DETAILED ACTION
This action is filed in response to the application filed on 8/23/2025.
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
Acknowledgement is made of Applicant’s Information Disclosure Statements (IDS) form PTO-1149 filed on 8/23/2025. This IDS has been considered.
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 and 4 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing mental steps without significantly more. Claim 1 recites the following abstract concepts in BOLD of:
A clock skew tracking method based on weighted observation fusion and timestamp-free interaction, characterized in that the method comprises: performing listening synchronization by an implicit node S within an overlapping communication range between a reference node R and multiple active nodes A1, A2, ..., AL, and after multiple pairs of timestamp- free communication messages are successfully overheard, using multiple extended Kalman filtering algorithms to perform weighted fusion of multiple observed values on multiple obtained tracking results based on a scalar weighted linear minimum variance information fusion criterion, thus realizing timestamp-free relative skew fusion tracking of the implicit node Si:
wherein according to an internal relationship of clock skew among the active nodes, the reference node and the implicit node, a relative clock skew ρ(SR) between the reference node and the implicit node is estimated, the weighted observation fusion skew tracking of the implicit node is realized, and a specific internal relationship is:
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where ρ(AR)[n] represents a relative clock skew the active nodes and the reference node, and ρ(AS)[n] represents a relative clock skew between the active nodes and the implicit node;
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wherein calculation formulas to the multiple extended Kalman filtering algorithms are: prediction:
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predicted minimum mean square error matrix:
Kalman gain:
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283
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correction:
minimum mean square error matrix:
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where k represents a kth parallel extended Kalman filter executed,
is the prediction of xk[n] by considering a state matrix A and a previous round state
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and Mk [n[n-1] is the predicted minimum mean square error matrix without observation correction; after the Kalman gain Kk[n] is calculated, a corrected estimated value and Mk[n|n] are obtained, wherein Mk[n|n] is the minimum mean square error matrix of the corresponding relative frequency offset obtained after performing the extended Kalman filter; I represents a unit matrix, H represents an observation matrix, C and are a state covariance matrix and an observation noise variance, Q’k[n] represents an observed value at time n, and represents an observed value without noise influence;
wherein the step of performing weighted fusion of multiple values on multiple obtained tracking results based on a scalar weighted linear minimum variance information fusion criterion specifically comprises following steps:
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S1: calculating an optimal information fusion matrix in the scalar weighted linear minimum variance information fusion criterion, and an expression is:
where a represents an estimator component weight; and a fusion weight condition of a1 + a2 + … + aL = 1 is obtained based on unbiased estimation;
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S2: obtaining an optimal mean square error matrix according to a fusion estimation error, and an expression is:
S3: calculating a fusion performance evaluation parameter, and an expression is:
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Where tr represents a trace of the matrix, and Mkl[n|n] represents a cross covariance; and a problem of optimal fusion is transformed to selecting a1, a2, …aL to minimize Г.
Under Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category as Claim 1 recites a method.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps. The steps of performing listening and overhearing timestamp free communication messages can be interpreted as mental processes that can be performed in the human mind.
Furthermore the steps of using Kalman filtering algorithms, reciting the internal relationship of clock skew among active nodes, the calculation formulas of the multiple Kalman filtering algorithms, and all three steps pertaining to the performance of weighted fusion of multiple observed values all claim the performance of mathematics and are therefore abstract ideas.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner further notes there are no improvements claimed as the abstract ideas cannot themselves contain the improvement. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites no additional elements beyond the above cited abstract ideas.
Claim 4 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea, as it further limits the mathematics performed in claim 1.
Allowable Subject Matter
There are no prior art rejections for claims 1 and 4. The following is a statement of reasons for indication of allowable subject matter:
Regarding Claim 1, Examiner notes the closest prior art to be Wang (CN110460553 A), Wang, Heng et al. “Estimation of Clock Skew for Time Synchronization Based on Two-Way Message Exchange Mechanism in Industrial Wireless Sensor Networks.” IEEE transactions on industrial informatics 14.11 (2018): 4755–4765. Web., and Sharma (WO2021118675 A1).
Examiner notes Wang teaches a clock skew tracking method based on weighted observation fusion and timestamp-free interaction (e.g. see [0002] “This invention belongs to the field of wireless sensor network technology and relates to a method for estimating the implicit node clock frequency offset without timestamp interaction”), characterized in that the method comprises: performing listening synchronization by an implicit node S within an overlapping communication range between a reference node R and multiple active nodes A1, A2, ..., AL, and after multiple pairs of timestamp- free communication messages are successfully overheard (e.g. see [0011] “A time-stamp-free interaction method for estimating the clock frequency offset of hidden nodes is proposed. This method integrates a time-stamp-free interaction mechanism with a listening mechanism and unifies the clock relationships between the master node M, slave node S, and hidden node F to the same reference scale, forming a maximum likelihood estimation or a low-complexity estimation method to estimate the clock frequency offset of hidden node F relative to slave node S and master node M”),
wherein according to an internal relationship of clock skew among the active nodes, the reference node and the implicit node, a relative clock skew ρ(SR) between the reference node and the implicit node is estimated, the weighted observation fusion skew tracking of the implicit node is realized, and a specific internal relationship is:
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187
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where ρ(AR)[n] represents a relative clock skew the active nodes and the reference node, and ρ(AS)[n] represents a relative clock skew between the active nodes and the implicit node
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(e.g. see [equation (4)]);
Examiner notes none of the cited prior art teaches or renders obvious the method as claimed comprising, “ wherein calculation formulas to the multiple extended Kalman filtering algorithms are:
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prediction:
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predicted minimum mean square error matrix:
Kalman gain:
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26
283
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22
359
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correction:
minimum mean square error matrix:
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media_image8.png
23
61
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where k represents a kth parallel extended Kalman filter executed,
is the prediction of xk[n] by considering a state matrix A and a previous round state
PNG
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20
90
media_image9.png
Greyscale
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91
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and Mk [n[n-1] is the predicted minimum mean square error matrix without observation correction; after the Kalman gain Kk[n] is calculated, a corrected estimated value and Mk[n|n] are obtained, wherein Mk[n|n] is the minimum mean square error matrix of the corresponding relative frequency offset obtained after performing the extended Kalman filter; I represents a unit matrix, H represents an observation matrix, C and are a state covariance matrix and an observation noise variance, Q’k[n] represents an observed value at time n, and represents an observed value without noise influence;
wherein the step of performing weighted fusion of multiple values on multiple obtained tracking results based on a scalar weighted linear minimum variance information fusion criterion specifically comprises following steps:
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media_image13.png
36
376
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Greyscale
S1: calculating an optimal information fusion matrix in the scalar weighted linear minimum variance information fusion criterion, and an expression is:
where a represents an estimator component weight; and a fusion weight condition of a1 + a2 + … + aL = 1 is obtained based on unbiased estimation;
PNG
media_image14.png
70
192
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Greyscale
S2: obtaining an optimal mean square error matrix according to a fusion estimation error, and an expression is:
S3: calculating a fusion performance evaluation parameter, and an expression is:
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438
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Where tr represents a trace of the matrix, and Mkl[n|n] represents a cross covariance; and a problem of optimal fusion is transformed to selecting a1, a2, …aL to minimize Г.
Claim 4 would be allowable based on its dependence on Claim 1.
Conclusion
There are no prior art rejections, but Examiner is unable to comment on the allowability of the claims until the 35 U.S.C. 101 Rejections are addressed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NYLA GAVIA whose telephone number is (703)756-1592. The examiner can normally be reached M-F 8:30-5:30pm.
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/NYLA GAVIA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857