Electronic device and control method therefof

Abstract

An electronic device is disclosed. One or more processors, when instructions are individually or collectively executed, cause the electronic device to: when app data corresponding to a user activity is obtained from each of a plurality of applications, classify at least one piece of log data included in the app data for each preset item; when second log data related to first log data is identified from among a plurality of pieces of log data classified into the same item, obtain integrated data by integrating first app data corresponding to the first log data and second app data corresponding to the second log data; identify a first category corresponding to the integrated data from among a category list stored in a memory; and generate user activity-based diary data on the basis of the first category and the integrated data.

Description

Electronic device and method of controlling the same

[0001] The present disclosure relates to an electronic device and a method for controlling the same.

[0002] With the recent advancements in mobile devices equipped with artificial intelligence technology, technologies are being developed to automatically generate weather data using log data based on user activity.

[0003] Traditionally, log data obtained from specific applications has been used to automatically generate weather data based on user activity. However, generating weather data using log data obtained from multiple applications has been criticized for its limitations in not comprehensively considering multiple user activities.

[0004] An electronic device according to one or more embodiments of the present disclosure includes one or more processors including memory for storing instructions and processing circuitry.

[0005] According to one or more embodiments, the one or more processors, when the instructions are individually or collectively executed, cause the electronic device to, when app data corresponding to user activity is obtained from each of a plurality of applications, classify at least one log data included in the app data by a preset item, when second log data related to first log data is identified among the plurality of log data classified by the same item, integrate the first app data corresponding to the first log data and the second app data corresponding to the second log data to obtain integrated data, identify a first category corresponding to the integrated data among a category list stored in the memory, and generate user activity-based diary data based on the first category and the integrated data.

[0006] According to one or more embodiments, the memory stores category-specific priority information for generating the weather data, and the instructions, when individually or collectively executed by the one or more processors, cause the electronic device to, when a plurality of integrated data are acquired, identify a category of each of the plurality of integrated data based on the category list, identify first integrated data among the plurality of integrated data based on the category of each of the plurality of integrated data and the category-specific priority information, and generate the user activity-based weather data based on the first integrated data.

[0007] According to one or more embodiments, the instructions, when individually or collectively executed by the one or more processors, cause the electronic device to identify category-specific user preference information based on the app data and the category list obtained from each of the plurality of applications, identify second integrated data among the plurality of integrated data based on the category-specific priority information and the category-specific user preference information, and generate the user activity-based diary data based on the second integrated data.

[0008] According to one or more embodiments, the instructions, when individually or collectively executed by the one or more processors, cause the electronic device to identify an amount of log data corresponding to each category included in the category list among the app data obtained from each of the plurality of applications, and to identify user preference information for each category based on the amount of log data corresponding to each category.

[0009] According to one or more embodiments, the instructions, when individually or collectively executed by the one or more processors, cause the electronic device to input the first category and the integrated data into an artificial intelligence model to generate the weather data, wherein the artificial intelligence model is a model trained to generate the weather data based on format information identified based on the category information and the integrated data when category information and integrated data are input.

[0010] According to one or more embodiments, the electronic device further includes a display, and the instructions, when individually or collectively executed by the one or more processors, cause the electronic device to, when a plurality of weather data corresponding to different dates are generated, identify a size of a UI area corresponding to the plurality of weather data based on an integrated data amount of each of the plurality of weather data, and display a UI corresponding to the plurality of weather data through the display based on the size of the UI area corresponding to the plurality of weather data.

[0011] According to one or more embodiments, the instructions, when individually or collectively executed by the one or more processors, cause the electronic device to identify a user's daily pattern information based on the user activity-based diary data and to provide recommended schedule information based on the user's daily pattern information.

[0012] According to one or more embodiments, the instructions, when individually or collectively executed by the one or more processors, cause the electronic device to, when app data corresponding to user activity is acquired in real time from each of the plurality of applications, identify diary data corresponding to the app data acquired in real time among previously generated diary data, and, when a user input related to the app data acquired in real time is received, identify a keyword related to the user input among the identified diary data, and provide the keyword related to the user input.

[0013] According to one or more embodiments, the electronic device further includes a display, and the instructions, when individually or collectively executed by the one or more processors, cause the electronic device to generate the plurality of item-specific diary data based on the plurality of item-specific data included in the integrated data, and display a UI through the display that guides recommended phrases for modifying the plurality of item-specific diary data.

[0014] According to one or more embodiments, the preset item includes at least one of a person item, a place item, an object item, and a time item.

[0015] A method for controlling an electronic device according to one or more embodiments of the present disclosure includes: when app data corresponding to a user activity is acquired from each of a plurality of applications, an operation of classifying at least one log data included in the app data by a preset item; when second log data related to first log data is identified among a plurality of log data classified by the same item, an operation of acquiring integrated data by integrating first app data corresponding to the first log data and second app data corresponding to the second log data; an operation of identifying a first category corresponding to the integrated data from a category list stored in the electronic device; and an operation of generating user activity-based diary data based on the first category and the integrated data.

[0016] A non-transitory computer-readable storage medium storing computer instructions that, when executed by a processor of an electronic device according to one or more embodiments of the present disclosure, cause the electronic device to perform an operation, the operation includes: when app data corresponding to a user activity is obtained from each of a plurality of applications, classifying at least one log data included in the app data by a preset item; when second log data related to first log data is identified among a plurality of log data classified by the same item, integrating first app data corresponding to the first log data and second app data corresponding to the second log data to obtain integrated data; identifying a first category corresponding to the integrated data from a category list stored in the electronic device; and generating user activity-based diary data based on the first category and the integrated data.

[0017] FIG. 1 is a diagram illustrating the operation of an electronic device according to one or more embodiments.

[0018] FIG. 2 is a block diagram illustrating a configuration of an electronic device according to one or more embodiments.

[0019] FIG. 3 is a block diagram illustrating a detailed configuration of an electronic device according to one or more embodiments.

[0020] FIG. 4 is a diagram illustrating a process for acquiring app data of an electronic device according to one or more embodiments.

[0021] FIG. 5 is a diagram illustrating a process for identifying log data corresponding to the same item of an electronic device according to one or more embodiments.

[0022] FIG. 6 is a diagram illustrating an integrated data acquisition process of an electronic device according to one or more embodiments.

[0023] FIG. 7 is a diagram illustrating a category identification process of an electronic device according to one or more embodiments.

[0024] FIG. 8 is a diagram illustrating a process for generating weather data based on priority information of an electronic device according to one or more embodiments.

[0025] FIG. 9 is a diagram illustrating a process for generating weather data based on user preference information by category of an electronic device according to one or more embodiments.

[0026] FIG. 10 is a diagram illustrating a process for generating weather data of an electronic device according to one or more embodiments.

[0027] FIGS. 11a, 11b, and 11c are diagrams for explaining a weather data UI display process of an electronic device according to one or more embodiments.

[0028] FIG. 12a and FIG. 12b are diagrams illustrating a process for providing a recommended schedule according to one or more embodiments.

[0029] FIG. 13 is a diagram illustrating a keyword providing process related to a user input of an electronic device according to one or more embodiments.

[0030] FIG. 14 is a diagram illustrating a recommended phrase guide UI for modifying weather data of an electronic device according to one or more embodiments.

[0031] FIG. 15 is a drawing for explaining a data generation guide UI of an electronic device according to one or more embodiments.

[0032] FIG. 16 is a diagram illustrating a weather data grouping process of an electronic device according to one or more embodiments.

[0033] FIG. 17 is a diagram illustrating a process for generating a representative image of weather data of an electronic device according to one or more embodiments.

[0034] FIG. 18 is a diagram illustrating a category classification UI of an electronic device according to one or more embodiments.

[0035] FIG. 19 is a drawing for explaining an operation method of an electronic device according to one or more embodiments.

[0036] The terms used in the various embodiments of this disclosure have been selected from widely used, current terms, taking into account the functions of this disclosure. However, these terms may vary depending on the intentions of those skilled in the art, precedents, the emergence of new technologies, etc. Furthermore, in certain cases, terms may be arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the description of the relevant disclosure. Therefore, the terms used in this disclosure should be defined based on the meaning of the terms and the overall content of this disclosure, rather than simply their names.

[0037] In this disclosure, expressions such as “has,” “can have,” “includes,” or “may include” indicate the presence of a corresponding feature (e.g., a component such as a number, function, operation, or part), and do not exclude the presence of additional features.

[0038] The expression "at least one of A and / or B" should be understood to mean either "A" or "B" or "A and B".

[0039] The expressions “first,” “second,” “first,” or “second,” etc., used in this disclosure can describe various components, regardless of order and / or importance, and are only used to distinguish one component from another, but do not limit the components.

[0040] When it is said that a component (e.g., a first component) is “(operatively or communicatively) coupled with / to” or “connected to” another component (e.g., a second component), it should be understood that the component may be directly coupled to the other component, or may be connected through another component (e.g., a third component).

[0041] Singular expressions include plural expressions unless the context clearly dictates otherwise. In this disclosure, terms such as "comprise" or "consist of" are intended to indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but should be understood not to preclude the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0042] In the present disclosure, a "module" or "part" performs at least one function or operation and may be implemented as hardware or software, or as a combination of hardware and software. Furthermore, multiple "modules" or multiple "parts" may be integrated into at least one module and implemented as at least one processor (not shown), excluding any "modules" or "parts" that need to be implemented as specific hardware.

[0043] In this disclosure, the term user may refer to a person using an electronic device or a device used by the person.

[0044] An embodiment of the present disclosure will be described in more detail with reference to the attached drawings below.

[0045] FIG. 1 is a diagram illustrating the operation of an electronic device according to one or more embodiments.

[0046] According to one embodiment, the electronic device (100) can generate user activity-based diary data based on app data acquired from each of a plurality of applications. Here, the electronic device (100) can be implemented as various types of electronic devices such as a user terminal device, a smart TV, a monitor, a kiosk, a tablet PC, an electronic picture frame, a large format display (LFD), a digital signage, a digital information display (DID), a video wall, a projector display, etc.

[0047] According to one embodiment, the electronic device (100) may acquire integrated data by integrating multiple app data acquired from different applications. The electronic device (100) may compare at least one log data included in each of the multiple app data, and if log data containing the same value is identified, the electronic device (100) may acquire integrated data by integrating the multiple app data.

[0048] According to one embodiment, the electronic device (100) can identify a category corresponding to the acquired integrated data. The electronic device (100) can identify the category corresponding to the integrated data based on a category list stored in memory. For example, the category may be a classification item corresponding to a specific event, such as travel, exercise, or food.

[0049] According to one embodiment, when a category corresponding to the integrated data is identified, the electronic device (100) can generate weather data based on the identified category and the acquired integrated data. The weather data may include data recording specific events by hour, day, or period.

[0050] According to one embodiment, the electronic device (100) can automatically generate weather data by inputting integrated data and identified categories into an artificial intelligence model. The electronic device (100) can generate weather data including text data, image data, and media data through the artificial intelligence model based on categories corresponding to multiple app data and integrated data.

[0051] Referring to FIG. 1, the electronic device (100) can obtain app data (10-1 to 10-4) corresponding to each of a plurality of applications based on user activity. For example, the electronic device (100) can obtain current user location data (10-1) from a map application, contacted user data (10-2) from a text message application, schedule data (10-3) from a calendar application, and image data (10-4) from a photo application.

[0052] The electronic device (100) can classify at least one log data contained in each of a plurality of app data (10-1 to 10-4) by preset items. The preset items may be items classified based on attributes of the log data.

[0053] When the electronic device (100) identifies common log data among multiple log data classified by preset items, it can acquire integrated data by integrating multiple app data corresponding to the log data. The electronic device (100) can identify a category corresponding to the integrated data based on a category list stored in the memory. For example, the electronic device (100) can identify the "Travel" category included in the category list from the multiple app data (10-1 to 10-4).

[0054] The electronic device (100) can generate weather data (20) based on the "travel" category and integrated data. For example, the electronic device (100) can generate weather data (20) including text data, image data, and media data based on the integrated data.

[0055] Hereinafter, various embodiments in which an electronic device (100) obtains integrated data from multiple app data and generates user activity-based diary data based on a category corresponding to the integrated data will be described with reference to the drawings.

[0056] FIG. 2 is a block diagram illustrating a configuration of an electronic device according to one or more embodiments.

[0057] According to FIG. 2, the electronic device (100) includes a memory (110) and one or more processors (120). However, the present invention is not limited thereto, and the electronic device (100) may be implemented in a form in which some components are excluded, or may be implemented in a form in which other components are further included.

[0058] The memory (110) can store at least one command, data, program, etc. required for the operation of the electronic device (100). For example, the memory (110) can store outline highlight processing information and location information corresponding to a selected image.

[0059] The memory (110) may be implemented in the form of memory embedded in the electronic device (100) or in the form of memory that can be attached or detached from the electronic device (100) depending on the purpose of data storage. For example, data for driving the electronic device (100) may be stored in a memory embedded in the electronic device (100), and data for expanding the functions of the electronic device (100) may be stored in a memory that can be attached or detached from the electronic device (100).

[0060] In the case of memory embedded in the electronic device (100), it may be implemented as at least one of volatile memory (e.g., dynamic RAM (DRAM), static RAM (SRAM), or synchronous dynamic RAM (SDRAM)), non-volatile memory (e.g., one time programmable ROM (OTPROM), programmable ROM (PROM), erasable and programmable ROM (EPROM), electrically erasable and programmable ROM (EEPROM), mask ROM, flash ROM, flash memory (e.g., NAND flash or NOR flash), hard drive, or solid state drive (SSD)).

[0061] The memory (110) may be implemented as a single memory that stores data generated from various operations according to the present disclosure, but is not limited thereto, and the memory (110) may be implemented to include multiple memories that each store different types of data or each store data generated at different stages.

[0062] One or more processors (120) control the overall operation of the electronic device (100). Specifically, one or more processors (120) may be connected to each component of the electronic device (100) to control the overall operation of the electronic device (100). For example, one or more processors (120) may be electrically connected to the memory (110) to control the overall operation of the electronic device (100). One or more processors (120) may include a processing circuit and may be configured with one or more processors.

[0063] One or more processors (120) can perform operations of the electronic device (100) according to various embodiments by executing one or more commands stored in the memory (110).

[0064] The one or more processors (120) may include one or more of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), an Accelerated Processing Unit (APU), a Many Integrated Core (MIC), a Digital Signal Processor (DSP), a Neural Processing Unit (NPU), a hardware accelerator, or a machine learning accelerator. The one or more processors (120) may control one or any combination of other components of the electronic device, and may perform operations related to communication or data processing. The one or more processors (120) may execute one or more programs or instructions stored in a memory. For example, the one or more processors may perform a method according to one or more embodiments of the present disclosure by executing one or more instructions stored in a memory.

[0065] When a method according to one or more embodiments of the present disclosure includes multiple operations, the multiple operations may be performed by one processor or by multiple processors. For example, when a first operation, a second operation, and a third operation are performed by a method according to one or more embodiments, the first operation, the second operation, and the third operation may all be performed by the first processor, or the first operation and the second operation may be performed by the first processor (e.g., a general-purpose processor) and the third operation may be performed by the second processor (e.g., an artificial intelligence-specific processor).

[0066] One or more processors (120) may be implemented as a single core processor including one core, or may be implemented as one or more multicore processors including multiple cores (e.g., homogeneous multicores or heterogeneous multicores). When one or more processors (120) are implemented as a multicore processor, each of the multiple cores included in the multicore processor may include an internal processor memory, such as a cache memory or an on-chip memory, and a common cache shared by the multiple cores may be included in the multicore processor. In addition, each of the multiple cores (or some of the multiple cores) included in the multicore processor may independently read and execute a program instruction for implementing a method according to one or more embodiments of the present disclosure, or all (or some) of the multiple cores may be linked to read and execute a program instruction for implementing a method according to one or more embodiments of the present disclosure.

[0067] When a method according to one or more embodiments of the present disclosure includes a plurality of operations, the plurality of operations may be performed by one core among the plurality of cores included in a multi-core processor, or may be performed by the plurality of cores. For example, when a first operation, a second operation, and a third operation are performed by a method according to one or more embodiments, the first operation, the second operation, and the third operation may all be performed by a first core included in the multi-core processor, or the first operation and the second operation may be performed by a first core included in the multi-core processor, and the third operation may be performed by a second core included in the multi-core processor.

[0068] In the embodiments of the present disclosure, a processor may mean a system on a chip (SoC) in which one or more processors and other electronic components are integrated, a single-core processor, a multi-core processor, or a core included in a single-core processor or a multi-core processor, wherein the core may be implemented as a CPU, a GPU, an APU, a MIC, a DSP, an NPU, a hardware accelerator, or a machine learning accelerator, but the embodiments of the present disclosure are not limited thereto. Hereinafter, for the convenience of explanation, one or more processors (120) will be referred to as a processor (120).

[0069] According to one embodiment, when app data corresponding to user activity is obtained from each of a plurality of applications, the processor (120) may classify at least one log data included in the app data by preset items.

[0070] App data may include data generated or recorded when a user uses an application. App data may include user activity data, user preferences, location data, log data, and media data.

[0071] The predefined items may be classified based on the attributes of the log data. The predefined items may include at least one of a person item, a place item, an object item, and a time item. For example, the processor (120) may classify app data into log data corresponding to a person item, log data corresponding to a place item, log data corresponding to an object item, and log data corresponding to a time item.

[0072] According to one embodiment, when second log data related to first log data is identified among a plurality of log data classified as the same item, the processor (120) may acquire integrated data by integrating first app data corresponding to the first log data and second app data corresponding to the second log data.

[0073] For example, if log data commonly included in first log data corresponding to a time item included in first app data and second log data corresponding to a time item included in second app data are identified, or if log data related to each other are identified, the processor (120) can integrate the first app data and the second app data to obtain integrated data.

[0074] According to one embodiment, the processor (120) may identify a first category corresponding to the integrated data among the category list stored in the memory (110). The category list stored in the memory (110) may be set during the manufacturing stage of the electronic device (100) or may be set according to user input.

[0075] For example, the processor (120) may identify a first category corresponding to the integrated data among the category list based on keywords, images, and tagging information included in the integrated data.

[0076] In one embodiment, the processor (120) may generate user activity-based diary data based on the first category and the integrated data.

[0077] FIG. 3 is a block diagram illustrating a detailed configuration of an electronic device according to one or more embodiments.

[0078] According to FIG. 3, the electronic device (100) includes a memory (110), one or more processors (120), a display (130), a communication circuit (140), and an input / output interface (150). Among the configurations illustrated in FIG. 3, a detailed description of configurations that overlap with those illustrated in FIG. 2 will be omitted.

[0079] The display (130) is a configuration for providing content including a plurality of scenes. The display (130) may be implemented as a display module including a self-luminous element or a display module including a non-luminous element and a backlight. In addition, the display (130) may be implemented as an LFD display according to the above-described content. For example, the display may be implemented as various types of displays such as an LCD (Liquid Crystal Display), an OLED (Organic Light Emitting Diodes) display, an LED (Light Emitting Diodes), a micro LED, a Mini LED, a PDP (Plasma Display Panel), a QD (Quantum dot) display, a QLED (Quantum dot light-emitting diodes), etc. The display (130) may also include a driving circuit, a backlight unit, etc., which may be implemented in a form such as an a-si TFT, an LTPS (low temperature poly silicon) TFT, an OTFT (organic TFT), etc.

[0080] The communication circuit (140) may include wired or wireless input / output interfaces (or input / output terminals) according to various standards. The communication circuit (140) may be configured to communicate with various types of external devices according to various types of communication methods. The communication circuit (140) may include a wireless communication module or a wired communication module. Here, each communication module may be implemented in the form of at least one hardware chip.

[0081] The communication circuit (140) may include various interfaces such as HDMI (High Definition Multimedia Interface), MHL (Mobile High-Definition Link), USB (Universal Serial Bus), DP (Display Port), Thunderbolt, VGA (Video Graphics Array) port, RGB port, D-SUB (D-subminiature), DVI (Digital Visual Interface), Bluetooth, Zigbee, wired / wireless LAN (Local Area Network), WAN (Wide Area Network), Ethernet, IEEE 1394, AES / EBU (Audio Engineering Society / European Broadcasting Union), optical, coaxial, etc.

[0082] The input / output interface (150) can be connected to a communication circuit (110). The input / output interface (150) can transmit information received from an external device to the communication circuit (110) or transmit information received through the communication circuit (110) to the external device.

[0083] FIG. 4 is a diagram illustrating a process for acquiring app data of an electronic device according to one or more embodiments.

[0084] According to one embodiment, the electronic device (100) can obtain app data classified by preset items from each of a plurality of applications. The electronic device (100) can classify at least one log data included in the app data by preset items.

[0085] For example, if the electronic device (100) includes multiple log data in the app data, the multiple log data can be classified into log data corresponding to a time item and log data corresponding to a location item. For example, if app data for a specific photo is acquired from a photo application, the app data can be classified into log data for the time item '2024.12.01' and log data for the location item 'Gangnam-gu, Seoul'.

[0086] Referring to FIG. 4, the electronic device (100) can obtain a plurality of app data (410-1 to 410-5) from a plurality of applications. The electronic device (100) can classify at least one log data included in the plurality of app data (410-1 to 410-5) by preset items. For example, the electronic device (100) can classify the log data included in the app data (410-1 to 410-5) into person items, place items, object items, and time items.

[0087] For example, the electronic device (100) can classify app data (410-1) obtained from a photo application into log data (420) corresponding to a time item, log data (430) corresponding to a place item, log data (440) corresponding to an object item, and log data (450) corresponding to a person item.

[0088] FIG. 5 is a diagram illustrating a process for identifying log data corresponding to the same item of an electronic device according to one or more embodiments.

[0089] According to one embodiment, when second log data related to first log data is identified among a plurality of log data classified as the same item, the electronic device (100) can acquire integrated data by integrating first app data corresponding to the first log data and second app data corresponding to the second log data.

[0090] Referring to FIG. 5, the electronic device (100) can identify log data corresponding to a time item from each of a plurality of app data (410-1 to 410-5). The electronic device (100) can identify "2023.12.07" log data (510) corresponding to the time item from the photo app data (410-1). The electronic device (100) can identify "2023.12.07 7pm ~ 8pm" log data (520) corresponding to the time item from the media app data (410-2). The electronic device (100) can identify "2023.12.07 B and 2 others" log data (530) corresponding to the time item from the message app data (410-4). The electronic device (100) can identify the log data (540) corresponding to the time item “2023.12.07 6pm ~ 12am” from the map app data (410-5).

[0091] When the electronic device (100) identifies related log data among the log data (510 to 540) corresponding to multiple time items, the electronic device (100) can integrate app data including the corresponding log data. For example, when the log data (510 to 540) corresponding to multiple time items includes a data value corresponding to "2023.12.07", the electronic device (100) can integrate the photo app data (410-1), the media app data (410-2), the message app data (410-4), and the map app data (410-5) to obtain a single integrated data.

[0092] FIG. 6 is a diagram illustrating an integrated data acquisition process of an electronic device according to one or more embodiments.

[0093] According to one embodiment, the electronic device (100) can acquire integrated data by integrating app data acquired from different applications.

[0094] For example, when second log data related to first log data is identified among a plurality of log data classified as the same item, the electronic device (100) can acquire integrated data by integrating the entire log data of the first app data corresponding to the first log data and the entire log data of the second app data corresponding to the second log data.

[0095] Referring to FIG. 6, the electronic device (100) can acquire integrated data (650) by integrating the entire log data (640) included in the photo app data (410-1), the entire log data (610) included in the message app data (410-4), the entire log data (620) included in the map app data (410-5), and the entire log data (630) included in the media app data (410-2). That is, rather than acquiring integrated data by integrating only the log data (510 to 540) corresponding to the time item, if any one of the preset items is identified as being related to each other, the electronic device (100) can acquire integrated data by integrating the entire log data included in the app data.

[0096] For example, the electronic device (100) may acquire integrated data (650) by integrating multiple log data (610 to 640) acquired from different applications by preset items. For example, the electronic device (100) may acquire integrated data (650) by integrating log data corresponding to location items (e.g., Gapyeong, Infinity Pool, Camping, Glam Tree) from the multiple log data (610 to 640).

[0097] FIG. 7 is a diagram illustrating a category identification process of an electronic device according to one or more embodiments.

[0098] According to one embodiment, the electronic device (100) can identify a category corresponding to the integrated data (650) based on a category list stored in the memory (110).

[0099] For example, the category list (710) may include new categories added or existing categories deleted based on user input. For example, the category list may include "Travel," "Exercise," "Food," and "Study." The category list may be stored in memory (110) or received from an external server.

[0100] According to one embodiment, the electronic device (100) can identify a category included in a category list (710) based on a plurality of log data included in the integrated data. The electronic device (100) can identify the most relevant category among the categories included in the category list based on data values, keywords, and tagging information of the log data.

[0101] Referring to FIG. 7, the electronic device (100) can identify a category corresponding to the integrated data based on a category list (710) stored in the memory (110). The electronic device (100) can identify the most relevant category among the category list (710) based on a plurality of log data included in the integrated data.

[0102] For example, if the log data including the integrated data includes “water play,” “glamping,” and “reservation information,” the electronic device (100) can identify the “travel” category among the category list (710) as a category corresponding to the integrated data.

[0103] In this case, the electronic device (100) can identify the first category corresponding to the integrated data as travel (720).

[0104] FIG. 8 is a diagram illustrating a process for generating weather data based on priority information of an electronic device according to one or more embodiments.

[0105] According to one embodiment, when a plurality of pieces of integrated data are acquired, the electronic device (100) can identify a category of each piece of integrated data based on a category list.

[0106] For example, when a plurality of pieces of integrated data are acquired, the electronic device (100) can identify a category corresponding to each piece of integrated data through the method described above.

[0107] According to one embodiment, the electronic device (100) can identify first integrated data among a plurality of integrated data based on the category and priority information for each category of the plurality of integrated data.

[0108] When multiple pieces of integrated data are acquired, the electronic device (100) can generate weather data based on a single piece of integrated data. However, the present invention is not limited thereto, and when multiple pieces of integrated data are acquired, the electronic device (100) can generate weather data corresponding to each piece of the multiple pieces of integrated data, or can generate weather data corresponding to two or three pieces of integrated data.

[0109] Meanwhile, in the present disclosure, if it is assumed that the electronic device (100) generates weather data based on a single piece of integrated data, when multiple pieces of integrated data are acquired, it must be identified which of the integrated pieces of data will be used to generate the weather data. In this case, the electronic device (100) can identify the integrated data corresponding to the highest priority based on priority information stored in the memory (110), and generate weather data based on the identified integrated data.

[0110] Priority information may include categories for which weather data is to be generated. Priority information may be set during the device manufacturing process or based on user input. Priority information may be stored in memory (110) or received from an external server. For example, priority information may be prioritized in the order of travel, exercise, food, and study.

[0111] According to one embodiment, the electronic device (100) can generate user activity-based diary data based on the first integrated data.

[0112] Referring to FIG. 8, the electronic device (100) can identify categories (810 to 830) corresponding to each of a plurality of integrated data based on the category list (710). The electronic device (100) can identify integrated data for generating weather data among the plurality of integrated data based on priority information (840) stored in the memory (110).

[0113] For example, when travel integrated data (810), food integrated data (820), and exercise integrated data (830) are acquired, the electronic device (100) can identify the travel integrated data (810) as the first integrated data (850) based on priority information (840) in which 'travel' is the top priority.

[0114] In one example, the electronic device (100) can generate user activity-based diary data based on travel integration data (850).

[0115] FIG. 9 is a diagram illustrating a process for generating weather data based on user preference information by category of an electronic device according to one or more embodiments.

[0116] According to one embodiment, the electronic device (100) can identify user preference information by category based on app data and category lists obtained from each of a plurality of applications.

[0117] User preference information by category may include information on categories preferred by users, identified based on app data, from the categories included in the category list. For example, if log data, tagging information, and keywords included in the app data include exercise-related data, the exercise category may be the user's top preference.

[0118] According to one embodiment, the electronic device (100) can identify the amount of log data corresponding to each category included in the category list among the app data obtained from each of a plurality of applications, and identify user preference information for each category based on the amount of log data corresponding to each category.

[0119] According to one embodiment, the electronic device (100) can identify second integrated data among a plurality of integrated data based on category-specific priority information and category-specific user preference information.

[0120] For example, the electronic device (100) may acquire travel integration data and exercise integration data. If the travel category is set as the highest priority in the priority information, and the amount of data or number of keywords corresponding to exercise integration data in the app data is greater than the amount of data or number of keywords corresponding to travel integration data, the exercise category may be set as the highest priority in the user preference information. In this case, even if travel is set as the highest priority based on the priority information, the electronic device (100) may generate diary data based on the exercise integration data.

[0121] According to one embodiment, the electronic device (100) can assign weights to each of priority information and user preference information, and identify a priority score based on the weights. Based on the priority score, the electronic device (100) can identify second integrated data from which to generate weather data among a plurality of integrated data.

[0122] According to one embodiment, the electronic device (100) can generate user activity-based diary data based on the second integrated data.

[0123] Referring to FIG. 9, the electronic device (100) can identify user preference information (920) by category based on app data (910) and a category list (710). For example, if the log data, tagging information, and keyword information included in the app data contain the largest amount of information corresponding to the exercise category, the exercise category may have the highest priority in the user preference information (920) by category.

[0124] The electronic device (100) can identify the second integrated data (930) based on the category-specific priority information (840) and the category-specific user preference information (920). For example, since the exercise category has the highest priority in the category-specific user preference information (920) and the exercise category has the second priority in the priority information (840), the electronic device (100) can identify the exercise category as the highest priority. The electronic device (100) can identify the integrated data corresponding to the exercise category as the second integrated data (930).

[0125] FIG. 10 is a diagram illustrating a process for generating weather data of an electronic device according to one or more embodiments.

[0126] According to one embodiment, the electronic device (100) can generate weather data by inputting the first category and integrated data into an artificial intelligence model.

[0127] In one embodiment, the artificial intelligence model may be a model trained to generate weather data based on format information identified based on the category information and the integrated data when category information and integrated data are input.

[0128] Format information may include different format information for each category. For example, for the exercise category, format information may include text, media information, and image information in a preset order and arrangement. For example, for the study category, format information may include text, schedule information, and media information in a preset order and arrangement.

[0129] Here, the learning of an artificial intelligence model means that a basic artificial intelligence model (e.g., an artificial intelligence model including any random parameters) is trained using a learning algorithm using a large number of training data, thereby creating a predefined set of operation rules or an artificial intelligence model set to perform a desired characteristic (or purpose). This learning may be performed through a separate server and / or system, but is not limited thereto, and may also be performed in a cooking device. Examples of learning algorithms include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0130] Here, the artificial intelligence model can be implemented as, for example, a Large Language Model (LLM), a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), a Restricted Boltzmann Machine (RBM), a Deep Belief Network (DBN), a Bidirectional Recurrent Deep Neural Network (BRDNN), or a Deep Q-Network, but is not limited thereto.

[0131] Referring to FIG. 10, the electronic device (100) can input travel integration data (1010) corresponding to a travel category into an artificial intelligence model (1020). The artificial intelligence model (1020) can be a model trained to perform large-scale data learning through LLM technology and generate diary data based on the input categories. The electronic device (100) can generate diary data (1020) including a title corresponding to a first category, text based on the integrated data, image information, person information, and media information through the artificial intelligence model (1020).

[0132] FIGS. 11a, 11b, and 11c are diagrams for explaining a weather data UI display process of an electronic device according to one or more embodiments.

[0133] According to one embodiment, when multiple weather data corresponding to different dates are generated, the electronic device (100) can display the multiple weather data using various types of UIs (User Interfaces).

[0134] For example, the electronic device (100) may display multiple weather data in a grid view type including multiple grid areas. For example, the electronic device (100) may display multiple weather data in a list view type in the form of a list.

[0135] Referring to FIG. 11a, the electronic device (100) can display a plurality of weather data corresponding to different dates in a grid view type (1110) including grids of different sizes.

[0136] Referring to FIG. 11b, the electronic device (100) can display a plurality of weather data corresponding to different dates in a list view type (1120) in a list form in order from top to bottom.

[0137] According to one embodiment, when multiple weather data corresponding to different dates are generated, the electronic device (100) can identify the size of a UI area corresponding to the multiple weather data based on the integrated data amount of each of the multiple weather data.

[0138] For example, if the amount of integrated data is large, the size of the UI area corresponding to the weather data may be larger than if the amount of integrated data is small.

[0139] Referring to FIG. 11c, the electronic device (100) can display multiple weather data corresponding to different dates in a grid view type (1110). The electronic device (100) can identify the amount of integrated data included in each of the multiple weather data.

[0140] For example, the electronic device (100) can identify the size (1130) of the UI area corresponding to unit area 1 when the amount of integrated data is the smallest. For example, the electronic device (100) can identify the size (1140) of the UI area corresponding to unit area 2 when the amount of integrated data is medium. For example, the electronic device (100) can identify the size (1150) of the UI area corresponding to unit area 3 when the amount of integrated data is the largest.

[0141] According to one embodiment, the electronic device (100) may display a UI corresponding to a plurality of weather data through the display (130) based on the size of the UI area corresponding to the plurality of weather data.

[0142] FIG. 12a and FIG. 12b are diagrams illustrating a process for providing a recommended schedule according to one or more embodiments.

[0143] According to one embodiment, the electronic device (100) may identify the user's daily pattern information based on user activity-based diary data and provide recommended schedule information based on the user's daily pattern information.

[0144] A user's date-specific pattern information may include information about recurring events based on specific dates, days of the week, or time zones. For example, a user's date-specific pattern information may include information about recurring events based on specific dates, such as anniversaries or birthdays.

[0145] Referring to FIG. 12A, the electronic device (100) may display a UI (1210) corresponding to recommended schedule information through the display (130) based on the user's date-specific pattern information. For example, if December 7th is the user's family's birthday, the electronic device (100) may display a UI (1210) corresponding to recommended schedule information for message delivery through a message app on December 7th through the display (130).

[0146] Referring to FIG. 12b, if a travel schedule for December 10 is identified based on the user's date-specific pattern information, the electronic device (100) can display a UI (1220) corresponding to recommended schedule information related to the travel location through the display (130).

[0147] For example, the electronic device (100) may identify recommended information based on at least one of time information, weather information, and user schedule information, and provide the same to the user.

[0148] Time information can include time zone information, such as morning, lunch, and dinner. Weather information can include current weather information, tomorrow's weather information, weekly weather information, or monthly weather information. User schedule information can include activity pattern cycle information and exercise pattern cycle information.

[0149] For example, the electronic device (100) may provide the user with recommendation information including a text phrase such as "Good morning!" based on time information (e.g., morning time). For example, the electronic device (100) may provide the user with recommendation information including a text phrase such as "Bring an umbrella!" based on weather information (e.g., rain). For example, the electronic device (100) may provide the user with recommendation information including a text phrase such as "Today is a day to exercise! Be sure to stretch!" based on exercise pattern cycle information among user schedule information.

[0150] FIG. 13 is a diagram illustrating a keyword providing process related to a user input of an electronic device according to one or more embodiments.

[0151] According to one embodiment, when app data corresponding to user activity is acquired in real time from each of a plurality of applications, the electronic device (100) can identify weather data corresponding to the app data acquired in real time among the previously generated weather data.

[0152] For example, when app data is acquired in real time, the electronic device (100) can identify log data included in the acquired app data. The electronic device (100) can identify weather data that includes data values ​​of the identified log data among previously generated weather data.

[0153] According to one embodiment, when a user input related to app data acquired in real time is received, the electronic device (100) can identify a keyword related to the user input from the identified weather data. When a user input related to app data is received, the electronic device (100) can identify a keyword related to the user input from the integrated data of the identified weather data. For example, the electronic device (100) can identify a keyword included in the same item as the user input as a keyword related to the user input.

[0154] According to one embodiment, when a user input related to app data acquired in real time is received, the electronic device (100) may provide a keyword related to the user input.

[0155] Referring to FIG. 13, the electronic device (100) can identify weather data (1320) corresponding to the acquired app data among the previously generated weather data based on the app data acquired in real time. When the electronic device (100) receives the app data acquired in real time and a user input (e.g., glamping) (1310), the electronic device (100) can identify a plurality of keywords (1330) related to the user input from the identified weather data (1320).

[0156] When a user input (1310) related to app data acquired in real time is received, the electronic device (100) can provide a plurality of keywords (1330) related to the user input.

[0157] For example, the electronic device (100) may provide a plurality of keywords based on app data corresponding to user activity and provide the user with a text phrase including the reason for providing the keywords. For example, if the user's location is identified as location "A" based on app data corresponding to the user activity, the electronic device (100) may provide the user with a text phrase such as "You are currently at location A! Check out past memories related to location A!"

[0158] Thereafter, the electronic device (100) can identify a plurality of keywords (1330) related to app data corresponding to user input and user activity from the identified weather data (1320), and provide the identified plurality of keywords to the user through the display (130).

[0159] For example, the electronic device (100) may provide weather data based on at least one of location information, time information, external device information, and keyword information among app data corresponding to user activity.

[0160] For example, if the current date is an anniversary, the electronic device (100) may provide the user with diary data including text phrases such as "Today is our wedding anniversary! Take a look at past memories" and the anniversary keyword.

[0161] For example, when device B information is received from device B, the electronic device (100) can provide the user with diary data including the keyword B, such as text phrase "Are you with B? Take a look at past memories."

[0162] According to one embodiment, when a search term is identified based on user input from an input box displayed through a display (130), the electronic device (100) may provide the user with keywords related to the search term. Even when the search term is not fully entered, the electronic device (100) may identify diary data that includes some words of the search term among a plurality of diary data, and identify keywords related to the search term from the identified diary data.

[0163] For example, if a keyword such as “Wednesday lunch” is identified from a user input from an input box, the electronic device (100) can identify weather data including “Wednesday lunch” among a plurality of weather data, and identify keywords such as “Gangnam Station,” “hamburger,” and “friend C” from the identified weather data.

[0164] For example, even if the user only enters “Wednesday,” the electronic device (100) can identify keywords such as “Wednesday lunch,” “Gangnam Station,” “hamburger,” and “friend C” and provide them to the user.

[0165] FIG. 14 is a diagram illustrating a recommended phrase guide UI for modifying weather data of an electronic device according to one or more embodiments.

[0166] According to one embodiment, the electronic device (100) may generate a plurality of item-specific diary data based on a plurality of item-specific data included in the integrated data, and display a UI guiding recommended phrases for modifying the plurality of item-specific diary data through the display (130).

[0167] For example, the electronic device (100) may generate weather data based on time, place, person, and object items included in the integrated data. When a user input for modifying weather data corresponding to any one of the multiple item-specific weather data is received, the electronic device (100) may display a UI guiding recommended phrases for modifying the weather data via the display (130).

[0168] Referring to FIG. 14, the electronic device (100) can generate diary data (1410) based on person and place items included in the integrated data. When a user input for modifying diary data corresponding to a place item is received, the electronic device (100) can display a UI (1420) guiding recommended phrases for modifying the diary data through the display (130).

[0169] For example, the electronic device (100) may provide a user with a UI for recommending different location data rather than location data generated from weather data based on integrated data.

[0170] According to one embodiment, the electronic device (100) may provide a user with a diary data writing guide UI. The diary data writing guide UI may be a UI that guides the user by a plurality of preset items so that the user can easily write diary data.

[0171] For example, when a keyword of diary data to be written by a user is identified, the electronic device (100) may provide the user with associated data (e.g., images, location information, time information, media information) related to the identified keyword. The electronic device (100) may provide the user with associated data corresponding to each preset item.

[0172] For example, when a user input corresponding to diary data is identified, the electronic device (100) may provide the user with associated data related to the diary data in the form of keywords. For example, the electronic device (100) may provide the user with associated data related to previously written diary data and keywords for the associated data, so that the user can easily write a diary.

[0173] For example, if there is insufficient data to automatically generate diary data, the electronic device (100) may provide the user with a guide UI that prompts the user to write a diary entry. For example, if location information is not identified in the diary data, the electronic device (100) may provide the user with a guide UI that prompts the user to write a diary entry corresponding to the location entry.

[0174] FIG. 15 is a drawing for explaining a data generation guide UI of an electronic device according to one or more embodiments.

[0175] According to one embodiment, the electronic device (100) may provide a user with a guide UI for generating weather data when the amount of integrated data is insufficient.

[0176] For example, if the amount of log data included in the integrated data is insufficient to create weather data, the electronic device (100) may provide the user with log data obtained from the app data and provide the user with a guide UI for creating weather data.

[0177] Referring to FIG. 15, the electronic device (100) may provide the user with image data (1520) obtained from a photo application. The electronic device (100) may provide the user with a UI (1510) including guide text for generating weather data in relation to the image data (1520).

[0178] FIG. 16 is a diagram illustrating a weather data grouping process of an electronic device according to one or more embodiments.

[0179] According to one embodiment, the electronic device (100) can group multiple weather data corresponding to different dates based on a user input and display the grouped weather data.

[0180] Referring to FIG. 16, the electronic device (100) can display multiple weather data corresponding to different dates in a grid view type (1610). When a user input (1620) for grouping multiple weather data corresponding to different dates is received, the electronic device (100) can group multiple weather data corresponding to different dates based on the user input.

[0181] The electronic device (100) can display grouped weather data in a grid view type UI through a display (130).

[0182] FIG. 17 is a diagram illustrating a process for generating a representative image of weather data of an electronic device according to one or more embodiments.

[0183] According to one embodiment, the electronic device (100) may acquire an app data image of each of the plurality of app data based on integrated data including a plurality of app data, and integrate the plurality of app data images to generate a representative image of the integrated data.

[0184] The image of the app data may be an image generated based on representative log data included in the app data. For example, an app data image of a game console image may be obtained from game app data.

[0185] Referring to FIG. 17, the electronic device (100) can identify log data of each of the plurality of app data from integrated data including the plurality of app data. The electronic device (100) can obtain app data images from each of the plurality of applications. The electronic device (100) can integrate the plurality of app data images to generate a representative image for the integrated data.

[0186] The electronic device (100) can display weather data through a display (130) as a UI including a representative image.

[0187] FIG. 18 is a diagram illustrating a category classification UI of an electronic device according to one or more embodiments.

[0188] According to one embodiment, the electronic device (100) can classify weather data by preset categories based on user input and display a UI including the classified weather data through the display (130).

[0189] Referring to FIG. 18, the electronic device (100) can classify weather data into preset categories based on user input, such as user activity (My days), economic activity (My Works), and family activity (My Family). The electronic device (100) can display a UI including the classified weather data through the display (130).

[0190] According to one embodiment, the electronic device (100) can identify log data corresponding to a user input in real time and provide the user with a recommended phrase or recommended search word related to the user input.

[0191] According to one embodiment, the electronic device (100) can identify user activity-based log data acquired in real time and assign a preset priority to the log data. Based on the preset priority, the electronic device (100) can provide the user with recommended phrases or recommended search terms corresponding to the user input.

[0192] According to one embodiment, the electronic device (100) may provide a chatbot service related to weather data to a user through a display (130). When a preset user action related to weather data is received, the electronic device (100) may provide the chatbot service related to weather data to the user.

[0193] Predefined user actions may include touching for a preset period of time, double-clicking, or sliding.

[0194] For example, when a preset user action is received from an image included in the weather data, the electronic device (100) may display an interactive search window through the display (130) and provide a chatbot service according to the user query. For example, when the user clicks on an image in the weather data for a preset period of time or longer, the electronic device (100) may provide the user with a chatbot service including text such as "If you have any questions regarding A's birthday party on December 7, 2023, please ask."

[0195] For example, when a user query is received for a chatbot service, the electronic device (100) may provide a response corresponding to the user query based on associated data associated with the data selected by the user. For example, when a user query such as "Who did you talk to at that birthday party?" is received, the electronic device (100) may identify the call data based on associated data associated with the corresponding image data and provide the user with a response corresponding to the user query.

[0196] FIG. 19 is a drawing for explaining an operation method of an electronic device according to one or more embodiments.

[0197] Referring to FIG. 19, in operation 1910, the electronic device (100) can obtain app data corresponding to user activity from each of a plurality of applications.

[0198] In operation 1920, the electronic device (100) can classify at least one log data included in the app data by preset items.

[0199] In operation 1930, when second log data related to first log data is identified among a plurality of log data classified as the same item, the electronic device (100) can acquire integrated data by integrating first app data corresponding to the first log data and second app data corresponding to the second log data.

[0200] In operation 1940, the electronic device (100) can identify a first category corresponding to the integrated data among the category list stored in the electronic device (100).

[0201] At operation 1950, the electronic device (100) may generate user activity-based diary data based on the first category and the integrated data.

[0202] Since the specific method of obtaining integrated data and identifying the first category has been described through the above-described embodiments, a description thereof will be omitted.

[0203] The control method described in FIG. 19 can be performed by an electronic device (100) having the configuration of FIG. 2 described above, but is not necessarily limited thereto, and can also be performed by electronic devices having various configurations.

[0204] The various embodiments described above may be implemented as a single embodiment, or at least one embodiment may be combined with each other in whole or in part and implemented together in one device.

[0205] According to the various embodiments described above, the electronic device (100) can automatically generate user activity-based diary data by utilizing app data obtained from multiple applications.

[0206] Meanwhile, the various embodiments described above may be applied to a product as an embodiment alone, but at least some of the contents may be implemented in combination with other embodiments of the present disclosure.

[0207] The various embodiments described above can be implemented as software including instructions stored in a machine-readable storage medium that can be read by a machine (e.g., a computer). The device is a device that can call instructions stored in the storage medium and operate according to the called instructions, and may include an electronic device (e.g., electronic device (100)) according to the disclosed embodiments. When an instruction is executed by a processor, the processor can perform a function corresponding to the instruction directly or by using other components under the control of the processor. The instruction may include code generated or executed by a compiler or interpreter. The machine-readable storage medium can be provided in the form of a non-transitory computer-readable storage medium. Here, 'non-transitory' means that the storage medium does not contain a signal and is tangible, but does not distinguish between data being stored semi-permanently or temporarily in the storage medium.

[0208] Additionally, according to one embodiment of the present disclosure, the method according to the various embodiments described above may be provided as included in a computer program product.

[0209] Specifically, a non-transitory readable storage medium or a computer program product storing computer instructions may be provided that perform the following actions: when app data corresponding to user activity is acquired from each of a plurality of applications, classifying at least one log data included in the app data by a preset item; when second log data related to first log data is identified among a plurality of log data classified by the same item, integrating first app data corresponding to the first log data and second app data corresponding to the second log data to acquire integrated data; identifying a first category corresponding to the integrated data from a category list stored in an electronic device; and generating user activity-based diary data based on the first category and the integrated data.

[0210] The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)) or online through an application store (e.g., Play Store™). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created in a storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0211] In addition, computer instructions or programs for performing the control methods of electronic devices according to the various embodiments described above may be stored in a non-transitory computer-readable medium. The computer instructions stored in such a non-transitory computer-readable medium, when executed by a processor of a specific device, cause the specific device to perform processing operations in the device according to the various embodiments described above. A non-transitory computer-readable medium refers to a medium that stores data semi-permanently and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specific examples of non-transitory computer-readable media may include a CD, DVD, hard disk, Blu-ray disk, USB, memory card, or ROM.

[0212] Although the preferred embodiments of the present disclosure have been illustrated and described above, the present disclosure is not limited to the specific embodiments described above, and various modifications may be made by a person skilled in the art to which the present disclosure pertains without departing from the gist of the present disclosure as claimed in the claims, and such modifications should not be understood individually from the technical idea or prospect of the present disclosure.

Claims

1. In electronic devices, memory that stores instructions; and one or more processors including processing circuitry; One or more of the above processors, When the above instructions are executed individually or collectively, the electronic device, When app data corresponding to user activity is acquired from each of multiple applications, at least one log data included in the app data is classified by preset items, When second log data related to first log data is identified among multiple log data classified as the same item, first app data corresponding to the first log data and second app data corresponding to the second log data are integrated to obtain integrated data, Identifying the first category corresponding to the integrated data among the category list stored in the above memory, An electronic device that generates user activity-based diary data based on the first category and the integrated data.

2. In paragraph 1, The above memory is, Stores priority information by category for generating the above weather data, The above instructions, when individually or collectively executed by the one or more processors, cause the electronic device to: When multiple integrated data are acquired, the category of each of the multiple integrated data is identified based on the category list, Identifying first integrated data among the plurality of integrated data based on the category of each of the plurality of integrated data and priority information for each category, An electronic device that generates user activity-based diary data based on the first integrated data.

3. In paragraph 2, The above instructions, when individually or collectively executed by the one or more processors, cause the electronic device to: Identifying user preference information by category based on the app data and the category list obtained from each of the plurality of applications, Identifying second integrated data among the plurality of integrated data based on the category-specific priority information and category-specific user preference information, An electronic device that generates user activity-based diary data based on the second integrated data.

4. In paragraph 3, The above instructions, when individually or collectively executed by the one or more processors, cause the electronic device to: Identify the amount of log data corresponding to each category included in the category list among the app data obtained from each of the plurality of applications, An electronic device that identifies user preference information for each category based on the amount of log data corresponding to each of the above categories.

5. In paragraph 1, The above instructions, when individually or collectively executed by the one or more processors, cause the electronic device to: The above first category and the above integrated data are input into an artificial intelligence model to generate the above weather data, The above artificial intelligence model, An electronic device, wherein when category information and integrated data are input, a model is trained to generate the weather data based on format information identified based on the category information and the integrated data.

6. In paragraph 1, The above electronic device, including display; The above instructions, when individually or collectively executed by the one or more processors, cause the electronic device to: When multiple weather data corresponding to different dates are generated, the size of the UI area corresponding to the multiple weather data is identified based on the integrated data amount of each of the multiple weather data, An electronic device that displays a UI corresponding to the plurality of weather data through the display based on the size of the UI area corresponding to the plurality of weather data.

7. In paragraph 1, The above instructions, when individually or collectively executed by the one or more processors, cause the electronic device to: Identifying the user's daily pattern information based on the above user activity-based diary data, An electronic device that provides recommended schedule information based on the user's date-specific pattern information.

8. In paragraph 1, The above instructions, when individually or collectively executed by the one or more processors, cause the electronic device to: When app data corresponding to user activity is acquired in real time from each of the above multiple applications, the weather data corresponding to the app data acquired in real time is identified among the previously generated weather data, When a user input related to the app data acquired in real time is received, a keyword related to the user input is identified among the identified weather data, An electronic device that provides keywords related to the above user input.

9. In paragraph 1, The above electronic device, including display; The above instructions, when individually or collectively executed by the one or more processors, cause the electronic device to: Generating multiple item-specific diary data based on multiple item-specific data included in the above integrated data, An electronic device that displays a UI that guides recommended phrases for modifying the above multiple item-specific diary data through the display.

10. In paragraph 1, The above preset items are: An electronic device comprising at least one of a person item, a place item, a thing item, and a time item.

11. In a method for controlling an electronic device, When app data corresponding to user activity is acquired from each of multiple applications, an operation of classifying at least one log data included in the app data by preset items; An operation of integrating first app data corresponding to the first log data and second app data corresponding to the second log data to obtain integrated data when second log data related to the first log data is identified among multiple log data classified as the same item; An operation of identifying a first category corresponding to the integrated data among a list of categories stored in the electronic device; and A control method comprising: an operation of generating user activity-based diary data based on the first category and the integrated data.

12. In paragraph 11, When multiple pieces of integrated data are acquired, an operation of identifying a category of each piece of integrated data based on the category list; An operation of identifying first integrated data among the plurality of integrated data based on the category of each of the plurality of integrated data and priority information for each category for generating the weather data stored in the electronic device; and A control method, comprising: an operation of generating the user activity-based diary data based on the first integrated data; 13. In paragraph 12, An operation of identifying user preference information by category based on the app data and the category list obtained from each of the plurality of applications; An operation of identifying second integrated data among the plurality of integrated data based on the category-specific priority information and category-specific user preference information; and A control method comprising: an operation of generating the user activity-based diary data based on the second integrated data; 14. In paragraph 13, An operation of identifying the amount of log data corresponding to each category included in the category list among the app data obtained from each of the plurality of applications; and A control method comprising: an operation of identifying user preference information for each category based on the amount of log data corresponding to each category; 15. A non-transitory computer-readable storage medium storing computer instructions that, when executed by a processor of an electronic device, cause the electronic device to perform an operation, the operation comprising: When app data corresponding to user activity is acquired from each of multiple applications, an operation of classifying at least one log data included in the app data by preset items; An operation of integrating first app data corresponding to the first log data and second app data corresponding to the second log data to obtain integrated data when second log data related to the first log data is identified among multiple log data classified as the same item; An operation of identifying a first category corresponding to the integrated data among a list of categories stored in the electronic device; and A non-transitory computer-readable storage medium comprising: an operation for generating user activity-based diary data based on the first category and the integrated data.

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