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 .
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 1-20 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-8, 9-16, 17-20 are directed to a method, an apparatus and a computer program product of selective breeding for NNs in an edge computer environment. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 9 and 17 these claims recite
deploying a plurality of copies of a centralized neural network respectively to a corresponding plurality of edge servers, wherein each of the copies of the centralized neural network is independently operated and trained at one of the edge servers based on inputs received at that edge server and becomes an independently trained neural network; sending, by each of the edge servers at periodic intervals, a copy of the independently trained neural network at that edge server to other ones of the edge servers; and updating, at each of one or more of the edge servers, the independently trained neural network at that edge server, including performing neural network breeding based on the independently trained neural network at that edge server and one or more copies of the independently trained neural networks sent to the edge server from other ones of the edge servers.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such that they are disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0038-0052], etc. Fig. 1. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 9, 17 these claims
This limitation recites using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and 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 2106.0S(f))
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 9 and 17: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites the additional elements of “deploying…”, “sending…”, “updating…” etc. These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2, 10, 18
Claim 2, 10, 18 merely recite other additional elements that define storing a set of data points associated with the NN which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3, 11, 19
Claim 3, 11, 19 merely recite other additional elements that define calculating a fitness measure for each of the one or more copies of the trained NNs which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4, 12
Claim 4, 12 merely recite other additional elements that define discarding the NNs with fitness measure blow a threshold which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5, 13
Claim 5, 13 merely recite other additional elements that define selecting the top performing NNs which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 6, 14, 20
Claim 6, 14, 20 merely recite other additional elements that define calculating a weighted averages of the trained NNs which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7, 15
Claim 7, 15 merely recite other additional elements that define the periodic interval which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 8, 16
Claim 8, 16 merely recite other additional elements that define the periodic interval is defined by a preset model drift which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 6-7, 9-11, 14-15, 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Roth et al. (Roth) US 20220366220
In regard to claim 1, Roth disclose A method for selective breeding for divergent neural networks in an edge computing environment, comprising: ([0077]-[0092] [0102]-[0104] generating a trained NN from various aggregated NNs from other edge devices in an edge device)
deploying a plurality of copies of a centralized neural network respectively to a corresponding plurality of edge servers, wherein each of the copies of the centralized neural network is independently operated and trained at one of the edge servers based on inputs received at that edge server and becomes an independently trained neural network; ([0071]-[0092] [0096]-[0104][0503][0057] [0570] send copies of each local model (NN) receive from each edge device to each edge device and each local model is independently trained at the edge devices based on the local data received at the edge device and trained using the local data, the edge device is a computer system which can be a server too. Note: please further define a centralized NN and please use functional description language to help move forward the prosecution, call to discuss if necessary.)
sending, by each of the edge servers at periodic intervals, a copy of the independently trained neural network at that edge server to other ones of the edge servers; ([0071]-[0094] [0096]-[0104] [0503] [0570] sending, by each edge device, a copy of the local model trained at the edge device at predetermined period of time to others edge devices) and
updating, at each of one or more of the edge servers, the independently trained neural network at that edge server, including performing neural network breeding based on the independently trained neural network at that edge server and one or more copies of the independently trained neural networks sent to the edge server from other ones of the edge servers. ([0071]-[0094] [0096]-[0104] [0503] [0570] updating, at the each edge devices, the local model by aggregating weights according to a metric, such as local model accuracy, from the other local models received from other edge devices)
In regard to claim 2, Roth disclose The method of claim 1,
Roth disclose and further comprising:
storing, at each of the edge servers, a set of data points associated with the independently trained neural network at that edge server. ([0066] [0071]-[0094] [0096]-[0104]-[0116][0119] [0149] [0503] [0570] store information associated with the trained NN at the edge device)
In regard to claim 3, Roth disclose The method of claim 2,
Roth disclose and further comprising:
calculating, at each of the edge servers based on the set of data points stored at the edge server, a fitness measure for each of the one or more copies of the independently trained neural networks sent to the edge server from the other ones of the edge servers. ([0074]-[0094] [0395] calculate, at each edge devices based on the information stored, a metric value such as a degree of accuracy for each of the local models from other edge devices)
In regard to claim 6, Roth disclose The method of claim 3,
Roth disclose wherein the neural network breeding includes performing, at each of the edge servers, a hyperNEAT calculation that includes determining a weighted average, based on the fitness measures, of parameters extracted from neurons in the one or more copies of the independently trained neural networks sent to the edge server. ([0091]-[00104] calculating, at the edge device, a weighted average, based on the metric, of parameters extracted from neurons in the trained NN sent to the edge device. Note: please use functional language to define hyperNEAT, since non-functional language has not much patent weight.)
In regard to claim 7, Roth disclose The method of claim 1,
Roth disclose wherein the periodic interval is defined by a preset period of time. ([0071]-[0094] [0102]-[0104] [0503] [0570] sending, by each edge device, a copy of the local model trained at the edge device at predetermined period of time to others edge devices)
In regard to claims 9-11, 14-15, claims 9-11, 14-15 are apparatus claims corresponding to the method claims 1-3, 6-7 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-3, 6-7.
In regard to claims 17-20, claims 17-20 are computer program product claims corresponding to the method claims 1-3, 6 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-3, 6.
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.
Claims 4-5, 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Roth et al. (Roth) US 20220366220 as applied to claim 1, further in view of Shin US 20250023930
In regard to claim 4, Roth disclose The method of claim 3,
Roth disclose any of the one or more copies of the independently trained neural networks sent to the edge server; ([0071]-[0094] [0102]-[0104] [0503] [0570] a copy of the local model trained at the edge device sent to others edge devices)
But Roth fail to explicitly disclose “wherein the neural network breeding includes discarding, at each of the edge servers, the models that have a corresponding fitness measure below a threshold.”
Shin disclose wherein the neural network breeding includes discarding, at each of the edge servers, the models that have a corresponding fitness measure below a threshold. ([0070] discard the trained ML models’ accuracy below a minimum threshold)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Shin‘s ML training into Roth’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Shin‘s ML training based on the accuracy threshold would help to provide more validation criteria into Roth’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more validation criteria with accuracy threshold to train the ML model would help to improve training accuracy of the ML model.
In regard to claim 5, Roth and Shin disclose The method of claim 3,
Roth disclose ones of the one or more copies of the independently trained neural networks sent to the edge server. ([0071]-[0094] [0102]-[0104] [0503] [0570] a copy of the local model trained at the edge device sent to others edge devices)
But Roth fail to explicitly disclose “wherein the neural network breeding includes selecting, at each of the edge servers based on the fitness measures, top performing ones.”
Shin disclose wherein the neural network breeding includes selecting, at each of the edge servers based on the fitness measures, top performing ones. ([0070] select the trained ML model has the highest accuracy of the trained ML models)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Shin‘s ML training into Roth’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Shin‘s ML training based on the accuracy threshold would help to provide more validation criteria into Roth’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more validation criteria with accuracy threshold to train the ML model would help to improve training accuracy of the ML model.
In regard to claims 12-13, claims 12-13 are apparatus claims corresponding to the method claims 4-5 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 4-5.
Claims 8, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Roth et al. (Roth) US 20220366220 as applied to claim 1, further in view of Sankarapu et al. (Sankarapu) US 20230076559
In regard to claim 8, Roth disclose The method of claim 1,
But Roth fail to explicitly disclose “wherein the periodic interval is defined by a preset amount of drift occurring in one or more of the copies of the independently trained neural networks.”
Sankarapu disclose wherein the periodic interval is defined by a preset amount of drift occurring in one or more of the copies of the independently trained neural networks. ([0107]-[0119][0130] the predefined time interval is determined on the basis on the criteria defined by the user on model drift)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Sankarapu‘s ML training into Roth’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Sankarapu‘s MLtraining based on the model drifting would help to provide more validation criteria into Roth’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more validation criteria with model drafting to train the ML model would help to improve training accuracy of the ML model.
In regard to claim 16, claim 16 is an apparatus claim corresponding to the method claim 8 above and, therefore, are rejected for the same reasons set forth in the rejections of claim 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
PATENT PUB. # PUB. DATE INVENTOR(S) TITLE
US 11170320 B2 2021-11-09 Garg et al.
Updating Machine Learning Models On Edge Servers
Garg et al. disclose Systems and techniques are described herein for updating a machine learning model on edge servers. Local parameters of the machine learning model are updated at a plurality of edge servers using fresh data on the edge servers, rather than waiting for the data to reach a global server to update the machine learning model. Hence, latency is significantly reduced, making the systems and techniques described herein suitable for real-time services that support streaming data. Moreover, by updating global parameters of the machine learning model at a global server in a deterministic manner based on parameter updates from the edge servers, rather than by including randomization steps, global parameters of the converge quickly to their optimal values. The global parameters are sent from the global server to the plurality of edge servers at each iteration, thereby synchronizing the machine learning model on the edge servers…. See abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
Prosecution Insights