System

Abstract

An object of the system according to the embodiment is to dynamically generate a dialogue with a player and a scenario and realize a realistic emotional expression.SOLUTION: A system according to an embodiment includes a dialogue generation unit, a scenario generation unit, and an emotion expression unit. The dialogue generator generates a dialogue with the player in real time. The scenario generation unit dynamically generates a scenario in accordance with a player's selection or action. The emotion expression unit generates a dialogue and an action for realistically expressing the emotion of the character.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] With conventional technology, romance simulation games had the problem that the dialogue with the player and the scenario were fixed, making it difficult to express realistic emotions.

[0005] The system according to the embodiment aims to dynamically generate dialogues and scenarios with the player, and to realize realistic emotional expression. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue generation unit, a scenario generation unit, and an emotion expression unit. The dialogue generation unit generates dialogue with the player in real time. The scenario generation unit dynamically generates a scenario according to the player's choices and actions. The emotion expression unit generates dialogue and actions to realistically express the character's emotions. [Effects of the Invention]

[0007] The system according to the embodiment can dynamically generate dialogues with the player and scenarios, and can realize realistic emotional expression. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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 dating simulation game according to an embodiment of the present invention is a system equipped with a generation AI, in which the player aims to build a romantic relationship with a virtual character. This system uses the generation AI to generate dialogue with the player in real time, dynamically generate a scenario based on the player's choices and actions, and realistically express the character's emotions. This allows the dating simulation game to provide the player with a more realistic and diverse romantic experience.

[0029] A dating simulation game according to an embodiment includes a dialogue generation unit, a scenario generation unit, and an emotion expression unit. The dialogue generation unit generates dialogue with a player in real time. For example, the generation AI generates appropriate responses for a character based on text entered by the player and options selected. The generation AI generates responses to the player's input using a text generation AI (e.g., GPT-3). The generation AI can also analyze a player's past dialogue history to generate a dialogue style optimized for each individual player. For example, a character can generate responses tailored to the player's frequently used phrases and tone. The scenario generation unit dynamically generates scenarios based on the player's choices and actions. For example, if a player selects a date with a character, the generation AI generates a scenario for that date and provides it to the player. The generation AI can also analyze the player's behavior history and predict scenarios based on past choices. For example, if a player has frequently selected dates with a specific character in the past, the generation AI generates a scenario that deepens the player's relationship with that character. The emotion expression unit generates dialogue and actions to realistically express the character's emotions. For example, the generation AI can make a character express emotions such as joy, anger, and sadness in response to the player's actions and choices. The generation AI can also use its emotion estimation function to estimate the player's emotions and generate dialogue based on those emotions. For example, if a player sends a sad message, the character can generate a comforting response. This allows the dating simulation game according to the embodiment to provide players with a more realistic and diverse dating experience. For example, players can enjoy their own unique story and experience changes in emotions as they deepen their relationships with characters. Furthermore, the multiple endings increase the enjoyment of replaying the game.

[0030] The dialogue generation unit can analyze a player's past dialogue history and generate a dialogue style optimized for each individual player. For example, the dialogue generation unit analyzes the content of the player's past dialogues and learns the player's preferences and speaking patterns. For example, the character generates a response that matches the player's frequently used phrases and tone. The dialogue generation unit also provides a character with topics that match the player's interests and concerns based on the player's dialogue history. For example, if the player previously talked about sports, the character will continue that topic. The dialogue generation unit also analyzes the player's dialogue history and learns the player's preferred dialogue style (e.g., humorous conversation or serious speech) and generates the character's responses based on that. This makes it possible to provide the player with a more personalized dialogue experience.

[0031] The dialogue generation unit can generate humorous or sarcastic responses to player statements. The dialogue generation unit generates humorous responses from a character in response to the content of a player's statement. For example, if the player makes a joke, the character responds in a way that elicits laughter. The dialogue generation unit also generates sarcastic responses from a character to a player's statement. For example, if the player says, "I don't have anything to do today," the character replies, "Then let's find something fun to do together." The dialogue generation unit also increases the variety of dialogue by generating humorous or sarcastic responses from a character to a player's statement. For example, if the player says, "I'm tired today," the character replies, "Then relax and let's talk." This increases the variety of dialogue and provides the player with a more enjoyable experience.

[0032] The dialogue generation unit can learn the player's hobbies and interests and provide topics based on them. For example, the dialogue generation unit learns the hobbies and interests that the player talks about during a dialogue, and the character provides topics based on them. For example, if the player says that they like music, the character will ask, "What kind of music have you been listening to lately?" The dialogue generation unit also learns the player's hobbies and interests, and the character will provide related topics based on them. For example, if the player learns that they like movies, the character will ask, "What movie have you seen recently?" The dialogue generation unit also allows the character to learn the player's hobbies and interests during a dialogue and provide topics based on them. For example, if the player says that they like traveling, the character will ask, "Where do you want to go next?" This makes it possible to provide a more personalized experience by providing topics based on the player's hobbies and interests.

[0033] The dialogue generation unit can generate responses according to the player's real-time environment (weather, time of day, etc.). The dialogue generation unit, for example, acquires the player's real-time environmental information (weather, time of day, etc.) and generates responses according to it. For example, on a rainy day, a character might suggest, "It's raining today, so let's relax at home." The dialogue generation unit also allows the character to engage in dialogue according to the player's real-time environment. For example, if the player is playing a game late at night, the character might say, "It's getting late, so you should get some rest now." The dialogue generation unit also allows the character to generate appropriate responses based on the player's real-time environmental information. For example, in the morning, the character might greet the player with, "Good morning! I wonder what kind of day it will be today?" This allows the player to have a more realistic experience by providing responses according to the player's real-time environment.

[0034] The scenario generation unit can increase the branching points of the scenario based on the player's selection, thereby realizing a more complex story development. The scenario generation unit, for example, increases the branching points of the scenario based on the player's selection, and generates multiple story developments. For example, if the player makes a specific choice, a new scenario develops in accordance with that choice. Furthermore, by increasing the branching points of the scenario, the scenario generation unit can realize a different story development for each player. For example, each time the player makes a different choice, a new scenario is generated. Furthermore, the scenario generation unit increases the branching points of the scenario based on the player's selection, thereby realizing a more complex story development. For example, if the player chooses to deepen their relationship with a specific character, a new scenario related to that character is generated. This makes it possible to provide a different story development for each player.

[0035] The scenario generation unit can analyze the player's behavior history and predict a scenario based on past selections. The scenario generation unit, for example, analyzes the player's past behavior history and predicts a scenario based on that data. For example, if the player has deepened their relationship with a specific character in the past, a scenario related to that character is predicted. The scenario generation unit also predicts a scenario according to past selections based on the player's behavior history. For example, it analyzes the pattern of scenarios selected by the player in the past and predicts the next scenario. The scenario generation unit also analyzes the player's behavior history and predicts a scenario based on past selections. For example, if the player frequently selects a specific event, it predicts a scenario related to that event. This makes it possible to achieve a more consistent story development by providing a scenario based on the player's past selections.

[0036] The scenario generation unit can compare the choices and actions of different players and extract common scenario patterns. The scenario generation unit, for example, compares the choices and actions of different players and extracts common scenario patterns. For example, if multiple players make the same choice, it identifies a scenario pattern based on that choice. The scenario generation unit also builds a system that compares the choices and actions of players and extracts common scenario patterns. For example, if a particular option is chosen frequently, it extracts a scenario pattern related to that option. The scenario generation unit also analyzes the actions of different players and extracts common scenario patterns. For example, if a player often makes choices that deepen their relationship with a particular character, it identifies a scenario pattern related to that character. In this way, by comparing the choices and actions of different players, it is possible to extract common scenario patterns and provide a more diverse story development.

[0037] The scenario generation unit can provide a scenario based on a real-world location, taking into account the player's real-time location information. The scenario generation unit, for example, acquires the player's real-time location information and generates a scenario based on that information. For example, if the player is in a specific location, it provides a scenario related to that location. The scenario generation unit also generates a scenario based on a real-world location, based on the player's location information. For example, if the player is in a tourist spot, it provides a scenario related to that tourist spot. The scenario generation unit also generates a scenario based on the player's location, taking into account the real-time location information. For example, if the player is in a cafe, it provides a scenario related to that cafe. In this way, by providing a scenario based on the player's real-time location information, it is possible to provide a more realistic experience.

[0038] The emotion expression unit can take into account the player's past behavioral history and provide consistent emotion expression. The emotion expression unit, for example, analyzes the player's past behavioral history and makes the character's emotion expression consistent based on that. For example, if the player has frequently praised the character in the past, the character will express affectionate emotions toward the player. The emotion expression unit also builds a system that takes into account the player's past behavioral history when the character expresses emotion. For example, if the player has ignored the character in the past, the character will be cold toward the player. The emotion expression unit also provides consistent emotion expression for the character based on the player's behavioral history. For example, if the player has behaved kindly toward the character in the past, the character will express gratitude toward the player. In this way, consistent emotion expression can be provided by taking into account the player's past behavioral history.

[0039] The emotion expression unit can customize the emotional expressions of a character according to different cultures and backgrounds. For example, the emotion expression unit builds a system that customizes the emotional expressions of a character according to different cultures and backgrounds. For example, different emotional expressions depending on the culture (e.g., ways of greeting or expressions of gratitude) are incorporated. The emotion expression unit also customizes the character's dialogue and behavior to generate emotional expressions according to different cultures and backgrounds. For example, if emotions are expressed more subtly in a particular culture, the emotion expression unit expresses emotions that match that culture. The emotion expression unit also provides a more familiar experience for the player by customizing the character's emotional expressions according to different cultures and backgrounds. For example, the emotion expression unit expresses emotions that match the player's culture. In this way, by providing emotional expressions that match different cultures and backgrounds, a more familiar experience can be provided for the player.

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

[0041] The dialogue generation unit can enable characters to suggest surprise events based on the player's dialogue history, according to the player's preferences and interests. For example, if the player has frequently chosen to go on dates with a particular character in the past, that character can suggest a surprise date to the player. Also, if the player has frequently talked about a particular hobby, the dialogue generation unit can suggest events related to that hobby. Furthermore, the dialogue generation unit can analyze the player's dialogue history and suggest new events based on events the player has enjoyed in the past. This can provide the player with a more personalized experience.

[0042] The dialogue generation unit can generate responses to player utterances that involve physical actions by the character. For example, if the player says to the character, "Let's high-five," the character can perform a high-five motion. Also, if the player says, "Let's dance together," the character can perform a dance motion. Furthermore, the dialogue generation unit can increase the variety of dialogue by generating responses that involve physical actions by the character in response to the player's utterances. For example, if the player says, "I'm tired today," the character can perform a shoulder massage motion. This can provide the player with a more interactive experience.

[0043] The dialogue generation unit allows a character to provide relevant news and information to a player based on the player's hobbies and interests. For example, if the player says that they like music, the character can provide the latest music news. Also, if a character learns that the player likes movies, it can provide the latest movie information. Furthermore, the dialogue generation unit allows a character to suggest relevant events and activities based on the player's hobbies and interests. For example, if the player says that they like traveling, the character can provide recommended information on the next travel destination. This makes it possible to provide a more personalized experience by providing information based on the player's hobbies and interests.

[0044] The dialogue generation unit enables a character to provide appropriate advice to a player based on the player's real-time environmental information. For example, if the player goes out on a cold day, the character may advise the player to "wear warm clothes." Also, if the player is playing a game late at night, the character may say, "It's getting late, so you should get some rest." Furthermore, the dialogue generation unit enables a character to suggest appropriate activities to the player based on the player's real-time environmental information. For example, on a rainy day, the character may suggest, "Let's stay home and watch a movie today." This allows the character to provide advice and suggestions based on the player's real-time environment, providing a more realistic experience.

[0045] The scenario generation unit can cause a character to propose a surprise event to the player based on the player's selection. For example, if the player chooses to deepen their relationship with a specific character, that character can propose a surprise date to the player. Also, if the player chooses to participate in a specific event, the scenario generation unit can generate a surprise scenario related to that event. Furthermore, the scenario generation unit can cause a character to propose a special gift to the player based on the player's selection. For example, if the player chooses to deepen their relationship with a specific character, that character can propose a special gift to the player. This can provide the player with a more personalized experience.

[0046] The scenario generation unit can analyze the player's behavior history and generate a new scenario based on scenarios that the player enjoyed in the past. For example, if the player has frequently selected dates with a specific character in the past, a new date scenario related to that character can be generated. Also, if the player has frequently selected a specific event, a new scenario related to that event can be generated. Furthermore, the scenario generation unit can analyze patterns of scenarios that the player has enjoyed in the past based on the player's behavior history and generate a new scenario based on those patterns. This allows for a more consistent story development by providing scenarios based on the player's past behavior history.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The dialogue generation unit generates dialogue with the player in real time. The generation AI generates appropriate responses from the character based on the text and options entered by the player. The generation AI uses text generation AI (e.g., GPT-3) to generate responses to the player's input. The generation AI can also analyze the player's past dialogue history and generate a dialogue style optimized for each individual player. For example, the character generates responses that match the player's frequently used phrases and tone. Step 2: The scenario generation unit dynamically generates a scenario based on the player's choices and actions. If the player chooses to go on a date with a character, the generation AI generates a scenario for that date and provides it to the player. The generation AI can also analyze the player's behavioral history and predict scenarios based on past choices. For example, if the player has chosen to go on many dates with a particular character in the past, it will generate a scenario that deepens the player's relationship with that character. Step 3: The emotion expression unit generates dialogue and actions to realistically express the character's emotions. The generation AI makes the character express emotions such as joy, anger, and sadness according to the player's actions and choices. The generation AI can also use its emotion estimation function to estimate the player's emotions and generate dialogue that corresponds to those emotions. For example, if the player sends a sad-sounding message, the character will generate a comforting response.

[0049] (Example 2) A dating simulation game according to an embodiment of the present invention is a system equipped with a generation AI, in which the player aims to build a romantic relationship with a virtual character. This system uses the generation AI to generate dialogue with the player in real time, dynamically generate a scenario based on the player's choices and actions, and realistically express the character's emotions. This allows the dating simulation game to provide the player with a more realistic and diverse romantic experience.

[0050] A dating simulation game according to an embodiment includes a dialogue generation unit, a scenario generation unit, and an emotion expression unit. The dialogue generation unit generates dialogue with a player in real time. For example, the generation AI generates appropriate responses for a character based on text entered by the player and options selected. The generation AI generates responses to the player's input using a text generation AI (e.g., GPT-3). The generation AI can also analyze a player's past dialogue history to generate a dialogue style optimized for each individual player. For example, a character can generate responses tailored to the player's frequently used phrases and tone. The scenario generation unit dynamically generates scenarios based on the player's choices and actions. For example, if a player selects a date with a character, the generation AI generates a scenario for that date and provides it to the player. The generation AI can also analyze the player's behavior history and predict scenarios based on past choices. For example, if a player has frequently selected dates with a specific character in the past, the generation AI generates a scenario that deepens the player's relationship with that character. The emotion expression unit generates dialogue and actions to realistically express the character's emotions. For example, the generation AI can make a character express emotions such as joy, anger, and sadness in response to the player's actions and choices. The generation AI can also use its emotion estimation function to estimate the player's emotions and generate dialogue based on those emotions. For example, if a player sends a sad message, the character can generate a comforting response. This allows the dating simulation game according to the embodiment to provide players with a more realistic and diverse dating experience. For example, players can enjoy their own unique story and experience changes in emotions as they deepen their relationships with characters. Furthermore, the multiple endings increase the enjoyment of replaying the game.

[0051] The dialogue generation unit can analyze a player's past dialogue history and generate a dialogue style optimized for each individual player. For example, the dialogue generation unit analyzes the content of the player's past dialogues and learns the player's preferences and speaking patterns. For example, the character generates a response that matches the player's frequently used phrases and tone. The dialogue generation unit also provides a character with topics that match the player's interests and concerns based on the player's dialogue history. For example, if the player previously talked about sports, the character will continue that topic. The dialogue generation unit also analyzes the player's dialogue history and learns the player's preferred dialogue style (e.g., humorous conversation or serious speech) and generates the character's responses based on that. This makes it possible to provide the player with a more personalized dialogue experience.

[0052] The dialogue generation unit can estimate the player's emotions and generate dialogue that corresponds to those emotions. For example, the dialogue generation unit estimates emotions from the player's input text and options, and generates a response that corresponds to those emotions. For example, if the player sends a sad message, the character generates a comforting response. The dialogue generation unit also analyzes the player's emotions in real time, and the character engages in dialogue that matches those emotions. For example, if the player is excited, the character responds in a similarly excited tone. The dialogue generation unit also uses the emotion estimation function to generate dialogue that corresponds to the player's emotions and improves the player's mood. For example, if the player is feeling down, the character will offer words of encouragement. This allows for providing dialogue that corresponds to the player's emotions, thereby further strengthening the emotional connection.

[0053] The dialogue generation unit can generate humorous or sarcastic responses to player statements. The dialogue generation unit generates humorous responses from a character in response to the content of a player's statement. For example, if the player makes a joke, the character responds in a way that elicits laughter. The dialogue generation unit also generates sarcastic responses from a character to a player's statement. For example, if the player says, "I don't have anything to do today," the character replies, "Then let's find something fun to do together." The dialogue generation unit also increases the variety of dialogue by generating humorous or sarcastic responses from a character to a player's statement. For example, if the player says, "I'm tired today," the character replies, "Then relax and let's talk." This increases the variety of dialogue and provides the player with a more enjoyable experience.

[0054] The dialogue generation unit can learn the player's hobbies and interests and provide topics based on them. For example, the dialogue generation unit learns the hobbies and interests that the player talks about during a dialogue, and the character provides topics based on them. For example, if the player says that they like music, the character will ask, "What kind of music have you been listening to lately?" The dialogue generation unit also learns the player's hobbies and interests, and the character will provide related topics based on them. For example, if the player learns that they like movies, the character will ask, "What movie have you seen recently?" The dialogue generation unit also allows the character to learn the player's hobbies and interests during a dialogue and provide topics based on them. For example, if the player says that they like traveling, the character will ask, "Where do you want to go next?" This makes it possible to provide a more personalized experience by providing topics based on the player's hobbies and interests.

[0055] The dialogue generation unit can generate responses according to the player's real-time environment (weather, time of day, etc.). The dialogue generation unit, for example, acquires the player's real-time environmental information (weather, time of day, etc.) and generates responses according to it. For example, on a rainy day, a character might suggest, "It's raining today, so let's relax at home." The dialogue generation unit also allows the character to engage in dialogue according to the player's real-time environment. For example, if the player is playing a game late at night, the character might say, "It's getting late, so you should get some rest now." The dialogue generation unit also allows the character to generate appropriate responses based on the player's real-time environmental information. For example, in the morning, the character might greet the player with, "Good morning! I wonder what kind of day it will be today?" This allows the player to have a more realistic experience by providing responses according to the player's real-time environment.

[0056] The dialogue generation unit estimates the player's emotions and generates dialogue corresponding to those emotions, thereby improving the player's mood. The dialogue generation unit, for example, analyzes the player's emotions in real time and generates dialogue corresponding to those emotions. For example, if the player is feeling down, a character will offer words of encouragement. The dialogue generation unit also uses the emotion estimation function to generate dialogue corresponding to the player's emotions and improve the player's mood. For example, if the player is happy, a character will respond in a way that shares that joy. The dialogue generation unit also estimates the player's emotions and generates dialogue corresponding to those emotions, thereby improving the player's mood. For example, if the player is feeling anxious, a character will offer words of reassurance. In this way, the player's mood can be improved by providing dialogue corresponding to the player's emotions.

[0057] The scenario generation unit can increase the branching points of the scenario based on the player's selection, thereby realizing a more complex story development. The scenario generation unit, for example, increases the branching points of the scenario based on the player's selection, and generates multiple story developments. For example, if the player makes a specific choice, a new scenario develops in accordance with that choice. Furthermore, by increasing the branching points of the scenario, the scenario generation unit can realize a different story development for each player. For example, each time the player makes a different choice, a new scenario is generated. Furthermore, the scenario generation unit increases the branching points of the scenario based on the player's selection, thereby realizing a more complex story development. For example, if the player chooses to deepen their relationship with a specific character, a new scenario related to that character is generated. This makes it possible to provide a different story development for each player.

[0058] The scenario generation unit can analyze the player's behavior history and predict a scenario based on past selections. The scenario generation unit, for example, analyzes the player's past behavior history and predicts a scenario based on that data. For example, if the player has deepened their relationship with a specific character in the past, a scenario related to that character is predicted. The scenario generation unit also predicts a scenario according to past selections based on the player's behavior history. For example, it analyzes the pattern of scenarios selected by the player in the past and predicts the next scenario. The scenario generation unit also analyzes the player's behavior history and predicts a scenario based on past selections. For example, if the player frequently selects a specific event, it predicts a scenario related to that event. This makes it possible to achieve a more consistent story development by providing a scenario based on the player's past selections.

[0059] The scenario generation unit can estimate the player's emotions and generate a scenario according to those emotions. The scenario generation unit, for example, analyzes the player's emotions in real time and generates a scenario according to those emotions. For example, if the player is happy, it generates a positive scenario. The scenario generation unit also uses the emotion estimation function to generate a scenario according to the player's emotions. For example, if the player is sad, it generates a comforting scenario. The scenario generation unit also improves the player's experience by estimating the player's emotions and generating a scenario according to those emotions. For example, if the player is excited, it generates a scenario including an action scene. In this way, by providing a scenario according to the player's emotions, it is possible to further strengthen the emotional connection.

[0060] The scenario generation unit can compare the choices and actions of different players and extract common scenario patterns. The scenario generation unit, for example, compares the choices and actions of different players and extracts common scenario patterns. For example, if multiple players make the same choice, it identifies a scenario pattern based on that choice. The scenario generation unit also builds a system that compares the choices and actions of players and extracts common scenario patterns. For example, if a particular option is chosen frequently, it extracts a scenario pattern related to that option. The scenario generation unit also analyzes the actions of different players and extracts common scenario patterns. For example, if a player often makes choices that deepen their relationship with a particular character, it identifies a scenario pattern related to that character. In this way, by comparing the choices and actions of different players, it is possible to extract common scenario patterns and provide a more diverse story development.

[0061] The scenario generation unit can provide a scenario based on a real-world location, taking into account the player's real-time location information. The scenario generation unit, for example, acquires the player's real-time location information and generates a scenario based on that information. For example, if the player is in a specific location, it provides a scenario related to that location. The scenario generation unit also generates a scenario based on a real-world location, based on the player's location information. For example, if the player is in a tourist spot, it provides a scenario related to that tourist spot. The scenario generation unit also generates a scenario based on the player's location, taking into account the real-time location information. For example, if the player is in a cafe, it provides a scenario related to that cafe. In this way, by providing a scenario based on the player's real-time location information, it is possible to provide a more realistic experience.

[0062] The scenario generation unit estimates the player's emotions and generates a scenario according to those emotions, thereby increasing emotional satisfaction. The scenario generation unit, for example, analyzes the player's emotions in real time and generates a scenario according to those emotions. For example, if the player is happy, it generates a positive scenario. The scenario generation unit also uses the emotion estimation function to generate a scenario according to the player's emotions. For example, if the player is sad, it generates a comforting scenario. The scenario generation unit also estimates the player's emotions and generates a scenario according to those emotions, thereby improving the player's experience. For example, if the player is excited, it generates a scenario including an action scene. In this way, by providing a scenario according to the player's emotions, it is possible to increase emotional satisfaction.

[0063] The emotion expression unit can generate the character's emotional expressions, including non-verbal elements such as facial expressions and tone of voice. For example, the emotion expression unit generates the character's facial expressions in real time and changes them according to the player's actions and choices. For example, if the player praises the character, the character will smile. The emotion expression unit also changes the character's tone of voice according to the emotion. For example, if the character is angry, the tone of voice will become lower. The emotion expression unit also generates the character's emotional expressions, including non-verbal elements (for example, gestures and posture). For example, if the character is sad, the character will slump their shoulders. This makes the character's emotional expressions more realistic, thereby providing the player with a stronger emotional connection.

[0064] The emotional expression unit can change the emotional reaction of the character to the player's actions over time. For example, the emotional expression unit changes the emotional reaction of the character to the player's actions over time. For example, if the player continues to ignore the character, the character gradually becomes sad. The emotional expression unit also builds a system that changes the emotional reaction of the character over time. For example, after the player gives the character a gift, the character's joy fades over time. The emotional expression unit also achieves more realistic emotional expression by changing the emotional reaction of the character to the player's actions over time. For example, if the player frequently converses with the character, the character's trust increases. In this way, more realistic emotional expression can be provided by changing the emotional reaction of the character to the player's actions over time.

[0065] The emotion expression unit can estimate the player's emotion and generate an emotional expression for the character corresponding to that emotion. The emotion expression unit, for example, analyzes the player's emotion in real time and generates an emotional expression for the character corresponding to that emotion. For example, if the player is happy, the character also expresses joy. The emotion expression unit also uses the emotion estimation function to generate an emotional expression for the character corresponding to the player's emotion. For example, if the player is sad, the character expresses comfortingly. The emotion expression unit also estimates the player's emotion and generates an emotional expression for the character corresponding to that emotion, thereby strengthening the emotional connection with the player. For example, if the player is angry, the character apologizes. In this way, by providing an emotional expression for the character corresponding to the player's emotion, the emotional connection with the player can be strengthened.

[0066] The emotion expression unit can take into account the player's past behavioral history and provide consistent emotion expression. The emotion expression unit, for example, analyzes the player's past behavioral history and makes the character's emotion expression consistent based on that. For example, if the player has frequently praised the character in the past, the character will express affectionate emotions toward the player. The emotion expression unit also builds a system that takes into account the player's past behavioral history when the character expresses emotion. For example, if the player has ignored the character in the past, the character will be cold toward the player. The emotion expression unit also provides consistent emotion expression for the character based on the player's behavioral history. For example, if the player has behaved kindly toward the character in the past, the character will express gratitude toward the player. In this way, consistent emotion expression can be provided by taking into account the player's past behavioral history.

[0067] The emotion expression unit can customize the emotional expressions of a character according to different cultures and backgrounds. For example, the emotion expression unit builds a system that customizes the emotional expressions of a character according to different cultures and backgrounds. For example, different emotional expressions depending on the culture (e.g., ways of greeting or expressions of gratitude) are incorporated. The emotion expression unit also customizes the character's dialogue and behavior to generate emotional expressions according to different cultures and backgrounds. For example, if emotions are expressed more subtly in a particular culture, the emotion expression unit expresses emotions that match that culture. The emotion expression unit also provides a more familiar experience for the player by customizing the character's emotional expressions according to different cultures and backgrounds. For example, the emotion expression unit expresses emotions that match the player's culture. In this way, by providing emotional expressions that match different cultures and backgrounds, a more familiar experience can be provided for the player.

[0068] The emotion expression unit estimates the player's emotion and generates an emotional expression of the character corresponding to that emotion, thereby eliciting empathy from the player. The emotion expression unit, for example, analyzes the player's emotion in real time and generates an emotional expression of the character corresponding to that emotion. For example, if the player is happy, the character also expresses joy. The emotion expression unit also uses the emotion estimation function to generate an emotional expression of the character corresponding to the player's emotion. For example, if the player is sad, the character expresses comfortingly. The emotion expression unit also estimates the player's emotion and generates an emotional expression of the character corresponding to that emotion, thereby strengthening the emotional connection with the player. For example, if the player is angry, the character apologizes. In this way, by providing an emotional expression of the character corresponding to the player's emotion, it is possible to elicit empathy from the player.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The dialogue generation unit can also analyze the tone of the player's voice and the speed of speech, and generate a character's response based on that. For example, if the player is excited and speaking quickly, the character will respond in a similarly fast manner. On the other hand, if the player speaks slowly and calmly, the character will respond in a tone that matches that. Furthermore, the dialogue generation unit can analyze the emotion in the player's voice and generate a response that corresponds to that emotion. For example, if the player is angry, the character can respond calmly to calm the player's emotions. This makes it possible to provide a more realistic dialogue experience by providing dialogue that corresponds to the player's tone of voice and speaking style.

[0071] The dialogue generation unit can enable characters to suggest surprise events based on the player's dialogue history, according to the player's preferences and interests. For example, if the player has frequently chosen to go on dates with a particular character in the past, that character can suggest a surprise date to the player. Also, if the player has frequently talked about a particular hobby, the dialogue generation unit can suggest events related to that hobby. Furthermore, the dialogue generation unit can analyze the player's dialogue history and suggest new events based on events the player has enjoyed in the past. This can provide the player with a more personalized experience.

[0072] The dialogue generation unit can estimate the player's emotions and play music in the background that corresponds to those emotions. For example, if the player is sad, a character can play comforting music. Alternatively, if the player is happy, a character can play upbeat music that shares the player's joy. Furthermore, the dialogue generation unit can select music that corresponds to the player's emotions to improve the player's mood. For example, if the player is depressed, a character can play encouraging music. This allows for a stronger emotional connection by providing music that corresponds to the player's emotions.

[0073] The dialogue generation unit can generate responses to player utterances that involve physical actions by the character. For example, if the player says to the character, "Let's high-five," the character can perform a high-five motion. Also, if the player says, "Let's dance together," the character can perform a dance motion. Furthermore, the dialogue generation unit can increase the variety of dialogue by generating responses that involve physical actions by the character in response to the player's utterances. For example, if the player says, "I'm tired today," the character can perform a shoulder massage motion. This can provide the player with a more interactive experience.

[0074] The dialogue generation unit allows a character to provide relevant news and information to a player based on the player's hobbies and interests. For example, if the player says that they like music, the character can provide the latest music news. Also, if a character learns that the player likes movies, it can provide the latest movie information. Furthermore, the dialogue generation unit allows a character to suggest relevant events and activities based on the player's hobbies and interests. For example, if the player says that they like traveling, the character can provide recommended information on the next travel destination. This makes it possible to provide a more personalized experience by providing information based on the player's hobbies and interests.

[0075] The dialogue generation unit enables a character to provide appropriate advice to a player based on the player's real-time environmental information. For example, if the player goes out on a cold day, the character may advise the player to "wear warm clothes." Also, if the player is playing a game late at night, the character may say, "It's getting late, so you should get some rest." Furthermore, the dialogue generation unit enables a character to suggest appropriate activities to the player based on the player's real-time environmental information. For example, on a rainy day, the character may suggest, "Let's stay home and watch a movie today." This allows the character to provide advice and suggestions based on the player's real-time environment, providing a more realistic experience.

[0076] The dialogue generation unit can estimate the player's emotions and generate a character's facial expression according to those emotions. For example, if the player is happy, the character will show a smile. Also, if the player is sad, the character can show a sad expression. Furthermore, the dialogue generation unit can strengthen the emotional connection with the player by generating a character's facial expression according to the player's emotions. For example, if the player is angry, the character will show an apologetic expression. In this way, by providing a character's facial expression according to the player's emotions, the emotional connection can be further strengthened.

[0077] The scenario generation unit can cause a character to propose a surprise event to the player based on the player's selection. For example, if the player chooses to deepen their relationship with a specific character, that character can propose a surprise date to the player. Also, if the player chooses to participate in a specific event, the scenario generation unit can generate a surprise scenario related to that event. Furthermore, the scenario generation unit can cause a character to propose a special gift to the player based on the player's selection. For example, if the player chooses to deepen their relationship with a specific character, that character can propose a special gift to the player. This can provide the player with a more personalized experience.

[0078] The scenario generation unit can analyze the player's behavior history and generate a new scenario based on scenarios that the player enjoyed in the past. For example, if the player has frequently selected dates with a specific character in the past, a new date scenario related to that character can be generated. Also, if the player has frequently selected a specific event, a new scenario related to that event can be generated. Furthermore, the scenario generation unit can analyze patterns of scenarios that the player has enjoyed in the past based on the player's behavior history and generate a new scenario based on those patterns. This allows for a more consistent story development by providing scenarios based on the player's past behavior history.

[0079] The scenario generation unit can estimate the player's emotions and generate a scenario ending that corresponds to those emotions. For example, if the player is happy, a positive ending can be generated. Also, if the player is sad, a comforting ending can be generated. Furthermore, the scenario generation unit can improve the player's experience by generating a scenario ending that corresponds to the player's emotions. For example, if the player is excited, an ending that includes an action scene can be generated. In this way, by providing a scenario ending that corresponds to the player's emotions, it is possible to further strengthen the emotional connection.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The dialogue generation unit generates dialogue with the player in real time. The generation AI generates appropriate responses from the character based on the text and options entered by the player. The generation AI uses text generation AI (e.g., GPT-3) to generate responses to the player's input. The generation AI can also analyze the player's past dialogue history and generate a dialogue style optimized for each individual player. For example, the character generates responses that match the player's frequently used phrases and tone. Step 2: The scenario generation unit dynamically generates a scenario based on the player's choices and actions. If the player chooses to go on a date with a character, the generation AI generates a scenario for that date and provides it to the player. The generation AI can also analyze the player's behavioral history and predict scenarios based on past choices. For example, if the player has chosen to go on many dates with a particular character in the past, it will generate a scenario that deepens the player's relationship with that character. Step 3: The emotion expression unit generates dialogue and actions to realistically express the character's emotions. The generation AI makes the character express emotions such as joy, anger, and sadness according to the player's actions and choices. The generation AI can also use its emotion estimation function to estimate the player's emotions and generate dialogue that corresponds to those emotions. For example, if the player sends a sad-sounding message, the character will generate a comforting response.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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 dating simulation game equipped with a generative AI, The goal is for players to develop romantic relationships with virtual characters. Using generative AI, a dialogue generation unit that generates dialogue with a player in real time; a scenario generation unit that dynamically generates a scenario in response to the player's choices and actions; An emotion expression unit that generates dialogue and actions to realistically express the emotions of the character. A system characterized by:

2. The dialogue generation unit Analyzing the past interaction history of the player and generating an interaction style optimized for each individual player 2. The system of claim 1.

3. The dialogue generation unit Generate responses based on the player's real-time environment (weather, time of day) 2. The system of claim 1.

4. The scenario generation unit Based on the player's choices, the scenario will have more branching points, allowing for more complex story development.

2. The system of claim 1.

5. The scenario generation unit Considering the player's real-time location, providing a scenario based on real locations 2. The system of claim 1.

6. The emotion expression unit Generate the character's emotional expressions, including non-verbal elements such as facial expressions and tone of voice.

2. The system of claim 1.

7. The emotion expression unit Estimating the player's emotion and generating an emotional expression for the character in accordance with the emotion 2. The system of claim 1.

8. The dialogue generation unit Estimate the player's emotions and generate dialogue according to those emotions.

2. The system of claim 1.

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