DETAILED ACTION
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 .
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.
Claims 1-20 are subject to review.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/22/2024 is being considered by the examiner.
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 reciting significantly more.
Step 1 – is the claim directed to a process, machine, manufacture, or composition of matter?
Claims 1-10 are directed to a “method” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Claims 11-20 are directed to a “system” which describes one of the four statutory categories of patentable subject matter, i.e., a machine.
Regarding Claim 1
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 1 recites an abstract idea, substantially as follows:
“for each respective training sample in the plurality of training samples: determining, using the respective training sample, a first loss of the client ML model;” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing calculating a loss, which is considered to be a mathematical calculation.
“determining, using the respective training sample, a second loss of a server machine learning (ML) model; and” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing calculating a loss, which is considered to be a mathematical calculation.
“determining a respective score based on the first loss and the second loss;” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing calculating a score, which is considered to be a mathematical calculation.
“selecting, based on each respective score of each respective training sample in the plurality of training samples, a subset of training samples from the plurality of training samples; and” – is directed to the abstract idea of a mental process i.e., selecting a subset of training samples based on the results of the scoring function mirrors the cognitive activity of a person observing the score results, evaluating them, and making a conclusive judgement are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 1 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“receiving, from a client device, a client machine learning (ML) model, the client ML model trained locally on the client device” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
“obtaining a set of training data comprising a plurality of training samples;” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)).
“training the server ML model using the subset of training samples.” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 1 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“receiving, from a client device, a client machine learning (ML) model, the client ML model trained locally on the client device” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
“obtaining a set of training data comprising a plurality of training samples;” – the broadest reasonable interpretation of this imitation is found to be merely receiving data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
“training the server ML model using the subset of training samples.” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 2
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 2 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 2 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the client ML model is trained using a local training data set stored locally at the client device.” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 2 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the client ML model is trained using a local training data set stored locally at the client device.” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 3
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 3 recites an abstract idea, substantially as follows:
“wherein each respective score is based on a difference between the first loss and the second loss. “ – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing the score based on the difference of numerical values, and is considered to be a mathematical formula or equation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 3 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 3 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 4
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 4 recites an abstract idea, substantially as follows:
“wherein selecting the subset of training samples comprises selecting data points from the plurality of training samples with a respective score that satisfies a first threshold.” – is directed to the abstract idea of a mental process i.e., selecting a subset of training samples based on the results of the scoring function mirrors the cognitive activity of a person observing the score results, evaluating them, and making a conclusive judgement are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 4 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 4 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 5
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 5 recites an abstract idea, substantially as follows:
“wherein selecting the subset of training samples further comprises selecting each training sample from the plurality of training samples with a respective score that satisfies a second threshold.” – is directed to the abstract idea of a mental process i.e., selecting a subset of training samples based on the results of the scoring function mirrors the cognitive activity of a person observing the score results, evaluating them, and making a conclusive judgement are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 5 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 5 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 6
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 6 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 6 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the first threshold is an upper limit threshold and the second threshold is a lower limit threshold.” – is merely indicating a field of use or technological environment directed towards the technology of relevance filtering (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 6 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the first threshold is an upper limit threshold and the second threshold is a lower limit threshold.” – is merely indicating a field of use or technological environment directed towards the technology of relevance filtering (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 7
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 7 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 7 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the operations further comprise filtering the set of training samples to remove outlier data points.” – is merely selecting a particular data source or type of data to be manipulated, considered an insignificant extra-solution activity (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 7 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the operations further comprise filtering the set of training samples to remove outlier data points.” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)). Further, the insignificant extra-solution data gathering is also WURC, see MPEP 2106.05(d)(II) “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iii. Electronic recordkeeping”. [Examiner Note: the removal of outlier data points from the set of training samples amounts to updating and maintaining the data set which falls under electronic recordkeeping]
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 8
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 8 recites an abstract idea, substantially as follows:
“wherein the first loss of the client ML model comprises a reducible holdout loss (RHO-Loss).” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing the use of RHO-Loss, which is considered to be a mathematical formula or equation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 8 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 8 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 9
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 9 recites an abstract idea, substantially as follows:
“wherein the second loss of the server ML model comprises a reducible holdout loss (RHO-Loss).” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing the use of RHO-Loss, which is considered to be a mathematical formula or equation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 9 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 9 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 10
2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 10 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 10 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the client ML model is trained locally on the client device using a set of client training data that is different than the set of training data.” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 10 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the client ML model is trained locally on the client device using a set of client training data that is different than the set of training data.” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 11
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 11 recites an abstract idea, substantially as follows:
“for each respective training sample in the plurality of training samples: determining, using the respective training sample, a first loss of the client ML model;” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing calculating a loss, which is considered to be a mathematical calculation.
“determining, using the respective training sample, a second loss of a server machine learning (ML) model; and” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing calculating a loss, which is considered to be a mathematical calculation.
“determining a respective score based on the first loss and the second loss;” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing calculating a score, which is considered to be a mathematical calculation.
“selecting, based on each respective score of each respective training sample in the plurality of training samples, a subset of training samples from the plurality of training samples; and” – is directed to the abstract idea of a mental process i.e., selecting a subset of training samples based on the results of the scoring function mirrors the cognitive activity of a person observing the score results, evaluating them, and making a conclusive judgement are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 11 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“data processing hardware;” – is directed to merely applying an abstract idea using a generic computer as a tool, considered mere instructions to apply an exception (see MPEP 2106.05(f)(2), 2106.04(d)).
“memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations comprising:” – is directed to merely applying an abstract idea using a generic computer as a tool, considered mere instructions to apply an exception (see MPEP 2106.05(f)(2), 2106.04(d)).
“receiving, from a client device, a client machine learning (ML) model, the client ML model trained locally on the client device;” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
“obtaining a set of training data comprising a plurality of training samples;” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)).
“training the server ML model using the subset of training samples.” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 11 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“data processing hardware;” – is directed to merely applying an abstract idea using a generic computer as a tool, considered mere instructions to apply an exception (see MPEP 2106.05(f)(2), 2106.04(d)).
“memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations comprising:” – is directed to merely applying an abstract idea using a generic computer as a tool, considered mere instructions to apply an exception (see MPEP 2106.05(f)(2), 2106.04(d)).
“receiving, from a client device, a client machine learning (ML) model, the client ML model trained locally on the client device” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
“obtaining a set of training data comprising a plurality of training samples;” – the broadest reasonable interpretation of this imitation is found to be merely receiving data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
“training the server ML model using the subset of training samples.” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 12
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 12 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 12 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the client ML model is trained using a local training data set stored locally at the client device.” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 12 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the client ML model is trained using a local training data set stored locally at the client device.” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 13
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 13 recites an abstract idea, substantially as follows:
“wherein each respective score is based on a difference between the first loss and the second loss. “ – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing the score based on the difference of numerical values, and is considered to be a mathematical formula or equation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 13 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 13 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 14
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 14 recites an abstract idea, substantially as follows:
“wherein selecting the subset of training samples comprises selecting data points from the plurality of training samples with a respective score that satisfies a first threshold.” – is directed to the abstract idea of a mental process i.e., selecting a subset of training samples based on the results of the scoring function mirrors the cognitive activity of a person observing the score results, evaluating them, and making a conclusive judgement are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 14 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 14 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 15
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 15 recites an abstract idea, substantially as follows:
“wherein selecting the subset of training samples further comprises selecting each training sample from the plurality of training samples with a respective score that satisfies a second threshold.” – is directed to the abstract idea of a mental process i.e., selecting a subset of training samples based on the results of the scoring function mirrors the cognitive activity of a person observing the score results, evaluating them, and making a conclusive judgement are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 15 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 15 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 16
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 16 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 16 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the first threshold is an upper limit threshold and the second threshold is a lower limit threshold.” – is merely indicating a field of use or technological environment directed towards the technology of relevance filtering (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 16 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the first threshold is an upper limit threshold and the second threshold is a lower limit threshold.” – is merely indicating a field of use or technological environment directed towards the technology of relevance filtering (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 17
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 17 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 17 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the operations further comprise filtering the set of training samples to remove outlier data points.” – is merely selecting a particular data source or type of data to be manipulated, considered an insignificant extra-solution activity (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 17 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the operations further comprise filtering the set of training samples to remove outlier data points.” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)). Further, the insignificant extra-solution data gathering is also WURC, see MPEP 2106.05(d)(II) “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iii. Electronic recordkeeping”. [Examiner Note: the removal of outlier data points from the set of training samples amounts to updating and maintaining the data set which falls under electronic recordkeeping]
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 18
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 18 recites an abstract idea, substantially as follows:
“wherein the first loss of the client ML model comprises a reducible holdout loss (RHO-Loss).” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing the use of RHO-Loss, which is considered to be a mathematical formula or equation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 18 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 18 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 19
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 19 recites an abstract idea, substantially as follows:
“wherein the second loss of the server ML model comprises a reducible holdout loss (RHO-Loss).” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing the use of RHO-Loss, which is considered to be a mathematical formula or equation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 19 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 19 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 20
2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 20 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 20 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the client ML model is trained locally on the client device using a set of client training data that is different than the set of training data.” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 20 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the client ML model is trained locally on the client device using a set of client training data that is different than the set of training data.” – is merely indicating a field of use or technological environment directed towards the technology of federated learning (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
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.
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) 1-3, 10-13, and 20 are rejected under 35 U.S.C 103 as being unpatentable over Malik et al. (US 20210117780 A1), hereinafter referred to as Malik, in view of Li et al. (US 20230082173 A1), hereinafter referred to as Li.
Regarding Claim 1
Malik discloses:
“receiving, from a client device, a client machine learning (ML) model, the client ML model trained locally on the client device;” (Malik at [0117]: Client system 130a may then train the received global neural network model 820a together with the local personalization model 830a on the pluralities of examples [Examiner Note: mapped to trained locally] 530a to generate a plurality of updated federated model parameters and a plurality of updated local model parameters. [...] Client system 130a may then send the trained global neural network model [Examiner Note: mapped to receiving from a client device] 820a including the updated federated model parameters to server 510 without sending any of the examples 530a to server 510. )
“determining a respective score based on […] loss;” (Malik at [0099]: For example, user valuation [Examiner Note: mapped to score] v.sub.m.sup.t+1 may be calculated based on a loss value function)
“selecting, based on each respective score […] a subset of training samples from the plurality of training samples;” (Malik at [0098]: Active federated learning may improve on conventional federated learning techniques by providing a cheap, simple, and intuitive sampling scheme to optimize the selection of client systems based on the benefit of training a model on client systems 130 having high-value user data (i.e., training data that is useful for model training; Malik at [0107]: At step 730, client system 130 may calculate a user valuation 540 associated with client system 130, wherein the user valuation represents a measure of utility of training the neural network model on the plurality of examples 530) [Examiner Note: selecting client systems based on relevancy of their training data is equivalent to selecting a subset of training samples based on a score]
“and training the server ML model using the subset of training samples.” (Malik at [0100]: As discussed herein, user valuation v.sub.m.sup.t+1 may be associated with a likelihood of selected client system [Examiner Note: mapped to the subset of training samples] S.sub.m.sup.t being selected to train the model in a subsequent training round.)
However, Malik does not disclose:
“obtaining a set of training data comprising a plurality of training samples;”
“for each respective training sample in the plurality of training samples: determining, using the respective training sample, a first loss of the […] ML model;”
“determining, using the respective training sample, a second loss of a […] machine learning (ML) model; and”
On the other hand, Li discloses:
“obtaining a set of training data comprising a plurality of training samples;” (Li at [0121]: A data collection device 260 is configured to collect or generate training data, and store the training data in a database 230. In this embodiment of this application, the training data may be a plurality of images, a plurality of voice segments, or the like with tags [Examiner Note: mapped to training samples].)
“for each respective training sample in the plurality of training samples: determining, using the respective training sample, a first loss of the […] ML model;” (Li at [0186]: Specifically, the server stores a first loss value corresponding to the first machine learning model, the server obtains a second loss value based on the loss function of the second machine learning model, calculates a difference between the first loss value and the second loss value, and determines whether the difference is less than or equal to a preset threshold.)
“determining, using the respective training sample, a second loss of a […] machine learning (ML) model; and” (Li at [0186]: Specifically, the server stores a first loss value corresponding to the first machine learning model, the server obtains a second loss value based on the loss function of the second machine learning model, calculates a difference between the first loss value and the second loss value, and determines whether the difference is less than or equal to a preset threshold.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Malik with the above teachings of Li by using a method federated learning that trains a server model using filtered training data samples, as taught by Malik, and a method of calculating losses of models based on training data samples, as taught by Li. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve data privacy as suggested by Li at Abstract: “The foregoing manner can improve security of data exchange between the server and the terminal”.
As per Claim 11, this a system claim corresponding to method Claim 1, and is rejected for similar reasons.
Regarding Claim 2
The combination of Malik and Li discloses: “The method of claim 1” and the limitations are shown in the rejection above.
The combination of Malik and Li further discloses:
“wherein the client ML model is trained using a local training data set stored locally at the client device.” (Malik at [0103]: Client systems 130b and 130d may each receive a current version of the neural network model from server 510. Client systems 130b and 130d may then each retrieve the respective plurality of examples [Examiner Note: mapped to a local training data set] 530b and 530d from the local data store of the client system. Client systems 130b and 130d may then each train the neural network model [Examiner Note: mapped to the client ML model] on the respective pluralities of examples and 530d to generate a plurality of updated model parameters.)
As per Claim 12, this a system claim corresponding to method Claim 2, and is rejected for similar reasons.
Regarding Claim 3
The combination of Malik and Li discloses: “The method of claim 1” and the limitations are shown in the rejection above.
The combination of Malik and Li further discloses:
“wherein each respective score is based on a difference between the first loss and the second loss.” (Li at [0186]: Specifically, the server stores a first loss value corresponding to the first machine learning model, the server obtains a second loss value based on the loss function of the second machine learning model, calculates a difference between the first loss value and the second loss value, and determines whether the difference is less than or equal to a preset threshold.)
The same motivation that was utilized for combining Malik with Li, as set forth in Claim 1, is equally applicable to Claim 3.
As per Claim 13, this a system claim corresponding to method Claim 3, and is rejected for similar reasons.
Regarding Claim 10
The combination of Malik and Li discloses: “The method of claim 1” and the limitations are shown in the rejection above.
The combination of Malik and Li further discloses:
“wherein the client ML model is trained locally on the client device using a set of client training data that is different than the set of training data.” (Malik at [0103]: Client systems 130b and 130d may each receive a current version of the neural network model from server 510. Client systems 130b and 130d may then each retrieve the respective plurality of examples 530b and 530d from the local data store of the client system. Client systems 130b and 130d may then each train the neural network model on the respective pluralities of examples 530b and 530d to generate a plurality of updated model parameters.) [Examiner Note: client systems training the models using their own data stored In their local data store is equivalent to using a set of client training data that is different the set of training data]
As per Claim 20, this a system claim corresponding to method Claim 10, and is rejected for similar reasons.
Claim(s) 4-6, and 14-16 are rejected under 35 U.S.C 103 as being unpatentable over Malik in view of Li and further in view of Serita, Susumu (US 20220405161 A1) hereinafter referred to as Serita.
Regarding Claim 4
The combination of Malik and Li discloses: “The method of claim 1” and the limitations are shown in the rejection above.
However the combination of Malik and Li does not disclose:
“wherein selecting the subset of training samples comprises selecting data points from the plurality of training samples with a respective score that satisfies a first threshold.”
On the other hand, Seria discloses:
“wherein selecting the subset of training samples comprises selecting data points from the plurality of training samples with a respective score that satisfies a first threshold.” (Serita at [0106]: For example, when the selection condition of the training data is expressed as “x>10 AND x<20”, the training data selection unit 118 extracts a region in which the sensor value of the “sensor 3” of the sensor data 31 satisfies this condition, [Examiner Note: mapped to satisfying a first threshold] that is, a range between a lower limit value “10” and an upper limit value “20” )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Malik and Li with the above teachings of Serita using a method of federated learning that trains a server model using filtered training data samples, as taught by Malik and Li, and a method of selecting a subset of training samples that satisfy a certain threshold, as taught by Serita. The modification would have been obvious because one of ordinary skill in the art would be motivated to select a relevant portion of training data in large datasets to train a model as suggested by Serita at [0011]: ”An object of the invention is to provide a technique capable of assisting selection of suitable training data to be used for sign detection even when the sign detection of a device is performed based on large-scale measurement data or measurement data having a complicated change pattern”.
As per Claim 14, this a system claim corresponding to method Claim 4, and is rejected for similar reasons.
Regarding Claim 5
The combination of Malik, Li and Serita discloses: “The method of claim 4,” and the limitations are shown in the rejection above.
The combination of Malik, Li and Serita further discloses:
“wherein selecting the subset of training samples further comprises selecting each training sample from the plurality of training samples with a respective score that satisfies a second threshold.” (Serita at [0106]: For example, when the selection condition of the training data is expressed as “x>10 AND x<20”, the training data selection unit 118 extracts a region in which the sensor value of the “sensor 3” of the sensor data 31 satisfies this condition, [Examiner Note: mapped to satisfying a second threshold] that is, a range between a lower limit value “10” and an upper limit value “20” )
The same motivation that was utilized for combining Malik and Li with Serita, as set forth in Claim 4, is equally applicable to Claim 5.
As per Claim 15, this a system claim corresponding to method Claim 5, and is rejected for similar reasons.
Regarding Claim 6
The combination of Malik, Li and Serita discloses: “The method of claim 5,” and the limitations are shown in the rejection above.
The combination of Malik, Li and Serita further discloses:
“wherein the first threshold is an upper limit threshold and the second threshold is a lower limit threshold.” (Serita at [0106]: For example, when the selection condition of the training data is expressed as “x>10 AND x<20”, the training data selection unit 118 extracts a region in which the sensor value of the “sensor 3” of the sensor data 31 satisfies this condition, that is, a range between a lower limit value [Examiner Note: mapped to second threshold is a lower limit threshold] “10” and an upper limit value [Examiner Note: mapped to first threshold is an upper limit threshold] “20” )
The same motivation that was utilized for combining Malik and Li with Serita, as set forth in Claim 4, is equally applicable to Claim 6.
As per Claim 16, this a system claim corresponding to method Claim 6, and is rejected for similar reasons.
Claim(s) 7 and 17 rejected under 35 U.S.C 103 as being unpatentable over Malik in view of Li and further in view of Kida, Luis (US 20180307741 A1) hereinafter referred to as Kida.
Regarding Claim 7
The combination of Malik and Li discloses: “The method of claim 1,” and the limitations can be found in the rejection above.
However, the combination of Malik and Li does not disclose:
“wherein the operations further comprise filtering the set of training samples to remove outlier data points.”
On the other hand, Kida discloses:
“wherein the operations further comprise filtering the set of training samples to remove outlier data points.” (Kida at [0029] After classifying the samples in the training data, various samples may be filtered from the training data using the classification of the samples. For example, all noise and outlier isolated samples may be removed to create the training data used to train a model. )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Malik and Li with the above teachings of Kida by using a method of federated learning that trains a server model using filtered training data samples, as taught by Malik and Li, and a method of filtering a training set to remove outliers, as taught by Kida. The modification would have been obvious because one of ordinary skill in the art would be motivated to use a method that generates a model that is less complex and more efficient as suggested by Kida at [0015]: “the filtered training data may be used to create a model. This model may be less complex compared to a model created with the entire training data. A less complex model generally requires less memory and less computation power to create and operate compared to the model trained on the full training data set”.
As per Claim 17, this a system claim corresponding to method Claim 7, and is rejected for similar reasons.
Claim(s) 8-9 and 18-19 are rejected under 35 U.S.C 103 as being unpatentable over Malik in view of Li and further in view of NPL reference Soren et al. “Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt”, hereinafter referred to as Soren.
Regarding Claim 8
The combination of Malik and Li discloses: “The method of claim 1,” and the limitations can be found in the rejection above.
The combination of Malik and Li further discloses:
“wherein the first loss of the client ML model comprises […]” (Li at [0186]: Specifically, the server stores a first loss value corresponding to the first machine learning model, […])
However, the combination of Malik and Li does not disclose:
“a reducible holdout loss (RHO-Loss).”
On the other hand, Soren discloses:
“a reducible holdout loss (RHO-Loss).” (Soren at Pg. 3, second column, third paragraph
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Malik and Li with the above teachings of Soren by using a method of federated learning that trains a server model using filtered training data samples, as taught by Malik and Li, and a method of using RHO to compute the loss of a model, as taught by Soren. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve training speed, efficiency, and accuracy as suggested by Soren at Abstract: “RHO-LOSS trains in far fewer steps than prior art, improves accuracy, and speeds up training on a wide range of datasets, hyperparameters, and architectures (MLPs, CNNs, and BERT)”.
As per Claim 18, this a system claim corresponding to method Claim 8, and is rejected for similar reasons.
Regarding Claim 9
The combination of Malik and Li discloses: “The method of claim 1,” and the limitations can be found in the rejection above.
The combination of Malik and Li further discloses:
“wherein the second loss of the server ML model comprises […]” (Li at [0186]: […] the server obtains a second loss value based on the loss function of the second machine learning model, […])
However, the combination of Malik and Li does not disclose:
“a reducible holdout loss (RHO-Loss).”
On the other hand, Soren discloses:
“a reducible holdout loss (RHO-Loss).” (Soren at Pg. 3, second column, third paragraph
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The same motivation that was utilized for combining Malik and Li with Soren, as set forth in Claim 8, is equally applicable to Claim 9.
As per Claim 19, this a system claim corresponding to method Claim 9, and is rejected for similar reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20200090045 A1 – recites a method of training a machine learning system on a subset of data that is selected based on an error parameter.
US 20160267380 A1 – recites a method and system of training a neural network wherein models are updated using respective subsets of data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMIYAH KABIR whose telephone number is (571)270-0722. The examiner can normally be reached Monday-Thursday 8am-6pm.
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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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/SAMIYAH KABIR/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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