System

Abstract

An object of a system according to an embodiment is to enable a user who uses a dating site or an application to effectively perform pre-face-to-face communication or speaking.SOLUTION: A system according to an embodiment includes a conversation substitute part, a speech substitute part, a profile analysis part, a reaction analysis part, and a date plan proposal part. The conversation substitute unit generates a conversation content based on a user's instruction. The dictation acting part generates a dictation phrase on the basis of the conversation content generated by the conversation acting part. The profile analysis unit analyzes a profile of a user. The reaction analysis unit analyzes the reaction of the other party. The date plan proposing section proposes a date plan.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 made it difficult for users of dating sites and apps to effectively communicate and flirt with others before meeting in person.

[0005] The system according to the embodiment aims to enable users of dating sites and apps to effectively communicate and flirt with each other before meeting in person. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation agent unit, a flirting agent unit, a profile analysis unit, a reaction analysis unit, and a date plan suggestion unit. The conversation agent unit generates conversation content based on instructions from a user. The flirting agent unit generates flirting lines based on the conversation content generated by the conversation agent unit. The profile analysis unit analyzes the user's profile. The reaction analysis unit analyzes the other person's reaction. The date plan suggestion unit suggests a date plan. [Effects of the Invention]

[0007] The system according to the embodiment enables users of dating sites and apps to effectively communicate and flirt before meeting in person. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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) A communication proxy system according to an embodiment of the present invention is a system that supports a user in smoothly communicating and persuading a person before meeting. As a result, the communication proxy system enables the user to smoothly communicate and persuade a person before meeting and build a relationship with the other person.

[0029] The communication proxy system according to the embodiment includes a conversation proxy unit, a flirting proxy unit, a profile analysis unit, a response analysis unit, and a date plan suggestion unit. The conversation proxy unit generates conversation content based on a user's instructions. For example, when a user inputs an instruction such as "I want to talk about my hobbies" into the generation AI, the generation AI generates appropriate conversation content based on the instruction and sends it to the other party. The generation AI generates conversation content using a text generation AI (e.g., LLM). The generation AI can also generate conversation content using a multimodal generation AI. The generation AI can also learn the user's past conversation history and generate conversations that reflect the user's personality. The flirting proxy unit generates flirting lines based on the user's instructions. For example, when a user inputs an instruction such as "I want to ask you out on a date" into the generation AI, the generation AI generates appropriate flirting lines based on the instruction and sends them to the other party. The generation AI can also learn the user's past successes and failures to generate the most effective flirting lines. The profile analysis unit analyzes the user's profile. For example, the generation AI analyzes a user's social media accounts and automatically generates optimal profile information. The generation AI can also analyze the user's past message history and generate the most appealing profile text. The reaction analysis unit analyzes the other party's reactions. For example, the generation AI analyzes the emotional tone of the other party's messages and provides feedback on the other party's emotional state toward the user. The generation AI can also analyze the frequency and timing of the other party's messages to evaluate the other party's level of interest. The date plan suggestion unit suggests date plans. For example, the generation AI generates customized date plans based on the user and the other party's shared hobbies and interests. The generation AI can also analyze the user and the other party's geographic location information to suggest optimal date spots. This allows the communication proxy system according to the embodiment to smoothly communicate and flirt with the other party before meeting and build a relationship with the other party. For example, even users who are not good at conversation can approach the other party with confidence with the support of the generation AI. Furthermore, analyzing the other party's reactions can provide information to use as reference for the next conversation.Furthermore, by suggesting the best date plan, you can smoothly proceed with planning your date.

[0030] The conversation agent unit can learn the user's past conversation history and generate conversations that reflect the user's personality. For example, the conversation agent unit uses a generation AI to analyze the user's past conversation history and learn the user's speaking style and vocabulary. For example, it identifies phrases and expressions that the user frequently uses and reflects them in the conversation. The conversation agent unit also uses a generation AI to generate conversations that reflect the user's personality based on the user's past conversation history. For example, if the user likes humor, it will insert appropriate humor into the conversation. This makes it possible to generate conversations that reflect the user's personality.

[0031] The conversation agent can insert appropriate humor or jokes depending on the content of the conversation. For example, the generation AI analyzes the context of the conversation and inserts humor or jokes at the appropriate time. For example, if the other person sends a smiling emoji, the generation AI can return a light-hearted joke. The conversation agent can also insert appropriate humor or jokes depending on the content of the conversation. For example, if the other person is nervous, it can provide humor to relax them. This makes the conversation more natural and enjoyable.

[0032] The conversation agent unit can automatically suggest topics related to the user's hobbies and interests. For example, the generation AI in the conversation agent unit analyzes the user's profile information and suggests topics related to the user's hobbies and interests during the conversation. For example, if the user likes music, the latest music news will be the topic of conversation. The conversation agent unit also automatically suggests related topics during the conversation based on the user's hobbies and interests. For example, if the user likes sports, the latest sports news will be the topic of conversation. This makes it possible to automatically suggest topics to liven up the conversation.

[0033] The conversation proxy unit can automatically translate the content of the conversation and support communication between users who speak different languages. For example, the conversation proxy unit uses a generation AI to translate the content of the conversation in real time, supporting communication between users who speak different languages. For example, it realizes a smooth conversation between a user who speaks Japanese and a user who speaks English. The conversation proxy unit can also use a generation AI to automatically translate the content of the conversation and support communication between users who speak different languages. For example, it realizes a smooth conversation between a user who speaks French and a user who speaks Spanish. This makes it possible to support communication between users who speak different languages.

[0034] The pick-up agent unit can learn from the user's past successes and failures and generate the most effective pick-up lines. For example, the pick-up agent unit uses a generation AI to analyze the user's past successes and failures in pick-up attempts and generate the most effective pick-up lines. For example, it learns the patterns of successful pick-up lines and creates new pick-up lines based on those. The pick-up agent unit also uses a generation AI to generate the most effective pick-up lines based on the user's past successes and failures. For example, it creates new lines that avoid the patterns of unsuccessful pick-up lines. This allows the generation of the most effective pick-up lines.

[0035] The pick-up agent unit can analyze the profile information of the other party and generate pick-up lines that match the other party's interests and values. In the pick-up agent unit, for example, the generation AI analyzes the profile information of the other party and generates pick-up lines that match the other party's interests and values. For example, if the other party likes to travel, it will create pick-up lines that incorporate topics related to travel. In addition, the pick-up agent unit generates pick-up lines that match the other party's interests and values ​​based on the profile information of the other party using the generation AI. For example, if the other party likes music, it will create pick-up lines that incorporate topics related to music. In this way, it is possible to generate pick-up lines that match the other party's interests and values.

[0036] The pick-up agent unit can analyze the tone and pace of the user's voice and send pick-up lines at the optimal timing. For example, the generation AI of the pick-up agent unit analyzes the tone and pace of the user's voice and sends pick-up lines at the optimal timing. For example, lines are sent when the user is relaxed. The pick-up agent unit also sends pick-up lines at the optimal timing based on the tone and pace of the user's voice. For example, calm lines are sent when the user is excited. This allows pick-up lines to be sent at the optimal timing.

[0037] The seduction agent unit can propose an optimal seduction scenario based on the user's past dating experiences. For example, the seduction agent unit uses a generation AI to analyze the user's past dating experiences and propose an optimal seduction scenario. For example, it learns successful dating patterns and creates a new scenario based on them. The seduction agent unit also uses a generation AI to propose an optimal seduction scenario based on the user's past dating experiences. For example, it creates a new scenario that avoids unsuccessful dating patterns. This makes it possible to propose an optimal seduction scenario.

[0038] The profile analysis unit can analyze a user's social media accounts and automatically generate optimal profile information. In the profile analysis unit, for example, a generation AI analyzes a user's social media accounts and automatically generates optimal profile information. For example, an attractive profile statement is created based on the user's posted content and photos. In addition, the profile analysis unit can automatically generate optimal profile information based on a user's social media accounts using a generation AI. For example, an attractive profile statement is created based on the user's follower information and number of likes. This allows optimal profile information to be automatically generated.

[0039] The profile analysis unit can analyze a user's past message history and generate the most attractive profile text. For example, the profile analysis unit uses a generation AI to analyze a user's past message history and generate the most attractive profile text. For example, it incorporates positive expressions used by the user in the past. The profile analysis unit also uses a generation AI to generate the most attractive profile text based on the user's past message history. For example, it learns the patterns of messages that the user has used successfully in the past and creates a new profile text based on that. This allows the generation of the most attractive profile text.

[0040] The profile analysis unit can analyze the user's photos and suggest the most attractive profile picture. For example, the generation AI in the profile analysis unit analyzes the user's photos and suggests the most attractive profile picture. For example, it may prioritize selecting photos of the user smiling. The profile analysis unit can also suggest the most attractive profile picture based on the user's photos using the generation AI. For example, it may analyze the user's facial expressions and background and select the most attractive photo. This allows it to suggest the most attractive profile picture.

[0041] The profile analysis unit can suggest recommended activities and interests to add to the profile based on the user's hobbies and interests. In the profile analysis unit, for example, the generation AI analyzes the user's hobbies and interests and suggests recommended activities and interests to add to the profile. For example, if the user likes the outdoors, hiking and camping are suggested. In addition, the profile analysis unit can suggest recommended activities and interests to add to the profile based on the user's hobbies and interests. For example, if the user likes cooking, cooking classes and recipes are suggested. This makes it possible to suggest recommended activities and interests to add to the profile.

[0042] The reaction analysis unit can analyze the frequency and timing of the other person's messages and evaluate the other person's level of interest. For example, the generation AI analyzes the frequency and timing of the other person's messages and evaluates the other person's level of interest. For example, if the other person sends messages frequently, it indicates a high level of interest. The reaction analysis unit can also evaluate the other person's level of interest based on the frequency and timing of the other person's messages. For example, if the other person replies at short intervals, it indicates a high level of interest. This makes it possible to evaluate the other person's level of interest.

[0043] The reaction analysis unit analyzes the content of the other person's message and can suggest topics related to the other person's hobbies and interests. For example, the generation AI analyzes the content of the other person's message and suggests topics related to the other person's hobbies and interests. For example, if the other person likes sports, the latest sports news will be the topic of conversation. The reaction analysis unit also uses the generation AI to suggest topics related to the other person's hobbies and interests based on the content of the other person's message. For example, if the other person likes music, the latest music news will be the topic of conversation. This makes it possible to suggest topics related to the other person's hobbies and interests.

[0044] The reaction analysis unit can analyze the writing style and language of the other party's message and provide feedback on the other party's impression of the user. For example, the generation AI can analyze the writing style and language of the other party's message and provide feedback on the other party's impression of the user. For example, if the other party uses polite language, the reaction analysis unit can convey that the other party has a favorable impression of the user. The reaction analysis unit can also provide feedback on the other party's impression of the user based on the writing style and language of the other party's message. For example, if the other party uses casual language, the reaction analysis unit can convey that the other party is relaxed. This allows feedback on the other party's impression of the user.

[0045] The date plan suggestion unit can generate a customized date plan based on the common hobbies and interests of the user and the other person. For example, the generation AI of the date plan suggestion unit analyzes the common hobbies and interests of the user and the other person and generates a customized date plan. For example, if the common hobby is cooking, it will suggest a date plan to go to a cooking class. Furthermore, the date plan suggestion unit generates a customized date plan based on the common hobbies and interests of the user and the other person. For example, if the common hobby is watching movies, it will suggest a date plan to go to the movie theater. In this way, a customized date plan can be generated.

[0046] The date plan suggestion unit can analyze the geographical location information of the user and the other party and suggest the best date spot. For example, the generation AI in the date plan suggestion unit analyzes the geographical location information of the user and the other party and suggests the best date spot. For example, it can suggest a cafe or restaurant that is halfway between the two parties. The date plan suggestion unit can also suggest the best date spot based on the geographical location information of the user and the other party. For example, it can suggest a park or tourist spot near where both parties live. This makes it possible to suggest the best date spot.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The communication proxy system may further include a voice analysis unit that analyzes the tone and pace of the user's voice. For example, if the user is nervous, the voice analysis unit provides advice to relax. The voice analysis unit also provides guidance to advance the conversation at the optimal timing based on the user's tone and pace of voice. For example, if the user is speaking too quickly, the voice analysis unit advises the user to speak more slowly. This allows the user to have a more natural conversation.

[0049] The communication proxy system can further include a history analysis unit that analyzes the user's past message history and proposes the most effective conversation pattern. The history analysis unit, for example, learns the user's past successful conversation patterns and proposes new conversation patterns based on them. The history analysis unit also proposes new conversation patterns that avoid the user's past failures. This allows the user to have more effective conversations.

[0050] The communication proxy system can further include a hobby suggestion unit that automatically suggests related topics during a conversation based on the user's hobbies and interests. For example, if the user likes music, the hobby suggestion unit may suggest the latest music news as a topic of conversation. The hobby suggestion unit also automatically suggests related topics during a conversation based on the user's hobbies and interests. For example, if the user likes sports, the hobby suggestion unit may suggest the latest sports news as a topic of conversation. This makes it possible to automatically suggest topics that will liven up the conversation.

[0051] The communication proxy system may further include a voice analysis unit that analyzes the tone and pace of the user's voice and transmits pick-up lines at the optimal timing. The voice analysis unit may, for example, advise the user to transmit pick-up lines when the user is relaxed. The voice analysis unit may also transmit pick-up lines at the optimal timing based on the tone and pace of the user's voice. This allows the pick-up lines to be transmitted at the optimal timing.

[0052] The communication proxy system can further include a dating experience analysis unit that proposes an optimal seduction scenario based on the user's past dating experiences. The dating experience analysis unit, for example, learns successful dating patterns and creates a new scenario based on them. The dating experience analysis unit also creates a new scenario that avoids unsuccessful dating patterns. This allows the system to propose an optimal seduction scenario.

[0053] The communication agent system may further include a photo analysis unit that analyzes the user's photos and suggests the most attractive profile photo. For example, the photo analysis unit may preferentially select photos of the user smiling. The photo analysis unit may also analyze the user's facial expression and background to select the most attractive photo. This allows the system to suggest the most attractive profile photo.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The conversation agent generates conversation content based on the user's instructions. For example, if the user inputs an instruction such as "I want to talk about my hobbies" into the generation AI, the generation AI will generate appropriate conversation content based on that instruction and send it to the other party. The generation AI generates conversation content using a text generation AI (e.g., LLM). The generation AI can also generate conversation content using a multimodal generation AI. The generation AI can also learn the user's past conversation history and generate conversation that reflects the user's personality. Step 2: The pick-up agent generates pick-up lines based on the user's instructions. For example, if the user inputs an instruction such as "I want to ask you out on a date," the generation AI will generate appropriate pick-up lines based on that instruction and send them to the other person. The generation AI can also learn from the user's past successes and failures to generate the most effective pick-up lines. Step 3: The profile analysis unit analyzes the user's profile. For example, the generation AI can analyze the user's social media accounts and automatically generate the most suitable profile information. The generation AI can also analyze the user's past messaging history and generate the most attractive profile text. Step 4: The reaction analysis unit analyzes the other person's reaction. For example, the generation AI analyzes the emotional tone of the other person's message and provides feedback on the other person's emotional state toward the user. The generation AI can also analyze the frequency and timing of the other person's messages to evaluate the other person's level of interest. Step 5: The date plan suggestion unit proposes a date plan. For example, the generation AI generates a customized date plan based on the shared hobbies and interests of the user and the other person. The generation AI can also analyze the geographical location information of the user and the other person to suggest the best date spots.

[0056] (Example 2) A communication proxy system according to an embodiment of the present invention is a system that supports a user in smoothly communicating and persuading a person before meeting. As a result, the communication proxy system enables the user to smoothly communicate and persuade a person before meeting and build a relationship with the other person.

[0057] The communication proxy system according to the embodiment includes a conversation proxy unit, a flirting proxy unit, a profile analysis unit, a response analysis unit, and a date plan suggestion unit. The conversation proxy unit generates conversation content based on a user's instructions. For example, when a user inputs an instruction such as "I want to talk about my hobbies" into the generation AI, the generation AI generates appropriate conversation content based on the instruction and sends it to the other party. The generation AI generates conversation content using a text generation AI (e.g., LLM). The generation AI can also generate conversation content using a multimodal generation AI. The generation AI can also learn the user's past conversation history and generate conversations that reflect the user's personality. The flirting proxy unit generates flirting lines based on the user's instructions. For example, when a user inputs an instruction such as "I want to ask you out on a date" into the generation AI, the generation AI generates appropriate flirting lines based on the instruction and sends them to the other party. The generation AI can also learn the user's past successes and failures to generate the most effective flirting lines. The profile analysis unit analyzes the user's profile. For example, the generation AI analyzes a user's social media accounts and automatically generates optimal profile information. The generation AI can also analyze the user's past message history and generate the most appealing profile text. The reaction analysis unit analyzes the other party's reactions. For example, the generation AI analyzes the emotional tone of the other party's messages and provides feedback on the other party's emotional state toward the user. The generation AI can also analyze the frequency and timing of the other party's messages to evaluate the other party's level of interest. The date plan suggestion unit suggests date plans. For example, the generation AI generates customized date plans based on the user and the other party's shared hobbies and interests. The generation AI can also analyze the user and the other party's geographic location information to suggest optimal date spots. This allows the communication proxy system according to the embodiment to smoothly communicate and flirt with the other party before meeting and build a relationship with the other party. For example, even users who are not good at conversation can approach the other party with confidence with the support of the generation AI. Furthermore, analyzing the other party's reactions can provide information to use as reference for the next conversation.Furthermore, by suggesting the best date plan, you can smoothly proceed with planning your date.

[0058] The conversation agent unit can learn the user's past conversation history and generate conversations that reflect the user's personality. For example, the conversation agent unit uses a generation AI to analyze the user's past conversation history and learn the user's speaking style and vocabulary. For example, it identifies phrases and expressions that the user frequently uses and reflects them in the conversation. The conversation agent unit also uses a generation AI to generate conversations that reflect the user's personality based on the user's past conversation history. For example, if the user likes humor, it will insert appropriate humor into the conversation. This makes it possible to generate conversations that reflect the user's personality.

[0059] The conversation agent can insert appropriate humor or jokes depending on the content of the conversation. For example, the generation AI analyzes the context of the conversation and inserts humor or jokes at the appropriate time. For example, if the other person sends a smiling emoji, the generation AI can return a light-hearted joke. The conversation agent can also insert appropriate humor or jokes depending on the content of the conversation. For example, if the other person is nervous, it can provide humor to relax them. This makes the conversation more natural and enjoyable.

[0060] The conversation agent unit can use the emotion estimation function to estimate the emotional state of the other party in real time and generate conversation content that corresponds to that emotion. For example, the conversation agent unit's generation AI analyzes the emotional tone of the other party's message, and if the other party has positive emotions, it will offer more positive topics. For example, if the other party is happy, it will continue with happy topics. The conversation agent unit also uses the generation AI to estimate the other party's emotional state in real time and generate conversation content that corresponds to that emotion. For example, if the other party is sad, it will offer comforting topics. This makes it possible to generate conversation content that corresponds to the other party's emotions.

[0061] The conversation agent unit can automatically suggest topics related to the user's hobbies and interests. For example, the generation AI in the conversation agent unit analyzes the user's profile information and suggests topics related to the user's hobbies and interests during the conversation. For example, if the user likes music, the latest music news will be the topic of conversation. The conversation agent unit also automatically suggests related topics during the conversation based on the user's hobbies and interests. For example, if the user likes sports, the latest sports news will be the topic of conversation. This makes it possible to automatically suggest topics to liven up the conversation.

[0062] The conversation proxy unit can automatically translate the content of the conversation and support communication between users who speak different languages. For example, the conversation proxy unit uses a generation AI to translate the content of the conversation in real time, supporting communication between users who speak different languages. For example, it realizes a smooth conversation between a user who speaks Japanese and a user who speaks English. The conversation proxy unit can also use a generation AI to automatically translate the content of the conversation and support communication between users who speak different languages. For example, it realizes a smooth conversation between a user who speaks French and a user who speaks Spanish. This makes it possible to support communication between users who speak different languages.

[0063] The conversation agent unit can use the emotion estimation function to suggest relaxing topics to reduce the stress and anxiety the user feels during a conversation. For example, the conversation agent unit uses the emotion estimation function to detect the stress or anxiety the user feels during a conversation and suggest relaxing topics. For example, when the user is feeling nervous, the conversation agent unit suggests topics about hobbies that will help them relax. In addition, the conversation agent unit uses the generation AI to analyze the user's emotional state in real time, and when the user is feeling stressed, suggests relaxing topics. For example, when the user is feeling anxious, the conversation agent unit suggests relaxing topics. This reduces the stress and anxiety the user feels during a conversation.

[0064] The pick-up agent unit can learn from the user's past successes and failures and generate the most effective pick-up lines. For example, the pick-up agent unit uses a generation AI to analyze the user's past successes and failures in pick-up attempts and generate the most effective pick-up lines. For example, it learns the patterns of successful pick-up lines and creates new pick-up lines based on those. The pick-up agent unit also uses a generation AI to generate the most effective pick-up lines based on the user's past successes and failures. For example, it creates new lines that avoid the patterns of unsuccessful pick-up lines. This allows the generation of the most effective pick-up lines.

[0065] The pick-up agent unit can analyze the profile information of the other party and generate pick-up lines that match the other party's interests and values. In the pick-up agent unit, for example, the generation AI analyzes the profile information of the other party and generates pick-up lines that match the other party's interests and values. For example, if the other party likes to travel, it will create pick-up lines that incorporate topics related to travel. In addition, the pick-up agent unit generates pick-up lines that match the other party's interests and values ​​based on the profile information of the other party using the generation AI. For example, if the other party likes music, it will create pick-up lines that incorporate topics related to music. In this way, it is possible to generate pick-up lines that match the other party's interests and values.

[0066] The pick-up agent unit can use the emotion estimation function to estimate the emotional state of the other person in real time and generate pick-up lines that correspond to those emotions. For example, the pick-up agent unit can use the emotion estimation function to analyze the emotional state of the other person in real time and generate pick-up lines that correspond to those emotions. For example, when the other person is happy, it will suggest more positive lines. In addition, the pick-up agent unit uses the generation AI to estimate the emotional state of the other person in real time and generate pick-up lines that correspond to those emotions. For example, if the other person is sad, it will suggest comforting lines. This makes it possible to generate pick-up lines that correspond to the other person's emotions.

[0067] The pick-up agent unit can analyze the tone and pace of the user's voice and send pick-up lines at the optimal timing. For example, the generation AI of the pick-up agent unit analyzes the tone and pace of the user's voice and sends pick-up lines at the optimal timing. For example, lines are sent when the user is relaxed. The pick-up agent unit also sends pick-up lines at the optimal timing based on the tone and pace of the user's voice. For example, calm lines are sent when the user is excited. This allows pick-up lines to be sent at the optimal timing.

[0068] The seduction agent unit can propose an optimal seduction scenario based on the user's past dating experiences. For example, the seduction agent unit uses a generation AI to analyze the user's past dating experiences and propose an optimal seduction scenario. For example, it learns successful dating patterns and creates a new scenario based on them. The seduction agent unit also uses a generation AI to propose an optimal seduction scenario based on the user's past dating experiences. For example, it creates a new scenario that avoids unsuccessful dating patterns. This makes it possible to propose an optimal seduction scenario.

[0069] The pick-up agent unit can use the emotion estimation function to provide positive feedback to increase the user's confidence when sending pick-up lines. The pick-up agent unit, for example, uses the emotion estimation function to provide positive feedback to increase the user's confidence when sending pick-up lines. For example, sending an encouraging message when the user is nervous. The pick-up agent unit also analyzes the user's emotional state in real time using the generation AI to provide positive feedback to increase the user's confidence. For example, when the user is feeling anxious, it presents success stories to increase the user's confidence. This can increase the user's confidence when sending pick-up lines.

[0070] The profile analysis unit can analyze a user's social media accounts and automatically generate optimal profile information. In the profile analysis unit, for example, a generation AI analyzes a user's social media accounts and automatically generates optimal profile information. For example, an attractive profile statement is created based on the user's posted content and photos. In addition, the profile analysis unit can automatically generate optimal profile information based on a user's social media accounts using a generation AI. For example, an attractive profile statement is created based on the user's follower information and number of likes. This allows optimal profile information to be automatically generated.

[0071] The profile analysis unit can analyze a user's past message history and generate the most attractive profile text. For example, the profile analysis unit uses a generation AI to analyze a user's past message history and generate the most attractive profile text. For example, it incorporates positive expressions used by the user in the past. The profile analysis unit also uses a generation AI to generate the most attractive profile text based on the user's past message history. For example, it learns the patterns of messages that the user has used successfully in the past and creates a new profile text based on that. This allows the generation of the most attractive profile text.

[0072] The profile analysis unit uses an emotion estimation function to generate a profile statement that corresponds to the user's emotional state, thereby maximizing the user's appeal. The profile analysis unit, for example, uses the emotion estimation function to generate a profile statement that corresponds to the user's emotional state. For example, when a user has positive emotions, the unit creates a profile statement that reflects those emotions. The profile analysis unit also uses a generation AI to analyze the user's emotional state in real time and generate a profile statement that maximizes the user's appeal. For example, when a user is happy, the unit creates a profile statement that reflects those emotions. This makes it possible to generate a profile statement that maximizes the user's appeal.

[0073] The profile analysis unit can analyze the user's photos and suggest the most attractive profile picture. For example, the generation AI in the profile analysis unit analyzes the user's photos and suggests the most attractive profile picture. For example, it may prioritize selecting photos of the user smiling. The profile analysis unit can also suggest the most attractive profile picture based on the user's photos using the generation AI. For example, it may analyze the user's facial expressions and background and select the most attractive photo. This allows it to suggest the most attractive profile picture.

[0074] The profile analysis unit can suggest recommended activities and interests to add to the profile based on the user's hobbies and interests. In the profile analysis unit, for example, the generation AI analyzes the user's hobbies and interests and suggests recommended activities and interests to add to the profile. For example, if the user likes the outdoors, hiking and camping are suggested. In addition, the profile analysis unit can suggest recommended activities and interests to add to the profile based on the user's hobbies and interests. For example, if the user likes cooking, cooking classes and recipes are suggested. This makes it possible to suggest recommended activities and interests to add to the profile.

[0075] The profile analysis unit can use the emotion estimation function to provide guidance to help the user reduce stress when creating a profile. The profile analysis unit, for example, uses the emotion estimation function to provide guidance to help the user reduce stress when creating a profile. For example, it provides advice to relax when the user is feeling nervous. In addition, the profile analysis unit uses the generation AI to analyze the user's emotional state in real time, and provides guidance to relax when the user is feeling stressed. For example, it provides advice to relax when the user is feeling anxious. This helps reduce stress when the user creates a profile.

[0076] The reaction analysis unit can analyze the emotional tone of the other party's message and provide feedback on the other party's emotional state toward the user. In the reaction analysis unit, for example, the generation AI analyzes the emotional tone of the other party's message and provides feedback on the other party's emotional state toward the user. For example, if the other party has positive emotions, that information is conveyed to the user. In addition, the reaction analysis unit can feed back the other party's emotional state toward the user based on the emotional tone of the other party's message. For example, if the other party has negative emotions, that information is conveyed to the user. In this way, the other party's emotional state toward the user can be provided as feedback.

[0077] The reaction analysis unit can analyze the frequency and timing of the other person's messages and evaluate the other person's level of interest. For example, the generation AI analyzes the frequency and timing of the other person's messages and evaluates the other person's level of interest. For example, if the other person sends messages frequently, it indicates a high level of interest. The reaction analysis unit can also evaluate the other person's level of interest based on the frequency and timing of the other person's messages. For example, if the other person replies at short intervals, it indicates a high level of interest. This makes it possible to evaluate the other person's level of interest.

[0078] The reaction analysis unit can use the emotion estimation function to suggest the next conversation topic based on the other person's emotional state. The reaction analysis unit, for example, uses the emotion estimation function to suggest the next conversation topic based on the other person's emotional state. For example, if the other person is feeling positive, it will suggest a fun topic. The reaction analysis unit also uses the generation AI to analyze the other person's emotional state in real time and suggest the next conversation topic. For example, if the other person is feeling negative, it will suggest a comforting topic. This makes it possible to suggest the next conversation topic based on the other person's emotional state.

[0079] The reaction analysis unit analyzes the content of the other person's message and can suggest topics related to the other person's hobbies and interests. For example, the generation AI analyzes the content of the other person's message and suggests topics related to the other person's hobbies and interests. For example, if the other person likes sports, the latest sports news will be the topic of conversation. The reaction analysis unit also uses the generation AI to suggest topics related to the other person's hobbies and interests based on the content of the other person's message. For example, if the other person likes music, the latest music news will be the topic of conversation. This makes it possible to suggest topics related to the other person's hobbies and interests.

[0080] The reaction analysis unit can analyze the writing style and language of the other party's message and provide feedback on the other party's impression of the user. For example, the generation AI can analyze the writing style and language of the other party's message and provide feedback on the other party's impression of the user. For example, if the other party uses polite language, the reaction analysis unit can convey that the other party has a favorable impression of the user. The reaction analysis unit can also provide feedback on the other party's impression of the user based on the writing style and language of the other party's message. For example, if the other party uses casual language, the reaction analysis unit can convey that the other party is relaxed. This allows feedback on the other party's impression of the user.

[0081] The reaction analysis unit uses the emotion estimation function to monitor the other person's emotional reactions in real time and suggest the most appropriate reply to the user. The reaction analysis unit, for example, uses the emotion estimation function to monitor the other person's emotional reactions in real time and suggest the most appropriate reply to the user. For example, if the other person is feeling positive, it will suggest an even more positive reply. The reaction analysis unit also uses the generation AI to analyze the other person's emotional reactions in real time and suggest the most appropriate reply to the user. For example, if the other person is feeling negative, it will suggest a comforting reply. This makes it possible to monitor the other person's emotional reactions in real time and suggest the most appropriate reply.

[0082] The date plan suggestion unit can generate a customized date plan based on the common hobbies and interests of the user and the other person. For example, the generation AI of the date plan suggestion unit analyzes the common hobbies and interests of the user and the other person and generates a customized date plan. For example, if the common hobby is cooking, it will suggest a date plan to go to a cooking class. Furthermore, the date plan suggestion unit generates a customized date plan based on the common hobbies and interests of the user and the other person. For example, if the common hobby is watching movies, it will suggest a date plan to go to the movie theater. In this way, a customized date plan can be generated.

[0083] The date plan suggestion unit can use the emotion estimation function to suggest a relaxed date plan that matches the emotional state of the user and the other person. For example, the date plan suggestion unit uses the emotion estimation function to suggest a relaxed date plan that matches the emotional state of the user and the other person. For example, when both parties are relaxed, it suggests a date plan at a relaxing cafe. In addition, the date plan suggestion unit uses the generation AI to analyze the emotional states of the user and the other party in real time and suggest a relaxed date plan. For example, when both parties are tense, it suggests a date plan at a relaxing park. This makes it possible to suggest a relaxed date plan.

[0084] The date plan suggestion unit can analyze the geographical location information of the user and the other party and suggest the best date spot. For example, the generation AI in the date plan suggestion unit analyzes the geographical location information of the user and the other party and suggests the best date spot. For example, it can suggest a cafe or restaurant that is halfway between the two parties. The date plan suggestion unit can also suggest the best date spot based on the geographical location information of the user and the other party. For example, it can suggest a park or tourist spot near where both parties live. This makes it possible to suggest the best date spot.

[0085] The date plan suggestion unit can use the emotion estimation function to provide feedback on the date plan based on the emotional states of the user and the other person. The date plan suggestion unit, for example, uses the emotion estimation function to provide feedback on the date plan based on the emotional states of the user and the other person. For example, the emotional states of both parties can be analyzed after a date and reflected in the next date plan. In addition, the date plan suggestion unit uses the generation AI to analyze the emotional states of the user and the other party in real time and provide feedback on the date plan. For example, the emotional states of both parties can be monitored during a date and feedback can be provided in real time. This makes it possible to provide feedback on the date plan.

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

[0087] The communication proxy system may further include a voice analysis unit that analyzes the tone and pace of the user's voice. For example, if the user is nervous, the voice analysis unit provides advice to relax. The voice analysis unit also provides guidance to advance the conversation at the optimal timing based on the user's tone and pace of voice. For example, if the user is speaking too quickly, the voice analysis unit advises the user to speak more slowly. This allows the user to have a more natural conversation.

[0088] The communication proxy system can further include a history analysis unit that analyzes the user's past message history and proposes the most effective conversation pattern. The history analysis unit, for example, learns the user's past successful conversation patterns and proposes new conversation patterns based on them. The history analysis unit also proposes new conversation patterns that avoid the user's past failures. This allows the user to have more effective conversations.

[0089] The communication proxy system can further include a hobby suggestion unit that automatically suggests related topics during a conversation based on the user's hobbies and interests. For example, if the user likes music, the hobby suggestion unit may suggest the latest music news as a topic of conversation. The hobby suggestion unit also automatically suggests related topics during a conversation based on the user's hobbies and interests. For example, if the user likes sports, the hobby suggestion unit may suggest the latest sports news as a topic of conversation. This makes it possible to automatically suggest topics that will liven up the conversation.

[0090] The communication proxy system can further include an emotion monitoring unit that monitors the user's emotional state in real time and provides advice to help the user relax if the user feels stressed. For example, the emotion monitoring unit advises the user to take a deep breath if the user feels nervous. The emotion monitoring unit also suggests topics to help the user relax based on the user's emotional state. This reduces the stress the user feels during conversation.

[0091] The communication proxy system may further include a timing suggestion unit that suggests the optimal timing for a conversation based on the user's emotional state. The timing suggestion unit advises the user to start a conversation when the user is relaxed, for example. The timing suggestion unit also provides guidance for advancing the conversation at the optimal timing based on the user's emotional state. This allows the user to have a more natural conversation.

[0092] The communication proxy system may further include a voice analysis unit that analyzes the tone and pace of the user's voice and transmits pick-up lines at the optimal timing. The voice analysis unit may, for example, advise the user to transmit pick-up lines when the user is relaxed. The voice analysis unit may also transmit pick-up lines at the optimal timing based on the tone and pace of the user's voice. This allows the pick-up lines to be transmitted at the optimal timing.

[0093] The communication proxy system can further include a dating experience analysis unit that proposes an optimal seduction scenario based on the user's past dating experiences. The dating experience analysis unit, for example, learns successful dating patterns and creates a new scenario based on them. The dating experience analysis unit also creates a new scenario that avoids unsuccessful dating patterns. This allows the system to propose an optimal seduction scenario.

[0094] The communication agent system may further include a confidence-boosting unit that analyzes the user's emotional state in real time and provides positive feedback to help the user feel more confident. For example, the confidence-boosting unit may send an encouraging message when the user feels nervous. The confidence-boosting unit may also present success stories based on the user's emotional state to boost the user's confidence. This may increase the user's confidence when sending pick-up lines.

[0095] The communication agent system may further include a photo analysis unit that analyzes the user's photos and suggests the most attractive profile photo. For example, the photo analysis unit may preferentially select photos of the user smiling. The photo analysis unit may also analyze the user's facial expression and background to select the most attractive photo. This allows the system to suggest the most attractive profile photo.

[0096] The communication proxy system may further include an emotion guidance unit that analyzes the user's emotional state in real time and provides guidance to reduce stress when the user creates a profile. For example, the emotion guidance unit provides advice to help the user relax when they are feeling tense. The emotion guidance unit also provides guidance to help the user relax based on the user's emotional state. This reduces stress when the user creates a profile.

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

[0098] Step 1: The conversation agent generates conversation content based on the user's instructions. For example, if the user inputs an instruction such as "I want to talk about my hobbies" into the generation AI, the generation AI will generate appropriate conversation content based on that instruction and send it to the other party. The generation AI generates conversation content using a text generation AI (e.g., LLM). The generation AI can also generate conversation content using a multimodal generation AI. The generation AI can also learn the user's past conversation history and generate conversation that reflects the user's personality. Step 2: The pick-up agent generates pick-up lines based on the user's instructions. For example, if the user inputs an instruction such as "I want to ask you out on a date," the generation AI will generate appropriate pick-up lines based on that instruction and send them to the other person. The generation AI can also learn from the user's past successes and failures to generate the most effective pick-up lines. Step 3: The profile analysis unit analyzes the user's profile. For example, the generation AI can analyze the user's social media accounts and automatically generate the most suitable profile information. The generation AI can also analyze the user's past messaging history and generate the most attractive profile text. Step 4: The reaction analysis unit analyzes the other person's reaction. For example, the generation AI analyzes the emotional tone of the other person's message and provides feedback on the other person's emotional state toward the user. The generation AI can also analyze the frequency and timing of the other person's messages to evaluate the other person's level of interest. Step 5: The date plan suggestion unit proposes a date plan. For example, the generation AI generates a customized date plan based on the shared hobbies and interests of the user and the other person. The generation AI can also analyze the geographical location information of the user and the other person to suggest the best date spots.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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 conversation proxy unit that generates conversation content based on a user's instruction; a pick-up line agent that generates pick-up lines based on the conversation content generated by the conversation agent unit; a profile analysis unit that analyzes a user's profile; A reaction analysis unit that analyzes the other person's reaction, A date plan suggestion unit that suggests a date plan is provided. A system characterized by:

2. The conversation proxy unit The emotional state of the other person is estimated in real time using an emotion estimation function, and the conversation content is generated according to that emotion.

2. The system of claim 1.

3. The persuasion agency unit The emotional state of the other person is estimated in real time using an emotion estimation function, and pick-up lines are generated according to that emotion.

2. The system of claim 1.

4. The profile analysis unit Using an emotion estimation function, a profile sentence is generated according to the emotional state of the user, thereby maximizing the user's appeal.

2. The system of claim 1.

5. The reaction analysis unit Using emotion estimation function, suggest the next conversation based on the other person's emotional state 2. The system of claim 1.

6. The date plan suggestion unit Using an emotion estimation function, a relaxed date plan is proposed according to the emotional state of the user and the other person.

2. The system of claim 1.

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