DETAILED ACTION
This action is responsive to the Application filed on 08/10/2026. Claims 1-25 are pending in the case. Claims 1, 11 and 20 are independent claims. Claims 1, 11, 15 and 20 have been amended.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/10/2026 has been entered.
Response to Arguments
With respect to the rejection under 35 U.S.C. 101:
Applicant's arguments filed 08/10/2026 with respect to the 35 U.S.C 101 rejection have been fully considered but they are not persuasive.
Applicant quotes claim 1 and asserts that the claim cannot be performed in the human mind and as such is not directed to a mental process. Further, noting that the claimed elements are performed on image data to improve motion classification and that the claims require trained neural networks.
Examiner disagrees. While the claims recite using a neural network to determine a classification of motion and therefore indeed require a neural network, the claims nevertheless recite a mental process. Abstract decisions about data such as a classification of motion of objects in an image is nevertheless a process which can be performed in the mind. Such determinations do not require neural networks because decisions about classifications of data as well as identification and comparisons of such data are all processes which can be performed in the mind. At least under Step 2A prong 1 the claims therefore recite mental processes. While the claim recites a neural network, this is considered an additional element broadly recited for performing a mental process (see MPEP 2106.05(f)).
Further, Applicant argues the claims do not merely invoke a generic computer but a specific technological process highlighting a first neural network generation motion classifications while a second neural network analyzes relationships thus improving the accuracy of motion classification.
Examiner disagrees. Firstly, at least claim 1 does not require such an embodiment and therefore such features are not recited in the claims. Each Independent claim only requires a single neural network for performing the classification. Indeed, dependent claim 15 recites two neural network networks, however the pair of neural network each individually perform abstract ideas and rather than improving the functioning of neural network technology, the disclosure describes an improvement to a mental process, i.e improvements to the accuracy of classification. As noted previously, classification of abstract data is a mental process and therefore cannot be considered an improvement in technology. The disclosure does not describe how particular functioning of image processing is improved.
While the disclosure generally relates to improving motion classification, for such an improvement to be considered a practical application the claims should reflect an improvement to how particular technology functions rather that claiming the solution itself as improvement.
Applicant continues noting that the following is a concrete improvement in image processing “improves the accuracy of motion classification by detecting and correcting structured errors in classifications generated for individual images based on relationships between classifications associated with different images”.
Examiner disagrees. Merely correcting labels based on relationships of classification describes an abstract idea alone. As noted in the MPEP the judicial exception alone cannot be the basis for an improvement in technology.
Applicant notes that claim 15 recites specific technological implementation using multiple neural networks and as such is directed to an improvement in computer-based time series processing.
Examiner disagrees. The mere presence of multiple neural network itself does not make the claim eligible. The claim recites two additional elements (i.e a first a second neural network) which each respectively perform recited judicial exceptions. As noted in the rejection the first neural network is merely used as a tool to generate a prediction, (MPEP 2016.05(f)). No details for how the first neural network is particularly suited to make improved predictions is described or reflected in the claims. Further, the claim describes the second neural network makes use of the predictions to generate corrected labels and learn errors, which again is using a neural network to perform a recited abstract idea. While the claim describes using the output of the first neural network to train the second neural network, there are no details regarding how the training is performed to suggest that the claims reflects an improvement to a training process or neural network functioning such that a particular technology is improved to increase motion classification.
Therefore, the rejection is maintained.
With respect to the rejection under 35 U.S.C. 112(a)
Applicant's arguments filed 08/10/2026 with respect to the 35 U.S.C 101 rejection have been fully considered but they are not persuasive.
Applicant cites paragraph 0014-0019, 0024-0025 and 0030 and figure 1 which describe “generating labels for individual time-associated data points, processing sequences of those labels, learning structured errors within those sequences, and correcting the labels based on the learned relationships.” And therefore, provides support for the claims “comparing the classification…” as claimed.
Examiner disagrees. Such cited section indeed describe correcting labels based on learned relationships. However, neither the cited sections nor any other section of the disclosure particularly describe:
“comparing the classification of at least one object determined for a first image of the two or more images with a classification of the at least one object determined for a second image of the two or more images to identify differences in the classification of the at least one object determined for the first image and the classification of the at least one object determined for the second image… to correct errors … based, at least in part, on the comparison.”
Comparing a classification of a first image with a classification of a second image and subsequently performing an error correction based on the resulting comparison is a wholly different analysis process than merely generating labels for data points to learn error to be corrected. No such comparison between classification of two images is described in the disclosure.
Therefore, the rejection is maintained.
With respect to the rejection under prior art:
Applicant's arguments filed 08/10/2026 with respect to the cited prior art have been fully considered and are persuasive.
As noted by Applicant, the step of combining outputs from different sub-models to generate a final classification does not identify differences in classification of an object for the first image and a classification of an object for the second image as claimed.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-25 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
Regarding Claim 1/11/20
Under step 1, claim 1 is directed to a method, which is directed to a process, one of the statutory categories. Under step 1, claim 11 is directed to one or more processors, which is directed to a machine, one of the statutory categories. Under step 1, claim 20 is directed to a non-transitory computer-readable storage medium, which is directed to a product of manufacture, one of the statutory categories.
Under Step 2A Prong 1, the claim(s) recites the following limitations which are considered mental evaluations “update at least one classification corresponding to one or more types of motion of an object by… to determine, for each of two or more images, a classification of motion of one or more objects at least partially depicted in the two or more images… comparing the classification of at least one object determined for a first image of the two or more images with a classification of the at least one object determined for a second image of the two or more images… to identify differences in the classification of the at least one object determined for the first image and the classification of the at least one object determined for the second image …to correct errors in at least one of the classifications based, at least in part, on the comparison.”
The human mind is capable of updating a classification, which is a mere label about abstract data, as well as making corrections, determination, comparisons and identifications about the data and/or images.
Step 2A Prong Two Analysis: The judicial exception in not integrated into a practical application. In particular, the claims recite the additional element(s) the limitations “using one or more neural networks… circuitry to use one or more computer processes … medium having stored thereon a set of instructions, which if performed by one or more processors, causes the one or more processors to use one or more neural networks… use one or more computer processes” amounts to mere instructions to apply a computer technology to an abstract idea, see MPEP 2106.05(f) consideration (2).
Accordingly, the recited additional elements, when taken alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, nor do they amount to significantly more, under Step 2B, than the judicial exception because they do not impose any meaningful limits on practicing the abstract idea.
Regarding Claim 2/12
The claim is dependent upon a rejected claim. The claim recites more abstract ideas: “to identify motion of the one or more objects by predicting labels for the one or more objects utilizing time-series regression.” Under Step 2A Prong 1, these limitations correspond to a mental evaluation.
The claim recites the following additional element(s), in addition to those already identified in the parent claim: (“using the one or more neural networks”) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The recited additional elements do not integrate the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 3
The claim is dependent upon a rejected claim. The claim recites more abstract ideas: “to generate one or more labels corresponding to the one or more objects in each frame of the video… to correct errors in the generated one or more labels by indicating which of the generated one or more labels is correct.” Under Step 2A Prong 1, these limitations correspond to a mental evaluation.
The claim recites the following additional element(s), in addition to those already identified in the parent claim: (“using the one or more neural networks”) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The recited additional elements do not integrate the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 4/14
The claim depends upon a rejected claim. The claim recites the following additional element(s), in addition to those already identified in the parent claim: (“wherein the one or more neural networks comprise a one- dimensional (ID) convolutional neural network (CNN).”) is generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h). The recited additional elements do not integrate the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 5
The claim is dependent upon a rejected claim. The claim recites additional details which further describe the previously recited abstract idea: “wherein the classification of motion of one or more objects at least partially depicted in the two or more images comprise motion information about the one or more objects” Under Step 2A Prong 1, these limitations correspond to a mental evaluation.
The claim does not recite any more additional elements beyond those identified in the parent claim. These additional elements do not integrate the abstract idea into a practical application nor provide significantly more.
Regarding Claim 6/22
The claim is dependent upon a rejected claim. The claim recites more abstract ideas: “to perform error correction on one or more labels determined from the classification of the at least one object determined for the first image and the classification of the at least one object determined for the second image using ground truth labels corresponding to the one or more objects” Under Step 2A Prong 1, these limitations correspond to a mental evaluation.
The claim recites the following additional element(s), in addition to those already identified in the parent claim: (“the one or more neural networks… wherein the set of instructions, which if performed by the one or more processors, cause the one or more processors to use the one or more neural networks”) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The recited additional elements do not integrate the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 7
The claim is dependent upon a rejected claim. The claim recites more abstract ideas: “to perform error correction on one or more labels determined from the classification of the at least one object determined for the first image and the classification of the at least one object determined for the second image by creating and returning error-corrected labels” Under Step 2A Prong 1, these limitations correspond to a mental evaluation.
The claim recites the following additional element(s), in addition to those already identified in the parent claim: (“the one or more neural networks”) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The recited additional elements do not integrate the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 8/16
The claim is dependent upon a rejected claim. The claim recites more abstract ideas: “to perform error correction on one or more labels of a different set of the one or more objects.” Under Step 2A Prong 1, these limitations correspond to a mental evaluation.
The claim recites the following additional element(s), in addition to those already identified in the parent claim: (“the one or more neural networks”) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The recited additional elements do not integrate the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 9/19
The claim is dependent upon a rejected claim. The claim recites additional details which further describe the previously recited abstract idea: “wherein the two or more images comprise a plurality of time-associated motion capture (mocap) data points.”, “the two or more images include mocap data.” Under Step 2A Prong 1, these limitations correspond to a mental evaluation.
The claim does not recite any more additional elements beyond those identified in the parent claim. These additional elements do not integrate the abstract idea into a practical application nor provide significantly more.
Regarding Claim 10/17/18
The claim is dependent upon a rejected claim. The claim recites additional details which further describe the previously recited abstract idea: “wherein the at least one of the classifications comprise one or more predicted labels that correspond to a phase value for each instance of mocap data… wherein the classifications comprise phase value information about the one or more objects. … to generate a predicted phase value to each image of the two or more images” Under Step 2A Prong 1, these limitations correspond to a mental evaluation.
The claim does not recite any more additional elements beyond those identified in the parent claim. These additional elements do not integrate the abstract idea into a practical application nor provide significantly more.
Regarding Claim 13
The claim depends upon a rejected claim. The claim recites the following additional element(s), in addition to those already identified in the parent claim: (“train the one or more neural networks using one or more labels from the at least one of the classifications and one or more ground truth labels corresponding to the one or more objects.”) is generally linking the use of the judicial exception to a particular technological environment or field of use because no limitation provide specifics or details regarding how training is performed stating that labels of object motion are used merely link the judicial exception to a field of use , see MPEP 2106.05(h). The recited additional elements do not integrate the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 15
The claim is dependent upon a rejected claim. The claim recites more abstract ideas: “to generate predicted labels corresponding to classifications of motion of one or more objects for a sequence of time-associated data points associated with the two or more images… to learn structured temporal prediction errors generated by the first neural network… one or more error-corrected labels for the sequence of time-associated data points analyzing one or more labels of the at least one of the classifications. ” Under Step 2A Prong 1, these limitations correspond to a mental evaluation. Generation of labels and their corrections is an analysis of data which can be performed in the mind. Similarly, learning errors generated by a model is an evaluation of a performance or results of the model and does not require any mental execution of the model itself.
The claim recites the following additional element(s), in addition to those already identified in the parent claim: (“wherein the circuitry is to use the one or more neural networks… by the second neural network”) that are mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
(“training a second neural network comprising a one-dimensional convolutional neural network using the predicted labels and ground-truth labels corresponding to the sequence of time-associated data points.”) is generally linking the use of the judicial exception to a particular technological environment or field of use because no limitation provide specifics or details regarding how training is performed stating that labels of object motion are used merely link the judicial exception to a field of use , see MPEP 2106.05(h)
The recited additional elements do not integrate the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 21
The claim is dependent upon a rejected claim. The claim recites more abstract ideas: “to generate one or more labels in each of the first image and the second image and in the two or more images to indicate which part of the motion is being performed.” Under Step 2A Prong 1, these limitations correspond to a mental evaluation.
The claim recites the following additional element(s), in addition to those already identified in the parent claim: (“wherein the set of instructions, which if performed by the one or more processors, cause the one or more processors to use the one or more neural networks”) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The recited additional elements do not integrate the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 22
The claim is dependent upon a rejected claim. The claim recites more abstract ideas: “to perform error correction on an identification of the motion of the one or more objects by using one or more labels generated by the one or more neural networks.” Under Step 2A Prong 1, these limitations correspond to a mental evaluation.
The claim recites the following additional element(s), in addition to those already identified in the parent claim: (“wherein the set of instructions, which if performed by the one or more processors, cause the one or more processors to use the one or more neural networks”) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The recited additional elements do not integrate the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 23
The claim is dependent upon a rejected claim. The claim recites more abstract ideas: “to indicate whether an identification of the motion of one or more objects by the one or more neural networks is correct” Under Step 2A Prong 1, these limitations correspond to a mental evaluation.
The claim recites the following additional element(s), in addition to those already identified in the parent claim: (“wherein the set of instructions, which if performed by the one or more processors, cause the one or more processors to use the one or more neural networks”) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The recited additional elements do not integrate the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 24
The claim is dependent upon a rejected claim. The claim recites additional details which further describe the previously recited abstract idea: “wherein the two or more images are included in a single video” Under Step 2A Prong 1, these limitations correspond to a mental evaluation.
The claim does not recite any more additional elements beyond those identified in the parent claim. These additional elements do not integrate the abstract idea into a practical application nor provide significantly more.
Regarding Claim 25
The claim is dependent upon a rejected claim. The claim recites additional details which further describe the previously recited abstract idea: “wherein the at least one of the classifications comprise motion data about the one or more objects in the two or more images.” Under Step 2A Prong 1, these limitations correspond to a mental evaluation.
The claim does not recite any more additional elements beyond those identified in the parent claim. These additional elements do not integrate the abstract idea into a practical application nor provide significantly more.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitation. The disclosure makes no mention of
“comparing the classification of at least one object determined for a first image of the two or more images with a classification of the at least one object determined for a second image of the two or more images;
to identify differences in the classification of the at least one object determined for the first image and the classification of the at least one object determined for the second image
and using the one or more neural networks to correct errors in at least one of the classifications based, at least in part, on the comparison.” (claim 1, 11 and 20).
Specifically, while the cited portions of the disclosure describe producing error corrected labels of classifications. The disclosure does not describe comparing a classification of a first image with a classification of a second image in order to identify differences in classifications. Therefore, the disclosure does not describe correcting such errors based on said comparison.
Claim 2-10, 12-19, 21-25 are rejected by virtue of dependency.
Allowable Subject Matter
Claims 1-25 are rejected under 35 U.S.C 101 and 35 U.S.C. 112(a).
Specifically, none of the reference of record either alone or in combination fairly disclose or suggest the limitations of claim 1
comparing the classification of at least one object determined for a first image of the two or more images with a classification of the at least one object determined for a second image of the two or more images to identify differences in the classification of the at least one object determined for the first image and the classification of the at least one object determined for the second image;
and using the one or more neural networks to correct errors in at least one of the classifications based, at least in part, on the comparison.
The closest cited art of record El-Ghaish et al. “Human action recognition using a multi-modal hybrid deep learning model” describes merging features from a plurality of images in a final classification of motion, the model is trained on a plurality of classification over a plurality of instances to correct errors based on fine tuning the model. However, El-Ghaish does not describe identification of difference in classifications as claimed. Further Gidaris et al “Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling” describes refining and correcting the labels generated by an initial trained neural network for a given image via classifications made on the same image with a second refinement neural network model. The network does not explicitly identify differences between classifications of two source image classifications and rather corrects the classifications for a given single image. Further, Denton et al. “Unsupervised Learning of Disentangled Representations from Video” describes comparing the hidden state representation of a first and second video frame to correct errors in labeling via a loss function which identifies differences in features maps, the network does not however identify differences in classification of at least one object.
Specifically, none of the reference of record either alone or in combination fairly disclose or suggest the limitations of the independent claim. It would not have been obvious to one of ordinary skill in the art before the effective filing date to combine the references cited to teach at least the limitation above.
Conclusion
Prior art:
Gidaris et al “Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling” describes refining and correcting the labels generated by an initial trained neural network for a given image via classifications made on the same image with a second refinement neural network model.
Denton et al. “Unsupervised Learning of Disentangled Representations from Video” describes comparing the hidden state representation of a first and second video frame to correct errors in labeling via a loss function which identifies differences in features maps.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNATHAN R GERMICK whose telephone number is (571)272-8363. The examiner can normally be reached M-F 7:30-4:30.
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/J.R.G./
Examiner, Art Unit 2122
/KAKALI CHAKI/ Supervisory Patent Examiner, Art Unit 2122
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