Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Response to Amendment
This is in response to applicant’s amendment/response filed on 03/11/2026, which has been entered and made of record. Claims 1 – 3, 7-10, 14-17, 20 have been amended. No claim has been cancelled. No claim has been added. Claims 1-20 are pending in the application.
Response to Arguments
Applicant’s arguments on 03/11/2026 have been fully considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 6, 8-10, 13, 15-17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Newburn et al. (US Pub 2025/0251951 A1) in view of Yang et al. (US Pub 2024/0412029 A1).
As to claim 1, Newburn discloses a system for dynamically configuring and generating user interface components based on interface interaction data (Newburn, abstract, “generating a customized display and UX based on a user chronotype may include displaying an emotional design questionnaire, receiving a response, determining a chronotype of a user based on the response, and generating the customized display and UX based on the user chronotype.”), the system comprising:
a processing device (Newburn, ¶0068, “such elements as a user interface, a memory, a processor, various sensors, a locator, and one or more communicators.”);
a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of (Newburn, ¶0068, “such elements as a user interface, a memory, a processor, various sensors, a locator, and one or more communicators.”):
identify a user device associated with a user account (Newburn, ¶0012, “generate a customized display and UX based on user data that indicates a specific display and UX implemented on a client device associated with the user data.” ¶0068, “a user-oriented computing device such as a smartphone, a personal computer, a tablet computer”);
identify at least one user access to a platform from the user device (Newburn, Fig. 2, ¶0076, “a digital, AI-driven lifestyle management system 200” “Data from the user devices 202 may be communicated to the server device 204. The server device 204 may have implemented thereon a global AI engine that processes the data and generates other data. The server device 204 may communicate the other data to one or more of the user devices 202, which may use the data received from the server device 204 to execute various functions.”);
determine, by an emotional artificial intelligence (AI) engine, at least one user platform preference for the user account, wherein the emotional AI engine is pre-trained on historical user platform preference data for the user account (Newburn, ¶0003, “the client device may include a local artificial intelligence (AI) engine that may be trained to determine a user chronotype based on a response to the emotional design questionnaire.” ¶0086, ““What's your preference for exercise?” ¶0091, “The AI engine may be trained to have general intelligence or may be trained with specific intelligence. The AI engine may be trained to make specific determinations based on data provided to it, and/or to generate other data based on the determinations it makes. For example, the AI engine may be specifically trained to determine a user chronotype based on a response to an emotional design questionnaire. The AI engine may be trained to generate a customized display and/or UX based on the user chronotype. The AI engine may be trained to determine application content based on the user chronotype. The content may be associated with one or more applications, which applications may be implemented on a device associated with the user. The AI engine may be trained to update the customized display and/or UX based on use data associated with the customized display and/or UX. The AI engine may be trained to determine features of the customized display and/or UX based on data associated with one or more applications implemented on a device associated with the user, e.g., a calendar application, a finance or personal application, a health application, and so forth. The AI engine may be trained to determine features of the customized display and/or UX based on data indicative of an objective of a user of the device on which the customized display and/or UX is implemented. The AI engine may be trained to determine an evolution of the customized display and/or UX based on data indicative of the objective and/or a current status of the user relative to the objective. The AI engine may be trained to determine an activity for a user based on data indicative of the objective and/or current status of the user relative to the objective. The AI engine may be trained to determine the activity based on data indicative of a personal goal and/or a lifestyle goal of the user. The AI engine may be trained to determine whether the personal and/or lifestyle goal is associated with a relationship between the user and another person or entity.” ¶0105, “The data provided from the smartphone to the cloud server may include the user's response to the emotional design questionnaire, data indicative of the customized display and UX implemented on the smartphone, the use data, and or data indicative of the updated customized display and UX. The data provided to the server device may be used to train the global AI engine to more accurately generate initial displays and UXs.” Fig. 6A, ¶0106, “At 604, the method may include obtaining device usage data from the device associated with the first user. At 606, the method may include repeating the previous two steps for a plurality of other users and their associated devices. At 608, the method may include providing the plurality of chronotypes and the plurality of device usage data to the AI engine.” ¶0109, “the device usage data may indicate a display and UX associated with the device. A plurality of chronotypes may be determined and a plurality of device usage data may be obtained.” “The positive input may indicate the response display and UX may be correct. The negative input may indicate the response display and UX may be incorrect.” Fig. 9, ¶0115. Fig. 24, ¶0142);
wherein the emotional AI engine is trained based on physical characteristic data of the user collected via at least one sensor on the user device as the user interacts with a graphical user interface of the user device (Newburn, abstract, “Use data may be generated that describes how the device is used, or how the customized display or UX is interacted with” ¶0009, “the plurality of device usage data may be provided to the AI engine” ¶0091, “The AI engine may be trained to determine features of the customized display and/or UX based on data indicative of an objective of a user of the device on which the customized display and/or UX is implemented. The AI engine may be trained to determine an evolution of the customized display and/or UX based on data indicative of the objective and/or a current status of the user relative to the objective. The AI engine may be trained to determine an activity for a user based on data indicative of the objective and/or current status of the user relative to the objective. The AI engine may be trained to determine the activity based on data indicative of a personal goal and/or a lifestyle goal of the user.”),
generate, by the emotional AI engine, a user platform interface component based on the at least one user platform preference (Newburn, ¶0097, “a first chronotype may correspond to a first customized display and UX, a second chronotype may correspond to a second customized display and UX, a third chronotype may correspond to a third customized display and UX, and a fourth chronotype may correspond to a fourth customized display and UX.”); and
transmit the user platform interface component to the user device, wherein the transmission of the user platform interface component triggers a configuration of the graphical user interface of the user device (Newburn, ¶0099, “the customized display and UX may be implemented on the client device by an application-level operating system installed on the client device. The application-level operating system may include a device module that communicates with a native operating system of the client device. The application-level operating system may include an application module that communicates with a plurality of applications installed on the client device. The application-level operating system may include a UX module that generates a UX based on device data communicated from the native operating system to the application-level operating system and/or from application data communicated from the plurality of applications. The application-level operating system may include a display module that generates a UI on the device based on the UX. The application-level operating system may receive, process, and display data from the device module and/or the application module according to the customized display and UX.”).
Newburn does not explicitly disclose wherein the physical characteristic data comprises at least one of pupil dilation, eye movement data, facial expression data, or micro-expression data;
Yang teaches wherein the emotional AI engine is trained based on physical characteristic data of the user collected via at least one sensor on the user device as the user interacts with a graphical user interface of the user device, and wherein the physical characteristic data comprises at least one of pupil dilation, eye movement data, facial expression data, or micro-expression data (Yang, ¶0037, “The empathic prompting module 228 augments the prompt 214 based on the emotion 226 to generate an emotion augmented prompt 230, and then feeds the emotion augmented prompt 230 to the generative AI 212.” ¶0071, “The vision-based models 502 can be trained using supervised or unsupervised learning, considering real and/or synthetic images or frames. The vision-based models 502 can recognize emotions of the user 210 from the video data by analyzing visual characteristics such as facial expressions, body posture, and/or gestures.” ¶0075, “selecting a subset of representative facial landmarks that are part of interesting facial features for expression recognition (e.g., the facial landmarks representing the eyes, eyebrows, and lips).” ¶0091, “one prediction by pre-trained models from data collected in a laboratory setting and another prediction by personalized models trained exclusively on the personal data collected from the user's current setting. The two predictions can be combined into one prediction output in several different ways, including aggregating, appending, averaging, maximum, minimum, weighting, etc. Therefore, the emotion service is better capable of understanding and interpreting, for example, the specific user's jokes, sense of humor, body language (e.g., gestures, posture, etc.), facial expressions, behavior (e.g., rolling eyes), speech pattern, word choice (e.g., dialect), vocal tones, heart rate, EEG signals, etc. Accordingly, the emotion service can be trained to be a personalized real-time empathic model that is fine-tuned to the specific user.”).
Newburn and Yang are considered to be analogous art because all pertain to generating user interface based on the execution of an artificial intelligence (AI) model. It would have been obvious before the effective filing date of the claimed invention to have modified Newburn with the features of “the emotional AI engine is trained based on physical characteristic data of the user collected via at least one sensor on the user device as the user interacts with a graphical user interface of the user device, and wherein the physical characteristic data comprises at least one of pupil dilation, eye movement data, facial expression data, or micro-expression data.” as taught by Yang. The suggestion/motivation would have been non-verbal cues provide important context for effectively communicating using words (Yang, ¶00014).
As to claim 2, claim 1 is incorporated and the combination of Newburn and Yang discloses the at least one user platform preference comprises at least one data point mapped within the graphical user interface of the user device (Newburn, ¶0012, “generate a customized display and UX based on user data that indicates a specific display and UX implemented on a client device associated with the user data. In some implementations, the user data further indicates a chronotype of a user associated with the client device.” ¶0091, “The AI engine may be trained to update the customized display and/or UX based on use data associated with the customized display and/or UX. The AI engine may be trained to determine features of the customized display and/or UX based on data associated with one or more applications implemented on a device associated with the user, e.g., a calendar application, a finance or personal application, a health application, and so forth. The AI engine may be trained to determine features of the customized display and/or UX based on data indicative of an objective of a user of the device on which the customized display and/or UX is implemented.”).
As to claim 3, claim 1 is incorporated and the combination of Newburn and Yang discloses the data point is a location identifier of a pixel within the graphical user interface (Newburn, ¶0091, “The AI engine may be trained to update the customized display and/or UX based on use data associated with the customized display and/or UX. The AI engine may be trained to determine features of the customized display and/or UX based on data associated with one or more applications implemented on a device associated with the user, e.g., a calendar application, a finance or personal application, a health application, and so forth. The AI engine may be trained to determine features of the customized display and/or UX based on data indicative of an objective of a user of the device on which the customized display and/or UX is implemented.” The customized display inherently identifies a location of a pixel within the graphical user interface.).
As to claim 6, claim 1 is incorporated and the combination of Newburn and Yang discloses the emotional AI engine is trained with at least one feedback for the at least one user platform preference as it is rendered in the user platform interface component (Newburn, ¶0009, “The response display and UX may be compared to the test display and UX. Various implementations may include, in response to the response display and UX matching the test display and UX, providing a positive input to the AI engine that indicates the response display and/or UX are correct. In response to the response display and UX not matching the test display and UX, a negative input may be provided to the AI engine, indicating the response display and/or UX are incorrect. In some implementations, the positive input further indicates which portions of the response display and UX match the test display and UX. In some implementations, the negative input further indicates which portions of the response display and UX do not match the test display and UX.”).
As to claim 8, the combination of Newburn and Yang discloses a computer program product for dynamically configuring and generating user interface components based on interface interaction data, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to: identify a user device associated with a user account; identify at least one user access to a platform from the user device; determine, by an emotional artificial intelligence (AI) engine, at least one user platform preference for the user account, wherein the emotional AI engine is pre-trained on historical user platform preference data for the user account, wherein the emotional Al engine is trained based on physical characteristic data of the user collected via at least one sensor on the user device as the user interacts with a graphical user interface of the user device, and wherein the physical characteristic data comprises at least one of pupil dilation, eye movement data, facial expression data, or micro-expression data; generate, by the emotional AI engine, a user platform interface component based on the at least one user platform preference; and transmit the user platform interface component to the user device, wherein the transmission of the user platform interface component triggers a configuration of the graphical user interface of the user device (See claim 1 for detailed analysis.).
As to claim 9, claim 8 is incorporated and the combination of Newburn and Yang discloses the at least one user platform preference comprises at least one data point mapped within the graphical user interface of the user device (See claim 2 for detailed analysis.).
As to claim 10, claim 9 is incorporated and the combination of Newburn and Yang discloses the data point is a location identifier of a pixel within the graphical user interface (See claim 3 for detailed analysis.).
As to claim 13, claim 8 is incorporated and the combination of Newburn and Yang discloses the emotional AI engine is trained with at least one feedback for the at least one user platform preference as it is rendered in the user platform interface component (See claim 6 for detailed analysis.).
As to claim 15, the combination of Newburn and Yang discloses a computer implemented method for dynamically configuring and generating user interface components based on interface interaction data, the computer implemented method comprising: identifying a user device associated with a user account; identifying at least one user access to a platform from the user device; determining, by an emotional artificial intelligence (AI) engine, at least one user platform preference for the user account, wherein the emotional Al engine is trained based on physical characteristic data of the user collected via at least one sensor on the user device as the user interacts with a graphical user interface of the user device, and wherein the physical characteristic data comprises at least one of pupil dilation, eye movement data, facial expression data, or micro-expression data; wherein the emotional AI engine is pre-trained on historical user platform preference data for the user account; generating, by the emotional AI engine, a user platform interface component based on the at least one user platform preference; and transmitting the user platform interface component to the user device, wherein the transmission of the user platform interface component triggers a configuration of the graphical user interface of the user device (See claim 1 for detailed analysis.).
As to claim 16, claim 15 is incorporated and the combination of Newburn and Yang discloses the at least one user platform preference comprises at least one data point mapped within the graphical user interface of the user device (See claim 2 for detailed analysis.).
As to claim 17, claim 16 is incorporated and the combination of Newburn and Yang discloses the data point is a location identifier of a pixel within the graphical user interface (See claim 3 for detailed analysis.).
As to claim 19, claim 15 is incorporated and the combination of Newburn and Yang discloses the emotional AI engine is trained with at least one feedback for the at least one user platform preference as it is rendered in the user platform interface component (See claim 6 for detailed analysis.).
Claims 4-5, 7, 11, 12, 14, 18, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Newburn et al. (US Pub 2025/0251951 A1) in view of Yang et al. (US Pub 2024/0412029 A1) and Tao et al. (US Pub 2025/0231775 A1).
As to claim 4, claim 1 is incorporated and Newburn does not disclose the non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of: collect a first dataset of user platform preferences for the user account at a first instance, wherein the first dataset comprises rendering data of the graphical user interface of the user device; generate, based on the collection of the first dataset, a first training dataset for the emotional AI engine; collect a second dataset of user platform preferences for the user account at a second instance, wherein the second dataset comprises graphic data on the graphical user interface of the user device; generate, based on the collection of the second dataset, a second training dataset for the emotional AI engine; and train the emotional AI engine by applying the first training dataset and the second training dataset to the emotional AI engine.
Tao teaches collect a first dataset of user platform preferences for the user account at a first instance, wherein the first dataset comprises rendering data of the graphical user interface of the user device (Tao, ¶0008, “train an artificial intelligence (AI) model to learn user interface preferences of a plurality of user devices during the communication session” ¶0012, “one or more of rendering a graphical user interface within a software application including a plurality of elements, modifying locations of the plurality of elements within the graphical user interface based on user inputs on the graphical user interface, generating a dynamic mapping of the graphical user interface including the modified locations of the plurality of elements based on an execution of an artificial intelligence (AI) model on the rendered graphical user interface, and storing the dynamic mapping of the graphical user interface within a storage.” ¶0037, “a GenAI model may be trained to understand a correlation between content types and graphical user interface locations (e.g., X-, Y-, Z-coordinates, etc.). For example, the training may include logged graphical user interface activity of the user. Furthermore, the GenAI model may also render content within a graphical user interface based on the graphical user interface preferences learned by the GenAI model.” ¶0049, “The training data for training the GenAI model 122 may be obtained from a data store 124 that includes historical graphical user interface data of the user, such as logged user activities on the graphical user interface including movement of graphical user interface objects and content types. The historical graphical user interface data may include coordinate points (e.g., X, Y, and Z) which represent dimensional locations of the objects with respect to an outer perimeter of the graphical user interface.”¶0078, “The logged data may include pixel locations of the window 612 and the window 614, changes to the pixel locations of the window 612 and the window 614 (e.g., deltas, etc.), content types of the window 612 and the window 614, and the like. The logged data may be used to train a GenAI model as shown in FIG. 6B.”);
generate, based on the collection of the first dataset, a first training dataset for the emotional AI engine (Tao, ¶0037, ““a GenAI model may be trained to understand a correlation between content types and graphical user interface locations (e.g., X-, Y-, Z-coordinates, etc.). For example, the training may include logged graphical user interface activity of the user.” ¶0043, “the GenAI model may be retrained as the user changes preferences for content types and display locations. Data from the user's activity on the graphical user interface can be logged and ingested by the GenAI model to learn updated display location preferences of the user over time.”);
collect a second dataset of user platform preferences for the user account at a second instance, wherein the second dataset comprises graphic data on the graphical user interface of the user device (Tao, ¶0018, “training an artificial intelligence (AI) model to learn location preferences for a content type on the user interface based on the logged user actions including the respective content types” ¶0072, “the GenAI model 522 may be trained to identify content types and locations of such content types that are preferred on the respective user interfaces of the different devices.” ¶0108, “the training may include executing the AI model on call content and visual content from previous meetings”);
generate, based on the collection of the second dataset, a second training dataset for the emotional AI engine (Tao, ¶0060, “The responses may include indications of whether the generated content is correct, and if not, what aspects of the placement are incorrect. This data may be captured and stored within a runtime log 325 or other data store within the live environment and can be subsequently used to retrain the GenAI model 322.”); and
train the emotional AI engine by applying the first training dataset and the second training dataset to the emotional AI engine (Tao, ¶0060, “the training process may use executional results that have already been generated/output by the GenAI model 322 in a live environment (including any customer feedback, etc.) to retrain the GenAI model 322. For example, predicted outputs/graphical user interface placements that are custom generated by the GenAI model 322 and the user feedback of the placements may be used to retrain the model to further enhance the images that are generated for all users. The responses may include indications of whether the generated content is correct, and if not, what aspects of the placement are incorrect. This data may be captured and stored within a runtime log 325 or other data store within the live environment and can be subsequently used to retrain the GenAI model 322.”).
Newburn and Tao are considered to be analogous art because all pertain to generating user interface based on the execution of an artificial intelligence (AI) model. It would have been obvious before the effective filing date of the claimed invention to have modified Newburn with the features of “collect a first dataset of user platform preferences for the user account at a first instance, wherein the first dataset comprises rendering data of the graphical user interface of the user device; generate, based on the collection of the first dataset, a first training dataset for the emotional AI engine; collect a second dataset of user platform preferences for the user account at a second instance, wherein the second dataset comprises graphic data on the graphical user interface of the user device; generate, based on the collection of the second dataset, a second training dataset for the emotional AI engine; and train the emotional AI engine by applying the first training dataset and the second training dataset to the emotional AI engine.” as taught by Tao. The suggestion/motivation would have been the users may have preferences for the type of content they prefer to see on the dashboard and locations for the type of content within the dashboard (Tao, ¶0001).
As to claim 5, claim 1 is incorporated and the combination of Newburn and Tao discloses the at least one user platform preference comprises at least one of a sound preference, an image type preference, a location preference, a sound length preference, or a video type preference (Tao, ¶0001, “users may have preferences for the type of content they prefer to see on the dashboard and locations for the type of content within the dashboard.” ¶0043, “The GenAI model can apply what it has learned through its training to choose a display location/placement of the page/window of the content type on the screen.”).
As to claim 7, claim 1 is incorporated and the combination of Newburn and Tao discloses the non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of:
identify, based on the user account, a user image (Tao, ¶0001, “the advisor and the client may establish their preferences for the dashboard. For example, users may have preferences for the type of content they prefer to see on the dashboard and locations for the type of content within the dashboard” ¶0050, “the user devices 110 and 130 may exchange speech, text, images, communications”);
generate, by the emotional AI engine, a background image based on the at least one user platform preference (Tao, ¶0055, “the payload of data may be in text format, image format, audio format, and the like. In response, the AI engine 222 may convert the payload of data into a format that is readable by the model 224, such as a vector or other encoding. The vector may then be input into the model 224.” ¶0058, “a GenAI model that can receive text as input and generate custom imagery, text, etc., which can be displayed on a user interface/dashboard of a software application that displays content during meetings between user devices.” Fig. 8B-Fig. 8C. ¶0087. “an outer perimeter of the user interface 810.”);
create a preference image by overlaying the user image onto the background image (Tao, Fig. 8B-Fig. 8C. ¶0087, “The bitmap 826 may include a digital image that preserves the sizes and the locations of the display elements 811, 812, 813, and 814.”);
generate a user image platform interface component based on the preference image (Tao, Fig. 8B-Fig. 8C, ¶0088, “FIG. 8B illustrates a process 800B of generating multiple different bitmaps 831, 832, and 833, representing the pixel locations of the display elements 811, 812, 813, and 814 within different sizes of display screens belonging to different device types. Here, the GenAI model 824 may receive, via the software application 822 shown in FIG. 8A, identifiers of the different devices of the user including screen sizes and generate different bitmaps representing the different screen sizes. The different bitmaps may include different sizes, shapes, locations, etc. for the display elements therewithin, in comparison to the other bitmaps.”); and
transmit the user image platform interface component to the user device and cause a trigger of a configuration of the graphical user interface of the user device with the user image platform interface component (Tao, ¶0038, “create realistic display content on the graphical user interface including dynamically generated image and text content.” ¶0104, “identifying a device type of a user device where the software application is being opened and generating the image of the graphical user interface based on executing the AI model on the device type of the user device.” ¶0137, “Processor A sends a message to the software application's processor (Processor C) with instructions on how to configure the user interface elements based on the retrieved dynamic mapping or image.”).
As to claim 11, claim 8 is incorporated and the combination of Newburn and Tao discloses collect a first dataset of user platform preferences for the user account at a first instance, wherein the first dataset comprises rendering data of the graphical user interface of the user device; generate, based on the collection of the first dataset, a first training dataset for the emotional AI engine; collect a second dataset of user platform preferences for the user account at a second instance, wherein the second dataset comprises graphic data on the graphical user interface of the user device; generate, based on the collection of the second dataset, a second training dataset for the emotional AI engine; and train the emotional AI engine by applying the first training dataset and the second training dataset to the emotional AI engine (See claim 4 for detailed analysis.).
As to claim 12, claim 8 is incorporated and the combination of Newburn and Tao discloses the at least one user platform preference comprises at least one of a sound preference, an image type preference, a location preference, a sound length preference, or a video type preference (See claim 5 for detailed analysis.).
As to claim 14, claim 8 is incorporated and the combination of Newburn and Tao discloses identify, based on the user account, a user image; generate, by the emotional AI engine, a background image based on the at least one user platform preference; create a preference image by overlaying the user image onto the background image; generate a user image platform interface component based on the preference image; and transmit the user image platform interface component to the user device and cause a trigger of a configuration of the graphical user interface of the user device with the user image platform interface component (See claim 7 for detailed analysis.)..
As to claim 18, claim 15 is incorporated and the combination of Newburn and Tao discloses the at least one user platform preference comprises at least one of a sound preference, an image type preference, a location preference, a sound length preference, or a video type preference (See claim 5 for detailed analysis.).
As to claim 20, claim 15 is incorporated and the combination of Newburn and Tao discloses identifying, based on the user account, a user image; generating, by the emotional AI engine, a background image based on the at least one user platform preference; creating a preference image by overlaying the user image onto the background image; generating a user image platform interface component based on the preference image; and transmitting the user image platform interface component to the user device and cause a trigger of a configuration of the graphical user interface of the user device with the user image platform interface component (See claim 7 for detailed analysis.).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YU CHEN whose telephone number is (571)270-7951. The examiner can normally be reached on M-F 8-5 PST Mid-day flex.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xiao Wu can be reached on 571-272-7761. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YU CHEN/
Primary Examiner, Art Unit 2613
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