CTNF 18/384,525 CTNF 92113 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION 2. This action is in response to the original filing on 10/27/2023. Claims 1-20 are pending and have been considered below. Information Disclosure Statement 3. The information disclosure statement (IDS(s)) submitted on 10/27/2023 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 4. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea without significantly more. Step 1 , the claims are directed to a process, machine, and manufacture. Step 2A Prong 1, Claims 1, 9, and 16 recite, in part the HDC model having a number of dimensions and a number of prototypes, each dimension of the HDC model corresponding to a protype of the number of prototypes (Mathematical concepts, mathematical relationships) . Step 2A Prong 2 , this judicial exception is not integrated into a practical application. The additional elements: memory; and processing circuitry (mere instructions to apply the exception using a generic computer component). transmit the trained one or more independent sub models of the HDC model to another computing device (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity). Step 2B , the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination. The additional elements: memory; and processing circuitry (mere instructions to apply the exception using a generic computer component). transmit the trained one or more independent sub models of the HDC model to another computing device (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity). Claims 2-8, 10-15, 17-20 provide further limitations to the abstract idea ( Mathematical concepts and/or Mental processes ) as rejected in claims 1, 9, 16, however, they do not disclose any additional elements that would amount to a practical application or significantly more than an abstract idea ( data gathering / insignificant extra-solution activity and/or generic computer component ). Claim Rejections – 35 USC § 103 07-20-aia AIA 5. 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 of this title, 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 . 07-21-aia AIA 6. Claims 1 and 5-8 are r ejected under 35 U.S.C. 103 as being unpatentable over X u et al. (U.S. Patent Application Pub. No. US 20230409871 A1) in view of Sharad et al. (U.S. Patent Application Pub. No. US 20200285980 A1), and further in view of Sahand et al. (U.S. Patent Application Pub. No. US 20210334703 A1). C laim 1: Xu teaches a device to train a hyperdimensional computing (HDC) model (i.e. hyperdimensional computing (HDC) for classification has been emerging as a lightweight machine learning framework targeting inference on resource-constrained edge devices. HDC classifiers mimic the brain cognition process by representing an object as a vector (e.g., a hypervector) with a very high dimension … the training process of an HDC classifier is extremely simple—simply averaging over the hypervectors of labeled training samples to derive the corresponding class hypervectors; para. [0042, 0045]) , comprising: memory (i.e. memory; para. [0049]) ; and processing circuitry (i.e. processor; para. [0049]) to: train one or more sub models of the HDC model (i.e. Given K classes, the training process obtains K class hypervectors 205, each for one class; para. [0060, 0061]) , the HDC model having a number of dimensions (i.e. HDC classifiers mimic the brain cognition process by representing an object as a vector (e.g., a hypervector) with a very high dimension. The dimension can be on the order of thousands of bits, and potentially even higher numbers of bits; para. [0042, 0048, 0051]) and a number of prototypes (i.e. Given K classes, the training process obtains K class hypervectors 205, each for one class, re-training leads to an adjusted centroid for each class; para. [0060, 0061]) , dimension of the HDC model corresponding to a protype of the number of prototypes (i.e. The HDC encodes all the values, features, and samples as hyperdimensional bipolar vectors, e.g., H ∈ {1, −1} D=10,000; para. [0051, 0056, 0061, 0074]) ; and transmit the to another computing device (i.e. Client computer(s)/devices 50 and server computer(s) 60 provide processing, storage, and input/output devices executing application programs and the like. Client computer(s)/devices 50 can also be linked through communications network 70 to other computing devices, including other client devices/processes 50 and server computer(s) 60; para. [0104]) . Xu does not explicitly teach train one or more independent sub models; each dimension of the model corresponding to a protype; transmit the trained one or more independent sub models of the model to another computing device. However, Sharad teaches train one or more independent sub models of the HDC model (i.e. the aggregated group updates are obtained from a locally trained model L_i using local training data by each participant; para. [0019, 0020]) ; and transmit the trained one or more independent sub models of the HDC model to another computing device (i.e. the selected clients send updates to the central server for improving the global model based on their local data; para. [0014, 0020, 0045]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Xu to include the feature of Sharad. One would have been motivated to make this modification because it enables distributed or federated training of the HDC model, to reduce centralized data collection and preserve local data privacy. However, Salamat teaches each dimension of the HDC model corresponding to a protype of the number of prototypes (i.e. The similarity in the fixed-point and power-of-two number representations is defined as calculating the cosine similarity, which is obtained by multiplying each dimension in the query vector to the corresponding dimension of the class hypervectors, and adding up the partial products; para. [0005, 0034, 0036]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Xu and Sharad to include the feature of Salamat. One would have been motivated to make this modification because it provides a clearer dimension-wise correspondence between the HDC representation and the class representative vectors used for classification. Claim 5: Xu, Sharad, and Salamat teach the device of claim 1. Xu further teaches wherein to train the one or more sub models of the HDC model, the processing circuitry is to: transform one or more training data points to one or more hyperdimensional representations (i.e. FIG. 2 is a block diagram 200 illustrating an example an overview of an existing HDC model. In this example, an input sample is represented as a vector 201 with N features F={f 1 , f 2 , . . . f N }, where the value range for each feature is normalized and uniformly discretized into M values, i.e., f i ∈ {1, . . . , M} for i=1, . . . , N. The HDC encodes all the values, features, and samples as hyperdimensional bipolar vectors; para. [0051, 59, 60]) ; initialize a prototype using the one or more hyperdimensional representations of the one or more training data points (i.e. Given K classes, the training process obtains K class hypervectors 205, each for one class; para. [0060]) , wherein the initialized prototype is a sub-model of the one or more sub models of the HDC model (i.e. Given K classes, the training process obtains K class hypervectors 205, each for one class; para. [0060]) ; and iteratively train the initialized prototype (i.e. To improve accuracy, existing HDC models have also added re-training as part of the training process. Concretely, re-training fine tunes the class hypervectors C 205 derived ; para. [0061]) . Xu does not explicitly teach train the one or more independent sub models. However, Sharad further teaches train the one or more independent sub models (i.e. the aggregated group updates are obtained from a locally trained model L_i using local training data by each participant; para. [0019, 0020]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Xu and Salamat to include the feature of Sharad. One would have been motivated to make this modification because it enables distributed or federated training of the HDC model, to reduce centralized data collection and preserve local data privacy. Claim 6: Xu, Sharad, and Salamat teach the device of claim 5. Xu further teaches wherein to transform the one or more training data points to the one or more hyperdimensional representations comprises mapping the one or more training data points into a hyperdimensional vector (i.e. An encoding module 220 encodes input sample as a sample hypervector 204 by fetching the pre-generated value and feature hypervectors 202 and 203 from the item memory (IM) 206. Specifically, by combining each feature hypervector 203 with its corresponding value hypervector 202, the encoding output for an input sample is given by: S=sgn (Σ i=1 N F i ºV fi ) where f i is the i-th feature value, V fi is the corresponding value hypervector, is the Hadamard product, and sgn(·) is the sign function that binarizes the encoded sample hypervector. As a tiebreaker, sgn(0)=1; para. [0059, 0060]) . Xu does not explicitly teach using random projection-based HDC mapping. However, Salamat further teaches using random projection-based HDC mapping (i.e. base hypervectors can be selected randomly; para. [0031-0033]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Xu and Sharad to include the feature of Salamat. One would have been motivated to make this modification because it provides a clearer dimension-wise correspondence between the HDC representation and the class representative vectors used for classification. Claim 7: Xu, Sharad, and Salamat teach the device of claim 5. Xu further teaches wherein the prototype has one or more classes, the one or more training data points corresponding to a class of the prototype (i.e. Given K classes, the training process obtains K class hypervectors 205, each for one class; para. [0056, 0060, 0061]) . Claim 8: Xu, Sharad, and Salamat teach the device of claim 5. Xu further teaches wherein the processing circuitry is further to: for a transformed data point of the one or more transformed data points: determine a distance between the transformed data points to each prototype of the HDC model; compare distances between the transformed data points and the prototypes of the HDC model; and select a prototype with a smallest distance based on comparison of the distances (i.e. re-training leads to an adjusted centroid for each class. The most similar class hypervector 205 with the lowest Hamming distance indicates the classification result: arg min k Hamm(S q , C k ). Due to the equivalence of Hamming distance and cosine similarity, the classification rule (arg min k Hamm(S q , C k )) is equivalent to arg max k S q T C k which essentially transforms the bit-wise comparison to vector multiplication and is instrumental for establishing the equivalence between an HDC model and a neural network; para. [0061-0063]) . 07-21-aia AIA 7. Claim s 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Sharad, Salamat, and further in view of Chahal et al. (U.S. Patent Application Pub. No. US 20230409967 A1) . Claim 2: Xu, Sharad, and Salamat teach the device of claim 1. Xu does not explicitly teach wherein the another computing device is to: aggregate the trained one or more independent sub models with one or more trained additional sub models received from one or more additional devices; concatenate the aggregated sub models to create the model; and transmit the model to the device and the one or more additional devices. However, Sharad further teaches wherein the another computing device is to: aggregate the trained one or more independent sub models with one or more trained additional sub models received from one or more additional devices (i.e. the aggregated group updates are obtained from a locally trained model L_i using local training data by each participant; para. [0019, 0020]) ; the aggregated sub models to create the HDC model (i.e. The server obtains aggregated group updates AU 1 , . . . , AU g from each group and compares the aggregated group updates and identifies suspicious aggregated group updates. The server combines the aggregated group updates by excluding the updates identified as suspicious, to obtain an aggregated update U final . The server derives a new global model G r from the previous model G r-1 and the aggregated update U final and shares G r with the plurality of participants; para. [0005, 0018]) ; and transmit the HDC model to the device and the one or more additional devices (i.e. the server combines the aggregated group updates by excluding, or reducing the impact of, the updates identified as suspicious, obtaining an aggregated update U final . At step 520, the server derives a new global model G r from the previous model G r-1 and the aggregated update U final , and shares G r with all participants; para. [0050]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Xu and Salamat to include the feature of Sharad. One would have been motivated to make this modification because it enables distributed or federated training of the HDC model, to reduce centralized data collection and preserve local data privacy. However, Chahal teaches concatenate the aggregated sub models to create the model (i.e. concurrently reading and concatenating, the plurality of aggregated segment level DL models by each of the plurality of workers to obtain an aggregated model generated by the current training instance, wherein the aggregated model is used by each of the plurality of workers during the successive training instances using a successive mini batch; para. [0009]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Xu, Sharad, and Salamat to include the feature of Chahal. One would have been motivated to make this modification because it improves scalability in distributed training. Claim 3: Xu, Sharad, Salamat, and Chahal teach the device of claim 2. Xu further teaches wherein the device and the one or more additional devices use the HDC model to perform an inference on test data (i.e. device 101 for the inference process of the proposed LDC classifier; para. [0049, 0062]) stored at the one or more additional devices to retrain at least a portion of the HDC model (i.e. retraining improves the inference accuracy against the basic HDC; para. [0091]) . Xu does not explicitly teach test data stored locally at the one or more additional devices. However, Sharad further teaches wherein the device and the one or more additional devices use the HDC model to perform an inference on test data stored locally at the one or more additional devices (i.e. the system performs local training (LT). Each of the selected contributors P i retrieves the current global model G, and proceeds with training a local model L i starting from G and using their own dataset D ; para. [0019, 0039]) to retrain at least a portion of the HDC model (i.e. a federated learning protocol generates a global model (G) throughout several rounds of training; para. [0014]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Xu, Salamat, and Chahal to include the feature of Sharad. One would have been motivated to make this modification because it enables distributed or federated training of the HDC model, to reduce centralized data collection and preserve local data privacy . 07-21-aia AIA 8. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Sharad, Salamat, Chahal, and further in view of Satheesh Kumar et al. (U.S. Patent Application Pub. No. US 20230297844 A1) . Claim 4: Xu, Sharad, Salamat, and Chahal teach the device of claim 2. Xu further teaches wherein at least one of the device or the one or more additional devices selects a subset of the HDC model to retrain the HDC model (i.e. retraining improves the inference accuracy against the basic HDC; para. [0091]) and transmits to the another computing device (i.e. Client computer(s)/devices 50 and server computer(s) 60 provide processing, storage, and input/output devices executing application programs and the like. Client computer(s)/devices 50 can also be linked through communications network 70 to other computing devices, including other client devices/processes 50 and server computer(s) 60; para. [0104]) . Xu does not explicitly teach selects a random subset of the model, transmits an updated model to the another computing device. However, Sharad further teaches wherein at least one of the device or the one or more additional devices selects a subset of the HDC model to retrain the HDC model (i.e. a federated learning protocol generates a global model (G) throughout several rounds of training; para. [0014]) and transmits an updated HDC model to the another computing device (i.e. the selected clients send updates to the central server for improving the global model based on their local data; para. [0014, 0020, 0045]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Xu, Salamat, and Chahal to include the feature of Sharad. One would have been motivated to make this modification because it enables distributed or federated training of the HDC model, to reduce centralized data collection and preserve local data privacy. However, Satheesh Kumar teaches wherein at least one of the device or the one or more additional devices selects a random subset of the model to retrain the model and transmits an updated model to the another computing device (i.e. the central computing device or server 102 may transmit to the users a global model (e.g., newly initialized or partially trained through previous rounds of federated learning). The users 104 may train their individual models locally with their own data. The results of such local training may then be reported back to central computing device or server 102, which may pool the results and update the global model. This process may be repeated iteratively. Further, at each round of training the global model, central computing device or server 102 may select a subset of all registered users 104 (e.g., a random subset) to participate in the training round; para. [0036]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Xu, Salamat, Sharad and Chahal to include the feature of Satheesh Kumar. One would have been motivated to make this modification because it reduces training burden while still allowing iterative refinement of the overall model. 9. Claims 9-20 are similar in scope to Claims 1-8 and are rejected under a similar rationale . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Khaleghi et al. (Pub. No. US 20240273407 A1), Disclosed herein are techniques and architectures for encoding data within a hyperdimensional computing (HDC) framework, enabling the transformation of input data into high-dimensional vector space representations. It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson , 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)) . 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If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /TAN H TRAN/Primary Examiner, Art Unit 2141 Application/Control Number: 18/384,525 Page 2 Art Unit: 2141 Application/Control Number: 18/384,525 Page 3 Art Unit: 2141 Application/Control Number: 18/384,525 Page 4 Art Unit: 2141 Application/Control Number: 18/384,525 Page 5 Art Unit: 2141 Application/Control Number: 18/384,525 Page 6 Art Unit: 2141 Application/Control Number: 18/384,525 Page 7 Art Unit: 2141 Application/Control Number: 18/384,525 Page 8 Art Unit: 2141 Application/Control Number: 18/384,525 Page 9 Art Unit: 2141 Application/Control Number: 18/384,525 Page 10 Art Unit: 2141 Application/Control Number: 18/384,525 Page 11 Art Unit: 2141
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