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 Remarks
The Office Action has been made issued in response to amendment filed March 31, 2026. Claims 1-4, 6-14 and 16-20 are pending. Applicant’s arguments have been carefully and respectfully considered in light of the instant amendment, and are not persuasive. Accordingly, this action has been made FINAL.
Claim Interpretation – 35 USC section § 112(f)
Claims 4 and 14
Applicant has amended the claims 4 and 14 so that they no longer contain functional language. Therefore, the claims are no longer interpreted under 112(f).
Claim 11
Applicant has not acknowledged the Examiner interpretation of a processing unit being a processor. The Examiner suggests Applicant acknowledges the Examiner’s interpretation.
Claim Rejections – 35 USC section § 102/103
On pages 11-12 of the Response, Applicant argued that the Chen does not teach “the retrieval model of the present disclosure for retrieving from the repository a motion feature that matches the set of previous reference positions”; the Examiner agrees.
The Examiner however, introduces Ye to cure this deficiencies of Chen. Ye teaches the video retrieval model 121 configured to provide a video, a video segment, or an image corresponding to a query, the video summarization model 122 configured to provide a set of representative video frames that have been stitched in chronological order to form a summary of the video, and a video captioning model 123 configured to automatically generate description of the video based on visual features, motion features, and/or audio features extracted from the video (see [p][0058]). Moreover, Ye teaches in [p][0133] that “the video retrieval model may identify a video that matches or corresponds to the query, for example, using the feature extractor 122, the query encoder 223, and the similarity score calculator 224.” Furthermore, the “video retrieval model, a video having a highest similarity score with the input query may be identified as the video that matches the query” (see [p][0133]). Moreover, Ye teaches , “receive as input a sequence of frames of a video” and “video may be a streaming video provided from a content providing server, a video stored on a local memory of an electronic device” (see [p][0112]) and thus clearly teaches the repository.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function.
Claim 11, recites limitations that use words like “means” (or “step”) or similar terms with functional language but do not invoke 35 U.S.C. 112(f):
Claims 11; recites the limitation, “at least one processing unit, configured to……,” [Line 1].
Such claim limitation(s) is/are:
(i) “processing unit….” have a structure associated with it a processor.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
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 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 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.
Claims 1-4, 6-14 and 17-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Chen et al (Pub No.: 20190304102) in view of Ye et al (Pub No.: 20230259779).
Regarding claim 1, Chen teaches a method of tracking a target object in a video based on an instance motion of the target object (video analytics method for detecting and tracking objects – see [p][0002]), comprising: for a set of previous frames (previous blob in a previous frame and history of location – see [p][0071]) prior to a target frame in the video (location of blob in current frame – see [p][0071]), obtaining a set of previous positions of the target object in the set of previous frames (history of locations – see [p][0071]), respectively; determining, based on the set of previous positions (term Ct-1(Ct-1x, Ct-1y) denotes the center position (x and y) of a bounding box of the tracker in a previous frame and The term Ct(Ctx, Cty) denotes the center position of a bounding box of the tracker in a current frame – see [p][0073]), a predicted value of a position of the target object in the target frame (prediction of the location of the blob tracker in the current frame can be based on the location of the blob in the previous frame – see [p][0071]) with a motion model (motion model can be maintained for a blob tracker and a motion model for a blob tracker can determine and maintain two locations of the blob tracker for each frame – see [p][0071-0072]); determining a measured value of a position of an object in the target frame ([t]he velocity of a blob tracker can include the displacement of a blob tracker between consecutive frames – see [p][0073]); and tracking the target object in the video based on a similarity between the predicted value and the measured value ([u]sing the blob detection system 104 and the object tracking system 106, the video analytics system 100 can perform blob generation and detection for each frame or picture of a video sequence – see [p][0074] and “[t]he object tracking system 206A can perform data association to associate or match the blob trackers (e.g., blob trackers generated or updated based on a previous frame or newly generated blob trackers) and blobs 208A using the calculated costs (e.g., using a cost matrix or other suitable association technique”) – see [p][0075]).
Chen teaches the determining, based on the retrieved motion feature, the predicted value of the position of the target object in the target frame (the location of a blob tracker in a current frame may need to be predicted based on information from a previous frame (e.g., using a location of a blob associated with the blob tracker in the previous frame) – see [p][0104]).
Chen does not expressly teach retrieving, with a retrieval model in the motion model and based on the set of previous positions, a motion feature from a repository in the motion model that matches the set of previous positions.
Ye explicitly teaches retrieving, with a retrieval model in the motion model and based on the set of previous positions, a motion feature from a repository in the motion model that matches the set of previous positions (the video retrieval model 121 configured to provide a video, a video segment, or an image corresponding to a query, the video summarization model 122 configured to provide a set of representative video frames that have been stitched in chronological order to form a summary of the video, and a video captioning model 123 configured to automatically generate description of the video based on visual features, motion features, and/or audio features extracted from the video – see [p][0058]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Chen electronic device, comprising: at least one processing unit with the teachings of Ye of having retrieving, with a retrieval model in the motion model and based on the set of previous positions, a motion feature from a repository in the motion model that matches the set of previous positions.
Wherein having Chen retrieving, with a retrieval model in the motion model and based on the set of previous positions, a motion feature from a repository in the motion model that matches the set of previous positions.
The motivation behind the modification would have been to reduce the processing time by only selecting salient feature for summarization of each frame thus not requiring the processing of the entire frame since both Chen and Ye are method and systems for object tracking wherein Chen performs the classification task in the current frame for the one or more blobs associated with the one or more classification requests (in which case the entire picture of the current frame would need to be accessed), the classification task can be performed for the one or more blobs in a next video frame using an image patch from the next video frame instead of the entire video frame while Ye selects salient frames to represent a video for a downstream multimodal task and discard non-informative frames. (Please see Chen et al (Pub No.: 20190304102), [p][0007] and Ye et al (Pub No.: 20230259779), [p][0006]).
Regarding claim 2, Chen in view of Ye teaches the method of claim 1, Chen teaches wherein determining the predicted value of the position of the target object in the target frame comprises: obtaining the motion model that describes an association between a position of an object in a target frame in a video and a set of positions of the object in a set of previous frames respectively prior to the target frame ([t]he object tracking system 206A can perform data association to associate or match the blob trackers (e.g., blob trackers generated or updated based on a previous frame or newly generated blob trackers) and blobs 208A using the calculated costs (e.g., using a cost matrix or other suitable association technique) – see [p][0075]); and determining, based on the motion model and the set of previous positions, the predicted value of the position of the target object in the target frame (a blob tracker's state and location for the video frame A 202A can be calculated and updated. The blob tracker's location in a next video frame N 202N can also be predicted from the current video frame A 202A. For example, the predicted location of a blob tracker for the next video frame N 202N can include the location of the blob tracker (and its associated blob) in the current video frame A -see [p][0075]).
Regarding claim 3, Chen in view of Ye teaches the method of claim 2, Chen teaches wherein obtaining the motion model comprises: obtaining a reference position of a reference object in a target reference frame in a reference video (W′×W′ patch can be denoted as the restricted memory location patch (RMLP) or a reference image patch – see [p][0175]), and a set of previous reference positions of the reference object in a set of previous reference frames respectively prior to the target reference frame (history or motion model containing history of location for tracking purposes – see [p][0071]); and determining a motion feature in a repository in the motion model based on the reference position and the set of previous reference positions, the motion feature describing an association corresponding to the reference object ([f]oreground blobs are generated from the foreground pixels using morphology operations and spatial analysis. Further, blob trackers from previous frames need to be associated with the foreground blobs in a current frame -see [p][0074]).
Regarding claim 4, Chen in view of Ye teaches the method of claim 3, further comprising: retrieving, with the retrieval model in the motion model and based on the set of previous reference positions, a motion feature from the repository in the motion model that matches the set of previous reference positions (generate a foreground mask with foreground pixels based on the result of background subtraction. For example, the foreground mask can include a binary image containing the pixels making up the foreground objects (e.g., moving objects) in a scene and the pixels of the background – see [p][0082]).
Regarding independent claim 6, Chen in view of Ye teaches the method of claim 5, Chen teaches wherein determining the predicted value further comprises: updating the motion feature with a feature of the set of previous positions (When blobs (making up at least portions of objects) are detected from an input video frame, blob trackers from the previous video frame need to be associated to the blobs in the input video frame according to a cost calculation. The blob trackers can be updated based on the associated foreground blobs. In some instances, the steps in object tracking can be conducted in a series manner – see [p][0097]); determining, based on the updated motion feature, the predicted value of the position of the target object in the target frame ([a] cost determination engine 412 of the object tracking system 106 can obtain the blobs 408 of a current video frame from the blob detection system 10); and updating the motion features with a feature of the measured value (The cost determination engine 412 can also obtain the blob trackers 410A updated from the previous video frame (e.g., video frame A 202A). A cost function can then be used to calculate costs between the blob trackers 410A and the blobs 408 – see [p][0098]).
Regarding claim 7, Chen in view of Ye teaches the method of claim 1, Chen teaches wherein tracking the target object based on the similarity comprises: in response to determining that the similarity meets a threshold condition, identifying the object at a position corresponding to the measured value in the target frame as the target object ([f]or example, if the size ratio for a tracker is larger than a size comparison threshold (denoted as TSize), the tracker passes the object size based re-confirmation – see [p][0155]).
Regarding claim 8, Chen in view of Ye teaches the method of claim 1, Chen teaches wherein tracking the target object based on the similarity comprises: in response to determining that the similarity does not meet a threshold condition, identifying the object at a position corresponding to the measured value in the target frame as an object other than the target object (At a later point in time (e.g., at a current frame), the person may move closer to the camera, in which case the size of the tracker tracking the person will become bigger. The size based re-confirmation can pass for the tracker when the size ratio of the tracker becomes larger than the threshold TSize (e.g., the current bounding box of the tracker is at least TSize-times bigger than the previous bounding box of the tracker), at which point a new classification request can be generated for the tracker – see [p][0156]).
Regarding independent claim 9, Chen teaches the method of claim 1, further comprising: for a next target frame subsequent to the target frame in the video, obtaining a further set of previous positions of the target object in a further set of previous frames prior to the next target frame, respectively (see [p][0261]); determining, based on the further set of previous positions, a further predicted value of a position of the target object in the next target frame (the predicted location of an object tracker in the next video frame includes a location in the current frame of the blob with which the object tracker was associated (and thus the actual location of the tracker bounding box in the current frame – see [p][0263]); determining a further measured value of a position of an object in the next target frame (see [p][0263]); and tracking the target object in the video based on a similarity between the further predicted value and the further measured value ([u]sing the blob detection system 104 and the object tracking system 106, the video analytics system 100 can perform blob generation and detection for each frame or picture of a video sequence – see [p][0074] and “[t]he object tracking system 206A can perform data association to associate or match the blob trackers (e.g., blob trackers generated or updated based on a previous frame or newly generated blob trackers) and blobs 208A using the calculated costs (e.g., using a cost matrix or other suitable association technique”) – see [p][0075]).
Regarding claim 10, Chen in view of Ye teaches the method of claim 9, Chen teaches wherein obtaining the further set of previous positions respectively comprises: taking the measured value of the position of the target object determined from the target frame as the previous position of the target object in the target frame ([u]sing the blob detection system 104 and the object tracking system 106, the video analytics system 100 can perform blob generation and detection for each frame or picture of a video sequence – see [p][0074] and “[t]he object tracking system 206A can perform data association to associate or match the blob trackers (e.g., blob trackers generated or updated based on a previous frame or newly generated blob trackers) and blobs 208A using the calculated costs (e.g., using a cost matrix or other suitable association technique”) – see [p][0075]).
Regarding independent claim 11, Chen teaches an electronic device (see Fig 1), comprising: at least one processing unit (a processor – see [p][0278]); and at least one memory (see [p][0293]) coupled to the at least one processing unit (see [p][0293]) and storing instructions (program code – see [p][0293]) for execution by the at least one processing unit (see [p][0293]), the instructions, when executed by the at least one processing unit, causing the electronic device to perform actions of tracking a target object in a video based on an instance motion of the target object (video analytics system for detecting and tracking objects – see [p][0002]), the actions comprising: for a set of previous frames (previous blob in a previous frame and history of location – see [p][0071]) prior to a target frame in the video (location of blob in current frame – see [p][0071]), obtaining a set of previous positions of the target object in the set of previous frames (history of locations – see [p][0071]), respectively; determining, based on the set of previous positions (term Ct-1(Ct-1x, Ct-1y) denotes the center position (x and y) of a bounding box of the tracker in a previous frame and The term Ct(Ctx, Cty) denotes the center position of a bounding box of the tracker in a current frame – see [p][0073]), a predicted value of a position of the target object in the target frame (prediction of the location of the blob tracker in the current frame can be based on the location of the blob in the previous frame – see [p][0071]) with a motion model (motion model can be maintained for a blob tracker and a motion model for a blob tracker can determine and maintain two locations of the blob tracker for each frame – see [p][0071-0072]); determining a measured value of a position of an object in the target frame ([t]he velocity of a blob tracker can include the displacement of a blob tracker between consecutive frames – see [p][0073]); and tracking the target object in the video based on a similarity between the predicted value and the measured value ([u]sing the blob detection system 104 and the object tracking system 106, the video analytics system 100 can perform blob generation and detection for each frame or picture of a video sequence – see [p][0074] and “[t]he object tracking system 206A can perform data association to associate or match the blob trackers (e.g., blob trackers generated or updated based on a previous frame or newly generated blob trackers) and blobs 208A using the calculated costs (e.g., using a cost matrix or other suitable association technique”) – see [p][0075]).
Chen does not expressly teach retrieving, with a retrieval model in the motion model and based on the set of previous positions, a motion feature from a repository in the motion model that matches the set of previous positions.
Ye explicitly teaches retrieving, with a retrieval model in the motion model and based on the set of previous positions, a motion feature from a repository in the motion model that matches the set of previous positions (the video retrieval model 121 configured to provide a video, a video segment, or an image corresponding to a query, the video summarization model 122 configured to provide a set of representative video frames that have been stitched in chronological order to form a summary of the video, and a video captioning model 123 configured to automatically generate description of the video based on visual features, motion features, and/or audio features extracted from the video – see [p][0058]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Chen electronic device, comprising: at least one processing unit with the teachings of Ye of having retrieving, with a retrieval model in the motion model and based on the set of previous positions, a motion feature from a repository in the motion model that matches the set of previous positions.
Wherein having Chen retrieving, with a retrieval model in the motion model and based on the set of previous positions, a motion feature from a repository in the motion model that matches the set of previous positions.
The motivation behind the modification would have been to reduce the processing time by only selecting salient feature for summarization of each frame thus not requiring the processing of the entire frame since both Chen and Ye are method and systems for object tracking wherein Chen performs the classification task in the current frame for the one or more blobs associated with the one or more classification requests (in which case the entire picture of the current frame would need to be accessed), the classification task can be performed for the one or more blobs in a next video frame using an image patch from the next video frame instead of the entire video frame while Ye selects salient frames to represent a video for a downstream multimodal task and discard non-informative frames. (Please see Chen et al (Pub No.: 20190304102), [p][0007] and Ye et al (Pub No.: 20230259779), [p][0006]).
Regarding claim 12, which corresponds to claim 2 except for reciting a different statutory category of a device. Therefore, the rejection analysis of claim 2 are fully applicable to claim 12.
Regarding claim 13, which corresponds to claim 3 except for reciting a different statutory category of a device. Therefore, the rejection analysis of claim 3 are fully applicable to claim 13.
Regarding claim 14, which corresponds to claim 4 except for reciting a different statutory category of a device. Therefore, the rejection analysis of claim 4 are fully applicable to claim 14.
Regarding claim 16, which corresponds to claim 6 except for reciting a different statutory category of a device. Therefore, the rejection analysis of claim 6 are fully applicable to claim 16.
Regarding claim 17, which corresponds to claims 7-8 except for reciting a different statutory category of a device. Therefore, the rejection analysis of claims 7-8 are fully applicable to claim 17.
Regarding claim 18, which corresponds to claim 9 except for reciting a different statutory category of a device. Therefore, the rejection analysis of claim 9 are fully applicable to claim 18.
Regarding claim 19, which corresponds to claim 10 except for reciting a different statutory category of a device. Therefore, the rejection analysis of claim 10 are fully applicable to claim 19.
Regarding independent claim 20, Chen teaches a non-transitory computer readable storage medium (computer-readable medium – see [p][0293]) having stored there on a computer program (program code – see [p][0293]) which, when executed by a processor (a processor – see [p][0278]), causes the processor to implement actions of tracking a target object in a video based on an instance motion of the target object (video analytics method for detecting and tracking objects – see [p][0002]), the actions comprising: for a set of previous frames (previous blob in a previous frame and history of location – see [p][0071]) prior to a target frame in the video (location of blob in current frame – see [p][0071]), obtaining a set of previous positions of the target object in the set of previous frames (history of locations – see [p][0071]), respectively; determining, based on the set of previous positions (term Ct-1(Ct-1x, Ct-1y) denotes the center position (x and y) of a bounding box of the tracker in a previous frame and The term Ct(Ctx, Cty) denotes the center position of a bounding box of the tracker in a current frame – see [p][0073]), a predicted value of a position of the target object in the target frame (prediction of the location of the blob tracker in the current frame can be based on the location of the blob in the previous frame – see [p][0071]) with a motion model (motion model can be maintained for a blob tracker and a motion model for a blob tracker can determine and maintain two locations of the blob tracker for each frame – see [p][0071-0072]); determining a measured value of a position of an object in the target frame ([t]he velocity of a blob tracker can include the displacement of a blob tracker between consecutive frames – see [p][0073]); and tracking the target object in the video based on a similarity between the predicted value and the measured value ([u]sing the blob detection system 104 and the object tracking system 106, the video analytics system 100 can perform blob generation and detection for each frame or picture of a video sequence – see [p][0074] and “[t]he object tracking system 206A can perform data association to associate or match the blob trackers (e.g., blob trackers generated or updated based on a previous frame or newly generated blob trackers) and blobs 208A using the calculated costs (e.g., using a cost matrix or other suitable association technique”) – see [p][0075]).
Chen does not expressly teach retrieving, with a retrieval model in the motion model and based on the set of previous positions, a motion feature from a repository in the motion model that matches the set of previous positions.
Ye explicitly teaches retrieving, with a retrieval model in the motion model and based on the set of previous positions, a motion feature from a repository in the motion model that matches the set of previous positions (the video retrieval model 121 configured to provide a video, a video segment, or an image corresponding to a query, the video summarization model 122 configured to provide a set of representative video frames that have been stitched in chronological order to form a summary of the video, and a video captioning model 123 configured to automatically generate description of the video based on visual features, motion features, and/or audio features extracted from the video – see [p][0058]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Chen electronic device, comprising: at least one processing unit with the teachings of Ye of having retrieving, with a retrieval model in the motion model and based on the set of previous positions, a motion feature from a repository in the motion model that matches the set of previous positions.
Wherein having Chen retrieving, with a retrieval model in the motion model and based on the set of previous positions, a motion feature from a repository in the motion model that matches the set of previous positions.
The motivation behind the modification would have been to reduce the processing time by only selecting salient feature for summarization of each frame thus not requiring the processing of the entire frame since both Chen and Ye are method and systems for object tracking wherein Chen performs the classification task in the current frame for the one or more blobs associated with the one or more classification requests (in which case the entire picture of the current frame would need to be accessed), the classification task can be performed for the one or more blobs in a next video frame using an image patch from the next video frame instead of the entire video frame while Ye selects salient frames to represent a video for a downstream multimodal task and discard non-informative frames. (Please see Chen et al (Pub No.: 20190304102), [p][0007] and Ye et al (Pub No.: 20230259779), [p][0006]).
Conclusion
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.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRAE S ALLISON whose telephone number is (571)270-1052. The examiner can normally be reached on Monday-Friday 9am-5pm EST.
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/ANDRAE S ALLISON/Primary Examiner, Art Unit 2673
June 12, 2026
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