Information guide device in electronic device and operating method thereof

Abstract

The present disclosure relates to an electronic device for processing content data of a communication application. The electronic device may analyze content by at least considering target contents based on a specific communication application, and generate summary content on the basis of the analyzed content in response to an occurrence of a predetermined event according to the analysis. The electronic device may, in response to the occurrence of a specific event included in the predetermined event, generate one or more response contents corresponding to the generated summary content, and determine one response content to be recommended to a user from among the generated one or more response contents.

Description

Information guidance device in an electronic device and its operating method

[0001] The present disclosure relates to an electronic device for processing content data of a communication application and a method of operating the same.

[0002] An artificial neural network (ANN) refers to a computational architecture that models the biological brain. Technologies such as deep learning or machine learning can be implemented based on the ANN. As an example of the ANN, a deep neural network or deep learning may have a multi-layer structure comprising multiple layers.

[0003] AI models are being used in a variety of ways to analyze visual and audio data. To ensure effective operation of AI models on mobile devices, active research and development is underway on hardware technologies related to AI models. For example, research is being conducted on improving hardware architectures that take AI models into account, aiming to optimize the MAC (multiply-accumulate) operations performed in deep learning AI models.

[0004] The above information may be provided as background information to aid in understanding this document. None of the above is claimed to be prior art related to this document or can be used to determine prior art.

[0005] In various embodiments of the present disclosure, a device and an operating method thereof for processing content data of a communication application by considering a driving situation based on AI in an electronic device can be provided.

[0006] According to one embodiment, an electronic device may include a communication circuit. The electronic device may include at least one sensor. The electronic device may include a memory including one or more storage media for storing instructions. The electronic device may include at least one processor (210) including a processing circuit. When the instructions are individually or collectively executed by the at least one processor, the instructions may cause the electronic device to perform at least one operation. The at least one operation may include an operation of analyzing content by at least considering target contents based on a specific communication application. The at least one operation may include an operation of generating summary content based on the analyzed content in response to the occurrence of a predetermined event according to the analysis. The predetermined event may include a first event occurring due to a change in the topic of the analyzed content, a second event occurring due to a need for a response to the analyzed content, or a third event occurring due to the analyzed content corresponding to an emergency situation. The information type and / or information amount of the above summary content - the amount of information being the amount of content to be confirmed by the above summary content - may be determined based on at least one contextual piece of information related to the movement of the electronic device.

[0007] According to one embodiment, a method of operating an electronic device may be provided. The method may include an operation of analyzing content by considering at least target content based on a specific communication application. The method may include an operation of generating summary content based on the analyzed content in response to the occurrence of a predetermined event according to the analysis. The predetermined event may include a first event occurring due to a change in the topic of the analyzed content, a second event occurring due to a need for a response to the analyzed content, or a third event occurring due to the analyzed content corresponding to an emergency situation. The information type and / or information amount of the summary content—the information amount being the amount of content to be confirmed by the summary content—may be determined based on at least one piece of contextual information related to the movement of the electronic device.

[0008] According to one embodiment, a storage medium storing computer-readable instructions may be provided. The instructions, when executed by at least a portion of at least one processor of an electronic device, may cause the electronic device to perform at least one operation. The at least one operation may include analyzing content by at least considering target content based on a specific communication application. The at least one operation may include generating summary content based on the analyzed content in response to the occurrence of a predetermined event according to the analysis. The predetermined event may include a first event occurring due to a change in the topic of the analyzed content, a second event occurring due to a need for a response to the analyzed content, or a third event occurring due to the analyzed content corresponding to an emergency situation. The information type and / or information amount of the summary content—the information amount being the amount of content to be confirmed by the summary content—may be determined based on at least one contextual piece of information related to the movement of the electronic device.

[0009] In connection with the description of the drawings, the same or similar reference numerals may be used for the same or similar components.

[0010] FIG. 1 is a block diagram of an electronic device within a network environment according to various embodiments.

[0011] FIG. 2 is a block diagram of an exemplary electronic device capable of performing the operations described in this document.

[0012] FIG. 3 is an exemplary block diagram for providing generative artificial intelligence (AI) functionality in an electronic device according to one embodiment.

[0013] FIG. 4 is a block diagram of an exemplary AI system capable of performing the operations described in this document.

[0014] FIG. 5 is a block diagram for generating content summarizing a received message in an electronic device according to one embodiment.

[0015] FIGS. 6A and 6B are control flowcharts for generating content summarizing a received message in an electronic device according to one embodiment.

[0016] FIG. 7 is a diagram illustrating an example of an operation of analyzing a received message and providing a summary or response message in an electronic device according to one embodiment.

[0017] FIG. 8 is an example diagram of a user interface that provides a summary of received messages by situation in an electronic device according to one embodiment.

[0018] FIG. 9 is a diagram illustrating an example of an electronic device adaptively providing a response message by taking into account a user's situation according to one embodiment.

[0019] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings so that those skilled in the art can easily implement the present disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein. In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components. Furthermore, in the drawings and related descriptions, descriptions of well-known functions and configurations may be omitted for clarity and conciseness.

[0020] FIG. 1 is a block diagram of an electronic device (101) within a network environment (100) according to various embodiments.

[0021] Referring to FIG. 1, in a network environment (100), an electronic device (101) may communicate with an electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of an electronic device (104) or a server (108) via a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0022] The processor (120) may, for example, execute software (e.g., a program (140)) to control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) and perform various data processing or calculations. According to one embodiment, as at least a part of the data processing or calculations, the processor (120) may store commands or data received from other components (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the commands or data stored in the volatile memory (132), and store result data in a non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or a secondary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor)) that can operate independently or together therewith. For example, if the electronic device (101) includes a main processor (121) and a secondary processor (123), the secondary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a specified function. The secondary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0023] The auxiliary processor (123) may control at least a portion of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), 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), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0024] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., program (140)) and input data or output data for commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0025] The program (140) may be stored as software in memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0026] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0027] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. In one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0028] The display module (160) can visually provide information to an external party (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. In one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0029] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0030] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0031] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0032] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0033] A haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. In one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0034] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0035] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented, for example, as at least a part of a power management integrated circuit (PMIC).

[0036] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0037] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (194) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0038] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) can support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0039] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). In one embodiment, the antenna module (197) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). In one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (198) or the second network (199), may be selected from the plurality of antennas by, for example, the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device through the selected at least one antenna. In some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0040] According to various embodiments, the antenna module (197) may form a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0041] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0042] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0043] FIG. 2 is a block diagram of an exemplary electronic device (200) (e.g., the electronic device (101) of FIG. 1) capable of performing the operations described in this document.

[0044] Referring to FIG. 2, the electronic device (200) may be one of various forms of electronic devices, such as a notebook (290), smartphones (291) having various form factors (e.g., a bar-type smartphone (291-1), a foldable-type smartphone (291-2), or a sliderable (or rollable) type smartphone (291-3)), a tablet (192), a cellular phone (not shown), and other similar computing devices (not shown). The components, their relationships, and their functions illustrated in FIG. 2 are exemplary only and do not limit the implementations described or claimed in this document. The electronic device (200) may be referred to as a mobile device, a user device, a multi-function device, a portable device, or a server.

[0045] The electronic device (200) may include components including at least one processor (210) (hereinafter, referred to as 'processor (210)') (e.g., processor (120) of FIG. 1), at least one memory (220) (hereinafter, referred to as 'memory (220)') (e.g., memory (130) of FIG. 1), at least one display (240) (hereinafter, referred to as 'display (240)'), at least one image sensor (250) (hereinafter, referred to as 'image sensor (250)'), at least one communication circuit (260) (hereinafter, referred to as 'communication circuit (260)') (e.g., communication module (190) of FIG. 1), and / or at least one sensor (270) (hereinafter, referred to as 'sensor (270)') (e.g., sensor module (176) of FIG. 1). The components are merely exemplary. For example, the electronic device (200) may include other components (e.g., power management integrated circuitry (PMIC), audio processing circuitry, an antenna, a rechargeable battery, or input / output interfaces). For example, some components may be omitted from the electronic device (200). For example, some components may be integrated into a single component.

[0046] The processor (210) may be implemented as one or more IC (integrated circuit (or circuitry)) chips and may perform various data processing. The processor (210) may include at least one electrical circuit and may individually or collectively perform distributed processing of instructions (or programs, data, etc.) stored in the memory (220). The processor (210) may include a processor assembly including one or more processing circuits. The processor (210) may include any processing circuit operative to control the performance and operations of one or more components (e.g., the memory (220), the display (240), the image sensor (250), the communication circuit (260), and / or the sensor (270)) of the electronic device (200). For example, the processor (210) (e.g., the application processor (AP)) may be implemented as a system on chip (SoC) (e.g., a single chip or chipset). For example, the processor (210) may be implemented with multiple cores (or at least one core circuit), multiple chips, or multiple chipsets. For example, the processor (210) may include one or more processing circuits. For example, the processor (210) may include one or more processing circuits configured to individually and / or collectively perform various functions of the present disclosure. As a non-limiting example, at least a portion of the processor (210) may be included in a first chip of the electronic device (200), and at least another portion of the processor (210) may be included in a second chip of the electronic device (200) that is different from the first chip of the electronic device (200).

[0047] For example, the processor (210) may include a central processing unit (CPU) (211), a graphics processing unit (GPU) (212), a neural processing unit (NPU) (213), an image signal processor (ISP) (214), a display controller (215), a memory controller (216), a storage controller (217), a communication processor (CP) (218), and / or a sensor interface (219). These components of the processor (210) are merely exemplary. For example, the processor (210) may further include other components. For example, some components of the processor (210) may be omitted from the processor (210). For example, some components of the processor (210) may be included as separate components of the electronic device (200) outside the processor (210). For example, some components of the processor (210) (e.g., memory controller (216)) may be included within other components (e.g., at least a portion of memory (220), an interface (e.g., available for connection to at least one component of the electronic device (200)), a display (240) and / or an image sensor (250)).

[0048] The processor (210) may cause other components of the electronic device (200) to perform various operations by executing instructions stored in the memory (220). The CPU (211) (or central processing circuit) may be configured to control components of the processor (210) based on the execution of instructions stored in the memory (220) (e.g., volatile memory (221) (e.g., volatile memory (132) of FIG. 1) and / or non-volatile memory (222) (e.g., non-volatile memory (134) of FIG. 1)). The GPU (212) (or graphics processing circuit) may be configured to execute parallel operations (e.g., rendering). The NPU (213) (or neural processing circuit, or artificial intelligence (AI) chip) may be configured to execute operations for an AI model (e.g., convolution computation). The ISP (214) (or image signal processing circuit) may be configured to process a raw image acquired through the image sensor (250) into a format suitable for a component within the electronic device (200) or a component of the processor (210). The display controller (215) (or display control circuit, or DPU (display processing unit)) may be configured to process an image acquired from the CPU (211), the GPU (212), the ISP (214), or the memory (220) (e.g., volatile memory (221)) into a format suitable for the display (240). The memory controller (216) (or memory control circuit) may be configured to control reading data from the volatile memory (221) and writing data to the volatile memory (221).The storage controller (217) (or storage control circuit) may be configured to control reading data from the non-volatile memory (222) and writing data to the non-volatile memory (222). The CP (218) (communication processing circuit) may be configured to process data obtained from a component of the processor (210) into a format suitable for transmitting to another electronic device via the communication circuit (260), or to process data obtained from another electronic device via the communication circuit (260) into a format suitable for processing by a component of the processor (210). For example, the communication circuit (260) may include one or more communication circuits. The sensor interface (219) (or sensing data processing circuit, sensor hub) may be configured to process data on the state of the electronic device (200) and / or the state of the surroundings of the electronic device (200), obtained via the sensor (270), into a format suitable for a component of the processor (210).

[0049] The memory (220) may include one or more storage media (or one or more storage devices). For example, the memory (220) may include a memory assembly including one or more storage media. For example, the one or more storage media may include permanent memory (e.g., non-volatile memory (122)) such as a hard drive, flash memory, read-only memory (ROM), semi-permanent memory (e.g., volatile memory (221)) such as random access memory (RAM), any other suitable type of storage (or storage assembly), or any combination thereof. The memory (220) may include a cache memory, which is one or more different types of memory used to temporarily store data for a function or feature of the electronic device (200). As a non-limiting example, the cache memory may be included within the processor (210). The memory (220) may be fixedly embedded within the electronic device (200) or incorporated into one or more suitable types of components (e.g., a subscriber identity module (SIM) card and / or a secure digital (SD) card) that may be repeatedly inserted into and removed from the electronic device (200).

[0050] For example, the memory (220) may store one or more software applications, such as an operating system (or system) software application, a firmware software application, a driver software application, a plug-in (e.g., add-in, add-on, and / or applet) software application, and / or any other suitable software applications. For example, the one or more software applications may include instructions executable by the processor (210). For example, the memory (220) may store instructions callable by an application programming interface (API). For example, the memory (220) may store instructions within a library.

[0051] According to an example, the electronic device (200) can execute at least one instance of an AI model. The instance may be an object corresponding to a program (or application), such as an AI model, for example. The instance may be named a replica, a pod, a container, or a virtual machine, and there is no limitation on the name thereof. The number of instances may correspond to the size of a resource (e.g., the GPU (212) or the NPU (213)), and accordingly, the number of instances may be used interchangeably with the size of the resource, or the instances may be used interchangeably with the resource.

[0052] As an example, a plurality of user requests may be input to the electronic device (200). The user requests may be associated with a service. The user request may be processed by a first instance of a first AI model, and a first processing result may be provided from the first instance of the first AI model. The first processing result may be processed by a first instance of a second AI model, and accordingly, a second processing result may be provided by the first instance of the second AI model. By serial processing of the processing results, the first instance of the M-th AI model may receive and process the N-1-th processing result. The first instance of the M-th AI model may provide the N-th processing result as a response. Accordingly, a response corresponding to the user request may be provided.

[0053] Based on the above-described process, responses corresponding to each of a plurality of user requests may be provided. Meanwhile, since processing must be performed by an instance, the time required to provide responses corresponding to each of a plurality of user requests (hereinafter referred to as “response time”) may take a relatively long time. The response time may affect the latency of the instance. In order to reduce the response time, the electronic device (200) may increase the number of instances of at least one AI model, which may be referred to as scaling out. However, there may be limitations in increasing the number of instances due to hardware and / or software constraints of the electronic device (200) and / or parameter restrictions of the AI ​​model (e.g., large language model (LLM)).

[0054] FIG. 3 is an exemplary block diagram for providing a generative artificial intelligence (AI) function in an electronic device (e.g., electronic device (101) of FIG. 1 or electronic device (200) of FIG. 2) (hereinafter referred to as 'electronic device (200)') according to one embodiment.

[0055] Referring to FIG. 3, the electronic device (200) may include a processor (210) (e.g., the processor (210) of FIG. 2), a memory (220) (e.g., the memory (220) of FIG. 2), and / or an interface (IF) (220). The electronic device (200) may be a device for providing a service linked to at least one AI system (320) (hereinafter, referred to as 'AI system (320)').

[0056] The AI ​​system (320) may include at least one AI model (hereinafter referred to as “AI model”). For example, the AI ​​system (320) may analyze received messages to generate a summary message. The received message may include at least one content (hereinafter referred to as “target content”). The at least one target content may be digitally produced data. The at least one target content may be digital data produced by combining at least one or two or more of text, code, image, audio, or video. For example, the summary message may include one or more contents (hereinafter referred to as “summary content”) that are reprocessed from the received messages so that a user can easily recognize the content of the received messages. The one or more summary contents may be digitally produced data. The one or more summary contents may be digital data produced by combining at least one or two or more of text, code, image, audio, or video.

[0057] The AI ​​system (320) may be based on natural language processing (NLP). The NLP is, for example, a technology that allows the electronic device (200) to understand or process natural language input (hereinafter referred to as a "prompt (330)") that can be expressed in voice and / or text. The electronic device (200) can understand natural language through NLP, and based on this, can understand human intention or convey information in a language that humans can understand. In order to understand human language, the NLP can learn the order of words or tokens and predict the probability of the next word or token in a given text. The token is a basic unit for processing or understanding the prompt (330) in the AI ​​model. The main technologies of the NLP include tokenization, part-of-speech tagging, syntax analysis, named entity recognition, or sentiment analysis for the prompt (330) corresponding to the user's input.

[0058] The I / F (310) may receive the prompt (330) and transmit the received prompt (330) to the processor (210). The prompt (330) may be a medium that guides the AI ​​system (320) to perform a task or generate a result in a desired direction. The prompt (330) may be the only window through which the user can communicate with the AI ​​system (320). The prompt (330) needs to be clear and specific in order to obtain an answer close to the desired result from the AI ​​system (320). According to an example, the I / F (310) may receive a response result (340) (e.g., a summary message and / or a response message) processed by the AI ​​system (320) based on the prompt (330) (e.g., received messages), and output the response result (340) converted into a natural language form (e.g., text, image, audio, or video) that can be recognized by humans. The above I / F (310) can input or output natural language in the form of voice and / or text, for example, through at least one component such as a keyboard, a touch panel, a display, and / or a speaker.

[0059] The processor (210) may execute software (e.g., a program) to control at least one other component (e.g., a hardware or software component) of the electronic device (200) that is electrically connected thereto. The processor (210) may perform various data processing or operations. As at least a part of the data processing or operations, the processor (210) may store commands or data received from other components (e.g., the I / F (310)) in a memory (220) (e.g., a volatile memory, but without limitation). As at least a part of the data processing or operations, the processor (210) may process commands or data stored in the memory (220) (e.g., a volatile memory, but without limitation). As at least a part of the data processing or operations, the processor (210) may store data resulting from processing commands or data in the memory (220) (e.g., a non-volatile memory, but without limitation).

[0060] The memory (220) may store various data used by at least one component (e.g., processor (210) and / or I / F (310)) of the electronic device (200). The data may include, for example, input data or output data for software (e.g., program) and commands related thereto. The memory (220) may also store at least one AI model (e.g., LLM, LVM (large vision models), LMM (large multimodal models)) for instance execution.

[0061] The memory (220) can store at least one instruction. The processor (210) can execute at least one instruction stored in the memory (220). The at least one instruction, when executed by the processor (210), can cause the electronic device (200) to perform at least one operation. For example, as the at least one instruction is executed by the processor (210), at least one other component may be controlled, and / or various data processing or calculations may be performed. The performance of one operation by the processor (210) may mean, for example, that the operation is performed by (or under the control of) one entity included in the processor (210) (for example, the main processor, but without limitation). The performance of one operation may mean, for example, that a specific operation is performed by (or under the control of) multiple entities (for example, multiple processors). The fact that multiple operations are performed may mean, for example, that all of the multiple operations are performed by (or under the control of) one entity (e.g., but not limited to, a main processor (e.g., the main processor (121) of FIG. 1)). The fact that multiple operations are performed may mean, for example, that some of the multiple operations are performed by at least one entity, and some of the remaining operations are performed by at least one other entity. At least one instruction causing the performance of one or more operations may be stored, for example, in one memory, or may be stored distributedly in each of a plurality of memories.

[0062] In the electronic device (200), the AI ​​system (320) may share resources (e.g., data processing or computational power) corresponding to part or all of at least one processor included in the processor (210) and / or resources (e.g., data recording area) corresponding to part or all of the memory (220). For example, the AI ​​system (320) may be operated by at least one of the CPU (211), the GPU (212), or the NPU (213). The AI ​​system (320) may be executed solely by the CPU (211), for example, by being allocated at least a portion of the memory (220). The AI ​​system (320) may be executed solely by the GPU (212), for example, by being allocated at least a portion of the memory (220). The AI ​​system (320) may be executed solely by the NPU (213), for example, by being allocated at least a portion of the memory (220). The AI ​​system (320) may be performed by, for example, the CPU (211) and the GPU (212) in cooperation with each other by being allocated at least a portion of the memory (220). The AI ​​system (320) may be performed by, for example, the CPU (211) and the NPU (213) in cooperation with each other by being allocated at least a portion of the memory (220). The AI ​​system (320) may be performed by, for example, the GPU (212) and the NPU (213) in cooperation with each other by being allocated at least a portion of the memory (220). The AI ​​system (320) may be performed by, for example, the CPU (211), the GPU (212), and the NPU (213) in cooperation with each other by being allocated at least a portion of the memory (220). The various embodiments to be described later in the present disclosure are not limited to the combination of components for performing the AI ​​system (320), and may be implemented and / or applied based on any combination.

[0063] FIG. 4 is a block diagram of an exemplary AI system (e.g., AI system (320) of FIG. 3) capable of performing the operations described in this document. The AI ​​system (320) may be a generative AI system, but will be referred to as the “AI system (320)” hereinafter.

[0064] Referring to FIG. 4, the AI ​​system (320) may include an interface (User Query / Response Interface) (410) (e.g., I / F (310) of FIG. 3) (hereinafter referred to as 'I / F (410)'), an AI framework (420), a generative AI model (430) (hereinafter referred to as 'AI model'), a database (440), or an application / service component (Application / Service Component) (450).

[0065] The above I / F (410) can receive input (e.g., user input or data acquired or generated by the terminal, etc.). The data acquired or generated by the terminal may include image or video data generated using a processor, values ​​transmitted through a sensor or sensor hub (e.g., external illumination, angle of the terminal, temperature of the display or terminal, display size or expansion / reduction information, captured images of an image sensor, etc.). The user input may be in the form of natural language, touch coordinates or stylus coordinates acquired through a touch panel or digitizer included in the display, images, and / or videos. In addition, context information may also be transmitted when transmitting the user input. The context information may include various additional information at the time of the user input. For example, information on the application currently being used by the user or information on the user's location. In addition, the user input may also be in a form that mixes the above-described natural language, images, sounds, and context information. In addition, the user input may also be in a non-natural language form, such as selecting a menu. The I / F (410) can output the results of the AI ​​system (320) and / or the results of analyzing the input to the user. The output can be in the form of natural language or specific content, and can also be provided in the form of an action requested by the user. The output can also be provided in the form of a specific value designated by the user. The I / F (410) can output the results of the AI ​​system (320) to the user. The output can be in the form of natural language or specific content, and can also be provided in the form of an action requested by the user.

[0066] The AI ​​framework (420) can receive user input and coordinate and control each component necessary to carry out the user's intention based on the user's query. For example, the AI ​​framework (420) can include a prompt design component (421), a management component (API / Plug-in management component) (423), or an output modification component (or refiner component) (425).

[0067] The user input received from the I / F (410) may be transmitted to the prompt design component (421). The prompt design component (421) may be used to generate a prompt (e.g., the prompt (330) of FIG. 3) suitable for inputting the user input into the AI ​​model (430) (e.g., LLM, LVM or LMM). The prompt design component (421) may be an AI component that uses a machine learning algorithm or a neural network to develop a better prompt (330) over time. Although not shown, the prompt design component (421) may access a knowledge component including user preference data, a prompt library, and prompt examples based on the user input to generate the prompt (330), and transmit the generated prompt (330) to the AI ​​model (430).

[0068] The above management component (423) may communicate with external information when there is a request for additional information when transmitting user input as input to a generative model. The management component (423) may establish a channel for communicating with the outside of the AI ​​interface through an API, and may access various data sources (e.g., knowledge repositories (445)) through the established channel. When the management component (423) must perform an action that performs the user's input as a final result rather than an intermediate result in an application / service, the action may be requested to the application / service component (450) through an API. Information obtained from the outside may be used to generate a prompt (330) in the prompt design component (421) together with the user input, or may be transmitted as an input to the AI ​​model (430).

[0069] The output processing component (425) can fine-tune or reprocess the results output from the AI ​​model (430). For example, the output processing component (425) can verify whether the content generated through the AI ​​model (430) is irrelevant, does not contain biased content, or does not contain harmful content. The output processing component (425) can also determine to what extent it matches the result desired by the user and, if additional processing is required, can proceed with the process. The output processing component (425) can additionally configure and provide the user with hints to avoid unwanted output.

[0070] The AI ​​model (430) may generally refer to an AI neural network that generates new types of data based on user input information. The AI ​​model (430) may include a model that generates images and / or a model that generates language. The model that generates images may include, for example, a generative adversarial network (GAN) or a variational auto encoder (VAE). The model that generates images may be, for example, a diffusion-based AI model that uses the VAE and a transformer structure. The model that generates language may be a model trained to output the most statistically appropriate output value based on input values. Representative examples include models such as CHAT-GPT 3 and CHAT-GPT 4. In addition, there is also an LMM as an AI model (430) that can recognize various types of data inputs such as text, images, and voice and generate new data corresponding thereto.

[0071] FIG. 5 is a block diagram for generating content summarizing a received message (hereinafter referred to as “summary content”) in an electronic device (e.g., electronic device (101) of FIG. 1 or electronic device (200) of FIG. 2) (hereinafter referred to as “electronic device (200)”) according to one embodiment.

[0072] Referring to FIG. 5, the electronic device (200) may include a situation analysis module (510), a prompt generation module (520), or a content analysis / processing module (530) as components for generating summary content.

[0073] The above-mentioned situation analysis module (510) can analyze information related to the movement of the electronic device (200). For example, the above-mentioned situation analysis module (510) can analyze information regarding the user's information verification ability based on the collected information. The information regarding the information verification ability may include the verification time acquired through repeated learning regarding the time the user spends to verify content. The information verification ability may be determined by content topic. This takes into account that people may have different reading speeds depending on the topic.

[0074] The above situation analysis module (510) can analyze information about vehicle driving based on the collected information. The information about vehicle driving may include at least one of the following: whether the user is driving, the driver's behavior, the vehicle's driving speed, the vehicle's shaking, the vehicle's pulling, the expected arrival time at the destination, or the vehicle's stopping time.

[0075] For example, the situation analysis module (510) may include a driving situation analysis module (511) for analyzing vehicle information, a route situation analysis module (513) for analyzing driving information, or an indoor situation analysis module (515) for analyzing environmental information. The vehicle information may be information that can confirm the status or situation of a driving vehicle.

[0076] The above driving situation analysis module (511) can obtain vehicle information such as driving mode (e.g., autonomous driving, semi-autonomous driving, manual driving), driving environment, driving speed, or ride comfort (e.g., shaking, pulling) by analyzing sensing data of at least one sensor (e.g., sensor (270) of FIG. 2).

[0077] The above-mentioned route situation analysis module (513) can analyze data regarding route guidance provided by a route guidance device to obtain driving situation information related to vehicle driving, such as departure time, expected arrival time, travel route, congestion section, stop time, or expected possession time.

[0078] The indoor situation analysis module (515) can obtain environmental information related to the indoor situation by analyzing data collected regarding the presence of passengers, the content of conversation with at least one passenger, the driver's behavior, and the view angle, focus, or display gaze of passengers (e.g., the driver and / or passenger). The indoor situation analysis module (515) can predict the level of intimacy with passengers by analyzing the content of conversations taking place within the vehicle. The indoor situation analysis module (515) can predict the level of intimacy by analyzing information such as words, speech patterns, or titles used during conversation, for example.

[0079] The above prompt generation module (520) can generate a prompt (e.g., the prompt (330) of FIG. 3) to be used by an AI model (e.g., the AI ​​model (430) of FIG. 4) to reconstruct the conversation content and generate a summary message appropriate for the current situation (e.g., the operation of the prompt design component (421) of FIG. 4). The above prompt generation module (520) can transmit the generated prompt to the above content analysis / processing module (530). The above prompt generation module (520) can generate a more improved prompt using a machine learning algorithm or a neural network over time.

[0080] The content analysis / processing module (530) may generate a summary message by reprocessing received messages in response to a prompt generated by the prompt generation module (520). The content analysis / processing module (530) may consider at least one of vehicle information, driving information, or environmental information acquired by the situation analysis module (510) to generate the summary message. This is to provide a summary message that can be easily checked when necessary, taking into account the user's situation. The time when the summary message is required may be one of the following: when the topic of the conversation changes, when a reply and / or response is required, or when urgency is recognized. The content analysis / processing module (530) may generate a response message appropriate to the analyzed results and suggest it to the user at the time when a reply and / or response is required. In this case, the user may transmit the response message to the other party simply by selecting the corresponding response message or requesting transmission. The response message may include at least one response content. The at least one response content may be digitally produced data. The at least one response content may be digital data created using at least one or a combination of two or more of text, symbols, images, audio, or video, for example. The content analysis / processing module (530) may select information to be displayed through the summary message, taking into account the level of intimacy between the passenger and the driver, if the vehicle has passengers.

[0081] below is an example that summarizes the operation and results of the situation analysis module (510), the prompt generation module (520), or the content analysis / processing module (530).

[0082] Analysis of moving or driving content information Analysis of driving situation Analysis of indoor situation Analysis of travel time Analysis of target content Analysis of sensitive information Analysis of driving-related information Analysis of passengers Analysis of driver Determining the amount of data that can be confirmed Confirming the occurrence of an event Confirming sensitive information Confirming speed and / or ride comfort Confirming intimacy Confirming the degree of leeway Generating a prompt Determining summary depth Determining summary depth, provision time and / or whether a response is required Determining summary depth considering sensitive information Determining summary depth and / or output method Determining the level of sharing of sensitive information considering intimacy Determining the time of information provision Generating summary data Generating summary data and / or response message Generating summary data Generating summary data considering output method Delivering information with the determined sharing level Outputting information at the determined time

[0083] FIGS. 6A and 6B are control flowcharts for generating content summarizing a received message in an electronic device (e.g., electronic device (101) of FIG. 1 or electronic device (200) of FIG. 2) (hereinafter referred to as 'electronic device (200)') according to one embodiment.

[0084] Referring to FIGS. 6A and 6B , the electronic device (200) may activate an adaptive message confirmation function in operation 611. The adaptive message confirmation function may be a function that analyzes a conversation in a chat based on a specific communication application (e.g., an actual conversation (710) in FIG. 7) and provides a summarized message (hereinafter, referred to as a “summary message”) (e.g., a content summarized and delivered by AI in FIG. 7 (720)). The conversation may include messages received from at least one other party in the specific chat (hereinafter, referred to as “received messages”) and messages sent by the user (hereinafter, referred to as “sent messages”). The adaptive message confirmation function may be activated or deactivated according to a user’s setting. The adaptive message confirmation function may be activated, for example, in a situation where the user cannot check the received message in real time. The adaptive message confirmation function may be deactivated, for example, in a situation where the user can check the received message in real time.

[0085] The electronic device (200), in operation 613, may perform an operation according to the specific chat. For example, the electronic device (200) may receive one or more received messages from at least one counterpart in the specific chat. The one or more received messages may be received, for example, based on a chat application for the specific chat. The various embodiments proposed in this document may be substantially equally applicable to all applications that provide one-to-one or one-to-many conversations, regardless of the method or type of the chat application. For example, the received messages may include one or more contents of a specific type produced in digital format (hereinafter referred to as "target contents"). The target contents may be data produced digitally. The target contents may be digital data produced by at least one or a combination of two or more of text, symbols, images, audio, or videos. For example, the transmitted messages may also include content that is digital data produced by at least one or a combination of two or more of text, symbols, images, audio, or videos.

[0086] The electronic device (200) may determine, at operation 615, whether a situation is suitable for checking a received message. The suitable situation may occur when the user can check the received message in real time. Accordingly, an unsuitable situation for checking a received message may occur when the user cannot check the received message in real time. The inability to check the received message in real time may occur when there are special circumstances that make it difficult for the user to check the received message. The special circumstances may, for example, be the user driving a vehicle. The special circumstances may, for example, be the user making a voice call. The special circumstances may, for example, be the presence of a passenger in the vehicle. The special circumstances may, for example, be the user conversing with the passenger. The special circumstances may, for example, be the driving speed exceeding a predetermined threshold level. The special circumstances may, for example, be the vehicle driving on a road that is difficult or requires concentration, such as mountainous terrain, a curved road, or a fork in the road. The above special circumstances may, for example, correspond to a situation where a toll is being paid. The above special circumstances may correspond to a weather condition requiring careful driving, such as heavy rain or heavy snow. The above special circumstances may correspond to a situation where a user is studying in a library. The above special circumstances may correspond to a situation where a user is attending a meeting. Furthermore, the special circumstances that prevent a user from checking received messages in real time may vary, and the various embodiments proposed in this document can be applied substantially equally to most situations where a user cannot check received messages in real time. The above operation 615 may be omitted if necessary.For example, if the adaptive message confirmation function is activated because the user cannot confirm the message in real time, the electronic device (200) may omit the operation 615.

[0087] For example, even if the electronic device (200) is not in a suitable situation to check the received message, it may generate a summary message and provide it to the user only when a predetermined event occurs. The electronic device (200) may analyze the conversation content based on the received messages and determine whether the predetermined event occurs based on the analysis result. The predetermined event may include, for example, a first event that may occur when a topic in the analyzed conversation content changes (for example, a change from a conversation related to an appointment time (711) to a conversation related to an appointment place (713) in FIG. 7) (see reference numeral 731 in FIG. 7). The predetermined event may include, for example, a second event that may occur when it is recognized that a situation requires a response (for example, a reply or reaction) based on the analyzed conversation content (see reference numeral 733 in FIG. 7). The above-described event may include, for example, a third event that may occur in response to a situation where an emergency is recognized based on the analyzed conversation content (see reference numeral 735 in FIG. 7). Furthermore, the above-described event may occur for various reasons, and the various embodiments proposed in this document can be substantially equally applied to the occurrence of such events.

[0088] If the electronic device (200) determines that the user can check the received message in real time, it may output the received message in operation 617. The received message may be output as visual content, such as text, images, or videos, through a display, for example. The received message may be output as auditory content, such as an audible signal, through an audio output means, such as a speaker, for example.

[0089] If the electronic device (200) determines that the user is unable to check the received message in real time, the electronic device (200) may collect at least one piece of situation information in operation 619 (operation of the situation analysis module (510) of FIG. 5). The at least one piece of situation information may include information regarding items to be considered for generating a summary message (hereinafter, referred to as “consideration item information”). The at least one piece of situation information may be, for example, information related to the movement of the electronic device (200). As an example, the at least one piece of situation information may include information regarding the user’s ability to check information. The information regarding the ability to check information may include a topic-specific check time of the corresponding content acquired through iterative learning regarding the time the user spends to check the content. As an example, the at least one piece of situation information may include information regarding the user’s ability to check information and information regarding vehicle driving. The information regarding the ability to check information may include a topic-specific check time of the corresponding content acquired through iterative learning regarding the time the user spends to check the content. The information about the vehicle driving may include at least one of information about whether the user is driving, the driver's behavior, the vehicle's driving speed, the vehicle's shaking, the vehicle's pulling, the expected arrival time at the destination, or the stopping time.

[0090] According to one example, the electronic device (200) may collect vehicle information, driving information, or environmental information as information related to the vehicle driving. The vehicle information may be information that can confirm the state or situation of the driving vehicle. The vehicle information may be information that can be acquired by sensing data of at least one sensor (e.g., sensor (270) of FIG. 2), such as driving mode, driving environment, driving speed, or ride comfort (e.g., shaking, pulling). The driving mode may include, for example, an autonomous driving mode, a semi-autonomous driving mode, or various driving modes. As an example, the vehicle information may be collected or analyzed by the driving situation analysis module (511) of FIG. 5. The driving information may be driving situation information related to vehicle driving, such as, for example, departure time, expected arrival time, travel route, congested section, stop time, or expected possession time. The driving information may be acquired from a device that provides a route guidance service. For example, the driving information may be collected or analyzed by the route situation analysis module (513) of FIG. 5. The environmental information may be information related to an indoor situation, such as whether there are passengers, the content of a conversation with at least one passenger, the driver's behavior, the viewing angle, focus, or whether the passengers (e.g., the driver and / or passenger) are looking at a display. For example, the environmental information may be collected or analyzed by the indoor situation analysis module (515) of FIG. 5. For example, the electronic device (200) may predict the intimacy between the driver and the passenger. For example, if there is a passenger in the vehicle, the electronic device (200) may analyze the content of a conversation taking place in the vehicle. Based on the analysis result, the electronic device (200) may predict the intimacy with the passenger.The electronic device (200) can predict the intimacy by analyzing information such as words, speech, or titles used during a conversation, for example.

[0091] The electronic device (200), in operation 621, may determine a setting for generating a summary message (hereinafter, referred to as a 'summary message generation option') based on at least one of the collected contextual information, i.e., a consideration item (operation of the context analysis module (510) of FIG. 5). In order to determine the summary message generation option, the electronic device (200) may additionally consider a content type included in a received message. The content type may be, for example, text. The content type may be, for example, a symbol. The content type may be, for example, an image such as an emoticon. The content type may be, for example, audio. As an example, the audio may be voice. The content type may be, for example, a video. The content type may be, for example, a combination of at least two or more of text, an image, audio, or a video.

[0092] According to one example, the electronic device (200) may determine a summary message generation option including at least one of summary depth, layout, font, character option, or content type. The summary depth may determine the amount of information of the summary message to be generated. The amount of information may be information regarding the amount of content that the user will check through the summary message to be generated. The electronic device (200) may determine the amount of information by considering at least one contextual information (e.g., driving mode). As an example, the summary depth may be determined in proportion to the driver's available time to check the summary message. For example, if the vehicle is driving in autonomous driving mode, it may be predicted that the driver will have relatively more available time to check the summary message compared to when the vehicle is driving in manual driving mode or semi-autonomous driving mode. In this case, the summary depth for the summary message may be determined to be substantially close to the original text. For example, the longer the expected time to arrive at the destination, the more available time to check the summary message compared to when the expected time is short. In this case, the summary depth for the summary message can be determined in proportion to the expected waiting time. For example, after stopping for reasons such as waiting at a traffic light, it can be predicted that the longer the expected waiting time, the more time there will be to check the summary message compared to when the expected waiting time is short. In this case, the summary depth for the summary message can be determined in proportion to the expected waiting time. For example, it can be predicted that the time there will be to check the summary message at a relatively low driving speed compared to when the driving speed is relatively high. In this case, the summary depth for the summary message can be determined in proportion to the driving speed.

[0093] The above summary message may include one or more contents of a specific type (hereinafter referred to as "summary content"). The summary content may be, for example, digital data created by combining at least one or two or more of text, symbols, images, audio, or video. If the summary content to be generated is text, the amount of information may indicate the number of words (see FIG. 8). If the summary content is video, the amount of information may indicate the playback time (see FIG. 9). The layout may determine the structure of a paragraph, such as line spacing and / or indentation. The font may determine the shape of a letter. The character options may determine the size, thickness, and / or shape of the letter. The content type may determine the type of data, such as text, symbols, images, audio, or video.

[0094] The electronic device (200), in operation 623, may analyze the conversation content corresponding to the subject of summary and generate a summary message. The electronic device (200) may consider a summary message generation option when generating the summary message. The summary message may include summary data that summarizes the conversation content into a predetermined amount of information. The predetermined amount of information may correspond to the amount of content that the user is to confirm through the summary message. The summary data may include one or more contents of a specific information type (hereinafter referred to as “summary contents”). The summary contents may be, for example, digital data created by combining at least one or two or more of text, symbols, images, audio, or videos.

[0095] According to one example, the electronic device (200) may generate a prompt based on at least one piece of collected contextual information (e.g., vehicle information, driving information, or environmental information) (operation of the prompt generation module (520) of FIG. 5). The prompt may request generation of a summary message. The electronic device (200) may generate a prompt requesting, for example, to summarize a received message with a predetermined summary depth (e.g., number of words or playback time) and a predetermined content type. The electronic device (200) may determine the predetermined summary depth and / or the predetermined content type according to the summary message generation option.

[0096] According to one example, the electronic device (200) may, in response to the prompt, reprocess the received messages into summary content of a predetermined content type having a predetermined amount of information (operation of the content analysis / processing module (530) of FIG. 5).

[0097] For example, the electronic device (200) may consider the intimacy between the driver and passenger when generating the summary message. Table 2 below shows examples of summary messages (e.g., reconstructed messages) that may be generated from received messages by considering the level of detail.

[0098] Received Message Reconstruction Message Cheolsu. Do you want to go to Burning Sun tomorrow? ① Cheolsu. Do you want to go to Burning Sun tomorrow? ② Cheolsu. Do you want to go to a club tomorrow? ③ Cheolsu. Do you want to go to the place we went to together last Thursday night tomorrow? ④ Cheolsu. Do you want to go there tomorrow? ⑤ Cheolsu. Do you want to go tomorrow?

[0099] According to the above , when there is a passenger, the electronic device (200) can reconstruct a message by hiding information that is judged to be sensitive or replacing it with other information to protect personal information. For example, when generating a reconstructed message, the relationship (e.g., intimacy) between the passenger and the driver can be analyzed, and words to be used in the reconstructed message can be selectively selected considering the intimacy according to the analysis. In the above , the original characters ①, ②, ③, ④, and ⑤ correspond to the order of the reconstructed message that can be selected considering the intimacy. For example, when the intimacy with the passenger is determined to be very high, the electronic device (200) can generate a reconstructed message at the same level as the received message, such as 'Cheolsu. Do you want to go to Burning Sun tomorrow?' However, when the intimacy with the passenger is determined to be very low, the electronic device (200) can generate a reconstructed message such as 'Cheolsu. Do you want to go tomorrow?' containing content that others who respond to the same received message cannot understand.

[0100] As described above, the electronic device (200) can transmit a message without changing the content when it is determined that the relationship between the driver and the passenger is close and sensitive information can be disclosed, but can transmit a message reconstructed differently depending on the stage of the relationship when it is determined that the relationship between the driver and the passenger is distant and the driver wants to hide sensitive information.

[0101] The electronic device (200) may, at step 625, determine whether a response is required for previously analyzed received messages. For example, the electronic device (200) may analyze the received messages and determine that a response is required if the other party asks for the other party's opinion or requests a decision. Furthermore, situations requiring a response may vary and are not limited in their format.

[0102] If the electronic device (200) determines that a response is necessary, it may generate a response message in operation 627. The response message may be generated by an AI model in response to content (e.g., a question) analyzed from the received message. For example, the electronic device (200) may generate at least one response message corresponding to a summary message generated in response to the occurrence of a specific event. The specific event may be one of predetermined events used as a condition for determining the generation of the summary message. For example, the specific event may include a second event in which a response is determined to be necessary in a conversation referring to the received message, or a third event in which an emergency is determined in a conversation referring to the received message. The electronic device (200) may also operate to propose multiple response messages so that the user may select one response message. The response message may include at least one response content. The at least one response content may be digitally produced data. The at least one response content may be digital data created using at least one or a combination of two or more of text, symbols, images, audio, or video, for example. The electronic device (200) may determine whether a response is required before generating a summary message, and may operate to generate the summary message based on whether a response is required.

[0103] The electronic device (200) may, in operation 629, monitor an appropriate time to output a summary message. The electronic device (200) may determine the time to output the summary message by considering at least one of, for example, a change in the topic of the conversation, a time when a reply and / or response is deemed necessary, or a time when urgency is recognized. As the three exemplary time points have been previously described, further description thereof will be omitted.

[0104] The electronic device (200) may determine, in operation 631, whether the outputtable time of the summary message has arrived, taking into account the monitoring result. The outputtable time may be determined based on whether the user is in a situation where he or she can check the content. If the summary message is delivered while the driver is talking to a passenger or needs to focus on driving, the driver may not be able to check the summary message. Therefore, it may be desirable to reconstruct the summary message and then provide the summary message at a predetermined time when it is determined that the driver's situation will allow for easy confirmation. If the electronic device (200) has determined an appropriate outputtable time (hereinafter referred to as the "outputtable time") for generating the summary message, the electronic device may omit operations 629 and 631.

[0105] If the electronic device (200) determines that the output is possible at the time of output, in operation 633, the electronic device may output a summary message including at least one summary content or a summary and response message including at least one summary content. The summary message or the summary / response message may be output as visual content such as text, symbols, images, or videos through a display, for example. The summary message or the summary / response message may be output as auditory content such as an audible signal through an audio output means such as a speaker, for example. As an example, the electronic device (200) may operate an AI model to output the summary message and / or the response message considering readability at the output possible time. The readability may be adjusted, for example, by a font type such as a font size and / or a paragraph type such as a line spacing when the summary message and / or the response message is text. For example, text with a large font size may be relatively more readable than text with a small font size. For example, text with wider line spacing may be relatively more readable than text with narrower line spacing. This readability may be determined, for example, by the amount and / or type of information in the summary content and / or the recommended response content.

[0106] The electronic device (200) may, at operation 635, monitor whether transmission of the response message is requested. If transmission of the response message is requested, the electronic device (200) may transmit the response message at operation 637. The electronic device (200) may transmit the response message, for example, through a communication application that is in an activated state.

[0107] FIG. 7 is a drawing exemplarily illustrating an operation of analyzing a received message and providing a summary or response message in an electronic device (e.g., the electronic device (101) of FIG. 1 or the electronic device (200) of FIG. 2) (hereinafter referred to as 'electronic device (200)') according to one embodiment.

[0108] Referring to FIG. 7, the electronic device (200) can analyze actual conversation content (710) to monitor whether a predetermined event occurs.

[0109] The above-described event may include an event in which the topic of the conversation changes. For example, the topic may change from a conversation about an appointment time (711) to a conversation about an appointment location (713). In this case, the electronic device (200) may recognize that the topic of the conversation has changed and output a screen (721) that includes a summary message (M1) that analyzes the conversation content (711) regarding the topic before the change, along with function icons for selecting how to handle the summary message (e.g., ignore (a1) or join the conversation (a2)).

[0110] The above-described predetermined event may include an event that requires a response (e.g., a reply or reaction) based on the analyzed conversation content. For example, a message asking a user a question (e.g., why doesn't C confirm?) (715) may be received. In this case, the electronic device (200) may determine that a reply / reaction is required (733). The electronic device (200) may analyze the target received messages and output a screen (723) including a message (M2) that includes a summary (e.g., a summary message) and a content inquiring about whether to respond (e.g., a response message), and function icons for selecting the processing of the corresponding message (e.g., ignore (b1), reply with new content (b2), or reply with suggested content (b3)).

[0111] The above-described predetermined event may include an event determined to be urgent based on the analyzed conversation content. For example, a message (719) containing content requiring an urgent decision may be received from the other party. In this case, the electronic device (200) may determine that the time is considered urgent (735). The electronic device (200) may analyze the target received message and output a screen (725) including a message (M3) containing a summary of the content and function icons for selecting the processing of the message (e.g., ignore (c1), reply with new content (c2), or reply with suggested content (c3)).

[0112] FIG. 8 is an exemplary diagram of a user interface that provides a summary of received messages by situation in an electronic device (e.g., the electronic device (101) of FIG. 1 or the electronic device (200) of FIG. 2) (hereinafter referred to as 'electronic device (200)') according to one embodiment.

[0113] Referring to FIG. 8, an electronic device (200) may receive a message (810) from a counterpart. The electronic device (200) may display the received message on the screen (811). If the electronic device (200) determines that the user has sufficient time to check the message, the electronic device (200) may display the content of the received message as is on the screen (821). If the electronic device (200) does not determine that the user has sufficient time to check the message, the electronic device (200) may determine the level of summary of the message by considering the expected time required to reach the destination. For example, if the expected time required is 10 minutes, the electronic device (200) may display a summary message that has been reprocessed into a summary depth that can be displayed on one screen (831). If the expected time required is 5 minutes, the electronic device (200) may display a summary message that has been reprocessed into a summary depth that can be displayed on half of the screen (841).

[0114] FIG. 9 is a diagram illustrating an example of adaptively providing a response message by taking into account a user's situation in an electronic device (e.g., the electronic device (101) of FIG. 1 or the electronic device (200) of FIG. 2) (hereinafter referred to as 'electronic device (200)') according to one embodiment.

[0115] Referring to FIG. 9, the electronic device (200) may output a received message (910) on the screen without substantially summarizing it in the autonomous driving mode and / or semi-autonomous driving mode, or without changing the font type such as the font size and / or the paragraph type such as the line spacing provided in the normal driving mode (911). For example, when the driving mode is switched to the manual driving mode, the electronic device (200) may output a reprocessed message on the screen in which the font type such as the font size and / or the paragraph type such as the line spacing are adjusted to readable values ​​(921). For example, when the driving mode is switched to the manual driving mode, the electronic device (200) may convert the received message into a type of content (e.g., a video) that is easy to check, and output the converted content on the screen (923).

[0116] According to one embodiment, an electronic device (200) may include a communication circuit (260). The electronic device (200) may include a memory (220) including one or more storage media for storing instructions. The electronic device (200) may include at least one processor (210) including a processing circuit. When the instructions are individually or collectively executed by the at least one processor (210), the instructions may cause the electronic device (200) to perform at least one operation. The at least one operation may include an operation of analyzing content by at least considering target contents based on a specific communication application. The at least one operation may include an operation of generating summary content based on the analyzed content in response to the occurrence of a predetermined event according to the analysis. The predetermined event may include a first event occurring when a topic in the analyzed content changes, a second event occurring when a response to the analyzed content is required, or a third event occurring when the analyzed content corresponds to an emergency situation. The information type and / or information amount of the above summary content - the amount of information is the amount of content to be confirmed by the above summary content - can be determined based on at least one contextual information related to the movement of the electronic device (200).

[0117] According to an example, the at least one operation may include an operation of generating at least one response content corresponding to the generated summary content in response to the occurrence of a specific event included in the predetermined event. The at least one operation may include an operation of an artificial intelligence (AI) model to recommend one response content from among the at least one generated response content. The specific event may include the second event or the third event. The target content, the summary content, or the response content may be digital data produced using at least one or a combination of two or more of text, symbols, images, audio, or video.

[0118] In one example, the at least one piece of contextual information may include information about the user's ability to verify information.

[0119] For example, information about the ability to verify information may include a subject-specific verification time of the content obtained by repeatedly learning the content verification time consumed by the user.

[0120] For example, the at least one piece of contextual information may include information regarding the user's ability to ascertain information and information regarding vehicle driving. The information regarding the user's ability to ascertain information may include the subject-specific ascertainment time of the content, obtained through repeated learning of the content learning time consumed by the user.

[0121] For example, the information about the vehicle driving may include information corresponding to at least one of whether the user is driving, the driver's behavior, the vehicle's driving speed, the vehicle's shaking, the vehicle's pulling, the expected arrival time at the destination, or the waiting time for the vehicle to stop.

[0122] In one example, the at least one operation may include analyzing the content of a conversation within the vehicle, if there is a passenger in the vehicle. The at least one operation may include obtaining a level of intimacy with the passenger based on the analysis result. The at least one operation may include an operation of an AI model that determines one or more words to be used to generate the summary content, taking into account the obtained level of intimacy.

[0123] For example, the target contents may be contents that are not confirmed by the user among contents based on the specific communication application.

[0124] In one example, the at least one operation may include an operation of an AI model that outputs the summary content and / or the recommended response content at a predetermined point in time determined by whether the user is in a situation where the content can be confirmed, taking readability into account. The readability may be determined by the amount and / or type of information of the summary content and / or the recommended response content. The amount of information may correspond to the amount of content that the user is to confirm through the summary content and / or the recommended response content.

[0125] According to one example, a method of operating an electronic device (200) may be provided. The method may include an operation of analyzing content by taking into account at least target contents based on a specific communication application. The method may include an operation of generating summary content based on the analyzed content in response to the occurrence of a predetermined event according to the analysis. The predetermined event may include a first event occurring due to a change in the topic of the analyzed content, a second event occurring due to a need for a response to the analyzed content, or a third event occurring due to the analyzed content corresponding to an emergency situation. The information type and / or information amount of the summary content—the information amount being the amount of content to be confirmed by the summary content—may be determined based on at least one piece of situational information related to the movement of the electronic device (200).

[0126] According to one example, the method may include an operation of generating at least one response content corresponding to the generated summary content in response to the occurrence of a specific event included in the predetermined event. The method may include an operation of an artificial intelligence (AI) model to recommend one response content from among the at least one generated response content. The specific event may include the second event or the third event. The target content, the summary content, or the response content may be digital data produced using at least one or a combination of two or more of text, symbols, images, audio, or video.

[0127] In one example, the at least one piece of contextual information may include information about the user's ability to verify information.

[0128] For example, information about the ability to verify information may include a subject-specific verification time of the content obtained by repeatedly learning the content verification time consumed by the user.

[0129] For example, the at least one piece of contextual information may include information regarding the user's ability to view information and information regarding vehicle driving. The information regarding the user's ability to view information may include a topic-specific viewing time for the content, obtained by repeatedly learning the content viewing time consumed by the user.

[0130] For example, the information about the vehicle driving may include information corresponding to at least one of whether the user is driving, the driver's behavior, the vehicle's driving speed, the vehicle's shaking, the vehicle's pulling, the expected arrival time at the destination, or the waiting time for the vehicle to stop.

[0131] In one example, the method may include analyzing conversations within the vehicle, if a passenger is present. The method may include obtaining a level of intimacy with the passenger based on the analysis results. The method may include determining one or more words to be used to generate the summary content, using an AI model that considers the obtained level of intimacy.

[0132] For example, the target contents may be contents that are not confirmed by the user among contents based on the specific communication application.

[0133] In one example, the method may include the operation of an AI model that outputs the summary content and / or the determined response content at a predetermined point in time determined by whether the user is in a situation where the content can be confirmed, taking readability into account. The readability may be determined by the amount and / or type of information of the summary content and / or the determined response content. The amount of information may correspond to the amount of content that the user will confirm through the summary content and / or the recommended response content.

[0134] According to one example, a storage medium storing computer-readable instructions may be provided. The instructions, when executed by at least a portion of at least one processor (210) of the electronic device (200), may cause the electronic device (200) to perform at least one operation. The at least one operation may include an operation of analyzing content by at least considering target contents based on a specific communication application. The at least one operation may include an operation of generating summary content based on the analyzed content in response to the occurrence of a predetermined event according to the analysis. The predetermined event may include a first event occurring due to a change in the topic of the analyzed content, a second event occurring due to a need for a response to the analyzed content, or a third event occurring due to the analyzed content corresponding to an emergency situation. The information type and / or information amount of the summary content—the information amount being the amount of content to be confirmed by the summary content—may be determined based on at least one situational information related to the movement of the electronic device (200).

[0135] According to an example, the at least one operation may include an operation of generating at least one response content corresponding to the generated summary content in response to the occurrence of a specific event included in the predetermined event. The at least one operation may include an operation of an artificial intelligence (AI) model to recommend one response content from among the at least one generated response content. The specific event may include the second event or the third event. The target content, the summary content, or the response content may be digital data produced using at least one or a combination of two or more of text, symbols, images, audio, or video.

[0136] In one example, the at least one piece of contextual information may include information about the user's ability to verify information.

[0137] For example, information about the ability to verify information may include a subject-specific verification time of the content obtained by repeatedly learning the content verification time consumed by the user.

[0138] For example, the at least one piece of contextual information may include information regarding the user's ability to view information and information regarding vehicle driving. The information regarding the user's ability to view information may include a topic-specific viewing time for the content, obtained by repeatedly learning the content viewing time consumed by the user.

[0139] For example, the information about the vehicle driving may include information corresponding to at least one of whether the user is driving, the driver's behavior, the vehicle's driving speed, the vehicle's shaking, the vehicle's pulling, the expected arrival time at the destination, or the waiting time for the vehicle to stop.

[0140] In one example, the at least one operation may include analyzing the content of a conversation within the vehicle, if there is a passenger in the vehicle. The at least one operation may include obtaining a level of intimacy with the passenger based on the analysis result. The at least one operation may include an operation of an AI model that determines one or more words to be used to generate the summary content, taking into account the obtained level of intimacy.

[0141] For example, the target contents may be contents that are not confirmed by the user among contents based on the specific communication application.

[0142] In one example, the at least one operation may include an operation of an AI model that outputs the summary content and / or the determined response content at a predetermined point in time determined by whether the user is in a situation where the content can be confirmed, taking readability into account. The readability may be determined by the amount and / or type of information of the summary content and / or the determined response content. The amount of information may correspond to the amount of content that the user will confirm through the summary content and / or the recommended response content.

[0143] It should be understood that the embodiments of this document and the terminology used herein are not intended to limit the technical features described in this document to a specific embodiment, but include various modifications, equivalents, or substitutes of the embodiment. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the item, unless the context clearly indicates otherwise. In this document, each of the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can include any one of the items listed together in the corresponding phrase among those phrases, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0144] The term "module" used in one embodiment of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0145] An embodiment of the present document may be implemented as software including one or more instructions stored in a storage medium (e.g., memory (220)) readable by a machine (e.g., electronic device (200)). For example, a processor (e.g., processor (210)) of the machine (e.g., electronic device (200)) may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0146] According to one embodiment, the method according to one embodiment disclosed in the present document may be provided as a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) via an application store (e.g., Play Store™) or directly between two user devices (e.g., smart phones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0147] According to one embodiment, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to one embodiment, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to one embodiment, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

1. In an electronic device (200), Communication circuit (260); At least one sensor (270); A memory (220) including one or more storage media for storing instructions; and At least one processor (210) comprising a processing circuit, Here, when the above instructions are individually or collectively executed by at least one processor (210), Analyze the content, at least considering the target contents based on a specific communication application, In response to the occurrence of a predetermined event according to the above analysis, summary content is generated based on the above analyzed content. Here, the predetermined event includes a first event that occurs when the subject of the analyzed content changes, a second event that occurs when a response to the analyzed content is required, or a third event that occurs when the analyzed content corresponds to an emergency situation. An electronic device (200), wherein the information type and / or information amount of the above summary content - the amount of information being the amount of content to be confirmed by the above summary content - is determined based on at least one contextual information related to the movement of the electronic device (200).

2. In paragraph 1, When the above instructions are executed individually or collectively by at least one processor (210), Generate at least one response content corresponding to the generated summary content in response to the occurrence of a specific event included in the above-mentioned predetermined event, An artificial intelligence (AI) model operates to recommend one response content from among at least one response content generated above, Here, the specific event includes the second event or the third event, The above target content, the above summary content, or the above response content is digital data created by a combination of at least one or two of text, symbols, images, audio, or video, in an electronic device (200).

3. In paragraph 1 or 2, At least one of the above contextual information includes information about the user's ability to verify information, Here, information regarding the information verification ability includes an electronic device (200) including a subject-specific verification time of the content obtained by repeatedly learning the content verification time consumed by the user.

4. In paragraph 1 or 2, The at least one contextual information includes information about the user's ability to check information and information about vehicle driving, Here, information about the ability to confirm the information includes the subject-specific confirmation time of the content obtained by repeatedly learning the content confirmation time consumed by the user, The information regarding the driving of the vehicle is an electronic device (200) that includes information corresponding to at least one of whether the user is driving, the driver's behavior, the driving speed of the vehicle, the shaking of the vehicle, the pulling of the vehicle, the expected arrival time at the destination, or the stopping time.

5. In any one of paragraphs 1 to 4, When the above instructions are executed individually or collectively by at least one processor (210), If there are passengers in the vehicle, the conversation within the vehicle is analyzed, Based on the above analysis results, intimacy with the passenger is obtained, An electronic device (200) in which an AI model operates to determine one or more words to be used to generate the summary content, taking into account the acquired intimacy.

6. In any one of paragraphs 1 to 5, The above target contents are contents that are not confirmed by the user among contents based on the specific communication application, electronic device (200).

7. In paragraph 2, When the above instructions are executed individually or collectively by at least one processor (210), The AI ​​model operates to output the summary content and / or the recommended response content considering readability at a predetermined point in time determined by whether the user is in a situation where the content can be confirmed. Here, the readability is determined by the amount of information and / or information type of the summary content and / or the recommended response content, The amount of information is an electronic device (200) corresponding to the amount of content to be confirmed by the user through the summary content and / or the recommended response content.

8. In the operating method of the electronic device (200), An action to analyze content, at least considering target contents based on a specific communication application; and Including an action of generating summary content based on the analyzed content in response to the occurrence of a predetermined event according to the above analysis, The above-mentioned predetermined event includes a first event that occurs when the subject of the analyzed content changes, a second event that occurs when a response to the analyzed content is required, or a third event that occurs when the analyzed content corresponds to an emergency situation. Here, a method in which the information type and / or information amount of the summary content - the amount of information being the amount of content to be confirmed by the summary content - is determined based on at least one contextual information related to the movement of the electronic device (200).

9. In paragraph 8, An action of generating at least one response content corresponding to the generated summary content in response to the occurrence of a specific event included in the above-described event; An operation for determining one response content from at least one response content generated above, Here, the specific event includes the second event or the third event, A method wherein the target content, the summary content, or the response content is digital data created by combining at least one or two of text, symbols, images, audio, or video.

10. In paragraph 8 or 9, At least one of the above contextual information includes information about the user's ability to verify information, Here, the information regarding the information verification ability includes a subject-specific verification time of the content obtained by repeatedly learning the content verification time consumed by the user.

11. In paragraph 8 or 9, The at least one contextual information includes information about the user's ability to check information and information about vehicle driving, Here, information about the ability to confirm the information includes the subject-specific confirmation time of the content obtained by repeatedly learning the content confirmation time consumed by the user, A method in which the information regarding the driving of the vehicle includes information corresponding to at least one of whether the user is driving, the driver's behavior, the driving speed of the vehicle, the shaking of the vehicle, the pulling of the vehicle, the expected arrival time at the destination, or the waiting time for stopping.

12. In any one of paragraphs 8 to 11, If there are passengers in the vehicle, an action is taken to analyze the conversation within the vehicle; An action to acquire intimacy with the passenger based on the above analysis results; and A method comprising an action of determining one or more words to be used to generate the summary content by taking into account the acquired intimacy.

13. In any one of paragraphs 8 to 12, The above target contents are contents that are not confirmed by the user among contents based on the specific communication application.

14. In paragraph 8, It includes an action of outputting the summary content or the determined response content considering readability at a predetermined point in time determined by whether the user is in a situation where the content can be confirmed, Here, the readability is determined by the amount of information and / or information type of the summary content and / or the determined response content, The above amount of information corresponds to the amount of content that the user will confirm through the summary content and / or the recommended response content.

15. In a storage medium that stores instructions that can be read by a computer, The above instructions, when executed by at least a part of at least one processor (210) included in the electronic device (200), cause the electronic device (200) to perform at least one operation; At least one of the above actions: An action to analyze content, at least considering target contents based on a specific communication application; and Including an action of generating summary content based on the analyzed content in response to the occurrence of a predetermined event according to the above analysis, The above-mentioned predetermined event includes a first event that occurs when the subject of the analyzed content changes, a second event that occurs when a response to the analyzed content is required, or a third event that occurs when the analyzed content corresponds to an emergency situation. A storage medium wherein the information type and / or information amount of the summary content - the amount of information being the amount of content to be confirmed by the summary content - is determined based on at least one contextual information related to the movement of the electronic device.

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