DETAILED ACTION
This action is in response to the initial filing filed on December 27, 2024.
Claim 1-10 are cancelled.
Claims 11-21 are new.
Claim 11-21 have been examined this application.
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed on 12/27/2024 has been acknowledged.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 .
Claim Objections
Claims 16, 18, and 19 are objected to because of the following informalities: claims 16, and 18-19 depend on a cancelled claim. For this action claim 16 and 18 will depend on claim 11 and claim 19 will depend on claim 17. Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 11-13 and 16-21 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al (CN 112710306 A) in view of Hoang et al (IAENG, 2012).
Regarding Claim 11, Zhao teaches a method for measuring a geographical position of a device based on a network of satellites in a predictive system with a filter of variable gain [0002 for all-weather high-precision positioning information and 0037 for having an adjustable filter gain],
said gain being represented by a vector of variable gain coefficients, said method, implemented by the device [0004 for using a combined position fusion filter],
comprising at each iteration following a time t the steps of [0025 and 0035]:
- receiving signals transmitted by a plurality of satellites in the satellite network [0003 for positioning accuracy in existing BDS and INS combined navigation and positioning methods (means to receive positioning signals)],
- determining the position and/or the speed of the device at a time from the signals received, referred to as "observation at time" [0036 for identifying abnormal observations for BDS signal also translation page 21, last paragraph equation (15)],
- calculating the best prediction of the state of the system at time on the basis of a predetermined estimation of the state of the system at time and a model representing the system between time and time [0052 for step 2: time update with page 22, 0053 equation (18)],
- calculating the prediction of the observation at time as the product of a predetermined observation matrix and the best prediction of the state of the system at time calculated [0058-0063 for step 4, with page 22, 0058-0062 and equations 21-22)],
- calculating the innovation of the filter at time as the difference between the observation at time and the prediction of the observation at time [0063 for state predictions and observations with page 17, equations (1-2)],
- calculating the estimation of the state of the system at time as being the sum of the best prediction of the state of the system at time calculated and the product of the gain at time by the innovation of the filter at time calculated [0058-0063 and page 20, 0011-0015 equation (4)],
- determining the position of the device from the estimation of the state of the system at time calculated [0058-0063 and page 23, 006-0070 equation (26)].
Zhao fails to explicitly teach determining the vector of gain coefficients at time by minimizing the square of the norm of the innovation of the filter at time calculated in the previous step, calculating the gain at time by making a correction by stochastic approximation using time averaging of the gain coefficients of the vector of gain coefficients determined.
Hoang has a method for properly initializing the filter gain, and for efficient optimization of the filter performance [page 1, left column, abstract] and teaches determining the vector of gain coefficients at time by minimizing the square of the norm of the innovation of the filter at time calculated in the previous step [page 3, left column, first two paragraphs and equation (8) with page 6, right column, last paragraph and figure 3 for time averaged rms (root mean square) of the position errors produced by four filters],
calculating the gain at time by making a correction by stochastic approximation using time averaging of the gain coefficients of the vector of gain coefficients determined [page 5, left column, second paragraph for applying the stochastic approximation with equation (17)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the GNSS position techniques, as disclosed by Zhao, further including the filter calculations as taught by Hoang for the purpose to design a low-cost procedure for computing the optimal gain. (Hoang, right column, last two paragraphs).
Regarding Claim 17, Zhang teaches a measurement module for measuring the geographical position of a device based on a network of satellites in a predictive system with a filter of variable gain [0002 for all-weather high-precision positioning information and 0037 for having an adjustable filter gain],
said gain being represented by a vector of variable parameters, said measurement module, embedded in said device, being configured for [0004 for using a combined position fusion filter]:
- receiving signals transmitted by a plurality of satellites in the satellite network [0003 for positioning accuracy in existing BDS and INS combined navigation and positioning methods (means to receive positioning signals)],
- determining the position and/or the speed of the device at a time from the signals received, referred to as "observation at time" [0036 for identifying abnormal observations for BDS signal also translation page 21, last paragraph equation (15)],
- calculating the best prediction of the state of the system at time on the basis of a predetermined estimation of the state of the system at time and a model representing the system between time and time [0052 for step 2: time update with page 22, 0053 equation (18)],
- calculating the prediction of the observation at time as the product of a predetermined observation matrix and the best prediction of the state of the system at time calculated [0058-0063 for step 4, with page 22, 0058-0062 and equations 21-22)],
- calculating the filter innovation at time as the difference between the observation at time and the prediction of the observation at time [0063 for state predictions and observations with page 17, equations (1-2)],
- calculating the estimation of the state of the system at time as the sum of the best prediction of the state of the system at time calculated and the product of the gain at time by the filter innovation at time calculated [0058-0063 and page 20, 0011-0015 equation (4)],
- determining the position of the device from the estimation of the state of the system at time calculated [0058-0063 and page 23, 006-0070 equation (26)].
Zhao fails to explicitly teach determining the vector of gain coefficients at time by minimizing the square of the norm of the filter innovation at time, calculating the gain at time by correcting by stochastic approximation using time averaging of the gain coefficients of the vector of gain coefficients determined.
Hoang has a method for properly initializing the filter gain, and for efficient optimization of the filter performance [page 1, left column, abstract] and teaches determining the vector of gain coefficients at time by minimizing the square of the norm of the innovation of the filter at time calculated in the previous step [page 3, left column, first two paragraphs and equation (8) with page 6, right column, last paragraph and figure 3 for time averaged rms (root mean square) of the position errors produced by four filters],
calculating the gain at time by making a correction by stochastic approximation using time averaging of the gain coefficients of the vector of gain coefficients determined [page 5, left column, second paragraph for applying the stochastic approximation with equation (17)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the GNSS position techniques, as disclosed by Zhao, further including the filter calculations as taught by Hoang for the purpose to design a low-cost procedure for computing the optimal gain. (Hoang, right column, last two paragraphs).
Regarding Claim 12 and 18, Zhao fails to explicitly teach at each iteration, a step of bounding the variable gain coefficients between a minimum and a maximum.
Hoang has a method for properly initializing the filter gain, and for efficient optimization of the filter performance [page 1, left column, abstract] and teaches at each iteration, a step of bounding the variable gain coefficients between a minimum and a maximum [page 3, left column, first two paragraph and equations 7-8 for the filters are stable for all theta].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the GNSS position techniques, as disclosed by Zhao, further including the filter calculations as taught by Hoang for the purpose to design a low-cost procedure for computing the optimal gain. (Hoang, right column, last two paragraphs).
Regarding Claim 13, Zhao fails to explicitly teach the minimum is equal to a bounding variable s and the maximum is equal to (2 - s).
Hoang has a method for properly initializing the filter gain, and for efficient optimization of the filter performance [page 1, left column, abstract] and teaches the minimum is equal to a bounding variable s and the maximum is equal to (2 - s) [page 3, left column, last two paragraph and equation 11 for ensuring its stability, the space].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the GNSS position techniques, as disclosed by Zhao, further including the filter calculations as taught by Hoang for the purpose to design a low-cost procedure for computing the optimal gain. (Hoang, right column, last two paragraphs).
Regarding Claim 16, Zhang teaches a computer program product comprising an assembly of program code instructions which, when executed by one or more processors, is configured to cause the processor or processors to implement a method [0003-0004 for improved BDS and INS combined navigation and positioning method for trains (satellite, navigation, and vehicle devices)].
Regarding Claim 19, Zhang teaches device, comprising a measurement module [0003 for improved BDS and INS combined navigation and positioning method for trains (satellite, navigation, and vehicle devices)].
Regarding Claim 20, Zhang teaches satellite-based geolocation system, said system comprising a plurality of satellites, each configured to transmit geolocation signals, and at least one measurement module and at least one device comprising the measurement module [0003 for improved BDS and INS combined navigation and positioning method for trains (satellite, navigation, and vehicle devices)].
Regarding Claim 21, Zhang teaches a satellite-based geolocation system, said system comprising a plurality of satellites, each configured to transmit geolocation signals, and at least one device [0003 for improved BDS and INS combined navigation and positioning method for trains (satellite, navigation, and vehicle devices)].
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al (CN 112710306 A) in view of Hoang et al (IAENG, 2012), as applied to Claim 11 above, and further in view of Revach et al (IEEE, 2022).
Regarding Claim 14 Zhao fails to explicitly teach the calculations are carried out by a neural network on the basis of a predetermined sample.
Revach has structural S-model with a dedicated recurrent neural network module [page 1532, left column, abstract] and teaches the calculations are carried out by a neural network on the basis of a predetermined sample [page 1533, left column, second paragraphs for KF as a critical component encapsulating the dependency on noise statistics and domain knowledge, and replace it with a compact RNN of limited complexity and using deep learning].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the GNSS position techniques, as disclosed by Zhao, further including the model calculations as taught by Revach for the purpose to have real time state estimation for continuous-value SS models (Revach, page 1533, left column, second paragraph).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al (CN 112710306 A) in view of Hoang et al (IAENG, 2012), as applied to Claim 11 above, and further in view of Banjac et al (Tehnicki Vjesnik, 2022).
Regarding Claim 15, Zhao fails to explicitly teach the model describing the transition from the state of the system at time t to the state at time is devoid of acceleration.
Banjac has a robustified Kalman filtering technique [page 907, left column, abstract] and teaches the model describing the transition from the state of the system at time t to the state at time is devoid of acceleration [page 911, right column, third paragraph and equation (35) for target state vector containing its position and velocity].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the GNSS position techniques, as disclosed by Zhao, further including the model calculations as taught by Banjac for the purpose to compensate the effects of mismodeling (Banjac, page 911, right column, third paragraph).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zeitzew et al (US 2016/0377736 A1) a tracking module processes the determined correlations to track a carrier of the received composite signal for estimation of a change in phase over a time period between a receiver antenna and one or more satellite transmitters.
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/SAMARINA MAKHDOOM/
Examiner, Art Unit 3648
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