Electronic device for providing results output through shared artificial intelligence model to plurality of applications and control method therefor

Abstract

An electronic device according to an embodiment of the present disclosure may comprise a memory and at least one processor including a processing circuit. The at least one processor may be individually and / or collectively configured to cause the electronic device to: transmit, to a shared application, first user input data, which is input through a first application corresponding to a first vendor and stored in the memory, and second user input data, which is input through a second application corresponding to a second vendor and stored in the memory; enable a first artificial intelligence model of the shared application to perform learning on the basis of the first user input data and the second user input data; after the artificial intelligence model has performed learning, estimate, through the first artificial intelligence model, results of third user input data, which is input through the first application or the second application and transmitted to the shared application; and determine a priority of the estimated results to transmit, to the first application or the second application, information about the determined priority and the estimated results.

Description

An electronic device and its control method for providing results output through a shared artificial intelligence model to multiple applications

[0001] The present disclosure relates to an electronic device and a control method thereof that provides results output through a shared artificial intelligence model to multiple applications.

[0002] The variety of services and additional features offered through electronic devices, such as smartphones, is steadily increasing. To enhance the utility of these devices and satisfy the diverse needs of users, telecommunications service providers and electronic device manufacturers are competitively developing electronic devices that offer diverse features and differentiate themselves from competitors. Accordingly, the various functions offered through wearable devices are also becoming increasingly sophisticated.

[0003] The above information may be provided as background art to aid in understanding the present disclosure. No claim or determination is made as to whether any of the above is applicable as prior art related to the present disclosure.

[0004] In conventional machine learning methods, such as federated learning, instead of user data, user data input through an electronic device can be processed by the electronic device and the processing results can be transmitted to a server. A server according to the conventional technique can generate a global model based on the results obtained from multiple electronic devices. The server according to the conventional technique can transmit the generated global model to each electronic device. According to such conventional techniques, an electronic device can obtain the results of performing a machine learning process without transmitting the user data input through the electronic device to the server. However, such conventional machine learning methods have the problem of relying on an external server. Furthermore, since the machine learning methods according to the conventional technique perform learning only with model parameters, not with the actual user data input, the quality and / or accuracy of the machine learning results cannot be guaranteed.

[0005] Embodiments of the present disclosure provide an electronic device capable of providing a user with results having improved quality and / or accuracy compared to conventional technologies by training an artificial intelligence model using user data input through multiple applications provided by various vendors, based on confidential computing technology.

[0006] Embodiments of the present disclosure provide an electronic device capable of providing a user with results having improved quality and / or accuracy compared to conventional technologies by performing training on an artificial intelligence model using user data actually input into the electronic device, based on confidential computing technology.

[0007] Embodiments of the present disclosure provide a method for controlling an electronic device, which can provide a user with results having improved quality and / or accuracy compared to conventional techniques by training an artificial intelligence model using user data input through multiple applications provided by various vendors in the electronic device based on confidential computing technology.

[0008] Embodiments of the present disclosure provide a method for controlling an electronic device, which can provide a user with results having improved quality and / or accuracy compared to conventional techniques, by performing training on an artificial intelligence model using user data actually input into the electronic device, based on confidential computing technology.

[0009] An electronic device according to an exemplary embodiment of the present disclosure includes a memory and at least one processor including a processing circuit, wherein the at least one processor is configured to individually and / or integrally cause the electronic device to: transmit first user input data input through a first application corresponding to a first vendor, stored in the memory, and second user input data input through a second application corresponding to a second vendor, stored in the memory, to a shared application, and a first artificial intelligence model of the shared application performs learning based on the first user input data and the second user input data, and based on the learning performed by the artificial intelligence model, estimate results of third user input data input through the first application and / or the second application, transmitted to the shared application, through the first artificial intelligence model, and determine priorities for the estimated results, and transmit information about the estimated results and the determined priorities to the first application and / or the second application.

[0010] A method for controlling an electronic device according to an exemplary embodiment of the present disclosure may include: transmitting first user input data input through a first application corresponding to a first vendor, stored in a memory of the electronic device, and second user input data input through a second application corresponding to a second vendor, stored in the memory, to a shared application; performing learning by a first artificial intelligence model of the shared application based on the first user input data and the second user input data; estimating, based on the learning performed by the artificial intelligence model, results of third user input data input through the first application and / or the second application, transmitted to the shared application, through the first artificial intelligence model; and determining priorities for the estimated results, and transmitting information about the estimated results and the determined priorities to the first application or the second application.

[0011] The above and other aspects, features and advantages of specific embodiments of the present disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0012] FIG. 1 is a block diagram of an exemplary electronic device within a network environment according to various embodiments.

[0013] FIG. 2 is a flowchart illustrating an exemplary function or operation of an electronic device (e.g., a first artificial intelligence model) according to various embodiments performing learning using first user data input through multiple applications and providing result data for second user data output based on the results of learning to at least one application.

[0014] FIG. 3A is a block diagram illustrating an exemplary configuration of an electronic device including, separately from a shared artificial intelligence model (e.g., a first artificial intelligence model), artificial intelligence models (e.g., a second artificial intelligence model and a third artificial intelligence model) corresponding to each of a plurality of applications (e.g., a first application and a second application), according to various embodiments.

[0015] FIG. 3b is a block diagram illustrating an exemplary configuration of an electronic device that includes only a shared artificial intelligence model (e.g., a first artificial intelligence model) and does not include artificial intelligence models (e.g., a second artificial intelligence model and a third artificial intelligence model) corresponding to each of a plurality of applications (e.g., a first application and a second application).

[0016] FIG. 4A is a diagram illustrating an exemplary function or operation in which a first application and a second application are controlled to provide first input data, first result data, and first feedback information associated with the first application, and second input data, second result data, and second feedback information associated with the second application, for learning a first artificial intelligence model in a shared application according to various embodiments.

[0017] FIG. 4B is a diagram illustrating an exemplary function or operation in which a first application and a second application are controlled so that first input data and first feedback information associated with the first application, and second input data and second feedback information associated with the second application, are provided to a shared application according to various embodiments for learning a first artificial intelligence model.

[0018] FIG. 5A is a diagram illustrating an exemplary function or operation in which a first application is controlled to provide first input data and first result data associated with the first application to obtain result data estimated from an artificial intelligence model of the shared application according to various embodiments.

[0019] FIG. 5b is a diagram illustrating an exemplary function or operation in which a first application is controlled to provide first input data associated with the first application to obtain estimated result data from an artificial intelligence model of the shared application, according to various embodiments.

[0020] FIGS. 6A and 6B are exemplary drawings for explaining a function or operation in which a shared application is controlled so that result data estimated by an artificial intelligence model (e.g., a first artificial intelligence model) of a shared application according to various embodiments is provided to a first application.

[0021] FIG. 7 is a diagram illustrating an exemplary function or operation in which result data estimated by an artificial intelligence model of a shared application (e.g., a first artificial intelligence model) is provided to at least one application (e.g., a first application) according to a data policy according to various embodiments.

[0022] FIGS. 8A and 8B are diagrams illustrating exemplary functions or operations of an electronic device according to various embodiments, wherein the electronic device provides a user with result data different from the result data before learning based on a result learned based on input data input through a plurality of applications.

[0023] FIG. 1 is a block diagram of an exemplary electronic device (101) within a network environment (100), according to various embodiments.

[0024] 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). In 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)).

[0025] 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 operations. According to one embodiment, as at least a part of the data processing or operations, 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 an auxiliary 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 with the main processor (121). For example, when the electronic device (101) includes the main processor (121) and the auxiliary processor (123), the auxiliary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a given function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as a part thereof. Accordingly, a “processor” or a “model” in the present disclosure may include a processing circuit and / or may include a plurality of processors. For example, the terms "processor" or "model" as used in this disclosure, including the claims, may include various processing circuits including at least one processor, one or more of which may be individually and / or collectively configured to perform the various functions described herein.When "processor," "at least one processor," "model," "at least one model," and "one or more processors" are described as being configured to perform multiple functions as used herein, these terms encompass, but are not limited to, situations in which one processor and / or model performs some of the recited functions and other processor(s) and / or model(s) perform other parts of the recited functions, and situations in which a single processor and / or model can perform all of the recited functions. Furthermore, the at least one processor may comprise a combination of processors that perform the various recited / disclosed functions, which may be implemented in a distributed manner, for example. The at least one processor may execute program instructions to achieve or perform the various functions. Similarly, the at least one model may comprise a combination of circuits and / or processors that perform the various recited or disclosed functions, which may be implemented in a distributed manner, for example. The at least one processor and / or model may execute program instructions to achieve or perform the various functions.

[0026] 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.

[0027] 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).

[0028] The program (140) may be stored as software in the memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0029] 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).

[0030] 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.

[0031] 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. According to 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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).

[0036] The 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. According to one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0037] 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.

[0038] 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 as, for example, at least a part of a power management integrated circuit (PMIC).

[0039] 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.

[0040] 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).

[0041] 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.

[0042] 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, for example, by the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device via the at least one selected 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).

[0043] According to various embodiments, the antenna module (197) may form a mmWave antenna module. In 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 side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0044] 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)).

[0045] 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.

[0046] FIG. 2 is a flow chart illustrating an exemplary function or operation of an electronic device (101) (e.g., a first artificial intelligence model (332) (see FIGS. 3A and 3B)) according to various embodiments performing learning using first user data input through multiple applications and providing result data for second user data output based on the results of learning to at least one application.

[0047] Referring to FIG. 2, an electronic device (101) according to an embodiment of the present disclosure may transmit, in operation 210, first user input data (e.g., first input data (410) of FIG. 4A) input through a first application (e.g., first application (310) of FIGS. 3A and 3B) corresponding to a first vendor (e.g., Samsung®) and second user input data (e.g., second input data (440) of FIG. 4A) input through a second application (e.g., second application (320) of FIGS. 3A and 3B) corresponding to a second vendor to a shared application (e.g., shared application (330) of FIGS. 3A and 3B). An electronic device (101) according to one embodiment of the present disclosure can perform learning on a first artificial intelligence model (e.g., the first artificial intelligence model (332) of FIGS. 3A and 3B) based on first user input data (e.g., the first input data (410) of FIG. 4A) and second user input data (e.g., the second input data (440) of FIG. 4A) in operation 220.

[0048] FIG. 3A is a block diagram illustrating an exemplary configuration of an electronic device (101) including, according to various embodiments, a shared artificial intelligence model (e.g., a first artificial intelligence model (332)) and, separately, artificial intelligence models (e.g., a second artificial intelligence model (312) and a third artificial intelligence model (322)) corresponding to each of a plurality of applications (e.g., a first application (310) and a second application (320)).

[0049] Referring to FIG. 3A, an electronic device (101) according to an embodiment of the present disclosure may include a shared application (330), a first artificial intelligence model (e.g., including circuits and / or executable program instructions) (332) stored in the electronic device (101) in association with the shared application, and a data policy (334). The shared application (330), the first artificial intelligence model (332) stored in the electronic device (101) in association with the shared application, and the data policy (334) according to an embodiment of the present disclosure may be stored at least temporarily in a memory (130). The first artificial intelligence model (332) according to an embodiment of the present disclosure may include an artificial intelligence model (e.g., a reward model) configured to estimate and / or determine priorities for a plurality of result values ​​based on a user's preference. The shared application (330) according to an embodiment of the present disclosure may include an application configured to manage the first artificial intelligence model (332). A first application (310) according to an embodiment of the present disclosure may include an application (e.g., Samsung® Bixby®) provided by a vendor of the electronic device (101). A first application (310) according to an embodiment of the present disclosure may include a language model. A second artificial intelligence model (e.g., including circuits and / or executable computer instructions) (312) according to an embodiment of the present disclosure may be an artificial intelligence model stored in the electronic device (101) in association with the first application (310). A second artificial intelligence model (312) according to an embodiment of the present disclosure may include an artificial intelligence model configured to estimate and / or determine result data of input data input through the first application (310). A second application (310) according to an embodiment of the present disclosure may include an application (e.g., a third-party application) provided by a vendor other than the vendor of the electronic device (101).A first application (310) according to one embodiment of the present disclosure may include a language model. A third artificial intelligence model (e.g., including circuits and / or executable computer instructions) (322) according to one embodiment of the present disclosure may be an artificial intelligence model stored in an electronic device (101) in association with a second application (320). A third artificial intelligence model (322) according to one embodiment of the present disclosure may include an artificial intelligence model configured to estimate and / or determine result data of input data input through the second application (320). A data policy (334) according to one embodiment of the present disclosure may include a policy for determining eligibility for whether at least one application (e.g., the first application (310)) can be provided with result data estimated by the first artificial intelligence model (332). A data policy (334) according to one embodiment of the present disclosure may include a policy established to determine that only applications that have provided data to a shared application (330) a specified number of times or more are eligible to receive the result data estimated by the first artificial intelligence model (332). According to one embodiment of the present disclosure, data stored for the first application (310) may not be directly transmitted to the second application (320).

[0050] FIG. 3B is a block diagram illustrating an exemplary configuration of an electronic device (101) that includes only a shared artificial intelligence model (e.g., a first artificial intelligence model (332)) and does not include artificial intelligence models (e.g., a second artificial intelligence model (312) and a third artificial intelligence model (322)) corresponding to each of a plurality of applications (e.g., a first application (310) and a second application (320)). Referring to FIG. 3B , an electronic device (101) according to an embodiment of the present disclosure may include a shared application (330), a first artificial intelligence model (332) associated with the shared application and stored in the electronic device (101), and a data policy (334). The shared application (330) according to an embodiment of the present disclosure, the first artificial intelligence model (332) associated with the shared application and stored in the electronic device (101), and the data policy (334) may be stored at least temporarily in a memory (130). A first artificial intelligence model (332) according to one embodiment of the present disclosure may include an artificial intelligence model configured to estimate and / or determine priorities for a plurality of result values ​​based on a user's preference. A shared application (330) according to one embodiment of the present disclosure may include an application configured to manage the first artificial intelligence model (332). A first application (310) according to one embodiment of the present disclosure may include an application provided by a vendor of the electronic device (101) (e.g., Samsung® Bixby®). A second application (310) according to one embodiment of the present disclosure may include an application provided by a vendor other than the vendor of the electronic device (101) (e.g., a third-party application). A data policy (334) according to one embodiment of the present disclosure may include a policy for determining eligibility for whether at least one application (e.g., the first application (310)) can receive result data estimated by the first artificial intelligence model (332).

[0051] According to one embodiment of the present disclosure, the first application (310), the second application (320), and the shared application (330) may operate under a confidential computing environment. The confidential computing environment according to one embodiment of the present disclosure may include a computing environment in which the corresponding code may be public and the corresponding code can be attested by the electronic device (101) and / or a third-party device. The confidential computing environment according to one embodiment of the present disclosure may include a computing environment in which the security of the application can be guaranteed even if an operating system (OS) with higher authority than the application operating in the application layer is hacked. Under the confidential computing environment according to one embodiment of the present disclosure, even the vendor of the electronic device (101) (e.g., Samsung®) cannot check the data stored in the application (e.g., the first application (310) and / or the second application (320)). The dotted lines illustrated in FIGS. 3A and 3B indicate that the first application (310), the second application (320), and the shared application (330) operate under a confidential computing environment. According to one embodiment of the present disclosure, the first application (310), the second application (320), and the shared application (330) may communicate using a secure channel, and the secure channel may include a communication channel protected under the confidential computing environment.

[0052] FIG. 4A is a diagram illustrating an exemplary function or operation in which a first application (310) and a second application (320) are controlled so that first input data (410), first result data (420), and first feedback information (430) associated with a first application (310), and second input data (440), second result data (450), and second feedback information (460) associated with a second application (320) are provided to a shared application (330) according to various embodiments for training a first artificial intelligence model (332).

[0053] Referring to FIG. 4A, an electronic device (101) (e.g., a first application (310)) according to an embodiment of the present disclosure may call a designated function (e.g., feed_data(input, output)) configured to transmit first input data (410), first result data (420), and first feedback information (430) to a shared application (330). An electronic device (101) (e.g., a first application (310)) according to an embodiment of the present disclosure may transmit first input data (410), first result data (420), and first feedback information (430) to a shared application (330) through the designated function. Similarly, an electronic device (101) (e.g., a second application (320)) according to an embodiment of the present disclosure may call a designated function (e.g., feed_data(input, output)) configured to transmit second input data (440), second result data (450), and second feedback information (460) to a sharing application (330). An electronic device (101) (e.g., a second application (320)) according to an embodiment of the present disclosure may transmit second input data (440), second result data (450), and second feedback information (460) to a sharing application (330) through a designated function. The first input data (410) according to an embodiment of the present disclosure may include, for example, a user utterance such as “Show me the photos I recently saved to the gallery and a description of the photos.” The first result data (420) according to one embodiment of the present disclosure may include, for example, at least one result (e.g., at least one image and descriptions according to various description methods for the image) for a user utterance (e.g., “Show me the photos I recently saved to the gallery and descriptions for the photos”) and / or priority information for the results.The first feedback information (430) according to one embodiment of the present disclosure may include user feedback (e.g., satisfaction) regarding one result finally provided to the user based on a priority. The first artificial intelligence model (332) according to one embodiment of the present disclosure may perform learning (e.g., training) using the first input data (410), the first result data (420), and the first feedback information (430). The second input data (410) according to one embodiment of the present disclosure may include, for example, a user utterance such as “Show me recently uploaded photos and descriptions of the photos.” The second result data (450) according to one embodiment of the present disclosure may include, for example, at least one result (e.g., at least one image and descriptions according to various description methods for the image) and / or priority information regarding the result in response to the user utterance (e.g., “Show me recently uploaded photos and descriptions of the photos”). The second feedback information (460) according to one embodiment of the present disclosure may include the user's feedback (e.g., satisfaction) regarding one result finally provided to the user based on the priority. The first artificial intelligence model (332) according to one embodiment of the present disclosure may perform learning (e.g., training) using the second input data (440), the second result data (450), and the second feedback information (460). Learning of the first artificial intelligence model (332) according to one embodiment of the present disclosure may be performed when the electronic device (101) (e.g., the first application (310)) has the authority to call the first artificial intelligence model (332). The electronic device (101) (e.g., the shared application (330)) according to one embodiment of the present disclosure may determine whether the first application (310) and / or the second application (320) has the authority to call the first artificial intelligence model (332) based on the data policy (334).An electronic device (101) (e.g., a shared application (330)) according to an embodiment of the present disclosure may allow the first application (310) and / or the second application (320) to call the first artificial intelligence model (332) if it is determined that the first application (310) and / or the second application (320) has the authority to call the first artificial intelligence model (332). An electronic device (101) (e.g., a first application (310)) according to an embodiment of the present disclosure may call a designated function (e.g., run(input, output)) set to call the first artificial intelligence model (332) if the calling authority is granted by the shared application (330). An electronic device (101) (e.g., a first artificial intelligence model (332)) according to an embodiment of the present disclosure may perform learning based on data transmitted from each application when the first artificial intelligence model (332) is called through a designated function (e.g., run(input, output)). A data policy (334) according to an embodiment of the present disclosure is described in more detail below with reference to FIG. 7. An electronic device (101) (e.g., a shared application (330)) according to an embodiment of the present disclosure may, during the learning process, provide a result according to the priority for the first result data and a result according to the priority for the second result data to the first application (310) and the second application (320), respectively.

[0054] FIG. 4b is a diagram illustrating an exemplary function or operation in which a first application (310) and a second application (320) are controlled so that first input data (410) and first feedback information (430) associated with a first application (310), and second input data (440) and second feedback information (460) associated with a second application (320) are provided to a shared application (330) for learning a first artificial intelligence model (332) according to various embodiments.

[0055] Referring to FIG. 4B, an electronic device (101) (e.g., a first application (310)) according to an embodiment of the present disclosure may call a designated function (e.g., feed_data(input, output)) set to transmit first input data (410) and first feedback information (430) to a shared application (330). If the second artificial intelligence model (312) and the third artificial intelligence model (322) are not included according to an embodiment of the present disclosure, data corresponding to an output value in the designated function may not be included in the designated function (e.g., feed_data(input, output)). Since the data corresponding to the output value according to an embodiment of the present disclosure may be estimated and / or determined by the first artificial intelligence model (332), data corresponding to the output value in the designated function may not be included in the designated function (e.g., feed_data(input, output)). An electronic device (101) (e.g., a first application (310)) according to an embodiment of the present disclosure may transmit first input data (410) and first feedback information (440) to a shared application (330) through a designated function. Similarly, an electronic device (101) (e.g., a second application (320)) according to an embodiment of the present disclosure may call a designated function (e.g., feed_data(input, output)) set to transmit second input data (440) and second feedback information (460) to the shared application (330). An electronic device (101) (e.g., a second application (320)) according to an embodiment of the present disclosure may transmit second input data (440) and second feedback information (460) to the shared application (330) through a designated function. A first artificial intelligence model (332) according to one embodiment of the present disclosure can perform learning (e.g., training) using first input data (410), first feedback information (430), second input data (440), and second feedback information (460).

[0056] Returning to FIG. 2 again, the electronic device (101) according to one embodiment of the present disclosure may, at operation 230, estimate the results of third user input data (e.g., input data (510) (see, e.g., FIGS. 5A and 5B )) input through the first application (310) or the second application (320) and transmitted to the shared application (330) after the artificial intelligence model (e.g., the first artificial intelligence model (332)) performs learning. The electronic device (101) according to one embodiment of the present disclosure may determine priorities for the estimated results and transmit information about the estimated results and the determined priorities to the first application (310) or the second application (320) at operation 240 of FIG. 2.

[0057] FIG. 5A is a diagram illustrating an exemplary function or operation by which a first application (310) is controlled so that first input data and first result data associated with the first application (310) are provided to obtain result data estimated from an artificial intelligence model (e.g., a first artificial intelligence model (332)) of a shared application (330) according to various embodiments. FIG. 6A and FIG. 6B are diagrams illustrating an exemplary function or operation by which a shared application (330) is controlled so that result data estimated by an artificial intelligence model (e.g., a first artificial intelligence model (332)) of the shared application (330) is provided to the first application (310) according to various embodiments.

[0058] Referring to FIG. 5A, a first application (310) according to an embodiment of the present disclosure may transmit input data (510) input by a user and result data (520) estimated and / or determined by a second artificial intelligence model (312) to a shared application (330). The first application (310) according to an embodiment of the present disclosure may transmit data to the shared application (330) under a guarantee that the shared application (330) does not leak data provided from the first application (310). Such guarantee may be performed through an application attestation function or operation of confidential computing according to an embodiment of the present disclosure. Application attestation according to an embodiment of the present disclosure may include a function or operation of verifying the integrity of the application through integrity testing and measurement. The input data (510) according to an embodiment of the present disclosure may include, for example, a user utterance such as "Show me photos and descriptions recently uploaded from App A (e.g., the second application (320))." The result data (520) according to one embodiment of the present disclosure may include at least one image and descriptions according to various description methods for the image. In this case, although not illustrated in FIG. 5A, if user feedback information for the result data (520) is stored in the electronic device (101), the user feedback information for the result data (520) may also be transmitted to the first artificial intelligence model (332). The first artificial intelligence model (332) according to one embodiment of the present disclosure may perform learning using the transmitted feedback information. The first artificial intelligence model (332) according to one embodiment of the present disclosure may estimate and / or determine a priority for at least one result included in the result data (520) based on the result of learning.A sharing application (330) according to one embodiment of the present disclosure can transmit result data (610) according to preference (e.g., see FIGS. 6A and 6B) to a first application (310).

[0059] FIG. 5b is a diagram illustrating an exemplary function or operation in which a first application (310) is controlled to provide first input data associated with the first application (310) to obtain estimated result data from an artificial intelligence model (e.g., a first artificial intelligence model (332)) of the shared application (330) according to various embodiments.

[0060] Referring to FIG. 5B, a first application (310) according to an embodiment of the present disclosure may transmit input data (510) input by a user to a sharing application (330). The input data (510) according to an embodiment of the present disclosure may include, for example, a user utterance such as, “Show me photos and descriptions recently uploaded from app A (e.g., the second application (320)).” In this case, although not shown in FIG. 5B, if user feedback information on the input data (510) is stored in the electronic device (101), the user feedback information on the result data (520) may also be transmitted to the first artificial intelligence model (332). The first artificial intelligence model (332) according to an embodiment of the present disclosure may perform learning using the transmitted feedback information. The first artificial intelligence model (332) according to an embodiment of the present disclosure may estimate and / or determine at least one result value based on the result of learning. According to one embodiment of the present disclosure, a first artificial intelligence model (332) can estimate and / or determine priorities for at least one estimated and / or determined result value. According to one embodiment of the present disclosure, a shared application (330) can transmit result data (610) (e.g., see FIGS. 6A and 6B ) according to preferences based on the estimated and / or determined priorities to the first application (310) in operation 240 of FIG. 2 .

[0061] Returning to FIG. 2 again, the electronic device (101) according to one embodiment of the present disclosure may, at operation 250, provide the user with at least one result for the third user input data based on the transmitted result. The electronic device (101) according to one embodiment of the present disclosure may provide the user with the result for the third user input data through the display module (160) for the result with the highest priority.

[0062] FIG. 7 is a flow chart illustrating an exemplary function or operation in which result data estimated by an artificial intelligence model (e.g., a first artificial intelligence model) of a shared application (330) is provided to at least one application (e.g., a first application) according to a data policy (334) according to various embodiments.

[0063] Referring to FIG. 7, an electronic device (101) according to an embodiment of the present disclosure may control a first application (310) or a second application (320) to transmit input data from the first application (310) or the second application (320) to a shared application (330) in operation 710. An electronic device (101) according to an embodiment of the present disclosure may determine whether the first application (310) or the second application (320) satisfies a data policy (334) in operation 720. Operation 720 according to an embodiment of the present disclosure may be performed by, for example, the shared application (330). The data policy (334) according to an embodiment of the present disclosure may include, for example, a policy that provides result data when data is transmitted to the shared application (330) a specified number of times or more. According to one embodiment of the present disclosure, if data is not transmitted to the shared application (330) more than a specified number of times, the first application (310) or the second application (320) may not receive result data from the shared application (330) (operation 720 - No). According to one embodiment of the present disclosure, in operation 730, the electronic device (101) may transmit result data to the first application (310) or the second application (320) if the first application (310) or the second application (320) is an application that satisfies the data policy (334) (operation 720 - Yes). Verification of the data policy (334) according to one embodiment of the present disclosure may be performed, for example, when a specified function (e.g., feed_data()) is called, but is not limited thereto.

[0064] FIGS. 8A and 8B are diagrams illustrating exemplary functions or operations of an electronic device (101) according to various embodiments, which provides a user with result data different from the result data before learning based on a result learned based on input data input through a plurality of applications (e.g., a first application (310) and a second application (320)).

[0065] Referring to FIGS. 8A and 8B , an electronic device (101) according to an embodiment of the present disclosure may provide result data (e.g., a first description (810) and a second result image (820)) using a first application (310) before learning using data associated with a second application (320), and may provide result data (e.g., a first description (810) and a third result image (830)) using the first application (310) after learning using data associated with a second application (320), thereby transmitting result data that is more in line with the user's intention than in the prior art. Here, the second result image (820) and the third result image (830) may include images associated with the second application (320) or stored for the second application (320).

[0066] An electronic device (e.g., electronic device (101) of FIG. 1) according to an exemplary embodiment of the present disclosure may include at least one processor (e.g., processor (120) of FIG. 1), including a memory (e.g., memory (130) of FIG. 1), and a processing circuit. The at least one processor may be configured to, individually and / or collectively, cause the electronic device to: transmit first user input data input through a first application corresponding to a first vendor, stored in the memory, and second user input data input through a second application corresponding to a second vendor, stored in the memory, to a shared application, and a first artificial intelligence model of the shared application performs learning based on the first user input data and the second user input data, and based on the learning performed by the artificial intelligence model, estimates results of third user input data input through the first application or the second application, transmitted to the shared application, through the first artificial intelligence model, and determines priorities for the estimated results, and transmits information about the estimated results and the determined priorities to the first application or the second application.

[0067] According to an exemplary embodiment of the present disclosure, at least one processor may be configured to control the first application and the second application to, individually and / or collectively, cause the electronic device to: provide, to the shared application, first result data estimated by a second artificial intelligence model of the first application for the first user input data and first feedback information for the first result data, and second result data estimated by a third artificial intelligence model of the second application for the second user input data and second feedback information for the second result data.

[0068] According to an exemplary embodiment of the present disclosure, at least one processor may be configured to, individually and / or collectively, cause the electronic device to: determine whether the first application and the second application are applications that satisfy a specified privacy policy, wherein the specified privacy policy may include a policy configured to transmit information about the estimated results and the determined priority for an application that has transmitted specified data to the shared application a specified number of times or more.

[0069] According to an exemplary embodiment of the present disclosure, the first application, the second application, and the shared application can be executed under a confidential computing environment.

[0070] According to an exemplary embodiment of the present disclosure, the first vendor and the second vendor include different vendors, and data stored for the first application may not be directly provided to the second application.

[0071] According to an exemplary embodiment of the present disclosure, at least one processor may be configured to, individually and / or collectively, cause the electronic device to: control the first application and the second application such that, for the learning, first feedback information for the first user input data and first feedback information for the second user input data are provided from the first application and the second application to the shared application, respectively.

[0072] According to an exemplary embodiment of the present disclosure, at least one processor may be configured to, individually and / or collectively, cause the electronic device to: control the first application or the second application to transmit at least one of the first user data or the second user data to the shared application based on a result of application attestation for the shared application.

[0073] Electronic devices according to various embodiments disclosed in the present disclosure may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, home appliances, and the like. Electronic devices according to embodiments of the present disclosure are not limited to the aforementioned devices.

[0074] The various embodiments of the present disclosure and the terminology used therein are not intended to limit the technical features described in the present disclosure to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the embodiments. 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 items, unless the context clearly indicates otherwise. In the present disclosure, 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 the 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.

[0075] The term "module" used in various embodiments of the present disclosure may include a unit implemented by hardware, software, or firmware, or any combination thereof, 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).

[0076] Various embodiments of the present disclosure may be implemented as software (e.g., a program (140)) including one or more instructions stored in a storage medium (e.g., an internal memory (136) or an external memory (138)) readable by a machine (e.g., an electronic device (101)). For example, a processor of the machine (e.g., an electronic device (101)) 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, a "non-transitory" storage medium is a tangible device, may 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.

[0077] According to one embodiment, the method according to various embodiments disclosed in the present disclosure may be provided as included in 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) through 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 a relay server.

[0078] According to one embodiment of the present disclosure, 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 separately arranged in other components. According to one embodiment of the present disclosure, 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 this 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 of the present disclosure, 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.

[0079] While the present disclosure has been illustrated and described with reference to various exemplary embodiments, it should be understood that the various exemplary embodiments are intended to be illustrative and not restrictive. Those skilled in the art will readily appreciate that various modifications, alternatives, and / or variations of the various exemplary embodiments may be made without departing from the true spirit and full scope of the present disclosure, including the appended claims and their equivalents. Furthermore, it should be understood that any embodiment(s) described in the present disclosure may be used in conjunction with any other embodiment(s) described in the present disclosure.

Claims

1. In electronic devices, a memory for storing one or more computer programs, and At least one processor, including a processor circuit, communicatively coupled to the memory, The one or more computer programs comprise instructions executable by a computer, and the at least one processor is individually and / or collectively configured to execute the instructions and cause the electronic device to: Transmitting first user input data inputted through a first application corresponding to a first vendor stored in the above memory and second user input data inputted through a second application corresponding to a second vendor stored in the above memory to a shared application, The first artificial intelligence model of the shared application performs learning based on the first user input data and the second user input data, After the above artificial intelligence model performs learning, the results of the third user input data input through the first application or the second application transmitted to the shared application are estimated through the first artificial intelligence model, and An electronic device characterized in that it is set to determine priorities for the estimated results and transmit information about the estimated results and the determined priorities to the first application or the second application.

2. In paragraph 1, The at least one processor, individually and / or collectively, causes the electronic device to: For the above learning, the first application is controlled to provide the first result data estimated by the second artificial intelligence model of the first application for the first user input data and the first feedback information for the first result data to the shared application, and An electronic device characterized in that, for the above learning, the second application is set to be controlled to provide the second result data estimated by the third artificial intelligence model of the second application for the second user input data and second feedback information for the second result data to the shared application.

3. In paragraph 1, The at least one processor, individually and / or collectively, causes the electronic device to: It is set to check whether the first application and the second application are applications that satisfy the specified privacy policy, An electronic device characterized in that the above-mentioned designated privacy policy includes a policy set to transmit information about the estimated results and the determined priority to an application that has transmitted the designated data more than a designated number of times to the shared application.

4. In paragraph 1, An electronic device, characterized in that the first application, the second application, and the shared application are executed under a confidential computing environment.

5. In paragraph 1, The first vendor and the second vendor include different vendors, An electronic device, characterized in that data stored for the first application is set so as not to be directly provided to the second application.

6. In paragraph 1, The at least one processor, individually and / or collectively, causes the electronic device to: For the above learning, the first application is provided with first feedback information on the first user input data to the sharing application, and An electronic device characterized in that the second application is set to control the second application to provide first feedback information about the second user input data to the sharing application.

7. In any one of paragraphs 1 to 6, The at least one processor, individually and / or collectively, causes the electronic device to: An electronic device characterized in that, based on the result of application attestation for the shared application, the first application or the second application is set to be controlled to transmit at least one of the first user data or the second user data to the shared application.

8. In a method of controlling an electronic device, An operation of transmitting first user input data input through a first application corresponding to a first vendor, stored in the memory of the electronic device, and second user input data input through a second application corresponding to a second vendor, stored in the memory, to a shared application; An operation in which the first artificial intelligence model of the shared application performs learning based on the first user input data and the second user input data; After the above artificial intelligence model performs learning, an operation of estimating the results of third user input data input through the first application or the second application transmitted to the shared application through the first artificial intelligence model, and A method for controlling an electronic device, characterized in that it includes an operation of determining a priority for the estimated results and transmitting information about the estimated results and the determined priority to the first application or the second application.

9. In paragraph 8, A method of controlling the above electronic device, For the above learning, an operation of controlling the first application to provide the first result data estimated by the second artificial intelligence model of the first application for the first user input data and first feedback information for the first result data to the shared application, and A method for controlling an electronic device, characterized in that it further includes an operation of controlling the second application to provide the second result data estimated by the third artificial intelligence model of the second application for the second user input data and second feedback information for the second result data to the shared application for the learning.

10. In paragraph 8, The method of controlling the electronic device further includes an operation of checking whether the first application and the second application are applications that satisfy a specified privacy policy, A method for controlling an electronic device, characterized in that the above-mentioned designated privacy policy includes a policy set to transmit information about the estimated results and the determined priority to an application that has transmitted the designated data more than a designated number of times to the shared application.

11. In paragraph 8, A method for controlling an electronic device, characterized in that the first application, the second application, and the shared application are executed under a confidential computing environment.

12. In paragraph 8, The first vendor and the second vendor include different vendors, A method for controlling an electronic device, characterized in that data stored for the first application is not directly provided to the second application.

13. In paragraph 8, A method for controlling the electronic device, characterized in that the method further includes an operation of providing first feedback information for the first user input data and first feedback information for the second user input data to the shared application from the first application and the second application, respectively, for the learning.

14. In paragraph 8, A method for controlling an electronic device, characterized in that the method further comprises an operation of transmitting at least one of the first user data or the second user data to the shared application based on a result of application attestation for the shared application.

15. A non-transitory computer-readable storage medium storing one or more computer programs, wherein the one or more computer programs include computer-executable instructions, which, when individually and / or collectively executed by at least one processor of an electronic device including a processing circuit, cause the electronic device to perform operations, the operations comprising: An operation of transmitting first user input data inputted through a first application corresponding to a first vendor, stored in the memory of the electronic device, and second user input data inputted through a second application corresponding to a second vendor, stored in the memory, to a shared application; An operation in which the first artificial intelligence model of the shared application performs learning based on the first user input data and the second user input data; After the above artificial intelligence model performs learning, an operation of estimating the results of third user input data input through the first application or the second application transmitted to the shared application through the first artificial intelligence model, and A non-transitory computer-readable storage medium characterized by including an operation of determining priorities for the estimated results and transmitting information about the estimated results and the determined priorities to the first application or the second application.

Images