DETAILED ACTION
Response to Amendment
The amendment filed August 12, 2026 has been entered.
Claim 1, 6, 12, 17, and 20 are amended.
Claims 1-20 are pending this application.
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 1-4, 10, 12-15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Meissner et al (US 2020/0292660 A1) in view of Rock et al (ArXiv, 2019).
Regarding Claim 1, Meissner teaches a method comprising [0030 for radar interference]:
transmitting, by a vehicle radar system, first radar signals into an environment of a vehicle [0030-0031 on-coming (transmitted) vehicle interference];
receiving, by the vehicle radar system, first electromagnetic energy propagating in the environment [0030-0031 for receiving radar echoes for vehicles];
filtering, using a first model, the first electromagnetic energy to remove a first portion of the first electromagnetic energy that corresponds to one or more interferer signals, [0034 for filtering for suppressing undesired (interference) signals and figure 16, element 44 for using a CNN (trained model) for filtering signal, 0078 for filtering data to reduce interference signal];
generating a first representation of the environment using a second portion of the first electromagnetic energy, wherein the second portion of the first electromagnetic energy comprises one or more reflections of the first radar signals transmitted by the vehicle radar system [0044 for making range doppler maps (representation) with figure 15, block 43 for target detection and 0070 for using filtered range doppler maps];
and causing the vehicle to perform a first control strategy based on the first representation of the environment [0044 for extracting target information and performing interference suppression].
Meissner fails to explicitly teach wherein the first model is trained based on a first data set that comprising a first plurality of interferer signals and labels that convey transmission patterns of the first plurality of interferer signals.
Rock has a method for ability of CNNs to find structured information in data while preserving local information enables superior denoising performance (page 1 abstract) and teaches wherein the first model is trained based on a first data set that comprising a first plurality of interferer signals and labels that convey transmission patterns of the first plurality of interferer signals [page 3, right column, last paragraph for interferer parameters are uniformly sampled within the ranges with page 4, left column, table 1].
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 interference mitigation techniques, as disclosed by Meissner, further including the training calculations as taught by Rock for the purpose to generate test datasets for training and validation (Rock, page 4, left column, first tow paragraphs).
Regarding Claim 12, Meissner teaches a system comprising [0030 for radar interference]:
a vehicle radar system coupled to a vehicle [0030 for using four vehicles];
and a computing system configured to [0029 for semiconductor chip]:
cause the vehicle radar system to transmit first radar signals into an environment of a vehicle [0030-0031 on-coming (transmitted) vehicle interference];
receive, from the vehicle radar system, first electromagnetic energy propagating in the environment [0030-0031 for receiving radar echoes for vehicles];
and filter, using a first model, the first electromagnetic energy to remove a first portion of the first electromagnetic energy that corresponds to one or more interferer signals [0034 for filtering for suppressing undesired (interference) signals and figure 16, element 44 for using a CNN (trained model) for filtering signal, 0078 for filtering data to reduce interference signal];
generate a first representation of the environment using a second portion of the first electromagnetic energy, wherein the second portion of the first electromagnetic energy comprises one or more reflections of the first radar signals transmitted by the vehicle radar system [0044 for making range doppler maps (representation) with figure 15, block 43 for target detection and 0070 for using filtered range doppler maps];
and cause the vehicle to perform a first control strategy based on the first representation of the environment [0044 for extracting target information and performing interference suppression].
Meissner fails to explicitly teach wherein the first model is trained based on a first data set that comprising a first plurality of interferer signals and labels that convey transmission patterns of the first plurality of interferer signals.
Rock has a method for ability of CNNs to find structured information in data while preserving local information enables superior denoising performance (page 1 abstract) and teaches wherein the first model is trained based on a first data set that comprising a first plurality of interferer signals and labels that convey transmission patterns of the first plurality of interferer signals [page 3, right column, last paragraph for interferer parameters are uniformly sampled within the ranges with page 4, left column, table 1].
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 interference mitigation techniques, as disclosed by Meissner, further including the training calculations as taught by Rock for the purpose to generate test datasets for training and validation (Rock, page 4, left column, first tow paragraphs).
Regarding Claim 20, Meissner teaches a vehicle system comprising [0030 for radar interference]:
a vehicle radar system [0030];
and a computing system configured to perform operations comprising [0029]:
causing the vehicle radar system to transmit first radar signals into an environment of a vehicle [0030-0031 on-coming (transmitted) vehicle interference];
receiving, from the vehicle radar system, first electromagnetic energy propagating in the environment [0030-0031 for receiving radar echoes for vehicles];
and filtering, using a first model, the first electromagnetic energy to remove a first portion of the first electromagnetic energy that corresponds to one or more interferer signals [0034 for filtering for suppressing undesired (interference) signals and figure 16, element 44 for using a CNN (trained model) for filtering signal, 0078 for filtering data to reduce interference signal];
generating a first representation of the environment using a second portion of the first electromagnetic energy, wherein the second portion of the first electromagnetic energy comprises one or more reflections of the first radar signals transmitted by the vehicle radar system [0044 for making range doppler maps (representation) with figure 15, block 43 for target detection and 0070 for using filtered range doppler maps];
and causing the vehicle to perform a first control strategy based on the first representation of the environment [0044 for extracting target information and performing interference suppression].
Meissner fails to explicitly teach wherein the first model is trained based on a first data set that comprising a first plurality of interferer signals and labels that convey transmission patterns of the first plurality of interferer signals.
Rock has a method for ability of CNNs to find structured information in data while preserving local information enables superior denoising performance (page 1 abstract) and teaches wherein the first model is trained based on a first data set that comprising a first plurality of interferer signals and labels that convey transmission patterns of the first plurality of interferer signals [page 3, right column, last paragraph for interferer parameters are uniformly sampled within the ranges with page 4, left column, table 1].
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 interference mitigation techniques, as disclosed by Meissner, further including the training calculations as taught by Rock for the purpose to generate test datasets for training and validation (Rock, page 4, left column, first tow paragraphs).
Regarding Claim 2 and 13, Meissner teaches transmitting second radar signals into the environment [figure 16 elements Rx1-Rxi];
receiving second electromagnetic energy propagating in the environment [0054-0055 for receiving interference propagation];
and filtering, using a second model, the second electromagnetic energy to remove a first portion of the second electromagnetic energy that corresponds to respective interferer signals [0054 for reducing and eliminating interference],
wherein the second model is trained based on a second data set that conveys transmission patterns for a second plurality of interferer signals [figure 10A-D for four different neural network structures and 0054-0055].
Regarding Claim 3 and 14, Meissner teaches generating a second representation of the environment further based on a second portion of the second electromagnetic energy, wherein the second portion of the second electromagnetic energy comprises one or more reflections of the second radar signals transmitted by the vehicle radar system [figure 16 element 43 and 0074-0075 for a range doppler map];
and causing the vehicle to perform a second control strategy based on the second representation of the environment [0044 for extracting target information and performing interference suppression].
Regarding Claim 4 and 15, Meissner teaches obtaining the first model at a first period [0046-0047 for using discrete time values];
and subsequently obtaining the second model at a second period, wherein the second period is after the first period [0046 for using fast time models, and 0049 for using slow time models].
Regarding Claim 10, Meissner teaches causing the vehicle radar system to switch from using the first model to a second model [figure 10A-D for four different neural network structures and 0054-0055].
Meissner fails to explicitly teach wherein the second model is trained based on a second data set that conveys transmission patterns for a second plurality of interferer signals.
Rock has a method for ability of CNNs to find structured information in data while preserving local information enables superior denoising performance (page 1 abstract) and teaches wherein the second model is trained based on a second data set that conveys transmission patterns for a second plurality of interferer signals [page 3, right column, last paragraph for interferer parameters are uniformly sampled within the ranges with page 4, left column, table 1].
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 interference mitigation techniques, as disclosed by Meissner, further including the training calculations as taught by Rock for the purpose to generate test datasets for training and validation (Rock, page 4, left column, first two paragraphs).
Claims 5-9, 11, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Meissner et al (US 2020/0292660 A1) in view of Rock et al (ArXiv, 2019) as applied to claims 1 and 12 above, and further in view of McCloskey et al (US 2016/0061935).
Regarding Claim 5 and 16, Meissner teaches and causing the vehicle radar system to switch from using the first model to process electromagnetic energy to using the second model to process electromagnetic energy [figure 10A-D for four different neural network structures and 0054-0055].
Meissner fails to explicitly teach the method further comprises: receiving the second model from a remote computing system via an over-the-air update.
McCloskey has a method is provided that includes a vehicle receiving data from an external computing device (abstract) and teaches the method further comprises: receiving the second model from a remote computing system via an over-the-air update [0075 for using servers with wireless connections and 0128 for having remotely located devices].
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 interference mitigation techniques, as disclosed by Meissner, further including the remote calculations as taught by McCloskey for the purpose to send data/requests to the vehicles and/or to receive data from the vehicles (McCloskey, 0074).
Regarding Claim 6 and 17, Meissner fails to explicitly teach the method further comprises: training the first model based on the first data set; applying one or more modifications to the first data set to generate a second set that conveys transmission patterns for a second plurality of interferer signals; and training the second model based on the second data set.
Rock has a method for ability of CNNs to find structured information in data while preserving local information enables superior denoising performance (page 1 abstract) and teaches the method further comprises: training the first model based on the first data set [page4, right column Section A Data sets];
applying one or more modifications to the first data set to generate a second set that conveys transmission patterns for a second plurality of interferer signals [page4, right column Section A Data sets (3 data sets)];
and training the second model based on the second data set [page 3, right column, last paragraph for interferer parameters are uniformly sampled within the ranges with page 4, left column, table 1].
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 interference mitigation techniques, as disclosed by Meissner, further including the training calculations as taught by Rock for the purpose to generate test datasets for training and validation (Rock, page 4, left column, first two paragraphs).
Regarding Claim 7, Meissner fails to explicitly teach distributing the first model to a plurality of vehicles comprising the vehicle; and subsequently distributing the second model to the plurality of vehicles comprising the vehicle.
McCloskey has a method is provided that includes a vehicle receiving data from an external computing device (abstract) and teaches distributing the first model to a plurality of vehicles comprising the vehicle [0074 for having data request to multiple vehicles];
and subsequently distributing the second model to the plurality of vehicles comprising the vehicle [0075 for using servers with wireless connections and 0128 for having remotely located devices].
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 interference mitigation techniques, as disclosed by Meissner, further including the remote calculations as taught by McCloskey for the purpose to send data/requests to the vehicles and/or to receive data from the vehicles (McCloskey, 0074).
Regarding Claim 8 and 18, Meissner fails to explicitly teach the first model is trained based on the first data set by a training system that is positioned remotely from the vehicle, and wherein the training system is configured to provide the first model to the vehicle via an over-the-air update.
McCloskey has a method is provided that includes a vehicle receiving data from an external computing device (abstract) and teaches the first model is trained based on the first data set by a training system that is positioned remotely from the vehicle, and wherein the training system is configured to provide the first model to the vehicle via an over-the-air update [0075 for using servers with wireless connections and 0128 for having remotely located devices].
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 interference mitigation techniques, as disclosed by Meissner, further including the remote calculations as taught by McCloskey for the purpose to send data/requests to the vehicles and/or to receive data from the vehicles (McCloskey, 0074).
Regarding Claim 9 and 19, Meissner teaches obtaining a second model to replace the first model, wherein the second model is trained based on a second data set by the training system [figure 10A-C for having multiple training models with neural networks and FFTs].
Meissner fails to explicitly teach and wherein the training system is configured to provide the second model to the vehicle via an additional over-the-air update.
McCloskey has a method is provided that includes a vehicle receiving data from an external computing device (abstract) and teaches and wherein the training system is configured to provide the second model to the vehicle via an additional over-the-air update [0075 for using servers with wireless connections and 0128 for having remotely located devices].
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 interference mitigation techniques, as disclosed by Meissner, further including the remote calculations as taught by McCloskey for the purpose to send data/requests to the vehicles and/or to receive data from the vehicles (McCloskey, 0074).
Regarding Claim 11 Meissner fails to explicitly teach causing the vehicle radar system to switch from using the first model to the second model comprises: causing the vehicle radar system to switch to the second model based on a location of the vehicle.
McCloskey has a method is provided that includes a vehicle receiving data from an external computing device (abstract) and teaches causing the vehicle radar system to switch from using the first model to the second model comprises: causing the vehicle radar system to switch to the second model based on a location of the vehicle [0085-0086 for using location sensors to determine likelihood of interference].
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 interference mitigation techniques, as disclosed by Meissner, further including the remote calculations as taught by McCloskey for the purpose to facilitate determining the likelihood of interference (McCloskey, 0085).
Response to Arguments
Applicant’s arguments with respect to claims 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.
In applicant’s arguments page 8, first paragraph of applicant’s arguments, the applicant states that Mun fails to explicitly teach the first model is training based on a first data set that conveys transmission patterns. The examiner thanks the applicant for the amendments. New reference Rock teaches SIR and SNR are used to scale the interference and noise powers relative to the object signal power [Rock, page 3, right column last paragraph and page 4 Table 1].
In applicant’s arguments page 8, second paragraph of applicant’s arguments, the applicant states that Messinger fails to explicitly teach labels. The examiner respectfully disagrees: Messinger teaches the respective signal segment or some of the samples contained therein are impacted by interference. The result of this detection is a yes/no decision [Messinger, 0053] the yes no decision trains the network on how to classify (label) that signal.
In applicant’s arguments page 8, last paragraph of applicant’s arguments, the applicant states that Messinger fails to explicitly teach claims 2, 10 and 13. The examiner respectfully disagrees: Messinger teaches digital radar data that are overlaid with noise and interfering signals [Messinger, 0077] or rerunning the same simulation with different interferer signal to build a second updated training set.
In applicant’s arguments page 9, second paragraph of applicant’s arguments, the applicant states that Messinger fails to explicitly teach claims 4 and 15. The examiner respectfully disagrees: Messinger splits its radar processing in to fast (neural network tuned) and slow (separate network), these are trained and deployed as distinct models rather than one single network [Messinger 0046-0047].
In applicant’s arguments page 9, second paragraph of applicant’s arguments, the applicant states that McClosky fails to explicitly teach claims 11. The examiner respectfully disagrees: McClosky teaches determining that the at least one other vehicle is within a threshold distance to the vehicle based on the location [McClosky, 0086] and adjusting the sensor responsiveness based on likelihood of interference, functionally the same location based sensor adjustments.
In applicant’s arguments page 9, fourth paragraph of applicant’s arguments, the applicant argues the combination of Messinger and Mun. The examiner thanks the applicant for the amendments, Mun is not used in this office action.
The examiner acknowledges that this is a broader interpretation than Applicant’s. However, examiners are not only allowed to apply broad interpretations, but are required to do so, as it reduces the possibility that the claims, once issued, will be interpreted more broadly than is justified. MPEP §2111. Patentability is determined by the “broadest reasonable interpretation
consistent with the specification” (MPEP §2111), not the narrowest reasonable interpretation. And Applicant does not have an explicit lexicographical statement in line with MPEP §2111.01
subsection IV requiring a specific interpretation of the relevant phrases which forces the examiner to interpret them only one way.
The express, implicit, and inherent disclosures of a prior art reference may be relied upon in the rejection of claims under 35 U.S.C. 102 or 103. "The inherent teaching of a prior art reference, a question of fact, arises both in the context of anticipation and obviousness." In re Napier, 55 F.3d 610, 613, 34 USPQ2d 1782, 1784 (Fed. Cir. 1995).
For applicant’s benefit, portions of the cited reference(s) have been cited to aid in the review of the rejection(s). While every attempt has been made to be thorough and consistent within the rejection it is noted that the PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, including disclosures that teach away from the claims. See MPEP 2141.02 VI.
“The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments. Merck & Co. v.Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005) See MPEP 2123.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMARINA MAKHDOOM whose telephone number is (703)756-1044. The examiner can normally be reached Monday – Thursdays from 8:30 to 5:30 pm eastern time.
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/SAMARINA MAKHDOOM/
Examiner, Art Unit 3648
Prosecution Insights