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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy of Application No. JP2022-190207, filed on November 29, 2022 has been electronically retrieved by USPTO.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on May 15, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
In page 33, line 3, “at least one of the local model” should read “at least one of the local models.”
In page 33, line 13, “at least one of the local model” should read “at least one of the local models.”
In page 34, lines 28-29, “at least one of the local model” should read “at least one of the local models.” Appropriate correction is required.
Claim Objections
Claims 5 is objected to because of the following informalities:
In claim 5, line 9, “at least one of the local model” should read “at least one of the local models.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 8, and 9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “the plurality of local models.” There is insufficient antecedent basis for this term.
Claim 8 recites “the plurality of local models.” There is insufficient antecedent basis for this term.
Claim 9 recites “the plurality of local models.” There is insufficient antecedent basis for this term.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-7 and 9 are device claims. Claim 8 is a method type claim. Therefore, the claims are directed to either a process, machine, manufacture or composition of matter.
Claim 1:
Regarding claim 1, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “an information processing apparatus.” This is a device or machine, which is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mathematical concept but for recitation of generic computer components:
“calculate contribution degree based on at least one of each of the plurality of local models or each the plurality of output data to the updated global model” (this is a mathematical concept; as described in the specification, Equation 1 is:
C
i
=
D
i
∑
k
=
1
n
D
k
, in which Ci is the contribution degree of the ith client, Di is the number of local data used for learning of the ith client, and n is the number of clients, see MPEP §2106.04(a)(2)(I)),
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mathematical concept but for the recitation of generic computer components, then it falls within the mathematical concepts grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
i. “An information processing apparatus comprising” (An information processing apparatus is considered a generic computer component being used as a tool to perform the judicial exception – see MPEP §2106.05(f))
ii. “circuitry configured to” (Circuitry is considered a generic computer component being used as a tool to perform the judicial exception – see MPEP §2106.05(f))
iii. “receive at least one of information indicating a local model or output data, from each a plurality of nodes, the information indicating the local model being obtained by learning a local data processed by the node based on a global model, the output data being obtained by inputting shared data to the local mode” (Receiving information is considered insignificant extra-solution activity of mere data gathering – see MPEP §2106.05(g))
iv. “update the global model based on at least one of a plurality of the information indicating the local model or a plurality of the output data received from the plurality of nodes” (Updating the global model is considered mere instructions to apply the exception using a generic computer – see MPEP §2106.05(g))
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount significantly more than the judicial exception.
As discussed above, the additional element iii recites insignificant extra-solution activity of mere data gathering, which is a well-understood routine and conventional activity, receiving or transmitting data over a network, e.g., using the Internet to gather data, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362. The additional element iv recites mere instructions to apply the exception using a generic computer. In addition, additional elements i and ii recite a generic computer component being used to perform the judicial exception, which are not indicative of significantly more.
Considering that the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 2
Regarding claim 2, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 2 recites the following additional elements:
“determine incentive for each of the plurality of nodes, based on the contribution degree of at least one of each of the plurality of local models for each of the plurality of output data” (this is a mental process, a person could mentally evaluate determining incentive for pluralities of nodes based on contribution degree)
If claim limitations, under their broadest reasonable interpretation, cover performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
“The information processing apparatus of claim 1, wherein the circuitry is further configured to…” (in step 2A, prong 2, this is considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 3:
Regarding claim 3, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 3 recites the following additional elements:
“The information processing apparatus of claim 1, wherein the circuitry is further configured to transmit the information indicating the global model to the plurality of nodes.” (in step 2A, prong 2, this is considered insignificant extra solution activity of mere data gathering, see MPEP § 2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of mere data gathering, which is well-understood, routine and conventional, activity, receiving or transmitting data over a network, e.g., using the Internet to gather data, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 4:
Regarding claim 4, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 4 recites the following additional elements:
“calculate the contribution degree, based on the number of items of data of each of the plurality of nodes” (this is a mathematical concept, as described in the specification, Equation 1 is:
C
i
=
D
i
∑
k
=
1
n
D
k
, in which Ci is the contribution degree of the ith client, Di is the number of local data used for learning of the ith client, and n is the number of clients, see MPEP §2106.04(a)(2)(I)),
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mathematical concept but for the recitation of generic computer components, then it falls within the mathematical concepts grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
“The information processing apparatus of claim 1, wherein the circuitry is further configured to” (in step 2A, prong 2, this is considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f)). In step 2B, this is also considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f).)
“receive a number of items the local data used in the learning of the local model from each of the plurality of nodes” (in step 2A, prong 2, this is considered insignificant extra-solution activity of mere data gathering – see MPEP §2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of mere data gathering, which is a well-understood routine and conventional activity, receiving or transmitting data over a network, e.g., using the Internet to gather data, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 5:
Regarding claim 5, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 5 recites the following additional elements:
“calculate the contribution degree of at least one of the local model or the output data of the specific node based on an evaluation of the first global model and an evaluation of the second global model” (this is a mathematical concept, as described in the specification, Equation 1 is:
C
i
=
D
i
∑
k
=
1
n
D
k
, in which Ci is the contribution degree of the ith client, Di is the number of local data used for learning of the ith client, and n is the number of clients, see MPEP §2106.04(a)(2)(I)),
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mathematical concept but for the recitation of generic computer components, then it falls within the mathematical concepts grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
“The information processing apparatus of claim 1, wherein the circuitry is further configured to” (in step 2A, prong 2, this is considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f)). In step 2B, this is also considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f).)
“update the global model to a first global model, based on at least one of the plurality of the information indicating the local model or the plurality of the output data received from the plurality of nodes indicating a specific node” (in step 2A, prong 2, this is considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f)). In step 2B, this is also considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f).)
“update the global model to a second global model based on the local model or the output data, received from at least one of the plurality of nodes excluding the specific node or the specific node” (in step 2A, prong 2, this is considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f)). In step 2B, this is also considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 6:
Regarding claim 6, it is dependent upon claim 5, and thereby incorporates the limitations of, and corresponding analysis to claim 5. Further, claim 6 recites the following additional elements:
“evaluate each of the first global model and second global model based on evaluation data” (this is a mental process, a person could mentally evaluate the global models based on evaluation data)
“The information processing apparatus of claim 5, wherein the circuitry is further configured to” (in step 2A, prong 2, this is considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 7:
Regarding claim 7, it is dependent upon claim 5, and thereby incorporates the limitations of, and corresponding analysis to claim 5. Further, claim 7 recites the following additional elements:
“The information processing apparatus of claim 5, wherein the circuitry is further configured to” (in step 2A, prong 2, this is considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f)). In step 2B, this is also considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f).)
“receive the evaluation of the first global model based on the local data and the evaluation of the second global model based on the local data” (in step 2A, prong 2, this is considered insignificant extra-solution activity of mere data gathering – see MPEP §2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of mere data gathering, which is a well-understood routine and conventional activity, see receiving or transmitting data over a network, e.g., using the Internet to gather data, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 8:
Regarding claim 8, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “an information processing method.” A method or process is one of the four statutory categories of invention.
In step 2A prong 1 one of the 101-analysis set forth by MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mathematical concept but for recitation of generic computer components:
“calculating contribution degree of at least one of each of the plurality of local models or each of the plurality of output data to the updated global model” (this is a mathematical concept, as described in the specification, Equation 1 is:
C
i
=
D
i
∑
k
=
1
n
D
k
, in which Ci is the contribution degree of the ith client, Di is the number of local data used for learning of the ith client, and n is the number of clients, see MPEP §2106.04(a)(2)(I)),
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mathematical concept but for the recitation of generic computer components, then it falls within the mathematical concepts grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
i. “An information processing method comprising” (an information processing method is considered mere instructions to apply an exception using a generic computer component – see MPEP § 2106.05(f)))
ii. “transmitting information indicating a global model to a plurality of nodes” (Transmitting information is considered insignificant extra-solution activity of mere data gathering – see MPEP §2106.05(g))
iii. “receiving at least one of information indicating a local model or output data, from each a plurality of nodes, the information indicating the local model being obtained by learning a local data processed by the node based on a global model, the output data being obtained by inputting shared data to the local model” (Receiving information is considered insignificant extra-solution activity of mere data gathering – see MPEP §2106.05(g))
iv. “updating the global model based on at least one of a plurality of the information indicating the local model or a plurality of the output data received from the plurality of nodes” (Updating the global model is considered mere instructions to apply the exception using a generic computer – see MPEP §2106.05(g))
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount significantly more than the judicial exception.
As discussed above, the additional elements ii and iii recite insignificant extra-solution activity of mere data gathering, which is a well-understood routine and conventional activity, receiving or transmitting data over a network, e.g., using the Internet to gather data, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362. In addition, additional elements i and iv recite mere instructions to apply the exception using generic computer components, which are not indicative of significantly more.
Considering that the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 9:
Regarding claim 9, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “a non-transitory recording medium.” This is a device or machine, which is one of the four statutory categories of invention.
In step 2A prong 1 one of the 101-analysis set forth by MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mathematical concept but for recitation of generic computer components:
“calculating contribution degree of at least one of each of the plurality of local models or each of the plurality of output data to the updated global model” (this is a mathematical concept, as described in the specification, Equation 1 is:
C
i
=
D
i
∑
k
=
1
n
D
k
, in which Ci is the contribution degree of the ith client, Di is the number of local data used for learning of the ith client, and n is the number of clients, see MPEP §2106.04(a)(2)(I)),
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
i. “A non-transitory recording medium storing a plurality of instructions which, when executed by one or more processors on an information processing apparatus, causes the processors to perform an information processing method comprising” (this is considered mere instructions to apply an exception using a generic computer component – see MPEP § 2106.05(f)))
ii. “receiving at least one of information indicating a local model or output data, from each a plurality of nodes, the information indicating the local model being obtained by learning a local data processed by the node based on a global model, the output data being obtained by inputting shared data to the local model” (Receiving information is considered insignificant extra-solution activity of mere data gathering – see MPEP §2106.05(g))
iii. “updating the global model based on at least one of a plurality of the information indicating the local model or a plurality of the output data received from the plurality of nodes” (Updating the global model is considered mere instructions to apply the exception using a generic computer – see MPEP §2106.05(g))
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount significantly more than the judicial exception.
As discussed above, the additional element ii recites insignificant extra-solution activity of mere data gathering, which is a well-understood routine and conventional activity, receiving or transmitting data over a network, e.g., using the Internet to gather data, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362. In addition, additional elements i and iii recite mere instructions to apply the exception using generic computer components, which are not indicative of significantly more.
Considering that the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. 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.
Claims 1, 3, 8, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Ouyang et al. (US 20240005341 A1) (hereinafter Ouyang) in view of Shi et al. (US 20230334370 A1) (hereinafter Shi).
Claim 1
Regarding claim 1, Ouyang teaches “An information processing apparatus comprising circuitry configured to;”
See Ouyang in paragraph 0009 where it describes “An aspect of the present disclosure relates to an electronic device for a service provider in a wireless communication system, the service provider capable of implementing customer experience perception with at least one other service provider in the wireless communication system through federated learning, the electronic device can comprise a processing circuit” Here, Ouyang describes an electronic device that has a processing circuit.
Further Ouyang teaches “receive at least one of information indicating a local model or output data, from each a plurality of nodes;”
See Ouyang in paragraph 0010 where it describes “(The) electronic device can comprise a processing circuit configured to receive intermediate information related to local model training from the at least two service providers which are obtained at the service providers by local model training by utilizing training data for training a global model related to the customer experience perception based on the federated learning, wherein the training data is related to the customer's experience for a specific service/product/business, and trains the global model by aggregating the intermediate information from the at least two service providers.” Here, Ouyang describes receiving information relating to a local model that is derived from at least two service providers, which act as nodes.
Further Ouyang teaches “the information indicating the local model being obtained by learning a local data processed by the node based on a global model, the output data being obtained by inputting shared data to the local model;”
See Ouyang in paragraph 0010 where it describes “receive intermediate information related to local model training from the at least two service providers which are obtained at the service providers by local model training by utilizing training data for training a global model related to the customer experience perception based on the federated learning, wherein the training data is related to the customer's experience for a specific service/product/business” Here, Ouyang teaches using training data that is based on a global model.
Further Ouyang teaches “update the global model based on at least one of a plurality of the information indicating the local model or a plurality of the output data received from the plurality of nodes;”
See Ouyang in paragraph 0010 where it describes “and trains the global model by aggregating the intermediate information from the at least two service providers.” Here, Ouyang teaches training the global model based on previously received information from the local model.
However, Ouyang did not explicitly teach “calculate contribution degree of at least one of each of the plurality of local models or each of the plurality of output data to the updated global model.”
However, Shi in the same field of art teaches “calculate contribution degree of at least one of each of the plurality of local models or each of the plurality of output data to the updated global model.”
See Shi in paragraph 0054 where it describes “In the embodiments of this specification, the participating node participating in the federated learning model include a plurality of participating nodes, and the degree of participation of the participating node can be represented by a contribution degree of the participating node in the federated learning. The determining a degree of participation of the participating node based on the node local gradient of the participating node and the global gradient can specifically include the following: determining a node contribution degree of each of the plurality of participating nodes based on a node local gradient of the participating node and the global gradient; and determining a relative contribution degree of the participating node based on a node contribution degree of the participating node and the node contribution degree of each participating node.” Here, Shi teaches a contribution degree given to the local models.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Ouyang with the teachings of Shi by using Ouyang’s teachings of an information processing apparatus comprising circuitry configured to receive local model information with nodes, with the local model information being derived from a global model, and updating the global model with local model information, and incorporate with Shi’s teachings of calculating a contribution degree.
One of ordinary skill in the art would be motivated to do so because by integrating Shi’s frameworks into the methods of Ouyang, one with ordinary skill in the art would be able to have a method that “satisfies the idea of contribution evaluation in cooperative games, overcomes the disadvantage of using only accuracy in related work as a contribution, and has smaller computational overheads than those of real Shapley values, thereby saving resources and improving computational efficiency.” (Shi, paragraph 0056)
Claim 3
Regarding claim 3, Ouyang in view of Shi teaches the limitations in claim 1.
However, Ouyang did not explicitly teach “transmit the information indicating the global model to the plurality of nodes.”
However, Shi in the same field of art teaches “transmit the information indicating the global model to the plurality of nodes.”
See Shi in paragraph 0097 where it describes “The server can send the actual model gradient corresponding to each participating node to each participating node, and each participating node can generate a federated learning model based on the actual model gradient that is received by the participating node.” Here, Shi teaches information associated with a model to a group of nodes.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Ouyang with the teachings of Shi by using Ouyang’s teachings of an information processing apparatus comprising circuitry configured to receive local model information with nodes, with the local model information being derived from a global model, and updating the global model with local model information, and incorporate with Shi’s teachings of transmitting global model information to a plurality of nodes.
One of ordinary skill in the art would be motivated to do so because by integrating Shi’s frameworks into the methods of Ouyang, one with ordinary skill in the art would be able to “train the latest version of the training model based on the data about the participating nodes, and feed back, to the server for aggregation, the node local gradient obtained from the training” (Shi, paragraph 0097).
Claim 8
Regarding claim 8, Ouyang teaches “an information processing method;”
See Ouyang in paragraph 0011 where it describes “Another aspect of the present disclosure relates to a method for a service provider in a wireless communication system, the service provider capable of implementing customer experience perception with at least one other service provider in the wireless communication system through federated learning” Here, Ouyang describes a method for processing information.
Further Ouyang teaches “receiving at least one of information indicating a local model or output data, from each a plurality of nodes;”
See Ouyang in paragraph 0010 where it describes “(The) electronic device can comprise a processing circuit configured to receive intermediate information related to local model training from the at least two service providers which are obtained at the service providers by local model training by utilizing training data for training a global model related to the customer experience perception based on the federated learning, wherein the training data is related to the customer's experience for a specific service/product/business, and trains the global model by aggregating the intermediate information from the at least two service providers.” Here, Ouyang describes receiving information relating to a local model that is derived from at least two service providers, which act as nodes.
Further Ouyang teaches “the information indicating the local model being obtained by learning a local data processed by the node based on a global model, the output data being obtained by inputting shared data to the local model;”
See Ouyang in paragraph 0010 where it describes “receive intermediate information related to local model training from the at least two service providers which are obtained at the service providers by local model training by utilizing training data for training a global model related to the customer experience perception based on the federated learning, wherein the training data is related to the customer's experience for a specific service/product/business” Here, Ouyang teaches using training data that is based on a global model.
Further Ouyang teaches “updating the global model based on at least one of a plurality of the information indicating the local model or a plurality of the output data received from the plurality of nodes;”
See Ouyang in paragraph 0010 where it describes “and trains the global model by aggregating the intermediate information from the at least two service providers.” Here, Ouyang teaches training the global model based on previously received information from the local model.
However, Ouyang did not explicitly teach “transmit the information indicating the global model to the plurality of nodes” and “calculating contribution degree of at least one of each of the plurality of local models or each of the plurality of output data to the updated global model.”
However, Shi in the same field of art teaches “transmit the information indicating the global model to the plurality of nodes.”
See Shi in paragraph 0097 where it describes “The server can send the actual model gradient corresponding to each participating node to each participating node, and each participating node can generate a federated learning model based on the actual model gradient that is received by the participating node.” Here, Shi teaches information associated with a model to a group of nodes.
“calculating contribution degree of at least one of each of the plurality of local models or each of the plurality of output data to the updated global model.”
See Shi in paragraph 0054 where it describes “In the embodiments of this specification, the participating node participating in the federated learning model include a plurality of participating nodes, and the degree of participation of the participating node can be represented by a contribution degree of the participating node in the federated learning. The determining a degree of participation of the participating node based on the node local gradient of the participating node and the global gradient can specifically include the following: determining a node contribution degree of each of the plurality of participating nodes based on a node local gradient of the participating node and the global gradient; and determining a relative contribution degree of the participating node based on a node contribution degree of the participating node and the node contribution degree of each participating node.” Here, Shi teaches a contribution degree given to the local models.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Ouyang with the teachings of Shi by using Ouyang’s teachings of an information processing apparatus comprising circuitry configured to receive local model information with nodes, with the local model information being derived from a global model, and updating the global model with local model information, and incorporate with Shi’s teachings of calculating a contribution degree and transmitting global model information to the plurality of nodes.
One of ordinary skill in the art would be motivated to do so because by integrating Shi’s frameworks into the methods of Ouyang, one with ordinary skill in the art would be able to have a method that “satisfies the idea of contribution evaluation in cooperative games, overcomes the disadvantage of using only accuracy in related work as a contribution, and has smaller computational overheads than those of real Shapley values, thereby saving resources and improving computational efficiency.” (Shi, paragraph 0056)
Claim 9
Regarding claim 9, Ouyang teaches “A non-transitory recording medium storing a plurality of instructions which, when executed by one or more processors on an information processing apparatus, causes the processors to perform an information processing method comprising;”
See Ouyang in paragraph 0013 where it describes “Yet another aspect of the present disclosure relates to a non-transitory computer-readable storage medium storing executable instructions that, when executed, implement the method as previously described.” Further see Ouyang in paragraph 0014 where it describes “Yet another aspect of the present disclosure relates to a device which includes a processor and a storage device, and the storage device stores executable instructions that, when executed, implement the method described above.” Here, Ouyang teaches a non-transitory recording medium that stores executable instructions, as well as a processor that executes these instructions.
Further Ouyang teaches “receiving at least one of information indicating a local model or output data, from each a plurality of nodes;”
See Ouyang in paragraph 0010 where it describes “(The) electronic device can comprise a processing circuit configured to receive intermediate information related to local model training from the at least two service providers which are obtained at the service providers by local model training by utilizing training data for training a global model related to the customer experience perception based on the federated learning, wherein the training data is related to the customer's experience for a specific service/product/business, and trains the global model by aggregating the intermediate information from the at least two service providers.” Here, Ouyang describes receiving information relating to a local model that is derived from at least two service providers, which act as nodes.
Further Ouyang teaches “the information indicating the local model being obtained by learning a local data processed by the node based on a global model, the output data being obtained by inputting shared data to the local model;”
See Ouyang in paragraph 0010 where it describes “receive intermediate information related to local model training from the at least two service providers which are obtained at the service providers by local model training by utilizing training data for training a global model related to the customer experience perception based on the federated learning, wherein the training data is related to the customer's experience for a specific service/product/business” Here, Ouyang teaches using training data that is based on a global model.
Further Ouyang teaches “updating the global model based on at least one of a plurality of the information indicating the local model or a plurality of the output data received from the plurality of nodes;”
See Ouyang in paragraph 0010 where it describes “and trains the global model by aggregating the intermediate information from the at least two service providers.” Here, Ouyang teaches training the global model based on previously received information from the local model.
However, Ouyang did not explicitly teach “calculating contribution degree of at least one of each of the plurality of local models or each of the plurality of output data to the updated global model.”
However, Shi in the same field of art teaches “calculating contribution degree of at least one of each of the plurality of local models or each of the plurality of output data to the updated global model.”
See Shi in paragraph 0054 where it describes “In the embodiments of this specification, the participating node participating in the federated learning model include a plurality of participating nodes, and the degree of participation of the participating node can be represented by a contribution degree of the participating node in the federated learning. The determining a degree of participation of the participating node based on the node local gradient of the participating node and the global gradient can specifically include the following: determining a node contribution degree of each of the plurality of participating nodes based on a node local gradient of the participating node and the global gradient; and determining a relative contribution degree of the participating node based on a node contribution degree of the participating node and the node contribution degree of each participating node.” Here, Shi teaches a contribution degree given to the local models.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Ouyang with the teachings of Shi by using Ouyang’s teachings of an information processing apparatus comprising circuitry configured to receive local model information with nodes, with the local model information being derived from a global model, and updating the global model with local model information, and incorporate with Shi’s teachings of calculating a contribution degree.
One of ordinary skill in the art would be motivated to do so because by integrating Shi’s frameworks into the methods of Ouyang, one with ordinary skill in the art would be able to have a method that “satisfies the idea of contribution evaluation in cooperative games, overcomes the disadvantage of using only accuracy in related work as a contribution, and has smaller computational overheads than those of real Shapley values, thereby saving resources and improving computational efficiency.” (Shi, paragraph 0056)
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Ouyang in view of Shi, and further in view of Wen et al. (US 20230419182 A1) (hereinafter Wen).
Claim 2
Regarding claim 2, Ouyang in view of Shi teaches the limitations in claim 1.
Neither Ouyang or Shi appear to teach “determine incentive for each of the plurality of nodes, based on the contribution degree of the at least one of each of the plurality of local models or each of the plurality of output data.”
However, Wen in the same field of art teaches “determine incentive for each of the plurality of nodes, based on the contribution degree of the at least one of each of the plurality of local models or each of the plurality of output data.”
See Wen in paragraph 0049 where it describes “In some embodiments, the determining the training reward of the each participant node based on the total training reward includes: determining a contribution degree of the each participant node; and determining the training reward of each participant node by allocating, based on the contribution degree of the each participant node, the total training reward proportionally.” Here, Wen describes determining a reward (or incentive) for each node based on the contribution degree.
Ouyang, Shi, and Wen are considered to be analogous to the claimed invention because they are in the same field of federated learning devices. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ouyang and Shi to incorporate the teachings of Wen to incorporate the teachings of Wen and determine incentive based on contribution degree.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Ouyang and Shi with the teachings of Wen by using Ouyang and Shi’s teachings of an apparatus for federating learning that receives information, updates a global model, and calculates contribution degree, and incorporate with Wen’s teachings of determining an incentive based on the contribution degree. One of ordinary skill in the art would be motivated to do so because by integrating Wen’s frameworks into the methods of Ouyang and Shi, one with ordinary skill in the art would “achieve an effective improvement of the motivation of each participant in the federal learning and help improve the model training effect” (Wen, paragraph 0021).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Ouyang in view of Shi, and further in view of Gao et al. (https://dl.acm.org/doi/abs/10.1145/3472456.3472469) (hereinafter Gao).
Claim 4
Regarding claim 4, Ouyang in view of Shi teaches the limitations of claim 1.
Ouyang teaches “receive a number of items of the local data used in learning of the local model, from each the plurality of nodes;”
See Ouyang in paragraph 0010 where it describes “(The) electronic device can comprise a processing circuit configured to receive intermediate information related to local model training from the at least two service providers which are obtained at the service providers by local model training by utilizing training data for training a global model related to the customer experience perception based on the federated learning, wherein the training data is related to the customer's experience for a specific service/product/business, and trains the global model by aggregating the intermediate information from the at least two service providers.” Here, Ouyang describes receiving information relating to a local model that is derived from at least two service providers, which act as nodes.
Neither Ouyang or Shi appear to teach “calculate the contribution degree based on the number of items of data of each of the plurality of nodes.”
However, Gao in the same field of art teaches “calculate the contribution degree based on the number of items of data of each of the plurality of nodes.”
See Gao in page 6, col. 2 where it describes “The relationship between the amount of training data and system revenue is expressed as the following utility function:
Ψ
=
log
1
+
n
[
33
]
, where
n
is the amount of training data and
Ψ
is the revenue.” Here, Gao describes calculating an incentive (or in other words, contribution degree) based on the amount of training data. The formula uses
n
, the amount of training data, to do this.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Ouyang and Shi with the teachings of Gao by using Ouyang and Shi’s teachings of an information processing apparatus comprising circuitry configured to receive a number of local data items from each the plurality of nodes that calculating a contribution degree, and further incorporate Gao’s teachings of calculating contribution degree based on the number of items.
One of ordinary skill in the art would be motivated to do so because by integrating Gao’s frameworks into the methods of Ouyang and Shi, one with ordinary skill in the art would “measure the trustworthiness of workers… to obtain stable system revenue” (Gao, page 7, col. 1).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ouyang in view of Shi, and further in view of Lee et al. (US 12632784 B2) (hereinafter Lee).
Claim 5
Regarding claim 5, Ouyang in view of Shi teaches the limitations in claim 1.
Shi teaches “calculate the contribution degree of at least one of the local model or the output data of the specific node based on an evaluation of the first global model and an evaluation of the second global model;”
See Shi in paragraph 0042 where it describes “In the embodiments of this specification, the participating node participating in the federated learning model can include a plurality of participating nodes. The obtaining a marginal loss of the participating node can specifically include the following: determining a first reference global model based on a node local gradient of each participating node in the plurality of participating nodes; determining a second reference global model based on a node local gradient of each participating node other than the participating node in the plurality of participating nodes” Here, Shi describes a federated learning model using a first and second global model. Further see Shi in paragraph 0054 where it describes “In the embodiments of this specification, the participating node participating in the federated learning model include a plurality of participating nodes, and the degree of participation of the participating node can be represented by a contribution degree of the participating node in the federated learning. The determining a degree of participation of the participating node based on the node local gradient of the participating node and the global gradient can specifically include the following: determining a node contribution degree of each of the plurality of participating nodes based on a node local gradient of the participating node and the global gradient; and determining a relative contribution degree of the participating node based on a node contribution degree of the participating node and the node contribution degree of each participating node.” Here, Shi teaches calculating a contribution degree based on local models.
Neither Ouyang or Shi teach “update the global model to a first global model, based on at least one of the plurality of the information indicating the local model or the plurality of the output data received from the plurality of nodes including a specific node” or “update the global model to a second global model based on the local model or the output data, received from at least one of the plurality of nodes excluding the specific node or the specific node”
However, Lee in the same field of art teaches “update the global model to a first global model, based on at least one of the plurality of the information indicating the local model or the plurality of the output data received from the plurality of nodes including a specific node”
See Lee in col 8, lines 12-23 where it describes “In step S1010, the central server 100 may transmit the global parameters of the global model to each client device 200. In step S1020, each client device 200 may apply a loss for the difference between the predicted value of the global model and the predicted value of the local model possessed by itself to the loss function to the train the local model, and transmit the trained local parameters to the central server 100. In step S1030, the central server 100 may receive the local parameters trained from each client device 200 and update the global model.” Here, Lee teaches receiving local parameters and updating the global model accordingly. These local parameters can be trained so that they include a specific node.
Further, Lee teaches “update the global model to a second global model based on the local model or the output data, received from at least one of the plurality of nodes excluding the specific node or the specific node”
See Lee in col 8, lines 12-23 where it describes “In step S1010, the central server 100 may transmit the global parameters of the global model to each client device 200. In step S1020, each client device 200 may apply a loss for the difference between the predicted value of the global model and the predicted value of the local model possessed by itself to the loss function to the train the local model, and transmit the trained local parameters to the central server 100. In step S1030, the central server 100 may receive the local parameters trained from each client device 200 and update the global model.” Here, Lee teaches receiving local parameters and updating the global model accordingly. These local parameters can be trained so that they exclude a specific node.
Ouyang, Shi, and Lee are considered to be analogous to the claimed invention because they are in the same field of federated learning devices. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ouyang and Shi to incorporate the teachings of Lee and update the global model based on an existing local model.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Ouyang and Shi with the teachings of Lee by using Ouyang and Shi’s teachings of an apparatus for federating learning that receives information, updates a global model, and calculates contribution degree, and incorporate with Lee’s teachings of updating the global model based on a local model. One of ordinary skill in the art would be motivated to do so because by integrating Lee’s frameworks into the methods of Ouyang and Shi, one with ordinary skill in the art would “(allow) the local models of each client device to train parameters that follow the learning direction based on the global model” (Lee, col. 5, lines 28-30).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Ouyang in view of Shi in view of Lee, and further in view of Duan et al. (https://ieeexplore.ieee.org/abstract/document/9644782) (hereinafter Duan).
Claim 6
Regarding claim 6, Ouyang in view of Shi in view of Lee teaches the limitations in claim 5.
Neither Ouyang, Shi, or Lee teaches “evaluate each of the first global model and the second global model based on evaluation data.”
However, Duan in the same field of art teaches “evaluate each of the first global model and the second global model based on evaluation data.”
See Duan in page 234, column 2 where it describes “Since each client has a local test set in our experimental setting, we evaluate its corresponding group model based on these data. For example, in FedAvg and FedProx we evaluate the global model based on the test set for all clients. And in FedGroup and FedGrouProx we evaluate the group model based on the test set for the clients in this group.” Here, Duan teaches evaluating sets of global models, for example, a first global model and a second global model, based on a given data set.
Ouyang, Shi, Lee, and Duan are considered to be analogous to the claimed invention because they are in the same field of federated learning devices. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ouyang, Shi, and Lee to incorporate the teachings of Duan and evaluate the global models.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Ouyang, Shi, and Lee with the teachings of Duan by using Ouyang, Shi, and Lee’s teachings of an apparatus for federating learning that receives information, updates a global model, and calculates contribution degree, and incorporate with Duan’s teachings of evaluating the global model based on defined data. One of ordinary skill in the art would be motivated to do so because by integrating Duan’s frameworks into the methods of Ouyang, Shi, and Lee, one with ordinary skill in the art would “(improve) test accuracy by +26.9%” and have a strategy that “is efficient and can achieve more performance improvements” (Duan, page 234, column 2).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ouyang in view of Shi in view of Lee, and further in view of Hoffman (US 20250022384 A1).
Claim 7
Regarding claim 6, Ouyang in view of Shi in view of Lee teaches the limitations in claim 5.
Neither Ouyang, Shi, or Lee teaches “receive the evaluation of the first global model based on the local data and the evaluation of the second global model based on the local data.”
However, Hoffman in the same field of art teaches “receive the evaluation of the first global model based on the local data and the evaluation of the second global model based on the local data.”
See Hoffman in paragraph 0028 where it describes “Learning system 130 may evaluate or score responses provided by learners 104, and may do so by engaging one or more models (e.g., entailment model 136) or interacting with one or more other computing systems.” Here, Hoffman describes a system that interacts with one or more models, for example, a first and second global model. Further see Hoffman in paragraph 0037 where it describes “Computing system 170 may provide feedback to the learner. For instance, again with reference to FIG. 1A, learning system 130 receives evaluation 125A from entailment model 136 in response to submitting model answer 113A and learner answer 123A to entailment model 136 (arrow labeled “6”).” Here, Hoffman describes receiving an evaluation of an entailment model (e.g., the first and second global models) based on an “answer” (data) that has been provided to it.
Ouyang, Shi, Lee, and Hoffman are considered to be analogous to the claimed invention because they are in the same field of machine learning models. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ouyang, Shi, and Lee to incorporate the teachings of Hoffman and receive a calculated evaluation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Ouyang, Shi, and Lee with the teachings of Hoffman by using Ouyang, Shi, and Lee’s teachings of an apparatus for federating learning that receives information, updates a global model, and calculates contribution degree, and incorporate with Hoffman’s teachings of receiving evaluation data. One of ordinary skill in the art would be motivated to do so because by integrating Hoffman’s frameworks into the methods of Ouyang, Shi, and Lee, one with ordinary skill in the art would “be able to more effectively test specific information retention” (Hoffman, paragraph 0048).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW CHARLES YORKS whose telephone number is (571)270-1803. The examiner can normally be reached M-F, 9am to 5pm ET.
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/ANDREW CHARLES YORKS/Examiner, Art Unit 2146 /USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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