System

Abstract

An object of a system according to an embodiment is to enable a user whose reading time is limited to efficiently grasp the contents of a book.SOLUTION: A system according to an embodiment includes a synopsis generation unit, a summary generation unit, and a review generation unit. The synopsis generation unit generates a synopsis that can be read in one minute. The summary generation part generates a summary corresponding to the time required for reading. The review generation unit collectively provides reviews from many readers.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 techniques have had the problem that they do not adequately provide users with limited reading time with a means to efficiently grasp the contents of a book.

[0005] The system according to the embodiment aims to enable users who have limited time to read to efficiently understand the contents of a book. [Means for solving the problem]

[0006] The system according to the embodiment includes a synopsis generation unit, a summary generation unit, and a review generation unit. The synopsis generation unit generates a synopsis that can be read in one minute. The summary generation unit generates a summary according to the time required to read it through. The review generation unit compiles and provides reviews from many readers. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user who has limited time to read to efficiently understand the contents of a book. [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 reading support system according to an embodiment of the present invention is a system for efficiently digesting a user's pile of books. This system uses a generation AI to provide a synopsis that can be read in one minute, a summary that corresponds to the time required to read through the book, and a collection of reviews from many readers. This allows the reading support system to efficiently digest a user's pile of books and improve the reading experience.

[0029] A reading support system according to an embodiment includes a synopsis generation unit, a summary generation unit, and a review generation unit. The synopsis generation unit generates a synopsis that can be read in one minute. For example, the generation AI analyzes the contents of a book specified by a user and generates a concise synopsis. The generation AI uses a text generation AI (e.g., LLM) to summarize major events and character relationships. The summary generation unit generates a summary based on the amount of time required to read the book. For example, the generation AI provides a summary tailored to the user's preferences, such as a summary that can be read in 10 minutes, 30 minutes, or one hour. The generation AI can also use a multimodal generation AI to generate summaries that combine text and images. The review generation unit compiles and provides reviews from many readers. For example, the generation AI collects reviews from multiple review sites and summarizes them in a balanced manner, including positive and negative opinions. The generation AI uses sentiment analysis technology to analyze the emotional responses of reviews and highlight key points for the user. This allows the reading support system according to an embodiment to efficiently digest piles of books and improve the reading experience. For example, users can quickly understand the contents of a book and use it as a reference when choosing their next book to read. Also, by referring to reviews, users can learn the opinions of other readers.

[0030] The plot generation unit can concisely summarize character relationships and background information to deepen understanding of the story. For example, the plot generation unit uses a generation AI to analyze character relationships and reflect them in the plot. For example, it can briefly explain the relationship between the main character and supporting characters. The plot generation unit also summarizes character background information to deepen understanding of the story. For example, it can concisely summarize a character's past and setting. This makes it easier for users to understand the character relationships and background information in the story.

[0031] The plot generation unit can add visual elements to make the plot easier to understand visually. For example, the plot generation unit uses a generation AI to automatically generate illustrations and diagrams related to the plot to make it easier to understand visually. For example, it can illustrate the main scenes of the story. The plot generation unit also uses diagrams to visually show the structure of the story and the relationships between characters. For example, it can generate a character relationship chart or a story flowchart. This makes it possible to provide a plot that is visually easy for users to understand.

[0032] The synopsis generation unit can generate similar synopses for books of different genres, reflecting the characteristics of each genre. For example, the synopsis generation unit uses a generation AI to generate synopses for books of different genres, reflecting the characteristics of each genre. For example, mystery novels emphasize elements of solving mysteries. The synopsis generation unit also analyzes the characteristics of each genre and reflects them in the synopsis. For example, romance novels emphasize changes in emotions, and science fiction novels emphasize technical elements. This makes it possible to provide appropriate synopses for books of different genres.

[0033] The summary generation unit can highlight important turning points and climaxes in a story, allowing users to understand the core of the story in a short amount of time. For example, the summary generation unit uses a generation AI to analyze important turning points and climaxes in a story and reflect them in the summary. For example, it can highlight scenes in which the protagonist makes important decisions. The summary generation unit also summarizes important scenes and events so that the core of the story can be understood in a short amount of time. For example, it can concisely summarize the peaks and important scenes of the story. This allows users to understand the core of the story in a short amount of time.

[0034] The summary generation unit can clearly show the theme and message of the story, allowing the reader to gain a deeper understanding. For example, the summary generation unit uses a generation AI to analyze the theme and message of the story and reflect this in the summary. For example, it can emphasize themes such as friendship and courage. The summary generation unit also clearly shows the message of the story, allowing the reader to gain a deeper understanding. For example, it can concisely summarize the moral or important message of the story. This allows the user to gain a deeper understanding of the theme and message of the story.

[0035] The summary generation unit can provide the summary content in audio format, allowing the user to listen to and understand the content while traveling or working. For example, the summary generation unit uses a generation AI to generate the summary content in audio format, allowing the user to listen to and understand the content while traveling or working. For example, the summary is provided as an audio file. The summary generation unit also uses speech synthesis technology to provide the summary content in a natural voice. For example, it converts text into audio and provides it to the user. This allows the user to listen to and understand the summary content while traveling or working.

[0036] The summary generation unit can automatically translate into different languages ​​and provide summaries in multiple languages. For example, the summary generation unit uses a generation AI to automatically translate the summary content into different languages ​​and provide summaries in multiple languages. For example, it translates into multiple languages ​​such as English, French, and Chinese. The summary generation unit also uses a translation engine to perform highly accurate translations. For example, it uses neural machine translation technology to provide natural translations. This allows users to use summaries in different languages.

[0037] The review generation unit analyzes the content of the review and can summarize positive and negative opinions in a balanced manner. For example, the review generation unit uses a generation AI to analyze the content of the review and reflect positive and negative opinions in the summary in a balanced manner. For example, it shows both good and bad points equally. The review generation unit also analyzes the review's evaluation criteria and provides a balanced summary. For example, it shows both particularly high and low rated parts of the review equally. This allows the user to understand the balanced review.

[0038] The review generation unit can visually display reviews and color-code positive and negative opinions to enable intuitive understanding. For example, the review generation unit uses a generation AI to analyze the content of the review and visually display positive and negative opinions by color. For example, positive opinions are displayed in green and negative opinions in red. The review generation unit also visually displays the content of the review using graphs and charts. For example, the ratio of positive opinions to negative opinions is displayed in a pie chart. This allows the user to intuitively understand the review.

[0039] The review generation unit can integrate reviews from different platforms and provide a comprehensive rating. For example, the review generation unit uses a generation AI to collect reviews from different platforms, integrate them, and reflect them in a summary. For example, it integrates reviews from Amazon, Goodreads, Google Books, etc. The review generation unit can also analyze reviews from different platforms and provide a comprehensive rating. For example, it unifies the evaluation criteria for reviews on each platform and provides a comprehensive rating. This allows users to obtain a comprehensive rating.

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

[0041] The reading support system also includes a reading progress management unit. The reading progress management unit can record the number of pages read by the user and the reading time, and visualize the progress. For example, it can display the number of pages read by the user in a graph, indicating the degree of goal achievement. The reading progress management unit can also analyze the user's reading pace and propose an appropriate reading plan. For example, it can calculate and notify the user of the number of pages they should read per day. This makes it easier for the user to manage their reading progress, and maintain motivation to achieve their goals.

[0042] The reading support system further includes a reading history analysis unit. The reading history analysis unit can analyze the user's past reading history and understand their reading tendencies. For example, it can analyze the genres and themes of books the user has read in the past and identify their preferences. The reading history analysis unit can also recommend new books based on the user's reading history. For example, it can recommend books with similar themes or genres to books the user has read in the past. This makes it easier for the user to find books that suit their preferences.

[0043] The reading support system further includes a reading community collaboration unit. The reading community collaboration unit enables users to share their reading experiences with other readers. For example, users can post their impressions and reviews of books they have read and exchange opinions with other readers. The reading community collaboration unit also provides information about book clubs and online discussions, allowing users to participate. This allows users to interact with other readers and enrich their reading experience.

[0044] The reading support system further includes a reading environment optimization unit. The reading environment optimization unit can analyze the user's reading environment and propose the optimal reading environment. For example, it can analyze the user's reading location and time of day and propose the optimal reading environment. The reading environment optimization unit can also adjust environmental elements such as lighting and music to provide an environment suitable for reading. This allows the user to enjoy reading in a comfortable environment.

[0045] The reading support system further includes a reading goal setting unit. The reading goal setting unit allows the user to set reading goals and manage the progress of those goals. For example, the reading goal setting unit sets the number of books or pages the user wants to read per year and tracks the progress. The reading goal setting unit can also provide advice and reminders for achieving the goal. For example, the reading goal setting unit can propose a reading plan for achieving the goal and periodically notify the user of progress. This makes it easier for the user to achieve their reading goal.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The synopsis generator generates a synopsis that can be read in one minute. For example, the generator analyzes the contents of a book specified by the user and generates a concise synopsis. The generator uses a text generation AI (e.g., LLM) to summarize key events and character relationships. Step 2: The summary generator generates a summary based on the amount of time it takes to read. For example, the generator AI can provide a summary that can be read in 10 minutes, 30 minutes, or 1 hour, depending on the user's preference. The generator AI can also use multimodal generator AI to generate summaries that combine text and images. Step 3: The review generator compiles and provides reviews from many readers. For example, the generator collects reviews from multiple review sites and summarizes them in a balanced manner, including both positive and negative opinions. The generator uses sentiment analysis technology to analyze the emotional responses of the reviews and highlight key points that are important to users.

[0048] (Example 2) A reading support system according to an embodiment of the present invention is a system for efficiently digesting a user's pile of books. This system uses a generation AI to provide a synopsis that can be read in one minute, a summary that corresponds to the time required to read through the book, and a collection of reviews from many readers. This allows the reading support system to efficiently digest a user's pile of books and improve the reading experience.

[0049] A reading support system according to an embodiment includes a synopsis generation unit, a summary generation unit, and a review generation unit. The synopsis generation unit generates a synopsis that can be read in one minute. For example, the generation AI analyzes the contents of a book specified by a user and generates a concise synopsis. The generation AI uses a text generation AI (e.g., LLM) to summarize major events and character relationships. The summary generation unit generates a summary based on the amount of time required to read the book. For example, the generation AI provides a summary tailored to the user's preferences, such as a summary that can be read in 10 minutes, 30 minutes, or one hour. The generation AI can also use a multimodal generation AI to generate summaries that combine text and images. The review generation unit compiles and provides reviews from many readers. For example, the generation AI collects reviews from multiple review sites and summarizes them in a balanced manner, including positive and negative opinions. The generation AI uses sentiment analysis technology to analyze the emotional responses of reviews and highlight key points for the user. This allows the reading support system according to an embodiment to efficiently digest piles of books and improve the reading experience. For example, users can quickly understand the contents of a book and use it as a reference when choosing their next book to read. Also, by referring to reviews, users can learn the opinions of other readers.

[0050] The synopsis generation unit can emphasize the main emotional changes in a story, making it easier to understand the flow of emotions. For example, the synopsis generation unit uses a generation AI to analyze the main emotional changes in a story and reflect them in the synopsis. For example, it can emphasize the emotional changes as the protagonist faces and overcomes difficulties. The synopsis generation unit also uses an emotion estimation algorithm to analyze the emotional flow of a story and provide it to the user. For example, it can generate a graph showing the intensity of emotions and the timing of changes. This makes it easier for users to understand the emotional flow of a story.

[0051] The plot generation unit can concisely summarize character relationships and background information to deepen understanding of the story. For example, the plot generation unit uses a generation AI to analyze character relationships and reflect them in the plot. For example, it can briefly explain the relationship between the main character and supporting characters. The plot generation unit also summarizes character background information to deepen understanding of the story. For example, it can concisely summarize a character's past and setting. This makes it easier for users to understand the character relationships and background information in the story.

[0052] The synopsis generation unit can use the emotion estimation function to generate a synopsis that emphasizes the parts that are most likely to interest the user. For example, the synopsis generation unit uses the emotion estimation function to identify the parts that are most likely to interest the user and reflect them in the synopsis. For example, it emphasizes climactic scenes and moving scenes. The synopsis generation unit also analyzes the user's past behavioral data and interest trends to emphasize parts that are likely to interest the user. For example, it identifies scenes that are likely to interest the user based on data on books the user has read in the past. This makes it possible to provide a synopsis that emphasizes the parts that are most likely to interest the user.

[0053] The plot generation unit can add visual elements to make the plot easier to understand visually. For example, the plot generation unit uses a generation AI to automatically generate illustrations and diagrams related to the plot to make it easier to understand visually. For example, it can illustrate the main scenes of the story. The plot generation unit also uses diagrams to visually show the structure of the story and the relationships between characters. For example, it can generate a character relationship chart or a story flowchart. This makes it possible to provide a plot that is visually easy for users to understand.

[0054] The synopsis generation unit can generate similar synopses for books of different genres, reflecting the characteristics of each genre. For example, the synopsis generation unit uses a generation AI to generate synopses for books of different genres, reflecting the characteristics of each genre. For example, mystery novels emphasize elements of solving mysteries. The synopsis generation unit also analyzes the characteristics of each genre and reflects them in the synopsis. For example, romance novels emphasize changes in emotions, and science fiction novels emphasize technical elements. This makes it possible to provide appropriate synopses for books of different genres.

[0055] The synopsis generation unit uses the emotion estimation function to generate a synopsis that matches the user's current emotional state, thereby increasing their motivation to read. The synopsis generation unit, for example, uses the emotion estimation function to analyze the user's current emotional state and generate a synopsis that matches it. For example, if the user is tired, the synopsis generation unit emphasizes relaxing content. The synopsis generation unit also adjusts the content of the synopsis according to the user's emotional state. For example, if the user is excited, the synopsis generation unit emphasizes action scenes. This provides a synopsis that matches the user's emotional state, thereby increasing their motivation to read.

[0056] The summary generation unit can highlight important turning points and climaxes in a story, allowing users to understand the core of the story in a short amount of time. For example, the summary generation unit uses a generation AI to analyze important turning points and climaxes in a story and reflect them in the summary. For example, it can highlight scenes in which the protagonist makes important decisions. The summary generation unit also summarizes important scenes and events so that the core of the story can be understood in a short amount of time. For example, it can concisely summarize the peaks and important scenes of the story. This allows users to understand the core of the story in a short amount of time.

[0057] The summary generation unit can clearly show the theme and message of the story, allowing the reader to gain a deeper understanding. For example, the summary generation unit uses a generation AI to analyze the theme and message of the story and reflect this in the summary. For example, it can emphasize themes such as friendship and courage. The summary generation unit also clearly shows the message of the story, allowing the reader to gain a deeper understanding. For example, it can concisely summarize the moral or important message of the story. This allows the user to gain a deeper understanding of the theme and message of the story.

[0058] The summary generation unit can use the emotion estimation function to provide a summary that emphasizes parts that the user is most likely to empathize with emotionally. The summary generation unit, for example, uses the emotion estimation function to identify parts that the user is most likely to empathize with emotionally and reflect these in the summary. For example, moving scenes and tense scenes are emphasized. The summary generation unit also analyzes the emotional state of the user and emphasizes parts that the user is most likely to empathize with. For example, scenes that the user is most likely to be moved by are identified and reflected in the summary. This makes it possible to provide a summary that emphasizes parts that the user is most likely to empathize with emotionally.

[0059] The summary generation unit can provide the summary content in audio format, allowing the user to listen to and understand the content while traveling or working. For example, the summary generation unit uses a generation AI to generate the summary content in audio format, allowing the user to listen to and understand the content while traveling or working. For example, the summary is provided as an audio file. The summary generation unit also uses speech synthesis technology to provide the summary content in a natural voice. For example, it converts text into audio and provides it to the user. This allows the user to listen to and understand the summary content while traveling or working.

[0060] The summary generation unit can automatically translate into different languages ​​and provide summaries in multiple languages. For example, the summary generation unit uses a generation AI to automatically translate the summary content into different languages ​​and provide summaries in multiple languages. For example, it translates into multiple languages ​​such as English, French, and Chinese. The summary generation unit also uses a translation engine to perform highly accurate translations. For example, it uses neural machine translation technology to provide natural translations. This allows users to use summaries in different languages.

[0061] The summary generation unit uses the emotion estimation function to generate a summary according to the user's emotional state, thereby personalizing the reading experience. For example, the summary generation unit uses the emotion estimation function to analyze the user's emotional state and generate a summary according to the emotional state. For example, if the user is tired, the summary generation unit emphasizes relaxing content. The summary generation unit also adjusts the content of the summary according to the user's emotional state. For example, if the user is excited, the summary generation unit emphasizes action scenes. This allows the user to receive a summary according to the user's emotional state, thereby personalizing the reading experience.

[0062] The review generation unit analyzes the content of the review and can summarize positive and negative opinions in a balanced manner. For example, the review generation unit uses a generation AI to analyze the content of the review and reflect positive and negative opinions in the summary in a balanced manner. For example, it shows both good and bad points equally. The review generation unit also analyzes the review's evaluation criteria and provides a balanced summary. For example, it shows both particularly high and low rated parts of the review equally. This allows the user to understand the balanced review.

[0063] The review generation unit can use the emotion estimation function to preferentially display reviews that the user can most easily empathize with. The review generation unit, for example, uses the emotion estimation function to identify reviews that the user can most easily empathize with and reflects these in the summary. For example, it can emphasize moving scenes or tense scenes. The review generation unit also analyzes the emotional state of the user and preferentially displays reviews that the user can most easily empathize with. For example, it can identify scenes that the user can most easily empathize with and reflect these in the review. This allows the review generation unit to preferentially display reviews that the user can most easily empathize with.

[0064] The review generation unit can visually display reviews and color-code positive and negative opinions to enable intuitive understanding. For example, the review generation unit uses a generation AI to analyze the content of the review and visually display positive and negative opinions by color. For example, positive opinions are displayed in green and negative opinions in red. The review generation unit also visually displays the content of the review using graphs and charts. For example, the ratio of positive opinions to negative opinions is displayed in a pie chart. This allows the user to intuitively understand the review.

[0065] The review generation unit can integrate reviews from different platforms and provide a comprehensive rating. For example, the review generation unit uses a generation AI to collect reviews from different platforms, integrate them, and reflect them in a summary. For example, it integrates reviews from Amazon, Goodreads, Google Books, etc. The review generation unit can also analyze reviews from different platforms and provide a comprehensive rating. For example, it unifies the evaluation criteria for reviews on each platform and provides a comprehensive rating. This allows users to obtain a comprehensive rating.

[0066] The review generation unit can use the emotion estimation function to display reviews that match the user's emotional state and support reading selection. The review generation unit, for example, uses the emotion estimation function to analyze the user's emotional state and display reviews that match it. For example, if the user is tired, reviews with relaxing content are displayed. The review generation unit also adjusts the content of the review according to the user's emotional state. For example, if the user is excited, action scenes are emphasized. This makes it possible to provide reviews that match the user's emotional state and support reading selection.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The reading support system also includes a reading progress management unit. The reading progress management unit can record the number of pages read by the user and the reading time, and visualize the progress. For example, it can display the number of pages read by the user in a graph, indicating the degree of goal achievement. The reading progress management unit can also analyze the user's reading pace and propose an appropriate reading plan. For example, it can calculate and notify the user of the number of pages they should read per day. This makes it easier for the user to manage their reading progress, and maintain motivation to achieve their goals.

[0069] The reading support system further includes a reading history analysis unit. The reading history analysis unit can analyze the user's past reading history and understand their reading tendencies. For example, it can analyze the genres and themes of books the user has read in the past and identify their preferences. The reading history analysis unit can also recommend new books based on the user's reading history. For example, it can recommend books with similar themes or genres to books the user has read in the past. This makes it easier for the user to find books that suit their preferences.

[0070] The reading support system further includes a reading community collaboration unit. The reading community collaboration unit enables users to share their reading experiences with other readers. For example, users can post their impressions and reviews of books they have read and exchange opinions with other readers. The reading community collaboration unit also provides information about book clubs and online discussions, allowing users to participate. This allows users to interact with other readers and enrich their reading experience.

[0071] The reading support system further includes a reading environment optimization unit. The reading environment optimization unit can analyze the user's reading environment and propose the optimal reading environment. For example, it can analyze the user's reading location and time of day and propose the optimal reading environment. The reading environment optimization unit can also adjust environmental elements such as lighting and music to provide an environment suitable for reading. This allows the user to enjoy reading in a comfortable environment.

[0072] The reading support system further includes a reading goal setting unit. The reading goal setting unit allows the user to set reading goals and manage the progress of those goals. For example, the reading goal setting unit sets the number of books or pages the user wants to read per year and tracks the progress. The reading goal setting unit can also provide advice and reminders for achieving the goal. For example, the reading goal setting unit can propose a reading plan for achieving the goal and periodically notify the user of progress. This makes it easier for the user to achieve their reading goal.

[0073] The reading support system further includes a reading recommendation unit that uses an emotion estimation function to recommend books based on the user's emotions. The reading recommendation unit can analyze the user's current emotional state and recommend appropriate books based on that analysis. For example, if the user is feeling stressed, it can recommend books that will help them relax. The reading recommendation unit can also suggest the timing and environment for reading based on the user's emotional state. This makes it easier for users to choose books that match their emotions.

[0074] The reading support system further includes a reading progress feedback unit that uses an emotion estimation function to provide feedback based on the user's emotions. The reading progress feedback unit can analyze the user's emotional state and provide feedback on the reading progress based on the analysis. For example, if the user loses motivation for reading, it can provide an encouraging message. The reading progress feedback unit can also adjust the reading pace and goals according to the user's emotional state. This makes it easier for the user to maintain motivation for reading.

[0075] The reading support system further includes a reading history analysis unit that uses an emotion estimation function to analyze the user's emotions. The reading history analysis unit can analyze the user's emotional state and analyze the reading history based on that. For example, it can identify books that the user has read in the past that particularly moved them and understand their reading trends. The reading history analysis unit can also recommend new books based on the user's emotional state. This makes it easier for the user to find books that match their emotions.

[0076] The reading support system further includes a reading community collaboration unit that uses an emotion estimation function to analyze the user's emotional state and support interactions with other readers based on that analysis. For example, it makes it easier for users to share their impressions of books that moved them. The reading community collaboration unit can also suggest appropriate reading groups and discussions based on the user's emotional state. This makes it easier for users to share reading experiences that match their emotions.

[0077] The reading support system further includes a reading environment optimization unit that uses an emotion estimation function to optimize the reading environment based on the user's emotions. The reading environment optimization unit can analyze the user's emotional state and suggest the optimal reading environment based on that analysis. For example, if the user wants to relax, it can suggest a quiet place. The reading environment optimization unit can also adjust environmental elements such as lighting and music according to the user's emotional state. This allows the user to enjoy reading in an environment that suits their emotions.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The synopsis generator generates a synopsis that can be read in one minute. For example, the generator analyzes the contents of a book specified by the user and generates a concise synopsis. The generator uses a text generation AI (e.g., LLM) to summarize key events and character relationships. Step 2: The summary generator generates a summary based on the amount of time it takes to read. For example, the generator AI can provide a summary that can be read in 10 minutes, 30 minutes, or 1 hour, depending on the user's preference. The generator AI can also use multimodal generator AI to generate summaries that combine text and images. Step 3: The review generator compiles and provides reviews from many readers. For example, the generator collects reviews from multiple review sites and summarizes them in a balanced manner, including both positive and negative opinions. The generator uses sentiment analysis technology to analyze the emotional responses of the reviews and highlight key points that are important to users.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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 synopsis generator that generates a synopsis that can be read in one minute, a summary generator that generates a summary according to the time required for reading; A review generation unit that compiles and provides reviews from many readers. A system characterized by:

2. The synopsis generation unit Highlight the major emotional changes in the story, making it easier to understand the emotional flow 2. The system of claim 1.

3. The synopsis generation unit Add visual elements to make it easier to understand 2. The system of claim 1.

4. The summary generation unit Highlights important turning points and climaxes in the story, allowing readers to understand the core of the story in a short amount of time.

2. The system of claim 1.

5. The summary generation unit Provides summaries in audio format so users can listen and understand while on the move or at work 2. The system of claim 1.

6. The review generation unit Extracting parts of reviews that generate particularly strong emotional responses and highlighting key points for users 2. The system of claim 1.

7. The review generation unit Prioritize reviews that users can relate to most 2. The system of claim 1.

8. The review generation unit Displaying reviews tailored to the user's emotional state to support reading choices 2. The system of claim 1.

Images