Electronic device and information processing method

Abstract

The application discloses an electronic device and an information processing method, and belongs to the technical field of electronics. The electronic device comprises a controller and a display screen. The display screen is used for displaying reading content. The controller is used for receiving operation data of a target object on the reading content, wherein the operation data comprises at least one of reading sub-data, adding note sub-data and sharing note sub-data. The controller is also used for acquiring the characteristics of the target object, wherein the characteristics of the target object comprise the characteristics of the operation data. The controller is further used for sending recommended content to the display screen based on the characteristics of the target object. The display screen is also used for displaying the recommended content. The application solves the problem that the function of the electronic device in the learning scene is relatively single. The application is used for making suitable content recommendation to the target object.

Description

Technical Field

[0001] This application relates to the field of electronic technology, and in particular to an electronic device and an information processing method. Background Technology

[0002] With the development of electronic technology, electronic devices (such as smartphones) have become indispensable in people's lives, and the requirements for electronic devices are getting higher and higher.

[0003] Currently, users can use electronic devices for learning, such as reading e-books or answering electronic exercises.

[0004] However, the functions of electronic devices in learning scenarios are relatively limited in related technologies. Summary of the Invention

[0005] This application provides an electronic device and an information processing method, which can solve the problem of the limited functionality of electronic devices in learning scenarios. The technical solution is as follows:

[0006] On the one hand, an electronic device is provided, the electronic device including a controller and a display screen;

[0007] The display screen is used to display reading content;

[0008] The controller is configured to: receive operation data of a target object on the reading content, the operation data including at least one of: reading aloud sub-data, adding notes sub-data, and sharing notes sub-data; acquire features of the target object, the features of the target object including the features of the operation data; and send recommended content to the display screen based on the features of the target object.

[0009] The display screen is also used to display the recommended content.

[0010] On the other hand, an information processing method is provided for an electronic device, the electronic device including a controller and a display screen, the method comprising:

[0011] The display screen shows the reading content;

[0012] The controller receives operation data from the target object regarding the reading content, the operation data including at least one of the following: reading aloud sub-data, adding notes sub-data, and sharing notes sub-data;

[0013] The controller acquires the characteristics of the target object, and the characteristics of the target object include the characteristics of the operation data;

[0014] The controller sends recommended content to the display screen based on the characteristics of the target object;

[0015] The display screen shows the recommended content.

[0016] The beneficial effects of the technical solution provided in this application include at least the following:

[0017] In this application, the electronic device can obtain the characteristics of the target object based on the target object's operation data regarding the reading content displayed on the screen. These characteristics can accurately reflect the target object's reading habits. Furthermore, the electronic device can recommend appropriate content to the target object based on these characteristics, which helps the target object's learning and enriches the functionality of the electronic device. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the structure of an information processing system provided in an embodiment of this application;

[0020] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0021] Figure 3 This is a flowchart of an information processing method provided in an embodiment of this application;

[0022] Figure 4 This is a flowchart of another information processing method provided in an embodiment of this application;

[0023] Figure 5 This is a schematic diagram of the display interface of an electronic device provided in an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0025] With the development of electronic technology, electronic devices (such as smartphones and smart TVs) are becoming increasingly sophisticated, enabling people to accomplish many tasks in their work and daily lives. Consequently, the demands on these devices are also rising. Currently, electronic devices can be used to analyze students' learning processes, providing targeted guidance and helping students learn more effectively. For example, in related technologies, after students read on an electronic device, they can be presented with assessment questions to answer. The device can then assess their reading ability based on the correctness of their answers. However, since a student's reading ability is not solely reflected in their comprehension of a particular book, assessing their reading ability solely based on their answers to certain questions is too simplistic. Furthermore, the analysis of students' reading progress by electronic devices in related technologies is relatively ineffective, with significant discrepancies between the analysis results and the students' actual abilities. Therefore, directly relying on these analysis results to guide students' learning is not very accurate.

[0026] The following embodiments of this application provide an electronic device and an information processing method that can analyze the characteristics of a target object based on its operational data regarding reading content, and then recommend content to the target object. This enriches the functionality of electronic devices in learning scenarios and allows for more accurate analysis of the target object's reading behavior, thereby providing more appropriate guidance for students' reading and learning.

[0027] In this embodiment, the electronic device can directly process the acquired information to analyze and obtain the characteristics of the target object. Optionally, the electronic device can also upload the acquired information to a server so that the server can analyze the information to obtain the characteristics of the target object.

[0028] Figure 1 This is a schematic diagram of the structure of an information processing system provided in an embodiment of this application. For example... Figure 1As shown, the information processing system 10 may include a terminal 101 and a server 102. The terminal 101 is communicatively connected to the server 102, and can interact with the server 102. The terminal 101 is the electronic device provided in this embodiment. The terminal 101 can be an electronic device used by a target object (such as a student or child). The target object can read and operate on the reading content through the terminal 101. The terminal 101 can be an electronic device such as a smartphone, tablet, laptop, or desktop computer. Optionally, the terminal 101 can also be a smart home device with display capabilities, such as a smart TV, smart lamp, or smart refrigerator. An information processing application (such as a reading application) may be installed on the terminal 101, and the server 102 can be a server providing services to the application. Users can launch the application to analyze the characteristics of the target object through the information processing system. Users can also trigger the terminal 101 to display reading content through the application, thereby reading e-books.

[0029] Please continue to refer to this. Figure 1 The information processing system may also include a smart bookshelf 103. Terminal 101 can interact with the smart bookshelf 103. If both terminal 101 and the smart bookshelf 103 are communicatively connected to server 102, terminal 101 can obtain information sent by the smart bookshelf 103 through server 102. Optionally, terminal 101 can also communicate directly with the smart bookshelf 103; this embodiment does not limit this. Optionally, terminal 101 can manage information about the books in the smart bookshelf 103, so that users can access this information through terminal 101. Terminal 101 can also control the smart bookshelf 103. Server 102 can also support information processing or storage for the smart bookshelf 103 and terminal 101. Server 102 can be an edge server deployed in a home LAN or a cloud server. Terminal 101 and the smart bookshelf 103, as well as terminal 101 and server 102, can be connected via wired or wireless networks. Among them, wired networks may include, but are not limited to, Universal Serial Bus (USB), and wireless networks may include, but are not limited to, Wireless Fidelity (WIFI), Bluetooth, infrared, Zigbee, data networks, etc.

[0030] For example, a bookshelf management application can be installed on terminal 101. Terminal 101 can launch the bookshelf management application to bind the corresponding smart bookshelf 103, and then manage the smart bookshelf 103 through the application. Users can control the smart bookshelf 103 to perform certain operations through terminal 101, and can also obtain information acquired by the smart bookshelf 103 or information stored in the smart bookshelf 103. For example, users can remotely obtain images captured by the camera in the smart bookshelf 103 through terminal 101 to monitor the area where the smart bookshelf 103 is located. When the smart bookshelf 103 determines that a book has been taken out or put back in, it can send a prompt message about the book's storage or retrieval status to terminal 101.

[0031] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device can be the terminal 101 described above. For example... Figure 2 As shown, the electronic device may include: a processor 1101, a crystal oscillator unit 120, a camera 1032, a display screen 130, a radio frequency (RF) circuit 150, an audio circuit 160, a wireless fidelity (Wi-Fi) module 170, a Bluetooth module 180, a power supply 190, and other components. The Wi-Fi module 170 and the Bluetooth module 180 can work together as a communication unit in the electronic device.

[0032] The processor 1101 is the control center (also known as the controller) of the electronic device. It connects various parts of the electronic device via various interfaces and lines, and performs various functions and processes data by running or executing software programs stored in the memory 140 and calling data stored in the memory 140. In some embodiments, the processor 1101 may include one or more processing units; the processor 1101 may also integrate an application processor (AP) and a baseband processor (BP), wherein the application processor mainly handles the operating system, user interface, and applications, and the baseband processor mainly handles wireless communication. It is understood that the baseband processor may not be integrated into the processor 1101. In this application, the processor 1101 can run the operating system and applications, control the user interface display, and implement the methods provided in the embodiments of this application. Furthermore, the processor 1101 is coupled to the input unit and the touch display screen 130.

[0033] Camera 1032 can be used to capture still images or videos. An object passes through the lens to generate an optical image that is projected onto a photosensitive element. The photosensitive element can be a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the light signal into an electrical signal, which is then transmitted to processor 1101 to be converted into a digital image signal.

[0034] The touch display screen 130 can be used to receive input digital or character information, generate signal inputs related to user settings and function control of the electronic device, and optionally, the touch display screen 130 can also be used to display information input by the user or information provided to the user, as well as various menus of the electronic device, forming a graphical user interface (GUI). The touch display screen 130 may include a display screen. The display screen may be configured in the form of a liquid crystal display or a light-emitting diode. The touch display screen 130 may also include a touch screen. The touch screen can collect touch operations on or near the user, such as clicking a button, dragging a scroll bar, etc. The touch screen can be overlaid on the display screen, or the touch screen can be integrated with the display screen to realize the input and output functions of the electronic device; the integrated screen can be simply referred to as a touch display screen.

[0035] The memory 140 can be used to store software programs and data. The processor 1101 executes various functions of the electronic device and data processing by running the software programs or data stored in the memory 140. The memory 140 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. The memory 140 stores an operating system that enables the electronic device to run. In this application, the memory 140 may store the operating system and various application programs, and may also store code that executes the information processing methods provided in the embodiments of this application.

[0036] RF circuit 150 can be used to receive and transmit signals during information transmission or calls. It can receive downlink data from the base station and pass it to processor 1101 for processing; it can also send uplink data to the base station. Typically, RF circuits include, but are not limited to, antennas, at least one amplifier, transceiver, coupler, low-noise amplifier, duplexer, etc. Audio circuit 160, speaker 161, and microphone 162 provide an audio interface between the user and the electronic device. Audio circuit 160 converts the received audio data into an electrical signal and transmits it to speaker 161, where speaker 161 converts it into a sound signal for output. The electronic device can also be equipped with volume buttons for adjusting the volume of the sound signal. On the other hand, microphone 162 converts the collected sound signal into an electrical signal, which is received by audio circuit 160, converted into audio data, and then output to RF circuit 150 for transmission to, for example, another electronic device, or to memory 140 for further processing. In this application, microphone 162 can acquire the user's voice.

[0037] Wi-Fi is a short-range wireless transmission technology. Electronic devices can use Wi-Fi module 170 to help users send and receive emails, browse web pages, and access streaming media, providing users with wireless broadband internet access. Bluetooth module 180 is used to interact with other Bluetooth devices via the Bluetooth protocol. For example, electronic devices can establish a Bluetooth connection with wearable management devices (such as smartwatches) that also have Bluetooth modules through Bluetooth module 180, thereby exchanging data.

[0038] The electronic device also includes a power supply 190 (such as a battery) to power various components. The power supply can be logically connected to the processor 1101 through a power management system, thereby enabling the management of charging, discharging, and power consumption. The electronic device may also be equipped with a power button for powering on and off, and for screen locking. The electronic device may also include at least one sensor 1110, such as a motion sensor 11101, a proximity sensor 11102, a fingerprint sensor 11103, and a temperature sensor 11104. The electronic device may also be equipped with other sensors such as a gyroscope, barometer, hygrometer, thermometer, and infrared sensor.

[0039] Figure 3 This is a flowchart of an information processing method provided in an embodiment of this application. This method can be used for... Figure 2 The electronic device shown can be Figure 1 Terminal 101 in the information processing system shown. For example... Figure 3 As shown, the method may include:

[0040] Step 301: The display screen shows the reading content.

[0041] Optionally, a reading application may be installed on the electronic device. The user can trigger the electronic device to run the reading application, and then obtain reading content based on the reading application. The screen of the electronic device can display the reading content for the user to read.

[0042] Step 302: The controller receives operation data from the target object for the reading content. The operation data includes at least one of the following: reading aloud sub-data, adding notes sub-data, and sharing notes sub-data.

[0043] Optionally, the electronic device needs to log in to a user account when running the reading application. The electronic device can distinguish different target objects through user accounts. It can consider all relevant data obtained during the reading process (such as operation data related to the reading content) obtained when logging into the same user account as reading data for the target object corresponding to that user account. Optionally, the electronic device can also distinguish different reading objects when logging into a single user account. For example, when each reading object is reading, the electronic device can identify that reading object, or the reading object can manually change the identifier recorded by the electronic device, so that the electronic device can bind the relevant data of each reading object during the reading process to that reading object's identifier.

[0044] The target user can interact with the reading content displayed on the electronic device, allowing the electronic device to acquire operation data related to that content. For example, the electronic device can mark at least a portion of the reading content as read-aloud content. After reading the read-aloud content, the target user can read it aloud according to the instructions of the electronic device. The electronic device can collect the target user's voice during the reading process and analyze and process the voice to obtain reading sub-data. As another example, while reading the content, the target user can select and mark any word, phrase, or paragraph to add notes, allowing the electronic device to generate note-adding sub-data based on the note-adding action. Furthermore, after adding notes, the target user can perform subsequent operations on them. For example, the electronic device can be controlled to display the notes added by the target user, and any note can be shared, such as sharing a link to the note to the target user's friends' accounts or to other social media platforms.

[0045] Step 303: The controller acquires the characteristics of the target object, including the characteristics of the operation data.

[0046] The characteristics of the target object can reflect its reading ability. For example, it can reflect the target object's reading characteristics, such as its expressive abilities while reading.

[0047] Step 304: The controller sends recommended content to the display screen based on the characteristics of the target object.

[0048] Optionally, the recommended content may be at least one of the following: information about books, course information, and reading plan information.

[0049] Step 305: The screen displays the recommended content.

[0050] After seeing the recommended content, the target audience can use it as a basis for further reading and learning, which helps them to learn more effectively.

[0051] In summary, the information processing method provided in this application allows an electronic device to obtain the characteristics of a target object based on the target object's operation data regarding the reading content displayed on the screen. These characteristics can accurately reflect the target object's reading habits. Furthermore, the electronic device can recommend suitable content to the target object based on these characteristics, which aids in the target object's learning and enriches the functionality of the electronic device.

[0052] In this embodiment, the electronic device may have a reading application installed. A user can click the icon of the reading application displayed on the screen of the electronic device to trigger the electronic device to start running the reading application. The electronic device can then display the interface of the reading application. For example, the electronic device can obtain reading content based on the reading application, and the screen of the electronic device can display the reading content for the user to read. The electronic device can also display reading-related recommendations, such as recommending suitable books, courses, and reading plans to the user. The electronic device can construct a reading profile of the target user to obtain appropriate recommended content based on the reading profile. (The following is a collection of appendices.) Figure 4 This paper describes in detail the process of constructing a reading profile of a target object on an electronic device and recommending suitable content to the user based on that profile.

[0053] Figure 4 This is a flowchart of an information processing method provided in an embodiment of this application. This method can be used for... Figure 2 The electronic device shown can be Figure 1 Terminal 101 in the information processing system shown. For example... Figure 4 As shown, the method may include:

[0054] Step 401: Obtain reading-related data of the target object. The reading-related data includes at least one of the following: reading habit data of the target object, reading history data, and the target object's comprehension ability data of the reading content, as well as the target object's operation data on the reading content.

[0055] Optionally, each reader can log in to their account on an electronic device when reading, such as using a reading application installed on the device, to identify themselves. This embodiment uses a reader as the target object, which can be any reader using the electronic device. When logging into the target object's account on the electronic device, all operations performed using the reading application are considered by the electronic device to be performed by the target object. For example, if the reading content displayed during the account login process is considered by the electronic device to be the content read by the target object, any marking operation on the displayed content is considered to be performed by the target object. The electronic device can obtain the target object's reading-related data based on the reading-related data generated when logging into the target object's account.

[0056] In this embodiment, the reading-related data of the target object may include at least one of the following: reading habit data, reading history data, and comprehension ability data of the target object regarding the reading content, as well as operational data of the target object regarding the reading content. This embodiment takes as an example where the reading-related data of the target object simultaneously includes the reading habit data, reading history data, comprehension ability data, and operational data. Optionally, during the target object's reading process, some of these four types of data may not be generated; in this case, the electronic device may only acquire a portion of the existing data. It should be noted that the electronic device may acquire the operational data, reading habit data, reading history data, and comprehension ability data simultaneously, or it may acquire any one of these four types of data first. This embodiment does not limit the order in which the electronic device acquires the various data.

[0057] It should be noted that the target object's reading-related data can be obtained based on all reading processes of the target object for which data has been recorded. In this case, the reading content mentioned in the target object's reading-related data is a collective term for all reading content in all reading processes. Alternatively, the target object's reading-related data can also be obtained based on the most recent target reading process. In this case, the reading content mentioned in the target object's reading-related data is a collective term for all reading content in that target reading process. In the embodiments of this application, any one of the following data can include m types of sub-data, where m ≥ 1: the target object's reading habit data, the target object's comprehension data of the reading content, and the target object's operational data on the reading content.

[0058] Optionally, the target object's operation data regarding the reading content may include at least one of the following: reading aloud sub-data, adding notes sub-data, and sharing notes sub-data. For example, for any reading content during a reading process, the electronic device may mark at least a portion of the reading content as reading aloud content. After the target object reads the reading aloud content, the electronic device may prompt the target object to choose to read the reading aloud content, and the target object may read the reading aloud content according to the prompt of the electronic device. The electronic device may collect the target object's voice during the reading aloud process, and then analyze and process the voice to obtain reading aloud sub-data. For example, the electronic device may use the total duration of the voice and the average pause duration between adjacent words in the voice as reading aloud sub-data. Optionally, each segment of reading aloud content may have a corresponding reference duration, and the reading aloud sub-data may also include the reference duration.

[0059] For example, while reading, the target audience can select and mark any words, phrases, or paragraphs, and input the desired annotations on the screen to add notes. The electronic device can then generate sub-data of these notes based on the target audience's note-adding actions. Furthermore, after adding a note, the target audience can perform subsequent operations on it. For instance, they can control the electronic device to display the content of the notes added by the target audience, and can share any note. This could involve sharing the note's link to the target audience's friends' accounts or to other social media platforms.

[0060] Optionally, the target audience's reading habit data may include at least one of the following sub-data categories: reading duration, reading frequency, reading speed, and reading time period. For example, the electronic device may start timing each time reading content is displayed and continue until the device stops displaying the content or exits the reading application to obtain a reading duration. The electronic device may also count the number of times the reading application is opened and the events to determine reading frequency. The electronic device may determine the target audience's reading speed based on the number of words in the reading content and the target audience's reading time. The electronic device may also count the relative position of each reading session within a day, week, or month to determine the target audience's reading time period.

[0061] Optionally, the m sub-data categories in the target audience's comprehension data of the reading content can correspond one-to-one with m question types, and each sub-data category includes the answer results for the corresponding question type. For example, some reading content may be accompanied by related test questions. Each test question has a corresponding question type, such as information extraction, content comprehension, reasoning analysis, or application comprehension. After reading the reading content, the target audience can answer the accompanying questions, and the electronic device can then obtain the target audience's comprehension data of the reading content based on the answer results. Possibly, each question may have a reference answer, and the comprehension data may also include the reference answers for each question.

[0062] Optionally, the target audience's reading history data may include: information on books the target audience has read in the past, the types of books the target audience has read the most, the types of books the target audience has never read, and information on books the target audience has marked as liked or disliked. For example, book types can be pre-defined at the time of publication, such as categories like art, history, geography, mathematics, physics, novels, and comics.

[0063] It should be noted that in this embodiment, the target object can read directly on the electronic device, and the electronic device can directly collect reading-related data of the target object during the reading process. Optionally, the electronic device can also acquire reading-related data of the target object reading paper books, or it can acquire reading-related data of the target object reading on other devices. For example, the target object can manually input reading-related data of reading paper books into the electronic device. Alternatively, when the target object takes a paper book from the smart bookshelf, the smart bookshelf can automatically record the target object's reading-related data. The smart bookshelf can establish a communication connection with the electronic device, and the electronic device can acquire the target object's reading-related data sent by the smart bookshelf through this communication connection. For example, the communication unit in the smart bookshelf can communicate with the communication unit in the electronic device. Specifically, the communication unit in the smart bookshelf sends at least a portion of the aforementioned reading-related data to the communication unit of the electronic device.

[0064] Optionally, the raw data of the target object directly collected by the electronic device during the reading process usually includes a large amount of useless data in addition to the reading-related data required in this embodiment. The electronic device can filter the raw data it collects to obtain reading-related data. For example, the electronic device can use principal component analysis or mutual information method to process the collected raw data, remove invalid information, and obtain reading-related data. Optionally, the electronic device can obtain the conditions that the data to be filtered out or the conditions that the data to be retained must meet, and then perform data filtering based on these conditions. For example, the electronic device can obtain the percentage of retained data relative to the original data. For reading time, the data to be filtered out can be data with a reading time shorter than a specified time. Optionally, the electronic device can also upload all the collected raw data to the server, whereby the server can filter the raw data and perform further subsequent processing, which is not limited in this embodiment. Optionally, the server can also store the reading-related data obtained after filtering.

[0065] Specifically, step 401 can be performed by a controller in the electronic device.

[0066] Step 402: Obtain the feature extraction model. The feature extraction model is used to extract features from the input data.

[0067] It should be noted that step 402 can be performed before or after step 401, and this embodiment does not limit this. The feature extraction model can be built by an electronic device or by other devices (such as a server). Optionally, the electronic device can receive the feature extraction model from the other device. This embodiment uses the example of an electronic device building its own feature extraction model. This feature extraction model can also be called a reading profile construction model.

[0068] For example, the feature extraction model may include an evaluation model for reading habits, a classification model for reading preferences, an evaluation model for comprehension ability, and an evaluation model for sharing and expression characteristics. For any of these four models, such as the evaluation model for reading habits: the electronic device can first construct an initial model for the evaluation model, and then obtain multiple training samples corresponding to the evaluation model and the label of each training sample. Then, the training samples and the label of each training sample are input into the initial model, and the initial model processes the training samples. If the difference between the processed result and the label of the training sample is large, the parameters in the initial model are adjusted and the training samples are processed again until the difference between the processed result and the corresponding label of each training sample is within an acceptable range. At this point, the training of the initial model is complete, and the initial model after training is the evaluation model for the reading habits. Optionally, the electronic device can use methods such as random forests or decision trees to build the model. Optionally, after acquiring new reading-related data, the electronic device can also use this reading-related data as training samples to further optimize the feature extraction model, thereby improving the feature extraction effect of the feature extraction model.

[0069] Specifically, step 402 can be performed by a controller in the electronic device.

[0070] Step 403: Input the reading-related data of the target object into the feature extraction model to obtain the features of the target object.

[0071] The characteristics of the target object can include features of each type of data in the reading-related data. For example, the characteristics of the target object include: features of the target object's reading habits, features of its reading history, features of its comprehension of the reading content, and features of its operational data related to the reading content. Electronic devices can use feature extraction models to obtain features of each type of data in the target object's reading-related data. Specifically, a reading habit evaluation model can be used to obtain features of the target object's reading habits; a reading preference classification model can be used to obtain features of the target object's reading history; a comprehension ability evaluation model can be used to obtain features of the target object's comprehension of the reading content; and a sharing and expression characteristic evaluation model can be used to obtain features of the target object's operational data related to the reading content. Based on the features of various data output by the feature extraction model, electronic devices can obtain the characteristics of the target object. These characteristics can be used to construct a reading profile of the target object.

[0072] Optionally, the characteristics of the target audience's reading history data may include: the types of content the target audience prefers. Alternatively, the characteristics of the target audience's reading history data may also include: the types of content the target audience is not interested in, the authors the target audience prefers, etc.

[0073] Optionally, for any one of the target audience's reading habit data, reading content comprehension data, and reading content operation data, the characteristics of this type of data may include the corresponding alternative level among n alternative levels, where n≥2. For different types of data, these n alternative levels can be the same. For example, for each type of data—reading habit data, reading content comprehension data, and reading content operation data—its characteristic may include one alternative level among the four alternative levels: "Needs Improvement," "Average," "Good," and "Excellent." Optionally, for different types of data, these n alternative levels can also be different. For example, for reading habit data, its characteristic may include one alternative level among the four alternative levels: "Needs Improvement," "Average," "Good," and "Excellent"; for comprehension data, its characteristic may include one alternative level among the three alternative levels: "Poor," "Average," and "Good."

[0074] Optionally, for any one of the target audience's reading habit data, reading comprehension data, and reading content operation data, the characteristics of such data may also include the characteristics of various sub-data within that type of data.

[0075] For example, the features of the reading duration sub-data in reading habit data include average daily reading time; the features of the reading frequency sub-data may include at least one of average daily reading volume and average time to finish a book; and the features of the reading speed sub-data may include the degree of deviation between the reading time period and a reference time period. This degree of deviation can be the deviation duration, such as a deviation of 2 hours if the reference time period is 3 PM to 6 PM and the target subject's actual reading time period is 1 PM to 4 PM. Alternatively, this degree of deviation can be other values ​​calculated based on the deviation duration, such as the ratio of the deviation duration to the reference time period. Optionally, the feature of the reading time period sub-data may also include the time period that appears most frequently during each reading session.

[0076] For example, in the target object's operation data on reading content, the features of the reading sub-data may include at least one of reading fluency and reading accuracy. For instance, an electronic device can determine reading fluency by determining the duration of the voice and the target object's speaking speed based on the voice collected during reading. The electronic device can also perform speech recognition on the voice to determine the similarity between the recognition result and the reading content, thereby determining the reading accuracy. The features of the note-adding sub-data may include at least one of the number of notes and the number of words in the notes. The features of the note-sharing sub-data may include at least one of the sharing frequency, the number of times the note is shared, the number of times the note is liked, and the number of times the note is saved.

[0077] For example, the data on the target audience's ability to understand reading content contains m sub-data categories that correspond one-to-one with m question types. Each sub-data category includes the answer results for the corresponding question type. The characteristics of each sub-data category can include the accuracy rate of the answer results. For example, the question types in m include information extraction, content comprehension, reasoning and analysis, and application comprehension.

[0078] Optionally, for any one of the following data categories—reading habit data, comprehension data of reading content, and operational data of reading content—the features of each sub-data category within the m sub-data categories can include its corresponding level among n candidate levels. For example, each sub-data category can have a feature set of n candidate levels. The level of the candidate level is positively correlated with the value of the feature in the feature set. The candidate level corresponding to the feature set with the highest membership degree for each sub-data category is the level corresponding to that sub-data category among the n candidate levels. The electronic device can determine the features of this data category based on the membership degree of each sub-data category to its feature set of n candidate levels. For example, the features of this data category can directly include the levels corresponding to the m sub-data categories. It should be noted that the feature sets of n candidate levels for different sub-data categories can all be different, or some sub-data categories can have the same feature sets of n candidate levels; this embodiment does not impose such limitations.

[0079] This application embodiment uses the target object's comprehension data of reading content as an example, where the n candidate levels include four progressively higher levels: "Needs Improvement," "Average," "Good," and "Excellent," to describe the method for determining the features of this comprehension data. The characteristics of the target object's reading habit data and operational data regarding the reading content can also be determined using the same method as the comprehension data feature determination, and will not be elaborated upon in this application embodiment. The method for determining the features of this comprehension data described below can also be the specific process executed by the feature extraction model.

[0080] For example, in each of the n candidate levels for each sub-data category, each candidate level corresponds to a standard feature, which can be a feature from the feature set corresponding to that candidate level. The standard features corresponding to each candidate level in different sub-data categories can all be different, or some sub-data categories may have the same standard features corresponding to their candidate levels; this embodiment does not impose such limitations. Electronic devices can directly acquire the standard features corresponding to each candidate level. These standard features can be set by the user, by staff, or be experience values. For comprehension data, the features of each sub-data category can include the accuracy rate of answering questions; this standard feature is the accuracy rate. For example, the standard feature corresponding to the "Needs Improvement" level is an accuracy rate of 0.3, the "Average" level is an accuracy rate of 0.5, the "Good" level is an accuracy rate of 0.75, and the "Excellent" level is an accuracy rate of 0.9. For comprehension data, the standard feature corresponding to each candidate level can also be called the standard accuracy rate corresponding to the candidate level.

[0081] For comprehension data, electronic devices can receive standard features corresponding to all candidate levels for each type of sub-data. For example, an electronic device can receive standard features from the feature set of the j-th candidate level of the i-th sub-data in m types of sub-data, where 1 ≤ i ≤ m, 1 ≤ j ≤ n. The value of the standard feature corresponding to the (j+1)-th candidate level is greater than the value of the standard feature corresponding to the j-th candidate level. Here, we take the i-th sub-data in m types of sub-data and the j-th candidate level among n candidate levels as an example. The i-th sub-data represents any sub-data in the m types of sub-data, and the j-th candidate level represents any candidate level among the n candidate levels. Based on the features of the i-th sub-data and the standard features in the feature set of the j-th candidate level, the electronic device can determine the membership degree *r* of the i-th sub-data to the feature set of the j-th candidate level. ij The membership degree of the i-th sub-data to the feature set of the j-th candidate level can also be called the membership degree of the i-th sub-data to the j-th candidate level, or the membership degree of the i-th sub-data to the standard features corresponding to the j-th candidate level.

[0082] Optionally, the membership degree of the i-th sub-data to the feature set of the first candidate level can be calculated using a sparse membership degree solution method, such as... The membership degree of the i-th sub-data to the feature set of each candidate level from the second to the (n-1)-th candidate level can be calculated using an intermediate membership degree calculation method, such as... The membership degree of the i-th sub-data to the feature set of the n-th candidate level can be calculated using a slanted membership degree solution method, such as... Where 2≤p≤n-1; x represents the feature of the i-th sub-data, a j The standard features represent the feature set of the j-th candidate level of the i-th sub-data.

[0083] After calculating the membership degree for the i-th sub-data, the electronic device can obtain the membership degree of the i-th sub-data to the feature set of each of the n candidate levels. For example, if n = 4, then the membership degree set r can be obtained. i ={r i1 r i2 r i3 r i4 Optionally, the electronic device can determine the candidate level with the highest membership degree of the i-th sub-data as the level corresponding to the i-th sub-data. Furthermore, the electronic device can determine that the characteristics of the comprehension ability data directly include the level corresponding to the m-th sub-data. For example, the characteristics of the comprehension ability data may include: excellent information extraction ability, good content comprehension ability, good reasoning and analysis ability, and needing improvement in comprehension and application ability.

[0084] Electronic devices can also further analyze the comprehensive understanding of the target object. For example, based on the same membership degree calculation method described above, the electronic device can obtain the membership degree of each sub-data category in m sub-data categories to its n candidate level feature sets, thus obtaining the first membership degree matrix. Each of the m sub-data categories can be assigned a weight, and the electronic device can also retrieve the weights of each sub-data category within the m sub-data categories. For example, the electronic device can retrieve the weight matrix S = (s1 s2 ... s...) composed of the weights of the m sub-data categories. m ), s i This represents the weight of the i-th sub-data category within the m-th sub-data categories. The weight of each sub-data category can be calculated based on the membership degree of the i-th sub-data category to each candidate level. For example... in, k = 1 / ln n, where ln represents the logarithm with the constant e as the base.

[0085] The electronic device can multiply the weight matrix by the first membership matrix to obtain the second membership matrix. The second membership matrix is ​​T = S * R = (t1 t2 ... t...). n ), where t j t is the product of the elements in the j-th column of S and R; j This corresponds to the j-th candidate level. jThis can also be referred to as the membership degree of comprehension ability data to the j-th candidate level. Electronic devices can determine the candidate level corresponding to the largest element in the second membership matrix as a feature of the comprehension ability data. For example, if n=4 and t4 is the largest in the second membership matrix, the electronic device can determine that the comprehension ability data includes the feature of excellent comprehension ability.

[0086] Specifically, step 403 can be performed by a controller in the electronic device.

[0087] Step 404: Display information about the characteristics of the target object.

[0088] After determining the characteristics of a target object, an electronic device can display information about those characteristics on a screen. For example, Figure 5 This is a schematic diagram of the display interface of an electronic device provided in an embodiment of this application. For example... Figure 5 As shown, the characteristics of the target object include features of operational data related to the reading content (i.e., reading expression), features of reading habit data, features of reading history data (i.e., reading preferences), and features of comprehension data of the reading content (i.e., reading ability). The reading profile of the target object can include its characteristics, such as... Figure 5 The information displayed about the characteristics of the target object can also be considered a reading profile of the target object.

[0089] Step 405: Obtain recommended content based on the characteristics of the target object.

[0090] After acquiring the features of a target object, the electronic device can obtain recommended content that matches the target object's reading characteristics based on those features. Optionally, the electronic device can select appropriate recommended content from a content database based on recommendation algorithms such as collaborative filtering or SVD.

[0091] For example, the recommended content could include information about books. An electronic device could obtain information about books of a specific type based on the target audience's preferred content, and then recommend books of types the target audience might like, as well as books by authors the target audience favors.

[0092] For example, the recommended content could include course information. Electronic devices could select courses suitable for improving the target audience's comprehension abilities based on their reading comprehension skills. If the target audience is weak in any of their reasoning, analytical, content comprehension, information extraction, or application abilities, a course designed to improve that ability could be selected to assist in targeted training of their reading skills.

[0093] For example, the recommended content could include a reading plan. The electronic device can select a reading plan that suits the target audience's reading habits and communication style, and that can improve those habits, based on the target audience's reading time and communication style. For target audiences who need to improve their communication skills, the electronic device can prompt them to regularly read paragraphs aloud and upload notes.

[0094] Step 406: Display recommended content.

[0095] After obtaining recommended content, electronic devices can display that content to help target users improve their reading skills more effectively.

[0096] It should be noted that the same reader may have different interests and comprehension abilities towards different categories of books, and reading performance is not simply a matter of understanding a particular book; a reader's reading time, reading list, habits, and preferences all reflect their overall reading situation. In this embodiment, multi-dimensional comprehensive data analysis replaces the single-dimensional data analysis of question answers used in related technologies. By considering multiple aspects such as reading ability, reading habits, reading preferences, and reading expression and sharing, a comprehensive reflection of the target audience's reading situation can be provided, resulting in a more accurate assessment. Based on this, suitable book recommendations, reading plans, and reading ability development courses can be recommended specifically to the target audience, thereby effectively helping to improve their overall reading literacy.

[0097] In summary, the information processing method provided in this application allows an electronic device to obtain the characteristics of a target object based on the target object's operation data regarding the reading content displayed on the screen. These characteristics can accurately reflect the target object's reading habits. Furthermore, the electronic device can recommend suitable content to the target object based on these characteristics, which aids in the target object's learning and enriches the functionality of the electronic device.

[0098] This application also provides a computer-readable storage medium storing instructions. When these instructions are executed on a computer, the computer performs the information processing method provided in the above embodiments.

[0099] This application also provides a computer program product containing instructions, which, when run on a computer, causes the computer to execute the information processing method provided in the above-described method embodiments.

[0100] It should be noted that the method embodiments provided in this application can be referenced with the corresponding device embodiments, and the various method embodiments and the various device embodiments can also be referenced with each other. This application does not limit these aspects. The order of the steps in the method embodiments provided in this application can be appropriately adjusted, and the steps can also be added or removed as appropriate. Any variations that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application, and therefore will not be elaborated further.

[0101] In this application, the term "comprising" as used throughout the specification and claims is an open-ended term and should therefore be interpreted as "comprising but not limited to". In the embodiments of this application, the terms "first", "second", etc., are used to distinguish identical or similar items with substantially the same function. It should be understood that there is no logical or temporal dependency between "first", "second", and "nth", nor is there a limitation on the quantity or execution order. In the embodiments of this application, the term "at least one" means one or more, and the term "multiple" means two or more.

[0102] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " generally indicates that the preceding and following related objects are in an "or" relationship. In cases involving mathematical formula calculations, the character " / " represents the "division" operator. The term "at least one of A and B" in this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, at least one of A and B can represent: A existing alone, A and B existing simultaneously, or B existing alone.

[0103] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An electronic device, characterized in that, The electronic device includes a controller and a display screen; The display screen is used to display reading content; The controller is configured to: receive operation data of the target object on the reading content, the operation data including at least one of the following: reading aloud sub-data, adding notes sub-data, and sharing notes sub-data; acquire the characteristics of the target object, the characteristics of the target object including the characteristics of the operation data, and at least one of the following: characteristics of reading habit data, characteristics of reading history data, and characteristics of comprehension ability data of the reading content; and send recommended content to the display screen based on the characteristics of the target object. The reading habit data, comprehension ability data, and operational data each include m types of sub-data, where m ≥ 1. Each type of sub-data has a feature set of n candidate levels, where n ≥ 2. The level of the candidate level is positively correlated with the value of the feature in the feature set. The controller is further configured to: obtain a weight matrix and a first membership matrix; multiply the weight matrix by the first membership matrix to obtain a second membership matrix; and determine the candidate level corresponding to the largest element in the second membership matrix as a feature of the data. The weight matrix is... The first membership matrix is The second membership matrix is ;s i r represents the weight of the i-th sub-data in the m-th sub-data, 1≤i≤m; ij The t represents the membership degree of the i-th sub-data to the feature set of its j-th candidate level, 1≤j≤n; j t is the product of the elements in the j-th column of S and R; j Corresponding to the j-th candidate level; The display screen is also used to display the recommended content.

2. The electronic device according to claim 1, characterized in that, The controller is used for: Obtain a feature extraction model, which is used to extract features from the input data; The operation data is input into the feature extraction model to obtain the features of the operation data output by the feature extraction model.

3. The electronic device according to claim 1, characterized in that, The controller is used for: Receive standard features from the feature set of the j-th candidate level of the i-th sub-data; Based on the features of the i-th sub-data and the standard features, determine the membership degree r of the i-th sub-data to the feature set of the j-th candidate level. ij , 1≤i≤m; in, , , ;r i1 r represents the membership degree of the i-th sub-data to the feature set of the first candidate level. ip The r represents the membership degree of the i-th sub-data to the feature set of the p-th candidate level, 2≤p≤n-1. in The degree of membership of the i-th sub-data type to the feature set of the n-th candidate level is represented by x; x represents the feature of the i-th sub-data type, a j This refers to the standard feature.

4. The electronic device according to claim 1, characterized in that, ； in, k = 1 / ln n, where ln represents the logarithm with the constant e as the base.

5. The electronic device according to any one of claims 1 to 4, characterized in that, The electronic device satisfies at least one of the following: The features of the reading sub-data include at least one of reading fluency and reading accuracy; the features of the added notes sub-data include at least one of the number of notes and the number of words in the notes; and the features of the shared notes sub-data include at least one of the sharing frequency, the number of times the notes are shared, the number of times the notes are liked, and the number of times the notes are saved. The reading habit data includes at least one of the following: reading duration sub-data, reading frequency sub-data, reading speed sub-data, and reading time period sub-data; the characteristics of the reading time period sub-data include the degree of deviation between the reading time period and the reference time period. The comprehension data contains m sub-data categories that correspond one-to-one with m question types. Each sub-data category includes the answer results for the corresponding question type. The feature of each sub-data category includes the accuracy rate of the answer results. Furthermore, the characteristics of the reading history data include the content types preferred by the target object.

6. The electronic device according to any one of claims 1 to 4, characterized in that, The electronic device also includes a communication unit for communicating with the smart bookshelf. The controller is used to receive at least one type of data and at least a portion of the operation data sent by the smart bookshelf through the communication unit.

7. An information processing method, characterized in that, For an electronic device, the electronic device including a controller and a display screen, the method includes: The display screen shows the reading content; The controller receives operation data from the target object regarding the reading content, the operation data including at least one of the following: reading aloud sub-data, adding notes sub-data, and sharing notes sub-data; The controller acquires the features of the target object, which include at least one of the following: features of the operation data, features of reading habit data, features of reading history data, and features of comprehension ability data of the reading content; wherein any one of the reading habit data, comprehension ability data, and operation data includes m types of sub-data, m≥1, and each type of sub-data has a feature set of n candidate levels, n≥2, and the level of the candidate level is positively correlated with the value of the feature in the feature set; the controller acquires the features of the target object by: acquiring a weight matrix and a first membership matrix; multiplying the weight matrix by the first membership matrix to obtain a second membership matrix; and determining the candidate level corresponding to the largest element in the second membership matrix as the feature of any one type of data; wherein the weight matrix is... The first membership matrix is The second membership matrix is ;s i r represents the weight of the i-th sub-data in the m-th sub-data, 1≤i≤m; ij The t represents the membership degree of the i-th sub-data to the feature set of its j-th candidate level, 1≤j≤n; j t is the product of the elements in the j-th column of S and R; j Corresponding to the j-th candidate level; The controller sends recommended content to the display screen based on the characteristics of the target object; The display screen shows the recommended content.

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