DETAILED ACTION
This action is in response to claims filed 19 May 2023 for application 18320276 filed 19 May 2023. Currently claims 1-6 are pending.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 20200134444) in view of Long et al. (Learning Transferable Features with Deep Adaptation Networks).
Regarding claims 1, 5 and 6, Chen discloses: A machine learning device comprising:
a domain adaptability determination unit that determines a domain adaptability based on a precision of inference from images of a second domain using a first model trained by using images of a first domain as training data, the first model being a neural network (“Thus, present principles describe systems and methods for performing domain adaptation and optimization. According to the present disclosure, this may be performed not just by back propagating from the output/activation layer of the neural network once an error has been identified by a human supervisor or system administrator, but by running different but related training data through both the target domain and source domain and selecting any given hidden or intermediate layer for each domain that are parallel to each other to determine whether the outputs are similar or even the same. If the outputs are not similar statistically, as might be defined by a supervisor or administration, certain weight adjustments for the intermediate target layer can be performed as described herein to minimize the difference in outputs from the parallel layers (e.g., to ensure that the abstraction for the parallel layers are similar/the same) and thereby further optimize the target domain for the different type of data. Then, after training, testing may also be done to ensure that optimization has been performed to an acceptable degree.” [0054], see [0057] for image data. Note: the similarity of outputs is interpreted as the domain adaptability precision of inference).
Chen does not explicitly disclose, however, Long teaches: a learning layer determining unit that determines a layer in the second model, which is a duplicate of the first model, targeted for training, based on the domain adaptability (“Figure 1. The DAN architecture for learning transferable features. Since deep features eventually transition from general to specific along the network, (1) the features extracted by convolutional layers conv1–conv3 are general, hence these layers are frozen, (2) the features extracted by layers conv4–conv5 are slightly less transferable, hence these layers are learned via fine-tuning, and (3) fully connected layers fc6–fc8 are tailored to fit specific tasks, hence they are not transferable and should be adapted with MK-MMD.” P3); and
a transfer learning unit that applied transfer learning to the layer in the second model targeted for training, by using images of the second domain as training data (“Figure 1. The DAN architecture for learning transferable features. Since deep features eventually transition from general to specific along the network, (1) the features extracted by convolutional layers conv1–conv3 are general, hence these layers are frozen, (2) the features extracted by layers conv4–conv5 are slightly less transferable, hence these layers are learned via fine-tuning, and (3) fully connected layers fc6–fc8 are tailored to fit specific tasks, hence they are not transferable and should be adapted with MK-MMD.” P3, see also §4.1 wherein the training data comprises images and a transfer task is adapting a model from a source to a target domain of the images).
Chen and Long are in the same field of endeavor of transfer learning in image tasks and are analogous. Chen discloses a method that determines an inference measure between a source and target domain to determine layers that must be trained. Long teaches a method that implements transfer learning between source and target network layers. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the known transfer learning of Chen with the specific layers and transfer learning as taught by Long to yield predictable results of reducing computation in large datasets (Long, conclusion, improves the embedding matching effectiveness, while an unbiased estimate of the mean embedding naturally leads to a linear time algorithm that is very desirable for deep learning from large-scale datasets).
Regarding claim 2, Chen discloses: The machine learning device according to claim 1, wherein
the learning layer determination unit ensures that the lower the domain adaptability, the larger the number of layers targeted for training, and the higher the domain adaptability, the smaller the number of layers targeted for training (“Beginning with the discrepancy function method in reference to FIG. 3, it is to be understood that a discrepancy function may be used to calculate the distance of the overall data distribution between source and target data. The discrepancy loss can be defined by different metrics from any subset of layers of the source/target models, such as probability based distance between the source and target data extracted from multiple layers of the models (as will be described further below in reference), or by regularizing the parameter difference between the source and target models (as will also be described further below), or a weighted sum of these two types of loss (as will also be described further below). By jointly training with the discrepancy function, the model will be optimized to reduce the distribution difference to increase the generalization capability.” [0060], note: the larger the discrepancy the more layers will need to be trained).
Regarding claim 3, Chen discloses:
The machine learning device according to claim 1, wherein
the learning layer determination unit includes more of layers near an input layer as layers targeted for training, (“The device may then identify a first output from a first layer, with the first layer being an output/activation layer of the first neural network and with the first output being based on the first training data. The device may also identify a second output from a second layer, with the second layer being an output/activation layer of the second neural network and with the second output being based on the second training data. The device may then, based on the first and second outputs, determine a first adjustment to one or more weights of a third layer, with the third layer being an intermediate layer of the second neural network. The first adjustment may be determined, for example, via back-propagation from the second layer of the second neural network (the output/activation layer of the second neural network) using a first discrepancy/loss function.” [0066]).
Chen does not explicitly disclose, however, Long teaches: as the domain adaptability becomes lower (“Figure 1. The DAN architecture for learning transferable features. Since deep features eventually transition from general to specific along the network, (1) the features extracted by convolutional layers conv1–conv3 are general, hence these layers are frozen, (2) the features extracted by layers conv4–conv5 are slightly less transferable, hence these layers are learned via fine-tuning, and (3) fully connected layers fc6–fc8 are tailored to fit specific tasks, hence they are not transferable and should be adapted with MK-MMD.” P3, note: low domain adaptability would indicate more training is needed on earlier layers in this framework).
Regarding claim 4, Chen does not explicitly disclose, however, Long teaches: The machine learning device according to claim 1, wherein
the learning layer determination unit determines only full-connected layers to be layers targeted for training when the domain adaptability is equal to or higher than a predetermined value (“Figure 1. The DAN architecture for learning transferable features. Since deep features eventually transition from general to specific along the network, (1) the features extracted by convolutional layers conv1–conv3 are general, hence these layers are frozen, (2) the features extracted by layers conv4–conv5 are slightly less transferable, hence these layers are learned via fine-tuning, and (3) fully connected layers fc6–fc8 are tailored to fit specific tasks, hence they are not transferable and should be adapted with MK-MMD.” P3).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Xu et al. (US 20210264236) discloses domain adaptation.
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/ERIC NILSSON/ Primary Examiner, Art Unit 2151