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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 15, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “403” has been used to designate both "Training Contribution Estimation Engine" and "Communication Circuit", when it should just be the “Communication Circuit”, and the “Training Contribution Estimation Engine should be labeled 401 per specification. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 2 is objected to because of the following informalities: In claim Appropriate correction is required.
Claim 6 is objected to because of the following informalities: In claim 6 lines 1-2 “the controller further performs the device the balanced client selection in real-time”, should read “the controller device further performs balanced client selection in real-time”. Appropriate correction is required.
Claim 19 is objected to because of the following informalities: In claim 19, line 2 ""performing, at first a mobile client", should read "performing, at a first mobile client", and line 9 “at a second mobile”, should read “at a second mobile client”. Appropriate correction is required.
Claim 20 depends on claim 19 and therefore inherits these deficiencies noted here without otherwise curing them. Hence, on the same grounds as claim 19, claim 20 is likewise objected to.
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: processor device, controller device, and communication device in claims 1, 3, 6, and 13.
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 7 and 17 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 7:
Claim 7 recites the limitation "The vehicle of claim " in 3. In the claim, the term “the additional local machine learning model” is not present in claim 7’s chain of claim dependency. It is not in claims 1 or 3 from which 7 depends on and claim 7 itself doesn’t introduce it. The term is found in claim 6. Hence, Applicant should consider amending “The vehicle of claim 2” to depend on 6 as appropriate in claim 7. As such, there is insufficient antecedent basis for this limitation in the claim.
Claim 17:
Claim 17 recites the limitation "The vehicle of claim 11, wherein the additional local machine learning model is received from the connected vehicle via a vehicle-to-vehicle (V2V) communication." in lines 1-2. In the claim, the term “the additional local machine learning model” is not present in claim 17’s chain of claim dependency. It is not in claims 1 or 11 from which 17 depends on and claim 17 itself doesn’t introduce it. The term is found in claims 6-10. Hence, Applicant should consider amending “The vehicle of claim 11” to depend from one of 6-10 as appropriate in claim 17. As such, there is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mathematical concept) without significantly more.
Claim 1:
Regarding claim 1, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A vehicle, comprising:”, and a vehicle or machine is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the 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:
“and a controller device performing balanced client selection in real-time to communicate the local machine learning model with a connected vehicle for decentralized machine learning.” (this is a mathematical concept, performing balanced client selection involves performing a mathematical calculation (see Specification paragraph [0023]), 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 concept 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:
“A vehicle, comprising: a processor...” (A vehicle comprising a processor is considered generic computer component being used as tool to perform functions of the judicial exception – see MPEP § 2106.05(f)),
“…training a machine learning model using local data, wherein the local machine learning model is trained at the vehicle using a machine learning scheme;” (training a machine learning model using local data at a vehicle using a machine learning scheme is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
a controller device” (Using a controller device is considered generic computer component being used as tool to perform functions of the judicial exception – see MPEP § 2106.05(f)),
“communicate the local machine learning model with a connected vehicle for decentralized machine learning.” (Communicating the local machine learning model for decentralized machine learning with a connected vehicle is considered insignificant extra-solution activity of mere data gathering – see MPEP § 2106.05(g)),
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
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 to significantly more than the judicial exception.
As discussed above, additional elements ii, iv recites generic computer component being used as tool to perform functions of the judicial exception, additional element iii recites mere instructions to apply an exception using generic computer, and additional element v recites 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, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362, which is not indicative of significantly more.
Considering 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:
“The vehicle of claim 1, wherein the local data comprises non-independent and identically distribution data.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 vehicle of claim 1, wherein the controller device performs balanced client selection during the training of the machine learning model at the vehicle.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 4:
Regarding claim 4, it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis to claim 3. Further, claim 4 recites the following additional elements:
“The vehicle of claim 3, wherein the balanced client selection performed during the training of the local machine learning model comprises calculating a class-wide contribution associated with the training of the local machine learning model.” (this is a mathematical concept, balanced client selection performed during training of a local machine learning model that comprises calculating a class-wide contribution associated with the training of the local machine learning model is a mathematical calculation, see MPEP § 2106.04(a)(2)(I)),
If claim limitations, under their broadest reasonable interpretation, covers 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.
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 4, and thereby incorporates the limitations of, and corresponding analysis to claim 4. Further, claim 5 recites the following additional elements:
“The vehicle of claim 4, wherein the class-wide contribution associated with the training of the local machine learning model estimates a weight associated with a distribution incurred by the non-IID data during the training of the local machine learning model.” (this is a mathematical concept, class-wide contribution associated with training of a local machine learning model estimating a weight associated with a distribution is a mathematical calculation, see MPEP § 2106.04(a)(2)(I)),
If claim limitations, under their broadest reasonable interpretation, covers 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.
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 3, and thereby incorporates the limitations of, and corresponding analysis to claim 3. Further, claim 6 recites the following additional elements:
“The vehicle of claim 3, wherein the controller further performs the device the balanced client selection in real-time to receive an additional local machine learning model from the connected vehicle for decentralized machine learning.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 2, and thereby incorporates the limitations of, and corresponding analysis to claim 2. Further, claim 7 recites the following additional elements:
“The vehicle of claim 2, wherein the controller further performs aggregating the additional local machine learning model from the connected vehicle with the local machine learning model trained at the vehicle.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 8:
Regarding claim 8, it is dependent upon claim 7, and thereby incorporates the limitations of, and corresponding analysis to claim 7. Further, claim 8 recites the following additional elements:
“The vehicle of claim 7, wherein the controller device performs the balanced client selection during the aggregation of the additional machine learning model at the vehicle.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 9:
Regarding claim 9, it is dependent upon claim 8, and thereby incorporates the limitations of, and corresponding analysis to claim 8. Further, claim 9 recites the following additional elements:
“The vehicle of claim 8, wherein the balanced client selection performed during the aggregation of the additional local machine learning model comprises calculating a class-wide contribution associated with the aggregation of the additional machine learning model at the vehicle.” (this is a mathematical concept, calculating a class-wide contribution associated with an aggregation is a mathematical calculation, see MPEP § 2106.04(a)(2)(I)),
If claim limitations, under their broadest reasonable interpretation, covers 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.
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 10:
Regarding claim 10, it is dependent upon claim 9, and thereby incorporates the limitations of, and corresponding analysis to claim 9. Further, claim 10 recites the following additional elements:
“The vehicle of claim 9, wherein the balanced client selection performed during the aggregation of the additional local machine learning model further comprises calculating an aggregation weight to assign to the local machine learning model and the additional local machine learning model to optimize performance.” (this is a mathematical concept, calculating an aggregation weight to assign to the local machine learning model is a mathematical calculation, see MPEP § 2106.04(a)(2)(I)),
If claim limitations, under their broadest reasonable interpretation, covers 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.
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 11:
Regarding claim 11, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 11 recites the following additional elements:
“The vehicle of claim 1, wherein the machine learning scheme used to train the local machine learning model comprises decentralized machine learning.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 12:
Regarding claim 12, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 12 recites the following additional elements:
“The vehicle of claim 1, wherein the machine learning scheme used to train the local machine learning model comprises federated machine learning.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 13:
Regarding claim 13, it is dependent upon claim 12, and thereby incorporates the limitations of, and corresponding analysis to claim 12. Further, claim 13 recites the following additional elements:
“The vehicle of claim 12, further comprises a communication device to connect to a hybrid machine learning infrastructure.” (In step 2A, prong 2, this is considered generic computer component being used as tool to perform functions of the judicial exception, see MPEP § 2106.05(f)). (In step 2B, this is also considered generic computer component being used as tool to perform functions of the judicial exception - 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 14:
Regarding claim 14, it is dependent upon claim 13, and thereby incorporates the limitations of, and corresponding analysis to claim 13. Further, claim 14 recites the following additional elements:
“The vehicle of claim 13, wherein the hybrid machine learning infrastructure comprises a central server connected to the vehicle in accordance with federated machine learning .” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 15:
Regarding claim 15, it is dependent upon claim 14, and thereby incorporates the limitations of, and corresponding analysis to claim 14. Further, claim 15 recites the following additional elements:
“The vehicle of claim 14, wherein training the local machine learning model using federated machine learning comprises communicating the local trained machine learning model to the central sever and receiving an aggregated machine learning model from the central server.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 16:
Regarding claim 16, it is dependent upon claim 15, and thereby incorporates the limitations of, and corresponding analysis to claim 15. Further, claim 16 recites the following additional elements:
“The vehicle of claim 15, wherein the aggregated machine learning model comprises a plurality of local machine learning models from a plurality of connected vehicles communicating with the central server in the hybrid machine learning infrastructure.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 17:
Regarding claim 17, it is dependent upon claim 11, and thereby incorporates the limitations of, and corresponding analysis to claim 11. Further, claim 17 recites the following additional elements:
“The vehicle of claim 11, wherein the additional local machine learning model is received from the connected vehicle via a vehicle-to-vehicle (V2V) communication.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 18:
Regarding claim 18, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 18 recites the following additional elements:
“The vehicle of claim 1, wherein the vehicle comprises an autonomous vehicle.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 19:
Regarding claim 19, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A method, comprising:”, and a method or process is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the 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:
“wherein the training comprises calculating a class-wide data contribution estimation corresponding to locally training the first machine learning model at the first mobile client;” (this is a mathematical concept, calculating a class-wide data contribution estimation involves performing a mathematical calculation, see MPEP § 2106.04(a)(2)(I)),
“wherein the aggregating comprises calculating a class-wide data contribution corresponding to aggregating the first machine learning model with the second machine learning model locally trained at a second mobile;” (this is a mathematical concept, calculating a class-wide data contribution estimation involves performing a mathematical calculation, 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 concept 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:
“A method, comprising: performing, at first a mobile client, training of a first local machine learning model in accordance with decentralized machine learning,” (Performing training of a local machine learning model is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
“aggregating, at the first mobile client, the first machine learning model and a second machine learning model in accordance with decentralized machine learning,” (Aggregating a first model and a second model is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
“updating, at the first mobile client, the first machine learning model based on the aggregating;” (Updating a machine learning model is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
“and executing, at the first mobile client, one or more vehicle-related functions based on predictive inferences performed by the updated first machine learning client.” (Executing vehicle-related functions based on inferences performed by an updated machine learning model is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
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 to significantly more than the judicial exception.
As discussed above, additional elements iii, iv, v, and vi recites mere instructions to apply an exception using generic computer, which is not indicative of significantly more.
Considering 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 20:
Regarding claim 20, it is dependent upon claim 19, and thereby incorporates the limitations of, and corresponding analysis to claim 19. Further, claim 20 recites the following additional elements:
“The method of claim 19, wherein the one or more vehicle-related functions comprises: autonomous driving, traffic prediction, predictive maintenance, fuel efficiency optimization, and advanced driver assistance.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Isaksson M. et al, (US. Patent Application Publication 20240296342 A1) effectively filed on June 10, 2022, (hereafter Isaksson), in view of Liang F. et al, "Semi-Synchronous Federated Learning Protocol With Dynamic Aggregation in Internet of Vehicles", available at https://ieeexplore.ieee.org/abstract/document/9706290, effectively published on February 7, 2022, (hereafter Liang).
Claim 1:
Regarding claim 1, Isaksson teaches “A vehicle, comprising: a processor device training a machine learning model using local data, wherein the local machine learning model is trained at the vehicle using a machine learning scheme;”
See Isaksson in paragraph [0116] describing, “Local computing device may also include an interface (such as a user interface) coupled with processing circuitry 403, and/or local computing device may be incorporated in a vehicle.” Here, Isaksson establishes a vehicle comprising a local computing device with processing circuitry which is being interpreted as the processor device here. Further, see Isaksson in paragraph [0052] describing, “In some embodiments, further operations include performing one or more training rounds on the set of data (e.g., the local training data), using the selected global ML model as a starting point at each client or local computing device 102.” Here, Isaksson establishes training of a ML model using local data. With the local computing device being incorporated in the vehicle the local ML model being trained can be seen as being trained at the vehicle. Further, see Isaksson in paragraph [0036] describing, “Various embodiments of the present disclosure are described with reference to a distributed and decentralized ML setting that includes K clients. Each client has access to a local data partition.” Here, Isaksson uses distributed and decentralized ML setting in the embodiment and that is being interpreted as the machine learning scheme here.
However, Isaksson did not explicitly teach “and a controller device performing balanced client selection in real-time to communicate the local machine learning model with a connected vehicle for decentralized machine learning.”
In the same field of art, Liang teaches, “and a controller device performing balanced client selection in real-time to communicate the local machine learning model with a connected vehicle for decentralized machine learning.”
See Liang in Abstract page 1 describing, “In an Internet of Vehicle (IoV) system, federated learning (FL) is a new approach to process real-time vehicle data in a distributed way, which can improve the driving experience and service quality. However, due to the high mobility and uncertainty of vehicles, the existing federated learning protocols are difficult to meet the full requirements from an IoV system, such as efficient resource allocation, high precision learning, and fast convergence of learning algorithm. To solve the above problems, in this paper, we propose a semi-synchronous federated learning (Semi-SynFed) protocol, to improve the performance of machine learning at Internet of Vehicles.” Here, Liang establishes real-time communication of a federated learning process of a Semi-SynFed protocol. This protocol is also in an Internet of Vehicle system which is known to be a system for communicating with connected vehicles. Further, see Liang in Section IV. Methodology on page 4 describing, “To solve the aforementioned problems, in this section, the Semi-SynFed protocol is proposed with two folds, namely client selection and dynamic aggregation.” Further, see Liang in Section IV. Methodology section A. Client Selection on page 4 describing, “The client selection scheme is proposed to ensure that the ICV acted as a federated learning node can have the stable performance and network status. Meanwhile, it can guarantee that the participating vehicles have valuable samples, and reduce the waste of computation resources by terminating the learning clients whose data are no longer important for the model training. In FL procedure, there is always a certain number of vehicular nodes, which cannot submit parameters within the required time in each global round, due to the delay in a poor communication environment or shutdown of the vehicle.” Here, Liang establishes the Semi-SynFed protocol, which is a decentralized machine learning protocol, only considering clients in around of training which are relevant by terminating the learning clients which is being interpreted as balanced client selection. This protocol is also in an Internet of Vehicle system which is known to be a system for communicating with connected vehicles. Further, see Liang in Section III. System Model section A. Federated Learning in IoV on page 3 describing, “In each learning round, the selected vehicles j download the training model parameter from the parameter server and update the model wj with the local loss ℓ(wj) according to the local dataset Dj. Subsequently, the selected vehicles upload the new model parameter to the parameter server that aggregates these local models to produce a new global model wt+1.” Here, Liang establishes communication of the local machine learning model with vehicles with the downloading of the model and updating using a local dataset in a round of training and communicating the local model back to global model. The controller device here is being interpreted as the server.
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Claim 2:
Regarding claim 2, Isaksson in view of Liang teaches the limitations of claim 1.
Further, Isaksson teaches “The vehicle of claim 1, wherein the local data comprises non-independent and identically distribution data.”
See Isaksson in paragraph [0037] describing, “The method of some embodiments concerns performance on the local test set in a non-IID setting.”
Claim 3:
Regarding claim 3, Isaksson in view of Liang teaches the limitations of claim 1.
Isaksson does not appear to explicitly teach “The vehicle of claim 1, wherein the controller device performs balanced client selection during the training of the machine learning model at the vehicle.”
Further, Liang teaches “The vehicle of claim 1, wherein the controller device performs balanced client selection during the training of the machine learning model at the vehicle.”
See Liang in Section IV. Methodology subsection A. Client Selection on page 4 describing, “The client selection scheme is proposed to ensure that the ICV acted as a federated learning node can have the stable performance and network status. Meanwhile, it can guarantee that the participating vehicles have valuable samples, and reduce the waste of computation resources by terminating the learning clients whose data are no longer important for the model training. In FL procedure, there is always a certain number of vehicular nodes, which cannot submit parameters within the required time in each global round, due to the delay in a poor communication environment or shutdown of the vehicle.” Here, Liang establishes the Semi-SynFed protocol, which is a decentralized machine learning protocol, only considering clients in a round of training which are relevant by terminating the learning clients which is being interpreted as balanced client selection. This protocol is also in an Internet of Vehicle system which is known to be a system for communicating with connected vehicles as established in previous limitations and the selection is being done during a round of training.
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Claim 4:
Regarding claim 4, Isaksson in view of Liang teaches the limitations of claim 3.
Isaksson does not appear to explicitly teach “The vehicle of claim 3, wherein the balanced client selection performed during the training of the local machine learning model comprises calculating a class-wide contribution associated with the training of the local machine learning model.”
Further, Liang teaches “The vehicle of claim 3, wherein the balanced client selection performed during the training of the local machine learning model comprises calculating a class-wide contribution associated with the training of the local machine learning model.”
See Liang in Section II. Related Work on page 2 describing, “The current FL protocols can be mainly divided into synchronous methods and asynchronous methods by aggregation types.” Further, see Liang in Section II. Related Work subsection A. Synchronization on page 2 describing, “In terms of improving convergence speed, the authors [15] propose FSVRG, in which the parameter server collects the local gradient from every client and computes the full gradient that can accelerate the convergence of local models in ends at local update. In [16], the authors propose a Semi-Asynchronous FL Protocol (SAFA). In SAFA, a caching scheme is designed to cache model parameters of the nodes that do not be involved in aggregation at current round. Cached parameters can wait for the next aggregation by setting up a fixed server waiting time. For classification problems, the authors [17] propose FL protocol based class-weighted aggregation (FedCA), in which the parameters of classification layer are used as weights for parameter aggregation.” Here, Liang establishes class-weighted aggregation for an FL protocol, which is seen as calculating class wide contribution. The FL protocol is established to be of the already established protocol from previous limitations which does the balanced client selection during training of the local machine learning model, an establishment of local model training is made before establishing the class-wide contribution. Further, see Liang in Section II. Related Work subsection B. Asynchronization on page 3 describing, “Different from the existing works, we integrate the advantages of synchronous and asynchronous methods to perform semi-synchronous aggregations.” Here, Liang establishes all the advantages of the aggregations being integrated further establishing its association.
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Claim 5:
Regarding claim 5, Isaksson in view of Liang teaches the limitations of claim 4.
Isaksson does not appear to explicitly teach “The vehicle of claim 4, wherein the class-wide contribution associated with the training of the local machine learning model estimates a weight associated with a distribution incurred by the non-IID data during the training of the local machine learning model.”
Further, Liang teaches “The vehicle of claim 4, wherein the class-wide contribution associated with the training of the local machine learning model estimates a weight associated with a distribution incurred by the non-IID data during the training of the local machine learning model.”
See Liang in Section V. Experiment subsection B. Training Tasks and Data Redistribution on page 8 describing, “Different from traditional distributed machine learning, the federated learning protocol has to consider the heterogeneity of data distribution among nodes. Therefore, it’s necessary to form a Non-Independent and Identically Distributed (Non-IID) dataset among nodes. To achieve data redistribution, all samples are randomly divided into a certain number of subsets according to the dirichlet distribution. The samples in each subset are unique and non-repetitive. The label distribution in each subset and the total number of samples between the subsets are Non-IID. Then, selecting a part of subsets randomly and the total sample number of these subsets is bigger than nj. These selected subsets constitute the local dataset of client j.” Here, Liang establishes a distribution that is incurred by Non-IID data that constitute or train a local dataset. Further, see Liang in Section II. Related Work subsection A. Synchronization on page 2 describing, “In terms of improving convergence speed, the authors [15] propose FSVRG, in which the parameter server collects the local gradient from every client and computes the full gradient that can accelerate the convergence of local models in ends at local update. In [16], the authors propose a Semi-Asynchronous FL Protocol (SAFA). In SAFA, a caching scheme is designed to cache model parameters of the nodes that do not be involved in aggregation at current round. Cached parameters can wait for the next aggregation by setting up a fixed server waiting time. For classification problems, the authors [17] propose FL protocol based class-weighted aggregation (FedCA), in which the parameters of classification layer are used as weights for parameter aggregation.” Here, Liang establishes class-weighted aggregation for an FL protocol, which is seen as calculating class wide contribution and estimating weights. The FL protocol is established to be of the already established protocol from previous limitations and uses a local dataset to train a machine learning model and that non-IID data is in association with the local dataset.
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Claim 6:
Regarding claim 6, Isaksson in view of Liang teaches the limitations of claim 3.
Isaksson does not appear to explicitly teach “The vehicle of claim 3, wherein the controller further performs the device the balanced client selection in real-time to receive an additional local machine learning model from the connected vehicle for decentralized machine learning.”
Further, Liang teaches “The vehicle of claim 3, wherein the controller further performs the device the balanced client selection in real-time to receive an additional local machine learning model from the connected vehicle for decentralized machine learning.”
See Liang in Section III. System Model subsection A. Federated Learning in IoV on page 8 describing, “As depicted in Fig. 1, the system model of the FL consists of intelligent vehicles, base stations (BSs), and cloud server in an IoV environment. We assume there are M ICVs in the road network, and each ICV is equipped with onboard sensors to collect the data (e.g., the status of the vehicle, driving behavior, traffic information) from the surrounding environment. Thereafter, these vehicles collaboratively learn a task via local dataset without raw data transfer. In each learning round, the selected vehicles j download the training model parameter from the parameter server and update the model wj with the local loss ℓ(wj) according to the local dataset Dj. Subsequently, the selected vehicles upload the new model parameter to the parameter server that aggregates these local models to produce a new global model wt+1. The learning process is repeated until the model reaches the expected accuracy.” Here, Liang establishes the receiving of the additional local model with the aggregation of the local models to the server, which is the controller, from selected vehicles which is all done during training in real time from selected vehicles. As established in previous limitations the FL protocol described consists of decentralized machine learning protocol.
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Claim 7:
Regarding claim 7, Isaksson in view of Liang teaches the limitations of claim 2.
Isaksson does not appear to explicitly teach “The vehicle of claim 2, wherein the controller further performs aggregating the additional local machine learning model from the connected vehicle with the local machine learning model trained at the vehicle.”
Further, Liang teaches “The vehicle of claim 2, wherein the controller further performs aggregating the additional local machine learning model from the connected vehicle with the local machine learning model trained at the vehicle.”
See Liang in Section III. System Model subsection A. Federated Learning in IoV on page 8 describing, “As depicted in Fig. 1, the system model of the FL consists of intelligent vehicles, base stations (BSs), and cloud server in an IoV environment. We assume there are M ICVs in the road network, and each ICV is equipped with onboard sensors to collect the data (e.g., the status of the vehicle, driving behavior, traffic information) from the surrounding environment. Thereafter, these vehicles collaboratively learn a task via local dataset without raw data transfer. In each learning round, the selected vehicles j download the training model parameter from the parameter server and update the model wj with the local loss ℓ(wj) according to the local dataset Dj. Subsequently, the selected vehicles upload the new model parameter to the parameter server that aggregates these local models to produce a new global model wt+1. The learning process is repeated until the model reaches the expected accuracy.” Here, Liang establishes the receiving of the additional local model with the aggregation of the local models to the server, which is the controller, from selected vehicles which is all done during training in real time from selected vehicles. The selected vehicles each train a downloaded global model and updated the model with local dataset.
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Claim 8:
Regarding claim 8, Isaksson in view of Liang teaches the limitations of claim 7.
Isaksson does not appear to explicitly teach “The vehicle of claim 7, wherein the controller device performs the balanced client selection during the aggregation of the additional machine learning model at the vehicle.”
Further, Liang teaches “The vehicle of claim 7, wherein the controller device performs the balanced client selection during the aggregation of the additional machine learning model at the vehicle.”
See Liang in Section III. System Model subsection A. Federated Learning in IoV on page 8 describing, “As depicted in Fig. 1, the system model of the FL consists of intelligent vehicles, base stations (BSs), and cloud server in an IoV environment. We assume there are M ICVs in the road network, and each ICV is equipped with onboard sensors to collect the data (e.g., the status of the vehicle, driving behavior, traffic information) from the surrounding environment. Thereafter, these vehicles collaboratively learn a task via local dataset without raw data transfer. In each learning round, the selected vehicles j download the training model parameter from the parameter server and update the model wj with the local loss ℓ(wj) according to the local dataset Dj. Subsequently, the selected vehicles upload the new model parameter to the parameter server that aggregates these local models to produce a new global model wt+1. The learning process is repeated until the model reaches the expected accuracy.” Here, Liang establishes the receiving of the additional local model with the aggregation of the local models to the server, which is the controller, from selected vehicles which is all done during training in real time from selected vehicles. The selected vehicles each train a downloaded global model and updated the model with local dataset.
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Claim 9:
Regarding claim 9, Isaksson in view of Liang teaches the limitations of claim 8.
Isaksson does not appear to explicitly teach “The vehicle of claim 8, wherein the balanced client selection performed during the aggregation of the additional local machine learning model comprises calculating a class-wide contribution associated with the aggregation of the additional machine learning model at the vehicle.”
Further, Liang teaches “The vehicle of claim 7, wherein the controller device performs the balanced client selection during the aggregation of the additional machine learning model at the vehicle.”
See Liang in Section II. Related Work on page 2 describing, “The current FL protocols can be mainly divided into synchronous methods and asynchronous methods by aggregation types.” Further, see Liang in Section II. Related Work subsection A. Synchronization on page 2 describing, “In terms of improving convergence speed, the authors [15] propose FSVRG, in which the parameter server collects the local gradient from every client and computes the full gradient that can accelerate the convergence of local models in ends at local update. In [16], the authors propose a Semi-Asynchronous FL Protocol (SAFA). In SAFA, a caching scheme is designed to cache model parameters of the nodes that do not be involved in aggregation at current round. Cached parameters can wait for the next aggregation by setting up a fixed server waiting time. For classification problems, the authors [17] propose FL protocol based class-weighted aggregation (FedCA), in which the parameters of classification layer are used as weights for parameter aggregation.” Here, Liang establishes class-weighted aggregation for an FL protocol, which is seen as calculating class wide contribution. The FL protocol is established to be of the already established protocol from previous limitations which does the balanced client selection during training of the local machine learning model, an establishment of local model training is made before establishing the class-wide contribution. Further, see Liang in Section II. Related Work subsection B. Asynchronization on page 3 describing, “Different from the existing works, we integrate the advantages of synchronous and asynchronous methods to perform semi-synchronous aggregations.” Here, Liang establishes all the advantages of the aggregations being integrated further establishing its association. Further, see Liang in Section III. System Model on page 3 describing, “In this section, we mainly introduce the federated learning process in the Internet of Vehicles environment, as well as the formulations of problems.” Liang establishes the same federated learning protocol in the IoV environment. See Liang in Section III. System Model subsection A. Federated Learning in IoV on page 8 describing, “As depicted in Fig. 1, the system model of the FL consists of intelligent vehicles, base stations (BSs), and cloud server in an IoV environment. We assume there are M ICVs in the road network, and each ICV is equipped with onboard sensors to collect the data (e.g., the status of the vehicle, driving behavior, traffic information) from the surrounding environment. Thereafter, these vehicles collaboratively learn a task via local dataset without raw data transfer. In each learning round, the selected vehicles j download the training model parameter from the parameter server and update the model wj with the local loss ℓ(wj) according to the local dataset Dj. Subsequently, the selected vehicles upload the new model parameter to the parameter server that aggregates these local models to produce a new global model wt+1. The learning process is repeated until the model reaches the expected accuracy.” Here, Liang establishes the receiving of the additional local model with the aggregation of the local models to the server, which is the controller, from selected vehicles which is all done during training in real time from selected vehicles. The selected vehicles each train a downloaded global model and updated the model with local dataset. The aggregation being done here can comprise calculating a class-wide contribution as it incorporates the FL process from before.
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Claim 10:
Regarding claim 10, Isaksson in view of Liang teaches the limitations of claim 9.
Isaksson does not appear to explicitly teach “The vehicle of claim 9, wherein the balanced client selection performed during the aggregation of the additional local machine learning model further comprises calculating an aggregation weight to assign to the local machine learning model and the additional local machine learning model to optimize performance.”
Further, Liang teaches “The vehicle of claim 9, wherein the balanced client selection performed during the aggregation of the additional local machine learning model further comprises calculating an aggregation weight to assign to the local machine learning model and the additional local machine learning model to optimize performance.”
See Liang in Section IV. Methodology subsection A. Client Selection on page 4 describing, “The client selection scheme is proposed to ensure that the ICV acted as a federated learning node can have the stable performance and network status. Meanwhile, it can guarantee that the participating vehicles have valuable samples, and reduce the waste of computation resources by terminating the learning clients whose data are no longer important for the model training. In FL procedure, there is always a certain number of vehicular nodes, which cannot submit parameters within the required time in each global round, due to the delay in a poor communication environment or shutdown of the vehicle.” Here, Liang establishes the client selection being performed to optimize performance. Further, see Liang in Section IV. Methodology subsection A. Client Selection on page 4 describing, “In Semi-SynFed, we further consider computational capability (CC) and network capability (NC) to evaluate the state of ICV dynamically. CC is an indicator to measure the overall status of device performance and computing resources. NC is an indicator to measure the communication speed. In general, the transmission power of ICV is less than BS, which makes the uploading rate of ICV become the bottleneck of communication. Therefore, the instantaneous upload rate of vehicle is selected to characterize NC. By setting threshold, vehicles with low CC and high NC are selected to join in FL procedure. The formula of CC and NC is as following:
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” Here, Liang establishes further establishes the optimizing of performance and gives a formula to calculate the selection of the vehicles and clients to join the FL procedure. Further, see Liang in Section IV. Methodology subsection A. Client Selection on page 4 describing, “Furthermore, to select the nodes who have bigger contribution to the global model, we consider gradient norm of local model at each round.” Here, Liang establishes further establishes the formula which can be seen as a weight calculation for assigning to the global model and the local model which can be seen as the additional and local models, as previous limitations have established the global model being downloadable to a local vehicle or client.
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Claim 11:
Regarding claim 11, Isaksson in view of Liang teaches the limitations of claim 1.
Further, Liang teaches “The vehicle of claim 1, wherein the machine learning scheme used to train the local machine learning model comprises decentralized machine learning.”
See Isaksson in paragraph [0002] describing, “As used herein, a “client” refers to a local computing device (e.g., a user equipment (UE), a communication device, a mobile phone, an IoT device, a radio base station, a computer, etc.). Unless otherwise noted, the term “client” may be used interchangeably hereon with “local computing device”.” Here, Isaksson establishes clients as local computing devices as well. Further, see Isaksson in paragraph [0036] describing, “Various embodiments of the present disclosure are described with reference to a distributed and decentralized ML setting that includes K clients. Each client has access to a local data partition.” Here, Isaksson uses distributed and decentralized ML setting in the embodiment and that is being interpreted as the machine learning scheme here, and clients here have access to the local data partition which is used to train the local machine learning model. Further, see Isaksson in paragraph [0052] describing, “In some embodiments, further operations include performing one or more training rounds on the set of data (e.g., the local training data), using the selected global ML model as a starting point at each client or local computing device 102.” Here, Isaksson establishes training of a ML model using local data.
Claim 12:
Regarding claim 12, Isaksson in view of Liang teaches the limitations of claim 1.
Further, Liang teaches “The vehicle of claim 1, wherein the machine learning scheme used to train the local machine learning model comprises federated machine learning.”
See Isaksson in paragraph [0003] describing, “Federated Learning (FL) is a decentralized approach to ML where clients collectively train a global ML model without the need to share potentially sensitive private data.” Here, Isaksson establishes Federated Learning. Further, see Isaksson in paragraph [0040] describing, “Another approach may be to use FL using a MoE. In order to construct a personalized ML model for each client, a local expert ML model can be added that is trained only on local data..” Here, Isaksson establishes training of a local ML model using federated machine learning.
Claim 13:
Regarding claim 13, Isaksson in view of Liang teaches the limitations of claim 12.
Isaksson does not appear to explicitly teach “The vehicle of claim 12, further comprises a communication device to connect to a hybrid machine learning infrastructure.”
Further, Liang teaches “The vehicle of claim 12, further comprises a communication device to connect to a hybrid machine learning infrastructure.”
See Liang in Section II. Related Work on page 3 describing, “The current FL protocols can be mainly divided into synchronous methods and asynchronous methods by aggregation types.” Here, Liang establishes the FL protocols of two methods. Further, see Liang in Section II. Related Work subsection B. Asynchronization on page 3 describing, “Different from the existing works, we integrate the advantages of synchronous and asynchronous methods to perform semi-synchronous aggregations.” Here, Liang establishes all the advantages of the aggregations of the two methods being integrated implying a hybrid machine learning infrastructure in the FL protocol. Further, see Liang in Section III. System Model on page 3 describing, “In this section, we mainly introduce the federated learning process in the Internet of Vehicles environment, as well as the formulations of problems.” Liang establishes the same federated learning protocol in the IoV environment. Further, see Liang in Section V. Experiment on page 7 describing, “To facilitate the data acquisition, we assume that the communication of IoV is realized through the wireless communication base stations that work as roadside communication devices.” Liang establishes a communication device for IoV which incorporates the hybrid ML infrastructure as established.
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Claim 14:
Regarding claim 14, Isaksson in view of Liang teaches the limitations of claim 13.
Isaksson does not appear to explicitly teach “The vehicle of claim 13, wherein the hybrid machine learning infrastructure comprises a central server connected to the vehicle in accordance with federated machine learning.”
Further, Liang teaches “The vehicle of claim 13, wherein the hybrid machine learning infrastructure comprises a central server connected to the vehicle in accordance with federated machine learning.”
See Liang in Section II. Related Work subsection B. Asynchronization on page 3 describing, “Different from the existing works, we integrate the advantages of synchronous and asynchronous methods to perform semi-synchronous aggregations. Meanwhile, we dynamically adjust maximum server waiting time according to the proportion of participating nodes in each round, allowing as many nodes as possible to participate in the aggregation, thereby reducing communication cost.” Here, Liang establishes all the advantages of the aggregations of the two methods being integrated implying a hybrid machine learning infrastructure in the FL protocol and a server is explicitly used in it. Further, see Liang in Section III. System Model subsection A. Federated Learning in IoV on page 8 describing, “As depicted in Fig. 1, the system model of the FL consists of intelligent vehicles, base stations (BSs), and cloud server in an IoV environment. We assume there are M ICVs in the road network, and each ICV is equipped with onboard sensors to collect the data (e.g., the status of the vehicle, driving behavior, traffic information) from the surrounding environment. Thereafter, these vehicles collaboratively learn a task via local dataset without raw data transfer. In each learning round, the selected vehicles j download the training model parameter from the parameter server and update the model wj with the local loss ℓ(wj) according to the local dataset Dj. Here, Liang establishes the receiving of the additional local model with the aggregation of the local models to the server from selected vehicles which is all done during training in real time from selected vehicles in the established FL protocol.
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Claim 15:
Regarding claim 15, Isaksson in view of Liang teaches the limitations of claim 14.
Isaksson does not appear to explicitly teach “The vehicle of claim 14, wherein training the local machine learning model using federated machine learning comprises communicating the local trained machine learning model to the central sever and receiving an aggregated machine learning model from the central server.”
Further, Liang teaches “The vehicle of claim 14, wherein training the local machine learning model using federated machine learning comprises communicating the local trained machine learning model to the central sever and receiving an aggregated machine learning model from the central server.”
See Liang in Section III. System Model subsection A. Federated Learning in IoV on page 8 describing, “As depicted in Fig. 1, the system model of the FL consists of intelligent vehicles, base stations (BSs), and cloud server in an IoV environment. We assume there are M ICVs in the road network, and each ICV is equipped with onboard sensors to collect the data (e.g., the status of the vehicle, driving behavior, traffic information) from the surrounding environment. Thereafter, these vehicles collaboratively learn a task via local dataset without raw data transfer. In each learning round, the selected vehicles j download the training model parameter from the parameter server and update the model wj with the local loss ℓ(wj) according to the local dataset Dj. Subsequently, the selected vehicles upload the new model parameter to the parameter server that aggregates these local models to produce a new global model wt+1. The learning process is repeated until the model reaches the expected accuracy.” Here, Liang establishes the receiving of the aggregated model from the server with the downloading of the global model from the server with the aggregation of the local models to the server, and communicating the local model to produce a global model to the server by training the model using local dataset in an FL protocol.
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Claim 16:
Regarding claim 16, Isaksson in view of Liang teaches the limitations of claim 15.
Isaksson does not appear to explicitly teach “The vehicle of claim 15, wherein the aggregated machine learning model comprises a plurality of local machine learning models from a plurality of connected vehicles communicating with the central server in the hybrid machine learning infrastructure.”
Further, Liang teaches “The vehicle of claim 15, wherein the aggregated machine learning model comprises a plurality of local machine learning models from a plurality of connected vehicles communicating with the central server in the hybrid machine learning infrastructure.”
See Liang in Section III. System Model subsection A. Federated Learning in IoV on page 8 describing, “As depicted in Fig. 1, the system model of the FL consists of intelligent vehicles, base stations (BSs), and cloud server in an IoV environment. We assume there are M ICVs in the road network, and each ICV is equipped with onboard sensors to collect the data (e.g., the status of the vehicle, driving behavior, traffic information) from the surrounding environment. Thereafter, these vehicles collaboratively learn a task via local dataset without raw data transfer. In each learning round, the selected vehicles j download the training model parameter from the parameter server and update the model wj with the local loss ℓ(wj) according to the local dataset Dj. Subsequently, the selected vehicles upload the new model parameter to the parameter server that aggregates these local models to produce a new global model wt+1. The learning process is repeated until the model reaches the expected accuracy.” Here, Liang a plurality of models being communicated by a plurality of vehicles with each of the selected vehicles contributing a updated local model to update a global model to the server.
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Claim 17:
Regarding claim 17, Isaksson in view of Liang teaches the limitations of claim 11.
Further, Liang teaches “The vehicle of claim 11, wherein the additional local machine learning model is received from the connected vehicle via a vehicle-to-vehicle (V2V) communication.”
See Isaksson in paragraph [0122] describing, “The method further comprises transmitting (607) the selected global ML model, or a gradient of the computing device from the selected global ML model to the global computing device (104).” Here, Isaksson establishes transmitting a selected model which can be seen as an additional model to a computing device. Further, see Isaksson in paragraph [0116] describing, “FIG. 4 is a block diagram illustrating elements of a local computing device 400 (also referred to as a mobile terminal, a mobile communication terminal, a wireless device, a wireless communication device, a wireless terminal, mobile device, a wireless communication terminal, user equipment, UE, a user equipment node/terminal/device, a computer, etc.) configured to provide operations according to embodiments of inventive concepts.” Here a computing device is established as a UE. Further, see Isaksson in paragraph [0147] describing, “A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V21), or vehicle-to-everything (V2X).” Here, the UE which is established to be able to receive a model also supports V2V communication.
Claim 18:
Regarding claim 18, Isaksson in view of Liang teaches the limitations of claim 1.
Further, Liang teaches “The vehicle of claim 1, wherein the vehicle comprises an autonomous vehicle.”
See Isaksson in paragraph [0158] describing, “A UE, when in the form of an Internet of Things (IoT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an IoT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door/window sensor, a flood/moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle”.
Claim 19:
Regarding claim 19, Isaksson teaches “A method, comprising: performing, at first a mobile client, training of a first local machine learning model in accordance with decentralized machine learning, wherein the training comprises calculating a class-wide data contribution estimation corresponding to locally training the first machine learning model at the first mobile client;”
See Isaksson in paragraph [0036] describing, “Various embodiments of the present disclosure are described with reference to a distributed and decentralized ML setting that includes K clients. Each client has access to a local data partition.” Here, Isaksson establishes decentralized machine learning for clients in the embodiment, where each client has access to local data. Further, see Isaksson in paragraph [0037] describing, “Some embodiments are described in the context of considering a multi-class classification problem where there are a number of measured data samples and output class labels is in a finite set. Each client partition of data is further divided into a set of training data and a local test set of data. The method of some embodiments concerns performance on the local test set in a non-IID setting. FIG. 1 is a diagram illustrating an example communications network 100 illustrating devices that may perform tasks of a computing device and a server according to some embodiments of the present disclosure.” Here, Isaksson establishes calculating class-wide data contribution estimation with the multi-class classification where data samples are measured, and they correspond to the data for each client’s partition of data into a training data set and a local test set. Further, see Isaksson in paragraph [0002] describing, “As used herein, a “client” refers to a local computing device (e.g., a user equipment (UE), a communication device, a mobile phone, an IoT device, a radio base station, a computer, etc.).” Here, Isaksson establishes a client as a mobile client with the examples of a mobile phone, IoT device, etc. Further, see Isaksson in paragraph [0040] describing, “Another approach may be to use FL using a MoE. In order to construct a personalized ML model for each client, a local expert ML model can be added that is trained only on local data. A gating ML model can be defined to learn to weight the local ML model and the global ML model. In this way, personalization can be performed, even when a client's data is different from the data of the population.” Here, Isaksson establishes each client having a personalized model that can be trained using their data, in which a first client can train a local model inherently.
Further, Isaksson teaches “aggregating, at the first mobile client, the first machine learning model and a second machine learning model in accordance with decentralized machine learning, wherein the aggregating comprises calculating a class-wide data contribution corresponding to aggregating the first machine learning model with the second machine learning model locally trained at a second mobile;”
See Isaksson in paragraph [0003] describing, “Federated Learning (FL) is a decentralized approach to ML where clients collectively train a global ML model without the need to share potentially sensitive private data. Such a FL approach avoids central collection of data and instead performs training of a ML model locally where the data is generated. The local ML model updates generated by clients, such as radio base stations, are then aggregated by parameter server or global computing device into a new global ML model. Unless otherwise noted, the term “server” or “parameter server” may be used interchangeably hereon with “global computing device”.” Here, Isaksson establishes aggregating local models for different clients in a decentralized machine learning process, the aggregation of local models from different clients can be seen as a first and second mobile client each training a different local model since each client is aggregating its own local model. Further, see Isaksson in paragraph [0037] describing, “Some embodiments are described in the context of considering a multi-class classification problem where there are a number of measured data samples and output class labels is in a finite set. Each client partition of data is further divided into a set of training data and a local test set of data. The method of some embodiments concerns performance on the local test set in a non-IID setting. FIG. 1 is a diagram illustrating an example communications network 100 illustrating devices that may perform tasks of a computing device and a server according to some embodiments of the present disclosure.” Here, Isaksson establishes calculating class-wide data contribution estimation with the multi-class classification where data samples are measured, and they correspond to the data for each client’s partition of data into a training data set and a local test set, since this is in the same embodiment as the aggregation in accordance with decentralized machine learning, the aggregating can be seen to comprise of calculating the class-wide data contribution described.
However, Isaksson did not explicitly teach “updating, at the first mobile client, the first machine learning model based on the aggregating; and executing, at the first mobile client, one or more vehicle-related functions based on predictive inferences performed by the updated first machine learning client.”
In the same field of art, Liang teaches, “updating, at the first mobile client, the first machine learning model based on the aggregating;”
See Liang in Section IV. System Model subsection B. Dynamic Aggregation on page 5 describing, “At server side, the server aggregates the uploaded models
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from node set Nt after waiting
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at each communication round t (line 5-11). The waiting time
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and the selected node set St for the next round are calculated at line 18-22. And then the server broadcasts the new global model wt to St (line 23). At client side, each vehicular node at St performs SGD to update local models
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(line 28-34) at each communication round. The testing loss ℓ(wj) is calculated (line 35), and then the updated local model
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and the testing loss ℓ(wj) are uploaded to the server.” Here, Liang establishes updating local models based on the aggregated initial uploaded models, at client side each node updates the local model so a first node or client is able to do the updating based on aggregating. Further, see Liang in Section III. System Model subsection A. Federated Learning in IoV on page 3 describing, “As depicted in Fig. 1, the system model of the FL consists of intelligent vehicles, base stations (BSs), and cloud server in an IoV environment.” Here, Liang establishes a IoV environment which is an internet of vehicles environment, inherently the vehicles and nodes of the vehicles can act as mobile clients in this environment.
In the same field of art, Liang teaches, “and executing, at the first mobile client, one or more vehicle-related functions based on predictive inferences performed by the updated first machine learning client.”
See Liang in Section V. Experiment subsection G. End-to-End Autonomous Driving on page 11 describing, “To further verify the advantage of the proposed method compared with central learning, we extend an end-to-end autonomous driving model with an open dataset apolloscape (v1.5), whose size is about 1,280,000 [36]. For the autonomous driving, the main challenge is to handle complex traffic scenarios (such as extreme weather and mixed traffic). Therefore, we apply federated learning to train autonomous driving model by collecting data within different traffic scenarios, which deals with various complex traffic scenarios.” Here, Liang establishes the vehicle related function of autonomous driving and traffic prediction being executed by the federated learning, as established this federated learning process is done in a IoV environment, so the vehicle that collects the data described can be seen as a mobile client.” Further, see Liang in Section I. Introduction on page 2 describing, “To solve the aforementioned challenges, we propose a novel semi-synchronous FL (Semi-SynFed) protocol for IoV systems. We firstly design a client selection scheme to evaluate the status of vehicular nodes. The vehicular nodes satisfying upper limit of communication rounds will participate in the aggregation synchronously.” Here, Liang establishes clients being selected to evaluate vehicular nodes in a FL protocol for IoV systems,, the Fl protocol is a machine learning protocol, the clients connection to this FL protocol makes it a machine learning client and as communication rounds are being performed to do aggregation this implies updating of the clients being performed.
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Claim 20:
Regarding claim 20, Isaksson in view of Liang teaches the limitations of claim 19.
Isaksson does not appear to explicitly teach “The method of claim 19, wherein the one or more vehicle-related functions comprises: autonomous driving, traffic prediction, predictive maintenance, fuel efficiency optimization, and advanced driver assistance.”
Further, Liang teaches “The method of claim 19, wherein the one or more vehicle-related functions comprises: autonomous driving, traffic prediction, predictive maintenance, fuel efficiency optimization, and advanced driver assistance.”
See Liang in Section V. Experiment subsection G. End-to-End Autonomous Driving on page 11 describing, “To further verify the advantage of the proposed method compared with central learning, we extend an end-to-end autonomous driving model with an open dataset apolloscape (v1.5), whose size is about 1,280,000 [36]. For the autonomous driving, the main challenge is to handle complex traffic scenarios (such as extreme weather and mixed traffic). Therefore, we apply federated learning to train autonomous driving model by collecting data within different traffic scenarios, which deals with various complex traffic scenarios.”
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 Isaksson with the teachings of Liang by using Isaksson’s teachings of training a model using local data using a machine learning scheme, and incorporate with Liang’s teachings of performing balanced client selection to communicate a local model with a connected vehicle.
One of ordinary skill in the art would be motivated to do so because by integrating Liang’s frameworks into the methods of Isaksson, which are both in relation to federated learning with vehicles, one of ordinary skill in the art would bring “a client selection scheme by considering computing capacity, network capacity, and gradient norm of vehicular nodes.” (Liang, section I. Introduction), “Semi-SynFed protocol, which aggregates local models of different generations through synchronous methods. In addition, Semi-SynFed can reduce resource and communication costs by adjusting server waiting time.” (Liang, section I. Introduction), and “a simulation of IoV based on the road network and BS location data of Guangzhou Higher Education Mega Center. Machine learning task experiments show that our algorithm performs well in accuracy, convergence speed, and resource cost under fixed system runtime.” (Liang, section I. Introduction).
Conclusion
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/HASSAN RAMADAN SESAY/Examiner, Art Unit 2146
/SHOURJO DASGUPTA/Primary Examiner, Art Unit 2144
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