DETAILED ACTION
This is a response to the Amendment to Application # 18/496,260 filed on December 4, 2025 in which claims 1, 10, and 12 were amended.
Continued Examination Under 37 C.F.R. § 1.114
A request for continued examination under 37 C.F.R. § 1.114, including the fee set forth in 37 C.F.R. § 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 C.F.R. § 1.114, and the fee set forth in 37 C.F.R. § 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 C.F.R. § 1.114. Applicant's submission filed on December 4, 2025 has been entered.
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 .
Status of Claims
Claims 1-12 are pending, of which claims 1-3, 7, and 9-12 are rejected under 35 U.S.C. § 102(a)(2) and claims 4-6 and 8 are rejected under 35 U.S.C. § 103.
Claim Rejections - 35 U.S.C. § 102
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 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 the appropriate paragraphs of 35 U.S.C. § 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 7, and 8-12 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by Cao et al., US Publication 2022/0358317 (hereinafter Cao).
Regarding claim 1, Cao discloses a method for a localization of a networked motor vehicle, which is configured to be driven in an at least semi-automated manner (Cao ¶ 12), the comprising the following steps “carrying out a first localization of the motor vehicle using an on-board localization system and generating first localization results” (Cao ¶ 34, Fig. 4) by storing localization data in the form of HD maps that are onboard in HD geospatial database 422. Additionally, Cao discloses “receiving, by the motor vehicle, external localization information from an external source including an external infrastructure system” (Cao ¶¶ 12, 22) by receiving localization information from a second set of sensors (Cao ¶ 22), which may use external localization information from an external source including an external infrastructure system, such as a GPS device (Cao ¶ 12). Further, Cao discloses “carrying out a second localization of the motor vehicle using the received external localization information to generate second localization results” (Cao ¶ 23) where the sensor data is used to determine the location of one or more objects. Moreover, Cao discloses “continuously checking the first localization via the second localization using a comparison of the first localization results with the second localization results, wherein the second localization results are included in the external localization information or are generated from the external localization information, identifying, based on the comparison, one or more faulty localization sources of the on-board localization system” (Cao ¶¶ 19, 43) by comparing the localization information from the sensors to the pre-labeled map in order to determine if the map is accurate or if it contains an error (i.e., is a faulty localization source, Cao ¶ 19). This is performed “continuously” because the map management system tracks changes as they occur, meaning that each time a change occurs the process is performed, making it “continuous” within the plain and ordinary meaning of the term. Finally, Cao discloses “compensating for at least one identified faulty localization source by using the second localization results from the external localization information to replace localization data of the faulty source” (Cao ¶ 30) by using the unavailable localization information from a redundant system and/or from remote data sources, which may be the second set of sensors.
Regarding claim 2, Cao discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Cao discloses “wherein the on-board localization system of the motor vehicle has at least two independent localization sources” (Cao ¶ 12) where multiple independent and on-board localization sources are listed that are in addition to the pre-labeled map. Further, Cao discloses “wherein, depending on a result of the checking, one or more of the localization sources of at least one of the on-board localization system or of the external infrastructure system are identified as unavailable” (Cao ¶ 30) where additional localization sources may be used in the event that one or more of them are unavailable.
Regarding claim 3, Cao discloses the limitations contained in parent claim 2 for the reasons discussed above. In addition, Cao discloses “wherein the localization sources of the on-board localization system that have been identified as unavailable are compensated for by fault-free localization sources of the external infrastructure system” (Cao ¶ 30) where working localization sources (i.e., fault-free) may be used in the event that one or more of them are unavailable.
Regarding claim 7, Cao discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Cao discloses “wherein, for the checking, at least one of an uncertainty or a confidence regarding positions determined as first and second localization results, is calculated” (Cao ¶ 29) where, during the checking, environmental uncertainties identified.
Regarding claim 9, Cao discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Cao discloses “wherein the first localization is carried out using data from at least one of a global navigation satellite system or using an environment model or using environment-related information from a vehicle sensor system” (Cao ¶ 34) where a map is an environmental model.
Regarding claim 10, Cao discloses a device for localization of a networked motor vehicle, wherein the motor vehicle is configured to be driven in an at least semi-automated manner (Cao ¶ 12), wherein the device comprises “a communication unit comprising at least one communication interface, wherein the communication interface is configured to receive localization information from an external infrastructure system.” (Cao ¶ 33). Additionally, Cao discloses “at least one computing unit comprising a processor and a memory.” (Cao ¶ 28). Further, Cao discloses “the processor configured to: carry out a first localization of the motor vehicle using data from an on-board localization system and to generate first localization results” (Cao ¶ 34, Fig. 4) by storing localization data in the form of HD maps that are onboard in HD geospatial database 422. Moreover, Cao discloses “carry out a second localization of the motor vehicle using the received localization information and to generate second localization results” (Cao ¶¶ 12, 22-23) by receiving localization information from a second set of sensors (Cao ¶ 22), which may use external localization information from an external source including an external infrastructure system, such as a GPS device (Cao ¶ 12). The sensor data is used to determine the location of one or more objects (Cao ¶ 23). Likewise, Cao discloses “wherein the computing unit is further configured to continuously check the first localization via the second localization, using a comparison of the first and second localization results, and to output a result of the check, identifying, based on the comparison, one or more faulty localization sources of the on-board localization system” (Cao ¶¶ 19, 43) by comparing the localization information from the sensors to the pre-labeled map to determine if the map is accurate or if it contains an error (i.e., is a faulty localization source, Cao ¶ 19). This is performed “continuously” because the map management system tracks changes as they occur, meaning that each time a change occurs the process is performed, making it “continuous” within the plain and ordinary meaning of the term. Finally, Cao discloses “compensating for at least one identified faulty localization source by using the second localization results from the external localization information to replace localization data of the faulty source” (Cao ¶ 30) by using the unavailable localization information from a redundant system and/or from remote data sources, which may be the second set of sensors.
Regarding claim 11, Cao discloses the limitations contained in parent claim 10 for the reasons discussed above. In addition, Cao discloses “wherein the device is a control device for the motor vehicle driven in an at least semi-automated manner.” (Cao ¶ 12).
Regarding claim 12, Cao discloses a networked motor vehicle which is configured to be driven in an at least semi-automated manner (Cao ¶ 12), comprising “a device for localization of the networked motor vehicle.” (Cao ¶ 11). Additionally, Cao discloses “the device including: a communication unit comprising at least one communication interface, wherein the communication interface is configured to receive localization information from an external infrastructure system.” (Cao ¶ 33). Further, Cao discloses “at least one computing unit comprising a processor and a memory.” (Cao ¶ 28). Moreover, Cao discloses “the processor configured to: carry out a first localization of the motor vehicle using data from an on-board localization system and to generate first localization results” (Cao ¶ 34, Fig. 4) by storing localization data in the form of HD maps that are onboard in HD geospatial database 422. Likewise, Cao discloses “to carry out a second localization of the motor vehicle using the received localization information and to generate second localization results” (Cao ¶¶ 12, 22-23) by receiving localization information from a second set of sensors (Cao ¶ 22), which may use external localization information from an external source including an external infrastructure system, such as a GPS device (Cao ¶ 12). The sensor data is used to determine the location of one or more objects (Cao ¶ 23). Cao also discloses “wherein the computing unit is further configured to continuously check the first localization via the second localization, using a comparison of the first and second localization results, and to output a result of the check, identifying, based on the comparison, one or more faulty localization sources of the on-board localization system” (Cao ¶¶ 19, 43) by comparing the localization information from the sensors to the pre-labeled map to determine if the map is accurate or if it contains an error (i.e., is a faulty localization source, Cao ¶ 19). This is performed “continuously” because the map management system tracks changes as they occur, meaning that each time a change occurs the process is performed, making it “continuous” within the plain and ordinary meaning of the term. Finally, Cao discloses “compensating for at least one identified faulty localization source by using the second localization results from the external localization information to replace localization data of the faulty source” (Cao ¶ 30) by using the unavailable localization information from a redundant system and/or from remote data sources, which may be the second set of sensors.
Claim Rejections - 35 U.S.C. § 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 of this title, 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.
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. Applicants are advised of the obligation under 37 C.F.R. § 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.
Claim 4 is rejected under 35 U.S.C. § 103 as being unpatentable over Cao in view of Lenser et al., US Publication 2008/0027591 (hereinafter Lenser), as cited on the Notice of References Cited dated May 2, 2025.
Regarding claim 4, Cao discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Cao does not appear to explicitly disclose “wherein the motor vehicle receives information about an imminent failure of certain localization sources from at least one of the external infrastructure system or from another external source or from an on-board source.”
However, Lenser discloses a method for localizing an autonomous vehicle “wherein the motor vehicle receives information about an imminent failure of certain localization sources from at least one of the external infrastructure system or from another external source or from an on-board source” (Lenser ¶¶ 149, 171) where a likely failure of the localization system is detected (Lenser ¶ 171), which includes systems such as GPS (i.e., external sources).
Cao and Lenser are analogous art because they are from the “same field of endeavor,” namely that of localizing autonomous vehicles.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Cao and Lenser before him or her to modify the localization method of Cao to include the likely failure detection of Lenser.
The motivation/rationale for doing so would have been that of applying a known technique to a known device. See KSR Int’l Co. v. Teleflex Inc., 550 US 398, 82 USPQ2d 1385, 1396 (U.S. 2007) and MPEP § 2143(I)(D). Cao teaches the “base device” for localizing an autonomous vehicle. Further, Lenser teaches the “known technique” of detecting likely localization system failures that is applicable to the base device of Rosenblum. One of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in an improved system.
Claim 5 is rejected under 35 U.S.C. § 103 as being unpatentable over Cao in view of Rosenblum et al., US Publication 2022/0333932 (hereinafter Rosenblum), as cited on the Notice of References Cited dated May 2, 2025.
Regarding claim 5, Cao discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Cao does not appear to explicitly disclose “wherein, for the checking, a distance of the motor vehicle from a specific point is calculated in each case as the first and second localization results, and a difference in the first and second localization results is formed, and the difference is compared with a threshold value.”
However, Rosenblum discloses “wherein, for the checking, a distance of the motor vehicle from a specific point is calculated in each case as the first and second localization results, and a difference in the first and second localization results is formed, and the difference is compared with a threshold value. (Rosenblum ¶¶ 184) where the process compares localization data including both the onboard and external sourced data, determines a variance (i.e., a distance) and compares it to a predetermined threshold.
Cao and Rosenblum are analogous art because they are from the “same field of endeavor,” namely that of localizing autonomous vehicles.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Cao and Rosenblum before him or her to modify the localization process of Cao to include the threshold comparison of Rosenblum.
The motivation for doing so would have been to further improve navigation accuracy between landmarks.
Claim 6 is rejected under 35 U.S.C. § 103 as being unpatentable over Cao in view of Lenser et al., US Publication 2008/0027591 (hereinafter Lenser), as cited on the Notice of References Cited dated May 2, 2025.
Regarding claim 6, Cao discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Cao discloses “wherein, for the checking, a comparison value of the first and second localization results is observed over a certain time.” (Cao ¶ 13).
Cao does not appear to explicitly disclose “a check of the first localization takes place by carrying out a sequential statistical test including at least one of a sequential probability ratio test or a Kolmogorov-Smirnov test.”
However, Shalev-Shwartz discloses a vehicle navigation method including “a check of the first time-series data takes place by carrying out a sequential statistical test including at least one of a sequential probability ratio test or a Kolmogorov-Smirnov test” (Shalev-Shwartz ¶ 517) by performing a Kolmogorov-Smirnov test on a time series of data.
A person of ordinary skill in the art prior to the effective filing date would have recognized that when Shalev-Shwartz was combined with Cao, the Kolmogorov-Smirnov test of Shalev-Shwartz would be applied to the time series data of a first localization observed over a certain time of Cao. Therefore, the combination of Cao and Shalev-Shwartz at least teaches and/or suggests “a check of the first localization takes place by carrying out a sequential statistical test including at least one of a sequential probability ratio test or a Kolmogorov-Smirnov test,” rendering it obvious.
Cao and Shalev-Shwartz are analogous art because they are from the “same field of endeavor,” namely that of vehicle navigation systems.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Cao and Shalev-Shwartz before him or her to modify the localization system of Cao to include the Kolmogorov-Smirnov test of Shalev-Shwartz.
The motivation for doing so would have been that the Kolmogorov-Smirnov test is widely known to be easy to implement.
Claim 8 is rejected under 35 U.S.C. § 103 as being unpatentable over Cao in view of Yu et al. A State-Domain Robust Chi-Square Test Method for GNSS/INS Integrated Navigation, Journal of Sensors, 2021, 1745383, 8 pages, 2021. https://doi.org/10.1155/2021/1745383 (hereinafter Yu), as cited on the Notice of References Cited dated May 2, 2025.
Regarding claim 8, Cao discloses the limitations contained in parent claim 7 for the reasons discussed above. In addition, Cao does not appear to explicitly disclose “wherein at least one of the uncertainty or a confidence is determined by performing a chi-square goodness-of-fit test to statistically test for equality of Gaussian distributions corresponding to the first and second localization results.”
However, Yu discloses that the user of a chi-square goodness-of-fit test to statistically test is a well-known test to detect false alerts (i.e., uncertainty) of distributions including Gaussian distributions. (Yu 3, § 3.2 The Innovation Chi-Square Test Method). Yu further discloses that these distributions may comprise localization data in the form of GNSS data and INS data. (Yu 1, § 1 Introduction).
A person of ordinary skill in the art prior to the effective filing date would have recognized that when Yu was combined with Cao, the uncertainty values the first and second localization results of Cao would be calculated according to the a chi-square goodness-of-fit test of Yu. Therefore, the combination of Rosenblum and Yu at least teach and/or suggest the claimed limitation “disclose “wherein at least one of the uncertainty or a confidence is determined by performing a chi-square goodness-of-fit test to statistically test for equality of Gaussian distributions corresponding to the first and second localization results,” rendering it obvious.
Cao and Yu are analogous art because they are from the “same field of endeavor,” namely that of statistical analysis.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Cao and Yu before him or her to modify the confidence calculations of Cao to include the a chi-square goodness-of-fit test of Yu.
The motivation for doing so would have been that the a chi-square goodness-of-fit test is known to be provide quick error correction caused by GPS errors. (Yu 2 § 1(ii) Innovation chi-square test methods).
Response to Arguments
Applicant’s arguments filed December 4, 2025, with respect to the rejections of claims 1-12 under 35 U.S.C. §§ 102 and 103, respectively (Remarks 6) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Cao.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Srikanth et al., US Publication 2024/0110812, System and method for comparing localized data to detect errors.
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/ANDREW R DYER/Primary Examiner, Art Unit 3662