DETAILED ACTION
1. Claims 1-20 are pending.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
3. Claims 2 and 14 are objected to because of the following informalities:
Claim 2 line 6 recites “the current window layout”. This limitation lacks antecedent basis and appears as though it should recite “a current window layout”.
Claim 14 line 6 recites “the current window layout”. This limitation lacks antecedent basis and appears as though it should recite “a current window layout”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
4. Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Jeon et al. (WO 2025/154986 A1).
NOTE: In the below rejections, the cited portions of Jeon et al. (WO 2025/154986 A1) are with respect to the provided machine translation appended to the end of the provided copy of Jeon et al. (WO 2025/154986 A1).
NOTE: In the below rejections, all relied on portions of Jeon et al. (WO 2025/154986 A1) are described in the priority document 10-2024-0032125, dated 3/6/2024, which is published as Jeon et al. (KR 20250112111 A).
In regard to claim 1, Jeon discloses a system, comprising: a processing system; and memory coupled to the processing system, the memory comprising computer executable instructions that, when executed by the processing system, causes the system to perform operations comprising (Fig. 1):
in response to a trigger event (Pg 17: “According to one embodiment, the processor (710) may detect an event that triggers the configuration of a multi-window layout while the first application is running. For example, the event that triggers the configuration of the multi-window layout is an event that triggers the second application, and may include events such as, but not limited to, occurrence of an input for selecting an application to run”), collecting or identifying first context signals, the first context signals including at least one of content, topic, subject, or domain of an open application window among a plurality of application windows that is displayed within at least one display screen of corresponding at least one display device (Pg. 17: “ According to one embodiment, the processor (710) may analyze at least some of the information (e.g. application information, device status information, user status/tendency information, or content data)stored in the memory (720) to analyze a user's usage pattern for a plurality of applications to be displayed in a multi-window layout” and Pg. 20: “According to one embodiment, the application information (812) may include information related to various applications installed and/or running on the electronic device. For example, the application information (812) may include at least some of the following: application information such as the type of each application (e.g., media player, messenger, document, calendar, calculator, or game), resolution, execution history, usage frequency, user preference, association with other applications, simultaneous execution frequency, execution screen size, ratio, and resolution; application information related to the multi-window, such as whether multi-window is supported, display position, size, and resolution when running multi-window, and other applications that are frequently run together with multi-window; and/or content information of the application, such as content (e.g., video, audio), messages, or notifications provided by the running application. The application information (812) described in this document is not limited to the examples described above…the execution history of applications, usage frequency, preferences, application search history, Internet search history, frequently executed applications (or relevance of applications) at the same time, or user tendency information such as a preferred form of multi-window layout, and a behavior pattern when a notification (e.g., receiving a message) occurs”);
providing the first context signals as input to a machine learning ("ML") model that has been trained to provide window layout suggestions based on a plurality of combinations of context signals (Pg. 15: “According to one embodiment, the electronic device (610, 620) may transmit a task request to the AI model (650) to cause the AI model (650) to perform an intended task based on a user input. For example, when a user of the electronic device (610, 620) inputs a task to be requested to the AI model (650) through a voice input via a microphone or a text input via a keyboard/keypad, the electronic device (610, 620) may generate a prompt including the task request and transmit it to the AI model(650)” and Pg. 17: “According to one embodiment, the processor (710) may generate a prompt including a task request for requesting an AI model (e.g., a generative AI model) to configure a multi-window layout”, Pg. 18: “Examples of prompts generated based on the analysis results of the current operating status information of the electronic device (700) and/or the user's usage pattern are as shown”, Pg. 21: “the application analysis module (822) can analyze execution history, usage frequency, or user preference to determine whether the content provided through each window is important content to the user. The application analysis module (822) can, during the analysis, be based on at least some of the device status information (814), the user status/tendency information (816), and/or the content data (818) in addition to the application information (812)”, and Pg. 22: “According to one embodiment, the electronic device may generate a prompt (830) based on the analysis result of the analysis module (820). For example, the electronic device may generate the prompt (830) including a task request for configuring a multi-window layout, information on the current operating status of the electronic device generated based on the analysis result of the analysis module, and/or information necessary for configuring the multi-window layout, such as usage pattern and preference information”);
receiving, as output from the ML model, windowing suggestions for at least one window layout for a plurality of application windows based on the first context signals (Pg. 15: “the AI model (650) can interpret a prompt received from an electronic device (610, 620), execute a task requested by a user, and generate the result of the executed task as text and/or image information and transmit it to the electronic device (610, 620) as a task response” and Pg. 17: “ Accordingly, the AI model may configure a multi-window layout according to the task request included in the prompt of the electronic device (700)and transmit a task response including information related thereto to the electronic device (700)”);
and performing at least one of: displaying the windowing suggestions for the at least one window layout for the plurality of application windows; or autonomously selecting a first window layout among the at least one window layout, and displaying the plurality of application windows within the at least one display screen of the corresponding at least one display device, based on the first window layout (Pg. 2: “to receive a task response from the AI model, the task response including information related to a multi-window layout to be configured in response to the first event, and to configure a multi-window layout including an execution screen of the first application and an execution screen of the second application based on the received task response, and to display the multi-window layout on at least a portion of an area of the display” and Pg. 19: “According to one embodiment, the processor (710) may configure a multi-window layout based on a task response received from an AI model and display the same on the display (730). For example, the processor (710) may determine the location, size, shape of a window including execution screens of running applications”).
In regard to claim 2, Jeon discloses wherein displaying the plurality of application windows within the at least one display screen of the corresponding at least one display device, based on the first window layout, comprises at least one of: changing display of one or more first application windows that are currently being displayed within the at least one display screen of the corresponding at least one display device from the current window layout to the first window layout; replacing a current window layout of one or more second application windows as currently displayed within the at least one display screen of the corresponding at least one display device with the first window layout of the one or more second application windows; mapping each application window being displayed in the current window layout to the corresponding application window according to the first window layout; or for each application window being displayed in the current window layout, changing at least one of a first size, a first position, or a first level of zoom of the application window to a corresponding at least one of a second size, a second position, or a second level of zoom for the application window according to the first window layout (Pg. 2: “a multi-window layout to be configured in response to the first event, and to configure a multi-window layout including an execution screen of the first application and an execution screen of the second application”, Fig. 13(a)-13(b), and Pg. 28 “Referring to (a) of FIG. 13, the electronic device can execute a lecture application (1310) and provide the contents of the lecture application (1310) in a single window layout.. can execute a note application (1320) as in (b) of Fig. 13, and display the lecture application (1310) and the note application (1320) in a multi-window layout”).
In regard to claim 3, Jeon discloses wherein the operations further comprise at least one of: launching, duplicating, or maximizing one or more application windows among the plurality of application windows, based on the first window layout (Pg. 2: “a multi-window layout to be configured in response to the first event, and to configure a multi-window layout including an execution screen of the first application and an execution screen of the second application”, Fig. 13(a)-13(b), and Pg. 28 “Referring to (a) of FIG. 13, the electronic device can execute a lecture application (1310) and provide the contents of the lecture application (1310) in a single window layout.. can execute a note application (1320) as in (b) of Fig. 13, and display the lecture application (1310) and the note application (1320) in a multi-window layout”); layering, cascading, stacking, or overlaying two or more application windows among the plurality of application windows over one or more other application windows among the plurality of application windows, based on the first window layout; tiling, snapping, or grouping two or more other application windows among the plurality of application windows within the at least one display screen of corresponding at least one display device, based on the first window layout; or closing or minimizing one or more other currently displayed application windows that are not among the plurality of application windows, based on the first window layout.
In regard to claim 4, Jeon discloses identifying two or more application windows among the plurality of application windows having at least one of common or related content, common or related topic, common or related subject, or common or related domain (Pg. 23: “According to one embodiment, the electronic device may determine the correlation between each application based on the similarity of keywords that can be extracted from each application execution screen of the multi-window. …According to one embodiment, the electronic device may determine the relevance between applications based on the positions of application windows arranged in a multi-window layout. For example, if a user directly configures a multi-window layout and frequently arranges a messenger application and a shopping mall application, or a gallery application and a file application, adjacent to each other, the relevance may be determined to be high”);
and grouping the two or more application windows within the at least on display screen, based on the first window layout (Pgs. 25-26: “In one embodiment, if the user is currently driving, the electronic device may determine, based on the analysis results, that the user's driving usage pattern/taste is to listen to music while looking at navigation… generate a task response including information related to a layout in the form of a multi-window layout in which a navigation application is disposed in a left window close to the user, a music application is disposed in a right window, and a size ratio of 8 to 2, and provide the task response to the electronic device”).
In regard to claim 5, Jeon discloses wherein the trigger event includes one of: a user-system interaction-based trigger event including one of: detecting a user logging into a user account; detecting a user unlocking a locked display screen; detecting a user launching, closing, duplicating, maximizing, minimizing, or resizing an application window for display (Pg 17: “According to one embodiment, the processor (710) may detect an event that triggers the configuration of a multi-window layout while the first application is running. For example, the event that triggers the configuration of the multi-window layout is an event that triggers the second application, and may include events such as, but not limited to, occurrence of an input for selecting an application to run”), or that is being displayed, within the at least one display screen of the corresponding at least one display device; detecting a user changing from a first user task to a second user task, each involving one or more application windows for display, or that are being displayed, within the at least one display screen of the corresponding at least one display device; or receiving a user input, the user input including one of: a user input to select from a suggested list of smart window layouts; a user input to select from one of a saved set of smart window layouts or a frequently used set of smart window layouts; a user input to trigger smart window layout functionality; or a user input to organize a cluttered desktop environment; or a system change-based trigger event including one of: detecting the system being booted up; detecting docking or undocking of a laptop computer; detecting a change from a first monitor setup to a second monitor setup, the second monitor setup having at least one of a second number, a second type, or a second size of monitor that is different from the first monitor setup having a corresponding at least one of a first number, a first type, or a first size of monitor; detecting tripping of a time-of-day-based trigger; detecting a cluttered condition of application windows displayed within the at least one display screen of the corresponding at least one display device; or detecting a number of displayed application windows exceeding a threshold number of application windows.
In regard to claim 6, Jeon discloses wherein the ML model is a neural network model including one of a convolutional neural network ("CNN") model, a recurrent neural network ("RNN"), or a deep neural network ("DNN") (Pg. 4: “the artificial intelligence model may include a plurality of 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), deep Q-networks, or a combination of two or more of the above, but is not limited to the examples described above”).
In regard to claim 7, Jeon discloses wherein the ML model is a local ML model (Pg. 19: “the on-device AI model”) that is trained and optimized local to the system, using the collected or identified context signals (Pg. 4: “Such learning may be performed, for example, in the electronic device (101) itself on which the artificial intelligence model is executed” and Pg. 17: “the AI model may be learned based on operation state information (e.g., application information, device state information, user state/tendency information, content data, usage pattern and/or preference information) of the electronic device (700) and the form of the multi-window layout”).
In regard to claim 8, Jeon discloses wherein the context signals further include at least one of window positioning, window sizing, window layering, window layout, window tiling, titles of application windows, types of applications displayed in the application windows, content of applications, content of application windows, or a number of monitors used, correlated with at least one of user, type of user, task, time-of-day, monitor setup, computing system setup, number of task switches, number of window layout changes, or user location (Pg. 20: “According to one embodiment, the application information (812) may include information related to various applications installed and/or running on the electronic device. For example, the application information (812) may include at least some of the following: application information such as the type of each application (e.g., media player, messenger, document, calendar, calculator, or game), resolution, execution history, usage frequency, user preference, association with other applications, simultaneous execution frequency, execution screen size, ratio, and resolution; application information related to the multi-window, such as whether multi-window is supported, display position, size, and resolution when running multi-window, and other applications that are frequently run together with multi-window; and/or content information of the application, such as content (e.g., video, audio), messages, or notifications provided by the running application. The application information (812) described in this document is not limited to the examples described above”).
In regard to claim 9, Jeon discloses wherein the context signals further include at least one of dwell time of layout of application windows, metadata of application windows (Pg. 20: “According to one embodiment, the application information (812) may include information related to various applications installed and/or running on the electronic device. For example, the application information (812) may include at least some of the following: application information such as the type of each application (e.g., media player, messenger, document, calendar, calculator, or game), resolution, execution history, usage frequency, user preference, association with other applications… execution screen size, ratio, and resolution; application information related to the multi-window, such as whether multi-window is supported”), or a list of top x-number of open application windows by z-order of the open application windows.
In regard to claim 10, Jeon discloses wherein collecting or identifying the first context signals further comprises at least one of: inferring the content of the open application window from one or more of a document title of the open application window (Pg. 28: “According to one embodiment, the electronic device may analyze the content provided through the running application and data stored in the electronic device…For example, the electronic device may analyze the title provided through the video application…to determine that the soccer broadcast currently provided through the video application”), a uniform resource identifier ("URI") of a resource that is displayed in the open application window, or a uniform resource locator ("URL") of the resource that is displayed in the open application window; based on a determination that the system has access to full content contained in an application that is displayed in the open application window, extracting the full content from the application; performing optical character recognition ("OCR") of one or more of title, content, or URI that is displayed in the open application window; extracting the at least one of content, topic, subject, or domain from the open application window, converting the extracted at least one of content, topic, subject, or domain into feature sets, wherein providing the first context signals as input to the ML model includes providing the feature sets as input to the ML model; or extracting at least one of elements, attributes, or content from a document object model ("DOM") tree of a document that is displayed in the open application window.
In regard to claim 11, Jeon discloses a computer-implemented method for smart window layout functionalities, the method comprising:
in response to triggering of a launch operation for an application (Pg 17: “According to one embodiment, the processor (710) may detect an event that triggers the configuration of a multi-window layout while the first application is running. For example, the event that triggers the configuration of the multi-window layout is an event that triggers the second application, and may include events such as, but not limited to, occurrence of an input for selecting an application to run”), calling, by a computing system, one or a series of artificial intelligence ("AI") models to identify at least one window layout for a plurality of application windows for display within at least one display screen of corresponding at least one display device (Pg 17: “According to one embodiment, the processor (710) may generate a prompt including a task request for requesting an AI model (e.g., a generative AI model) to configure a multi-window layout”), the plurality of application windows including at least one application window associated with the application being launched (Pg 2: “information related to a multi-window layout to be configured in response to the first event, and to configure a multi-window layout including an execution screen of the first application and an execution screen of the second application based on the received task response”), further based on collected data regarding historical use and layout of application windows displayed within the at least one display screen for the application or for similar types of applications (Pg. 17: “the AI model may be learned based on operation state information (e.g., application information, device state information, user state/tendency information, content data, usage pattern and/or preference information) of the electronic device (700) and the form of the multi-window layout. For example, the AI model may be learned based on current state information set by a user in various electronic devices (700) and/or defined by a developer of the electronic device (700) or an application and information of the currently set multi-window layout” and Pg. 20: “According to one embodiment, the application information (812) may include information related to various applications installed and/or running on the electronic device. For example, the application information (812) may include at least some of the following: application information such as the type of each application (e.g., media player, messenger, document, calendar, calculator, or game), resolution, execution history, usage frequency, user preference, association with other applications, simultaneous execution frequency, execution screen size, ratio, and resolution; application information related to the multi-window, such as whether multi-window is supported, display position, size, and resolution when running multi-window, and other applications that are frequently run together with multi-window; and/or content information of the application, such as content (e.g., video, audio), messages, or notifications provided by the running application. The application information (812) described in this document is not limited to the examples described above”);
and displaying, by the computing system, the plurality of application windows within the at least one display screen of the corresponding at least one display device, based on a first window layout among the at least one window layout (Pg. 2: “to receive a task response from the AI model, the task response including information related to a multi-window layout to be configured in response to the first event, and to configure a multi-window layout including an execution screen of the first application and an execution screen of the second application based on the received task response, and to display the multi-window layout on at least a portion of an area of the display” and Pg. 19: “According to one embodiment, the processor (710) may configure a multi-window layout based on a task response received from an AI model and display the same on the display (730). For example, the processor (710) may determine the location, size, shape of a window including execution screens of running applications’).
In regard to claim 12, Jeon discloses wherein the AI models each includes one of a language model ("LM") or a machine learning ("ML") model, wherein the LM includes one of a small language model ("SLM"), a large language model ("LLM"), or other natural language ("NL") -based LM, wherein the ML model is a neural network model including one of a convolutional neural network ("CNN") model, a recurrent neural network ("RNN"), or a deep neural network ("DNN") (Pg. 4: “the artificial intelligence model may include a plurality of 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), deep Q-networks, or a combination of two or more of the above, but is not limited to the examples described above”).
In regard to claim 13, Jeon discloses wherein displaying the plurality of application windows within the at least one display screen of the corresponding at least one display device, based on the first window layout, comprises: opening, by the computing system, the at least one application window associated with the application being launched, and displaying, by the computing system, the at least one application window within the at least one display screen, either in relation to already open application windows or overlayed over the already open application windows, based on the first window layout (Pg. 2: “a multi-window layout to be configured in response to the first event, and to configure a multi-window layout including an execution screen of the first application and an execution screen of the second application”, Fig. 13(a)-13(b), and Pg. 28 “Referring to (a) of FIG. 13, the electronic device can execute a lecture application (1310) and provide the contents of the lecture application (1310) in a single window layout.. can execute a note application (1320) as in (b) of Fig. 13, and display the lecture application (1310) and the note application (1320) in a multi-window layout”).
In regard to claim 14, Jeon discloses wherein displaying the plurality of application windows within the at least one display screen of the corresponding at least one display device, based on the first window layout, further comprises at least one of: changing display of one or more first application windows that are currently being displayed within the at least one display screen of the corresponding at least one display device from the current window layout to the first window layout (Pg. 2: “a multi-window layout to be configured in response to the first event, and to configure a multi-window layout including an execution screen of the first application and an execution screen of the second application”, Fig. 13(a)-13(b), and Pg. 28 “Referring to (a) of FIG. 13, the electronic device can execute a lecture application (1310) and provide the contents of the lecture application (1310) in a single window layout.. can execute a note application (1320) as in (b) of Fig. 13, and display the lecture application (1310) and the note application (1320) in a multi-window layout”); replacing a current window layout of one or more second application windows as currently displayed within the at least one display screen of the corresponding at least one display device with the first window layout of the one or more second application windows; mapping each application window being displayed in the current window layout to the corresponding application window according to the first window layout; or for each application window being displayed in the current window layout, changing at least one of a first size, a first position, or a first level of zoom of the application window to a corresponding at least one of a second size, a second position, or a second level of zoom for the application window according to the first window layout.
In regard to claim 15, Jeon discloses further comprising at least one of: launching, duplicating, or maximizing one or more application windows among the plurality of application windows, based on the first window layout (Pg. 2: “a multi-window layout to be configured in response to the first event, and to configure a multi-window layout including an execution screen of the first application and an execution screen of the second application”, Fig. 13(a)-13(b), and Pg. 28 “Referring to (a) of FIG. 13, the electronic device can execute a lecture application (1310) and provide the contents of the lecture application (1310) in a single window layout.. can execute a note application (1320) as in (b) of Fig. 13, and display the lecture application (1310) and the note application (1320) in a multi-window layout”); layering, cascading, stacking, or overlaying two or more application windows among the plurality of application windows over one or more other application windows among the plurality of application windows, based on the first window layout; tiling, snapping, or grouping two or more other application windows among the plurality of application windows within the at least one display screen of corresponding at least one display device, based on the first window layout; or closing or minimizing one or more other currently displayed application windows that are not among the plurality of application windows, based on the first window layout.
In regard to claim 16, Jeon discloses further comprising: collecting or identifying, by the computing system, context data regarding the at least one application window, the context data including at least one of dwell time of layout of the at least one application window, window layout changes involving the at least one application window, or use of the at least one application window, wherein the window layout changes include changes in one or more of window positioning, window sizing, level of zoom, position relative to other open application windows, or change in z-order of open application windows (Pg. 25: “According to one embodiment, in operation 1030, the electronic device may analyze usage patterns and preferences based on the analysis results of operations 1022 to 1028. For example, the electronic device may classify what the user's behavior (e.g., rejection, agreement, touch, long click, termination) was intended to do. The electronic device may pattern the user's behavior according to various situations, detect the user's habits, and learn objects (e.g., applications, contents, other people) that the user prefers or does not prefer. According to one embodiment, the electronic device may analyze the history related to the user's multi-window layout configuration, and analyze which application is configured with which layout according to the usage pattern.;
and performing, by the computing system, one of: based on a determination that layout of the at least one application window has not changed within a threshold period, updating either historical use and layout data or at least one AI model to reinforce preference of the first window layout with respect to the at least one application window; or based on a determination that layout of the at least one application window has changed to a second window layout within the threshold period, updating either the historical use and layout data or the at least one AI model to replace the first window layout with respect to the at least one application window to the second window layout with respect to the at least one application window (Pg. 4: “The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning” and Pg. 15: “the AI model (650) may be learned based on current state information (e.g., application information, device state information, user state/tendency information, stored data, usage pattern and/or preference information) of the electronic device (610, 620) and the form of the multi-window layout).
In regard to claim 17, Jeon discloses a system for implementing smart window layout functionalities, the system comprising: a processing system; and memory coupled to the processing system, the memory comprising computer executable instructions that, when executed by the processing system, causes the system to perform operations comprising (Fig. 1):
receiving a user prompt requesting to identify or arrange a window layout of application windows based on a user task to be performed involving the application windows (Pg. 15: “According to one embodiment, the electronic device (610, 620) may transmit a task request to the AI model (650) to cause the AI model (650) to perform an intended task based on a user input. For example, when a user of the electronic device (610, 620) inputs a task to be requested to the AI model (650) through a voice input via a microphone or a text input via a keyboard/keypad, the electronic device (610, 620) may generate a prompt including the task request and transmit it to the AI model(650)” and Pg. 25: “According to one embodiment, the electronic device may generate and transmit the prompt to the AI model when a touch input or voice input for the user's multi-window execution occurs”);
generating, for input into a first language model ("LM"), an LM prompt based on the user prompt (Pg. 15: “According to one embodiment, the electronic device (610, 620) may transmit a task request to the AI model (650) to cause the AI model (650) to perform an intended task based on a user input. For example, when a user of the electronic device (610, 620) inputs a task to be requested to the AI model (650) through a voice input via a microphone or a text input via a keyboard/keypad, the electronic device (610, 620) may generate a prompt including the task request and transmit it to the AI model(650)” and Pg. 17: “According to one embodiment, the processor (710) may generate a prompt including a task request for requesting an AI model (e.g., a generative AI model) to configure a multi-window layout”);
receiving, as output from the first LM, windowing suggestions for at least one window layout for a plurality of application windows for facilitating performance of the user task (Pg. 15: “the AI model (650) can interpret a prompt received from an electronic device (610, 620), execute a task requested by a user, and generate the result of the executed task as text and/or image information and transmit it to the electronic device (610, 620) as a task response” and Pg. 17: “ Accordingly, the AI model may configure a multi-window layout according to the task request included in the prompt of the electronic device (700)and transmit a task response including information related thereto to the electronic device (700)”);
and performing at least one of: displaying the windowing suggestions for the at least one window layout for the plurality of application windows; or autonomously selecting a first window layout among the at least one window layout, and displaying the plurality of application windows within at least one display screen of a corresponding at least one display device, based on the first window layout (Pg. 2: “to receive a task response from the AI model, the task response including information related to a multi-window layout to be configured in response to the first event, and to configure a multi-window layout including an execution screen of the first application and an execution screen of the second application based on the received task response, and to display the multi-window layout on at least a portion of an area of the display” and Pg. 19: “According to one embodiment, the processor (710) may configure a multi-window layout based on a task response received from an AI model and display the same on the display (730). For example, the processor (710) may determine the location, size, shape of a window including execution screens of running applications’).
In regard to claim 18, Jeon discloses wherein displaying the plurality of application windows within the at least one display screen comprises at least one of: changing display of one or more first application windows that are currently being displayed within the at least one display screen of the corresponding at least one display device from the current window layout to the first window layout (Pg. 2: “a multi-window layout to be configured in response to the first event, and to configure a multi-window layout including an execution screen of the first application and an execution screen of the second application”, Fig. 13(a)-13(b), and Pg. 28 “Referring to (a) of FIG. 13, the electronic device can execute a lecture application (1310) and provide the contents of the lecture application (1310) in a single window layout.. can execute a note application (1320) as in (b) of Fig. 13, and display the lecture application (1310) and the note application (1320) in a multi-window layout”); replacing a current window layout of one or more second application windows as currently displayed within the at least one display screen of the corresponding at least one display device with the first window layout of the one or more second application windows; mapping each application window being displayed in the current window layout to the corresponding application window according to the first window layout; or for each application window being displayed in the current window layout, changing at least one of a first size, a first position, or a first level of zoom of the application window to a corresponding at least one of a second size, a second position, or a second level of zoom for the application window according to the first window layout.
In regard to claim 19, Jeon discloses collecting or identifying context signals associated with one or more application windows that had been displayed within the at least one display screen of the corresponding at least one display device prior to receiving the user prompt, the context signals including at least one of window positioning, window sizing, window layering, window layout, window tiling, titles of application windows, types of applications displayed in the application windows, content of applications, content of application windows, or a number of monitors used, correlated with at least one of user, type of user, task, time-of-day, monitor setup, computing system setup, number of task switches, number of window layout changes, or user location (Pg. 17: “ According to one embodiment, the processor (710) may analyze at least some of the information (e.g. application information, device status information, user status/tendency information, or content data)stored in the memory (720) to analyze a user's usage pattern for a plurality of applications to be displayed in a multi-window layout” and Pg. 20: “According to one embodiment, the application information (812) may include information related to various applications installed and/or running on the electronic device. For example, the application information (812) may include at least some of the following: application information such as the type of each application (e.g., media player, messenger, document, calendar, calculator, or game), resolution, execution history, usage frequency, user preference, association with other applications, simultaneous execution frequency, execution screen size, ratio, and resolution; application information related to the multi-window, such as whether multi-window is supported, display position, size, and resolution when running multi-window, and other applications that are frequently run together with multi-window; and/or content information of the application, such as content (e.g., video, audio), messages, or notifications provided by the running application. The application information (812) described in this document is not limited to the examples described above…the execution history of applications, usage frequency, preferences, application search history, Internet search history, frequently executed applications (or relevance of applications) at the same time, or user tendency information such as a preferred form of multi-window layout, and a behavior pattern when a notification (e.g., receiving a message) occurs”);
wherein generating the LM prompt includes adding the context signals (Pg. 18: “Examples of prompts generated based on the analysis results of the current operating status information of the electronic device (700) and/or the user's usage pattern are as shown”, Pg. 21: “the application analysis module (822) can analyze execution history, usage frequency, or user preference to determine whether the content provided through each window is important content to the user. The application analysis module (822) can, during the analysis, be based on at least some of the device status information (814), the user status/tendency information (816), and/or the content data (818) in addition to the application information (812)”, and Pg. 22: “According to one embodiment, the electronic device may generate a prompt (830) based on the analysis result of the analysis module (820). For example, the electronic device may generate the prompt (830) including a task request for configuring a multi-window layout, information on the current operating status of the electronic device generated based on the analysis result of the analysis module, and/or information necessary for configuring the multi-window layout, such as usage pattern and preference information”).
In regard to claim 20, Jeon discloses further comprising the LM, wherein the LM is located in a local architecture of the system, wherein prompts provided as input to the LM include local user data Pg. 19: “the on-device AI model”, Pg. 20: “According to one embodiment, an electronic device (e.g., an electronic device (700) of FIG. 7) may store various data (810) necessary to configure a multi-window layout on a memory (e.g., a memory(720) of FIG. 7”), and Pg. 22: “According to one embodiment, the electronic device may generate a prompt (830) based on the analysis result of the analysis module (820). For example, the electronic device may generate the prompt (830) including a task request for configuring a multi-window layout, information on the current operating status of the electronic device generated based on the analysis result of the analysis module, and/or information necessary for configuring the multi-window layout, such as usage pattern and preference information”).
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ahn et al. (KR 102593826 B1).
Lee et al. (US 2022/0291818 A1), see at least the abstract.
Papamarcos et al. (US 2022/0187958 A1), see at least the abstract and Paragraphs 0037-0041.
Singh et al. (US 2021/0342042 A1), see at least the abstract.
Ko et al. (US 2018/0188910 A1), see at least the abstract.
Vranjes et al. (US 2016/0034159 A1), see at least the abstract.
Massand (US 2014/0380201 A1), see at least the abstract.
Urawaki et al. (US 2014/0047379 A1), see at least the abstract.
Oliver et al. (US 2008/0005693 A1), see at least the abstract.
Porter et al. (US 5838318), see at least the abstract.
Stille et al. (A2DL- An Adaptive Automatic Display Layout System, IEEE, 1996).
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS S ULRICH whose telephone number is (571)270-1397. The examiner can normally be reached M-F 8-4.
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/Nicholas Ulrich/Primary Examiner, Art Unit 2179