DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The information disclosure statement (IDS) was submitted on 08/06/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
The “edge device which acquires data to be used for learning, and executes a learning process to generate a first model to be used for inference relating to a task using the data” in claim 1.
The “server device which generates a second model using the first model generated by each of the plural edge devices, and transmits the second model to the plural edge devices” in claim 1.
The “server device for transmitting the second model to the plural edge devices” in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-10 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 limitation "using the first model generated by each of the plural edge devices” and “transmits the second model to the plural edge devices”. There is insufficient antecedent basis for these limitations in the claim. It is unclear what “the plural edge devices” is referring to. For the purposes of examination, the Examiner has interpreted these instances and all subsequent instances in the dependent claims as a plurality of edge devices.
Claim(s) 2-4 are rejected for at least the same reasons as claim 1 since they depend on claim 1.
Claim 3 recites the limitation "and a maximum value of a weight of the first model received from each of the plural edge devices”. There is insufficient antecedent basis for this limitation in the claim. It is unclear what “the plural edge devices” is referring to. For the purposes of examination, the Examiner has interpreted these instances and all subsequent instances in the dependent claims as a plurality of edge devices.
Claim 5 recites the limitation "using the first model generated by each of the plural edge devices” and “transmits the second model to the plural edge devices”. There is insufficient antecedent basis for these limitations in the claim. It is unclear what “the plural edge devices” is referring to. For the purposes of examination, the Examiner has interpreted these instances and all subsequent instances in the dependent claims as a plurality of edge devices.
Claim(s) 6-8 are rejected for at least the same reasons as claim 5 since they depend on claim 5.
Claim 7 recites the limitation "and a maximum value of a weight of the first model received from each of the plural edge devices”. There is insufficient antecedent basis for this limitation in the claim. It is unclear what “the plural edge devices” is referring to. For the purposes of examination, the Examiner has interpreted these instances and all subsequent instances in the dependent claims as a plurality of edge devices.
Claim 9 recites the limitation "using the first model generated by each of the plural edge devices” and “is connected to a server device for transmitting the second model to the plural edge devices”. There is insufficient antecedent basis for these limitations in the claim. It is unclear what “the plural edge devices” is referring to. For the purposes of examination, the Examiner has interpreted these instances and all subsequent instances in the dependent claims as a plurality of edge devices.
Claim(s) 10 are rejected for at least the same reasons as claim 9 since they depend on claim 9.
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) 1-10 are rejected under 35 U.S.C. 101 because the claims are directed towards an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a computing system and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites:
and executes a learning process to generate a first model to be used for inference relating to a task using the data (This limitation is a mental process as it encompasses a human mentally generating a model and is thus an evaluation.)
generates a second model using the first model generated by each of the plural edge devices (This limitation is a mental process as it encompasses a human mentally generating a model and is thus an evaluation.)
calculates a first parameter to be used for a reduction process to reduce a data size of a model based on the communication state information (This limitation is a mental process as it encompasses a human mentally calculating a first parameter and is thus an evaluation.)
Therefore, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of:
A computing system comprising: an edge device which acquires data to be used for learning (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of receiving data (see MPEP 2106.05(g)).)
and a server device which (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
and transmits the second model to the plural edge devices (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of transmitting data (see MPEP 2106.05(g)).)
wherein the edge device: acquires communication state information relating to a communication state of a communication path between the edge device and the server device (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of receiving data (see MPEP 2106.05(g)).)
executes the reduction process to the generated first model based on the first parameter (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
and transmits the first model subjected to the reduction process to the server device (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of transmitting data (see MPEP 2106.05(g)).)
Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
A computing system comprising: an edge device which acquires data to be used for learning is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
and a server device which uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
and transmits the second model to the plural edge devices is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
wherein the edge device: acquires communication state information relating to a communication state of a communication path between the edge device and the server device is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
executes the reduction process to the generated first model based on the first parameter uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
and transmits the first model subjected to the reduction process to the server device is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 1 is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites:
calculates a second parameter to be used for the reduction process based on the communication state information (This limitation is a mental process as it encompasses a human mentally calculating a parameter and is thus an evaluation.)
Therefore, claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 further recites additional elements of:
wherein the server device: acquires the communication state information (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of receiving data (see MPEP 2106.05(g)).)
executes the reduction process to the generated second model based on the second parameter (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
and transmits the second model subjected to the reduction process to the edge device (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of transmitting data (see MPEP 2106.05(g)).)
Therefore, claim 2 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 2 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
The computing system according to claim 1, wherein the server device: acquires the communication state information is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
executes the reduction process to the generated second model based on the second parameter uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
and transmits the second model subjected to the reduction process to the edge device is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 2 is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites:
wherein the first model and the second model are neural networks (This limitation is a mental process as it is further modifying the generated first and second models defined in claim 1.)
determines a weight of the second model based on an algorithm considering an average value, a mode, and a maximum value of a weight of the first model received from each of the plural edge devices (This limitation is a mental process as it encompasses a human mentally determining a weight and is thus an evaluation.)
Therefore, claim 3 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 further recites additional elements of:
and the server device (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 3 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
and the server device uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 3 is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites:
wherein the edge device generates a feature extraction model which extracts feature information from the data, and a task inference model which performs inference relating to the task using the feature information in the learning process (This limitation is a mental process as it encompasses a human mentally generating models and is thus an evaluation.)
and the first model is the feature extraction model (This limitation is a mental process as it is further modifying the generated first model defined in claim 1.)
Therefore, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 does not further recite any additional elements. Therefore, claim 4 is not
integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 4 does not provide significantly more than
the abstract idea itself, taken alone and in combination. Therefore, claim 4 is subject-matter
ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 1:
Claim 5 recites a method and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites:
and executes a learning process to generate a first model to be used for inference relating to a task using the data (This limitation is a mental process as it encompasses a human mentally generating a model and is thus an evaluation.)
generates a second model using the first model generated by each of the plural edge devices (This limitation is a mental process as it encompasses a human mentally generating a model and is thus an evaluation.)
a second step in which the edge device calculates a first parameter to be used for a reduction process to reduce a data size of a model based on the communication state information (This limitation is a mental process as it encompasses a human mentally calculating a first parameter and is thus an evaluation.)
Therefore, claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 further recites additional elements of:
A learning method to be implemented in a computing system, the computing system including: an edge device which acquires data to be used for learning (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of receiving data (see MPEP 2106.05(g)).)
and a server device which (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
and transmits the second model to the plural edge devices (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of transmitting data (see MPEP 2106.05(g)).)
the learning method comprising: a first step in which the edge device acquires communication state information relating to a communication state of a communication path between the edge device and the server device (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of receiving data (see MPEP 2106.05(g)).)
a third step in which the edge device executes the reduction process to the generated first model based on the first parameter (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
and a fourth step in which the edge device transmits the first model subjected to the reduction process to the server device (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of transmitting data (see MPEP 2106.05(g)).)
Therefore, claim 5 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 5 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
A learning method to be implemented in a computing system, the computing system including: an edge device which acquires data to be used for learning is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
and a server device which uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
and transmits the second model to the plural edge devices is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
the learning method comprising: a first step in which the edge device acquires communication state information relating to a communication state of a communication path between the edge device and the server device is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
a third step in which the edge device executes the reduction process to the generated first model based on the first parameter uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
and a fourth step in which the edge device transmits the first model subjected to the reduction process to the server device is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 5 is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites:
a step in which the server device calculates a second parameter to be used for the reduction process based on the communication state information (This limitation is a mental process as it encompasses a human mentally calculating a parameter and is thus an evaluation.)
Therefore, claim 6 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 further recites additional elements of:
further comprising: a step in which the server device acquires the communication state information (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of receiving data (see MPEP 2106.05(g)).)
a step in which the server device executes the reduction process to the generated second model based on the second parameter (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
and a step in which the server device transmits the second model subjected to the reduction process to the edge device (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of transmitting data (see MPEP 2106.05(g)).)
Therefore, claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 6 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
further comprising: a step in which the server device acquires the communication state information is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
a step in which the server device executes the reduction process to the generated second model based on the second parameter uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
and a step in which the server device transmits the second model subjected to the reduction process to the edge device is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 6 is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites:
wherein the first model and the second model are neural networks (This limitation is a mental process as it is further modifying the generated first and second models defined in claim 5.)
determines a weight of the second model based on an algorithm considering an average value, a mode, and a maximum value of a weight of the first model received from each of the plural edge devices (This limitation is a mental process as it encompasses a human mentally determining a weight and is thus an evaluation.)
Therefore, claim 7 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 further recites additional elements of:
and the learning method further includes a step in which the server device (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 7 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 7 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
and the learning method further includes a step in which the server device uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 7 is subject-matter ineligible.
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 8 recites:
further comprising a step in which the edge device generates a feature extraction model which extracts feature information from the data, and a task inference model which performs inference relating to the task using the feature information in the learning process (This limitation is a mental process as it encompasses a human mentally generating models and is thus an evaluation.)
wherein the first model is the feature extraction model (This limitation is a mental process as it is further modifying the generated first model defined in claim 5.)
Therefore, claim 8 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 8 does not further recite any additional elements. Therefore, claim 8 is not
integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 8 does not provide significantly more than
the abstract idea itself, taken alone and in combination. Therefore, claim 8 is subject-matter
ineligible.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 1:
Claim 9 recites a device and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 9 recites:
and executes a learning process to generate a first model to be used for inference relating to a task using the data (This limitation is a mental process as it encompasses a human mentally generating a model and is thus an evaluation.)
generates a second model using the first model generated by each of the plural edge devices (This limitation is a mental process as it encompasses a human mentally generating a model and is thus an evaluation.)
calculates a parameter to be used for a reduction process to reduce a data size of a model based on the communication state information (This limitation is a mental process as it encompasses a human mentally calculating a first parameter and is thus an evaluation.)
Therefore, claim 9 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 9 further recites additional elements of:
An edge device which acquires data to be used for learning (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of receiving data (see MPEP 2106.05(g)).)
the edge device including a processor, a memory connected to the processor, and a network interface to be connected to the processor (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
wherein the edge device (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
and is connected to a server device for transmitting the second model to the plural edge devices (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
acquires communication state information relating to a communication state of a communication path between the edge device and the server device (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of receiving data (see MPEP 2106.05(g)).)
executes the reduction process to the generated first model based on the first parameter (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
and transmits the first model subjected to the reduction process to the server device (This element does not integrate the abstract idea into a practical application because it recites the insignificant extra-solution activity of transmitting data (see MPEP 2106.05(g)).)
Therefore, claim 9 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 9 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
An edge device which acquires data to be used for learning is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
the edge device including a processor, a memory connected to the processor, and a network interface to be connected to the processor uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
wherein the edge device uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
and is connected to a server device for transmitting the second model to the plural edge devices uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
acquires communication state information relating to a communication state of a communication path between the edge device and the server device is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
executes the reduction process to the generated first model based on the first parameter uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
and transmits the first model subjected to the reduction process to the server device is the well understood, routine, and conventional activity of "transmitting or receiving data over a network" (see MPEP 2106.05(d)(II); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 9 is subject-matter ineligible.
Regarding Claim 10:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 10 recites:
wherein the edge device generates a feature extraction model which extracts feature information from the data, and a task inference model which performs inference relating to the task using the feature information in the learning process (This limitation is a mental process as it encompasses a human mentally generating models and is thus an evaluation.)
and the first model is the feature extraction model (This limitation is a mental process as it is further modifying the generated first model defined in claim 9.)
Therefore, claim 10 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 10 does not further recite any additional elements. Therefore, claim 10 is not
integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 10 does not provide significantly more than
the abstract idea itself, taken alone and in combination. Therefore, claim 10 is subject-matter
ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 4-6, 8-10 is/are rejected under 35 U.S.C. 103 as being obvious over Zeyin et al. (CN113595993A) in view of Abdelmoniem et al. (“DC2: Delay-aware Compression Control for Distributed Machine Learning”).
Regarding claim 1, Zeyin teaches
A computing system comprising: an edge device which acquires data to be used for learning (Zeyin, page 3, “The invention relates to the field of updating of edge intelligent equipment, in particular to a vehicle-mounted sensing equipment joint learning method for model structure optimization under edge calculation.” Zeyin, page 5, “Continuously collecting image data as private training data of each vehicle-mounted device (edge node).” Examiner notes the edge device is the vehicle-mounted sensing equipment, and the acquired data is the image data.)
and executes a learning process to generate a first model to be used for inference relating to a task using the data (Zeyin, page 4, “the model local training module is used for establishing a neural network model suitable for the vehicle-mounted equipment as a local model according to a target detection algorithm adopted by the vehicle-mounted equipment.” Examiner notes the first model is the local model.)
and a server device which generates a second model using the first model generated by each of the plural edge devices (Zeyin, page 4, “step 1.1, initializing parameters of the neural network model at the central server to obtain initialized model parameters. Obtaining initialized global model parameters by performing iterative training on the model j times through the target data set.” Zeyin, page 3, “After the vehicle-mounted equipment completes the gradient compression and uploading of the local model, the central server performs gradient aggregation: the superscript of each gradient parameter is represented by K, and the gradient of the uncompressed local model has K parameters; in the j global iteration, the local compressed gradient uploaded by the vehicle-mounted device i is represented as [Image Omitted] [Image Omitted] And the corresponding mask matrix is [Image Omitted] Global polymerization gradient Δ The aggregation can be performed in a j neuron-by-neuron manner.” Examiner notes the second model is the global model stored on the central server, which is generated by aggregating local model gradient parameters uploaded from each edge device.)
and transmits the second model to the plural edge devices (Zeyin, page 5, “By neuron-by-neuron aggregation, a global gradient of aggregation is obtained, as shown in FIG. 3. In the global aggregation gradient, the weight of each node is from different vehicle-mounted devices, some weights are cut in the compression process, the reserved weights are aggregated as the update weight of the global aggregation gradient only in a weighted average mode, and if the weights are not updated, the gradient value is zero.” Zeyin, page 5, “the vehicle-mounted equipment acquires the global aggregate gradient, updates the local model, and utilizes the updated model to sense the road, so that the performance of road sensing can be improved by utilizing the real-time updated model.” Examiner notes the global aggregation gradient contains the updated weights of the neural network model, and thus makes up the fundamental parameters of the model. Examiner further notes the global aggregation gradient is transmitted to the vehicle mounted equipment (edge device) from the server and used to update the model.)
and transmits the first model subjected to the reduction process to the server device (Zeyin, page 11, “When a plurality of vehicle-mounted devices carry out local model training, after gradient compression of a first layer of a neural network is completed for a certain vehicle-mounted device, the compression gradient of the first layer can be transmitted immediately; meanwhile, the other vehicle-mounted equipment is subjected to gradient compression operation and then transmitted, and the other vehicle-mounted equipment repeats the operation in a pipeline mode so as to quickly upload the local update model to the cloud server.” Examiner notes the reduction process is gradient compression for the local model.)
Zeyin does not, but Abdelmoniem teaches
wherein the edge device: acquires communication state information relating to a communication state of a communication path between the edge device and the server device (Abdelmoniem, page 3, Fig. 2. Abdelmoniem, page 3, “Delay Monitor: this component maintains certain measurements and statistics of the network-level communication properties.” Examiner notes the communication state information is the measurements and statistics of the network-level communication between the workers (edge devices) and network (server device) as seen in Fig. 2.)
calculates a first parameter to be used for a reduction process to reduce a data size of a model based on the communication state information (Abdelmoniem, page 3, “Delay Monitor: this component maintains certain measurements and statistics of the network-level communication properties. The monitoring data are the following time series over iteration j of training: dj, the communication phase delay; mj, the minimum communication delay as witnessed up until iteration j; aj, the average delay over the previous w samples (dj, ··· ,d(j−w)); rj, the average delay differences over the previous w samples (dj − d(j−1),··· ,d(j−w) − d(j−w−1)).” Examiner notes aj, the average delay over the previous w samples, is the calculated first parameter.)
executes the reduction process to the generated first model based on the first parameter (Abdelmoniem, page 1, “we advocate a more controlled use of compression and propose DC2, a delay-aware compression control mechanism. DC2 couples compression control and network delays in applying compression adaptively.” Abdelmoniem, page 4, “we now propose 5 algorithms inspired by traditional as well as recent network congestion control schemes. The algorithms are shown in Algorithm 1.” Abdelmoniem, page 4, Algorithm 1:
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Examiner notes the reduction process is model compression, which is executed based on the calculated first parameter aj in algorithms DA1, DA2, DA3, and DA5.)
Zeyin and Abdelmoniem both teach compression in a distributed AI model training system and thus are analogous to the claimed invention. It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the application to have modified Zeyin to utilize dynamic compression based on communication state information like Abdelmoniem. Doing so would have been advantageous because doing so will “make distributed training systems more robust and resilient to variations in network conditions so they can finish their jobs in a predictable time [27] and consequently meet their time (or monetary) budgets” (Abdelmoniem, page 1). Furthermore, the results of DC2 show that “DC2 improves training speed-ups of up to 41x and 5.3x over baselines with no compression and uniform compression, respectively” (Abdelmoniem, page 1).
Regarding claim 2, Zeyin and Abdelmoniem teach The computing system according to claim 1. Zeyin further teaches
and transmits the second model subjected to the reduction process to the edge device (Zeyin, page 4, “obtaining a global polymerization gradient through neuron-by-neuron polymerization; in the global aggregation gradient, some weights are cut in the compression process, and the reserved weights are aggregated according to a weighted average mode to serve as the update weights of the global aggregation gradient; and 5, acquiring the global aggregation gradient by the vehicle-mounted equipment, updating the local model, performing road perception by using the updated model, and improving the performance of road perception by using the real-time updated model.” Examiner notes the global aggregation gradient is made up of the compressed weights from the local models and goes through a compression process, which is the reduction process. Examiner further notes the global aggregation gradient makes up the global model, and the gradient is sent to each edge device.)
Abdelmoniem further teaches
wherein the server device: acquires the communication state information (Abdelmoniem, page 3, Fig. 2. Abdelmoniem, page 3, “Delay Monitor: this component maintains certain measurements and statistics of the network-level communication properties.” Examiner notes the communication state information is the measurements and statistics of the network-level communication between the workers (edge devices) and network (server device) as seen in Fig. 2.)
calculates a second parameter to be used for the reduction process based on the communication state information (Abdelmoniem, page 3, “Delay Monitor: this component maintains certain measurements and statistics of the network-level communication properties. The monitoring data are the following time series over iteration j of training: dj, the communication phase delay; mj, the minimum communication delay as witnessed up until iteration j; aj, the average delay over the previous w samples (dj, ··· ,d(j−w)); rj, the average delay differences over the previous w samples (dj − d(j−1),··· ,d(j−w) − d(j−w−1)).” Examiner notes rj, the average delay differences over the previous w samples, is the calculated second parameter.)
executes the reduction process to the generated second model based on the second parameter (Abdelmoniem, page 1, “we advocate a more controlled use of compression and propose DC2, a delay-aware compression control mechanism. DC2 couples compression control and network delays in applying compression adaptively.” Abdelmoniem, page 4, “we now propose 5 algorithms inspired by traditional as well as recent network congestion control schemes. The algorithms are shown in Algorithm 1.” Abdelmoniem, page 4, Algorithm 1:
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Examiner notes the reduction process is model compression, which is executed based on the calculated second parameter rj in algorithms DA4 and DA5.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Zeyin with the teachings of Abdelmoniem for the same reasons disclosed in claim 1.
Regarding claim 4, Zeyin and Abdelmoniem teach The computing system according to claim 1. Zeyin further teaches
wherein the edge device generates a feature extraction model which extracts feature information from the data, and a task inference model which performs inference relating to the task using the feature information in the learning process, and the first model is the feature extraction model (Zeyin, page 5, “establishing a neural network model suitable for the vehicle-mounted equipment as a local model according to a target detection algorithm adopted by the vehicle-mounted equipment.” Zeyin, page 5, “The target detection algorithm may be, for example, yolov3 algorithm, and the target data set is composed of road scene images acquired from driving perspectives.” Examiner notes the algorithm used by the local model may be yolov3, which is an object detection object that predicts class probabilities from full images. Examiner further notes object detection for images extract feature information and the model further performs inference (class prediction) using the extracted features, and thus is the first model is a feature extraction model and a task inference model.)
Regarding claim 5, Zeyin teaches
A learning method to be implemented in a computing system, the computing system including: an edge device which acquires data to be used for learning (Zeyin, page 3, “The invention relates to the field of updating of edge intelligent equipment, in particular to a vehicle-mounted sensing equipment joint learning method for model structure optimization under edge calculation.” Zeyin, page 5, “Continuously collecting image data as private training data of each vehicle-mounted device (edge node).” Examiner notes the edge device is the vehicle-mounted sensing equipment, and the acquired data is the image data.)
and executes a learning process to generate a first model to be used for inference relating to a task using the data (Zeyin, page 4, “the model local training module is used for establishing a neural network model suitable for the vehicle-mounted equipment as a local model according to a target detection algorithm adopted by the vehicle-mounted equipment.” Examiner notes the first model is the local model.)
and a server device which generates a second model using the first model generated by each of the plural edge devices (Zeyin, page 4, “step 1.1, initializing parameters of the neural network model at the central server to obtain initialized model parameters. Obtaining initialized global model parameters by performing iterative training on the model j times through the target data set.” Zeyin, page 3, “After the vehicle-mounted equipment completes the gradient compression and uploading of the local model, the central server performs gradient aggregation: the superscript of each gradient parameter is represented by K, and the gradient of the uncompressed local model has K parameters; in the j global iteration, the local compressed gradient uploaded by the vehicle-mounted device i is represented as [Image Omitted] [Image Omitted] And the corresponding mask matrix is [Image Omitted] Global polymerization gradient Δ The aggregation can be performed in a j neuron-by-neuron manner.” Examiner notes the second model is the global model stored on the central server, which is generated by aggregating local model gradient parameters uploaded from each edge device.)
and transmits the second model to the plural edge devices (Zeyin, page 5, “By neuron-by-neuron aggregation, a global gradient of aggregation is obtained, as shown in FIG. 3. In the global aggregation gradient, the weight of each node is from different vehicle-mounted devices, some weights are cut in the compression process, the reserved weights are aggregated as the update weight of the global aggregation gradient only in a weighted average mode, and if the weights are not updated, the gradient value is zero.” Zeyin, page 5, “the vehicle-mounted equipment acquires the global aggregate gradient, updates the local model, and utilizes the updated model to sense the road, so that the performance of road sensing can be improved by utilizing the real-time updated model.” Examiner notes the global aggregation gradient contains the updated weights of the neural network model, and thus makes up the fundamental parameters of the model. Examiner further notes the global aggregation gradient is transmitted to the vehicle mounted equipment (edge device) from the server and used to update the model.)
and a fourth step in which the edge device transmits the first model subjected to the reduction process to the server device (Zeyin, page 11, “When a plurality of vehicle-mounted devices carry out local model training, after gradient compression of a first layer of a neural network is completed for a certain vehicle-mounted device, the compression gradient of the first layer can be transmitted immediately; meanwhile, the other vehicle-mounted equipment is subjected to gradient compression operation and then transmitted, and the other vehicle-mounted equipment repeats the operation in a pipeline mode so as to quickly upload the local update model to the cloud server.” Examiner notes the reduction process is gradient compression for the local model.)
Zeyin does not, but Abdelmoniem teaches
the learning method comprising: a first step in which the edge device acquires communication state information relating to a communication state of a communication path between the edge device and the server device (Abdelmoniem, page 3, Fig. 2. Abdelmoniem, page 3, “Delay Monitor: this component maintains certain measurements and statistics of the network-level communication properties.” Examiner notes the communication state information is the measurements and statistics of the network-level communication between the workers (edge devices) and network (server device) as seen in Fig. 2.)
a second step in which the edge device calculates a first parameter to be used for a reduction process to reduce a data size of a model based on the communication state information (Abdelmoniem, page 3, “Delay Monitor: this component maintains certain measurements and statistics of the network-level communication properties. The monitoring data are the following time series over iteration j of training: dj, the communication phase delay; mj, the minimum communication delay as witnessed up until iteration j; aj, the average delay over the previous w samples (dj, ··· ,d(j−w)); rj, the average delay differences over the previous w samples (dj − d(j−1),··· ,d(j−w) − d(j−w−1)).” Examiner notes aj, the average delay over the previous w samples, is the calculated first parameter.)
a third step in which the edge device executes the reduction process to the generated first model based on the first parameter (Abdelmoniem, page 1, “we advocate a more controlled use of compression and propose DC2, a delay-aware compression control mechanism. DC2 couples compression control and network delays in applying compression adaptively.” Abdelmoniem, page 4, “we now propose 5 algorithms inspired by traditional as well as recent network congestion control schemes. The algorithms are shown in Algorithm 1.” Abdelmoniem, page 4, Algorithm 1:
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Examiner notes the reduction process is model compression, which is executed based on the calculated first parameter aj in algorithms DA1, DA2, DA3, and DA5.)
Zeyin and Abdelmoniem both teach compression in a distributed AI model training system and thus are analogous to the claimed invention. It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the application to have modified Zeyin to utilize dynamic compression based on communication state information like Abdelmoniem. Doing so would have been advantageous because doing so will “make distributed training systems more robust and resilient to variations in network conditions so they can finish their jobs in a predictable time [27] and consequently meet their time (or monetary) budgets” (Abdelmoniem, page 1). Furthermore, the results of DC2 show that “DC2 improves training speed-ups of up to 41x and 5.3x over baselines with no compression and uniform compression, respectively” (Abdelmoniem, page 1).
Regarding claim 6, Zeyin and Abdelmoniem teach The learning method according to claim 5. Zeyin further teaches
and a step in which the server device transmits the second model subjected to the reduction process to the edge device (Zeyin, page 4, “obtaining a global polymerization gradient through neuron-by-neuron polymerization; in the global aggregation gradient, some weights are cut in the compression process, and the reserved weights are aggregated according to a weighted average mode to serve as the update weights of the global aggregation gradient; and 5, acquiring the global aggregation gradient by the vehicle-mounted equipment, updating the local model, performing road perception by using the updated model, and improving the performance of road perception by using the real-time updated model.” Examiner notes the global aggregation gradient is made up of the compressed weights from the local models and goes through a compression process, which is the reduction process. Examiner further notes the global aggregation gradient makes up the global model, and the gradient is sent to each edge device.)
Abdelmoniem further teaches
further comprising: a step in which the server device acquires the communication state information (Abdelmoniem, page 3, Fig. 2. Abdelmoniem, page 3, “Delay Monitor: this component maintains certain measurements and statistics of the network-level communication properties.” Examiner notes the communication state information is the measurements and statistics of the network-level communication between the workers (edge devices) and network (server device) as seen in Fig. 2.)
a step in which the server device calculates a second parameter to be used for the reduction process based on the communication state information (Abdelmoniem, page 3, “Delay Monitor: this component maintains certain measurements and statistics of the network-level communication properties. The monitoring data are the following time series over iteration j of training: dj, the communication phase delay; mj, the minimum communication delay as witnessed up until iteration j; aj, the average delay over the previous w samples (dj, ··· ,d(j−w)); rj, the average delay differences over the previous w samples (dj − d(j−1),··· ,d(j−w) − d(j−w−1)).” Examiner notes rj, the average delay differences over the previous w samples, is the calculated second parameter.)
a step in which the server device executes the reduction process to the generated second model based on the second parameter (Abdelmoniem, page 1, “we advocate a more controlled use of compression and propose DC2, a delay-aware compression control mechanism. DC2 couples compression control and network delays in applying compression adaptively.” Abdelmoniem, page 4, “we now propose 5 algorithms inspired by traditional as well as recent network congestion control schemes. The algorithms are shown in Algorithm 1.” Abdelmoniem, page 4, Algorithm 1:
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Examiner notes the reduction process is model compression, which is executed based on the calculated second parameter rj in algorithms DA4 and DA5.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Zeyin with the teachings of Abdelmoniem for the same reasons disclosed in claim 5.
Regarding claim 8, Zeyin and Abdelmoniem teach The learning method according to claim 5. Zeyin further teaches
further comprising a step in which the edge device generates a feature extraction model which extracts feature information from the data, and a task inference model which performs inference relating to the task using the feature information, wherein the first model is the feature extraction model (Zeyin, page 5, “establishing a neural network model suitable for the vehicle-mounted equipment as a local model according to a target detection algorithm adopted by the vehicle-mounted equipment.” Zeyin, page 5, “The target detection algorithm may be, for example, yolov3 algorithm, and the target data set is composed of road scene images acquired from driving perspectives.” Examiner notes the algorithm used by the local model may be yolov3, which is an object detection object that predicts class probabilities from full images. Examiner further notes object detection for images extract feature information and the model further performs inference (class prediction) using the extracted features, and thus is the first model is a feature extraction model and a task inference model.)
Regarding claim 9, Zeyin teaches
An edge device which acquires data to be used for learning (Zeyin, page 3, “The invention relates to the field of updating of edge intelligent equipment, in particular to a vehicle-mounted sensing equipment joint learning method for model structure optimization under edge calculation.” Zeyin, page 5, “Continuously collecting image data as private training data of each vehicle-mounted device (edge node).” Examiner notes the edge device is the vehicle-mounted sensing equipment, and the acquired data is the image data.)
and executes a learning process to generate a first model to be used for inference relating to a task using the data (Zeyin, page 4, “the model local training module is used for establishing a neural network model suitable for the vehicle-mounted equipment as a local model according to a target detection algorithm adopted by the vehicle-mounted equipment.” Examiner notes the first model is the local model.)
the edge device including a processor, a memory connected to the processor, and a network interface to be connected to the processor (Zeyin, page 9, “A computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the model structure optimized vehicle-mounted sensing device joint learning method under the edge calculation.” Zeyin, page 6, “the onboard edge devices have inherent heterogeneous properties in terms of computing power, communication conditions, data distribution, etc.”)
wherein the edge device generates a second model using the first model generated by each of the plural edge devices (Zeyin, page 4, “step 1.1, initializing parameters of the neural network model at the central server to obtain initialized model parameters. Obtaining initialized global model parameters by performing iterative training on the model j times through the target data set.” Zeyin, page 3, “After the vehicle-mounted equipment completes the gradient compression and uploading of the local model, the central server performs gradient aggregation: the superscript of each gradient parameter is represented by K, and the gradient of the uncompressed local model has K parameters; in the j global iteration, the local compressed gradient uploaded by the vehicle-mounted device i is represented as [Image Omitted] [Image Omitted] And the corresponding mask matrix is [Image Omitted] Global polymerization gradient Δ The aggregation can be performed in a j neuron-by-neuron manner.” Examiner notes the second model is the global model stored on the central server, which is generated by aggregating local model gradient parameters uploaded from each edge device.)
and is connected to a server device for transmitting the second model to the plural edge devices (Zeyin, page 5, “By neuron-by-neuron aggregation, a global gradient of aggregation is obtained, as shown in FIG. 3. In the global aggregation gradient, the weight of each node is from different vehicle-mounted devices, some weights are cut in the compression process, the reserved weights are aggregated as the update weight of the global aggregation gradient only in a weighted average mode, and if the weights are not updated, the gradient value is zero.” Zeyin, page 5, “the vehicle-mounted equipment acquires the global aggregate gradient, updates the local model, and utilizes the updated model to sense the road, so that the performance of road sensing can be improved by utilizing the real-time updated model.” Examiner notes the global aggregation gradient contains the updated weights of the neural network model, and thus makes up the fundamental parameters of the second model. Examiner further notes the global aggregation gradient is transmitted to the vehicle mounted equipment (edge device) from the server and used to update the model.)
and transmits the first model subjected to the reduction process to the server device (Zeyin, page 11, “When a plurality of vehicle-mounted devices carry out local model training, after gradient compression of a first layer of a neural network is completed for a certain vehicle-mounted device, the compression gradient of the first layer can be transmitted immediately; meanwhile, the other vehicle-mounted equipment is subjected to gradient compression operation and then transmitted, and the other vehicle-mounted equipment repeats the operation in a pipeline mode so as to quickly upload the local update model to the cloud server.” Examiner notes the reduction process is gradient compression for the local model.)
Zeyin does not, but Abdelmoniem teaches
acquires communication state information relating to a communication state of a communication path between the edge device and the server device (Abdelmoniem, page 3, Fig. 2. Abdelmoniem, page 3, “Delay Monitor: this component maintains certain measurements and statistics of the network-level communication properties.” Examiner notes the communication state information is the measurements and statistics of the network-level communication between the workers (edge devices) and network (server device) as seen in Fig. 2.)
calculates a parameter to be used for a reduction process to reduce a data size of a model based on the communication state information (Abdelmoniem, page 3, “Delay Monitor: this component maintains certain measurements and statistics of the network-level communication properties. The monitoring data are the following time series over iteration j of training: dj, the communication phase delay; mj, the minimum communication delay as witnessed up until iteration j; aj, the average delay over the previous w samples (dj, ··· ,d(j−w)); rj, the average delay differences over the previous w samples (dj − d(j−1),··· ,d(j−w) − d(j−w−1)).” Examiner notes aj, the average delay over the previous w samples, is the calculated parameter.)
executes the reduction process to the generated first model based on the parameter (Abdelmoniem, page 1, “we advocate a more controlled use of compression and propose DC2, a delay-aware compression control mechanism. DC2 couples compression control and network delays in applying compression adaptively.” Abdelmoniem, page 4, “we now propose 5 algorithms inspired by traditional as well as recent network congestion control schemes. The algorithms are shown in Algorithm 1.” Abdelmoniem, page 4, Algorithm 1:
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Examiner notes the reduction process is model compression, which is executed based on the calculated first parameter aj in algorithms DA1, DA2, DA3, and DA5.)
Zeyin and Abdelmoniem both teach compression in a distributed AI model training system and thus are analogous to the claimed invention. It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the application to have modified Zeyin to utilize dynamic compression based on communication state information like Abdelmoniem. Doing so would have been advantageous because doing so will “make distributed training systems more robust and resilient to variations in network conditions so they can finish their jobs in a predictable time [27] and consequently meet their time (or monetary) budgets” (Abdelmoniem, page 1). Furthermore, the results of DC2 show that “DC2 improves training speed-ups of up to 41x and 5.3x over baselines with no compression and uniform compression, respectively” (Abdelmoniem, page 1).
Regarding claim 10, Zeyin and Abdelmoniem teach The edge device according to claim 9. Zeyin further teaches
wherein the edge device generates a feature extraction model which extracts feature information from the data, and a task inference model which performs inference relating to the task using the feature information in the learning process, and the first model is the feature extraction model (Zeyin, page 5, “establishing a neural network model suitable for the vehicle-mounted equipment as a local model according to a target detection algorithm adopted by the vehicle-mounted equipment.” Zeyin, page 5, “The target detection algorithm may be, for example, yolov3 algorithm, and the target data set is composed of road scene images acquired from driving perspectives.” Examiner notes the algorithm used by the local model may be yolov3, which is an object detection object that predicts class probabilities from full images. Examiner further notes object detection for images extract feature information and the model further performs inference (class prediction) using the extracted features, and thus is the first model is a feature extraction model and a task inference model.)
Claim(s) 3, 7 is/are rejected under 35 U.S.C. 103 as being obvious over Zeyin in view of Abdelmoniem in further view of Yue et al. (“Federated Learning via Plurality Vote”).
Regarding claim 3, Zeyin and Abdelmoniem teach The computing system according to claim 2. Zeyin further teaches
wherein the first model and the second model are neural networks (Zeyin, page 5, “establishing a neural network model suitable for the vehicle-mounted equipment as a local model according to a target detection algorithm adopted by the vehicle-mounted equipment, and performing parameter initialization on the neural network model at a central server to obtain initialized model parameters [Image Omitted] Obtaining initialized global model parameters by performing iterative training on the model j times through the target data set [Image Omitted] Stored in the central server.”)
and the server device determines a weight of the second model based on an algorithm considering an average value… and a maximum value of a weight of the first model received from each of the plural edge devices (Zeyin, page 5, “In the global aggregation gradient, the weight of each node is from different vehicle-mounted devices.” Zeyin, page 4, “weights are aggregated according to a weighted average mode to serve as the update weights of the global aggregation gradient.” Examiner notes all of the weights are sent and used by the edge devices to the server to determine a weight of the second model (global model). Examiner further notes all of the weights include a maximum value of the weights and thus a maximum value of the weights from the local model received is considered.)
Zeyin and Abdelmoniem do not, but Yue teaches
and the server device determines a weight of the second model based on an algorithm considering…a mode …of a weight of the first model received from each of the plural edge devices (Yue, page 1, “In this paper, we propose a new method called federated learning via plurality vote (FedVote). We train a neural network at each worker with a range normalization function applied to model parameters. After local updating, binary/ternary weight vectors are obtained via stochastic rounding and sent to the server. The global model is updated by a voting procedure, and the voting results are sent back to each worker for further optimization in the next round.” Yue, page 5, “Once the server gathers the binary weights from all workers, it will perform the aggregation via plurality vote, i.e., w(k+1)=sign M m=1w(k,τ) m . A tie in vote will be broken randomly. In the following lemma, we show that the probability of error reduces exponentially as the number of workers increases.” Examiner notes the server determines a weight of the second model (global model) by considering a mode (majority voting) of each of the weights sent by the local models (workers).)
Zeyin and Abdelmoniem both teach compression in a distributed AI model training system and thus are analogous to the claimed invention. Yue teaches a federated learning environment and thus is also analogous to the claimed invention. It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the application to have modified Zeyin and Abdelmoniem to use an algorithm considering the mode of received weights of local models to determine a global weight parameter like Yue. Doing so would have been advantageous because it considers “omniscient attackers which can access the datasets of normal workers and send the opposite of the aggregated results to the server. The number of attackers is 15, and the remaining 16 clients are normal workers…It can be observed that Byzantine FedVote exhibits much better resilience to Byzantine attacks with close to half adversaries” (Yue, page 8). Furthermore, “FedVote is particularly advantageous in simultaneously achieving a high compression ratio and good test accuracy. Given a fixed communication cost, FedVote improves model accuracy on the CIFAR-10 dataset by 5–10%, 15–20%, and 25–30% compared with FedPAQ (Reisizadeh et al., 2020), signSGD (Bernstein et al., 2018), and FedAvg, respectively” (Yue, page 1).
Claim(s) 7 recites substantially similar limitations to claim(s) 3, and is/are therefore rejected under the same analysis.
Conclusion
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/Z.O.C./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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