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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09.09.2026 has been entered.
Response to Amendment
The amendment filed 09/09/2026 has been entered. The status of the claims is as follows:
Claims 1-20 are pending in the application.
Claims 1, 10 and 16 are amended.
Response to Arguments
In reference to the rejections under 35 USC § 103:
Argument 1:
Wierzynski does not disclose the claimed three-part data arrangement. The claim requires: an initial set of data; a first portion of additional training data and a second portion of additional training data
Response to Argument 1:
This argument is not persuasive. Wierzynski expressly discloses a first training set D containing first data x and corresponding label y (¶[0067] – [0069]), a second training set D’ containing second data x’ and generated label y’ (¶[0072]), and a third training set D’’ containing third data x’’ and corresponding label y’’ (¶[0073]-[0077]). Wierzynski further teaches that the second network is trained using the second data and labels and is subsequently trained using the second data and labels augmented with the third data and labels (¶[0082], ¶[0086]). Accordingly, the first training set D corresponds to the claimed initial set of data, while the second and third training set D’ and D’’ constitute the set of additional training data, with D’ and D’’ respectively corresponding to its first and second portions.
Argument 2:
Anjaneyapura does not place the container on the client device, wherein claim 1 apparently requires the pruning procedure to be performed within a container located on the client device.
Response to Argument 2:
Applicant’s arguments filed 09.09.2026, with respect to the 103 rejections have been fully considered and are persuasive. The rejection of claims 1-15 has been withdrawn.
Argument 3:
Applicant asserts that Wierzynski fails to disclose providing additional training data through the interface, associated with an edge server storing pre-trained model, to cause the additional training, as recited in claim 16
Response to Argument 3:
Applicant’s argument is not persuasive because it addresses Wierzynski individually, whereas the rejection is based on the combined teachings of the cited references. Wierzynski is relied upon for teaching further training of a previously trained NN using additional training data, as disclosed in paragraphs ¶[0070]-¶[0077], ¶[0082] and ¶[0086]. Anjaneyapura further teaches that a user device interacts with a model-training system through a frontend interface and submits a training request identifying the input training data, as disclosed in paragraph ¶[0029]. Thus, the combined teachings disclose providing information identifying the additional training data through an interface to cause further training of the pre-trained model. It would have been obvious to provide Wierzynski’s additional training data through Anjaneyapura’s frontend interface to permit a user to initiate and control additional training through the model-training process. The references need not individually disclose every claimed feature or be bodily incorporated into one another. Accordingly, Wierzynski’s failure to independently describe the interface does not establish a deficiency in the rejection based on the references as a whole.
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.
Claim(s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over Wierzynsky (US 2017/0024641 A1) in view of Anjaneyapura et al. (WO 2019/104149 A1) (hereafter referred to as “Anjaneyapura”) and further in view of Nagaraju et al. (US 2018/0032915 A1)
Regarding Claim 16, Wierzynsky explicitly discloses:
providing, in response to the request for the stored pre-trained model, at least one pre-trained model stored in a local storage location of the edge server; and (Wierzynsky, ¶0036]: “In an aspect of the present disclosure, the instructions loaded into the general-purpose processor 102 may comprise code for receiving second labels generated by a first network using second data. The first network was previously trained on first labels and first data. The instructions loaded into the general-purpose processor 102 may also comprise code for training a second network on the second labels and the second data.”, ¶[0038]: “As illustrated in FIG. 2, the system 200 may have multiple local processing units 202 that may perform various operations of methods described herein. Each local processing unit 202 may comprise a local state memory 204 and a local parameter memory 206 that may store parameters of a neural network.”)
providing, in response to a second request for additional training corresponding to an application to be used with the at least one pre-trained model, additional training data through the interface to cause the additional training of the at least one pre-trained model using the additional training data for the at least one type of inference for one or more classes represented in the additional training data, (Wierzynsky, ¶[0011]: “In one configuration, the first network has been previously trained on first labels for first data.”, ¶[0033]: “Neural networks may be trained on a training set that includes labels and corresponding data to classify objects from an input. For example, a first neural network may be trained on labeled images of cars to identify different types of cars. In some cases, it may be desirable to add new classes and/or modify the boundaries of existing classes after a network has been trained.”, ¶[0070]: “In one configuration, the original training set may be augmented with additional data and labels to perform the incremental learning. That is, for incremental learning, it is desirable to augment the original training set with a new training set to avoid forgetting the classifications of the original training set. Incremental learning is not limited to augmenting classes or modifying the boundaries of existing classes as other incremental learning functions are also contemplated.”)
wherein the one or more classes, for the application, are separate from and in addition to one or more original classes for the pre-trained model, wherein the second request specifies at least one of the one or more classes. (Wierzynsky, ¶[0060]: “FIG. 5 is a block diagram illustrating the run-time operation 500 of an AI application on a smartphone 502. The AI application may include a pre-process module 504 that may be configured (using for example, the JAVA programming language) to convert the format of an image 506 and then crop and/or resize the image 508. The pre-processed image may then be communicated to a classify application 510 that contains a SceneDetect Backend Engine 512 that may be configured (using for example, the C programming language) to detect and classify scenes based on visual input.”, ¶[0070]: “In one configuration, the original training set may be augmented with additional data and labels to perform the incremental learning. That is, for incremental learning, it is desirable to augment the original training set with a new training set to avoid forgetting the classifications of the original training set.”, ¶[0071]: “In one configuration, when the first training set D is no longer available after training the first neural network F, a second neural network F' is specified to approximate the first neural network F. Specifically, when the first training set D is no longer available, the first neural network F may be applied to second data x'i, that does not include second labels y'i. In one configuration, the second data x'i, is substantially similar or identical to the first data x of the first training set D. Alternatively, the second data xi', may not be related to the first data x.”) [Examiner’s note: “a second portion of the set of additional training data” is being interpreted as the second data xi’, which is not related to (i.e., separate from) the first data x (i.e., the first portion set of additional training data)]
Wierzynsky fails to disclose:
receiving, through an interface associated with an edge server, a request for a stored pre-trained model to be provided which is able to perform at least one type of inference;
in response to the request for the stored pre-trained model
providing, in response to a second request for additional training of the at least one pre-trained model corresponding to an application to be used with the at least one pre-trained model, additional training data through the interface
However, Nagaraju explicitly discloses:
receiving, through an interface associated with an edge server, a request for a stored pre-trained model to be provided (Nagaraju, ¶[0061]: “The edge devices 12 discussed above can represent a broader category of computing devices commonly referred to as "client devices," which can each be operated under the control of a user. For example, FIG. 1 shows a client device 26 that can communicate with the components of the system 10 (e.g., the edge devices 12 or the server computer system 14) to receive or exchange information over the network 16. For example, a communication between the client device 26 and the components of the system 10 can include sending various requests and receiving data packets”, ¶[0062]: “the client device 26 or applications 28 running on the client device 26 may initiate communications with applications running on the edge devices 12 or the server computer system 14 to request specific content (e.g., edge data), and the applications at the edge devices 12 or the server computer system 14 may respond with the requested content stored in one or more data packets. Hence, the components of the system 10 can also represent a broader category of computing devices referred to as "host devices," which can host each other.”)
in response to the request for the stored pre-trained model (Nagaraju, ¶[0061]: “The edge devices 12 discussed above can represent a broader category of computing devices commonly referred to as "client devices," which can each be operated under the control of a user. For example, FIG. 1 shows a client device 26 that can communicate with the components of the system 10 (e.g., the edge devices 12 or the server computer system 14) to receive or exchange information over the network 16. For example, a communication between the client device 26 and the components of the system 10 can include sending various requests and receiving data packets”, ¶[0062]: “the client device 26 or applications 28 running on the client device 26 may initiate communications with applications running on the edge devices 12 or the server computer system 14 to request specific content (e.g., edge data), and the applications at the edge devices 12 or the server computer system 14 may respond with the requested content stored in one or more data packets. Hence, the components of the system 10 can also represent a broader category of computing devices referred to as "host devices," which can host each other.”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Wierzynsky and Nagaraju. Wierzynsky discloses a method of transfer learning in neural networks. Nagaraju discloses transmitting machine learning models to edge devices. One of ordinary skill would have motivation to combine Wierzynsky and Nagaraju because MPEP 2143 sets forth the Supreme Court rationales for obviousness including: (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results; (E): “Obvious to try” choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success; (F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of the ordinary skill in the art.
However Anjaneyapura explicitly discloses:
which is able to perform at least one type of inference; (Anjaneyapura, [0033]: “The model training system 120 can use the information provided by the user device 102 to train a machine learning model in one or more pre-established virtual machine instances 122 in some embodiments… The model training system 120 can automatically scale up and down based on the volume of training requests received from user devices 102 via frontend 129, thereby relieving the user from the burden of having to worry about over-utilization ( e.g., acquiring too little computing resources and suffering performance issues) or under-utilization (e.g., acquiring more computing resources than necessary to train the machine learning models, and thus overpaying).”, [00124]: “With these hosted models (e.g., inference code 1024 executed by a container 1022), client applications 1008 - whether hosted within the provider network 199 or external to the provider network 199 - can issue requests via one or more inference endpoints 1010 (e.g., as HTTP requests) to perform inference using the model”) [Examiner’s note: a pre-trained model i.e., the pre-established virtual machine instances]
in response to a second request for additional training corresponding to an application to be used with the at least one pre-trained model (Anjaneyapura, [0033]: “The model training system 120 can use the information provided by the user device 102 to train a machine learning model in one or more pre-established virtual machine instances 122 in some embodiments… The model training system 120 can automatically scale up and down based on the volume of training requests received from user devices 102 via frontend 129, thereby relieving the user from the burden of having to worry about over-utilization ( e.g., acquiring too little computing resources and suffering performance issues) or under-utilization (e.g., acquiring more computing resources than necessary to train the machine learning models, and thus overpaying).”, [00124]: “With these hosted models (e.g., inference code 1024 executed by a container 1022), client applications 1008 - whether hosted within the provider network 199 or external to the provider network 199 - can issue requests via one or more inference endpoints 1010 (e.g., as HTTP requests) to perform inference using the model”) [Examiner’s note: a pre-trained model i.e., the pre-established virtual machine instances]
providing additional training data through the interface (Anjaneyapura, ¶[0029]: “The user devices 102 can interact with the model training system 120 via frontend 129 of the model training system 120. For example, a user device 102 can provide a training request to the frontend 129 that includes a container image (or multiple container images, or an identifier of one or multiple locations where container images are stored), an indicator of input data (e.g., an address or location of input data),”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Wierzynsky, Nagaraju and Anjaneyapura. Wierzynsky teaches a method of transfer learning in neural networks. Anjaneyapura teaches Techniques for packaging and deploying algorithms utilizing containers for training flexible machine learning to perform inference tasks. Nagaraju discloses transmitting machine learning models to edge devices. One of ordinary skill would have motivation to combine Wierzynsky, Nagaraju and Anjaneyapura because the edge servers have the benefit of low latency as they are located closer to the end-users or devices, applying edge servers in training helps reducing the time it takes for data to travel between the server and the users (Anjaneyapura, ¶[00117])
Claim(s) 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wierzynsky (US 2017/0024641 A1) in view of Anjaneyapura et al. (WO 2019/104149 A1) (hereafter referred to as “Anjaneyapura”), Dai et al. (“Incremental Learning Using a Grow-and-Prune Paradigm with Efficient Neural Networks”) (hereafter referred to as “Dai”) and in further view of Nagaraju et al. (US 2018/0032915 A1)
Regarding Claim 17, the combination of Wierzynsky, Nagaraju and Anjaneyapura discloses all the limitations of Claim 16 (as shown in the rejections above).
Wierzynsky in view of Anjaneyapura and Nagaraju fail to disclose:
pruning each machine learning model after the additional training is performed; and
However, Dai explicitly discloses:
pruning each machine learning model after the additional training is performed; and (Dai, Page 2, Col. 1, ¶[1]: “We first grow and prune a model with the initial data. When new data arrive, the network undergoes a growth phase (first, based on new data and then on all available data) that increases its size to accommodate new data and knowledge. Then, we employ a pruning phase to remove redundant parameters to obtain a compact inference model”)
retraining the pruned machine learning models using the additional training data. (Dai, Page 4, Col. 2, Section 4.2.2, ¶[1]: “To reduce the training cost of a model update, we introduce a mechanism to speed up the growth phase. Specifically, we first employ connection growth and parameter training only on the previously unseen data for a pre-defined number of epochs whenever new data become available. Then, we merge the new data with all the previously available training data, and perform growth and training on all existing data.”) [Examiner’s note: a portion of the additional training data that was omitted from the first portion of additional training data i.e., all existing data]
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Wierzynsky, Anjaneyapura, Nagaraju and Dai. Wierzynsky teaches a method of transfer learning in neural networks. Dai teaches an incremental learning framework based on a grow-and-prune neural network synthesis paradigm. Nagaraju discloses transmitting machine learning models to edge devices. Anjaneyapura teaches techniques for packaging and deploying algorithms utilizing containers for training flexible machine learning to perform inference tasks. One of ordinary skill would have motivation to combine Wierzynsky, Anjaneyapura, Nagaraju and Dai to produce a smaller model for the fine-tune process because a pruned model is smaller and can be fine-tuned more efficiently on new tasks, allowing for faster convergence and reduced training time (Dai, Page 5, Col. 2, Section 4.3.1: “Thus, we prune away redundant connections for compactness and to ensure efficient inference after the growth phase.”)
Regarding Claim 20, the combination of Wierzynsky, Dai, Nagaraju and Anjaneyapura discloses all the limitations of Claim 16 (as shown in the rejections above).
Wierzynsky in view of Dai, Nagaraju and Anjaneyapura further discloses:
performing augmentation of the additional training data before performing additional training of the machine learning model, (Dai, Page 7, Col. 1, ¶[2]: “We split the training set (with 55K images) randomly into ten different parts of equal size. In the incremental learning experiments, we start with one part to train the initial model for subsequent updates. We then add one part as new data each time in the incremental learning scenario. For each update, we perform growth on new data and all data for 15 epochs and 20 epochs in the growth phase, respectively. Then, we prune the post-growth network for compactness.”) [Examiner’s note: performing augmentation of the additional training data i.e., add one part as new data each time]
the augmentation increasing an amount of the additional training data through adjustment of at least one of orientation, color, resolution, or noise. (Wierzynsky, ¶[0060]: “The SceneDetect Backend Engine 512 may be configured to further preprocess 514 the image by scaling 516 and cropping 518. For example, the image may be scaled and cropped so that the resulting image is 224 pixels by 224 pixels. These dimensions may map to the input dimensions
of a neural network. The neural network may be configured by a deep neural network block 520 to cause various processing blocks of the SOC 100 to further process the image pixels with a deep neural network.”)
Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wierzynsky (US 2017/0024641 A1) in view of Dai et al. (“Incremental Learning Using a Grow-and-Prune Paradigm with Efficient Neural Networks”) (hereafter referred to as “Dai”), Anjaneyapura et al. (WO 2019/104149 A1) (hereafter referred to as “Anjaneyapura”) ,Ragesh & Rajesh (“Pedestrian Detection in Automotive Safety: Understanding State-of-the-Art”) (hereafter referred to as “Ragesh”) and in further view of Nagaraju et al. (US 2018/0032915 A1)
Regarding Claim 18, the combination of Wierzynsky, Nagaraju, Dai, Ragesh and Anjaneyapura discloses all the limitations of Claim 16 (as shown in the rejections above).
Wierzynsky in view of Dai, Ragesh, Nagaraju and Anjaneyapura further discloses:
for performing at least one of the additional training, model pruning, data augmentation, and model export, (Dai, Page 2, Col. 1, ¶[1]: “We first grow and prune a model with the initial data. When new data arrive, the network undergoes a growth phase (first, based on new data and then on all available data) that increases its size to accommodate new data and knowledge. Then, we employ a pruning phase to remove redundant parameters to obtain a compact inference model”)
wherein the toolkit is provided in a software container for execution on a target computing device. . (Anjaneyapura, [00123]: “In some embodiments, the machine learning models may be "custom" algorithms developed by users, and/or use custom code to train using existing algorithms such as deep learning frameworks (e.g., TensorFlow, Apache MXNet, etc.).”, [00125]: “Accordingly, in some embodiments, the training and/or hosting of machine learning models can be performed without needing significant knowledge on the part of users as to how these models are to be trained or used. For example, in some embodiments users can select or create a container including machine learning related code - potentially using any language(s)/package(s) that the user desires”) [Examiner’s note: the toolkit is interpreted as the TensorFlow as it contains the custom algorithms and it is included in a container created by the users]
providing a toolkit including at least a common interface and one or more modules for performing at least one of the additional training, model pruning, data augmentation, and model export, (Ragesh, Page 47880, Col. 2, ¶[1]: “Many of these architectures can be freely downloaded and used with DL programming frameworks like TensorFlow, Caffe, CNTK, PyTorch, Keras, Deeplearning4j, Matlab Deep learning toolkit etc. to implement different DL solutions based on our requirement. We can either freshly train these networks for a new problem or can use the existing knowledge from the pre-trained models and additionally train for the new problem using transfer learning [93].”) [Examiner’s note: a common interface i.e., the frameworks, the toolkit i.e., Matlab Deep learning toolkit, one or more modules of the toolkit is interpreted as the pre-trained model as it is an important component of the toolkit]
Regarding Claim 19, the combination of Wierzynsky, Nagaraju, Dai, Ragesh and Anjaneyapura discloses all the limitations of Claim 16 (as shown in the rejections above).
Wierzynsky in view of Dai, Ragesh, Nagaraju and Anjaneyapura further discloses:
selecting the at least one pre-trained model from a set of pre-trained models based at least in part upon the at least one type of inference to be performed. (Ragesh, Page 47865, Col. 1, ¶[3]: “In feature classifier (FC) approach, predefined features are used to represent pedestrian objects and a classifier classifies an object to a pedestrian or not based on the similarity of the features of the object-of-interest to that of the pre-trained model. The focus here will be how best a feature can be derived to distinguish a pedestrian object from other objects as well as how we arrive at a best pre-trained model to match the features of the object-of-interest.”) [Examiner’s note: The fact of arriving with the best pre-trained model is the same concept with selecting the best pre-trained model in the set of pre-trained models]
Allowable Subject Matter
Claims 1- 15 are allowed.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMY TRAN whose telephone number is (571)270-0693. The examiner can normally be reached Monday - Friday 7:30 am - 5:00 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AMY TRAN/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
