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 .
Claim Interpretation
Due to clarification from applicant, the term “queue” in claims 5-8 and 15-16 is being interpreted in line with the applicant’s proposed definition that is recited in their claim interpretation arguments presented on page 10 of their response.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5-8 and 15-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “P” in claims 5-8 and 15-16 is used by the claim to mean “positive integer,” while the accepted meaning is “positive integer over 1.” The term is indefinite because the specification does not clearly redefine the term. The term “P” is being used as essentially a placeholder for some number of frames or sections to be used according to the definition provided by the applicant. However, the definition provided by the claims and applicant remarks allows for all positive integers which would include the number one. This causes an issue of indefiniteness as can a person just acquire 1 frame or 1 section and 1 map to perform the claimed process or does it actually require more than one? In this case, there are multiple remedies possible to overcome this rejection. A potential remedy would be to simply remove the term “P” altogether, and another potential remedy may be to further limit the definition to, “positive integers greater than 1”.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3 and 11-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al. (CN112580559A), hereinafter referred to as Zhang.
In regards to claim 1, Zhang discloses a method of operating an electronic device, the method comprising: generating sampling frames for each of a plurality of video clips at a first sampling interval that remains constant despite variation in behavior time among the plurality of video clips, such that different numbers of sampling frames are generated for the plurality of video clips (Page 4 from the Paragraph denoted S3 to the bottom of the page, The method generates sampling frames at a rate by sampling every other frame making the sampled frames the ”sampling frames” where further there is variation in the total number of sampling frames as each video takes about ten seconds as the videos will have different lengths, there will be different numbers of sampling frames); generating, for each of the plurality of video clips, a cumulative feature map generated based on temporal accumulation of skeleton feature points extracted from the sampling frames of the corresponding video clip, thereby integrating a variable number of sampling frames into a single cumulative feature representation (Last paragraph of page 4 and first paragraph of page 5, The disclosed multi-channel map is analogous to the disclosed cumulative feature map as it combines multiple channels together); and using the cumulative feature map for each of the plurality of video clips as input, to learn a behavior recognition model for determining a behavior of an object included in a target video clip (Paragraph 9 of page 6, This discloses that a final behavior is determined based upon the combined results of the previous models), wherein the plurality of video clips comprises the object performing a same behavior, and wherein the behavior time, represents a time consumed from start to end of the same behavior performed by the object and is different for each video clip (Paragraph 14 of page 4, The categories disclosed would contain people performing the same actions. It also merely discloses that the times are “about ten seconds” which is a relative term that would allow for the clips to be of different times).
In regards to claim 2, Zhang discloses wherein the generating of the cumulative feature map for each video clip comprises: extracting the object from the generated sampling frames for each video clip (Abstract, discloses that the skeleton features of a human are extracted from the video which would read upon an object being extracted from the video clip); extracting skeleton coordinates of the extracted object for each sampling frame and generating skeleton feature points based on the skeleton coordinates (Abstract, Discloses that the skeleton points are extracted and that skeleton feature information is generated from these points); and generating the cumulative feature map for each video clip by accumulating the generated skeleton feature points in a temporal order for each video clip (Abstract, The double flow feature extraction disclosed combines the elements in a way that is analogous to the cumulative feature map).
In regards to claim 3, Zhang discloses further comprising: determining the behavior of the object included in the target video clip using the behavior recognition model learned based on the cumulative feature map (Paragraph 9 of page 6, This discloses that a final behavior is determined based upon the combined results of the previous models).
In regards to claim 11, it is similar to claim 1, and it is similarly rejected.
In regards to claim 12, it is similar to claim 2, and it is similarly rejected.
In regards to claim 13, it is similar to claim 3, and it is similarly rejected.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (CN112580559A), hereinafter referred to as Zhang, in view of Yalin et al. (EP 4074563 A1), hereinafter referred to as Yalin.
Zhang does not explicitly disclose wherein the determining of the behavior of the object included in the target video clip using the behavior recognition model comprises: generating target sampling frames by sampling the target video clip at a second sampling interval; generating target skeleton coordinates for each target sampling frame by extracting skeleton coordinates of the object from each target sampling frame; and determining the behavior of the object based on the target skeleton coordinates for each target sampling frame, wherein the second sampling interval is equal to the first sampling interval.
However, Yalin does disclose wherein the determining of the behavior of the object included in the target video clip using the behavior recognition model comprises: generating target sampling frames by sampling the target video clip at a second sampling interval (Paragraph 57, This paragraph specifies that the skeleton points are given skeleton coordinates); generating target skeleton coordinates for each target sampling frame by extracting skeleton coordinates of the object from each target sampling frame (Paragraph 57, This paragraph specifies that the skeleton points are given skeleton coordinates); and determining the behavior of the object based on the target skeleton coordinates for each target sampling frame, wherein the second sampling interval is equal to the first sampling interval (Paragraph 10, Specifies that the process requires interval sampling of the skeleton node locations for a plurality of moments to show the behavior of the target object. Since no sampling interval is specified, the sampling interval used via claim one could work.).
It would have been prima facie obvious to combine the teachings of Yalin and Zhang as it would have led to a predictable increase in accuracy. Yalin discloses coordinates that would lead to a predictable increase in accuracy. The usage of coordinates would allow for more accurate measures of how far each of the skeleton points would move by giving each a specific value that can be mapped to a coordinate grid. As such, it would be prima facie obvious to combine these two references.
In regards to claim 14, it is similar to claim 4, and it is similarly rejected.
Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (CN112580559A), hereinafter referred to as Zhang, in view of Yalin et al. (EP 4074563 A1), hereinafter referred to as Yalin, as applied to claims 4 and 14 above, and further in view of Han (CN 115188076 A).
In regards to claim 5, Yalin discloses wherein the determining of the behavior of the object comprises: generating target skeleton feature points for each target sampling frame from the target skeleton coordinates for each target sampling frame (Paragraph 57, This paragraph specifies that the skeleton points are given skeleton coordinates); and Zhang discloses selecting P sections from the queue, P being a positive integer (Paragraph 9 of page 6, This discloses that a final behavior is determined based upon the combined results of the previous models with P target and p target cumulative maps being 1 or more sections); generating P target cumulative feature maps, each generated based on temporal accumulation of skeleton feature points, accumulating, for each selected section, the target skeleton feature points in temporal order; and determining the behavior of the object by inputting the P target cumulative feature maps into the behavior recognition model (Paragraph 9 of page 6, This discloses that a final behavior is determined based upon the combined results of the previous models with P target and p target cumulative maps being undefined they are being treated as generic maps that display a target and a p target is merely a target).
It would have been prima facie obvious to combine the teachings of Yalin and Zhang as it would have led to a predictable increase in accuracy. Yalin discloses coordinates that would lead to a predictable increase in accuracy. The usage of coordinates would allow for more accurate measures of how far each of the skeleton points would move by giving each a specific value that can be mapped to a coordinate grid. As such, it would be prima facie obvious to combine these two references.
Neither Zhang nor Yalin explicitly discloses storing the target skeleton feature points for each target sampling frame in a queue configured to store data according to a temporal order.
However, Han discloses storing the target skeleton feature points for each target sampling frame in a queue configured to store data according to a temporal order (Paragraph 9 of Page 8, The paragraph describes that the disclosed queue is a cache that is organized in temporal order that contains data on the classification features of a target.)
It would be prima facie obvious to combine these references. It would be simple substitution to substitute the queue of Han containing classification features with the skeleton feature points of Zhang. Both are information that pertains to a particular target with Zhang’s information merely being just more specific than that of Han. As such, it would be prima facie obvious to combine the teachings of these arts.
In regards to claim 6, Han discloses wherein the generating of the P target cumulative feature maps comprises selecting P sections adjacent in time based on a storage space of the queue corresponding to a point in time of a current target sampling frame and generating each of the P target cumulative feature maps by temporally accumulating the target skeleton feature points included in each selected section (Paragraph 9 of Page 8, The paragraph describes that the disclosed queue is a cache that is organized in temporal order, which would cover the concept of the various sections being adjacent in time based on the storage space of the queue, that contains data on the classification features of a target).
In regards to claim 15, it is similar to claim 5, and it is similarly rejected.
In regards to claim 16, it is similar to claim 6, and it is similarly rejected.
Claims 7-8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (CN112580559A), hereinafter referred to as Zhang, in view of Yalin et al. (EP 4074563 A1), hereinafter referred to as Yalin and in view of Han (CN 115188076 A).
In regards to claim 7, Zhang discloses and determining a behavior of the object by inputting the P target cumulative feature maps into a behavior recognition model, wherein the behavior recognition model is configured to be learned based on a plurality of video clips including the object performing a same behavior (Paragraph 14 of page 4, The categories disclosed would contain people performing the same actions. It also merely discloses that the times are “about ten seconds” which is a relative term that would allow for the clips to be of different times), and wherein the behavior time represents a time consumed from start to end of the same behavior performed by the object and is different for each video clip (Paragraph 14 of page 4, The categories disclosed would contain people performing the same actions. It also merely discloses that the times are “about ten seconds” which is a relative term that would allow for the clips to be of different times).
Zhang does not explicitly disclose a method of operating an electronic device, the method comprising: generating target sampling frames for a target video clip at a second sampling interval; generating target skeleton coordinates for each target sampling frame by extracting skeleton coordinates of an object included in the target video clip; generating target skeleton feature points for each target sampling frame by extracting skeleton feature points from the target skeleton coordinates for each target sampling frame; storing the target skeleton feature points for each target sampling frame in a queue configured to store data according to a temporal order; generating P target cumulative feature maps by temporally accumulating target skeleton feature points included in P respective sections selected from the queue, P being a positive integer, thereby integrating a variable number of target sampling frames into each target cumulative feature map.
Yalin does disclose a method of operating an electronic device, the method comprising: generating target sampling frames for a target video clip at a second sampling interval; generating target skeleton coordinates for each target sampling frame by extracting skeleton coordinates of an object included in the target video clip (Paragraph 57, This paragraph specifies that the skeleton points are given skeleton coordinates); generating target skeleton feature points for each target sampling frame by extracting skeleton feature points from the target skeleton coordinates for each target sampling frame (Paragraph 57, This paragraph specifies that the skeleton points are given skeleton coordinates for each frame provided).
It would have been prima facie obvious to combine the teachings of Yalin and Zhang as it would have led to a predictable increase in accuracy. Yalin discloses coordinates that would lead to a predictable increase in accuracy. The usage of coordinates would allow for more accurate measures of how far each of the skeleton points would move by giving each a specific value that can be mapped to a coordinate grid. As such, it would be prima facie obvious to combine these two references.
Yalin does not explicitly disclose storing the target skeleton feature points for each target sampling frame in a queue configured to store data according to a temporal order; generating P target cumulative feature maps by temporally accumulating target skeleton feature points included in P respective sections selected from the queue, P being a positive integer, thereby integrating a variable number of target sampling frames into each target cumulative feature map.
Han does disclose storing the target skeleton feature points for each target sampling frame in a queue configured to store data according to a temporal order; generating P target cumulative feature maps by temporally accumulating target skeleton feature points included in P respective sections selected from the queue, P being a positive integer, thereby integrating a variable number of target sampling frames into each target cumulative feature map (Paragraph 9 of Page 8, The paragraph describes that the disclosed queue is a cache that is organized in temporal order that contains data on the classification features of a target).
It would be prima facie obvious to combine these references. It would be simple substitution to substitute the queue of Han containing classification features with the skeleton feature points of Zhang. Both are information that pertains to a particular target with Zhang’s information merely being just more specific than that of Han. As such, it would be prima facie obvious to combine the teachings of these arts.
In regards to claim 8, it is similar to claim 6, and it is similarly rejected.
In regards to claim 10, Zhang discloses wherein the learning of the behavior recognition model comprises: generating sampling frames for each of a plurality of video clips at a first sampling interval that remains constant despite variation in behavior time among the plurality of video clips, such that different numbers of sampling frames are generated for the plurality of video clips(Page 4 from the Paragraph denoted S3 to the bottom of the page, The method generates sampling frames at a rate by sampling every other frame making the sampled frames the ”sampling frames”); generating, for each of the plurality of video clips, a cumulative feature map generated based on temporal accumulation of skeleton feature points extracted from the generated sampling frames for each video clip, thereby integrating a variable number of sampling frames into a single cumulative feature representation (Last paragraph of page 4 and first paragraph of page 5, The disclosed multi-channel map is analogous to the disclosed cumulative feature map as it combines multiple channels together); and using the cumulative feature map for each of the plurality of video clips as input, to learn the a behavior recognition model for determining the behavior of the object included in the target video clip (Paragraph 9 of page 6, This discloses that a final behavior is determined based upon the combined results of the previous models).
Zhang does not explicitly disclose wherein the first sampling interval is equal to the second sampling interval.
However, Yalin does disclose wherein the first sampling interval is equal to the second sampling interval (Paragraph 10, Specifies that the process requires interval sampling of the skeleton node locations for a plurality of moments to show the behavior of the target object. Since no sampling interval is specified, the sampling interval used via claim one could work).
Response to Amendment
The amendment entered 3/10/2026 has been considered in full. The amendment overcomes the objection to the title, and it overcomes a previous 112(b) rejection. However, amended language introduces a new 112(b) issue which examiner has suggested potential remedies for. Amendment entered does not overcome the previous grounds of rejection based on the prior art.
Response to Arguments
Applicant's arguments filed 3/10/2026 have been fully considered but they are not persuasive. Applicant alleges that Zhang’s sampling does not extend to other video clips. Firstly, Zhang is directed to sampling multiple videos, at least 600 videos for each category with a total of 600 categories, or at least 360,000 videos according to page 4 of Zhang. Zhang’s method is applied to all of those videos and video clips, so the argument that Zhang does not disclose performing this method across multiple videos and clips is not persuasive. Zhang’s method of sampling every other frame across videos of varying lengths would result in a different number of frames per video as the videos all have varying numbers of frames. Arguments (ii)-(iv) allege that both the feature maps of Zhang are different from the claimed maps without providing a real rationale as to why they are. Further, the alleged difference of “temporally accumulating skeleton feature points” would appear to be analogous to watching the video from the temporal beginning to the temporal ending or from start to finish and accumulating skeleton feature points, which is the primary point of spatiotemporal analysis. In argument (v), applicant alleges that Zhang does not recognize variation in behavior time as a technical limitation to be addressed. Zhang discloses that the clips can vary in length and that they are generally around ten seconds long. Zhang discloses that the behavior time is different for each clip as each clip varies in the amount of time. As such, argument (v) is not persuasive. Arguments (vi) and (vii) have been considered against Yalin and Han, but they are not persuasive. These arguments are not persuasive since neither Yalin or Han was used in the rejections of claims 1 or 11. Both of those claims were rejected under 35 U.S.C. 102, and neither Yalin or Han were relied upon for those rejections. So, these arguments are not persuasive.
In regards to claim 7, the arguments against Zhang are not persuasive for the reasons that were recited in the arguments against the 102 rejections of claims 1 and 11. The arguments against Yalin in regards to claim 7 are not persuasive. Yalin is not relied upon for any of the instances of the cumulative feature maps. Furthermore, even assuming, arguendo, that Yalin was relied upon for this, which Yalin is not, paragraphs 146 and 147 of Yalin disclose the usage of feature maps. As such, the arguments against Yalin are not persuasive. Further, in regards to the arguments against Han, they are not persuasive. Han is describing a queue that is organized temporally, but the queue described is a First in First out system. As shown in the last seven paragraphs of page 9 of Han, the queue stores the information frame by frame in chronological order which would align with the temporal accumulation. As such, the argument is not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bose et al. (US 20230260552 A1) was found in the further search, and a similar cache structure to the one disclosed by the claim interpretation and remarks by the applicant, and it was not relied upon as Han covers this concept already.
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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CONOR AIDAN. O'MALLEY
Examiner
Art Unit 2675
/CONOR A O'MALLEY/ Examiner, Art Unit 2675
/ANDREW M MOYER/ Supervisory Patent Examiner, Art Unit 2675
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