DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to pending claims 1-20 filed 6/8/2023.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over Ferdinand ("Attenuating catastrophic forgetting by joint contrastive and incremental learning", published 8/23/2022) in view of He ("Momentum contrast for unsupervised visual representation learning", published 2020).
For claim 1, Ferdinand discloses: a computer-implemented method for training a classification model (fig.1 gives overview of model training, with §5, §5.1 disclosing various computer vision tasks), the computer-implemented method comprising:
obtaining a labeled data set of images comprising data from a rehearsal memory and new input data (fig.1, “augmented minibatch”; §4.2 ¶1: augmented image minibatches are obtained; §4.1 ¶1, §5.2 ¶2: rehearsal memory and implementation);
augmenting the labeled data set to generate a first data set and a second data set, wherein each image of the first data set corresponds to a corresponding image of the second data set (ibid: contrastive and complementary image sets are obtained);
inputting the first data set into a query encoder and inputting the second data set into a key encoder to obtain encodings output by the query encoder and the key encoder (fig.1: augmented input is passed to identically structured query student and key teacher encoders, each of which generate respective projection (gamma) and feature (F) outputs, see §4.2 ¶1-2);
obtaining a contrastive loss based on the encodings using a sum of a first contrastive loss function and a second contrastive loss function (§4 gives overview of total loss as sum of contrastive losses, see also fig.1 outlining contrastive losses generated from supervised data on the query network (L_con) and contrastive distillation loss (L_DCon));
updating parameters of the query encoder based on the obtained contrastive loss (§4: updating parameters based on losses); and
updating parameters of the key encoder based on parameters of the query encoder (§2.1 ¶2: transferring parameters from query to key at each incremental step, with §5.1 ¶2 disclosing 250 epochs for each step).
Ferdinand does not disclose: wherein the key encoder is a momentum encoder.
He discloses: wherein the key encoder is a momentum encoder (§3.2 “Momentum update” gives overview of a momentum encoder with exponentially weighted moving average per iteration, see eq.(2)).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Ferdinand by incorporating the momentum encoding of He. Both concern the art of contrastive incremental learning, and the incorporation would have, according to He, improve performance in contrastive loss applications by better sampling the underlying visual space and hence providing more consistent representations (§1 ¶3-4).
For claim 2, Ferdinand modified by He discloses the method of claim 1, as described above. Ferdinand further discloses: updating the rehearsal memory with samples of the new input data (§5.1 ¶2: overview of rehearsal memory implementation with memory bank and using herding sampling to update).
For claim 3, Ferdinand modified by He discloses the method of claim 2, as described above. Ferdinand modified by He further discloses: wherein the rehearsal memory is updated based on a balanced-fine tuning such that a number of samples of data existing in the rehearsal memory from previous tasks is equal to a number of samples of the new input data to be stored in the rehearsal memory (He §3.2: “Dictionary as a Queue” discloses storing rehearsal memory as a queue, with the oldest removed and the newest added. Hence, the number from previous tasks will be equal to the number of new input as they are both a minibatch size).
For claim 4, Ferdinand modified by He discloses the method of claim 3, as described above. Ferdinand modified by He further discloses: wherein images of the samples of data existing in the rehearsal memory from previous tasks and images of the samples of the new input data to be stored in the rehearsal memory are both selected randomly (He §3.2 ¶1).
For claim 5, Ferdinand modified by He discloses the method of claim 1, as described above. Ferdinand further discloses: wherein the query encoder and the momentum encoder have a same size and configuration (§2.1 ¶2: as the key encoder is the previous step query encoder, they are identically sized and configured).
For claim 6, Ferdinand modified by He discloses the method of claim 1, as described above. Ferdinand further discloses: wherein the first contrastive loss function is configured to identify encodings of different views of a same input image as anchor-positive pairs in a feature space (fig.1: contrastive distillation loss performed on augmented versions of each image, see §4.3 ¶3, hence, different views or augmentations act as anchor positive pairs).
For claim 7, Ferdinand modified by He discloses the method of claim 6, as described above. Ferdinand further discloses: wherein the second contrastive loss function is configured to identify encodings of two different sample images from a same class as anchor-positive pairs in the feature space (fig.1: contrastive loss pushes together all images of a class, hence, two different images would act as anchor positive pairs, see §4.2 ¶1: minimizing according to samples of the same labels in set P).
For claim 8, Ferdinand modified by He discloses the method of claim 6, as described above. He further discloses: wherein parameters of the momentum encoder are updated based on exponentially weighted moving averages of the parameters of the query encoder (§3.2 eq.2 shows step-wise exponential moving average).
Claim 9-16 disclose computer media corresponding to the above limitations and hence are rejected for the same reasons.
Claim(s) 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ferdinand ("Attenuating catastrophic forgetting by joint contrastive and incremental learning", published 8/23/2022) in view of He ("Momentum contrast for unsupervised visual representation learning", published 2020) in view of Dunne (US 20200272899 A1).
For claim 17, Ferdinand discloses: obtain a labeled data set of images comprising data from a rehearsal memory stored in the memory and new input data (fig.1, “augmented minibatch”; §4.2 ¶1: augmented image minibatches are obtained; §4.1 ¶1, §5.2 ¶2: rehearsal memory and implementation);
augment the labeled data set to generate a first data set and a second data set, wherein each image of the first data set corresponds to a corresponding image of the second data set (ibid: contrastive and complementary image sets are obtained);
input the first data set into a query encoder and inputting the second data set into a key encoder to obtain encodings output by the query encoder and the key encoder (fig.1: augmented input is passed to identically structured query student and key teacher encoders, each of which generate respective projection (gamma) and feature (F) outputs, see §4.2 ¶1-2);
obtain a contrastive loss based on the encodings using a sum of a first contrastive loss function and a second contrastive loss function (§4 gives overview of total loss as sum of contrastive losses, see also fig.1 outlining contrastive losses generated from supervised data on the query network (L_con) and contrastive distillation loss (L_DCon));
update parameters of the query encoder based on the obtained contrastive loss (§4: updating parameters based on losses);
update parameters of the key encoder based on parameters of the query encoder (§2.1 ¶2: transferring parameters from query to key at each incremental step, with §5.1 ¶2 disclosing 250 epochs for each step); and
Ferdinand does not disclose: wherein the key encoder is a momentum encoder;
a computing device for training a classification model to be provided to an edge device, the computing device comprising:
a transceiver;
a memory; and
one or more processors configured to:
provide the classification model including parameters of the query encoder to the edge device via the transceiver.
He discloses: wherein the key encoder is a momentum encoder (§3.2 “Momentum update” gives overview of a momentum encoder with exponentially weighted moving average per iteration, see eq.(2)).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Ferdinand by incorporating the momentum encoding of He. Both concern the art of contrastive incremental learning, and the incorporation would have, according to He, improve performance in contrastive loss applications by better sampling the underlying visual space and hence providing more consistent representations (§1 ¶3-4).
Ferdinand modified by He does not disclose the remaining limitations.
Dunne discloses: a computing device for training a classification model to be provided to an edge device (fig.1 gives overview of model training and deployment to edge device), the computing device comprising:
a transceiver (fig.1B gives networking overview, with fig.4 showing sending and receiving between edge devices and a centralized training devices via a network, hence, transceiver);
a memory (fig.14: 1402); and
one or more processors (fig.4:1401) configured to:
provide the classification model including parameters of the query encoder to the edge device via the transceiver (fig.3:310 discloses deployment of classification model to an edge device via transceiver, see fig.4 showing sending and receiving of data, combination with Ferdinand and He yielding the provision of parameters of the trained query encoder to the edge device).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Ferdinand modified by He by incorporating the edge deployment of Dunne. Both concern the art of machine learning, and the incorporation would have, according to Dune, address current limitations in AI technology, such as computer power limitations (0002-3, 0047).
Claim 18-19 disclose devices corresponding to methods 2-3 and are hence rejected for the same reasons.
For claim 20, Ferdinand modified by He modified by Dunne discloses the method of claim 17, as described above. Ferdinand further discloses: wherein the first contrastive loss function is configured to identify encodings of different views of a same input image as anchor-positive pairs in a feature space (fig.1: contrastive distillation loss performed on augmented versions of each image, see §4.3 ¶3, hence, different views or augmentations act as anchor positive pairs), and
wherein the second contrastive loss function is configured to identify encodings of two different sample images from a same class as anchor-positive pairs in the feature space (fig.1: contrastive loss pushes together all images of a class, hence, two different images would act as anchor positive pairs, see §4.2 ¶1: minimizing according to samples of the same labels in set P).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lin ("Continual contrastive learning for image classification", published 2022) discloses contrastive loss with a distillation and momentum encoder, see fig.2.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG LI whose telephone number is (303)297-4263. The examiner can normally be reached Mon-Fri 9-12p, 3-11p MT (11-2p, 5-1a ET).
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/LIANG LI/
Primary examiner AU 2143