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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/31/2026 has been entered.
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 1, 3, 11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (CN112580559A), hereinafter referred to as Zhang, in view of Brown et al. (US 20220148296 A1), hereinafter referred to as Brown.
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 (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) by extracting the object from the sampling frames of the corresponding 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, 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 accumulating the generated skeleton feature points in temporal order for the corresponding video clip (Abstract, The double flow feature extraction disclosed combines the elements in a way that is analogous to the cumulative feature map), thereby integrating a variable number of sampling frames into a single cumulative skeleton 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 including the skeleton features), 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).
Zhang does not explicitly disclose wherein the skeleton coordinates comprise x and y coordinates of skeletons included in the object, and wherein the skeleton feature points are expressed as a multi-dimensional array through a plurality of transformations of the skeleton coordinates.
However, Brown does disclose wherein the skeleton coordinates comprise x and y coordinates of skeletons included in the object, and wherein the skeleton feature points are expressed as a multi-dimensional array through a plurality of transformations of the skeleton coordinates (Paragraph 19 and claims 8 and 15, these paragraphs disclose the usage of a multi-dimensional array with x and y coordinates that are used to map out specific key skeleton points such as joints).
It would be prima facie obvious to combine the teachings of Zhang and Brown. It would lead to a predictable increase in accuracy to include the array of Brown with the system of Zhang. Zhang is directed to the identification of specific movements and actions by identifying the skeleton feature points. Using the method of Brown, one could plot out how specific movements correspond to movement upwards or to the sides which would allow for more accurate spatiotemporal tracking. As such, these references would be obvious to combine.
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 13, it is similar to claim 3, and it is similarly rejected.
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, and Brown et al. (US 20220148296 A1), hereinafter referred to as Brown, and further 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 Brown et al. (US 20220148296 A1), hereinafter referred to as Brown, and 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.
Response to Amendment
The amendment entered 8/31/2026 has been considered in full. It overcomes the 112(b) rejections of claims 5-8 and 15-16. It further overcomes the 102 rejections of claims 1, 3, 11, and 13 along with the 103 rejections of claims 7-8 and 10. However, after further search, new art was identified in a further search to reject claims 1, 3, 11, and 13 under 35 U.S.C. 103.
Allowable Subject Matter
Claims 7-8 and 10 are allowed.
The following is a statement of reasons for the indication of allowable subject matter: The newly added language to these claims overcame the 35 U.S.C. rejections to these claims. Further search was required, but no pertinent prior art was identified that would read upon the claims. The closest piece of prior art found was McCaughan et al. (US 20180108121 A1), hereinafter referred to as McCaughan. In paragraph 158, McCaughan discloses a buffer that is configured to store a peak or most recent maximum content metric or metrics associated with an image. However, this is clearly deficient to read upon the size limitation imposed upon the queue structure as claimed by applicant as it does not limit the size of the queue to have a maximum of just these metrics. As such, these claims were considered allowable over the prior art.
Response to Arguments
Applicant’s arguments, see pages 8-11, particularly to the second to last paragraph of page 11 , filed 08/31/2026, with respect to the rejections of amended claims 1 and 11 under 35 U.S.C. 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Brown et al. (US 20220148296 A1), hereinafter referred to as Brown.
With particular regard to the arguments at the last paragraph of page 10, Applicant’s arguments towards Zhang not disclosing the single cumulative skeleton feature representation in a temporal order are not persuasive. Applicant alleges that Zhang’s multi-channel feature map is significantly different from their own cumulative feature map. Zhang’s method requires the collection of key skeleton points over the entirety of the video in order and providing a spatiotemporal analysis of those key points in an overall feature map. This would align with applicant’s invention. However, Applicant was correct that Zhang does not disclose “the skeleton coordinates comprise x and y coordinates of skeletons included in the object, and wherein the skeleton feature points are expressed as a multi-dimensional array through a plurality of transformations of the skeleton coordinates”. As such, this necessitated bringing in Brown as a reference to reject the claims under 35 U.S.C. 103. Applicant makes further arguments against Han and Yalin which are not persuasive.
Applicant's arguments, in regards to the dependent claims, filed 08/31/2026 have been fully considered but they are not persuasive.
Yalin is not being relied on for the areas that applicant alleges Zhang fails, so this argument is not persuasive. Applicant alleges that Han is deficient as their queue is not an ordinary queue, but this seemingly contradicts applicant’s response to the claim interpretation provided in the response to the non-final rejection. In that, applicant identified the term queue to not be a particular FIFO data structure, but that it was rather a “memory buffer configured to store target skeleton feature points according to a temporal order”. That previous definition is seemingly an ordinary queue which would be disclosed by Han. As such, this argument is not persuasive.
Conclusion
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/CONOR A O'MALLEY/Examiner, Art Unit 2675
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698
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