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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/14/2026 has been entered.
Status of the Claims
Claims 1, 3-15, 17-26, 29, and 32 have been amended. Claims 1-26 and 29-32 are currently pending and have been considered by the Examiner.
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.
Claim 30 recites “means for applying” and “means for transmitting.”
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-26 and 29-32 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 is rendered indefinite because it is unclear whether the machine learning-based model is applied at the user equipment or the base station. Examiner treats claim 1 to mean that the machine learning-based model is applied at the user equipment. Claims 2-14 and 31-32 are rejected for failing to cure the deficiencies of claim 1.
Claims 15 recites the same indefinite limitations as claim 1 and is therefore rejected for at least the same reasons. Claims 16-26 are rejected for failing to cure the deficiencies of claim 15.
Claim 29 recites the same indefinite limitations as claim 1 and is therefore rejected for at least the same reasons.
Claim 30 recites the same indefinite limitations as claim 1 and is therefore rejected for at least the same reasons.
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 3-9, 11-12, 17-23, 25-26, and 32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 3-9, 11-12, and 32 each recites a system comprising processors (a system), and claims 17-23 and 25-26 each recites a method. A system and a method each falls under at least one of the four statutory categories of patent-eligible subject matter.
Claims 3-9, 11-12, and 32 incorporate additional elements from claim 1. Any additional elements recited by the dependent claims are underlined in Step 2A Prong 2 and Step 2B.
Claims 17-23 and 25-26 incorporate additional elements from claim 15. Any additional elements recited by the dependent claims are underlined in Step 2A Prong 2 and Step 2B.
Claim 3
Step 2A Prong 1: Determine that a difference between a predicted parameter of a channel at a first time and a measured parameter of the channel at the first time satisfies an error threshold is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Determine a failure associated with a selected channel, wherein the selected channel is selected by the UE based at least in part on applying the machine learning-based model is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper. The claim recites an abstract idea.
Step 2A Prong 2: A user equipment (UE) for wireless communication amounts to a field of use and technological environment under MPEP 2106.05(h).
One or more memories and one or more processors, coupled to the one or more memories amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Apply a machine learning-based model to one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction amounts to a field of use and technological environment under MPEP 2106.05(h).
Transmit, to the base station, a second error report amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions amounts to a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions and a field of use that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: A user equipment (UE) for wireless communication amounts to a field of use and technological environment under MPEP 2106.05(h).
One or more memories and one or more processors, coupled to the one or more memories amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Apply a machine learning-based model to one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction amounts to a field of use and technological environment under MPEP 2106.05(h).
Transmit, to the base station, a second error report amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions amounts to a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions and a field of use that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 4
Step 2A Prong 1: Perform one or more measurements of a channel parameter based at least in part on the error event associated with the machine learning-based model is a mental process of observing a channel parameter which can reasonably be performed in the human mind with the aid of pencil and paper. The claim recites an abstract idea.
Step 2A Prong 2: A user equipment (UE) for wireless communication amounts to a field of use and technological environment under MPEP 2106.05(h).
One or more memories and one or more processors, coupled to the one or more memories amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Apply a machine learning-based model to one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction amounts to a field of use and technological environment under MPEP 2106.05(h).
Transmit, to the base station, a second error report amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions amounts to a field of use and technological environment under MPEP 2106.05(h).
The first error report indicates the one or more measurements amounts to a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions and a field of use that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: A user equipment (UE) for wireless communication amounts to a field of use and technological environment under MPEP 2106.05(h).
One or more memories and one or more processors, coupled to the one or more memories amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Apply a machine learning-based model to one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction amounts to a field of use and technological environment under MPEP 2106.05(h).
The first error report indicates the one or more measurements amounts to a field of use and technological environment under MPEP 2106.05(h).
Transmit, to the base station, a second error report amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions amounts to a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions and a field of use that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 5
Step 2A Prong 1: Determine one or more updated parameters of the machine learning-based model based at least in part on the error event associated with the machine learning-based model is a mathematical calculation. Specification paragraphs [0062] and [0071] disclose updating parameters using an updating equation
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, and calculating a gradient G that is based on an error event. The claim recites an abstract idea.
Step 2A Prong 2: A user equipment (UE) for wireless communication amounts to a field of use and technological environment under MPEP 2106.05(h).
One or more memories and one or more processors, coupled to the one or more memories amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Apply a machine learning-based model to one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction amounts to a field of use and technological environment under MPEP 2106.05(h).
The first error report indicates the one or more updated parameters of the machine learning-based model amounts to a field of use and technological environment under MPEP 2106.05(h).
Transmit, to the base station, a second error report amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions amounts to a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions and a field of use that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: A user equipment (UE) for wireless communication amounts to a field of use and technological environment under MPEP 2106.05(h).
One or more memories and one or more processors, coupled to the one or more memories amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Apply a machine learning-based model to one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction amounts to a field of use and technological environment under MPEP 2106.05(h).
The first error report indicates the one or more updated parameters of the machine learning-based model amounts to a field of use and technological environment under MPEP 2106.05(h).
Transmit, to the base station, a second error report amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions amounts to a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions and a field of use that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 6 incorporates the rejection of claim 5.
Step 2A Prong 1: The abstract ideas of claim 5 are incorporated. Determine the quantity of error predictions, wherein the quantity of error predictions is used to determine the one or more updated parameters of the machine learning-based model amounts to counting the number of error predictions. This is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: Processors amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 7
Step 2A Prong 1: Determine the quantity of error predictions based at least in part on applying the machine learning-based model to the one or more functions for wireless communication amounts to counting the number of error predictions. This is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Determine the first scalar quantity, wherein the first scalar quantity is based at least in part on the quantity of the error predictions is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper. The claim is directed to an abstract idea.
Step 2A Prong 2: A user equipment (UE) for wireless communication amounts to a field of use and technological environment under MPEP 2106.05(h).
One or more memories and one or more processors, coupled to the one or more memories amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Apply a machine learning-based model to one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction amounts to a field of use and technological environment under MPEP 2106.05(h).
Transmit, to the base station, a second error report amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions amounts to a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions and a field of use that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: A user equipment (UE) for wireless communication amounts to a field of use and technological environment under MPEP 2106.05(h).
One or more memories and one or more processors, coupled to the one or more memories amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Apply a machine learning-based model to one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction amounts to a field of use and technological environment under MPEP 2106.05(h).
Transmit, to the base station, a second error report amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions amounts to a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions and a field of use that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 8 incorporates the rejection of claim 7.
Step 2A Prong 1: The abstract ideas of claim 7 are incorporated.
Step 2A Prong 2 and Step 2B: The first error report includes: a set of input data associated with determining a predicted parameter of a channel at a first time, the predicted parameter of the channel at the first time, and one or more measurements of the predicted parameter of the channel at the first time amounts to a field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 9 incorporates the rejection of claim 7.
Step 2A Prong 1: The abstract ideas of claim 7 are incorporated. Determine the one or more correct predictions based at least in part on applying the machine learning-based model to the one or more functions for wireless communication is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: Processors amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Applying the machine learning-based model to the one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 11
Step 2A Prong 1: Determine, according to a periodic schedule configured by the base station, one or more updated parameters of the machine learning-based model based at least in part on the error event associated with the machine learning-based model is a mathematical calculation. Specification paragraphs [0062] and [0071] disclose updating parameters using an updating equation
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, and calculating a gradient G that is based on an error event. The claim recites an abstract idea.
Step 2A Prong 2: A user equipment (UE) for wireless communication amounts to a field of use and technological environment under MPEP 2106.05(h).
One or more memories and one or more processors, coupled to the one or more memories amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Apply a machine learning-based model to one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The one or more processors, to transmit the first error report, are configured to: … transmit, according to the periodic schedule and to the base station, the first error report amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction amounts to a field of use and technological environment under MPEP 2106.05(h).
Transmit, to the base station, a second error report amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions amounts to a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions and a field of use that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: A user equipment (UE) for wireless communication amounts to a field of use and technological environment under MPEP 2106.05(h).
One or more memories and one or more processors, coupled to the one or more memories amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Apply a machine learning-based model to one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The one or more processors, to transmit the first error report, are configured to: … transmit, according to the periodic schedule and to the base station, the first error report amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction amounts to a field of use and technological environment under MPEP 2106.05(h).
Transmit, to the base station, a second error report amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions amounts to a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions and a field of use that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 12
Step 2A Prong 1: Determine that a quantity of predictions associated with applying the machine learning-based model to the one or more functions for wireless communication satisfies a reporting threshold is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Determine one or more updated parameters of the machine learning-based model based at least in part on the quantity of predictions is a mathematical calculation. Specification paragraphs [0061]-[0062] discloses calculating a gradient G based on a total quantity of predictions, and updating parameters using an updating equation
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. The claim recites an abstract idea.
Step 2A Prong 2: A user equipment (UE) for wireless communication amounts to a field of use and technological environment under MPEP 2106.05(h).
One or more memories and one or more processors, coupled to the one or more memories amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Apply a machine learning-based model to one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction amounts to a field of use and technological environment under MPEP 2106.05(h).
The first error report indicates the one or more updated parameters of the machine-learning model amounts to a field of use and technological environment under MPEP 2106.05(h).
Transmit, to the base station, a second error report amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions amounts to a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions and a field of use that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: A user equipment (UE) for wireless communication amounts to a field of use and technological environment under MPEP 2106.05(h).
One or more memories and one or more processors, coupled to the one or more memories amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Apply a machine learning-based model to one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction amounts to a field of use and technological environment under MPEP 2106.05(h).
The first error report indicates the one or more updated parameters of the machine-learning model amounts to a field of use and technological environment under MPEP 2106.05(h).
Transmit, to the base station, a second error report amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions amounts to a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions and a field of use that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claims 15, 17-23 and 25-26 each recites a method which implements the same features as the system of claims 1, 3-9, and 11-12, respectively. Claims 17-23 and 25-26 are rejected for at least the same reasons as the corresponding system claims.
Claim 32
Step 2A Prong 1: A stochastic gradient for updating the machine learning-based model is based at least in part on the one or more error predictions and the first scalar quantity is a mathematical calculation. Specification paragraphs [0062] and [0071] disclose updating parameters using an updating equation
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, and calculating a gradient G that is based on error predictions and a first scalar quantity.
Step 2A Prong 2: A user equipment (UE) for wireless communication amounts to a field of use and technological environment under MPEP 2106.05(h).
One or more memories and one or more processors, coupled to the one or more memories amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Apply a machine learning-based model to one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction amounts to a field of use and technological environment under MPEP 2106.05(h).
Transmit, to the base station, a second error report amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
The second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions amounts to a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions and a field of use that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: A user equipment (UE) for wireless communication amounts to a field of use and technological environment under MPEP 2106.05(h).
One or more memories and one or more processors, coupled to the one or more memories amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Apply a machine learning-based model to one or more functions for wireless communication amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction amounts to a field of use and technological environment under MPEP 2106.05(h).
Transmit, to the base station, a second error report amounts to a well-understood, routine, conventional activity recognized by the courts of transmitting data over a network under MPEP 2106.05(d)(II).
The second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions amounts to a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions and a field of use that are implemented to perform the abstract ideas disclosed above. 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 7-9, 13-18, 21-23, 29-31 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20210342687 A1, cited in PTO-892 issued 08/06/2025) in view of Cook et al. (US 20160314255 A1, cited in PTO-892 issued 08/06/2025) and Roberts et al. (US 20190139646 A1).
Regarding claim 1, Wang teaches: A user equipment (UE) for wireless communication, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to: ([0043], lines 1-2)
apply a machine learning-based model to one or more functions for wireless communication; ([0098], where “a machine learning-based model” corresponds to multiple DNNs.)
transmit, to a base station, a first error report based at least in part on an error event associated with the machine learning-based model, wherein the first error report indicates a quantity of
transmit, to the base station, a second error report, the second error report indicating one or more
However, Wang does not explicitly teach: a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the UE, wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, wherein the first scalar quantity is a ratio of a quantity of overall predictions made by the machine learning-based model to the quantity of error predictions, and wherein an error prediction of the quantity of error predictions is a prediction that differs from a measured value associated with the prediction; and
the second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model, wherein the second scalar quantity is a ratio of a quantity of the one or more correct predictions to the quantity of overall predictions.
But Cook teaches: a first error report based at least in part on an error event associated with the machine learning-based model, wherein the first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the [system]
a second error report, the second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity, 7 to “SVM” in line 14; [0269] on page 21, left column, lines 5-8 below Table 7; and [0272] disclose evaluating accuracy as a classification performance of an SVM. A second error report is the accuracy expressed as a fraction. Cook’s accuracy ratio is a number of correct predictions to a total number of predictions, which expresses the same information as the second scalar quantity as claimed.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have calculated error and accuracy ratios in Wang’s user equipment, and to have included these in transmissions to Wang’s base station. A motivation for the combination is to evaluate the classification performance of a machine learning model. (Cook, [0267] on page 21, left column, lines 2-8 below Table 7)
However, Wang and Cook do not explicitly teach: wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model,
wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model
But Roberts teaches: wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model, ([0065], lines 1-3 and 8-end discloses using an error rate to weight a predictive model in an ensemble of models. A first scalar quantity is a weight, based on an error rate, for a first predictive model in the ensemble.)
wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model ([0065], lines 16-19 disclose less accurate members may be weighted less, implying more accurate members may be weighted more. A second scalar quantity is a weight, based on an accuracy, for a second predictive model in the ensemble.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have applied Roberts’ weights to an ensemble comprising Wang’s multiple DNNs 612. A motivation for the combination is to reduce the contribution of less accurate models. (Roberts, [0065])
Regarding claim 2, the combination of Wang, Cook, and Roberts teaches: The UE of claim 1, wherein the one or more processors are further configured to:
Wang teaches: receive, from the base station, a configuration for the machine learning-based model, wherein the configuration indicates one or more parameters associated with the machine learning-based model. ([0103], lines 1-6; [0106], lines 1-10)
Regarding claim 3, the combination of Wang, Cook, and Roberts teaches: The UE of claim 1, wherein the one or more processors are configured to:
Wang teaches: determine that a difference between a predicted parameter of a channel at a first time and a measured parameter of the channel at the first time satisfies an error threshold; or ([0114], lines 3-end and [0123], lines 4-14 together disclose that channel condition predictions satisfy an accuracy within a threshold range, which conveys the same information as satisfying an error threshold.)
determine a failure associated with a selected channel, wherein the selected channel is selected by the UE based at least in part on applying the machine learning-based model.
Regarding claim 4, the combination of Wang, Cook, and Roberts teaches: The UE of claim 1, wherein the one or more processors, to transmit the first error report, are configured to:
Wang teaches: perform one or more measurements of a channel parameter based at least in part on the error event associated with the machine learning-based model, wherein the first error report indicates the one or more measurements. ([0067], [0068], [0114], lines 3-end and [0123], lines 4-14 together disclose that ground truth data is measured and labeled based on binary data (channel parameters) to be predicted by the neural network.)
Regarding claim 7, the combination of Wang, Cook, and Roberts teaches: The UE of claim 1, wherein the one or more processors, to transmit the first error report, are configured to:
Wang teaches: determine the [error metrics]
…
transmit the first error report. ([0115])
However, Wang and Roberts do not explicitly teach: determine the quantity of error predictions
determine the first scalar quantity, wherein the first scalar quantity is based at least in part on the quantity of the error predictions; and
But Cook teaches: determine the quantity of error predictions ([0272]-[0273] discloses determining a number of incorrect predictions.)
determine the first scalar quantity, wherein the first scalar quantity is based at least in part on the quantity of the error predictions; and ([0273] discloses Cook’s error ratio is a number of incorrect predictions to a total number of predictions, which expresses the same information as the first scalar quantity as claimed.)
A motivation for the combination is the same as the motivation disclosed in claim 1.
Regarding claim 8, the combination of Wang, Cook, and Roberts teaches: The UE of claim 7,
Wang teaches:
the predicted parameter of the channel at the first time; and ([0067], lines 15-17)
one or more measurements of the predicted parameter of the channel at the first time. ([0067], lines 4-6 where a desired output is a measurement.)
Wang at [0114], lines 3-end discloses the UE generates types of metrics such as error metrics. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated input data, model predictions, and ground truth data into the UE’s metrics. A motivation for the modification is that the base station can use details about the UE model’s predictions to identify a second neural network formation configuration in step 830. (Wang, [0116], lines 1-3)
Regarding claim 9, the combination of Wang, Cook, and Roberts teaches: The UE of claim 7, wherein the one or more processors are further configured to:
Wang teaches: determine the one or more correct predictions based at least in part on applying the machine learning-based model to the one or more functions for wireless communication. ([0111], from “the UE” in line 8 to line 11; Page 12, col. 1, lines 10-13 discloses the UE downlink neural network accurately recovers information, which indicates at least some of the network’s predictions are “correct predictions” as recited in the claim.)
Regarding claim 13, the combination of Wang, Cook, and Roberts teaches: The UE of claim 1, wherein the one or more processors are further configured to:
Wang teaches: receive, from the base station, a configuration indicating information to be included in the first error report, (All [0106], where a configuration for downlink control channel processing indicates prediction errors will be included in the error report.)
wherein the one or more processors, to transmit the first error report, to the base station, are configured to transmit the first error report based at least in part on the configuration indicating information to be included in the first error report. ([0114], lines 3-end)
Regarding claim 14, the combination of Wang, Cook, and Roberts teaches: The UE of claim 1, wherein the one or more processors are further configured to:
Wang teaches: transmit, to the base station, an indication of an error reporting capability of the UE, wherein the one or more processors, to transmit the first error report to the base station, are configured to transmit the first error report based at least in part on transmitting the indication of the error reporting capability of the UE. ([0104], from line 1 to “information” in line 4; [0114], lines 3-end; and [0115] where an indication of an error reporting capability includes connectivity between the base station and UE.)
Claims 15-18 and 21-23 each recites a method which implements the same features as the system of claims 1-4 and 7-9, respectively, and are therefore rejected for at least the same reasons.
Claim 29 recites a product which implements the same features as the system of claim 1 and is therefore rejected for at least the same reasons.
Wang teaches: A non-transitory computer-readable medium storing one or more instructions for wireless communication, the one or more instructions comprising: one or more instructions that, when executed by one or more processors of a user equipment (UE), cause the one or more processors to: ([0043], lines 1-2 and 12-17)
Claim 30 recites an apparatus which implements the same features as the system of claim 1 and is therefore rejected for at least the same reasons.
Regarding claim 31, the combination of Wang, Cook, and Roberts teaches: The UE of claim 1, wherein the one or more processors are further configured to:
Wang teaches: receive, from the base station, a configuration indicating whether the UE is to determine updates to parameters of the machine learning-based model. ([0116], from line 1 to “configuration” in line 8; [0119], lines 1-3)
Claims 5-6, 11, 19-20, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20210342687 A1, cited in PTO-892 issued 08/06/2025) in view of Cook et al. (US 20160314255 A1, cited in PTO-892 issued 08/06/2025), Roberts et al. (US 20190139646 A1), and Choudhary et al. (US 20190385043 A1, cited in PTO-892 issued 02/04/2026).
Regarding claim 5, the combination of Wang, Cook, and Roberts teaches: The UE of claim 1, wherein the one or more processors, to transmit the first error report, are configured to:
Wang teaches: transmit, to the base station, the first error report, ([0115])
However, Wang, Cook, and Roberts do not explicitly teach: determine one or more updated parameters of the machine learning-based model based at least in part on the error event associated with the machine learning-based model; and
transmit, to the base station, the first error report, wherein the first error report indicates the one or more updated parameters of the machine learning-based model.
But Choudhary teaches: determine one or more updated parameters of the machine learning-based model based at least in part on the error event associated with the machine learning-based model; and ([0071])
transmit, to the [server]
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have updated the first neural network in Wang’s user equipment 110 via Choudhary’s federated learning. A motivation for the combination is to train a model utilizing private digital information. (Choudhary, [0002])
Regarding claim 6, the combination of Wang, Cook, Roberts, and Choudhary teaches: The UE of claim 5,
Wang teaches: wherein the one or more processors, to transmit the first error report, are configured to: determine the [error metrics]
However, Wang, Roberts, and Choudhary do not explicitly teach: quantity of error predictions
But Cook teaches: quantity of error predictions ([0273])
A motivation for the combination is the same as the motivation disclosed in claim 1.
Regarding claim 11, the combination of Wang, Cook, and Roberts teaches: The UE of claim 1, wherein the one or more processors, to transmit the first error report, are configured to:
Wang teaches transmitting an error report to the base station. However, Wang, Cook, and Roberts do not explicitly teach: determine, according to a periodic schedule configured by the base station, one or more updated parameters of the machine learning-based model based at least in part on the error event associated with the machine learning-based model; and transmit, according to the periodic schedule and to the base station, the first error report, wherein the error first report indicates the one or more updated parameters of the machine learning-based model.
But Choudhary teaches: determine, according to a periodic schedule configured by the [server]
transmit, according to the periodic schedule and to the [server]
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Choudhary’s asynchronous training system into the combination of Wang, Cook, and Roberts. A motivation for the combination is to ensure that slower client devices are not excluded from providing modified parameter indicators, and thus skew training of the global machine learning model. (Choudhary, [0089])
Claims 19-20 and 25 each recites a method which implements the same features as the system of claims 5-6 and 11, respectively, and are therefore rejected for at least the same reasons.
Claims 10 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20210342687 A1, cited in PTO-892 issued 08/06/2025) in view of Cook et al. (US 20160314255 A1, cited in PTO-892 issued 08/06/2025), Roberts et al. (US 20190139646 A1), and Schultalbers et al. (US 20220187772 A1).
Regarding claim 10, the combination of Wang, Cook, and Roberts teaches: The UE of claim 1,
However, Wang and Roberts do not explicitly teach: wherein the first error report includes a first indicator indicating that predictions included in the first error report are error predictions, and wherein the second error report includes a second indicator indicating that predictions included in the second error report are correct predictions.
But Cook teaches: the first error report and the second error report ([0272]-[0273] where the first error report is the error expressed as a fraction, and the second error report is the accuracy expressed as a fraction.)
A motivation for the combination is the same as the motivation given for claim 1.
However, Wang, Cook, and Roberts do not explicitly teach: wherein the first error report includes a first indicator indicating that predictions included in the first error report are error predictions, and wherein the second error report includes a second indicator indicating that predictions included in the second error report are correct predictions.
But Schultalbers teaches: wherein the first error report includes a first indicator indicating that predictions included in the first error report are error predictions, and ([0039] in col. 2, lines 7-end disclose a discriminator network creates an assessment that stores whether a predicted future value is a real future value. These are stored as correct R values.)
wherein the second error report includes a second indicator indicating that predictions included in the second error report are correct predictions.([0039] in col. 2, lines 7-end disclose a discriminator network creates an assessment that stores whether a predicted future value is an artificially generated future value. These are stored as incorrect F values.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have stored assessments of each training sample during machine learning in the combination of Wang, Cook, and Roberts. A motivation for the combination is to evaluate the performance of the machine learning model.
Claim 24 recites a method which implements the same features as the system of claim 10 and is therefore rejected for at least the same reasons.
Claims 12 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20210342687 A1, cited in PTO-892 issued 08/06/2025) in view of Cook et al. (US 20160314255 A1, cited in PTO-892 issued 08/06/2025), Roberts et al. (US 20190139646 A1), Choudhary et al. (US 20190385043 A1, cited in PTO-892 issued 02/04/2026), and Wang et al. (US 20170228645 A1, cited in PTO-892 issued 02/04/2026), hereinafter “Wang II”.
Regarding claim 12, the combination of Wang, Cook, and Roberts teaches: The UE of claim 1, wherein the one or more processors, to transmit the first error report, are configured to:
Wang teaches: determine
…
transmit, to the base station, the first error report,
However, Wang, Cook, and Roberts do not explicitly teach: determine that a quantity of predictions satisfies a reporting threshold;
determine one or more updated parameters of the machine learning-based model based at least in part on the quantity of predictions; and
wherein the first error report indicates the one or more updated parameters of the machine learning-based model.
But Choudhary teaches: determine one or more updated parameters of the machine learning-based model based at least in part on the quantity of predictions; and ([0071])
wherein the first error report indicates the one or more updated parameters of the machine learning-based model. ([0073], lines 1-4 and 14-18)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have updated the first neural network in Wang’s user equipment 110 via Choudhary’s federated learning. A motivation for the combination is to train a model utilizing private digital information. (Choudhary, [0002])
However, Wang, Cook, Roberts, and Choudhary do not explicitly teach: determine that a quantity of predictions satisfies a reporting threshold;
But Wang II teaches: determine that a quantity of predictions satisfies a reporting threshold; ([0045]-[0046] where the batch size is a reporting threshold.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have trained Wang’s DNNs on Wang II’s batches. The batch size is a reporting threshold because each training sample in the batch must be processed before the locally modified parameter may be sent to the server. A motivation for the combination is that training on a batch of samples (e.g., 20 samples) reduces the number of parameter updates when compared to training without batching, which equals a batch size of 1.
Claim 26 recites a method which implements the same features as the system of claim 12 and is therefore rejected for at least the same reasons.
Claim 32 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20210342687 A1, cited in PTO-892 issued 08/06/2025) in view of Cook et al. (US 20160314255 A1, cited in PTO-892 issued 08/06/2025), Roberts et al. (US 20190139646 A1), and Sawada et al. (US 20180025271 A1).
Regarding claim 32, the combination of Wang, Cook, and Roberts teaches: The UE of claim 1,
Cook at [0273] teaches the one or more error predictions and the first scalar quantity. However, Wang, Cook, and Roberts does not explicitly teach: wherein a stochastic gradient for updating the machine learning-based model is based at least in part on the one or more error predictions and the first scalar quantity.
But Sawada teaches: wherein a stochastic gradient for updating the machine learning-based model is based at least in part on the one or more error predictions and the first scalar quantity. ([0073]-[0075] and [0163] discloses training a neural network using gradient descent. A gradient depends on an error between the answer vector (ground truth) and output vector (model prediction). When the output vector does not match the answer vector, the output is an error prediction. The more incorrect predictions the model makes, the longer training will take and thus the stochastic gradient may reflect the error rate (first scalar quantity as claimed).)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Sawada’s gradient descent method for training Wang’s DNNs in the combination of Wang, Cook, and Roberts. A motivation for the combination is to gradient descent enables the neural network to make more accurate classifications. (Sawada, [0076])
Response to Arguments
Below is the Examiner’s response to the Applicant’s arguments filed 04/01/2026.
Applicant’s Arguments Under 35 U.S.C. 101 (Remarks pages 17-18): Applicant submits that the features of at least claim 1 cannot practically be performed in the human mind.
Examiner’s Response: Applicant’s arguments with respect to claims 1, 15, 29, and 30 have been fully considered and are persuasive. The rejection of claims 1, 15, 29, and 30 under 35 U.S.C. 101 has been withdrawn because these claims does not recite any judicial exceptions.
Claims 3-9, 11-12, and 32 recite abstract ideas and they inherit the limitations of parent claim 1 as additional elements. Claims 17-23 and 25-26 recite abstract ideas and they inherit the limitations of parent claim 15 as additional elements. These claims are rejected under 35 U.S.C. 101.
Applicant’s Arguments Under 35 U.S.C. 103 (Remarks pages 19-20): Applicant submits that Wang, Choudhary, and Cook do not disclose or suggest each and every feature in amended claim 1.
Examiner’s Response: Applicant's arguments have been fully considered but they are not persuasive. In the combination of Wang and Cook, Cook teaches: a first error report based at least in part on an error event associated with the machine learning-based model, wherein the first error report indicates a quantity of error predictions made by the machine learning-based model and a first scalar quantity determined by the [system] error ratio is a number of incorrect predictions to a total number of predictions, which expresses the same information as the first scalar quantity as claimed.)
a second error report, the second error report indicating one or more correct predictions made by the machine learning-based model and a second scalar quantity,
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have calculated error and accuracy ratios in Wang’s user equipment, and to have included these in transmissions to Wang’s base station. A motivation for the combination is to evaluate the classification performance of a machine learning model. (Cook, [0267] on page 21, left column, lines 2-8 below Table 7)
Applicant’s arguments with respect to the limitations “wherein the first scalar quantity comprises a first weight that is applied to the machine learning-based model” and “wherein the second scalar quantity comprises a second weight that is applied to the machine learning-based model” have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/A.H.J./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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