DETAILED ACTION
This action is in response to the Applicant Response filed 20 May 2026 for application 18/461,410 filed 05 September 2023.
Claim(s) 1, 6, 11, 16 is/are currently amended.
Claim(s) 1-20 is/are pending.
Claim(s) 1-20 is/are rejected.
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 .
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejection of claims 1-20 have been fully considered but are not persuasive.
Applicant first argues that the claims do not provide a generic mathematical concept, but instead recite a specific technique. However, the MPEP does not distinguish generic versus specific mathematical techniques. The MPEP simply states that a mathematical concept is an abstract idea.
Applicant next argues that receipt of model updates is a specific technical operation. Examiner respectfully disagrees. Receiving data from clients at a server provides nothing more that data transmission. The fact that the devices are wireless do not provide any specific technical operation. This transmission of data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is, as recognized by the MPEP and the courts, the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d)).
Applicant next argues that the remainder of the limitations provide an improvement. Examiner respectfully disagrees. As noted below, the remainder of the limitations provide abstract ideas. As noted in the MPEP, it is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. MPEP 2106.05(a). Therefore, any improvement would be, at best, an improvement in an abstract idea.
Applicant next argues that the claims do not provide evidence to support well-understood routine and conventional elements. Examiner respectfully disagrees. As noted below, when discussing transmitting data, Examiner provides evidence to support the data transmission as WURC as recognized by the courts. See MPEP 2106.05(d). Additionally, as noted above, the claims do not provide an improvement. As noted below, any other additional elements amount to mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)), indicate a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)), or amount to nothing more than insignificant extra-solution activity (2106.05(g)) and do not provide significantly more than the abstract idea.
The remainder of applicant’s arguments regarding the 35 U.S.C. 101 rejection of the claims are based on the newly amended subject matter. These arguments are addressed in the 35 U.S.C. 101 rejection of the claims below.
Therefore, the 35 U.S.C. 101 rejection of claim 1-20 is maintained.
Applicant’s arguments regarding the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims are based on the newly amended subject matter. All arguments are addressed in the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims below.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101, because the claim(s) is/are directed to an abstract idea, and because the claim elements, whether considered individually or in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. V. CLS Bank International et al., 573 US 208 (2014).
Regarding claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a(n) method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) processor-implemented method.
The limitation of determining a fixed local zone associated with each of the plurality of clients based on a common attribute of the plurality of clients, the fixed local zone having a first fixed boundary, the common attribute comprising at least one of a geographic location, a user interface theme, or a default language, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of updating model weights of a central machine learning model based on local machine learning updates for a local subset of the plurality of clients, the local subset corresponding to the fixed local zone, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating weight values.
The limitation of updating the model weights of the central machine learning model based on neighbor machine learning updates for a neighbor subset of the plurality of clients, the neighbor subset corresponding to a fixed neighbor zone that neighbors the fixed local zone, the neighbor machine learning updates having a different weight than the local machine learning updates when updating model weights, a value of the different weight corresponding to a similarity parameter, the fixed neighbor zone having a second fixed boundary, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating weight values.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – processor-implemented. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – federated learning system, central machine learning model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites receiving machine learning model updates wirelessly from a plurality of wireless clients in a federated learning system, which is simply receiving data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
processor-implemented amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
receiving data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
federated learning system, central machine learning model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 2, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) processor-implemented method. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 2 carries out the method of claim 1 but for the recitation of additional element(s) of learning the similarity parameter with machine learning training.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites learning the similarity parameter with machine learning training which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 3, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) processor-implemented method. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 3 carries out the method of claim 1 but for the recitation of additional element(s) of in which the similarity parameter comprises a self-attention coefficient.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the parameters and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the parameters do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 4, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) processor-implemented method. The Step 2A Prong One Analysis for claim 3 is applicable here since claim 4 carries out the method of claim 3 but for the recitation of additional element(s) of in which the self-attention coefficient normalizes a relationship between the local machine learning updates of the local subset and the neighbor machine learning updates of the neighbor subset.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the parameters and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the parameters do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 5, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) processor-implemented method. The Step 2A Prong One Analysis for claim 4 is applicable here since claim 5 carries out the method of claim 4 but for the recitation of additional element(s) of in which the relationship comprises an inner product.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 6, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus.
The limitation of determine a fixed local zone associated with each of the plurality of clients based on a common attribute of the plurality of clients, the fixed local zone having a first fixed boundary, the common attribute comprising at least one of a geographic location, a user interface theme, or a default language, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of update model weights of a central machine learning model based on local machine learning updates for a local subset of the plurality of clients, the local subset corresponding to the fixed local zone, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating weight values.
The limitation of update the model weights of the central machine learning model based on neighbor machine learning updates for a neighbor subset of the plurality of clients, the neighbor subset corresponding to a fixed neighbor zone that neighbors the fixed local zone, the neighbor machine learning updates having a different weight than the local machine learning updates when updating model weights, a value of the different weight corresponding to a similarity parameter, the fixed neighbor zone having a second fixed boundary, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating weight values.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – apparatus, at least one memory, at least one processor. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – federated learning system, central machine learning model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites receive machine learning model updates wirelessly from a plurality of wireless clients in a federated learning system, which is simply receiving data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
apparatus, at least one memory, at least one processor amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
receiving data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
federated learning system, central machine learning model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 7, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus. The Step 2A Prong One Analysis for claim 6 is applicable here since claim 7 carries out the apparatus of claim 6 but for the recitation of additional element(s) of learn the similarity parameter with machine learning training.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites learn the similarity parameter with machine learning training which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 8, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus. The Step 2A Prong One Analysis for claim 6 is applicable here since claim 8 carries out the apparatus of claim 6 but for the recitation of additional element(s) of in which the similarity parameter comprises a self-attention coefficient.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the parameters and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the parameters do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 9, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus. The Step 2A Prong One Analysis for claim 8 is applicable here since claim 9 carries out the apparatus of claim 8 but for the recitation of additional element(s) of in which the self-attention coefficient normalizes a relationship between the local machine learning updates of the local subset and the neighbor machine learning updates of the neighbor subset.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the parameters and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the parameters do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 10, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus. The Step 2A Prong One Analysis for claim 9 is applicable here since claim 10 carries out the apparatus of claim 9 but for the recitation of additional element(s) of in which the relationship comprises an inner product.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus.
The limitation of determining a fixed local zone associated with each of the plurality of clients based on a common attribute of the plurality of clients, the fixed local zone having a first fixed boundary, the common attribute comprising at least one of a geographic location, a user interface theme, or a default language, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of updating model weights of a central machine learning model based on local machine learning updates for a local subset of the plurality of clients, the local subset corresponding to the fixed local zone, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating weight values.
The limitation of updating the model weights of the central machine learning model based on neighbor machine learning updates for a neighbor subset of the plurality of clients, the neighbor subset corresponding to a fixed neighbor zone that neighbors the fixed local zone, the neighbor machine learning updates having a different weight than the local machine learning updates when updating model weights, a value of the different weight corresponding to a similarity parameter, the fixed neighbor zone having a second fixed boundary, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating weight values.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – apparatus. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – federated learning system, central machine learning model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites receiving machine learning model updates wirelessly from a plurality of wireless clients in a federated learning system, which is simply receiving data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
apparatus amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
receiving data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
federated learning system, central machine learning model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 12, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus. The Step 2A Prong One Analysis for claim 11 is applicable here since claim 12 carries out the apparatus of claim 11 but for the recitation of additional element(s) of learning the similarity parameter with machine learning training.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites learning the similarity parameter with machine learning training which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 13, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus. The Step 2A Prong One Analysis for claim 11 is applicable here since claim 13 carries out the apparatus of claim 11 but for the recitation of additional element(s) of in which the similarity parameter comprises a self-attention coefficient.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the parameters and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the parameters do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 14, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus. The Step 2A Prong One Analysis for claim 13 is applicable here since claim 14 carries out the apparatus of claim 13 but for the recitation of additional element(s) of in which the self-attention coefficient normalizes a relationship between the local machine learning updates of the local subset and the neighbor machine learning updates of the neighbor subset.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the parameters and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the parameters do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 15, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a(n) apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) apparatus. The Step 2A Prong One Analysis for claim 14 is applicable here since claim 15 carries out the apparatus of claim 14 but for the recitation of additional element(s) of in which the relationship comprises an inner product.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 16, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a(n) computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium.
The limitation of determine a fixed local zone associated with each of the plurality of clients based on a common attribute of the plurality of clients, the fixed local zone having a first fixed boundary, the common attribute comprising at least one of a geographic location, a user interface theme, or a default language, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of update model weights of a central machine learning model based on local machine learning updates for a local subset of the plurality of clients, the local subset corresponding to the fixed local zone, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating weight values.
The limitation of update the model weights of the central machine learning model based on neighbor machine learning updates for a neighbor subset of the plurality of clients, the neighbor subset corresponding to a fixed neighbor zone that neighbors the fixed local zone, the neighbor machine learning updates having a different weight than the local machine learning updates when updating model weights, a value of the different weight corresponding to a similarity parameter, the fixed neighbor zone having a second fixed boundary, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses calculating weight values.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – computer-readable medium, program code, processor. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – federated learning system, central machine learning model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites receive machine learning model updates wirelessly from a plurality of wireless clients in a federated learning system, which is simply receiving data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
computer-readable medium, program code, processor amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
receiving data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
federated learning system, central machine learning model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 17, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a(n) computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium. The Step 2A Prong One Analysis for claim 16 is applicable here since claim 17 carries out the computer-readable medium of claim 16 but for the recitation of additional element(s) of learn the similarity parameter with machine learning training.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites learn the similarity parameter with machine learning training which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 18, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a(n) computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium. The Step 2A Prong One Analysis for claim 16 is applicable here since claim 18 carries out the computer-readable medium of claim 16 but for the recitation of additional element(s) of in which the similarity parameter comprises a self-attention coefficient.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the parameters and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the parameters do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 19, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a(n) computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium. The Step 2A Prong One Analysis for claim 18 is applicable here since claim 19 carries out the computer-readable medium of claim 18 but for the recitation of additional element(s) of in which the self-attention coefficient normalizes a relationship between the local machine learning updates of the local subset and the neighbor machine learning updates of the neighbor subset.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the parameters and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the parameters do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 20, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a(n) computer-readable medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-readable medium. The Step 2A Prong One Analysis for claim 19 is applicable here since claim 20 carries out the computer-readable medium of claim 19 but for the recitation of additional element(s) of in which the relationship comprises an inner product.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
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.
Claim(s) 1-2, 6-7, 11-12, 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Servetnyk et al. (Unsupervised Federated Learning for Unbalanced Data, hereinafter referred to as "Servetnyk") in view of Hu et al. (Federated Region-Learning: An Edge Computing Based Framework for Urban Environment Sensing, hereinafter referred to as “Hu”).
Regarding claim 1 (Currently Amended), Servetnyk teaches a processor-implemented method (Servetnyk, section II.A - teaches agents performing local computations and a server aggregating the local updated into a global models [It would be obvious that the server has computing components to perform the steps]), comprising:
receiving machine learning model updates … from a plurality of … clients in a federated learning system (Servetnyk, section II – teaches receiving parameters from each of the agents for aggregation on the server [It would be obvious that agents and servers can communicate wirelessly]);
determining a fixed local zone associated with each of the plurality of clients based on a common attribute of the plurality of clients, the fixed local zone having a first fixed boundary (Servetnyk, section II – teaches that the agents are associated with sensor nodes; Servetnyk, section III – teaches determining which dataset clusters are associated with each of the agents in order to determine weights; see also Servetnyk Fig. 2) …;
updating model weights of a central machine learning model based on local machine learning updates for a local subset of the plurality of clients (Servetnyk, section II – teaches updating weights of the global model based on weighted model parameters of the agents), the local subset corresponding to the fixed local zone (Servetnyk, section II – teaches that the agents are associated with sensor nodes); and
updating the model weights of the central machine learning model based on neighbor machine learning updates for a neighbor subset of the plurality of clients (Servetnyk, section II – teaches updating the global model based on weighted local agent parameters, each agent associated with a sensor node; Servetnyk, section III – teaches determining the weights based the current agent as well as the neighboring agents), the neighbor subset corresponding to a fixed neighbor zone that neighbors the fixed local zone (Servetnyk, section II – teaches that the agents are associated with sensor nodes, each node corresponding to neighboring nodes), the neighbor machine learning updates having a different weight than the local machine learning updates when updating model weights (Servetnyk, section III – teaches determining the weights based the current agent as well as the neighboring agents), a value of the different weight corresponding to a similarity parameter (Servetnyk, section III – teaches determining weights for local model updated based on a similarity of data between the agents/nodes), the fixed neighbor zone having a second fixed boundary (Servetnyk, section II – teaches that the agents are associated with sensor nodes).
However, Servetnyk does not explicitly teach the common attribute comprising at least one of a geographic location, a user interface theme, or a default language.
Hu teaches
receiving machine learning model updates wirelessly from a plurality of wireless clients in a federated learning system (Hu, section II – teaches the regional federated learning using regional sites; see also Hu, section I – teaches federated learning using mobile device for local data and training [It is obvious that the communications between server and sited could be wireless]);
determining a fixed local zone associated with each of the plurality of clients based on a common attribute of the plurality of clients, the fixed local zone having a first fixed boundary, the common attribute comprising at least one of a geographic location, a user interface theme, or a default language (Hu, section II – teaches determining regional areas based on geographic location, each region comprising monitoring sites, to perform regional modeling which is used to update a global model; see also Hu, section I).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Servetnyk with the teachings of Hu in order to consider regional characteristics of distributed training samples to improve accuracy in the field of federated learning (Hu, Abstract – “Sparse sensory data caused by insufficient monitoring sites and their incomplete records becomes the main challenge of fine-grained environment sensing. In this paper, we develop a novel inference framework, named Federated Region Learning (FRL), for urban environment sensing. The proposed framework inherits the basic idea of federated learning, and also considers the regional characteristics during the distribution of training samples so as to improve the inference accuracy. Moreover, we exploit an edge computing architecture to implement the FRL for improving the computational efficiency. We also apply FRL to PM2.5 monitoring in Beijing. The evaluation shows that our FRL improves computational efficiency nearly 3 times than centralized training mode and increases accuracy by more than 5% compared with normal distributed training.”).
Regarding claim 2 (Original), Servetnyk in view of Hu teaches all of the limitations of the method of claim 1 as noted above. Servetnyk further teaches learning the similarity parameter with machine learning training (Servetnyk, section III – teaches determining weights for local model parameter updates based on self-organizing maps).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Servetnyk and Hu for the same reasons as disclosed in claim 1 above.
Regarding claim 6 (Currently Amended), it is the apparatus embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Servetnyk further teaches an apparatus, comprising:
at least one memory (Servetnyk, section II.A - teaches agents performing local computations and a server aggregating the local updated into a global models [It would be obvious that the server has computing components to perform the steps]); and
at least one processor coupled to the at least one memory, the at least one processor configured to (Servetnyk, section II.A - teaches agents performing local computations and a server aggregating the local updated into a global models [It would be obvious that the server has computing components to perform the steps]) …
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Servetnyk and Hu for the same reasons as disclosed in claim 1 above.
Regarding claim 7 (Original), the rejection of claim 6 is incorporated herein. Further, the limitations in this claim are taught by Servetnyk in view of Hu for the reasons set forth in the rejection of claim 2.
Regarding claim 11 (Apparatus), it is the apparatus embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1.
Regarding claim 12 (Original), the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Servetnyk in view of Hu for the reasons set forth in the rejection of claim 2.
Regarding claim 16 (Currently Amended), it is the computer-readable medium embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Servetnyk further teaches a non-transitory computer-readable medium having program code recorded thereon, the program code executed by a processor and comprising (Servetnyk, section II.A - teaches agents performing local computations and a server aggregating the local updated into a global models [It would be obvious that the server has computing components to perform the steps]) …
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Servetnyk and Hu for the same reasons as disclosed in claim 1 above.
Regarding claim 17 (Original), the rejection of claim 16 is incorporated herein. Further, the limitations in this claim are taught by Servetnyk in view of Hu for the reasons set forth in the rejection of claim 2.
Claim(s) 3-4, 8-9, 13-14, 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Servetnyk in view of Hu and further in view of Chu et al., (US 2021/0374617 A1 – Methods and Systems for Horizontal Federated Learning Using Non-IID Data, hereinafter referred to as “Chu”).
Regarding claim 3 (Original), Servetnyk in view of Hu teaches all of the limitations of the method of claim 1 as noted above. However, Servetnyk in view of Hu does not explicitly teach in which the similarity parameter comprises a self-attention coefficient.
Chu teaches in which the similarity parameter comprises a self-attention coefficient (Chu, [0068]-[0071] – teaches determining similarity of local client updates using collaboration coefficients).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Servetnyk in view of Hu with the teachings of Chu in order to improve service and efficiency in the field of federated learning (Chu, [0099] – “Other applications of the present disclosure include application in the context of autonomous driving (e.g., autonomous vehicles may provide data to learn an up-to-date model related to traffic, construction, or pedestrian behavior, to promote safe driving), or in the context of a network of sensors (e.g., individual sensors may perform local learning of a model, to avoid sending large amounts of data back to the central server). Other possible applications include applications in the context of mobile communication, where horizontal federated learning may be used to learn user behaviors to improve service and/or improve efficiency (e.g., to better manage power usage and/or CPU control). Example embodiments of the present disclosure may also have applications in the context of the internet of things (IoT), in which a client may be any IoT-capable device (e.g., lamp, fridge, oven, desk, door, window, air conditioner, etc. having IoT capabilities)”).
Regarding claim 4 (Original), Servetnyk in view of Hu and further in view of Chu teaches all of the limitations of the method of claim 3 as noted above. Chu further teaches in which the self-attention coefficient normalizes a relationship between the local machine learning updates of the local subset and the neighbor machine learning updates of the neighbor subset (Chu, [0072]-[0073] – teaches normalizing the collaboration coefficients).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Servetnyk, Hu and Chu in order to normalize the relationship between models to improve service and efficiency (Chu, [0099]).
Regarding claim 8 (Original), the rejection of claim 6 is incorporated herein. Further, the limitations in this claim are taught by Servetnyk in view of Hu and further in view of Chu for the reasons set forth in the rejection of claim 3.
Regarding claim 9 (Original), the rejection of claim 8 is incorporated herein. Further, the limitations in this claim are taught by Servetnyk in view of Hu and further in view of Chu for the reasons set forth in the rejection of claim 4.
Regarding claim 13 (Original), the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Servetnyk in view of Hu and further in view of Chu for the reasons set forth in the rejection of claim 3.
Regarding claim 14 (Original), the rejection of claim 13 is incorporated herein. Further, the limitations in this claim are taught by Servetnyk in view of Hu and further in view of Chu for the reasons set forth in the rejection of claim 4.
Regarding claim 18 (Original), the rejection of claim 16 is incorporated herein. Further, the limitations in this claim are taught by Servetnyk in view of Hu and further in view of Chu for the reasons set forth in the rejection of claim 3.
Regarding claim 19 (Original), the rejection of claim 18 is incorporated herein. Further, the limitations in this claim are taught by Servetnyk in view of Hu and further in view of Chu for the reasons set forth in the rejection of claim 4.
Claim(s) 5, 10, 15, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Servetnyk in view of Hu, further in view of Chu and further in view of Upadhyay et al. (US 2022/0391779 A1 – Systems and Methods for Reward-Driven Federated Learning, hereinafter referred to as “Upadhyay”).
Regarding claim 5 (Original), Servetnyk in view of Hu and further in view of Chu teaches all of the limitations of the method of claim 4 as noted above. However, Servetnyk in view of Hu and further in view of Chu does not explicitly teach in which the relationship comprises an inner product.
Upadhyay teaches in which the relationship comprises an inner product (Upadhyay, [0034]-[0040] – teaches that the relationship between models is a Frobenius norm [Frobenius inner product]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Servetnyk in view of Hu and further in view of Chu with the teachings of Upadhyay in order to provide proper contribution of local models in the field of federated learning (Upadhyay, [0005] – “This poses a challenge for federated learning that requires maintaining privacy of the individual models (and model gradients) as well as the anonymity of the model contributor. Current solutions address this challenge by dynamically generating asymmetric and symmetric keys for each federated learning round, with a caveat that the aggregation server node is a “consortium”—trusted infrastructure. Even with consortium-trusted aggregation server implementation, there is still a risk of a lack of contribution in the overall federated learning from individual nodes. In addition, malicious nodes could potentially send a misleading model that could skew the efficacy of the aggregated models. Current solutions address this by having an aggregation server detect such behavior and drop those contributors from the collaboration process.”).
Regarding claim 10 (Original), the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Servetnyk in view of Hu, further in view of Chu and further in view of Upadhyay for the reasons set forth in the rejection of claim 5.
Regarding claim 15 (Original), the rejection of claim 14 is incorporated herein. Further, the limitations in this claim are taught by Servetnyk in view of Hu, further in view of Chu and further in view of Upadhyay for the reasons set forth in the rejection of claim 5.
Regarding claim 20 (Original), the rejection of claim 19 is incorporated herein. Further, the limitations in this claim are taught by Servetnyk in view of Hu, further in view of Chu and further in view of Upadhyay for the reasons set forth in the rejection of claim 5.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125
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