System

Abstract

An object of the system according to the embodiment is to recommend a novel based on a user's preference.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a recommendation unit, and a learning unit. The collection unit collects a reading history or an evaluation of the user. The analysis unit analyzes the information collected by the collection unit and specifies the preference of the user. The recommendation unit recommends the novel specified by the analysis unit to the user. The learning unit continuously learns the evaluation of the novel recommended by the recommendation 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 techniques have had the problem that it is difficult for users to find novels that they really like.

[0005] The system according to the embodiment aims to recommend novels based on the user's preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a recommendation unit, and a learning unit. The collection unit collects a user's reading history or ratings. The analysis unit analyzes the information collected by the collection unit and identifies the user's preferences. The recommendation unit recommends novels identified by the analysis unit to the user. The learning unit continuously learns the ratings of novels recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend novels based on the user's preferences. [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 collects and analyzes a user's reading history and ratings, and recommends novels that match the user's preferences. The recommendation system collects the user's past reading history, ratings, and interests, and a recommendation AI analyzes the collected information to identify the user's preferences. The system then recommends the identified novels to the user and continuously learns the user's ratings to improve the accuracy of the recommendations. For example, the recommendation system collects the titles, genres, and ratings of novels the user has read in the past. This information is input into the recommendation AI. The recommendation AI then analyzes the collected information to identify the user's preferences. The recommendation AI analyzes the user's reading history, ratings, and interests to extract characteristics of the user's preferred novels. For example, if the user likes mystery novels, the recommendation AI identifies the mystery novel that is most suitable for the user. The recommendation AI then recommends the identified novel to the user. The user can browse the recommended novels and read them if they interest them. This allows users to find novels that suit their tastes without relying on other people's reviews or bestsellers. Recommendation AI also continuously learns from users' interests and improves the accuracy of its recommendations. For example, when a user inputs a rating for a novel they have recently read, the recommendation AI learns that information and reflects it in the next recommendation. This allows recommendations to be continually made that are tailored to the user's preferences. This allows the recommendation system to automatically recommend novels that suit the user's tastes, improving the user's reading experience. For example, users can find novels that suit their tastes without relying on other people's reviews or bestsellers. Recommendation systems also continuously learn from users' ratings and improve the accuracy of their recommendations, allowing them to provide more personalized recommendations.

[0029] A recommendation system according to an embodiment includes a collection unit, an analysis unit, a recommendation unit, and a learning unit. The collection unit collects a user's reading history or ratings. The user's reading history includes, for example, the titles of books read, the completion date, and ratings, but is not limited to these examples. The collection unit collects, for example, the titles, genres, and ratings of novels the user has read in the past. The collection unit can also collect the user's interests. For example, it collects genres and themes that interest the user. The analysis unit analyzes the information collected by the collection unit to identify the user's preferences. For example, the analysis unit analyzes the user's reading history, ratings, and interests to extract characteristics of the user's favorite novels. For example, if the user likes mystery novels, the analysis unit identifies the mystery novel that is best suited to the user. The analysis unit can also use AI to identify the user's preferences. For example, the analysis unit uses an AI model to analyze the user's reading history and ratings to identify the user's preferences. The recommendation unit recommends the novel identified by the analysis unit to the user. For example, the recommendation unit presents the identified novel to the user, allowing the user to read the novel if the user is interested. The recommendation unit can also use AI to make recommendations tailored to the user's preferences. For example, the recommendation unit uses an AI model to recommend the novel that is best suited to the user. The learning unit continuously learns the ratings of the novels recommended by the recommendation unit and improves the accuracy of the recommendations. For example, the learning unit receives a rating from the user of a newly read novel, learns the information, and reflects it in the next recommendation. The learning unit can also use AI to continuously learn the user's ratings. For example, the learning unit uses an AI model to learn the user's ratings and improve the accuracy of the recommendations. This allows the recommendation system according to the embodiment to recommend novels tailored to the user's preferences. For example, the user can find novels that suit their preferences without relying on other people's reviews or bestsellers.

[0030] The collection unit can analyze the user's past reading history and select an acquisition method. For example, the collection unit can acquire the reading history from a device that the user frequently used in the past. The collection unit can also acquire the reading history from an application that the user used in the past. The collection unit can also acquire the reading history from a website that the user accessed in the past. This makes it possible to acquire information in an optimal manner based on the user's past reading history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past reading history data into the generation AI and have the generation AI select the optimal acquisition method.

[0031] When acquiring the reading history, the collection unit can filter the reading history based on the user's current interests or living situation. For example, the collection unit prioritizes acquiring reading history in genres in which the user is currently interested. The collection unit can also acquire related reading history according to the user's living situation (work, vacation, etc.). The collection unit can also filter related reading history based on the user's current interests. This makes it possible to acquire information according to the user's current interests and living situation. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's current interest data into the generation AI and have the generation AI perform filtering.

[0032] When acquiring the reading history, the collection unit can select an acquisition means depending on the user's input method. For example, if the user uses voice input, the collection unit acquires the reading history from voice data. Furthermore, if the user uses text input, the collection unit can also acquire the reading history from text data. Furthermore, if the user uses image input, the collection unit can also acquire the reading history from image data. This makes it possible to acquire information by the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's voice data to a generation AI and have the generation AI acquire the reading history.

[0033] When acquiring a reading history, the collection unit can prioritize acquiring highly relevant history based on the user's geographical location information. For example, the collection unit prioritizes acquiring reading history related to the area where the user is currently located. The collection unit can also prioritize acquiring reading history related to places the user has visited in the past. The collection unit can also prioritize acquiring reading history related to places the user plans to visit in the future. This makes it possible to acquire highly relevant information based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to acquire highly relevant history.

[0034] When acquiring a reading history, the collection unit can analyze the user's social media activities and acquire related history. For example, the collection unit acquires the reading history of novels shared by the user on social media. The collection unit can also analyze the content of the user's social media posts to acquire related reading history. The collection unit can also acquire related reading history by referring to the activities of the user's friends on social media. This makes it possible to acquire related information based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to acquire related history.

[0035] When acquiring a reading history, the collection unit can customize the acquisition method by reflecting the user's past feedback. For example, the collection unit prioritizes acquisition of reading histories of novels that the user has previously rated highly. The collection unit can also exclude reading histories of novels that the user has previously rated poorly. The collection unit can also customize the optimal acquisition method based on the user's past feedback. This makes it possible to acquire information in the optimal method based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into a generation AI and have the generation AI customize the acquisition method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the reading history. For example, the analysis unit performs a detailed analysis on reading history with high importance. The analysis unit can also perform a concise analysis on reading history with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance. This allows analysis to be performed with an optimal level of detail according to the importance of the reading history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the reading history to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the reading history. For example, the analysis unit can apply a specific analysis algorithm to the reading history of mystery novels. The analysis unit can also apply a different analysis algorithm to the reading history of romance novels. The analysis unit can also select and apply the optimal analysis algorithm for each category. This allows the optimal analysis algorithm to be applied depending on the category of the reading history. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input category data of the reading history to the generation AI and have the generation AI apply the analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also utilize the user's past analysis results as feedback to continuously improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the reading history. For example, the analysis unit prioritizes analysis of the most recently submitted reading history. The analysis unit can also postpone the reading history that was submitted earlier. The analysis unit can also dynamically adjust the priority of analysis based on the time of submission. This allows analysis to be performed with optimal priority based on the time of submission of the reading history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of the reading history to the generation AI and have the generation AI determine the priority of analysis.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the reading history. For example, the analysis unit prioritizes analysis of highly relevant reading history. The analysis unit can also postpone analysis of less relevant reading history. The analysis unit can also dynamically adjust the order of analysis based on the relevance. This allows analysis to be performed in an optimal order based on the relevance of the reading history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the reading history to the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses a lot of technical terminology for users with high levels of expertise. The analysis unit can also avoid technical terminology for users with low levels of expertise. The analysis unit can also dynamically adjust the use of technical terminology in the analysis according to the user's level of expertise. This makes it possible to provide analysis results using the most appropriate technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0042] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the novel. For example, the recommendation unit makes a detailed recommendation for a novel with a high level of importance. The recommendation unit can also make a concise recommendation for a novel with a low level of importance. The recommendation unit can also dynamically adjust the level of detail of the recommendation according to the importance. This makes it possible to make a recommendation with an optimal level of detail according to the importance of the novel. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input importance data of the novel to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0043] When making a recommendation, the recommendation unit can apply different recommendation algorithms depending on the category of the novel. For example, the recommendation unit can apply a specific recommendation algorithm to mystery novels. The recommendation unit can also apply a different recommendation algorithm to romance novels. The recommendation unit can also select and apply the optimal recommendation algorithm for each category. This makes it possible to apply the optimal recommendation algorithm depending on the category of the novel. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input novel category data into the generation AI and cause the generation AI to apply the recommendation algorithm.

[0044] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to the user's past recommendation results. The recommendation unit, for example, adjusts the recommendation algorithm based on the user's past recommendation results. The recommendation unit can also improve the accuracy of the recommendation by referring to the user's past recommendation results. The recommendation unit can also utilize the user's past recommendation results as feedback to continuously improve the accuracy of the recommendation. In this way, the accuracy of the recommendation can be improved by referring to the user's past recommendation results. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past recommendation result data into the generation AI and cause the generation AI to improve the accuracy of the recommendations.

[0045] When making a recommendation, the recommendation unit can determine the priority of the recommendation based on the publication date of the novel. For example, the recommendation unit prioritizes recommending recently published novels. The recommendation unit can also postpone recommending older novels. The recommendation unit can also dynamically adjust the priority of recommendations based on the publication date. This allows recommendations to be made with optimal priority based on the publication date of the novel. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the publication date of the novel into the generation AI and have the generation AI determine the priority of the recommendations.

[0046] When making a recommendation, the recommendation unit can adjust the order of recommendations based on the relevance of the novels. For example, the recommendation unit prioritizes recommending highly relevant novels. The recommendation unit can also postpone recommending less relevant novels. The recommendation unit can also dynamically adjust the order of recommendations based on the relevance. This allows recommendations to be made in the optimal order based on the relevance of the novels. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input relevance data of novels into a generation AI and cause the generation AI to adjust the order of recommendations.

[0047] When making a recommendation, the recommendation unit can adjust the use of technical terminology in the recommendation according to the user's level of expertise. For example, the recommendation unit uses a lot of technical terminology for users with high levels of expertise. The recommendation unit can also avoid technical terminology for users with low levels of expertise. The recommendation unit can also dynamically adjust the use of technical terminology in the recommendation according to the user's level of expertise. This makes it possible to provide recommendations using the most appropriate technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0048] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, adjusts the learning algorithm based on past learning data. The learning unit can also improve the accuracy of learning by referring to past learning data. The learning unit can also continuously optimize the learning algorithm by utilizing past learning data as feedback. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to optimize the learning algorithm.

[0049] During learning, the learning unit can analyze fluctuations in the user's reading history and adjust the update frequency of the learning data. For example, if the user's reading history fluctuates frequently, the learning unit can increase the update frequency of the learning data. Furthermore, if the user's reading history is stable, the learning unit can also decrease the update frequency of the learning data. Furthermore, the learning unit can dynamically adjust the update frequency of the learning data based on fluctuations in the reading history. This allows the learning data to be updated at an optimal frequency depending on fluctuations in the user's reading history. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input fluctuation data of the user's reading history into the generation AI and cause the generation AI to adjust the update frequency of the learning data.

[0050] During learning, the learning unit can continuously learn user ratings and improve the accuracy of recommendations. The learning unit, for example, adjusts the learning algorithm based on user ratings. The learning unit can also refer to user ratings to improve the accuracy of recommendations. The learning unit can also utilize user ratings as feedback to continuously improve the accuracy of recommendations. In this way, by continuously learning user ratings, the accuracy of recommendations can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input user rating data into a generation AI and cause the generation AI to improve the accuracy of recommendations.

[0051] During learning, the learning unit can weight the learning data based on the time of submission of the reading history. For example, the learning unit can assign a higher weight to recently submitted reading history. The learning unit can also assign a lower weight to older submitted reading history. The learning unit can also dynamically adjust the weighting of the learning data based on the time of submission. This allows learning to be performed with optimal weighting based on the time of submission of the reading history. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data on the time of submission of the reading history to the generation AI and have the generation AI perform weighting of the learning data.

[0052] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. The learning unit adjusts the learning algorithm based on, for example, user feedback. The learning unit can also refer to user feedback to improve learning accuracy. The learning unit can also utilize user feedback as feedback to continuously optimize the learning algorithm. In this way, the learning algorithm can be optimized by reflecting user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to adjust the learning algorithm.

[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 may further include a social analysis unit that analyzes a user's social media activity. The social analysis unit can analyze the novels and posts the user has shared on social media to identify the user's interests. For example, if a user frequently shares novels of a particular genre on social media, novels related to that genre can be preferentially recommended. The system can also analyze novels shared by the user's friends to provide relevant recommendations to the user. This allows the system to provide more personalized recommendations based on the user's social media activity.

[0055] The recommendation system can further include a location analysis unit that analyzes the user's geographical location information. The location analysis unit can preferentially recommend novels related to the area where the user is currently located. For example, if the user is traveling, novels related to that area can be recommended. Novels related to places the user has visited in the past can also be recommended. This makes it possible to provide more relevant recommendations based on the user's geographical location information.

[0056] The recommendation system can further include a speed analysis unit that analyzes the user's reading speed. The speed analysis unit can analyze the reading speed of novels that the user has read in the past and make recommendations according to the user's reading speed. For example, if the user reads quickly, novels that can be read in a short time can be recommended preferentially. Also, if the user reads slowly, long novels can be recommended. This makes it possible to provide optimal recommendations according to the user's reading speed.

[0057] The recommendation system can further include an environment analysis unit that analyzes the user's reading environment. The environment analysis unit can analyze the environment in which the user reads (for example, indoors, outdoors, on the move, etc.) and recommend novels that are suitable for that environment. For example, if the user reads while on the move, short stories and essays can be recommended preferentially. Also, if the user reads indoors in a relaxed manner, full-length novels can be recommended. This makes it possible to provide optimal recommendations according to the user's reading environment.

[0058] The recommendation system can further include a fluctuation analysis unit that analyzes fluctuations in a user's reading history. If a user's reading history changes frequently, the fluctuation analysis unit can make recommendations according to those fluctuations. For example, if a user has recently become interested in a new genre, novels related to that genre can be recommended preferentially. Also, if a user's reading history is stable, novels in a genre that the user has previously rated highly can be recommended. This makes it possible to provide optimal recommendations according to fluctuations in the user's reading history.

[0059] The recommendation system can further include a timing analysis unit that analyzes when a user's reading history was submitted. The timing analysis unit can prioritize analysis of reading history recently submitted by the user and make recommendations based on the results. For example, it can recommend related novels based on the user's ratings of novels they have recently read. It can also postpone reading history submitted earlier. This makes it possible to provide optimal recommendations based on when a user submitted their reading history.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects the user's reading history or ratings. Specifically, it collects information such as the titles, completion dates, ratings, genres, and themes of books the user has read in the past. It can also collect the user's interests. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies the user's preferences. Specifically, it analyzes the user's reading history, ratings, and interests to extract the characteristics of the novels the user likes. Analysis can also be performed using an AI model. Step 3: The recommendation unit recommends the novels identified by the analysis unit to the user. Specifically, the identified novels are presented to the user, and if the user is interested, they can read them. Recommendations can also be made using an AI model. Step 4: The learning unit continuously learns from the ratings of novels recommended by the recommendation unit to improve the accuracy of recommendations. Specifically, the user inputs the rating of a newly read novel, and that information is learned and reflected in the next recommendation. Learning can also be done using an AI model.

[0062] (Example 2) A recommendation system according to an embodiment of the present invention collects and analyzes a user's reading history and ratings, and recommends novels that match the user's preferences. The recommendation system collects the user's past reading history, ratings, and interests, and a recommendation AI analyzes the collected information to identify the user's preferences. The system then recommends the identified novels to the user and continuously learns the user's ratings to improve the accuracy of the recommendations. For example, the recommendation system collects the titles, genres, and ratings of novels the user has read in the past. This information is input into the recommendation AI. The recommendation AI then analyzes the collected information to identify the user's preferences. The recommendation AI analyzes the user's reading history, ratings, and interests to extract characteristics of the user's preferred novels. For example, if the user likes mystery novels, the recommendation AI identifies the mystery novel that is most suitable for the user. The recommendation AI then recommends the identified novel to the user. The user can browse the recommended novels and read them if they interest them. This allows users to find novels that suit their tastes without relying on other people's reviews or bestsellers. Recommendation AI also continuously learns from users' interests and improves the accuracy of its recommendations. For example, when a user inputs a rating for a novel they have recently read, the recommendation AI learns that information and reflects it in the next recommendation. This allows recommendations to be continually made that are tailored to the user's preferences. This allows the recommendation system to automatically recommend novels that suit the user's tastes, improving the user's reading experience. For example, users can find novels that suit their tastes without relying on other people's reviews or bestsellers. Recommendation systems also continuously learn from users' ratings and improve the accuracy of their recommendations, allowing them to provide more personalized recommendations.

[0063] A recommendation system according to an embodiment includes a collection unit, an analysis unit, a recommendation unit, and a learning unit. The collection unit collects a user's reading history or ratings. The user's reading history includes, for example, the titles of books read, the completion date, and ratings, but is not limited to these examples. The collection unit collects, for example, the titles, genres, and ratings of novels the user has read in the past. The collection unit can also collect the user's interests. For example, it collects genres and themes that interest the user. The analysis unit analyzes the information collected by the collection unit to identify the user's preferences. For example, the analysis unit analyzes the user's reading history, ratings, and interests to extract characteristics of the user's favorite novels. For example, if the user likes mystery novels, the analysis unit identifies the mystery novel that is best suited to the user. The analysis unit can also use AI to identify the user's preferences. For example, the analysis unit uses an AI model to analyze the user's reading history and ratings to identify the user's preferences. The recommendation unit recommends the novel identified by the analysis unit to the user. For example, the recommendation unit presents the identified novel to the user, allowing the user to read the novel if the user is interested. The recommendation unit can also use AI to make recommendations tailored to the user's preferences. For example, the recommendation unit uses an AI model to recommend the novel that is best suited to the user. The learning unit continuously learns the ratings of the novels recommended by the recommendation unit and improves the accuracy of the recommendations. For example, the learning unit receives a rating from the user of a newly read novel, learns the information, and reflects it in the next recommendation. The learning unit can also use AI to continuously learn the user's ratings. For example, the learning unit uses an AI model to learn the user's ratings and improve the accuracy of the recommendations. This allows the recommendation system according to the embodiment to recommend novels tailored to the user's preferences. For example, the user can find novels that suit their preferences without relying on other people's reviews or bestsellers.

[0064] The collection unit can estimate the user's emotions and adjust the timing of acquiring the reading history based on the estimated user emotions. For example, if the user is relaxed, the collection unit can immediately acquire the reading history. Furthermore, if the user is feeling stressed, the collection unit can also postpone acquiring the reading history. Furthermore, if the user is concentrating, the collection unit can also not interrupt the acquisition of the reading history. This allows the reading history to be acquired at the optimal timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0065] The collection unit can analyze the user's past reading history and select an acquisition method. For example, the collection unit can acquire the reading history from a device that the user frequently used in the past. The collection unit can also acquire the reading history from an application that the user used in the past. The collection unit can also acquire the reading history from a website that the user accessed in the past. This makes it possible to acquire information in an optimal manner based on the user's past reading history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past reading history data into the generation AI and have the generation AI select the optimal acquisition method.

[0066] When acquiring the reading history, the collection unit can filter the reading history based on the user's current interests or living situation. For example, the collection unit prioritizes acquiring reading history in genres in which the user is currently interested. The collection unit can also acquire related reading history according to the user's living situation (work, vacation, etc.). The collection unit can also filter related reading history based on the user's current interests. This makes it possible to acquire information according to the user's current interests and living situation. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's current interest data into the generation AI and have the generation AI perform filtering.

[0067] When acquiring the reading history, the collection unit can select an acquisition means depending on the user's input method. For example, if the user uses voice input, the collection unit acquires the reading history from voice data. Furthermore, if the user uses text input, the collection unit can also acquire the reading history from text data. Furthermore, if the user uses image input, the collection unit can also acquire the reading history from image data. This makes it possible to acquire information by the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's voice data to a generation AI and have the generation AI acquire the reading history.

[0068] The collection unit can estimate the user's emotions and determine the priority of the reading history to be acquired based on the estimated user emotions. For example, when the user is relaxed, the collection unit prioritizes acquiring the most recent reading history. Furthermore, when the user is feeling stressed, the collection unit can also prioritize acquiring past reading history. Furthermore, when the user is concentrating, the collection unit can also prioritize acquiring reading history of a specific genre. This allows information to be acquired with optimal priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0069] When acquiring a reading history, the collection unit can prioritize acquiring highly relevant history based on the user's geographical location information. For example, the collection unit prioritizes acquiring reading history related to the area where the user is currently located. The collection unit can also prioritize acquiring reading history related to places the user has visited in the past. The collection unit can also prioritize acquiring reading history related to places the user plans to visit in the future. This makes it possible to acquire highly relevant information based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to acquire highly relevant history.

[0070] When acquiring a reading history, the collection unit can analyze the user's social media activities and acquire related history. For example, the collection unit acquires the reading history of novels shared by the user on social media. The collection unit can also analyze the content of the user's social media posts to acquire related reading history. The collection unit can also acquire related reading history by referring to the activities of the user's friends on social media. This makes it possible to acquire related information based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to acquire related history.

[0071] When acquiring a reading history, the collection unit can customize the acquisition method by reflecting the user's past feedback. For example, the collection unit prioritizes acquisition of reading histories of novels that the user has previously rated highly. The collection unit can also exclude reading histories of novels that the user has previously rated poorly. The collection unit can also customize the optimal acquisition method based on the user's past feedback. This makes it possible to acquire information in the optimal method based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into a generation AI and have the generation AI customize the acquisition method.

[0072] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. The analysis unit can also provide visually easy-to-understand analysis results when the user is concentrating. This allows the analysis results to be presented in an optimal way depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the reading history. For example, the analysis unit performs a detailed analysis on reading history with high importance. The analysis unit can also perform a concise analysis on reading history with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance. This allows analysis to be performed with an optimal level of detail according to the importance of the reading history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the reading history to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the reading history. For example, the analysis unit can apply a specific analysis algorithm to the reading history of mystery novels. The analysis unit can also apply a different analysis algorithm to the reading history of romance novels. The analysis unit can also select and apply the optimal analysis algorithm for each category. This allows the optimal analysis algorithm to be applied depending on the category of the reading history. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input category data of the reading history to the generation AI and have the generation AI apply the analysis algorithm.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also utilize the user's past analysis results as feedback to continuously improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. The analysis unit can also provide visually easy-to-understand analysis results when the user is concentrating. This allows the analysis results to be provided at an optimal length depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI adjust the length of the analysis.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the reading history. For example, the analysis unit prioritizes analysis of the most recently submitted reading history. The analysis unit can also postpone the reading history that was submitted earlier. The analysis unit can also dynamically adjust the priority of analysis based on the time of submission. This allows analysis to be performed with optimal priority based on the time of submission of the reading history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of the reading history to the generation AI and have the generation AI determine the priority of analysis.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the reading history. For example, the analysis unit prioritizes analysis of highly relevant reading history. The analysis unit can also postpone analysis of less relevant reading history. The analysis unit can also dynamically adjust the order of analysis based on the relevance. This allows analysis to be performed in an optimal order based on the relevance of the reading history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the reading history to the generation AI and have the generation AI adjust the order of analysis.

[0079] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses a lot of technical terminology for users with high levels of expertise. The analysis unit can also avoid technical terminology for users with low levels of expertise. The analysis unit can also dynamically adjust the use of technical terminology in the analysis according to the user's level of expertise. This makes it possible to provide analysis results using the most appropriate technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0080] The recommendation unit can estimate the user's emotions and adjust the way recommendations are expressed based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can provide detailed recommendations. If the user is stressed, the recommendation unit can also provide concise recommendations. If the user is concentrating, the recommendation unit can also provide visually easy-to-understand recommendations. This allows recommendations to be provided in an optimal way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recommendation unit can be performed using AI, or can be performed without AI. For example, the recommendation unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the way recommendations are expressed.

[0081] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the importance of the novel. For example, the recommendation unit makes a detailed recommendation for a novel with a high level of importance. The recommendation unit can also make a concise recommendation for a novel with a low level of importance. The recommendation unit can also dynamically adjust the level of detail of the recommendation according to the importance. This makes it possible to make a recommendation with an optimal level of detail according to the importance of the novel. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input importance data of the novel to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0082] When making a recommendation, the recommendation unit can apply different recommendation algorithms depending on the category of the novel. For example, the recommendation unit can apply a specific recommendation algorithm to mystery novels. The recommendation unit can also apply a different recommendation algorithm to romance novels. The recommendation unit can also select and apply the optimal recommendation algorithm for each category. This makes it possible to apply the optimal recommendation algorithm depending on the category of the novel. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input novel category data into the generation AI and cause the generation AI to apply the recommendation algorithm.

[0083] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to the user's past recommendation results. The recommendation unit, for example, adjusts the recommendation algorithm based on the user's past recommendation results. The recommendation unit can also improve the accuracy of the recommendation by referring to the user's past recommendation results. The recommendation unit can also utilize the user's past recommendation results as feedback to continuously improve the accuracy of the recommendation. In this way, the accuracy of the recommendation can be improved by referring to the user's past recommendation results. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past recommendation result data into the generation AI and cause the generation AI to improve the accuracy of the recommendations.

[0084] The recommendation unit can estimate the user's emotions and adjust the length of the recommendation based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can provide detailed recommendations. If the user is stressed, the recommendation unit can also provide concise recommendations. If the user is concentrating, the recommendation unit can also provide visually easy-to-understand recommendations. This allows recommendations to be provided with an optimal length depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recommendation unit can be performed using AI, or can be performed without AI. For example, the recommendation unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the length of the recommendation.

[0085] When making a recommendation, the recommendation unit can determine the priority of the recommendation based on the publication date of the novel. For example, the recommendation unit prioritizes recommending recently published novels. The recommendation unit can also postpone recommending older novels. The recommendation unit can also dynamically adjust the priority of recommendations based on the publication date. This allows recommendations to be made with optimal priority based on the publication date of the novel. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the publication date of the novel into the generation AI and have the generation AI determine the priority of the recommendations.

[0086] When making a recommendation, the recommendation unit can adjust the order of recommendations based on the relevance of the novels. For example, the recommendation unit prioritizes recommending highly relevant novels. The recommendation unit can also postpone recommending less relevant novels. The recommendation unit can also dynamically adjust the order of recommendations based on the relevance. This allows recommendations to be made in the optimal order based on the relevance of the novels. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input relevance data of novels into a generation AI and cause the generation AI to adjust the order of recommendations.

[0087] When making a recommendation, the recommendation unit can adjust the use of technical terminology in the recommendation according to the user's level of expertise. For example, the recommendation unit uses a lot of technical terminology for users with high levels of expertise. The recommendation unit can also avoid technical terminology for users with low levels of expertise. The recommendation unit can also dynamically adjust the use of technical terminology in the recommendation according to the user's level of expertise. This makes it possible to provide recommendations using the most appropriate technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0088] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, when the user is relaxed, the learning unit selects detailed training data. Furthermore, when the user is stressed, the learning unit can select concise training data. Furthermore, when the user is concentrating, the learning unit can select visually easy-to-understand training data. This allows optimal training data to be selected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's facial expression data into the generation AI and have the generation AI select the training data.

[0089] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, adjusts the learning algorithm based on past learning data. The learning unit can also improve the accuracy of learning by referring to past learning data. The learning unit can also continuously optimize the learning algorithm by utilizing past learning data as feedback. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to optimize the learning algorithm.

[0090] During learning, the learning unit can analyze fluctuations in the user's reading history and adjust the update frequency of the learning data. For example, if the user's reading history fluctuates frequently, the learning unit can increase the update frequency of the learning data. Furthermore, if the user's reading history is stable, the learning unit can also decrease the update frequency of the learning data. Furthermore, the learning unit can dynamically adjust the update frequency of the learning data based on fluctuations in the reading history. This allows the learning data to be updated at an optimal frequency depending on fluctuations in the user's reading history. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input fluctuation data of the user's reading history into the generation AI and cause the generation AI to adjust the update frequency of the learning data.

[0091] During learning, the learning unit can continuously learn user ratings and improve the accuracy of recommendations. The learning unit, for example, adjusts the learning algorithm based on user ratings. The learning unit can also refer to user ratings to improve the accuracy of recommendations. The learning unit can also utilize user ratings as feedback to continuously improve the accuracy of recommendations. In this way, by continuously learning user ratings, the accuracy of recommendations can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input user rating data into a generation AI and cause the generation AI to improve the accuracy of recommendations.

[0092] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit can increase the frequency of learning when the user is relaxed. The learning unit can also decrease the frequency of learning when the user is stressed. The learning unit can also dynamically adjust the frequency of learning when the user is concentrating. This allows learning to be performed at an optimal frequency depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the learning unit can be performed using an AI, for example, or without an AI. For example, the learning unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the frequency of learning.

[0093] During learning, the learning unit can weight the learning data based on the time of submission of the reading history. For example, the learning unit can assign a higher weight to recently submitted reading history. The learning unit can also assign a lower weight to older submitted reading history. The learning unit can also dynamically adjust the weighting of the learning data based on the time of submission. This allows learning to be performed with optimal weighting based on the time of submission of the reading history. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data on the time of submission of the reading history to the generation AI and have the generation AI perform weighting of the learning data.

[0094] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. The learning unit adjusts the learning algorithm based on, for example, user feedback. The learning unit can also refer to user feedback to improve learning accuracy. The learning unit can also utilize user feedback as feedback to continuously optimize the learning algorithm. In this way, the learning algorithm can be optimized by reflecting user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to adjust the learning algorithm. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and learning unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's reading history and ratings using the control unit 46A of the smart device 14. The analysis unit analyzes the information collected by the identification processing unit 290 of the data processing device 12 to identify the user's preferences. The recommendation unit recommends novels identified by the identification processing unit 290 of the data processing device 12 to the user. The learning unit continuously learns the user's ratings using the identification processing unit 290 of the data processing device 12 to improve the accuracy of recommendations. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and learning unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's reading history and ratings using the control unit 46A of the smart glasses 214. The analysis unit analyzes the information collected by the identification processing unit 290 of the data processing device 12 to identify the user's preferences. The recommendation unit recommends novels identified by the identification processing unit 290 of the data processing device 12 to the user. The learning unit continuously learns the user's ratings using the identification processing unit 290 of the data processing device 12 to improve the accuracy of recommendations. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and learning unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the user's reading history and ratings using the control unit 46A of the headset type terminal 314. The analysis unit analyzes the information collected by the identification processing unit 290 of the data processing device 12 and identifies the user's preferences. The recommendation unit recommends novels identified by the identification processing unit 290 of the data processing device 12 to the user. The learning unit continuously learns the user's ratings using the identification processing unit 290 of the data processing device 12 and improves the accuracy of recommendations. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and learning unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's reading history and ratings using the control unit 46A of the robot 414. The analysis unit analyzes the information collected by the identification processing unit 290 of the data processing device 12 and identifies the user's preferences. The recommendation unit recommends novels identified by the identification processing unit 290 of the data processing device 12 to the user. The learning unit continuously learns the user's ratings using the identification processing unit 290 of the data processing device 12 and improves the accuracy of recommendations.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The recommendation system may further include a social analysis unit that analyzes a user's social media activity. The social analysis unit can analyze the novels and posts the user has shared on social media to identify the user's interests. For example, if a user frequently shares novels of a particular genre on social media, novels related to that genre can be preferentially recommended. The system can also analyze novels shared by the user's friends to provide relevant recommendations to the user. This allows the system to provide more personalized recommendations based on the user's social media activity.

[0097] The recommendation system can further include an emotion timing unit that estimates the user's emotion and adjusts the timing of recommendations based on the estimated emotion. For example, the emotion timing unit can make recommendations immediately if the user is relaxed. Also, if the user is feeling stressed, the emotion timing unit can postpone the recommendation. Furthermore, if the user is concentrating, the recommendation can be prevented from being interrupted. This allows recommendations to be made at the optimal timing according to the user's emotion.

[0098] The recommendation system can further include a location analysis unit that analyzes the user's geographical location information. The location analysis unit can preferentially recommend novels related to the area where the user is currently located. For example, if the user is traveling, novels related to that area can be recommended. Novels related to places the user has visited in the past can also be recommended. This makes it possible to provide more relevant recommendations based on the user's geographical location information.

[0099] The recommendation system can further include a speed analysis unit that analyzes the user's reading speed. The speed analysis unit can analyze the reading speed of novels that the user has read in the past and make recommendations according to the user's reading speed. For example, if the user reads quickly, novels that can be read in a short time can be recommended preferentially. Also, if the user reads slowly, long novels can be recommended. This makes it possible to provide optimal recommendations according to the user's reading speed.

[0100] The recommendation system can further include an environment analysis unit that analyzes the user's reading environment. The environment analysis unit can analyze the environment in which the user reads (for example, indoors, outdoors, on the move, etc.) and recommend novels that are suitable for that environment. For example, if the user reads while on the move, short stories and essays can be recommended preferentially. Also, if the user reads indoors in a relaxed manner, full-length novels can be recommended. This makes it possible to provide optimal recommendations according to the user's reading environment.

[0101] The recommendation system can further include an emotional content unit that estimates the user's emotions and adjusts the content of recommendations based on the estimated emotions. For example, if the user is relaxed, the emotional content unit can recommend a novel with relaxing content. Also, if the user is feeling stressed, the emotional content unit can recommend a novel that helps relieve stress. Furthermore, if the user is concentrating, the emotional content unit can recommend a novel with content that will make the user think deeply. This makes it possible to provide recommendations with optimal content according to the user's emotions.

[0102] The recommendation system can further include a fluctuation analysis unit that analyzes fluctuations in a user's reading history. If a user's reading history changes frequently, the fluctuation analysis unit can make recommendations according to those fluctuations. For example, if a user has recently become interested in a new genre, novels related to that genre can be recommended preferentially. Also, if a user's reading history is stable, novels in a genre that the user has previously rated highly can be recommended. This makes it possible to provide optimal recommendations according to fluctuations in the user's reading history.

[0103] The recommendation system may further include an emotion frequency unit that estimates the user's emotion and adjusts the frequency of recommendations based on the estimated emotion. The emotion frequency unit may, for example, increase the frequency of recommendations when the user is relaxed. Alternatively, it may decrease the frequency of recommendations when the user is stressed. Furthermore, it may dynamically adjust the frequency of recommendations when the user is concentrating. This allows recommendations to be made at an optimal frequency depending on the user's emotion.

[0104] The recommendation system can further include a timing analysis unit that analyzes when a user's reading history was submitted. The timing analysis unit can prioritize analysis of reading history recently submitted by the user and make recommendations based on the results. For example, it can recommend related novels based on the user's ratings of novels they have recently read. It can also postpone reading history submitted earlier. This makes it possible to provide optimal recommendations based on when a user submitted their reading history.

[0105] The recommendation system may further include an emotion expression unit that estimates the user's emotion and adjusts the way in which recommendations are expressed based on the estimated emotion. For example, the emotion expression unit may provide detailed recommendations when the user is relaxed. Alternatively, it may provide concise recommendations when the user is stressed. Furthermore, it may provide visually easy-to-understand recommendations when the user is concentrating. This allows recommendations to be provided in the most appropriate expression method according to the user's emotion.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The collection unit collects the user's reading history or ratings. Specifically, it collects information such as the titles, completion dates, ratings, genres, and themes of books the user has read in the past. It can also collect the user's interests. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies the user's preferences. Specifically, it analyzes the user's reading history, ratings, and interests to extract the characteristics of the novels the user likes. Analysis can also be performed using an AI model. Step 3: The recommendation unit recommends the novels identified by the analysis unit to the user. Specifically, the identified novels are presented to the user, and if the user is interested, they can read them. Recommendations can also be made using an AI model. Step 4: The learning unit continuously learns from the ratings of novels recommended by the recommendation unit to improve the accuracy of recommendations. Specifically, the user inputs the rating of a newly read novel, and that information is learned and reflected in the next recommendation. Learning can also be done using an AI model.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

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

[0115] The 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.

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

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0119] Fig. 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.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0122] 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.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0138] 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.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] [Explanation of symbols]

[0180] 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 collection unit that collects users' reading histories or ratings; an analysis unit that analyzes the information collected by the collection unit and identifies user preferences; a recommendation unit that recommends the novel identified by the analysis unit to a user; a learning unit that continuously learns the evaluations of the novels recommended by the recommendation unit. A system characterized by:

2. The collecting unit Estimates the user's emotions and adjusts the timing of acquiring reading history based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit Analyze the user's past reading history and select the acquisition method 2. The system of claim 1.

4. The collecting unit When capturing reading history, filter it based on the user's current interests or life situation.

2. The system of claim 1.

5. The collecting unit When acquiring reading history, the acquisition method is selected according to the user's input method.

2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and prioritize the reading history to be acquired based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit When retrieving reading history, prioritize the most relevant history based on the user's geographic location.

2. The system of claim 1.

8. The collecting unit When acquiring reading history, analyze the user's social media activity and acquire related history.

2. The system of claim 1.

Images