System

Abstract

An object of a system according to an embodiment is to efficiently perform brush-up on generated materials and predict the fun of the materials.SOLUTION: A system includes a material generation part, a determination part, an brush-up part, and a prediction part. The material generation unit generates a material using the generation AI. The determination unit determines whether or not the material generated by the material generation unit is positive. The brush-up part performs brush-up of the material based on the positive comment by the determination part. The prediction part numerically predicts the interest of the material subjected to the brush-up by the brush-up part.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, the process of improving the quality of generated content is inefficient and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently refine generated material and predict its entertainingness. [Means for solving the problem]

[0006] The system according to the embodiment includes a joke generation unit, a judgment unit, a brush-up unit, and a prediction unit. The joke generation unit generates jokes using a generation AI. The judgment unit judges whether the joke generated by the joke generation unit is positive or not. The brush-up unit brushes up the joke based on positive comments made by the judgment unit. The prediction unit numerically predicts the funniness of the joke brushed up by the brush-up unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently refine generated material and predict its entertaining nature. [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) The joke generation system according to an embodiment of the present invention is a system that generates new jokes by combining jokes created using a generation AI with the user's existing jokes (RAGed), and evaluates and refines the humor of the jokes. This allows the joke generation system to efficiently create funny jokes and evaluate and improve the humor of the jokes.

[0029] A gag generation system according to an embodiment includes a gag generation unit, a determination unit, a polishing unit, and a prediction unit. The gag generation unit generates gag material using a generation AI. For example, the generation AI receives prompts containing instructions from a user as input and generates new material. The generation AI also simultaneously uses the user's existing material (RAGed) and combines them to create new material. The determination unit determines whether the generated material is positive or not. For example, the generation AI analyzes comments on the material and selects comments that the user finds "funny." The polishing unit polishes the material based on positive comments. For example, the material is improved by incorporating feedback obtained from positive comments. The prediction unit numerically predicts the funniness of the polished material. For example, the generation AI evaluates the funniness of the material based on past data and quantifies the evaluation. This allows the gag generation system according to an embodiment to efficiently create funny material and evaluate and improve the funniness of the material. For example, comedians can use this system when creating new material to make more audiences laugh. It can also be applied to creating content for social media and video platforms.

[0030] The joke generation unit can learn from users' past laughing reaction data and generate jokes optimized for each individual user. For example, the joke generation unit uses a generation AI to collect users' past laughing reaction data and generate jokes optimized for each individual user based on that data. For example, it analyzes patterns of jokes that made users laugh in the past and generates new jokes with similar patterns. This makes it possible to generate jokes optimized for each individual user based on the users' past reaction data.

[0031] The joke generation unit can learn humor from different cultures or languages ​​and generate jokes that are accepted globally. For example, the joke generation unit uses a generation AI to learn humor from different cultures or languages ​​and generate jokes that are accepted globally. For example, it analyzes the characteristics of humor in English-speaking, French-speaking, Japanese-speaking, etc., and generates jokes that combine them. This allows the AI ​​to learn humor from different cultures and languages ​​and generate jokes that are accepted globally.

[0032] The material generation unit can profile the user's hobbies and interests and generate material on themes based on those. For example, the material generation unit uses a generation AI to profile the user's hobbies and interests and generate material on themes based on those. For example, if the user is interested in sports, it can provide humor related to sports. This allows material to be generated on themes based on the user's hobbies and interests.

[0033] The material generation unit can generate material specialized for different genres. For example, the generation AI generates material specialized for different genres. For example, it learns the characteristics of each genre, such as political satire, black humor, and family-friendly content, and generates material based on that. This makes it possible to generate material specialized for different genres.

[0034] The judgment unit can analyze the reactions to each part of the joke in detail and identify which parts caused a particularly positive or negative reaction. For example, the generation AI can analyze the viewer's reaction to each part of the joke in detail and identify the parts that caused a particularly positive reaction. For example, if the viewer laughed at a particular joke, that part will be emphasized. This makes it possible to analyze the reaction to each part of the joke in detail and identify the parts that caused a particularly positive or negative reaction.

[0035] The judgment unit can analyze reactions on different platforms and determine whether each platform is positive or negative. For example, the judgment unit uses a generation AI to analyze reactions on social media and determine whether a post is positive or negative. For example, the evaluation is based on user comments and the number of likes. This makes it possible to analyze reactions on different platforms and determine whether a post is positive or negative for each platform.

[0036] The judgment unit can analyze reactions to a joke over time and identify the timing at which reactions, whether positive or negative, increase. For example, the generation AI analyzes reactions to a joke over time and identifies parts where positive reactions increase at specific times. For example, if laughter increases at the climax of a joke, that part is emphasized. This makes it possible to analyze reactions to a joke over time and identify parts where positive / negative reactions increase at specific times.

[0037] The brush-up section can analyze not only positive comments but also negative comments, identify the areas for improvement, and brush up the material. For example, the generation AI analyzes not only positive comments but also negative comments, identifies the areas for improvement, and brushes up the material. For example, based on a comment such as "this part is not funny," that part is corrected. This allows not only positive comments but also negative comments to be analyzed, and the areas for improvement in the material to be identified and brushed up.

[0038] The brush-up unit can generate different versions of the joke and select the optimal version based on the user's feedback. For example, the brush-up unit generates different versions of the joke using a generation AI and selects the optimal version based on user feedback. For example, the brush-up unit generates multiple jokes on the same theme and selects the most interesting version based on user evaluation. This allows different versions of the joke to be generated and the optimal version to be selected based on user feedback.

[0039] The brush-up unit can combine the material from different genres to create cross-genre humor. For example, the generation AI can generate material that combines political satire and family humor. This allows material from different genres to be combined to create cross-genre humor.

[0040] The brush-up unit can adjust the length and tempo of the material based on the user's feedback to achieve the optimal performance. For example, the generation AI of the brush-up unit adjusts the length and tempo of the material based on the user's feedback to achieve the optimal performance. For example, if the user feels that the material is too long, it shortens that part. This allows the length and tempo of the material to be adjusted based on the user's feedback to achieve the optimal performance.

[0041] The prediction unit can learn from past jokes and their reaction data and build a more accurate prediction model of laughter. For example, the prediction unit uses a generation AI to learn from past jokes and their reaction data and build a more accurate prediction model of laughter. For example, the prediction unit analyzes patterns of jokes that have received high ratings in the past and predicts laughter for new jokes based on that. In this way, past jokes and their reaction data can be learned and a more accurate prediction model of laughter can be built.

[0042] The prediction unit can analyze the humorous reactions of different audience segments (age, gender, region) and make the prediction for each audience segment. For example, the prediction unit uses a generation AI to analyze the humorous reactions of different audience segments (age, gender, region) and make a prediction for each audience segment. For example, the prediction unit analyzes the different humor patterns between young and elderly audiences and predicts jokes based on that. This allows the humorous reactions of different audience segments to be analyzed and a prediction for each audience segment to be made.

[0043] The prediction unit can analyze the reactions to the joke in different media (e.g., text, audio, video) and predict the laughter for each media. For example, the prediction unit uses a generation AI to analyze the reactions to the joke in text and predict the laughter for each media. For example, a prediction score is set based on the number of comments and likes for the text joke. This makes it possible to analyze the reactions to the joke in different media and predict the laughter for each media.

[0044] The prediction unit can predict laughter by taking into consideration not only the content of the joke but also the elements of the performance (e.g., tone of voice, gestures). For example, the prediction unit predicts laughter by having the generation AI analyze not only the content of the joke but also the tone of voice. For example, the funniness of the joke is evaluated based on the intonation and strength of the voice. This makes it possible to predict laughter by taking into consideration not only the content of the joke but also elements of the performance.

[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] The material generation system can further include a health condition acquisition unit that monitors the user's health condition. For example, if the user is tired, it can generate material that will help them relax. If the user is in good spirits, it can provide energetic material. This allows it to provide the most appropriate material according to the user's health condition.

[0047] The joke generation system can further include a purchase history analysis unit that analyzes the user's past purchase history. For example, the system can generate jokes related to products recently purchased by the user. If the user likes a particular brand, the system can provide humor related to that brand. This allows the system to provide personalized jokes based on the user's purchase history.

[0048] The joke generation system may further include a social media analysis unit that analyzes the user's social media activity. For example, the system may generate jokes related to posts that the user has recently "liked." The system may also provide humor related to influencers that the user follows. This allows the system to provide jokes based on the user's social media activity.

[0049] The material generation system can further include a location information acquisition unit that acquires real-time location information of the user. For example, if the user is traveling, the system can generate material related to the user's travel destination. Also, if the user is participating in a specific event, the system can provide humor related to the event. This allows the system to provide material based on the user's real-time location information.

[0050] The joke generation system can further include a music analysis unit that analyzes the user's musical preferences. For example, the system can generate jokes related to songs the user has recently listened to. It can also provide humor related to the user's favorite artists. This allows the system to provide jokes based on the user's musical preferences.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The material generation unit uses the generation AI to generate material. For example, the generation AI receives prompts containing instructions from the user as input and generates new material. It also uses the user's existing material (RAGed) and combines them to create new material. Step 2: The judgment unit determines whether the generated material is positive or not. For example, the generation AI analyzes comments on the material and picks out comments that it finds "interesting." Step 3: The polishing team polishes the material based on the positive comments. For example, they improve the material by reflecting the feedback they received from the positive comments. Step 4: The prediction unit numerically predicts the entertainingness of the polished material. For example, the generation AI evaluates the entertainingness of the material based on past data and quantifies the evaluation.

[0053] (Example 2) The joke generation system according to an embodiment of the present invention is a system that generates new jokes by combining jokes created using a generation AI with the user's existing jokes (RAGed), and evaluates and refines the humor of the jokes. This allows the joke generation system to efficiently create funny jokes and evaluate and improve the humor of the jokes.

[0054] A gag generation system according to an embodiment includes a gag generation unit, a determination unit, a polishing unit, and a prediction unit. The gag generation unit generates gag material using a generation AI. For example, the generation AI receives prompts containing instructions from a user as input and generates new material. The generation AI also simultaneously uses the user's existing material (RAGed) and combines them to create new material. The determination unit determines whether the generated material is positive or not. For example, the generation AI analyzes comments on the material and selects comments that the user finds "funny." The polishing unit polishes the material based on positive comments. For example, the material is improved by incorporating feedback obtained from positive comments. The prediction unit numerically predicts the funniness of the polished material. For example, the generation AI evaluates the funniness of the material based on past data and quantifies the evaluation. This allows the gag generation system according to an embodiment to efficiently create funny material and evaluate and improve the funniness of the material. For example, comedians can use this system when creating new material to make more audiences laugh. It can also be applied to creating content for social media and video platforms.

[0055] The joke generation unit can learn from users' past laughing reaction data and generate jokes optimized for each individual user. For example, the joke generation unit uses a generation AI to collect users' past laughing reaction data and generate jokes optimized for each individual user based on that data. For example, it analyzes patterns of jokes that made users laugh in the past and generates new jokes with similar patterns. This makes it possible to generate jokes optimized for each individual user based on the users' past reaction data.

[0056] The joke generation unit can learn humor from different cultures or languages ​​and generate jokes that are accepted globally. For example, the joke generation unit uses a generation AI to learn humor from different cultures or languages ​​and generate jokes that are accepted globally. For example, it analyzes the characteristics of humor in English-speaking, French-speaking, Japanese-speaking, etc., and generates jokes that combine them. This allows the AI ​​to learn humor from different cultures and languages ​​and generate jokes that are accepted globally.

[0057] The joke generation unit can use the emotion estimation function to analyze the user's real-time emotional state and generate jokes that best match the user's emotions at that time. For example, the joke generation unit can use the emotion estimation function to analyze the user's real-time emotional state and generate jokes that best match the user's emotions at that time. For example, if the user is feeling stressed, the joke generation unit can provide relaxing humor. This allows the generation of optimal jokes based on the user's real-time emotional state.

[0058] The material generation unit can profile the user's hobbies and interests and generate material on themes based on those. For example, the material generation unit uses a generation AI to profile the user's hobbies and interests and generate material on themes based on those. For example, if the user is interested in sports, it can provide humor related to sports. This allows material to be generated on themes based on the user's hobbies and interests.

[0059] The material generation unit can generate material specialized for different genres. For example, the generation AI generates material specialized for different genres. For example, it learns the characteristics of each genre, such as political satire, black humor, and family-friendly content, and generates material based on that. This makes it possible to generate material specialized for different genres.

[0060] The joke generation unit can use the emotion estimation function to identify a theme for which the user has the most positive emotion and generate a joke based on that theme. For example, the joke generation unit can use the emotion estimation function to identify a theme for which the user has the most positive emotion and generate a joke based on that theme. For example, if the user has positive emotions toward animals, humor related to animals can be provided. This makes it possible to generate a joke based on a theme for which the user has the most positive emotion.

[0061] The judgment unit can analyze not only the comments on the joke but also the viewers' facial expressions and tone of voice to determine whether they are positive or negative. For example, the judgment unit analyzes not only the comments on the joke but also the viewers' facial expressions to determine whether they are positive or negative. For example, if a viewer smiles, it will record this as a positive reaction. This allows for a more accurate positive / negative judgment by analyzing the viewers' facial expressions and tone of voice.

[0062] The judgment unit can analyze the reactions to each part of the joke in detail and identify which parts caused a particularly positive or negative reaction. For example, the generation AI can analyze the viewer's reaction to each part of the joke in detail and identify the parts that caused a particularly positive reaction. For example, if the viewer laughed at a particular joke, that part will be emphasized. This makes it possible to analyze the reaction to each part of the joke in detail and identify the parts that caused a particularly positive or negative reaction.

[0063] The determination unit can use the emotion estimation function to monitor changes in the user's emotions in real time and determine whether the emotion is positive or negative based on the changes. For example, the determination unit can use the emotion estimation function to monitor changes in the user's emotions in real time and determine whether the emotion is positive or negative based on the changes. For example, if the user changes from a smiling face to a serious face, this is recorded as a negative reaction. This makes it possible to monitor changes in the user's emotions in real time and determine whether the emotion is positive or negative based on the changes.

[0064] The judgment unit can analyze reactions on different platforms and determine whether each platform is positive or negative. For example, the judgment unit uses a generation AI to analyze reactions on social media and determine whether a post is positive or negative. For example, the evaluation is based on user comments and the number of likes. This makes it possible to analyze reactions on different platforms and determine whether a post is positive or negative for each platform.

[0065] The judgment unit can analyze reactions to a joke over time and identify the timing at which reactions, whether positive or negative, increase. For example, the generation AI analyzes reactions to a joke over time and identifies parts where positive reactions increase at specific times. For example, if laughter increases at the climax of a joke, that part is emphasized. This makes it possible to analyze reactions to a joke over time and identify parts where positive / negative reactions increase at specific times.

[0066] The determination unit can use the emotion estimation function to compare the emotional state of the user before and after watching a joke and determine the impact the joke has on emotions. For example, the determination unit can use the emotion estimation function to compare the emotional state of the user before and after watching a joke and determine the impact the joke has on emotions. For example, if a user who had negative emotions before watching a joke shows positive emotions after watching it, the joke is rated highly. This allows the emotional state of the user before and after watching a joke to be compared and the impact the joke has on emotions to be determined.

[0067] The brush-up section can analyze not only positive comments but also negative comments, identify the areas for improvement, and brush up the material. For example, the generation AI analyzes not only positive comments but also negative comments, identifies the areas for improvement, and brushes up the material. For example, based on a comment such as "this part is not funny," that part is corrected. This allows not only positive comments but also negative comments to be analyzed, and the areas for improvement in the material to be identified and brushed up.

[0068] The brush-up unit can generate different versions of the joke and select the optimal version based on the user's feedback. For example, the brush-up unit generates different versions of the joke using a generation AI and selects the optimal version based on user feedback. For example, the brush-up unit generates multiple jokes on the same theme and selects the most interesting version based on user evaluation. This allows different versions of the joke to be generated and the optimal version to be selected based on user feedback.

[0069] The brush-up unit can combine the material from different genres to create cross-genre humor. For example, the generation AI can generate material that combines political satire and family humor. This allows material from different genres to be combined to create cross-genre humor.

[0070] The brush-up unit can adjust the length and tempo of the material based on the user's feedback to achieve the optimal performance. For example, the generation AI of the brush-up unit adjusts the length and tempo of the material based on the user's feedback to achieve the optimal performance. For example, if the user feels that the material is too long, it shortens that part. This allows the length and tempo of the material to be adjusted based on the user's feedback to achieve the optimal performance.

[0071] The brush-up unit can use the emotion estimation function to extract the element of the joke that indicates the most positive emotion for the user and apply it to other jokes. For example, the brush-up unit uses the emotion estimation function to extract the element of the joke that indicates the most positive emotion for the user and apply it to other jokes. For example, if the user laughed at a particular joke, the pattern of that joke can be incorporated into other jokes. This allows the element of the joke that indicates the most positive emotion for the user to be extracted and applied to other jokes.

[0072] The prediction unit can learn from past jokes and their reaction data and build a more accurate prediction model of laughter. For example, the prediction unit uses a generation AI to learn from past jokes and their reaction data and build a more accurate prediction model of laughter. For example, the prediction unit analyzes patterns of jokes that have received high ratings in the past and predicts laughter for new jokes based on that. In this way, past jokes and their reaction data can be learned and a more accurate prediction model of laughter can be built.

[0073] The prediction unit can analyze the humorous reactions of different audience segments (age, gender, region) and make the prediction for each audience segment. For example, the prediction unit uses a generation AI to analyze the humorous reactions of different audience segments (age, gender, region) and make a prediction for each audience segment. For example, the prediction unit analyzes the different humor patterns between young and elderly audiences and predicts jokes based on that. This allows the humorous reactions of different audience segments to be analyzed and a prediction for each audience segment to be made.

[0074] The prediction unit can use the emotion estimation function to predict how much laughter the joke will elicit based on the real-time emotional state of the user. The prediction unit, for example, uses the emotion estimation function to predict how much laughter the joke will elicit based on the real-time emotional state of the user. For example, if the user smiles, the prediction score for the laughter of that joke is set high. This makes it possible to predict how much laughter the joke will elicit based on the real-time emotional state of the user.

[0075] The prediction unit can analyze the reactions to the joke in different media (e.g., text, audio, video) and predict the laughter for each media. For example, the prediction unit uses a generation AI to analyze the reactions to the joke in text and predict the laughter for each media. For example, a prediction score is set based on the number of comments and likes for the text joke. This makes it possible to analyze the reactions to the joke in different media and predict the laughter for each media.

[0076] The prediction unit can predict laughter by taking into consideration not only the content of the joke but also the elements of the performance (e.g., tone of voice, gestures). For example, the prediction unit predicts laughter by having the generation AI analyze not only the content of the joke but also the tone of voice. For example, the funniness of the joke is evaluated based on the intonation and strength of the voice. This makes it possible to predict laughter by taking into consideration not only the content of the joke but also elements of the performance.

[0077] The prediction unit can use the emotion estimation function to identify the element of the joke that indicates the most positive emotion for the user, and predict the laughter based on that element. For example, the prediction unit can use the emotion estimation function to identify the element of the joke that indicates the most positive emotion for the user, and predict laughter based on that element. For example, if the user laughs at a particular joke, a prediction is made based on the pattern of that joke. This allows the element of the joke that indicates the most positive emotion for the user to be identified, and prediction of laughter to be made based on that element.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The material generation system can further include a health condition acquisition unit that monitors the user's health condition. For example, if the user is tired, it can generate material that will help them relax. If the user is in good spirits, it can provide energetic material. This allows it to provide the most appropriate material according to the user's health condition.

[0080] The joke generation system can further include a purchase history analysis unit that analyzes the user's past purchase history. For example, the system can generate jokes related to products recently purchased by the user. If the user likes a particular brand, the system can provide humor related to that brand. This allows the system to provide personalized jokes based on the user's purchase history.

[0081] The joke generation system may further include a social media analysis unit that analyzes the user's social media activity. For example, the system may generate jokes related to posts that the user has recently "liked." The system may also provide humor related to influencers that the user follows. This allows the system to provide jokes based on the user's social media activity.

[0082] The material generation system can further include a location information acquisition unit that acquires real-time location information of the user. For example, if the user is traveling, the system can generate material related to the user's travel destination. Also, if the user is participating in a specific event, the system can provide humor related to the event. This allows the system to provide material based on the user's real-time location information.

[0083] The joke generation system can further include a music analysis unit that analyzes the user's musical preferences. For example, the system can generate jokes related to songs the user has recently listened to. It can also provide humor related to the user's favorite artists. This allows the system to provide jokes based on the user's musical preferences.

[0084] The material generation unit can use the emotion estimation function to identify the time period when the user is most relaxed and generate material that is optimal for that time period. For example, if the user is relaxing at night, humor that can be enjoyed at night can be provided. Also, if the user is relaxing on the weekend, material that can be enjoyed on the weekend can be generated. This makes it possible to provide material that is optimal for the time period when the user is most relaxed.

[0085] The material generation unit can use the emotion estimation function to identify the situation in which the user is feeling the most stress and generate material that corresponds to that situation. For example, if the user is feeling stressed at work, the unit can provide humor that allows the user to relax in between work. Also, if the user is feeling stressed during their commute, the unit can generate material that the user can enjoy during their commute. This makes it possible to provide material that corresponds to the situation in which the user is feeling the most stress.

[0086] The joke generation unit can use the emotion estimation function to identify the moment when the user feels happiest and generate the most suitable joke for that moment. For example, if the user feels happy spending time with family, humor that can be enjoyed by the family can be provided. Also, if the user feels happy when immersed in a hobby, jokes related to that hobby can be generated. This makes it possible to provide the most suitable joke for the moment when the user feels happiest.

[0087] The joke generation unit can use the emotion estimation function to identify the theme that excites the user the most and generate jokes based on that theme. For example, if the user is excited about sports, humor related to sports can be provided. Also, if the user is excited about movies, humor related to those movies can be generated. This makes it possible to provide jokes based on the theme that excites the user the most.

[0088] The joke generation unit can use the emotion estimation function to identify the moment when the user is most moved and generate the most appropriate joke for that moment. For example, if the user is moved while watching a moving movie, humor related to that movie can be provided. Also, if the user is moved while listening to moving music, jokes related to that music can be generated. This makes it possible to provide the most appropriate joke for the moment when the user is most moved.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The material generation unit uses the generation AI to generate material. For example, the generation AI receives prompts containing instructions from the user as input and generates new material. It also uses the user's existing material (RAGed) and combines them to create new material. Step 2: The judgment unit determines whether the generated material is positive or not. For example, the generation AI analyzes comments on the material and picks out comments that it finds "interesting." Step 3: The polishing team polishes the material based on the positive comments. For example, they improve the material by reflecting the feedback they received from the positive comments. Step 4: The prediction unit numerically predicts the entertainingness of the polished material. For example, the generation AI evaluates the entertainingness of the material based on past data and quantifies the evaluation.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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, in order to avoid confusion and to 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.

[0157] 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]

[0158] 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 material generation unit that generates material using a generation AI; a determination unit that determines whether the joke generated by the joke generation unit is positive; a brush-up unit that brushes up the material based on the positive comments made by the judgment unit; a prediction unit that predicts the entertainment value of the material brushed up by the brush-up unit in numerical terms. A system characterized by:

2. The material generation unit Analyzes the user's real-time emotional state and generates content that best suits that emotion.

2. The system of claim 1.

3. The determination unit Not only the comments on the material, but also the viewers' facial expressions and tone of voice are analyzed to determine whether they are positive or not.

2. The system of claim 1.

4. The brush-up unit Analyze not only positive comments but also negative comments to identify areas for improvement and refine the content.

2. The system of claim 1.

5. The prediction unit By learning from past jokes and their reactions, a more accurate prediction model for laughter can be built.

2. The system of claim 1.

Images