1.

8. The training section Using the emotion estimation function, the user is provided with relaxation techniques to reduce tension and anxiety felt during conversation.

2. The system of claim 1.

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