DETAILED ACTION
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 .
Response to Amendment
Applicants Amendments filed on June 3, 2026, has been entered and made of record.
Currently pending Claim(s): 1-15
Independent Claim(s): 1, 9
Amended Claim(s): 1, 8-9, 11-12, 14
Canceled Claim(s): 2, 10
Drawing Objections
In view of Applicant’s amendments to the Drawings filed on June 3, 2026, the objections to the Specification are withdrawn.
Specification Objections
In view of Applicant’s amendments to the Specification filed on June 3, 2026, the objections to the drawings are withdrawn.
Response to Arguments
Claim Rejections – 35 U.S.C § 102/103
This office action is responsive to the Applicant’s Arguments/Remarks Made in an Amendment
received on June 3, 2026.
In view of amendments filed on June 3, 2026, the Applicant has amended independent Claim 1 to recite the limitations of Claim 2. Originally, (the claim set dated April 26, 2024) Claim 1 was rejected under Trehan (US Pub No 2022/0072380). Claim 2 was rejected over Trehan in view of Rakshit (US Pub No 2021/0273892) and Newman (US Pub No 2023/00826823).
The Applicant explained that Trehan fails to teach the limitations of Claim 2. The Applicant explained (on Remarks, pg. 15, paragraph 2) that although Trehan teaches that text pertaining to the policy may be displayed, Trehan fails to teach, “ 1) receiving a policy from an external server; 2) analyzing information related to the policy using a natural language understanding model; 3) obtaining a plurality of detailed operations constituting an action defined by the policy; or 4) obtaining sequence information indicating an order of the plurality of detailed operations. Instead, Trehan merely analyzes a user's current exercise performance and compares the performance with a predefined target performance in order to provide corrective feedback”.
The Examiner agrees that Trehan fails to teach the claimed limitation. The Examiner previously rejected Claim 2 using Trehan in view of Rakshit and Newman. Rakshit teaches obtaining a policy from an external server (see paragraph [0030]). Rakshit further teaches that the knowledge corpus can be used to obtain a plurality of detailed operations for completing an objective by using the policy and information about a sequence of the plurality of detailed operations (see paragraph [0013]). Newman teaches that a plurality of detailed operations can be used compared to user activity to determine a policy performance level ( see paragraph [0048]). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventio to combine the teachings of Trehan, Rakshit and Newman. The motivation for doing so would be to better guide the user (see Rakshit, paragraph [0031], and Newman paragraph [0018]).
Thus, for all the reasons cited above, the Examiner maintains the rejection of Claim 1 under Trehan in view of Rakshit.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Trehan et al. (US Pub No 2022/0072380), hereinafter Trehan in view of Rakshit et al. (US Pub No 2021/0273892), hereinafter Rakshit, and further in view of Newman et al. (US Pub No 2023/0026823), hereinafter Newman.
As to Claim 1, Trehan teaches a method for providing a guide for user activity (see paragraph [0010], “The method further includes generating, by the AI model, feedback for the user based on comparison of the set of user performance parameters with the set of target activity performance parameters. The feedback includes at least one of corrective actions or alerts”, where the feedback is a guide for user activity),
performed by an augmented reality device (see paragraph [0008] , “it is beneficial to use a mirror display, Artificial Intelligence (AI), and Augmented Reality (AR) technology to solve such a problem”, where the mirror display is the augmented reality device).
obtaining, from a server of a policy provider, a policy (see paragraph [0100], “The disclosed methods and systems may be implemented on a conventional or a general-purpose computer system, such as a personal computer (PC) or server computer”, where the ‘server computer’ is the policy provider, and see paragraph [0030], “The memory may also store various data (for example, AI model data, a plurality of activity types, a plurality of activities, multimedia data, set of user performance parameters, target activity performance data, and the like”, where the ‘target activity performance data’ is the policy),
comprising at least one activity about at least one of a location, a time, a space, or an object (see paragraph [0036], “The gymnasium may include, for example, multiple exercise machines and equipment for performing multiple activities by the user. The user may use a smart mirror (for example, the smart mirror 100) or any other display device to select an activity from the activity categories and may correspondingly select an activity attribute that is associated the activity”, where the ‘object’ is the machines, and the ‘activity’ is relative to the ‘object’),
inputting an image obtained through a camera to an artificial intelligence model (see paragraph [0010], “The method further includes capturing in real-time, via at least one camera, multimedia data of current activity performance of the user corresponding to the activity type and the activity”, and see paragraph [0010], “The method further includes processing in real-time, by an Artificial Intelligence (AI) model, the captured multimedia data” where the multimedia data includes the image of the user),
and recognizing, from the image, at least one activity of a user (see paragraph [0082], “the AI model of the smart mirror (such as, the smart mirror 100) may determine whether the initial pose is correct. Further, the GUI 1000 displays a message 1008 for the user that the pose is recognized successfully for the exercise.” ),
determining a policy performance level of the user by comparing the recognized at least one activity of the user with the at least one activity in the policy (see paragraph [0010], “The method further includes comparing, by the AI model, the set of user performance parameters with a set of target activity performance parameters” and see paragraph [0083], “By way of an example, the GUI 1100 may display rep/step counters, percentage of reps completed, percentage of exercise completed, heart rate of the user, calories burnt by the user, or any other user performance parameter”, where the ‘user performance parameter’ is the policy performance level),
and outputting, based on the policy performance level a graphic user interface (UI) for providing the guide for the user activity on the policy (see paragraph [0074], “Further, the process 300 includes displaying the set of user performance parameters, the set of target activity performance parameters, and the target activity performance of the activity expert through the GUI”, where GUI stands for graphic user interface).
Trehan fails to teach obtaining, by analyzing text in the obtained policy, a plurality of detailed operations for recognizing the at least one activity defined by the policy and information about a sequence of the plurality of detailed operations.
However, Rakshit teaches an augmented reality device (see abstract), that can analyze text from a policy (see paragraph [0030], “In various embodiments of the present invention, chatbot 112 can analyze historically gathered videos, images, and documents (e.g., captured user dialog and/or retrieved instructions or explanations), wherein chatbot 112 can create knowledge corpus 114 based on the analyzed videos, images, and documents using machine learning”, where the documents contain text, and the knowledge corpus is the ‘policy’). Rakshit further teaches that the knowledge corpus can be used to obtain a plurality of detailed operations for completing an objective by using the policy and information about a sequence of the plurality of detailed operations (see paragraph [0013], “The knowledge corpus can recommend one or more solutions to the user, where the one or more solutions comprise identified actions (e.g., a list of user recommended actions) that can be visually presented to the user”, where the list of user recommended actions are the ‘detailed operations’).
Rakshit is combinable with Trehan as both are from the analogous field of image analysis and augmented reality. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the text analysis taught by Rakshit with the method of providing a user guide taught by Trehan. The motivation for doing so would be to provide solutions for the user by using data obtained from the text analysis. Rakshit teaches in paragraph [0031], “In various embodiments of the present invention knowledge corpus 114 can determine and output solutions or actions steps for the user for the identified user problem and/or user activity based on the identified user query and identified and retrieved information associated with the identified visual information.” The retrieved information can include the data gathered from analyzing documents. Thus, it would have been obvious to combine the text analysis taught by Rakshit with the teachings of Trehan.
Rakshit teaches that the knowledge corpus can be used to predict how the user will interact with the environment (see paragraph [0029]). However, Rakshit fails to explicitly teach that the detailed operations can be used to recognize the at least one activity. Instead, to recognize if the user has completed the activity, the augmented reality device monitors biometric signals, or asks the user for input (see paragraph [0051]).
From an analogous art, Newman teaches an augmented reality device (see abstract) that can obtain a policy (see paragraph [0037], “At step 120 one or more of the systems described herein may define, based on identifying the plurality of objects, an object-manipulation objective”, where the objective is the policy),
and then determine a plurality of detailed operations for the at least one activity defined by the policy and information about a sequence of the plurality of detailed operations (see paragraph [0048], “at step 130 one or more of the systems described herein may determine an action sequence that defines a sequence of action steps for manipulating the at least one of the plurality of objects to complete the object-manipulation objective. For example, sequence module 208 may determine action sequence 228 for manipulating objects 222 into end states 226 to achieve objectives 224”),
and that these operations can be used to determine a policy performance level of the user by comparing the at least one activity of the user with the plurality of detailed operations and comparing an order of the at least one activity of the user with the sequence of the plurality of detailed operations [0060], “For example, the notification may provide instructions or descriptions of one or more actions steps of action sequence 228. The notification may provide status updates, such as a percentage of completion of one or more action steps, changes to one or more relevant objects”, thus implying that the activity is recognized by the augmented reality device, and each activity is compared with the sequence of defined action steps).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the activity recognition taught by Newman with the teachings of Trehan and Rakshit. The motivation for doing so would be to provide users with more efficient guides. Newman teaches in paragraph [0017- 0018], “People often perform routine tasks, such as household chores, packing for a trip, etc., with little to no preplanning…. Thus, when the user is performing a task, an artificial-reality device may be able to analyze the user's environment and provide feedback in real time. By leveraging the computational resources of artificial-reality devices, the user may be able to perform the task more efficiently”). Thus, it would have been obvious to combine the teachings of Trehan, Rakshit, and Newman in order to obtain the invention as claimed in Claim 1.
As to Claim 3, Trehan teaches identifying a trigger point by monitoring whether at least one of the location, the time, the space, the object, or at least one activity of the user recognized from the image (see Trehan, paragraph [0080], “Referring now to FIG. 10, an exemplary GUI 1000 displaying current activity performance 1002 and pose skeletal model 1004 of a user is illustrated, in accordance with some embodiments. When the user acknowledges the message 908 and gets into an initial pose for the exercise, the AI model of the smart mirror (such as, the smart mirror 100) may determine whether the initial pose is correct. Further, the GUI 1000 displays a message 1008 for the user that the pose is recognized successfully for the exercise. The message 1008 may also be provided as an audio output. The user may be notified via text, graphic, visual, haptic, or audio output to begin the exercise”, where the initial pose is an activity of the user which is recognized).
Trehan teaches that wherein the determining the policy performance level comprises, based on the trigger point being identified, determining the policy performance level by comparing the recognized at least one activity of the user with the at least one activity in the policy (see Trehan, paragraph [0083], “When the user begins performing the exercise, the multimedia data associated with the current activity performance 1102 of the user is analyzed by the AI model of the smart mirror (such as, the smart mirror 100). The multimedia data of the current activity performance 1102 is compared with a target activity performance 1104 of the activity expert”).
As to Claim 4, Trehan teaches wherein the determining the policy performance level comprises: comparing at least one activity of the user recognized in real time from the image with at least one activity in the policy (see Trehan, paragraph [0029], “The smart mirror 100 further captures in real-time, via at least one camera, multimedia data of current activity performance of the user 102 corresponding to the activity type and the activity. The smart mirror 100 further processes in real-time, by an Artificial Intelligence (AI) model…. The smart mirror 100 further compares, by the AI model, the set of user performance parameters with a set of target activity performance parameters.”),
and determining whether the at least one activity of the user recognized in real time and the at least one activity in the policy correspond to each other (see Trehan, paragraph [0080], “Referring now to FIG. 10, an exemplary GUI 1000 displaying current activity performance 1002 and pose skeletal model 1004 of a user is illustrated, in accordance with some embodiments. When the user acknowledges the message 908 and gets into an initial pose for the exercise, the AI model of the smart mirror (such as, the smart mirror 100) may determine whether the initial pose is correct. Further, the GUI 1000 displays a message 1008 for the user that the pose is recognized successfully for the exercise”),
and updating a value of the policy performance level in real time by calculating the policy performance level based on whether the at least one activity of the user recognized in real time and the at least one activity in the policy correspond to each other (see Trehan, paragraph [0083], “The multimedia data of the current activity performance 1102 is compared with a target activity performance 1104 of the activity expert... When the user successfully moves from an initial pose to a subsequent pose, the rep/step counter will change in value from “1” to “2”, denoting that the user is now on second step”, where the ‘rep count’ is the value updated based on the user’s activity).
As to Claim 5, Trehan teaches wherein the outputting the graphic UI comprises displaying a graphic UI representing a reward determined based on the updated policy performance level (see Trehan, paragraph [0059], “In some configurations, scores related to user activities may be presented on a leader board as points for various users who use smart mirrors 100 and/or display devices. Badges may also be assigned to various users based on level of activities performed by them and may be displayed on social media platforms”).
As to Claim 6, Trehan teaches wherein the outputting the graphic UI comprises updating the graphic UI, based on the updated policy performance level (see Trehan, paragraph [0083], “The multimedia data of the current activity performance 1102 is compared with a target activity performance 1104 of the activity expert. By way of an example, the GUI 1100 may display rep/step counters, percentage of reps completed, percentage of exercise completed, heart rate of the user, calories burnt by the user, or any other user performance parameter. When the user successfully moves from an initial pose to a subsequent pose, the rep/step counter will change in value from “1” to “2”, denoting that the user is now on second step.”, where the GUI is updated by displaying a new value for the rep counter).
As to Claim 7, Trehan teaches an external device may be used to obtain location and object information about an activity (see Trehan, paragraph [0031], “In some embodiments, the smart mirror 100 may interact with the one or more external devices over a communication network” and see paragraph [0085], “The cameras may be used to track and record the activity of the user in the gymnasium as the user moves from one area or from one machine to another for performing various activities”, where the cameras are external devices, the ‘location’ is the gym, and the machines are ‘objects’ used for activities).
Trehan further teaches that the data from the external cameras can be compared to information in the policy to determine a policy performance level of the user (see Trehan, paragraph [0084], “The gymnasium may include, for example, multiple exercise machines and equipment for performing multiple activities by the user. The user may use a smart mirror (for example, the smart mirror 100) or any other display device to select an activity from the activity categories and may correspondingly select an activity attribute that is associated the activity. The multiple cameras may capture the activity of the user and may provide relevant instructions and feedback to the user for improvising the activities being performed”, where the multiple cameras are external cameras).
As to Claim 8, Trehan teaches wherein the obtaining the policy comprises receiving a plurality of policies (see Trehan, paragraph [0011], “The GUI is configured to render a plurality of activity types. Each of the plurality activity types includes a plurality of activities”, where each ‘activity type’ is a policy,
and wherein the determining the policy performance level comprises: determining the policy from among the plurality of received policies based on a priority set by the user input (see paragraph [0011], “The GUI is further configured to receive, via a user command, a user selection of at least one of an activity type from the plurality of activity types and an activity from the plurality of activities associated with activity type.”) ;
and determining the policy performance level by comparing at least one activity defined by the determined policy with at least one activity of the user recognized from the image. (see paragraph [0011], “The processor-executable instructions, on execution, further cause the processor to generate, by the AI model, feedback for the user based on comparison of the set of user performance parameters with the set of target activity performance parameters”).
Trehan in view of Newman fails to explicitly teach that plurality of polices are obtained from servers of a plurality of policy providers. However, Rakshit teaches an augmented reality system for instructing a user (see abstract) which can use a variety of different sources to obtain a policy (see paragraph [0024], “In other embodiments, server computer 120 can represent a server computing system utilizing multiple computers such as, but not limited to, a server system, such as in a cloud computing environment”, and see paragraph [0030], “In various embodiments of the present invention, knowledge corpus 114 can be created based on various sources of information on any particular domain previously accessed and/or stored in local storage 104 and/or shared storage 124 by chatbot 112 including retrieved data and user interaction”). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the multiple servers and sources taught by Rakshit with the teachings of Trehan and Newman. The motivation for doing so would be to better support the users by retrieving information. Rakshit teaches in paragraph [0031], “In various embodiments of the present invention, knowledge corpus 114 can support the user with textual or audio interaction by retrieving information”. Thus, it would have been obvious to combine the plurality of policy providers taught by Rakshit with the teachings Trehan and Newman in order to obtain the invention as claimed in Claim 8.
As to Claim 9, Trehan teaches an augmented reality device for providing a guide for user activity (see paragraph [0008] , “it is beneficial to use a mirror display, Artificial Intelligence (AI), and Augmented Reality (AR) technology to solve such a problem”, where the mirror display is the augmented reality device), the device comprising:
a communication interface configured to perform data communication with a server of a policy provider (see paragraph [0104], “The computing system 1200 may also include a communications interface 1218. The communications interface 1218 may be used to allow software and data to be transferred between the computing system 1200 and external devices”, and see paragraph [0100], “The disclosed methods and systems may be implemented on a conventional or a general-purpose computer system, such as a personal computer (PC) or server computer”, where the ‘server computer’ is the policy provider);
a camera configured to obtain an image by an object in a real space and a part of a body of a user (see paragraph [0093], “The method includes detecting a user, determining pose and body movement of a user using a camera,” and see paragraph [0085], “The cameras may be used to track and record the activity of the user in the gymnasium as the user moves from one area or from one machine to another for performing various activities”, where the gymnasium is a real space, and the machines are objects );
a display (see paragraph [0064], “In an embodiment, the GUI may be rendered on the display 214 via the GUI module 220.”)
and at least one processor (see paragraph [0030], “Further, the memory may store instructions that, when executed by the one or more processors, cause the one or more processors to analyse activity performance of the user 102 in real-time through the smart mirror 100”)
control the communication interface to receive, from the server of the policy provider, a policy comprising at least one activity (see paragraph [0030], “The memory may also store various data (for example, AI model data, a plurality of activity types, a plurality of activities, multimedia data, set of user performance parameters, target activity performance data, and the like”, where the ‘target activity performance data’ is the policy),
comprising at least one activity defined in connection with at least one of a location, a time, a space, or an object (see paragraph [0084], “The gymnasium may include, for example, multiple exercise machines and equipment for performing multiple activities by the user. The user may use a smart mirror (for example, the smart mirror 100) or any other display device to select an activity from the activity categories and may correspondingly select an activity attribute that is associated the activity”, where the ‘object’ is the machines, and the ‘activity’ is relative to the ‘object’);
input the image, obtained through the camera, to an artificial intelligence model (see paragraph [0010], “The method further includes processing in real-time, by an Artificial Intelligence (AI) model, the captured multimedia data” where the multimedia data includes the image of the user),
and recognize, from the image , at least one activity of the user interacting with at least one of a location, a time, a space, or an object, by using the artificial intelligence model (see paragraph [0082], “the AI model of the smart mirror (such as, the smart mirror 100) may determine whether the initial pose is correct. Further, the GUI 1000 displays a message 1008 for the user that the pose is recognized successfully for the exercise”, and see paragraph [0084], “The user may use a smart mirror (for example, the smart mirror 100) or any other display device to select an activity from the activity categories and may correspondingly select an activity attribute that is associated the activity. The multiple cameras may capture the activity of the user and may provide relevant instructions and feedback to the user for improvising the activities being performed”, thus implying that the activity of interacting with the machine is recognized);
determine a policy performance level of the user by comparing the recognized at least one activity of the user with the at least one activity in the policy (see paragraph [0010], “The method further includes comparing, by the AI model, the set of user performance parameters with a set of target activity performance parameters” and see paragraph [0083], “By way of an example, the GUI 1100 may display rep/step counters, percentage of reps completed, percentage of exercise completed, heart rate of the user, calories burnt by the user, or any other user performance parameter”, where the ‘user performance parameter’ is the policy performance level);
and control the display to output, based on the policy performance level, a graphic user interface (UI) for providing the guide for the user activity on the policy (see Trehan, paragraph [0074], “Further, the process 300 includes displaying the set of user performance parameters, the set of target activity performance parameters, and the target activity performance of the activity expert through the GUI”, where GUI stands for graphic user interface).
Trehan fails to teach obtaining, by analyzing text in the obtained policy, a plurality of detailed operations for recognizing the at least one activity defined by the policy and information about a sequence of the plurality of detailed operations.
However, Rakshit teaches an augmented reality device (see abstract), that can analyze text from a policy (see paragraph [0030], “In various embodiments of the present invention, chatbot 112 can analyze historically gathered videos, images, and documents (e.g., captured user dialog and/or retrieved instructions or explanations), wherein chatbot 112 can create knowledge corpus 114 based on the analyzed videos, images, and documents using machine learning”, where the documents contain text, and the knowledge corpus is the ‘policy’). Rakshit further teaches that the knowledge corpus can be used to obtain a plurality of detailed operations for completing an objective by using the policy and information about a sequence of the plurality of detailed operations (see paragraph [0013], “The knowledge corpus can recommend one or more solutions to the user, where the one or more solutions comprise identified actions (e.g., a list of user recommended actions) that can be visually presented to the user”, where the list of user recommended actions are the ‘detailed operations’).
Rakshit is combinable with Trehan as both are from the analogous field of image analysis and augmented reality. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the text analysis taught by Rakshit with the method of providing a user guide taught by Trehan. The motivation for doing so would be to provide solutions for the user by using data obtained from the text analysis (see Rakshit, paragraph [0031]).. Thus, it would have been obvious to combine the text analysis taught by Rakshit with the teachings of Trehan.
Rakshit teaches that the knowledge corpus can be used to predict how the user will interact with the environment (see paragraph [0029]). However, Rakshit fails to explicitly teach that the detailed operations can be used to recognize the at least one activity. Instead, to recognize if the user has completed the activity, the augmented reality device monitors biometric signals, or asks the user for input (see paragraph [0051]).
From an analogous art, Newman teaches an augmented reality device (see abstract) that can obtain a policy (see paragraph [0037], “At step 120 one or more of the systems described herein may define, based on identifying the plurality of objects, an object-manipulation objective”, where the objective is the policy),
and then determine a plurality of detailed operations for the at least one activity defined by the policy and information about a sequence of the plurality of detailed operations (see paragraph [0048], “at step 130 one or more of the systems described herein may determine an action sequence that defines a sequence of action steps for manipulating the at least one of the plurality of objects to complete the object-manipulation objective. For example, sequence module 208 may determine action sequence 228 for manipulating objects 222 into end states 226 to achieve objectives 224”),
and that these operations can be used to determine a policy performance level of the user by comparing the at least one activity of the user with the plurality of detailed operations and comparing an order of the at least one activity of the user with the sequence of the plurality of detailed operations [0060], “For example, the notification may provide instructions or descriptions of one or more actions steps of action sequence 228. The notification may provide status updates, such as a percentage of completion of one or more action steps, changes to one or more relevant objects.”, thus implying that the activity is recognized by the augmented reality device, and each activity is compared with the sequence of defined action steps).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the activity recognition taught by Newman with the teachings of Trehan and Rakshit. The motivation for doing so would be to provide users with more efficient guides (see Newman, paragraph [0017]-0018]). Thus, it would have been obvious to combine the teachings of Trehan, Rakshit, and Newman in order to obtain the invention as claimed in Claim 9.
As to Claim 11, Claim 11 claims the same limitation as Claim 3 and is dependent on a similarly
rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim
3.
As to Claim 12, Claim 12 claims the same limitation as Claim 4 and is dependent on a similarly
rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim
4.
As to Claim 13, Claim 13 claims the same limitation as Claim 5 and is dependent on a similarly
rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim
5.
As to Claim 14, Claim 14 claims the same limitation as Claim 6 and is dependent on a similarly
rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim
6.
As to Claim 15, Claim 15 claims the same limitation as Claim 7 and is dependent on a similarly
rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim
7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bruso et al. (US Pub No 2023/0036101) teaches a method for creating an augmented reality guide that includes analyzing text, images, and video to create a ‘AR pattern’ which can be used to guide a user.
Hong et al. (US Pub No 2022/0362631) teaches an augmented reality device that compares image and motion data to reference data to determine if the user has successfully completed an activity.
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOUMYA THOMAS whose telephone number is (571)272-8639. The examiner can normally be reached M-F 8:30-5:00.
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/S.T./Examiner, Art Unit 2664
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
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