System

Abstract

An object of the system according to the embodiment is to recommend an appropriate book based on a reading history and an interest of a user.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, and a recommendation unit. The reception unit receives a reading history or an interest / concern of a user. The analysis unit analyzes the information received by the reception unit. The recommendation unit recommends a book based on the analysis result obtained by the analysis 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 technologies have not been able to adequately recommend appropriate books based on a user's reading history and interests, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend appropriate books based on the user's reading history and interests. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a recommendation unit. The reception unit receives a user's reading history or interests. The analysis unit analyzes the information received by the reception unit. The recommendation unit recommends books based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend appropriate books based on the user's reading history and interests. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 recommendation system according to an embodiment of the present invention is a system that uses a recommendation algorithm to recommend library books based on a user's reading history and interests. In the recommendation system, a user logs in to an app and inputs their reading history and interests. The recommendation algorithm then analyzes this information and recommends books that are optimal for the user. For example, new books can be recommended based on books the user has read in the past and genres in which the user is interested. This allows the user to easily find books that match their interests. This also improves the utilization rate of library books and the user's reading experience. For example, by recommending new books based on the genres of books the user has read in the past, the user can discover new interests. This allows the recommendation system to recommend optimal books based on the user's reading history and interests. For example, by recommending new books based on the genres of books the user has read in the past, the user can discover new interests. This also improves the utilization rate of library books and the user's reading experience.

[0029] A recommendation system according to an embodiment includes a reception unit, an analysis unit, and a recommendation unit. The reception unit receives a user's reading history or interests. For example, the reception unit receives information such as the titles and author names of books the user has read in the past, and genres in which the user is interested. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the user's reading history and interests and recommends books that are most suitable for the user. The recommendation unit recommends books based on the analysis results obtained by the analysis unit. For example, if the user is interested in a particular genre, the recommendation unit preferentially recommends books related to that genre. This allows the recommendation system according to an embodiment to recommend books that are most suitable for the user based on the user's reading history and interests.

[0030] The reception unit can receive information on the titles or author names of books the user has read in the past, and genres in which the user is interested. For example, the reception unit receives information on the titles or author names of books the user has read in the past, and genres in which the user is interested. For example, by inputting the title of a book the user has read in the past, books related to that book can be recommended. Also, by inputting a genre in which the user is interested, books related to that genre can be recommended. This makes it possible to receive the user's detailed reading history and interests.

[0031] The analysis unit can analyze a user's reading history and interests to recommend books that are suitable for the user. The analysis unit can, for example, analyze a user's reading history and interests to recommend books that are best suited to the user. For example, it can recommend new books based on the genres and authors of books the user has read in the past. The analysis unit can also customize recommendation results based on the user's interests. For example, if a user is interested in a particular genre, it will prioritize recommending books related to that genre. This makes it possible to recommend books that are best suited to the user's reading history and interests.

[0032] The recommendation unit can adjust the recommendation results based on the user's interests. For example, the recommendation unit customizes the recommendation results based on the user's interests. For example, if the user is interested in a particular genre, books related to that genre are preferentially recommended. The recommendation unit can also recommend new books based on the user's past reading history. For example, new books are recommended based on the genres of books the user has read in the past. This makes it possible to recommend books customized based on the user's interests.

[0033] If a user is interested in a particular genre, the recommendation unit can preferentially recommend books related to that genre. For example, if a user is interested in a particular genre, the recommendation unit preferentially recommends books related to that genre. For example, if a user is interested in fiction, the recommendation unit preferentially recommends fiction books. Also, if a user is interested in a particular author, the recommendation unit can preferentially recommend books by that author. This makes it possible to preferentially recommend books related to the user's specific interests.

[0034] The recommendation unit can recommend new books based on the user's past reading history. The recommendation unit recommends new books based on the user's past reading history, for example. For example, new books can be recommended based on the genres and authors of books the user has read in the past. New books can also be recommended based on the themes of books the user has read in the past. This makes it possible to recommend new books based on the user's past reading history.

[0035] The reception unit can analyze the user's past reading history and select an appropriate input method. The reception unit, for example, analyzes the user's past reading history and selects the optimal input method. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Also, if the user has used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user has used image input in the past, the reception unit can preferentially suggest image input. In this way, the optimal input method can be suggested based on the user's past reading history.

[0036] The reception unit can filter the reading history based on the user's current areas of interest when the reading history is input. For example, the reception unit can filter the reading history based on the user's current areas of interest when the reading history is input. For example, the reception unit can suggest that the user input only reading history related to genres in which the user is currently interested. It can also suggest that the user input only books by authors in which the user is currently interested. It can also suggest that the user input only books related to themes in which the user is currently interested. In this way, the reading history can be filtered based on the user's current areas of interest.

[0037] The reception unit can select an appropriate input means according to the user's input method when inputting a reading history. For example, when inputting a reading history, the reception unit selects an appropriate input means according to the user's input method. For example, if the user selects voice input, the reading history can be input using voice recognition technology. Also, if the user selects text input, a text box can be provided to input the reading history. Also, if the user selects image input, the reading history can be input using image recognition technology. In this way, the reading history can be input using the optimal means according to the user's input method.

[0038] The reception unit can prioritize input of highly relevant history based on the user's geographical location information when inputting a reading history. For example, the reception unit prioritizes input of highly relevant history in consideration of the user's geographical location information when inputting a reading history. For example, the reception unit suggests that the user prioritize input of books related to the location where the user is currently located. It can also suggest that the user prioritize input of books related to places the user has visited in the past. It can also suggest that the user prioritize input of books related to places the user plans to visit in the future. This makes it possible to prioritize input of highly relevant reading history based on the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity and input related history when inputting a reading history. For example, the reception unit can analyze the user's social media activity and input related history when inputting a reading history. For example, the reception unit can suggest that the user input books that they have shared on social media with priority. It can also analyze the content of the user's social media posts and suggest that they input related books. It can also suggest that they input related books based on the activity of the user's friends on social media. In this way, it is possible to input related reading history based on the user's social media activity.

[0040] The reception unit can adjust the input method by reflecting the user's past feedback when inputting the reading history. For example, the reception unit adjusts the input method by reflecting the user's past feedback when inputting the reading history. For example, the reception unit preferentially suggests input methods that the user has preferred in the past. It can also suggest input methods that the user has been dissatisfied with in the past to avoid. It can also suggest the optimal input method based on the user's past feedback. In this way, it is possible to suggest the optimal input method based on the user's past feedback.

[0041] The analysis unit can set the level of detail of the analysis based on the importance of the reading history during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the reading history during analysis. For example, a detailed analysis can be performed on important reading history. A brief analysis can also be performed on less important reading history. A deep analysis can also be performed on reading history in which the user is particularly interested. This makes it possible to adjust the level of detail of the analysis according to the importance of the reading history.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the reading history during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the reading history during analysis. For example, for a reading history of fiction, the analysis unit can analyze the story themes and characters. For a reading history of non-fiction, the analysis unit can also analyze facts and data. For a reading history of specialized books, the analysis unit can also analyze technical terms and concepts. This makes it possible to apply the optimal analysis algorithm depending on the category of the reading history.

[0043] The analysis unit can improve the accuracy of the analysis based on the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis based on analysis results that the user preferred in the past. The analysis can also be performed to avoid analysis results that the user was dissatisfied with in the past. The optimal analysis algorithm can also be selected based on the user's past analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0044] The analysis unit can determine the priority of analysis based on the time of submission of the reading history during analysis. The analysis unit, for example, determines the priority of analysis based on the time of submission of the reading history during analysis. For example, the analysis unit prioritizes the analysis of reading histories that have been submitted recently. It is also possible to prioritize the analysis of reading histories that have been submitted by a user at a specific time. It is also possible to prioritize the analysis of reading histories that have been submitted by a user in the past. This makes it possible to determine the priority of analysis based on the time of submission of the reading history.

[0045] The analysis unit can set the order of analysis based on the relevance of the reading history during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the reading history during analysis. For example, it can prioritize analysis of highly relevant reading history. It can also postpone analysis of less relevant reading history. It can also prioritize analysis of reading history in which the user is particularly interested. This makes it possible to adjust the order of analysis based on the relevance of the reading history.

[0046] The analysis unit can set the use of technical terms for analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms for analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can use a lot of technical terms. Also, if the user does not have technical expertise, the analysis unit can avoid technical terms. Also, the analysis unit can use appropriate technical terms according to the user's level of expertise. This makes it possible to use appropriate technical terms according to the user's level of expertise.

[0047] The recommendation unit can set the level of detail of the recommendation based on the importance of the book when making a recommendation. For example, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the book when making a recommendation. For example, a detailed recommendation reason can be provided for an important book. A concise recommendation reason can be provided for a less important book. A more in-depth recommendation reason can be provided for a book in which the user is particularly interested. In this way, the level of detail of the recommendation can be adjusted according to the importance of the book.

[0048] The recommendation unit can apply different recommendation algorithms depending on the book category when making a recommendation. For example, the recommendation unit can apply different recommendation algorithms depending on the book category when making a recommendation. For example, for fiction books, recommendations can be made based on the theme or characters of the story. For non-fiction books, recommendations can be made based on facts and data. For specialized books, recommendations can be made based on specialized terms and concepts. This makes it possible to apply the optimal recommendation algorithm depending on the book category.

[0049] The recommendation unit can improve the accuracy of recommendations based on the user's past recommendation results when making a recommendation. For example, the recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation results when making a recommendation. For example, the recommendation unit can improve the accuracy of recommendations based on recommendation results that the user liked in the past. It can also make recommendations so as to avoid recommendation results that the user was dissatisfied with in the past. It can also select an optimal recommendation algorithm based on the user's past recommendation results. This makes it possible to improve the accuracy of recommendations based on the user's past recommendation results.

[0050] The recommendation unit can determine the priority of recommendations based on the time of submission of the books when making recommendations. For example, the recommendation unit can prioritize recommendations based on the time of submission of the books when making recommendations. For example, recently published books can be recommended preferentially. Also, books in which the user is interested at a specific time can be recommended preferentially. Also, books in which the user has been interested in in the past can be recommended preferentially. In this way, the priority of recommendations can be determined based on the time of submission of the books.

[0051] The recommendation unit can set the order of recommendation based on the relevance of books when making a recommendation. For example, the recommendation unit can adjust the order of recommendation based on the relevance of books when making a recommendation. For example, highly relevant books can be recommended with priority. Also, books with low relevance can be recommended later. Also, books in which the user is particularly interested can be recommended with priority. In this way, the order of recommendation can be adjusted based on the relevance of books.

[0052] The recommendation unit may set the use of technical terms for recommendations according to the user's level of expertise when making a recommendation. For example, the recommendation unit may adjust the use of technical terms for recommendations according to the user's level of expertise when making a recommendation. For example, if the user has technical expertise, the recommendation unit may use a lot of technical terms. On the other hand, if the user does not have technical expertise, the recommendation unit may avoid technical terms. Also, the recommendation unit may use appropriate technical terms according to the user's level of expertise. This allows the recommendation unit to use appropriate technical terms according to the user's level of expertise.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The recommendation system can further include a speed analysis unit that analyzes the user's reading speed. The speed analysis unit analyzes the reading time of books the user has read in the past and estimates the user's reading speed. For example, if a book that the user has finished reading in a short time is determined to be one that the user is likely to be interested in, books of a similar genre or theme can be preferentially recommended. In addition, if a book that the user has finished reading over a long period of time is likely to have difficult content, books with easier content can be recommended. This makes it possible to recommend optimal books based on the user's reading speed.

[0055] The recommendation system can further include an environment analysis unit that analyzes the user's reading environment. The environment analysis unit analyzes the place and time of day the user reads and recommends the most suitable books. For example, if the user reads during their commute, it can recommend books that can be read in a short time. Also, if the user reads at night, it can recommend books with relaxing content. This makes it possible to recommend the most suitable books based on the user's reading environment.

[0056] The recommendation system can further include a progress management unit that manages the reading progress based on the user's reading history. The progress management unit manages the progress of the book the user is currently reading and recommends new books at appropriate times. For example, if the user has stopped reading, the progress management unit notifies the user to resume reading. It can also recommend a new book if the user is close to finishing reading. This allows books to be recommended at optimal times based on the user's reading progress.

[0057] The recommendation system may further include a motivation enhancement unit that enhances the user's motivation to read based on the user's reading history. The motivation enhancement unit motivates the user to continue reading. For example, the motivation enhancement unit may visualize the number of books the user has read and the reading time, providing a sense of accomplishment. The motivation enhancement unit may also enable the user to set specific goals and track progress toward achieving those goals. This may enhance the user's motivation to read.

[0058] The recommendation system may further include a habit formation unit that forms a reading habit based on the user's reading history. The habit formation unit supports the user in reading regularly. For example, it may send a reminder to the user to read for a certain amount of time every day. It may also record the number of days the user has read and display the number of consecutive days of reading. This helps form a reading habit in the user and encourages continuous reading.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives the user's reading history or interests, such as the titles and authors of books the user has read in the past, and genres in which the user is interested. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it analyzes the user's reading history and interests, and generates data for recommending the most suitable books to the user. Step 3: The recommendation unit recommends books based on the analysis results obtained by the analysis unit. For example, if the user is interested in a particular genre, books related to that genre will be preferentially recommended.

[0061] (Example 2) A recommendation system according to an embodiment of the present invention is a system that uses a recommendation algorithm to recommend library books based on a user's reading history and interests. In the recommendation system, a user logs in to an app and inputs their reading history and interests. The recommendation algorithm then analyzes this information and recommends books that are optimal for the user. For example, new books can be recommended based on books the user has read in the past and genres in which the user is interested. This allows the user to easily find books that match their interests. This also improves the utilization rate of library books and the user's reading experience. For example, by recommending new books based on the genres of books the user has read in the past, the user can discover new interests. This allows the recommendation system to recommend optimal books based on the user's reading history and interests. For example, by recommending new books based on the genres of books the user has read in the past, the user can discover new interests. This also improves the utilization rate of library books and the user's reading experience.

[0062] A recommendation system according to an embodiment includes a reception unit, an analysis unit, and a recommendation unit. The reception unit receives a user's reading history or interests. For example, the reception unit receives information such as the titles and author names of books the user has read in the past, and genres in which the user is interested. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the user's reading history and interests and recommends books that are most suitable for the user. The recommendation unit recommends books based on the analysis results obtained by the analysis unit. For example, if the user is interested in a particular genre, the recommendation unit preferentially recommends books related to that genre. This allows the recommendation system according to an embodiment to recommend books that are most suitable for the user based on the user's reading history and interests.

[0063] The reception unit can receive information on the titles or author names of books the user has read in the past, and genres in which the user is interested. For example, the reception unit receives information on the titles or author names of books the user has read in the past, and genres in which the user is interested. For example, by inputting the title of a book the user has read in the past, books related to that book can be recommended. Also, by inputting a genre in which the user is interested, books related to that genre can be recommended. This makes it possible to receive the user's detailed reading history and interests.

[0064] The analysis unit can analyze a user's reading history and interests to recommend books that are suitable for the user. The analysis unit can, for example, analyze a user's reading history and interests to recommend books that are best suited to the user. For example, it can recommend new books based on the genres and authors of books the user has read in the past. The analysis unit can also customize recommendation results based on the user's interests. For example, if a user is interested in a particular genre, it will prioritize recommending books related to that genre. This makes it possible to recommend books that are best suited to the user's reading history and interests.

[0065] The recommendation unit can adjust the recommendation results based on the user's interests. For example, the recommendation unit customizes the recommendation results based on the user's interests. For example, if the user is interested in a particular genre, books related to that genre are preferentially recommended. The recommendation unit can also recommend new books based on the user's past reading history. For example, new books are recommended based on the genres of books the user has read in the past. This makes it possible to recommend books customized based on the user's interests.

[0066] If a user is interested in a particular genre, the recommendation unit can preferentially recommend books related to that genre. For example, if a user is interested in a particular genre, the recommendation unit preferentially recommends books related to that genre. For example, if a user is interested in fiction, the recommendation unit preferentially recommends fiction books. Also, if a user is interested in a particular author, the recommendation unit can preferentially recommend books by that author. This makes it possible to preferentially recommend books related to the user's specific interests.

[0067] The recommendation unit can recommend new books based on the user's past reading history. The recommendation unit recommends new books based on the user's past reading history, for example. For example, new books can be recommended based on the genres and authors of books the user has read in the past. New books can also be recommended based on the themes of books the user has read in the past. This makes it possible to recommend new books based on the user's past reading history.

[0068] The reception unit can estimate the user's emotions and adjust the timing of inputting the reading history based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the timing of inputting the reading history based on the estimated user emotions. For example, if the user is relaxed, the reception unit sends a notification prompting the user to input the reading history. Also, if the user is feeling stressed, the reception unit can suggest that the user postpone inputting the reading history. Also, if the user is concentrating, the reception unit can prompt the user to input the reading history immediately. This makes it possible to prompt the user to input the reading history at the optimal timing depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The reception unit can analyze the user's past reading history and select an appropriate input method. The reception unit, for example, analyzes the user's past reading history and selects the optimal input method. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Also, if the user has used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user has used image input in the past, the reception unit can preferentially suggest image input. In this way, the optimal input method can be suggested based on the user's past reading history.

[0070] The reception unit can filter the reading history based on the user's current areas of interest when the reading history is input. For example, the reception unit can filter the reading history based on the user's current areas of interest when the reading history is input. For example, the reception unit can suggest that the user input only reading history related to genres in which the user is currently interested. It can also suggest that the user input only books by authors in which the user is currently interested. It can also suggest that the user input only books related to themes in which the user is currently interested. In this way, the reading history can be filtered based on the user's current areas of interest.

[0071] The reception unit can select an appropriate input means according to the user's input method when inputting a reading history. For example, when inputting a reading history, the reception unit selects an appropriate input means according to the user's input method. For example, if the user selects voice input, the reading history can be input using voice recognition technology. Also, if the user selects text input, a text box can be provided to input the reading history. Also, if the user selects image input, the reading history can be input using image recognition technology. In this way, the reading history can be input using the optimal means according to the user's input method.

[0072] The reception unit can estimate the user's emotions and determine the priority of the reading history to be input based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of the reading history to be input based on the estimated user emotions. For example, if the user is relaxed, the reception unit can suggest that recently read books be input preferentially. Also, if the user is stressed, the reception unit can suggest that books in genres that the user is interested in be input preferentially. Also, if the user is concentrating, the reception unit can suggest that important books be input preferentially. In this way, the input priority of the reading history can be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The reception unit can prioritize input of highly relevant history based on the user's geographical location information when inputting a reading history. For example, the reception unit prioritizes input of highly relevant history in consideration of the user's geographical location information when inputting a reading history. For example, the reception unit suggests that the user prioritize input of books related to the location where the user is currently located. It can also suggest that the user prioritize input of books related to places the user has visited in the past. It can also suggest that the user prioritize input of books related to places the user plans to visit in the future. This makes it possible to prioritize input of highly relevant reading history based on the user's geographical location information.

[0074] The reception unit can analyze the user's social media activity and input related history when inputting a reading history. For example, the reception unit can analyze the user's social media activity and input related history when inputting a reading history. For example, the reception unit can suggest that the user input books that they have shared on social media with priority. It can also analyze the content of the user's social media posts and suggest that they input related books. It can also suggest that they input related books based on the activity of the user's friends on social media. In this way, it is possible to input related reading history based on the user's social media activity.

[0075] The reception unit can adjust the input method by reflecting the user's past feedback when inputting the reading history. For example, the reception unit adjusts the input method by reflecting the user's past feedback when inputting the reading history. For example, the reception unit preferentially suggests input methods that the user has preferred in the past. It can also suggest input methods that the user has been dissatisfied with in the past to avoid. It can also suggest the optimal input method based on the user's past feedback. In this way, it is possible to suggest the optimal input method based on the user's past feedback.

[0076] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the way the analysis is presented based on the estimated user's emotion. For example, if the user is relaxed, a detailed analysis result can be provided. If the user is stressed, a concise analysis result can be provided. If the user is concentrating, an in-depth analysis result can be provided. This makes it possible to provide the analysis result in an optimal presentation method depending on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The analysis unit can set the level of detail of the analysis based on the importance of the reading history during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the reading history during analysis. For example, a detailed analysis can be performed on important reading history. A brief analysis can also be performed on less important reading history. A deep analysis can also be performed on reading history in which the user is particularly interested. This makes it possible to adjust the level of detail of the analysis according to the importance of the reading history.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the reading history during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the reading history during analysis. For example, for a reading history of fiction, the analysis unit can analyze the story themes and characters. For a reading history of non-fiction, the analysis unit can also analyze facts and data. For a reading history of specialized books, the analysis unit can also analyze technical terms and concepts. This makes it possible to apply the optimal analysis algorithm depending on the category of the reading history.

[0079] The analysis unit can improve the accuracy of the analysis based on the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis based on analysis results that the user preferred in the past. The analysis can also be performed to avoid analysis results that the user was dissatisfied with in the past. The optimal analysis algorithm can also be selected based on the user's past analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0080] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. For example, if the user is relaxed, a longer analysis result can be provided. Also, if the user is stressed, a shorter analysis result can be provided. Also, if the user is concentrating, an analysis result of an appropriate length can be provided. This makes it possible to provide an analysis result of an optimal length according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] The analysis unit can determine the priority of analysis based on the time of submission of the reading history during analysis. The analysis unit, for example, determines the priority of analysis based on the time of submission of the reading history during analysis. For example, the analysis unit prioritizes the analysis of reading histories that have been submitted recently. It is also possible to prioritize the analysis of reading histories that have been submitted by a user at a specific time. It is also possible to prioritize the analysis of reading histories that have been submitted by a user in the past. This makes it possible to determine the priority of analysis based on the time of submission of the reading history.

[0082] The analysis unit can set the order of analysis based on the relevance of the reading history during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the reading history during analysis. For example, it can prioritize analysis of highly relevant reading history. It can also postpone analysis of less relevant reading history. It can also prioritize analysis of reading history in which the user is particularly interested. This makes it possible to adjust the order of analysis based on the relevance of the reading history.

[0083] The analysis unit can set the use of technical terms for analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms for analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can use a lot of technical terms. Also, if the user does not have technical expertise, the analysis unit can avoid technical terms. Also, the analysis unit can use appropriate technical terms according to the user's level of expertise. This makes it possible to use appropriate technical terms according to the user's level of expertise.

[0084] The recommendation unit can estimate the user's emotions and adjust the way in which recommendations are expressed based on the estimated user emotions. The recommendation unit, for example, estimates the user's emotions and adjusts the way in which recommendations are expressed based on the estimated user emotions. For example, if the user is relaxed, a detailed reason for recommendation can be provided. If the user is stressed, a concise reason for recommendation can be provided. If the user is concentrating, a more in-depth reason for recommendation can be provided. This makes it possible to provide recommendation results in an optimal way of expression depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The recommendation unit can set the level of detail of the recommendation based on the importance of the book when making a recommendation. For example, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the book when making a recommendation. For example, a detailed recommendation reason can be provided for an important book. A concise recommendation reason can be provided for a less important book. A more in-depth recommendation reason can be provided for a book in which the user is particularly interested. In this way, the level of detail of the recommendation can be adjusted according to the importance of the book.

[0086] The recommendation unit can apply different recommendation algorithms depending on the book category when making a recommendation. For example, the recommendation unit can apply different recommendation algorithms depending on the book category when making a recommendation. For example, for fiction books, recommendations can be made based on the theme or characters of the story. For non-fiction books, recommendations can be made based on facts and data. For specialized books, recommendations can be made based on specialized terms and concepts. This makes it possible to apply the optimal recommendation algorithm depending on the book category.

[0087] The recommendation unit can improve the accuracy of recommendations based on the user's past recommendation results when making a recommendation. For example, the recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation results when making a recommendation. For example, the recommendation unit can improve the accuracy of recommendations based on recommendation results that the user liked in the past. It can also make recommendations so as to avoid recommendation results that the user was dissatisfied with in the past. It can also select an optimal recommendation algorithm based on the user's past recommendation results. This makes it possible to improve the accuracy of recommendations based on the user's past recommendation results.

[0088] The recommendation unit can estimate the user's emotions and adjust the length of the recommendation based on the estimated user emotions. The recommendation unit, for example, estimates the user's emotions and adjusts the length of the recommendation based on the estimated user emotions. For example, if the user is relaxed, a longer reason for recommendation can be provided. Also, if the user is stressed, a shorter reason for recommendation can be provided. Also, if the user is concentrating, a reason of appropriate length for recommendation can be provided. This makes it possible to provide recommendation results of an optimal length according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The recommendation unit can determine the priority of recommendations based on the time of submission of the books when making recommendations. For example, the recommendation unit can prioritize recommendations based on the time of submission of the books when making recommendations. For example, recently published books can be recommended preferentially. Also, books in which the user is interested at a specific time can be recommended preferentially. Also, books in which the user has been interested in in the past can be recommended preferentially. In this way, the priority of recommendations can be determined based on the time of submission of the books.

[0090] The recommendation unit can set the order of recommendation based on the relevance of books when making a recommendation. For example, the recommendation unit can adjust the order of recommendation based on the relevance of books when making a recommendation. For example, highly relevant books can be recommended with priority. Also, books with low relevance can be recommended later. Also, books in which the user is particularly interested can be recommended with priority. In this way, the order of recommendation can be adjusted based on the relevance of books.

[0091] The recommendation unit may set the use of technical terms for recommendations according to the user's level of expertise when making a recommendation. For example, the recommendation unit may adjust the use of technical terms for recommendations according to the user's level of expertise when making a recommendation. For example, if the user has technical expertise, the recommendation unit may use a lot of technical terms. On the other hand, if the user does not have technical expertise, the recommendation unit may avoid technical terms. Also, the recommendation unit may use appropriate technical terms according to the user's level of expertise. This allows the recommendation unit to use appropriate technical terms according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and recommendation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the user's reading history and interests. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends books based on the analysis results. The reception unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the recommendation unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and recommendation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the user's reading history and interests. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends books based on the analysis results. The reception unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the recommendation unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and recommendation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives the user's reading history and interests. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends books based on the analysis results. The reception unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the recommendation unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and recommendation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the user's reading history and interests. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends books based on the analysis results. The reception unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the recommendation unit may be realized, for example, by the control unit 46A of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The recommendation system can further include a speed analysis unit that analyzes the user's reading speed. The speed analysis unit analyzes the reading time of books the user has read in the past and estimates the user's reading speed. For example, if a book that the user has finished reading in a short time is determined to be one that the user is likely to be interested in, books of a similar genre or theme can be preferentially recommended. In addition, if a book that the user has finished reading over a long period of time is likely to have difficult content, books with easier content can be recommended. This makes it possible to recommend optimal books based on the user's reading speed.

[0094] The recommendation system can further include an environment analysis unit that analyzes the user's reading environment. The environment analysis unit analyzes the place and time of day the user reads and recommends the most suitable books. For example, if the user reads during their commute, it can recommend books that can be read in a short time. Also, if the user reads at night, it can recommend books with relaxing content. This makes it possible to recommend the most suitable books based on the user's reading environment.

[0095] The recommendation system can further include a progress management unit that manages the reading progress based on the user's reading history. The progress management unit manages the progress of the book the user is currently reading and recommends new books at appropriate times. For example, if the user has stopped reading, the progress management unit notifies the user to resume reading. It can also recommend a new book if the user is close to finishing reading. This allows books to be recommended at optimal times based on the user's reading progress.

[0096] The recommendation system may further include a motivation enhancement unit that enhances the user's motivation to read based on the user's reading history. The motivation enhancement unit motivates the user to continue reading. For example, the motivation enhancement unit may visualize the number of books the user has read and the reading time, providing a sense of accomplishment. The motivation enhancement unit may also enable the user to set specific goals and track progress toward achieving those goals. This may enhance the user's motivation to read.

[0097] The recommendation system may further include a habit formation unit that forms a reading habit based on the user's reading history. The habit formation unit supports the user in reading regularly. For example, it may send a reminder to the user to read for a certain amount of time every day. It may also record the number of days the user has read and display the number of consecutive days of reading. This helps form a reading habit in the user and encourages continuous reading.

[0098] The recommendation system may further include an emotion progress management unit that estimates the user's emotion and manages the reading progress based on the estimated user emotion. The emotion progress management unit adjusts the reading progress according to the user's emotion. For example, if the user is relaxed, it may send a notification encouraging the user to continue reading. It may also suggest that the user stop reading if the user is feeling stressed. This makes it possible to manage the reading progress at the optimal timing according to the user's emotion.

[0099] The recommendation system may further include an emotion motivation improvement unit that estimates the user's emotion and improves the user's motivation to read based on the estimated emotion. The emotion motivation improvement unit motivates the user to read according to the user's emotion. For example, if the user is relaxed, the emotion motivation improvement unit may provide positive feedback to encourage the user to continue reading. Also, if the user is feeling stressed, the emotion motivation improvement unit may suggest that the user stop reading. This makes it possible to improve the user's motivation to read in an optimal way according to the user's emotion.

[0100] The recommendation system may further include an emotion habit formation unit that estimates the user's emotion and forms a reading habit based on the estimated user's emotion. The emotion habit formation unit supports the reading habit according to the user's emotion. For example, if the user is relaxed, the emotion habit formation unit may send a reminder to continue reading. Also, if the user is stressed, the emotion habit formation unit may suggest that the user stop reading. In this way, the reading habit can be formed in an optimal way according to the user's emotion.

[0101] The recommendation system may further include an emotional progress visualization unit that estimates the user's emotions and visualizes the reading progress based on the estimated user emotions. The emotional progress visualization unit displays the reading progress according to the user's emotions. For example, if the user is relaxed, a detailed progress status may be displayed. Alternatively, if the user is stressed, a concise progress status may be displayed. This makes it possible to visualize the reading progress in an optimal manner according to the user's emotions.

[0102] The recommendation system may further include an emotional goal setting unit that estimates the user's emotions and sets a reading goal based on the estimated user's emotions. The emotional goal setting unit sets a reading goal according to the user's emotions. For example, if the user is relaxed, a long-term reading goal may be set. Also, if the user is feeling stressed, a short-term reading goal may be set. In this way, a reading goal can be set in an optimal manner according to the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit receives the user's reading history or interests, such as the titles and authors of books the user has read in the past, and genres in which the user is interested. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it analyzes the user's reading history and interests, and generates data for recommending the most suitable books to the user. Step 3: The recommendation unit recommends books based on the analysis results obtained by the analysis unit. For example, if the user is interested in a particular genre, books related to that genre will be preferentially recommended.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[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 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.

[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. 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 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.

[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 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 reception unit that receives a user's reading history or interests; an analysis unit that analyzes the information received by the reception unit; a recommendation unit that recommends books based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The reception unit Accepts information about the titles or authors of books the user has read in the past, and genres they are interested in.

2. The system of claim 1.

3. The analysis unit Analyzes the user's reading history and interests to recommend books that are suitable for the user 2. The system of claim 1.

4. The recommendation unit Tailor recommendations based on user interests 2. The system of claim 1.

5. The recommendation unit If a user is interested in a particular genre, books related to that genre will be recommended first.

2. The system of claim 1.

6. The recommendation unit Recommend new books based on the user's past reading history 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and appropriately adjusts the timing of inputting reading history based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past reading history and select the appropriate input method 2. The system of claim 1.

9. The reception unit Filter reading history based on the user's current interests 2. The system of claim 1.

10. The reception unit When entering reading history, select the appropriate input method according to the user's input method.

2. The system of claim 1.

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