System

Abstract

An object of the system according to the embodiment is to optimize a novel written by a user based on characteristics of each prize.SOLUTION: A system includes a data learning part, a novel analysis part, an optimization part, and an output part. The data learning part learns data of each prize. The novel analyzing unit analyzes a novel input by a user. The optimizing unit optimizes the novel analyzed by the novel analyzing unit based on the feature of each prize. The output unit outputs the novel optimized by the optimization 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] Conventional technology has had the problem of making it difficult to optimize novels written by users to suit the characteristics of each award.

[0005] The system according to the embodiment aims to optimize novels written by users based on the characteristics of each award. [Means for solving the problem]

[0006] The system according to the embodiment includes a data learning unit, a novel analysis unit, an optimization unit, and an output unit. The data learning unit learns data on each award. The novel analysis unit analyzes the novel entered by the user. The optimization unit optimizes the novel analyzed by the novel analysis unit based on the characteristics of each award. The output unit outputs the novel optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the novels written by the user based on the characteristics of each award. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A novel optimization system according to an embodiment of the present invention uses a large-scale language model (LLM) to optimize the style and structure of a novel for a specific literary award. This allows the novel optimization system to optimize a user's novel to match the characteristics of each award, thereby increasing the chances of winning.

[0029] A novel optimization system according to an embodiment includes a data learning unit, a novel analysis unit, an optimization unit, and an output unit. The data learning unit learns data about each award. For example, the data learning unit inputs data about past award winners, application guidelines, judging criteria, and the like into the LLM and causes it to learn. The data learning unit can learn, for example, the characteristics of awards that favor moving stories. The data learning unit can also learn the characteristics of awards that value innovative ideas. The novel analysis unit analyzes novels entered by a user. For example, the novel analysis unit analyzes the writing style and structure of the novel entered by the user. The novel analysis unit can also analyze the theme and message of the novel entered by the user. The optimization unit optimizes the novel analyzed by the novel analysis unit based on the characteristics of each award. For example, the optimization unit adds emotional expressions and episodes for awards that favor moving stories. The optimization unit can also emphasize unique perspectives and new ideas for awards that value innovative ideas. The output unit outputs the novel optimized by the optimization unit. For example, the output unit displays the optimized novel to the user. The output unit can also save the optimized novel in a file format. As a result, the novel optimization system according to the embodiment can optimize the user's novel to match the characteristics of each award, thereby increasing the possibility of winning. For example, a user can adjust their own novel to match the characteristics of each award, thereby increasing the possibility of winning. Furthermore, a user can create a novel that is more in line with their intentions by emphasizing specific expressions or episodes.

[0030] The optimization unit can add expressions and episodes that bring out emotions, aiming for an award in which a moving story is preferred. The optimization unit can add expressions and episodes that bring out emotions, aiming for an award in which a moving story is preferred, for example. For example, the optimization unit can add an emotional climax. The optimization unit can also add sympathetic characters. The optimization unit can also add moving events. In this way, the optimization unit can be optimized for an award in which a moving story is preferred.

[0031] The optimization unit can emphasize original perspectives and new ideas for an award that recognizes innovative ideas. The optimization unit, for example, emphasizes original perspectives and new ideas for an award that recognizes innovative ideas. For example, the optimization unit emphasizes original perspectives. The optimization unit can also emphasize new concepts. The optimization unit can also emphasize unique perspectives. In this way, optimization can be performed for an award that recognizes innovative ideas.

[0032] The optimization unit can emphasize readability by frequently using short sentences and concise expressions. The optimization unit can emphasize readability by frequently using short sentences and concise expressions, for example. For example, the optimization unit frequently uses short sentences. The optimization unit can also frequently use concise expressions. The optimization unit can also frequently use concise language. This allows optimization that emphasizes readability.

[0033] The optimizer can add depth by using complex sentences and detailed descriptions. The optimizer can add depth by using complex sentences and detailed descriptions, for example. For example, the optimizer uses long sentences. The optimizer can also use sentences with complex structures. The optimizer can also use detailed descriptions. This allows for optimization with depth.

[0034] The optimization unit can emphasize specific expressions or episodes of the user. For example, the optimization unit emphasizes specific expressions or episodes of the user. For example, the optimization unit emphasizes expressions that the user particularly wants to emphasize. The optimization unit can also emphasize episodes that the user particularly wants to emphasize. The optimization unit can also emphasize important phrases. This allows optimization in line with the user's intentions.

[0035] The data learning unit analyzes the past comments and evaluation criteria of the judges for each award and trains the LLM to understand more specific judging criteria. The data learning unit, for example, analyzes the past comments and evaluation criteria of the judges for each award and trains the LLM to understand more specific judging criteria. For example, the data learning unit analyzes how the judges evaluated specific works. The data learning unit can also train the LLM to understand the judges' evaluation criteria. The data learning unit can also analyze the judges' comments. This allows for more specific judging criteria to be understood.

[0036] The data learning unit can learn not only the writing style and structure of the winning works of each award, but also the themes and messages, thereby promoting deeper understanding. For example, the data learning unit can learn not only the writing style and structure of the winning works of each award, but also the themes and messages, thereby promoting deeper understanding. For example, the data learning unit can extract the themes and messages of the winning works. The data learning unit can also analyze the writing style and structure of the winning works. The data learning unit can also have the LLM learn the messages of the winning works. This can promote deeper understanding.

[0037] The data learning unit can learn data on other literary awards in similar genres in addition to data on each award, allowing a broader range of trends to be grasped. For example, the data learning unit can learn data on other literary awards in similar genres in addition to data on each award, allowing a broader range of trends to be grasped. For example, the data learning unit can learn the winning works and application guidelines of multiple literary awards. The data learning unit can also collect data on other literary awards in similar genres. The data learning unit can also train the LLM on data on other literary awards. This allows a broader range of trends to be grasped.

[0038] When learning the data of each award, the data learning unit can incorporate an international perspective, including data on literary awards in different languages. For example, when learning the data of each award, the data learning unit incorporates an international perspective, including data on literary awards in different languages. For example, the data learning unit learns data on English literary awards. The data learning unit can also learn data on French literary awards. The data learning unit can also learn data on Chinese literary awards. This makes it possible to incorporate an international perspective.

[0039] The novel analysis unit can perform optimization for each chapter or section of the novel entered by the user, in accordance with the characteristics of each award. For example, the novel analysis unit can perform optimization for each chapter or section of the novel entered by the user, in accordance with the characteristics of each award. For example, the novel analysis unit can add expressions that bring out emotions for awards that favor moving stories. The novel analysis unit can also emphasize unique perspectives for awards that recognize innovative ideas. The novel analysis unit can also perform optimization for each chapter or section. This allows optimization to be performed for each chapter or section.

[0040] The optimization unit may provide an option to simultaneously optimize a novel input by a user for multiple literary awards. For example, the optimization unit may simultaneously optimize a novel input by a user for an award that favors moving stories and an award that recognizes innovative ideas. The optimization unit may also optimize for multiple literary awards. The optimization unit may also consider the characteristics of multiple awards simultaneously. This allows for simultaneous optimization for multiple literary awards.

[0041] The optimization unit can optimize not only the writing style and structure of a novel, but also the title and subtitle to suit each award. For example, the optimization unit can optimize not only the writing style and structure of a novel, but also the title and subtitle to suit each award. For example, the optimization unit can set an emotional title for an award that favors moving stories. The optimization unit can also set a unique title for an award that recognizes innovative ideas. The optimization unit can also adjust the subtitle. In this way, the title and subtitle can also be optimized.

[0042] The optimization unit can reflect the preferences and evaluation tendencies of the judges for each award when optimizing the writing style and composition. For example, the optimization unit reflects the preferences and evaluation tendencies of the judges for each award when optimizing the writing style and composition. For example, the optimization unit identifies the writing style and composition that the judges prefer and reflects them. The optimization unit can also analyze the evaluation tendencies of the judges. The optimization unit can also reflect the preferences of the judges. In this way, the preferences and evaluation tendencies of the judges can be reflected.

[0043] The optimization unit can also reconstruct the plot and story development of a novel to suit the trends of each award. For example, the optimization unit can add emotionally appealing episodes for awards that favor moving stories. The optimization unit can also emphasize unique perspectives for awards that recognize innovative ideas. The optimization unit can also reconstruct the plot and story development. This allows the plot and story development to be reconstructed.

[0044] The optimizer can incorporate elements of different genres and styles to create a new writing style when optimizing the writing style and structure. For example, the optimizer can incorporate elements of different genres and styles to create a new writing style when optimizing the writing style and structure. For example, the optimizer can add a mystery element to an emotional story. The optimizer can also add a romance element. The optimizer can also create a new writing style. In this way, it is possible to incorporate elements of different genres and styles to create a new writing style.

[0045] The optimization unit can also take visual elements into consideration when optimizing a novel. For example, the optimization unit also takes visual elements into consideration when optimizing a novel. For example, the optimization unit performs optimization including illustrations and layout. The optimization unit can also take visual elements into consideration. The optimization unit can also adjust the style of illustrations. This allows visual elements to be taken into consideration.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The novel optimization system further includes a voice analysis unit. The voice analysis unit analyzes the voice data of the novel read aloud by the user and can optimize the style and expression based on the intonation and rhythm of the voice. For example, the voice analysis unit can emphasize the intonation of the voice in moving scenes. The voice analysis unit can also speed up the rhythm in tense scenes. The voice analysis unit can also adjust the expression to match the tone of the user's voice. This makes it possible to optimize based on voice data.

[0048] The novel optimization system further includes a reader feedback collection unit. The reader feedback collection unit can collect feedback from readers and optimize the novel based on the data. For example, the reader feedback collection unit collects reader comments and ratings. The reader feedback collection unit can also analyze readers' preferences and opinions. The reader feedback collection unit can also improve specific parts of the novel based on the collected data. This enables optimization that reflects readers' opinions.

[0049] The novel optimization system further includes a cultural background analysis unit. The cultural background analysis unit can analyze the cultural background of the user's novel and perform optimization based on that background. For example, the cultural background analysis unit can emphasize expressions and themes unique to a particular culture or region. The cultural background analysis unit can also add elements to deepen understanding of different cultures. The cultural background analysis unit can also adjust expressions taking cultural nuances into account. This enables optimization based on the cultural background.

[0050] The novel optimization system further includes a visual element generation unit. The visual element generation unit can generate illustrations and diagrams based on the content of the novel and add visual elements. For example, the visual element generation unit can generate illustrations to match moving scenes. The visual element generation unit can also illustrate complex story developments. The visual element generation unit can also generate visuals of characters. This enables optimization that incorporates visual elements.

[0051] The novel optimization system further includes a music generation unit. The music generation unit can generate music that matches a scene in the novel and provide it to the user. For example, the music generation unit can generate emotional music that matches a moving scene. The music generation unit can also generate tense music that matches a tense scene. The music generation unit can also generate calm music that matches a relaxing scene. This makes it possible to incorporate music into optimization.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The data learning unit learns data about each award. For example, the data learning unit inputs data such as past winning works for each award, application guidelines, and judging criteria into the LLM and trains it. The data learning unit can learn the characteristics of awards that favor moving stories or awards that reward innovative ideas. Step 2: The novel analysis unit analyzes the novel entered by the user. For example, the novel analysis unit analyzes the style, structure, theme, and message of the novel entered by the user. Step 3: The optimization unit optimizes the novel analyzed by the novel analysis unit based on the characteristics of each award. For example, the optimization unit adds emotional expressions and episodes for awards that favor moving stories, and emphasizes unique perspectives and new ideas for awards that recognize innovative ideas. Step 4: The output unit outputs the novel optimized by the optimization unit. For example, the output unit can display the optimized novel to the user and save it in a file format.

[0054] (Example 2) A novel optimization system according to an embodiment of the present invention uses a large-scale language model (LLM) to optimize the style and structure of a novel for a specific literary award. This allows the novel optimization system to optimize a user's novel to match the characteristics of each award, thereby increasing the chances of winning.

[0055] A novel optimization system according to an embodiment includes a data learning unit, a novel analysis unit, an optimization unit, and an output unit. The data learning unit learns data about each award. For example, the data learning unit inputs data about past award winners, application guidelines, judging criteria, and the like into the LLM and causes it to learn. The data learning unit can learn, for example, the characteristics of awards that favor moving stories. The data learning unit can also learn the characteristics of awards that value innovative ideas. The novel analysis unit analyzes novels entered by a user. For example, the novel analysis unit analyzes the writing style and structure of the novel entered by the user. The novel analysis unit can also analyze the theme and message of the novel entered by the user. The optimization unit optimizes the novel analyzed by the novel analysis unit based on the characteristics of each award. For example, the optimization unit adds emotional expressions and episodes for awards that favor moving stories. The optimization unit can also emphasize unique perspectives and new ideas for awards that value innovative ideas. The output unit outputs the novel optimized by the optimization unit. For example, the output unit displays the optimized novel to the user. The output unit can also save the optimized novel in a file format. As a result, the novel optimization system according to the embodiment can optimize the user's novel to match the characteristics of each award, thereby increasing the possibility of winning. For example, a user can adjust their own novel to match the characteristics of each award, thereby increasing the possibility of winning. Furthermore, a user can create a novel that is more in line with their intentions by emphasizing specific expressions or episodes.

[0056] The optimization unit can add expressions and episodes that bring out emotions, aiming for an award in which a moving story is preferred. The optimization unit can add expressions and episodes that bring out emotions, aiming for an award in which a moving story is preferred, for example. For example, the optimization unit can add an emotional climax. The optimization unit can also add sympathetic characters. The optimization unit can also add moving events. In this way, the optimization unit can be optimized for an award in which a moving story is preferred.

[0057] The optimization unit can emphasize original perspectives and new ideas for an award that recognizes innovative ideas. The optimization unit, for example, emphasizes original perspectives and new ideas for an award that recognizes innovative ideas. For example, the optimization unit emphasizes original perspectives. The optimization unit can also emphasize new concepts. The optimization unit can also emphasize unique perspectives. In this way, optimization can be performed for an award that recognizes innovative ideas.

[0058] The optimization unit can emphasize readability by frequently using short sentences and concise expressions. The optimization unit can emphasize readability by frequently using short sentences and concise expressions, for example. For example, the optimization unit frequently uses short sentences. The optimization unit can also frequently use concise expressions. The optimization unit can also frequently use concise language. This allows optimization that emphasizes readability.

[0059] The optimizer can add depth by using complex sentences and detailed descriptions. The optimizer can add depth by using complex sentences and detailed descriptions, for example. For example, the optimizer uses long sentences. The optimizer can also use sentences with complex structures. The optimizer can also use detailed descriptions. This allows for optimization with depth.

[0060] The optimization unit can emphasize specific expressions or episodes of the user. For example, the optimization unit emphasizes specific expressions or episodes of the user. For example, the optimization unit emphasizes expressions that the user particularly wants to emphasize. The optimization unit can also emphasize episodes that the user particularly wants to emphasize. The optimization unit can also emphasize important phrases. This allows optimization in line with the user's intentions.

[0061] The data learning unit analyzes the past comments and evaluation criteria of the judges for each award and trains the LLM to understand more specific judging criteria. The data learning unit, for example, analyzes the past comments and evaluation criteria of the judges for each award and trains the LLM to understand more specific judging criteria. For example, the data learning unit analyzes how the judges evaluated specific works. The data learning unit can also train the LLM to understand the judges' evaluation criteria. The data learning unit can also analyze the judges' comments. This allows for more specific judging criteria to be understood.

[0062] The data learning unit can learn not only the writing style and structure of the winning works of each award, but also the themes and messages, thereby promoting deeper understanding. For example, the data learning unit can learn not only the writing style and structure of the winning works of each award, but also the themes and messages, thereby promoting deeper understanding. For example, the data learning unit can extract the themes and messages of the winning works. The data learning unit can also analyze the writing style and structure of the winning works. The data learning unit can also have the LLM learn the messages of the winning works. This can promote deeper understanding.

[0063] The data learning unit uses the emotion estimation function to analyze the emotional tone and atmosphere of the award-winning works and allows the LLM to learn the results. The data learning unit, for example, uses the emotion estimation function to analyze the emotional tone and atmosphere of the award-winning works and allows the LLM to learn the results. For example, the data learning unit analyzes the emotional characteristics of moving works. The data learning unit can also analyze the emotional characteristics of works that are tense. The data learning unit can also analyze emotional tone using the emotion estimation function. In this way, the emotional tone and atmosphere of the award-winning works can be learned.

[0064] The data learning unit can learn data on other literary awards in similar genres in addition to data on each award, allowing a broader range of trends to be grasped. For example, the data learning unit can learn data on other literary awards in similar genres in addition to data on each award, allowing a broader range of trends to be grasped. For example, the data learning unit can learn the winning works and application guidelines of multiple literary awards. The data learning unit can also collect data on other literary awards in similar genres. The data learning unit can also train the LLM on data on other literary awards. This allows a broader range of trends to be grasped.

[0065] When learning the data of each award, the data learning unit can incorporate an international perspective, including data on literary awards in different languages. For example, when learning the data of each award, the data learning unit incorporates an international perspective, including data on literary awards in different languages. For example, the data learning unit learns data on English literary awards. The data learning unit can also learn data on French literary awards. The data learning unit can also learn data on Chinese literary awards. This makes it possible to incorporate an international perspective.

[0066] The data learning unit uses the emotion estimation function to analyze readers' emotional reactions to the winning works of each award, and can also have the LLM learn that data. The data learning unit, for example, uses the emotion estimation function to analyze readers' emotional reactions to the winning works of each award, and can also have the LLM learn that data. For example, the data learning unit analyzes readers' reviews and comments. The data learning unit can also have the LLM learn readers' emotional data. The data learning unit can also use the emotion estimation function to analyze readers' emotional reactions. In this way, readers' emotional reactions can be learned.

[0067] The novel analysis unit can perform optimization for each chapter or section of the novel entered by the user, in accordance with the characteristics of each award. For example, the novel analysis unit can perform optimization for each chapter or section of the novel entered by the user, in accordance with the characteristics of each award. For example, the novel analysis unit can add expressions that bring out emotions for awards that favor moving stories. The novel analysis unit can also emphasize unique perspectives for awards that recognize innovative ideas. The novel analysis unit can also perform optimization for each chapter or section. This allows optimization to be performed for each chapter or section.

[0068] The novel analysis unit can use the emotion estimation function to analyze the emotional intensity of the user's novel and optimize it for each award. The novel analysis unit can, for example, use the emotion estimation function to analyze the emotional intensity of the user's novel and optimize it for each award. For example, the novel analysis unit can add expressions that enhance emotions for awards that favor moving stories. The novel analysis unit can also analyze emotional intensity. The novel analysis unit can also use the emotion estimation function to evaluate emotional intensity. This allows for analyzing and optimizing emotional intensity.

[0069] The optimization unit may provide an option to simultaneously optimize a novel input by a user for multiple literary awards. For example, the optimization unit may simultaneously optimize a novel input by a user for an award that favors moving stories and an award that recognizes innovative ideas. The optimization unit may also optimize for multiple literary awards. The optimization unit may also consider the characteristics of multiple awards simultaneously. This allows for simultaneous optimization for multiple literary awards.

[0070] The optimization unit can optimize not only the writing style and structure of a novel, but also the title and subtitle to suit each award. For example, the optimization unit can optimize not only the writing style and structure of a novel, but also the title and subtitle to suit each award. For example, the optimization unit can set an emotional title for an award that favors moving stories. The optimization unit can also set a unique title for an award that recognizes innovative ideas. The optimization unit can also adjust the subtitle. In this way, the title and subtitle can also be optimized.

[0071] The novel analysis unit can use the emotion estimation function to analyze the emotional tone of a novel entered by a user in real time and suggest optimal rewriting. The novel analysis unit can, for example, use the emotion estimation function to analyze the emotional tone of a novel entered by a user in real time and suggest optimal rewriting. For example, the novel analysis unit can suggest expressions that bring out the emotions for an award that favors moving stories. The novel analysis unit can also analyze emotional tone in real time. The novel analysis unit can also evaluate emotional tone using the emotion estimation function. This makes it possible to analyze emotional tone in real time and suggest optimal rewriting.

[0072] The optimization unit can reflect the preferences and evaluation tendencies of the judges for each award when optimizing the writing style and composition. For example, the optimization unit reflects the preferences and evaluation tendencies of the judges for each award when optimizing the writing style and composition. For example, the optimization unit identifies the writing style and composition that the judges prefer and reflects them. The optimization unit can also analyze the evaluation tendencies of the judges. The optimization unit can also reflect the preferences of the judges. In this way, the preferences and evaluation tendencies of the judges can be reflected.

[0073] The optimization unit can also reconstruct the plot and story development of a novel to suit the trends of each award. For example, the optimization unit can add emotionally appealing episodes for awards that favor moving stories. The optimization unit can also emphasize unique perspectives for awards that recognize innovative ideas. The optimization unit can also reconstruct the plot and story development. This allows the plot and story development to be reconstructed.

[0074] The optimization unit can use the emotion estimation function to evaluate the emotional impact of the optimized novel and make further adjustments. For example, the optimization unit can use the emotion estimation function to evaluate the emotional impact of the optimized novel and make further adjustments. For example, the optimization unit can add expressions that enhance emotions for a review that favors moving stories. The optimization unit can also evaluate the emotional impact. The optimization unit can also adjust the emotional impact using the emotion estimation function. In this way, the emotional impact can be evaluated and further adjusted.

[0075] The optimizer can incorporate elements of different genres and styles to create a new writing style when optimizing the writing style and structure. For example, the optimizer can incorporate elements of different genres and styles to create a new writing style when optimizing the writing style and structure. For example, the optimizer can add a mystery element to an emotional story. The optimizer can also add a romance element. The optimizer can also create a new writing style. In this way, it is possible to incorporate elements of different genres and styles to create a new writing style.

[0076] The optimization unit can also take visual elements into consideration when optimizing a novel. For example, the optimization unit also takes visual elements into consideration when optimizing a novel. For example, the optimization unit performs optimization including illustrations and layout. The optimization unit can also take visual elements into consideration. The optimization unit can also adjust the style of illustrations. This allows visual elements to be taken into consideration.

[0077] The optimization unit can use the emotion estimation function to monitor the reader's emotional response to the optimized novel in real time and make continuous adjustments. The optimization unit can, for example, use the emotion estimation function to monitor the reader's emotional response to the optimized novel in real time and make continuous adjustments. For example, the optimization unit can analyze the reader's facial expressions and voice and calculate an emotion score. The optimization unit can also monitor the reader's emotional response in real time. The optimization unit can also adjust the emotional response using the emotion estimation function. This allows the reader's emotional response to be monitored in real time and make continuous adjustments.

[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 novel optimization system further includes a voice analysis unit. The voice analysis unit analyzes the voice data of the novel read aloud by the user and can optimize the style and expression based on the intonation and rhythm of the voice. For example, the voice analysis unit can emphasize the intonation of the voice in moving scenes. The voice analysis unit can also speed up the rhythm in tense scenes. The voice analysis unit can also adjust the expression to match the tone of the user's voice. This makes it possible to optimize based on voice data.

[0080] The novel optimization system further includes a reader feedback collection unit. The reader feedback collection unit can collect feedback from readers and optimize the novel based on the data. For example, the reader feedback collection unit collects reader comments and ratings. The reader feedback collection unit can also analyze readers' preferences and opinions. The reader feedback collection unit can also improve specific parts of the novel based on the collected data. This enables optimization that reflects readers' opinions.

[0081] The novel optimization system further includes a cultural background analysis unit. The cultural background analysis unit can analyze the cultural background of the user's novel and perform optimization based on that background. For example, the cultural background analysis unit can emphasize expressions and themes unique to a particular culture or region. The cultural background analysis unit can also add elements to deepen understanding of different cultures. The cultural background analysis unit can also adjust expressions taking cultural nuances into account. This enables optimization based on the cultural background.

[0082] The novel optimization system further includes a visual element generation unit. The visual element generation unit can generate illustrations and diagrams based on the content of the novel and add visual elements. For example, the visual element generation unit can generate illustrations to match moving scenes. The visual element generation unit can also illustrate complex story developments. The visual element generation unit can also generate visuals of characters. This enables optimization that incorporates visual elements.

[0083] The novel optimization system further includes a music generation unit. The music generation unit can generate music that matches a scene in the novel and provide it to the user. For example, the music generation unit can generate emotional music that matches a moving scene. The music generation unit can also generate tense music that matches a tense scene. The music generation unit can also generate calm music that matches a relaxing scene. This makes it possible to incorporate music into optimization.

[0084] The optimization unit can estimate the user's emotions and suggest optimal expressions and episodes based on the estimated user's emotions. For example, if the user is emotional, the optimization unit can suggest expressions that bring out the emotion. Also, if the user is excited, the optimization unit can suggest tense episodes. Also, if the user is relaxed, the optimization unit can suggest calm expressions. This makes it possible to perform optimization based on the user's emotions.

[0085] The novel analysis unit can estimate the user's emotions and suggest optimal rewriting for each chapter or section based on the estimated user emotions. For example, if the user is moved, the novel analysis unit can suggest expressions that will bring out the emotion. Also, if the user is excited, the novel analysis unit can suggest tense episodes. Also, if the user is relaxed, the novel analysis unit can suggest calm expressions. This makes it possible to optimize based on the user's emotions.

[0086] The data learning unit uses the emotion estimation function to analyze the judges' emotional reactions to the winning works for each award, and can train the LLM based on that data. For example, the data learning unit analyzes the judges' comments and ratings. The data learning unit can also train the LLM to learn the judges' emotional reactions. The data learning unit can also analyze the judges' emotional reactions using the emotion estimation function. This allows the LLM to learn the judges' emotional reactions.

[0087] The optimization unit can use the emotion estimation function to optimize the title and subtitle of a novel based on the user's emotions. For example, if the user is emotional, the optimization unit can suggest a title that brings out the emotion. Also, if the user is excited, the optimization unit can suggest a tense title. Also, if the user is relaxed, the optimization unit can suggest a calm title. This makes it possible to optimize the title and subtitle based on the user's emotions.

[0088] The novel analysis unit can use the emotion estimation function to optimize the plot and story development of a novel based on the user's emotions. For example, if the user is moved, the novel analysis unit can add an episode that will enhance the emotion. Also, if the user is excited, the novel analysis unit can suggest a tense story development. Also, if the user is relaxed, the novel analysis unit can suggest a calm plot. This makes it possible to optimize the plot and story development based on the user's emotions.

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

[0090] Step 1: The data learning unit learns data about each award. For example, the data learning unit inputs data such as past winning works for each award, application guidelines, and judging criteria into the LLM and trains it. The data learning unit can learn the characteristics of awards that favor moving stories or awards that reward innovative ideas. Step 2: The novel analysis unit analyzes the novel entered by the user. For example, the novel analysis unit analyzes the style, structure, theme, and message of the novel entered by the user. Step 3: The optimization unit optimizes the novel analyzed by the novel analysis unit based on the characteristics of each award. For example, the optimization unit adds emotional expressions and episodes for awards that favor moving stories, and emphasizes unique perspectives and new ideas for awards that recognize innovative ideas. Step 4: The output unit outputs the novel optimized by the optimization unit. For example, the output unit can display the optimized novel to the user and save it in a file format.

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

[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, 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.

[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 data learning department that learns the data of each prize, a novel analysis unit that analyzes a novel input by a user; an optimization unit that optimizes the novel analyzed by the novel analysis unit based on the characteristics of each award; an output unit that outputs the novel optimized by the optimization unit; A system characterized by:

2. The optimization unit Highlight your unique perspective and new ideas for an award that recognizes innovative ideas.

2. The system of claim 1.

3. The data learning unit In addition to the data for each award, the data for other literary awards in similar genres is also studied to grasp broader trends.

2. The system of claim 1.

4. The novel analysis unit The optimization is performed for each chapter or section of the novel input by the user, in accordance with the characteristics of each award.

2. The system of claim 1.

5. The optimization unit In optimizing the writing style and structure, the preferences and evaluation tendencies of the judges of each award will be reflected.

2. The system of claim 1.

6. The data learning unit Using emotion estimation, the emotional tone and atmosphere of the winning works are analyzed and trained by the LLM.

2. The system of claim 1.

7. The novel analysis unit Using an emotion estimation function, the emotional intensity of the user's novel is analyzed and the optimization is performed for each award.

2. The system of claim 1.

8. The optimization unit Using an emotion estimation function to evaluate the emotional impact of the optimized novel and make further adjustments.

2. The system of claim 1.

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