System

Abstract

An object of a system according to an embodiment is to clarify an answer to a question in a reasoning game in which a player guesses an object, a person, or a place.SOLUTION: A system includes a selection unit and a question answering unit. The selection unit selects an object, a person, or a place. The question answering unit answers "yes" or "no" to a question asked by the player to guess the object, the person, or the place selected by the selection unit.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] In conventional techniques, in a mystery game in which a player must guess an object, person, or place, there is a problem in that the game does not progress smoothly because the answers to questions are ambiguous.

[0005] The system according to the embodiment aims to clarify the answer to a question in a guessing game in which a player guesses an object, person, or place. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit and a question-and-answer unit. The selection unit selects an object, a person, or a place. The question-and-answer unit answers questions posed by a player to guess the object, person, or place selected by the selection unit with "yes" or "no." [Effects of the Invention]

[0007] A system according to an embodiment can clarify answers to questions in a guessing game where players must guess an object, person, or place. [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) In a mystery game system according to an embodiment of the present invention, a generating AI thinks of an object, person, or place and asks a question that can be answered with "yes" or "no" to help the player guess it. This allows the mystery game system to test the player's ability to ask clever questions and have fun.

[0029] A mystery game system according to an embodiment includes a selection unit and a question-answering unit. The selection unit allows a generation AI to select an object, person, or place. For example, the generation AI may randomly select an object, person, or place from a pre-defined database. The generation AI may also make a selection by inputting a specific prompt. For example, if the prompt "Choose a famous person" is input, the generation AI will select a famous person based on the instruction. The question-answering unit responds to questions posed by a player with "yes" or "no." For example, if the player asks "Is that a living thing?", the generation AI will respond with "yes" or "no." Also, if the player asks "Is that an electronic device?", the generation AI will respond with "yes" or "no." This allows players to test their ability to ask smart questions.

[0030] The selection unit can customize objects, people, or locations based on the player's past question history and play style. For example, the selection unit analyzes the player's past question history and customizes the objects selected by the generation AI based on that data. For example, the selection unit may prioritize objects from categories that have been frequently asked about in the past. The selection unit can also customize the selection based on the player's play style. For example, the selection unit may select more challenging objects for a player with an aggressive play style. This allows the selection to be customized based on the player's past question history and play style.

[0031] The question answering unit can provide additional hints or related information in response to a player's question in addition to answering "yes" or "no." The question answering unit can provide additional hints in response to a player's question in addition to answering "yes" or "no." For example, in response to the question "Is that an electronic device?", it can provide a hint such as "Yes, it is a portable device." The question answering unit can also provide related information. For example, in response to the question "Is that an electronic device?", it can provide specific information such as "Yes, it is a smartphone." This makes it possible to provide additional hints or related information in response to a player's question.

[0032] The question answering unit may be provided with an assist function that analyzes the player's question history and suggests the next most effective question. The question answering unit may, for example, analyze the player's question history and suggest the next most effective question based on that data. For example, the question answering unit may suggest, "This question would be good to ask next," based on past question patterns. The question answering unit may also present examples of effective questions based on the player's question history. For example, the question "Is that an electronic device?" may be presented as an example of an effective question. This allows the player's question history to be analyzed and the next most effective question to be suggested.

[0033] The selection unit may have a mode for selecting an object, person, or place based on a specific theme. For example, the selection unit may have a mode for limiting the objects selected by the generation AI to a specific theme. For example, a mode for selecting only movie characters may be provided. Alternatively, a mode for selecting only historical figures may be provided. The specific theme may be set based on a category such as entertainment, science, or history. This allows a mode for making a selection based on a specific theme to be provided.

[0034] The selection unit may have a custom mode in which the player selects an object, person, or location from a list set in advance. The selection unit may have a custom mode in which the generation AI selects a target from a list set in advance by the player, for example. For example, the player may set a list of favorite movie characters and select from that list. The player may also set a list of historical figures in which the player is interested and select from that list. The list may be set in text format or image format, for example. This makes it possible to provide a custom mode in which the player selects from a list set in advance.

[0035] The question and answering unit may include a voice interface that allows the player to ask questions aloud and the generation AI to respond aloud. The question and answering unit may include, for example, a voice interface that allows the player to ask questions aloud and the generation AI to respond aloud. For example, the player may ask, "Is that an electronic device?" aloud, and the generation AI may respond aloud, "Yes." Alternatively, the player may ask, "Is that a living thing?" aloud, and the generation AI may respond aloud, "No." The voice interface may analyze the player's question using, for example, voice recognition technology. The generation AI's response may also be provided aloud using voice synthesis technology. This allows the player to ask questions aloud and the generation AI to respond aloud.

[0036] The question answering unit can complement a question by using an image or video when a player asks a question. For example, when a player asks a question, the question answering unit complements the question by using an image or video. For example, the question answering unit may ask a question such as, "Is it like this image?". It may also ask a question such as, "Is that what appears in this video?". The image or video may be provided in the form of, for example, a still image, animation, or video clip. This allows the player to complement the question by using an image or video when asking a question.

[0037] The system may be provided with an evaluation system that evaluates the quality of a player's question and awards points when the player asks a smart question. For example, the system may be provided with an evaluation system that evaluates the quality of a player's question and awards points when the player asks a smart question. For example, the question "Is that an electronic device?" is evaluated as a smart question and points are awarded. The system may also set criteria for evaluating the quality of questions. For example, the system may perform evaluation based on criteria such as the ease of eliciting information and the clarity of the question. This allows the quality of a player's question to be evaluated and points to be awarded when the player asks a smart question.

[0038] The system may be provided with a learning mode that analyzes patterns of questions previously asked by a player and presents examples of smart questions. For example, the system may analyze patterns of questions previously asked by a player and present examples of smart questions. For example, the system may suggest, "This question would be good to ask next" based on the player's past question history. The system may also set criteria for analyzing question patterns. For example, the system may perform analysis based on criteria such as frequency analysis or co-occurrence analysis. This allows the system to analyze patterns of questions previously asked by a player and present examples of smart questions.

[0039] The system may have a tutorial mode that provides hints and advice for asking smart questions. For example, the system may have a tutorial mode that provides hints and advice for asking smart questions. For example, a question such as "Is it an electronic device?" may be presented as an example of a smart question. The system may also configure the content of the hints and advice. For example, the system may provide hints and advice based on specific examples or guidelines. This allows the system to provide hints and advice for asking smart questions.

[0040] The system may have a battle mode in which a player competes with other players to see who can ask the smartest questions. For example, the system may have a battle mode in which a player competes with other players to see who can ask the smartest questions. For example, multiple players may ask questions at the same time, and the player who asks the smartest question wins. The system may also set rules for the battle mode. For example, the system may set rules based on the criteria for determining victory or defeat and the battle format. This allows a player to compete with other players to see who can ask the smartest questions.

[0041] The system may have a function to display the player's question history and its evaluation at the end of a game, and provide feedback on areas for improvement. For example, the system may have a function to display the player's question history and its evaluation at the end of a game, and provide feedback on areas for improvement. For example, the system may provide feedback such as, "Your question was clever, but please try asking a more specific question next time." The system may also set a method for saving the question history and evaluation criteria. For example, the system may provide feedback based on the question recording format, storage period, and evaluation criteria. This allows the system to display the player's question history and its evaluation at the end of a game, and provide feedback on areas for improvement.

[0042] The system may be equipped with an evaluation system that evaluates the player's reasoning ability at the end of the game and displays a score and rank. The system may be equipped with an evaluation system that evaluates the player's reasoning ability at the end of the game and displays a score and rank. For example, the evaluation may be displayed in the form of, "Your score is 80 points. Your rank is A." The system may also set evaluation criteria for reasoning ability and a method for calculating the score. For example, the evaluation may be based on the rate of correct answers or the speed of reasoning. In this way, the system may evaluate the player's reasoning ability at the end of the game and display a score and rank.

[0043] The system may have a function to display detailed information or trivia about the object, person, or place selected by the player at the end of the game. For example, the system may display detailed information or trivia about the object, person, or place selected by the player at the end of the game. For example, the system may provide information such as, "The person you selected is Albert Einstein. He proposed the theory of relativity." The system may also set the content of the detailed information or trivia. For example, the system may provide information based on historical background or interesting facts. This allows detailed information or trivia about the object, person, or place selected by the player to be displayed at the end of the game.

[0044] The system may include a social function that allows a player to share and compare results with other players at the end of a game. For example, the system may include a social function that allows a player to share and compare results with other players at the end of a game. For example, a player may share their score on a social networking site and compare their score with other players. The system may also configure the content of the social function. For example, the system may provide a function based on the method of sharing and comparing results. This allows a player to share and compare their results with other players at the end of a game.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0047] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0048] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0049] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0050] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0051] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0052] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0053] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0054] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0055] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: In the selection section, the generation AI selects an object, person, or place. For example, the generation AI randomly selects an object, person, or place from a pre-configured database. The generation AI can also make a selection by inputting a specific prompt. For example, if the prompt is "Select a famous person," the generation AI will select a famous person based on that instruction. Step 2: The question-answering unit answers the player's questions with "yes" or "no." For example, if the player asks "Is it a living thing?", the generated AI answers "yes" or "no." Also, if the player asks "Is it an electronic device?", the generated AI answers "yes" or "no."

[0058] (Example 2) In a mystery game system according to an embodiment of the present invention, a generating AI thinks of an object, person, or place and asks a question that can be answered with "yes" or "no" to help the player guess it. This allows the mystery game system to test the player's ability to ask clever questions and have fun.

[0059] A mystery game system according to an embodiment includes a selection unit and a question-answering unit. The selection unit allows a generation AI to select an object, person, or place. For example, the generation AI may randomly select an object, person, or place from a pre-defined database. The generation AI may also make a selection by inputting a specific prompt. For example, if the prompt "Choose a famous person" is input, the generation AI will select a famous person based on the instruction. The question-answering unit responds to questions posed by a player with "yes" or "no." For example, if the player asks "Is that a living thing?", the generation AI will respond with "yes" or "no." Also, if the player asks "Is that an electronic device?", the generation AI will respond with "yes" or "no." This allows players to test their ability to ask smart questions.

[0060] The selection unit can customize objects, people, or locations based on the player's past question history and play style. For example, the selection unit analyzes the player's past question history and customizes the objects selected by the generation AI based on that data. For example, the selection unit may prioritize objects from categories that have been frequently asked about in the past. The selection unit can also customize the selection based on the player's play style. For example, the selection unit may select more challenging objects for a player with an aggressive play style. This allows the selection to be customized based on the player's past question history and play style.

[0061] The question answering unit can provide additional hints or related information in response to a player's question in addition to answering "yes" or "no." The question answering unit can provide additional hints in response to a player's question in addition to answering "yes" or "no." For example, in response to the question "Is that an electronic device?", it can provide a hint such as "Yes, it is a portable device." The question answering unit can also provide related information. For example, in response to the question "Is that an electronic device?", it can provide specific information such as "Yes, it is a smartphone." This makes it possible to provide additional hints or related information in response to a player's question.

[0062] The question answering unit may be provided with an assist function that analyzes the player's question history and suggests the next most effective question. The question answering unit may, for example, analyze the player's question history and suggest the next most effective question based on that data. For example, the question answering unit may suggest, "This question would be good to ask next," based on past question patterns. The question answering unit may also present examples of effective questions based on the player's question history. For example, the question "Is that an electronic device?" may be presented as an example of an effective question. This allows the player's question history to be analyzed and the next most effective question to be suggested.

[0063] The question answering unit may be equipped with an emotion estimation function that adjusts the answer depending on the player's emotional state. The question answering unit may use the emotion estimation function to adjust the answer of the generation AI depending on the player's emotional state. For example, if the player is excited, the question answering unit may provide a more detailed answer. Alternatively, if the player is calm, the question answering unit may provide a more concise answer. The emotion estimation function may use facial expression recognition technology to estimate the player's emotional state. Alternatively, the question answering unit may use voice analysis technology to estimate the player's emotional state. This allows the answer to be adjusted depending on the player's emotional state.

[0064] The selection unit may have a mode for selecting an object, person, or place based on a specific theme. For example, the selection unit may have a mode for limiting the objects selected by the generation AI to a specific theme. For example, a mode for selecting only movie characters may be provided. Alternatively, a mode for selecting only historical figures may be provided. The specific theme may be set based on a category such as entertainment, science, or history. This allows a mode for making a selection based on a specific theme to be provided.

[0065] The selection unit may have a custom mode in which the player selects an object, person, or location from a list set in advance. The selection unit may have a custom mode in which the generation AI selects a target from a list set in advance by the player, for example. For example, the player may set a list of favorite movie characters and select from that list. The player may also set a list of historical figures in which the player is interested and select from that list. The list may be set in text format or image format, for example. This makes it possible to provide a custom mode in which the player selects from a list set in advance.

[0066] The selection unit may have an emotion estimation function that selects an object, person, or place based on the player's emotional state. The selection unit, for example, uses the emotion estimation function to select an object, person, or place according to the player's current emotional state. For example, if the player is relaxed, the selection unit may select a relaxing place. Alternatively, if the player is excited, the selection unit may select an object that will cause excitement. The emotion estimation function may, for example, estimate the player's emotional state using a machine learning algorithm. Alternatively, the selection unit may estimate the player's emotional state using sensor data. This makes it possible to provide an emotion estimation function that makes a selection based on the player's emotional state.

[0067] The question and answering unit may include a voice interface that allows the player to ask questions aloud and the generation AI to respond aloud. The question and answering unit may include, for example, a voice interface that allows the player to ask questions aloud and the generation AI to respond aloud. For example, the player may ask, "Is that an electronic device?" aloud, and the generation AI may respond aloud, "Yes." Alternatively, the player may ask, "Is that a living thing?" aloud, and the generation AI may respond aloud, "No." The voice interface may analyze the player's question using, for example, voice recognition technology. The generation AI's response may also be provided aloud using voice synthesis technology. This allows the player to ask questions aloud and the generation AI to respond aloud.

[0068] The question answering unit can complement a question by using an image or video when a player asks a question. For example, when a player asks a question, the question answering unit complements the question by using an image or video. For example, the question answering unit may ask a question such as, "Is it like this image?". It may also ask a question such as, "Is that what appears in this video?". The image or video may be provided in the form of, for example, a still image, animation, or video clip. This allows the player to complement the question by using an image or video when asking a question.

[0069] The question answering unit may be equipped with an emotion estimation function that changes the answer depending on the player's emotional state. The question answering unit, for example, uses the emotion estimation function to change the answer of the generation AI depending on the player's emotional state. For example, if the player is excited, a more detailed answer may be provided. Alternatively, if the player is calm, a concise answer may be provided. The emotion estimation function may, for example, estimate the player's emotional state using facial expression recognition technology. It may also be possible to estimate the player's emotional state using voice analysis technology. This makes it possible to change the answer depending on the player's emotional state.

[0070] The system may be provided with an evaluation system that evaluates the quality of a player's question and awards points when the player asks a smart question. For example, the system may be provided with an evaluation system that evaluates the quality of a player's question and awards points when the player asks a smart question. For example, the question "Is that an electronic device?" is evaluated as a smart question and points are awarded. The system may also set criteria for evaluating the quality of questions. For example, the system may perform evaluation based on criteria such as the ease of eliciting information and the clarity of the question. This allows the quality of a player's question to be evaluated and points to be awarded when the player asks a smart question.

[0071] The system may be provided with a learning mode that analyzes patterns of questions previously asked by a player and presents examples of smart questions. For example, the system may analyze patterns of questions previously asked by a player and present examples of smart questions. For example, the system may suggest, "This question would be good to ask next" based on the player's past question history. The system may also set criteria for analyzing question patterns. For example, the system may perform analysis based on criteria such as frequency analysis or co-occurrence analysis. This allows the system to analyze patterns of questions previously asked by a player and present examples of smart questions.

[0072] The system may have a tutorial mode that provides hints and advice for asking smart questions. For example, the system may have a tutorial mode that provides hints and advice for asking smart questions. For example, a question such as "Is it an electronic device?" may be presented as an example of a smart question. The system may also configure the content of the hints and advice. For example, the system may provide hints and advice based on specific examples or guidelines. This allows the system to provide hints and advice for asking smart questions.

[0073] The system may have a battle mode in which a player competes with other players to see who can ask the smartest questions. For example, the system may have a battle mode in which a player competes with other players to see who can ask the smartest questions. For example, multiple players may ask questions at the same time, and the player who asks the smartest question wins. The system may also set rules for the battle mode. For example, the system may set rules based on the criteria for determining victory or defeat and the battle format. This allows a player to compete with other players to see who can ask the smartest questions.

[0074] The system can be equipped with an emotion estimation function that enables the generation AI to provide an emotionally empathetic response when a player asks a smart question. For example, the system uses the emotion estimation function to enable the generation AI to provide an emotionally empathetic response when a player asks a smart question. For example, if the player asks, "Is that an electronic device?" and gets close to the correct answer, the system may empathize with the player by saying, "That's a great question!" The system can also set the content of the emotionally empathetic response. For example, the system may provide a response based on the way the empathy is expressed or the timing of the response. This allows the generation AI to provide an emotionally empathetic response when a player asks a smart question.

[0075] The system may have a function to display the player's question history and its evaluation at the end of a game, and provide feedback on areas for improvement. For example, the system may have a function to display the player's question history and its evaluation at the end of a game, and provide feedback on areas for improvement. For example, the system may provide feedback such as, "Your question was clever, but please try asking a more specific question next time." The system may also set a method for saving the question history and evaluation criteria. For example, the system may provide feedback based on the question recording format, storage period, and evaluation criteria. This allows the system to display the player's question history and its evaluation at the end of a game, and provide feedback on areas for improvement.

[0076] The system may be equipped with an evaluation system that evaluates the player's reasoning ability at the end of the game and displays a score and rank. The system may be equipped with an evaluation system that evaluates the player's reasoning ability at the end of the game and displays a score and rank. For example, the evaluation may be displayed in the form of, "Your score is 80 points. Your rank is A." The system may also set evaluation criteria for reasoning ability and a method for calculating the score. For example, the evaluation may be based on the rate of correct answers or the speed of reasoning. In this way, the system may evaluate the player's reasoning ability at the end of the game and display a score and rank.

[0077] The system can use the emotion estimation function to display results that will make the player feel positive emotions when the game ends. For example, the system can use the emotion estimation function to display results that will make the player feel positive emotions when the game ends. For example, the system can display the results in the form of, "Your score is 80 points! Great deduction skills!". The system can also set the content of the result display that will evoke positive emotions. For example, the system can display results based on expressions of satisfaction or joy. This makes it possible to display results that will make the player feel positive emotions when the game ends.

[0078] The system may have a function to display detailed information or trivia about the object, person, or place selected by the player at the end of the game. For example, the system may display detailed information or trivia about the object, person, or place selected by the player at the end of the game. For example, the system may provide information such as, "The person you selected is Albert Einstein. He proposed the theory of relativity." The system may also set the content of the detailed information or trivia. For example, the system may provide information based on historical background or interesting facts. This allows detailed information or trivia about the object, person, or place selected by the player to be displayed at the end of the game.

[0079] The system may include a social function that allows a player to share and compare results with other players at the end of a game. For example, the system may include a social function that allows a player to share and compare results with other players at the end of a game. For example, a player may share their score on a social networking site and compare their score with other players. The system may also configure the content of the social function. For example, the system may provide a function based on the method of sharing and comparing results. This allows a player to share and compare their results with other players at the end of a game.

[0080] The system can use the emotion estimation function to suggest the difficulty level and theme of the next game based on the emotions felt by the player at the end of the game. For example, the system can use the emotion estimation function to suggest the difficulty level and theme of the next game based on the emotions felt by the player at the end of the game. For example, if the player is excited, the system can suggest a more difficult game. Also, if the player is relaxed, the system can suggest a game with a relaxing theme. The emotion estimation function can estimate the player's emotional state using, for example, a machine learning algorithm. The system can also estimate the player's emotional state using sensor data. This makes it possible to suggest the difficulty level and theme of the next game based on the emotions felt by the player at the end of the game.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0083] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0084] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0085] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0086] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0087] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0088] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0089] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0090] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0091] When a player asks a question, the system can understand the intent of the question and provide relevant additional information. For example, if a player asks, "Is it an electronic device?", the system can provide additional information such as, "Yes, it is a portable device." Also, if a player asks, "Is it a living thing?", the system can provide specific information such as, "Yes, it is a mammal." This allows the system to understand the intent of the question and provide relevant additional information when a player asks a question.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: In the selection section, the generation AI selects an object, person, or place. For example, the generation AI randomly selects an object, person, or place from a pre-configured database. The generation AI can also make a selection by inputting a specific prompt. For example, if the prompt is "Select a famous person," the generation AI will select a famous person based on that instruction. Step 2: The question-answering unit answers the player's questions with "yes" or "no." For example, if the player asks "Is it a living thing?", the generated AI answers "yes" or "no." Also, if the player asks "Is it an electronic device?", the generated AI answers "yes" or "no."

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0100] The 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.

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

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

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

[0104] Fig. 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.

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

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

[0107] In the 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.

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

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

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The 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.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 selector for selecting an object, person, or place; a question and answer unit that answers "yes" or "no" to questions asked by a player in order to guess the object, the person, or the place selected by the selection unit. A system characterized by:

2. The selection unit Customizing the object, the person, or the location based on the player's past question history or play style.

2. The system of claim 1.

3. The question answering unit Providing additional hints or relevant information in addition to a "yes" or "no" response to the player's question 2. The system of claim 1.

4. The system comprises: A rating system is provided that evaluates the quality of the questions asked by the player and awards points to players who ask clever questions.

2. The system of claim 1.

5. The question answering unit An emotion estimation function that adjusts the response according to the emotional state of the player 2. The system of claim 1.

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