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 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. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “ acquiring unit” in claim 16 – The Specification notes that the acquiring unit may be programmed but does not provide any structure for the acquiring unit. “processing unit” in claim 16 – No structure is provided for the processing unit. “communication unit” in claim 16 – The Specification notes that an antenna is an exemplary communication unit. The communication unit is interpreted as an antenna or equivalent thereof. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 112 The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claims 16-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Regarding claim 16, “acquisition unit” and “processing unit” do not have the appropriate structural description in the Specification as required under 35 USC 112(f). The structure is therefore unknown and lacks written description. Claims 17-20 contain the same issue as claim 16 by virtue of dependency. 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 appl icant regards as his invention. Claim s 1 5 -20 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. Regarding claim 15, “the determined potential objects” lacks antecedent basis in the claims. Regarding claim 15, “their positions” and “their velocities” as they relate to “the determined potential objects” lack antecedent basis in the claims. Regarding claim 16, “acquisition unit” and “processing unit” do not have the appropriate structural description in the Specification as required under 35 USC 112(f). The structure is therefore unknown and the scope of the terms are indefinite. Regarding claim 17, “the object” lacks antecedent basis in the claims. Claims 17-20 are indefinite by virtue of dependency on claim 16 . Claim Rejections - 35 USC § 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 ( 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 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. Claim(s) 1 , 9-16 , and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kim (US10627823) . Regarding claim 1 , Kim teaches a computer-implemented method comprising: aggregating sensor data related to a plurality of vehicles (Col. 13, lines 15-43); filtering the sensor data according to one or more conditions, the conditions identifying an action of an advanced driver-assistance system/autonomous driving system (ADAS/AD system) of at least one of the plurality of vehicles (Col. 14, lines 34-58, “ Next, the learning device 100 may perform a process of inputting both the sensor values for training, acquired over the V2V communication in response to the fusion sensor information for training, and the nearby object information for training into the drive network 160, to thereby allow the drive network 160 to generate moving direction probabilities for training of said each of the m cooperatively-driving vehicles for training by referring to the sensor values for training and the nearby object information for training, and thus to drive the subject autonomous vehicle by referring to the moving direction probabilities for training. Herein, the drive network 160 may have been learned to generate the moving direction probabilities of the subject autonomous vehicle in the cooperative driving by referring to the sensor values and the nearby object information. ” – moving direction probabilities are conditions of the claimed system and are according to the filtered sensor data which has been filtered according to the moving probabilities because it is filtered such that the probabilities can be calculated, i.e. by virtue of the sensor data being filtered and the filtered data being able to generate moving probabilities, the sensor data is filtered according to those probabilities); identifying one or more objects in a vicinity of a target vehicle based on the filtered sensor data ( Id. “ By referring to FIG. 3 again, the learning device 100 may perform a process of inputting a road-driving video for training acquired over the V2V communication in response to the fusion sensor information for training into the detection network 150, to thereby allow the detection network 150 to detect at least part of the second virtual vehicles, the pedestrian, and the lane on the traveling road of the subject autonomous vehicle and thus to output the nearby object information for training.” ) ; and using the identified one or more objects to train a neural network to determine potential objects that cause a triggering of an ADAS/AD system of another vehicle (Col 14, lines 44-67, objects are used for training to determine which objects should cause a change in formation, i.e. cause a triggering of an ADAS/AD system of another vehicle to cause the other vehicle to move). Regarding claim 9 , Kim teaches all of the limitations of claim 1, wherein the one or more conditions identifying an action of the ADAS/AS system is one or more of: detection of a vehicle swarm anomaly; detection of a discrepancy with a traffic prediction; detection that a vehicle control unit rises a flag; detection that a path planning algorithm needs to correct its course due to an object that was not earlier seen by a perception system of the vehicle; detection that the ADAS/AD system of the vehicle disengages and asks a driver for intervention; detection of a rapid change of direction of a detected object (Col. 14, lines 34-58, “ Next, the learning device 100 may perform a process of inputting both the sensor values for training, acquired over the V2V communication in response to the fusion sensor information for training, and the nearby object information for training into the drive network 160, to thereby allow the drive network 160 to generate moving direction probabilities for training of said each of the m cooperatively-driving vehicles for training by referring to the sensor values for training and the nearby object information for training, and thus to drive the subject autonomous vehicle by referring to the moving direction probabilities for training. Herein, the drive network 160 may have been learned to generate the moving direction probabilities of the subject autonomous vehicle in the cooperative driving by referring to the sensor values and the nearby object information. ” – moving direction probabilities can be considered rapid changes in direction of a detected object because they are changes in direction of a detected object) ; detection of a collision; detection that a corrective action is performed to avoid a collision; or detection that a high-risk participant is identified. Regarding claim 10 , Kim teaches all of the limitations of claim 1, wherein the aggregated sensor data are synchronized sensor data (“ As one example, 3×n values may be outputted which represent probabilities of each piece of the sensor information acquired from each of the sensors being transmitted over the V2V communication if each of the three vehicles on the cooperative driving mode has n sensors. ” – the sensors on each vehicle are each represented by a single column and therefore are synchronized with respect to the matrix). Regarding claim 11 , Kim teaches all of the limitations of claim 10, wherein the aggregated sensor data are from at least one of: the plurality of vehicles recording sensor data simultaneously, or a virtual world simulation of the plurality of vehicles (“ First, by referring to FIG. 2, the learning device 100 may acquire (i) a driving image for training including (i-1) the subject autonomous vehicle, (i-2) m cooperatively-driving vehicles for training having first virtual vehicles performing the cooperative driving with the subject autonomous vehicle, and (i-3) second virtual vehicles performing a non-cooperative driving and (ii) multiple pieces of sensor status information for training on n sensors for training in each of the m cooperatively-driving vehicles for training. ”) Regarding claim 12 , Kim teaches all of the limitations of claim 1, wherein the aggregated sensor data are from at least one of: the plurality of vehicles recording sensor data simultaneously, or a virtual world simulation of the plurality of vehicles (“ First, by referring to FIG. 2, the learning device 100 may acquire (i) a driving image for training including (i-1) the subject autonomous vehicle, (i-2) m cooperatively-driving vehicles for training having first virtual vehicles performing the cooperative driving with the subject autonomous vehicle, and (i-3) second virtual vehicles performing a non-cooperative driving and (ii) multiple pieces of sensor status information for training on n sensors for training in each of the m cooperatively-driving vehicles for training. ”) Regarding claim 13 , Kim teaches all of the limitations of claim 1, wherein the trained neural network is a neural network trained for a specific traffic scene (“ If the logic for the nearby virtual vehicles is implemented as such, the traffic condition information may be acquired similarly to that of real-world cases. The subject autonomous vehicle may be implemented in the virtual world as such, and may be learned by adjusting the parameters of the neural network operation during a process of driving the subject autonomous vehicle in the virtual world. Upon implementation of the learning processes as above, environments similar to the real-world cases may be implemented in the virtual world, therefore a safe learning without any accidents, a traffic jam, driving on a winding road or on a road on hills, etc. may be performed for various situations in the virtual world. ”) Regarding claim 14 , Kim teaches all of the limitations of claim 13, wherein an output of the trained neural network includes: the determined potential objects, their positions, and their velocities; the determined potential objects and their positions (“ By referring to FIG. 3 again, the learning device 100 may perform a process of inputting a road-driving video for training acquired over the V2V communication in response to the fusion sensor information for training into the detection network 150 , to thereby allow the detection network 150 to detect at least part of the second virtual vehicles, the pedestrian, and the lane on the traveling road of the subject autonomous vehicle and thus to output the nearby object information for training. Herein, the detection network 150 may have been learned to detect objects on an input image ” – Objects are determined and their position can be “in the vicinity”); or the determined potential objects and their velocities. Regarding claim 15 , Kim teaches all of the limitations of claim 1, wherein an output of the trained neural network includes: the determined potential objects, their positions, and their velocities; the determined potential objects and their positions (“ By referring to FIG. 3 again, the learning device 100 may perform a process of inputting a road-driving video for training acquired over the V2V communication in response to the fusion sensor information for training into the detection network 150 , to thereby allow the detection network 150 to detect at least part of the second virtual vehicles, the pedestrian, and the lane on the traveling road of the subject autonomous vehicle and thus to output the nearby object information for training. Herein, the detection network 150 may have been learned to detect objects on an input image ” – Objects are determined and their position can be “in the vicinity”); or the determined potential objects and their velocities. Regarding claim 1 6 , Kim teaches a system comprising: an acquiring unit configured to acquire sensor-based data related to a plurality of vehicles (Col. 13, lines 15-43); a processing unit configured to: aggregate the sensor data (Col. 13, lines 15-43); filter the sensor data according to one or more conditions, the conditions identifying an action of an advanced driver-assistance system/autonomous driving system (ADAS/AD system) of at least one of the plurality of vehicles (Col. 14, lines 34-58, “ Next, the learning device 100 may perform a process of inputting both the sensor values for training, acquired over the V2V communication in response to the fusion sensor information for training, and the nearby object information for training into the drive network 160, to thereby allow the drive network 160 to generate moving direction probabilities for training of said each of the m cooperatively-driving vehicles for training by referring to the sensor values for training and the nearby object information for training, and thus to drive the subject autonomous vehicle by referring to the moving direction probabilities for training. Herein, the drive network 160 may have been learned to generate the moving direction probabilities of the subject autonomous vehicle in the cooperative driving by referring to the sensor values and the nearby object information. ” – moving direction probabilities are conditions of the claimed system and are according to the filtered sensor data which has been filtered according to the moving probabilities because it is filtered such that the probabilities can be calculated, i.e. by virtue of the sensor data being filtered and the filtered data being able to generate moving probabilities, the sensor data is filtered according to those probabilities); identify one or more objects in a vicinity of a target vehicle based on the filtered sensor data ( Id. “ By referring to FIG. 3 again, the learning device 100 may perform a process of inputting a road-driving video for training acquired over the V2V communication in response to the fusion sensor information for training into the detection network 150, to thereby allow the detection network 150 to detect at least part of the second virtual vehicles, the pedestrian, and the lane on the traveling road of the subject autonomous vehicle and thus to output the nearby object information for training.” ) ; us e the identified one or more objects to train a neural network to determine potential objects that cause a corrective action of an ADAS/AD system of another vehicle (Col 14, lines 44-67, objects are used for training to determine which objects should cause a change in formation, i.e. cause a triggering of an ADAS/AD system of another vehicle to cause the other vehicle to move) ; and a communication unit configured to report the determined one or more objects to at least one of another vehicle, a traffic infrastructure unit, or a cloud server (“ Next, the learning device 100 may perform a process of inputting both the sensor values for training, acquired over the V2V communication in response to the fusion sensor information for training, and the nearby object information for training into the drive network 160, to thereby allow the drive network 160 to generate moving direction probabilities for training of said each of the m cooperatively-driving vehicles for training by referring to the sensor values for training and the nearby object information for training, and thus to drive the subject autonomous vehicle by referring to the moving direction probabilities for training. ”) Regarding claim 20 , Kim teaches all of the limitations of claim 16, further comprising at least one of: a vehicle of the plurality of vehicles (see rejection of claim 16, multiple vehicles are present); the cloud server; or the traffic infrastructure unit. 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. 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. Claim(s) 2- 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US10627823) in view of Liu (Liu, Yen-Cheng, et al. "When2com: Multi-agent perception via communication graph grouping." Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition . 2020.) Regarding claim 2 , Kim teaches all of the limitations of claim 1, but does not teach wherein: the identifying includes an identification of a plurality of positions of the one or more objects. Liu teaches where the identifying includes an identification of a plurality of positions of the one or more objects (§4.1.2). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim such that the identifying includes an identification of a plurality of positions of the one or more objects in order to provide more context for the model to train from and increase accuracy in variable scenarios. Regarding claim 3 , Kim as modified teaches all of the limitations of claim 2, wherein the identified plurality of positions of the one or more objects are used to train the neural network (see rejections claim 2 and claim 1 – Kim provides objects for training and Kim as modified uses a plurality of positions of objects for training). Claim(s) 4- 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US10627823) in view of Liu (Liu, Yen-Cheng, et al. "When2com: Multi-agent perception via communication graph grouping." Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition . 2020.) , further in view of Luo (US20190147372A1). Regarding claim 4 , Kim as modified teaches all of the limitations of claim 2 . Kim as modified does not teach wherein the identifying includes at least one of: an identification of a plurality of velocities of the one or more objects, or an identification of a plurality of accelerations of the one or more objects. Luo teaches wherein the identifying includes at least one of: an identification of a plurality of velocities of the one or more objects (¶32), or an identification of a plurality of accelerations of the one or more objects (¶32). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim as modified such that the identifying includes at least one of an identification of a plurality of velocities of the one or more objects, or an identification of a plurality of accelerations of the one or more objects in order to provide additional context and tracking ability in the scheme of Kim as modified. Regarding claim 5 , Kim as modified teaches all of the limitations of claim 4, but does not teach wherein the at least one of the plurality of positions of the one or more objects, the plurality of velocities of the one or more objects, or the plurality of accelerations of the one or more objects are used to train the neural network. Luo teaches wherein the at least one of the plurality of positions of the one or more objects, the plurality of velocities of the one or more objects, or the plurality of accelerations of the one or more objects are used to train the neural network (¶32). Kim as modified and Luo are combinable under the same reasoning and rational as claim 4. Claim(s) 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US10627823) in view of Luo (US20190147372A1). Regarding claim 6 , Kim teaches all of the limitations of claim 1. Kim as modified does not teach wherein the identifying includes at least one of: an identification of a plurality of velocities of the one or more objects, or an identification of a plurality of accelerations of the one or more objects. Luo teaches wherein the identifying includes at least one of: an identification of a plurality of velocities of the one or more objects (¶32), or an identification of a plurality of accelerations of the one or more objects (¶32). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim as modified such that the identifying includes at least one of an identification of a plurality of velocities of the one or more objects, or an identification of a plurality of accelerations of the one or more objects in order to provide additional context and tracking ability in the scheme of Kim as modified. Regarding claim 7 , Kim as modified teaches all of the limitations of claim 6, but does not teach wherein the at least one of the plurality of positions of the one or more objects, the plurality of velocities of the one or more objects, or the plurality of accelerations of the one or more objects are used to train the neural network. Luo teaches wherein the at least one of the plurality of positions of the one or more objects, the plurality of velocities of the one or more objects, or the plurality of accelerations of the one or more objects are used to train the neural network (¶32). Kim as modified and Luo are combinable under the same reasoning and rational as claim 6. Regarding claim 8 , Kim as modified teaches all of the limitations of claim 1. Kim does not teach tracing the identified one or more objects back to previous sensor readings to track the one or more objects over a plurality of time steps. Luo teaches tracing the identified one or more objects back to previous sensor readings to track the one or more objects over a plurality of time steps (¶57). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim to include tracing the identified one or more objects back to previous sensor readings to track the one or more objects over a plurality of time steps in order to enable accurate object tracking. Claim(s) 17 -18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US10627823) in view of Huval (US20180373980A1). Regarding claim 17 , Kim teaches all of the limitations of claim 16, but does not teach wherein the processing unit is further configured to: label the determined one or more objects in a traffic scene with a binary information to report the object or to not report the objects. Huval teaches wherein the processing unit is further configured to: label the determined one or more objects in a traffic scene with a binary information to report the object or to not report the objects (¶54, the withholding of automated labels from the image can be considered not-reporting). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim such that the processing unit is further configured to: label the determined one or more objects in a traffic scene with a binary information to report the object or to not report the objects in order to ensure objects are accurately labeled. Regarding claim 18 , Kim teaches all of the limitations of claim 16, but does not teach wherein a number of the determined one or more objects is less than a number of objects identified in an environment of the system from the sensor-based data. Huval teaches that under-reporting can reduce bandwidth (¶60). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kim such that a number of the determined one or more objects is less than a number of objects identified in an environment of the system from the sensor-based data in order to reduce bandwidth consumption. Allowable Subject Matter Claim 19 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Claim 19 presents the output of confidence values for identified objects based on whether the objects are likely to cause corrective action of another vehicle, i.e. a vehicle that is not the subject vehicle. The prior art establishes causing corrective action of vehicles, but does not establish the claimed confidence values which describe the claimed likelihood. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT SCHYLER S SANKS whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-6125 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 06:30 - 15:30 Central Time, M-F . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Michael Huntley can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (303) 297-4307 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /SCHYLER S SANKS/ Primary Examiner, Art Unit 2129