DETAILED ACTION
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
This action is in reply to the response filed 2 March 2026.
Claims 1-2, 4, 6-7, 10-12 and 15-20 have been amended.
Claims 1-20 are pending and have been examined.
This action is FINAL.
Response to Amendments and Remarks
Claim Rejections - 35 USC § 112
Claims 4, 6-7, 10-12, 15-17 and 19-20 were 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.
The Applicant has amended the claims to overcome or render moot each of the rejections under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph,. Accordingly, the rejection of claims 4, 6-7, 10-12, 15-17 and 19-20 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, has been withdrawn.
Claim Rejections - 35 USC § 101
Claims 1-13 and 15-20 were rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Applicant’s arguments, see pages 11-14, filed 2 March 2026, with respect to the rejection(s) of claim(s) 1-13 and 15-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues:
The examiner previously indicated that claim 14 is patent-eligible because it "positively recites controlling the vehicle to maintain the first safety distance or second safety distance and thus integrates the abstract idea into a practical application." Amended claims 1, 15, and 18 now expressly include the same type of control element and therefore is also patent-eligible.
The examiner respectfully disagrees. Claim 14 recites “wherein the computer program code instructions are configured to, when executed, cause the apparatus to control maneuver of the subject vehicle to maintain the first safety distance or the second safety distance.” Thus, in claim 14, there is a step of controlling the vehicle within the safety distance determined. However, Applicant has amended independent claim 1 (and generally independent claims 15 and 18) to recite, “cause the apparatus to control maneuver of the subject vehicle or inform, via vehicle-to- vehicle communication, at least one vehicle of the set of vehicles”. Thus, the independent claims do not positively recite controlling the vehicle based on the safety distance determined. Rather, the claim only requires either to control the vehicle to perform an maneuver or to inform. Informing is considered extra solution activity as indicated in the updated rejection below. The examiner further notes that neither the control of the maneuver, nor the inform step are tied to the determination of the safety distance.
Applicant further argues that the claims do not recite a metal process, indicating that a human cannot cause the apparatus to control maneuver of the subject vehicle or inform, via vehicle-to- vehicle communication, at least one vehicle of the set of vehicles. In response to this argument the examiner notes that the claim language does not positively require control of a maneuver of the vehicle and further does not recite controlling the maneuver based on the safety distance. Rather the claim provides alternative language of “or inform, via vehicle-to- vehicle communication, at least one vehicle of the set of vehicles.”. The examiner agrees that this element cannot be performed in the human mind. However, the step of informing is considered extra-solution actively as applied in the rejection below.
Applicant again argues again that claim 1 includes similar technical feature as claim 14 previously indicated as integrating the exception into a technological application. The examiner notes that the claim language of claim 1 and the remaining independent claims simply requires controlling the vehicle to maneuver or to inform. As discussed above informing is considered extra solution activity as indicated in the updated rejection below.
Applicant further argues with respect to step 2B:
The claim controls maneuver of the subject vehicle to maintain a dynamically calculated second safety distance, thus changing real-world vehicle motion/spacing. Alternatively, the claim recites transmitting vehicle-to-vehicle communication data indicative of the second safety distance or the trigger event to at least one neighboring vehicle. This is not generic data output; it is inter-vehicle machine communication used to coordinate traffic maneuvers for collision-risk mitigation. Therefore, claim 1 recites requirements significantly more than any alleged abstract idea. Because independent claims 15 recite and 18 similar features in varying scope, they recites requirements significantly more than any alleged abstract idea under Step 2B for the same reasons as claim 1.
The examiner respectfully disagrees. As noted above, the claim does not controls the maneuver of the vehicle to maintain a dynamically calculated second safety distance. First, the claim has no recitation of “controls …to maintain a dynamically calculated second safety distance”. Further, the claim includes alternative language to inform. As discussed above informing is considered extra solution activity as indicated in the updated rejection below. The claim does not recite any details regarding what type of information is being provided and thus, Applicants argument that the informing step is not generic data output is not supported and it is not persuasive.
Claim Rejections - 35 USC § 103
Claims 1-3, 5, 9, 11, 14, 15, and 18 were rejected under 35 U.S.C. § 103 as being unpatentable over Lyngfelt (US Pub. No. 2024/0140422, in view of Luo (CN 115123226A). Claim 7 was rejected under 35 U.S.C. 103 as being unpatentable over Lyngfelt and Luo in view of Peterson (US Pub. No. 2019/0248379) and further in view of Gesang et al. (US Pub. No. 2023/0347872). Claim 8 was rejected under 35 U.S.C. 103 as being unpatentable over Lyngfelt and Luo in view of Gesang. Claim 10 was rejected under 35 U.S.C. 103 as being unpatentable over Lyngfelt and Luo in view of Wells et al. (US Patent 11,702,076) and further in view of Polisson et al. (US Pub. No. 2018/0050698). Claim 12 was rejected under 35 U.S.C. 103 as being unpatentable over Lyngfelt and Luo in view of Broll et. al. (US Pub. No. 2019/0232962) and in further view of Ping Wang et al. (CN 106314276A). Claim 13 was rejected under 35 U.S.C. 103 as being unpatentable over Lyngfelt and Luo in view of Eguchi et al. (US Pub. No. 2020/0380870) and in further view of Ping Wang et al.
Claim(s) 1-6, 9, 11 and 14-20 were also rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US Pub. No. 2022/0097697, hereinafter "Wang") in view of Luo. Claim 7 was rejected under 35 U.S.C. 103 as being unpatentable over Wang and Luo in view of Peterson and further in view of Gesang. Claim 8 was rejected under 35 U.S.C. 103 as being unpatentable over Wang and Luo in view of Gesang. Claim(s) 12 was rejected under 35 U.S.C. 103 as being unpatentable over Wang and Luo in view of Broll et. al. (US Pub. No. 2019/0232962, hereinafter "Broll") and in further view of Ping Wang et al. (CN 106314276A). Claim 13 was rejected under 35 U.S.C. 103 as being unpatentable over Wang and Luo in view of Eguchi and in further view of Ping Wang.
Applicant’s arguments, pages 15-16, filed 2 March 2026, with respect to the rejection(s) of claims 1-3, 5, 9, 11, 14, 15, and 18 under 35 U.S.C. § 103 as being unpatentable over Lyngfelt (US Pub. No. 2024/0140422, in view of Luo (CN 115123226A) have been fully considered and are persuasive. Accordingly, this rejection is withdrawn.
Similarly, Applicant’s arguments, see pages 16, filed 2 March 2026, with respect to the rejection(s) of claims(s) 1-6, 9, 11 and 14-20 under 35 U.S.C. 103 as being unpatentable over Wang et al. (US Pub. No. 2022/0097697, hereinafter "Wang") in view of Luo have been fully considered and are persuasive. However, upon further consideration, a new ground(s) of rejection is made in view of Wang and Luo in view of Ping Wang (of record). Further a new ground(s) of rejection is also made in view of Wang and Luo in view of Nagai (JP-6977633-B2).
Applicant argues that Wang, which continues to be relied upon in the present rejection, fails to disclose "second safety distance is less than the first and is to be maintained to avoid occupation of the gap." Further Indicating that:
Wang discloses reduction of headway "as close as possible" in a frequent cut-in scenario (Wang, paragraphs [0049]-[0054] and [0071]). However, Wang does not articulate the discrete "first distance" vs "second distance" state machine used to avoid occupation of the gap between the subject and the preceding vehicle. It is a continuous headway adjustment approach based on scenarios, not two-distance framework with an express avoid-occupation purpose.
This argument is unpersuasive. First the examiner notes that the phrase “the second safety distance… is maintained to avoid an occupation of the gap by at least one vehicle of the set of vehicles” is intended use limitation that does not limit the claim. In response to applicant's argument that “the second safety distance… is maintained to avoid an occupation of the gap by at least one vehicle of the set of vehicles”, a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim.
Wang teaches determining a minimum safety distance (i.e. a first safety distance) that the own vehicle maintains between the own vehicle and the preceding vehicle. Further, Wang teaches in response to the trigger of multiple cut-ins, “the safety distance no longer applies” and controls the vehicle to close the gap so that “the vehicle ahead should be kept as close as possible” such that the vehicle is at a second distance. This second distance is smaller than the first distance as the controller is no longer required to keep the minimum required distance. The distance of “close as possible” as described in Wang is a determined distance (a second safety distance) as the vehicle controller must determine a distance to follow without creating a collision with the preceding vehicle. Finally, the examiner notes that Wang teaches this is done to prevent the cutting-in of other vehicles that create driver discontent (see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.”).
Applicant provides no further substantive arguments other than to rely upon the purported deficiencies of Wang.
Claim Objections
Claim 1 objected to because of the following informalities:
Claim 1, line 17 recites “cause the apparatus to control maneuver of the subject vehicle…”. The examiner recommends reciting “cause the apparatus to control a maneuver of the subject vehicle…” to remedy a grammatical error. Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 applicant regards as his invention.
Claims 1-20 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.
Claim 1 recites “at least one vehicle of the set of vehicles” in line 15. Claim 1 previously recited “at least one vehicle of the set of vehicles” in line 10. It is not clear if the at least one vehicle in line 15 is the same or different than that recited in line 10. Further, claim 1 recites “at least one vehicle of the set of vehicles” in line 18. It is not clear if the at least one vehicle of line 18 is the same or different than the at least one vehicle recited in line 15 and/or line 10. Claim 15 and 18 have a similar recitation and are rejected for the same reasons
Claim 6 recites “at least one vehicle” in line 10. Claim 6 depends from claim 1 which previously recite “at least one vehicle”. It is not clear if the at least one vehicle of claim 6 is the same or different than the at least one vehicle recited multiple times in claim 1. Claims 17 and 20 have a similar recitation and are rejected for the same reason.
Claim 17 recites “the prediction result output by the second ML model”. There is insufficient antecedent basis for this limitation in the claim. The examiner notes that claim 17 depends from claim 16 with recites “a prediction result” of a first model. The prediction result of the second model does not appear to be the same as the prediction result of the first model. Claim 20 has a similar recitation and conflicts with the recitation of claim 19. Accordingly, claim 20 is rejected for the same reasons.
Claims 2-14 depend from claim 1 and are similarly rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, based on their dependency on claim 1.
Claims 16-17 depend from claim 15 and are similarly rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, based on their dependency on claim 15.
Claims 19-20 depends from claim 18 and are similarly rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, based on their dependency on claim 18.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 and 15-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Following the 2019 Revised Patent Subject Matter Eligibility Guidance (84 Fed. Reg. 50-57 and MPEP § 2106, hereinafter 2019 Guidance), the claim(s) appear to recite at least one abstract idea, as explained in the Step 2A, Prong I analysis below. Furthermore, the judicial exception(s) does/do not appear to be integrated into a practical application as explained in the Step 2A, Prong II analysis below. Further still, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s) as explained in the Step 2B analysis below.
STEP 1:
Step 1, of the 2019 Guidance, first looks to whether the claimed invention is directed to a statutory category, namely a process, machine, manufactures, and compositions of matter.
Claim 1 is directed toward an apparatus and is therefore eligible for further analysis.
Claim 15 is directed toward a method and is therefore eligible for further analysis.
Claim 18 is directed toward non-transitory computer-readable medium having computer program code instructions stored therein and is therefore eligible for further analysis.
STEP 2A, PRONG I:
Step 2A, prong I, of the 2019 Guidance, first looks to whether the claimed invention recites any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activities such as a fundamental economic practice, or mental processes).
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim(s) for the remainder of the 101 rejection.
Claim 1 recites:
An apparatus comprising at least one processor and at least one non- transitory memory including computer program code instructions, the computer program code instructions configured to, when executed, cause apparatus to:
determine or retrieve a first safety distance between a subject vehicle and a preceding vehicle among a set of vehicles within a pre-determined distance of the subject vehicle, wherein the preceding vehicle is traveling directly ahead of the subject vehicle;
obtain a set of features associated with one or more of the set of vehicles, one or more of a set of users driving the set of vehicles, or a combination thereof;
monitor a trigger event while maintaining the first safety distance, the trigger event being indicative of at least one vehicle of the set of vehicles trying to overtake the subject vehicle and to occupy a gap between the subject vehicle and the preceding vehicle;
determine a second safety distance to be maintained between the subject vehicle and the preceding vehicle based on the first safety distance and the set of features, wherein the second safety distance is less than the first safety distance and is maintained to avoid an occupation of the gap by at least one vehicle of the set of vehicles;
output the second safety distance on a user interface; and
cause the apparatus to control maneuver of the subject vehicle or inform, via vehicle-to- vehicle communication, at least one vehicle of the set of vehicles.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. Specifically, the “monitor a trigger event while maintaining the first safety distance, the trigger event being indicative of at least one vehicle of the set of vehicles trying to overtake the subject vehicle and to occupy a gap between the subject vehicle and the preceding vehicle” and “determine a second safety distance to be maintained between the subject vehicle and the preceding vehicle based on the first safety distance and the set of features, wherein the second safety distance is less than the first safety distance and is maintained to avoid an occupation of the gap by at least one vehicle of the set of vehicles” steps encompass a human observing another vehicle’s turn signal or looking in a side or rear-view mirror and identifying that the vehicle may try to overtake the subject vehicle and further making a determination regarding an updated safety distance based on receiving information about a first safety distance and features.
STEP 2A, PRONG II:
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
Claim 1 recites:
An apparatus comprising at least one processor and at least one non- transitory memory including computer program code instructions, the computer program code instructions configured to, when executed, cause apparatus to:
determine or retrieve a first safety distance between a subject vehicle and a preceding vehicle among a set of vehicles within a pre-determined distance of the subject vehicle, wherein the preceding vehicle is traveling directly ahead of the subject vehicle;
obtain a set of features associated with one or more of the set of vehicles, one or more of a set of users driving the set of vehicles, or a combination thereof;
monitor a trigger event while maintaining the first safety distance, the trigger event being indicative of at least one vehicle of the set of vehicles trying to overtake the subject vehicle and to occupy a gap between the subject vehicle and the preceding vehicle;
determine a second safety distance to be maintained between the subject vehicle and the preceding vehicle based on the first safety distance and the set of features, wherein the second safety distance is less than the first safety distance and is maintained to avoid an occupation of the gap by at least one vehicle of the set of vehicles;
output the second safety distance on a user interface; and
cause the apparatus to control maneuver of the subject vehicle or inform, via vehicle-to- vehicle communication, at least one vehicle of the set of vehicles.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application:
Regarding the additional limitations of “determine or retrieve a first safety distance between a subject vehicle and a preceding vehicle among a set of vehicles within a pre-determined distance of the subject vehicle, wherein the preceding vehicle is traveling directly ahead of the subject vehicle”, “obtain a set of features associated with one or more of the set of vehicles, one or more of a set of users driving the set of vehicles, or a combination thereof”, “output the second safety distance on a user interface” and “cause the apparatus to control maneuver of the subject vehicle or inform, via vehicle-to- vehicle communication, at least one vehicle of the set of vehicles” the examiner submits that these limitations merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use and do not integrate a judicial exception into a “practical application”.
The limitations of “retrieve a first safety distance between a subject vehicle and a preceding vehicle among a set of vehicles within a pre-determined distance of the subject vehicle, wherein the preceding vehicle is traveling directly ahead of the subject vehicle”, “obtain a set of features associated with one or more of the set of vehicles, one or more of a set of users driving the set of vehicles, or a combination thereof”, and “output the second safety distance on a user interface” and “cause the apparatus to control maneuver of the subject vehicle or inform, via vehicle-to- vehicle communication, at least one vehicle of the set of vehicles” are recited at a high level of generality (i.e. as a general means of data gathering or data output) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. See at least MPEP 2106.05(g). Thus, these additional elements merely reflect insignificant extra-solution activity.
The examiner notes that the courts have held that merely reciting the works “apply it” (or an equivalent) with the judicial exception, or merely including or are more than mere instructions to implement an abstract idea on a computer, or merely using the computer as a tool to perform an abstract idea, does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
STEP 2B:
Regarding Step 2B of the Revised Guidance, the representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Further, as discussed above, the additional limitations of retrieve a first safety distance between a subject vehicle and a preceding vehicle among a set of vehicles within a pre-determined distance of the subject vehicle, wherein the preceding vehicle is traveling directly ahead of the subject vehicle”, “obtain a set of features associated with one or more of the set of vehicles, one or more of a set of users driving the set of vehicles, or a combination thereof”, and “output the second safety distance on a user interface” and “cause the apparatus to control maneuver of the subject vehicle or inform, via vehicle-to- vehicle communication, at least one vehicle of the set of vehicles” the examiner submits are insignificant extra-solution activity. Hence, the claim is not patent eligible.
Claims 15 and 18 have similar recitations to claim 1 and the analysis above with respect to claim 1 also applies to claims 15 and 18.
Dependent claim(s) 2-13, 16-17, 19-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Specifically, the claims only recite limitations further defining the mental process and insignificant extra-solution activity. These limitations are considered mental process steps and additional steps that amount to necessary data output (e.g. second notification). These additional elements fail to integrate the abstract idea into a practical application because they do not impose meaningful limits on the claimed invention. As such, the additional elements individually and in combination do not amount to significantly more than the abstract idea. Therefore, when considering the combination of elements and the claimed invention as a whole, claims 2-13, 16-17, and 19-20 are not patent eligible.
Accordingly, claims 1-13, 15-20 are not patent eligible.
Claim 14 is patent eligible as it positively recites controlling the vehicle to maintain the first safety distance or second safety distance and thus integrates the abstract idea in to a practical application.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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) 1-6, 9, 11 and 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US Pub. No. 2022/0097697, hereinafter “Wang”) in view of Luo (CN 115123226A, hereinafter “Luo”, citations correspond to the machine translation provided in the Non-Final Office Action) and in further view of Ping Wang (CN 106314276A, Abstract provided in the IDS, hereinafter “Ping Wang”, the citations include line numbers of the machine translation).
Regarding claim 1, Wang discloses an apparatus comprising at least one processor and at least one non-transitory memory including computer program code instructions, the computer program code instructions (see at least Wang Figure 8 processor 801, memory 802, and [0169-0173] “As shown in FIG. 8, the electronic device includes: one or more processors 801, a memory 802, and interfaces for connecting various components, including a high-speed interface and a low-speed interface…The processor may process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of the GUI on an external input/output apparatus (such as a display apparatus coupled to an interface)…[0170] The memory 802 is a non-transitory computer-readable storage medium provided in the present disclosure. The memory stores instructions executable by at least one processor to enable the at least one processor to execute the vehicle control method provided in the present disclosure. The non-transitory computer-readable storage medium of the present disclosure stores computer instructions, and the computer instructions are used to make a computer execute the vehicle control method provided in the present disclosure.”) configured to, when executed, cause apparatus to:
determine or retrieve a first safety distance between a subject vehicle and a preceding vehicle among a set of vehicles within a pre-determined distance of the subject vehicle, wherein the preceding vehicle is traveling directly ahead of the subject vehicle (see at least Wang “safe distance” as described as a theoretical value that is known [0004] “ Existing automatic driving typically takes a safe distance derived from theoretical calculations of physics as the core in designing vehicle control solutions on active safety. For example, in adaptive cruise control, stop-and-go is performed according to a safety braking distance between a front and a rear vehicle. In an automatic overtaking, corresponding measures are taken according to a safe distance set between two vehicles.” See also [0049] “In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway.” See also Wang [0032] “However, for automatic driving in the prior art, control strategies are typically designed from the perspective of the vehicle. For example, an operation scheme of an adaptive cruise system is as follows: when there is no vehicle ahead, the adaptive cruise system will make the vehicle travel at a speed set by a user through controlling the throttle and the brake; when following a vehicle ahead, a control unit can keep a distance from the vehicle by braking until a safe distance is restored if the distance to the vehicle ahead is less than a safe vehicle-following distance; when the distance to the vehicle ahead is less than the safe vehicle-following distance, the host vehicle may still drive at a set speed. The safe distance is typically defined in terms of the speed of the host vehicle and the speed of the vehicle ahead, as well as the vehicle-following strategy adopted, such as time headway and so on.” See also [0063] and [0071] The examiner notes that while the majority of Wang discusses time headway, the time headway is based on the distance between the two vehicles and the speed of the vehicles, and thus is intrinsically linked to the distance between the vehicles, though, where possible, the examiner cites to the disclosure of safe distance rather than the time headway.);
obtain a set of features associated with one or more of the set of vehicles, one or more of a set of users driving the set of vehicles, or a combination thereof (see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.”);
monitor a trigger event while maintaining the first safety distance, see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.” See also Figure 1, S102 and S103 which controls the vehicle to a target headway, the examiner notes that to determine a target headway, the system must necessarily determine a target distance, for example see [0048] “It should be noted that the time headway is an important technical parameter index in automatic driving, which is calculated based on the distance between two vehicles and a current vehicle speed of the host vehicle.”).
determine a second safety distance to be maintained between the subject vehicle and the preceding vehicle based on the first safety distance and the set of features, wherein the second safety distance is less than the first safety distance and is maintained to avoid an occupation of the gap by at least one vehicle of the set of vehicles (see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.” See also Figure 1, S102 and S103 which controls the vehicle to a target headway, the examiner notes that to determine a target headway, the system must necessarily determine a target distance, for example see [0048] “It should be noted that the time headway is an important technical parameter index in automatic driving, which is calculated based on the distance between two vehicles and a current vehicle speed of the host vehicle.”)
cause the apparatus to control maneuver of the subject vehicle or inform, via vehicle-to-vehicle communication, at least one vehicle of the set of vehicles (see at least Wang [0071] “Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.” See also Figure 1, S102 and S103 which controls the vehicle to a target headway, the examiner notes that to determine a target headway, the system must necessarily determine a target distance, for example see [0048] “It should be noted that the time headway is an important technical parameter index in automatic driving, which is calculated based on the distance between two vehicles and a current vehicle speed of the host vehicle.”)
Wang discloses controlling the vehicle to the second safety distance, but does not teach outputting the second safety distances on a user interface.
Luo teaches outputting the second safety distances on a user interface (see at least Luo, lines 342-372 and Figures 7-9 “The on-board controller controls the instrument of the target vehicle to display a target image, wherein the target vehicle is a vehicle driven by the driver, and the target image is used to display the virtual image of the target vehicle and the first a virtual image of a following target, the first following target is a vehicle located in front of the target vehicle;…The adaptive cruise system is used to control the instrument of the target vehicle to display a real-time image through the on-board controller after acquiring the target following distance input by the user, wherein the real-time image is used to simulate the The process in which the distance between the target vehicle and the first following target is changed from the preset following distance to the target following distance. The real-time image includes: a virtual image of the target vehicle, the The virtual image and virtual parking space of the first following target, where the virtual parking space is where the virtual image of the target vehicle is when the distance between the target vehicle and the first following target is the target following distance. describe the location in the live image).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang with the teaching of Luo to display the second safety distance, with a reasonable expectation of success, because as Luo teaches, providing the driving scene improves the user experience (see Luo, lines 14-34).
The combination of Wang and Luo teach the method as claimed including monitoring a trigger event of vehicles overtaking the subject vehicle, but the combination does not explicitly teach wherein the trigger event being indicative of at least one vehicle of the set of vehicles trying to overtake the subject vehicle and to occupy a gap between the subject vehicle and the preceding vehicle.
Ping Wang teaches wherein the trigger event being indicative of at least one vehicle of the set of vehicles trying to overtake the subject vehicle and to occupy a gap between the subject vehicle and the preceding vehicle (see at least Ping Wang Abstract “ A rear car overtaking reminding system based on distance measuring is mainly composed of a distance measuring system, an overtaking identification system and an overtaking reminding system. The distance measuring system measures the distance between the left adjacent lane and a car, the distance between the right adjacent lane and the car and the distance between a car behind the lane where the car is located and the car; the overtaking identification system judges an overtaking intention of the rear car according to distance change information of the adjacent moments and the safety distance; the overtaking reminding system drives corresponding overtaking indicator lights and can give out voice prompt to remind a driver that the rear car is about to overtake from the left side or the right side according to the overtaking intention. By means of the rear car overtaking reminding system based on distance measuring, it can be guaranteed that the driver perceives approaching of the rear car and the overtaking intention of the rear car, the condition that lane changing is conducted when the rear car overtakes is avoided, and safe driving of the car is guaranteed.”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang and Luo with the teaching of Ping Wang, with a reasonable expectation of success to use the trigger event indicating a vehicle will overtake the subject vehicle, because as Ping Wang teaches this allows the driver to be aware of vehicles that will likely be cutting in and will reduce accidents (see at least Ping Wang page 1, lines 14-16).
Regarding claim 2, the combination of Wang, Luo, and Ping Wang teach the apparatus of claim 1, wherein, to determine the second safety distance, the computer program code instructions are configured to, when executed, cause the apparatus to:
monitor the trigger event indicative of a change in a value of at least one feature associated with the set of vehicles or the set of users (see at least Wang 0043] “In a possible design, the real-time monitoring data also includes a real-time road condition image, which is captured by a vehicle-mounted camera. By performing image recognition on the real-time road condition image, the presence of special vehicle around can be detected, and the frequent-cut-in scenario can be recognized.”) ; and
responsive to detecting the trigger event, determine the second safety distance,
wherein the trigger event is detected based on at least one of:
(i) whether any vehicle of the set of vehicles within the pre-determined distance of the subject vehicle is changing lanes (see at least Ping Wang Abstract “ A rear car overtaking reminding system based on distance measuring is mainly composed of a distance measuring system, an overtaking identification system and an overtaking reminding system. The distance measuring system measures the distance between the left adjacent lane and a car, the distance between the right adjacent lane and the car and the distance between a car behind the lane where the car is located and the car; the overtaking identification system judges an overtaking intention of the rear car according to distance change information of the adjacent moments and the safety distance; the overtaking reminding system drives corresponding overtaking indicator lights and can give out voice prompt to remind a driver that the rear car is about to overtake from the left side or the right side according to the overtaking intention. By means of the rear car overtaking reminding system based on distance measuring, it can be guaranteed that the driver perceives approaching of the rear car and the overtaking intention of the rear car, the condition that lane changing is conducted when the rear car overtakes is avoided, and safe driving of the car is guaranteed.” See also at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.” See also Figure 1, S102 and S103 which controls the vehicle to a target headway, the examiner notes that to determine a target headway, the system must necessarily determine a target distance, for example see [0048] “It should be noted that the time headway is an important technical parameter index in automatic driving, which is calculated based on the distance between two vehicles and a current vehicle speed of the host vehicle.”);
(ii) whether any vehicle of the set of vehicles is driving aggressively at high speed
(iii) whether any vehicle of the set of vehicles within the pre-determined distance of the subject vehicle is driving at uneven speed; or
(iv) whether at least one driver of the set of drivers driving the set of vehicles is looking at a side or a rear-view mirror.
Regarding claim 3, the combination of Wang, Luo, and Ping Wang teach the apparatus of claim 1, wherein the set of features is a second set of features, and wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
obtain a first set of features associated with: (i) information associated with the subject vehicle; (ii) vehicle information associated with the one or more of the set of vehicles; (iii) road information associated with a road on which the subject vehicle and the set of vehicles are being driven; (iv) traffic information associated with the road; (v) environmental information; (vi) distance information associated with a distance between the subject vehicle and a first lane of a set of lanes on the road; (vii) temporal information; or (viii) a combination thereof (see at least Wang, which teaches the features can be traffic information such as traffic speed, traffic congestion, frequency of cut-ions [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.”)
determine the first safety distance to be maintained between the subject vehicle and the preceding vehicle based on the first set of features (see at least Wang, which continuously monitors the road conditions and updates the safe distance as the target scenario changes, see for example [0055] “The target time headway is determined based on the monitored target travelling scenario. Furthermore, the state of the host vehicle is controlled dynamically, so as to reach the target time headway. Moreover, correction can be made in time if the target scenario changes during this process…[0056] Specifically, a target travelling scenario is determined according to a real-time monitoring data when a preset update condition has been fulfilled. Then, the target time headway is determined according to the target travelling scenario, and finally the vehicle is controlled according to the target time headway” See also [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.”); and
output the first safety distance on the user interface (see at least Luo, lines 342-372 and Figures 7-9 “The on-board controller controls the instrument of the target vehicle to display a target image, wherein the target vehicle is a vehicle driven by the driver, and the target image is used to display the virtual image of the target vehicle and the first a virtual image of a following target, the first following target is a vehicle located in front of the target vehicle;…The adaptive cruise system is used to control the instrument of the target vehicle to display a real-time image through the on-board controller after acquiring the target following distance input by the user, wherein the real-time image is used to simulate the The process in which the distance between the target vehicle and the first following target is changed from the preset following distance to the target following distance. The real-time image includes: a virtual image of the target vehicle, the The virtual image and virtual parking space of the first following target, where the virtual parking space is where the virtual image of the target vehicle is when the distance between the target vehicle and the first following target is the target following distance. describe the location in the live image).
Regarding claim 4, the combination of Wang, Luo, and Ping Wang teach the apparatus of claim 3, wherein, to determine the first safety distance, the computer program code instructions are configured to, when executed, cause the apparatus to:
apply a machine learning (ML) model on the first set of features, wherein the ML model is trained to output a prediction result based on the first set of features (see at least Wang [0049-0050] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions.” See [0095-0102] regarding calculation of base time headway using the scenario perception coefficient that is determined using the machine learning. Specifically, [0097] teaches “There are many ways to implement the time headway adjustment model, which can be statistical model of historical data, or self-learning model such as neural network.”, “See also [0084] “In this step, the scenario perception coefficient provides a medium for connecting the target travelling scenario with the adjustment of the time headway. The setting of the scenario perception coefficient can be obtained according to the target travelling scenario using big data statistics. Neural network models may also be used to implement automatic intelligent learning. For different regions and different users, different scenario perception coefficient settings can be applied.”); and
determine the first safety distance at least in part based on the prediction result output by the ML model (see at least Wang [0049-0054] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions… S103, control the vehicle according to the target time headway..[0053] In this step, the target time headway can be reached by setting to control the vehicle to go through different gradual processes based on different target travelling scenarios….[0054] For example, when the vehicle is in a frequent-cut-in scenario such as a congested road, and the current vehicle speed of the vehicle is low, the vehicle can be controlled to quickly reach the target time headway in order to prevent from being cut-in any further. When the vehicle is in a frequent-cut-in scenario with multiple vehicles moving in parallel on a highway, in order to avoid a traffic accident caused by an abrupt speed change of the vehicle, a smoother change process of the time headway can be set, so as to make the vehicle reach the target time headway more slowly. In this process, the target time headway can be modified further according to a change in the target travelling scenario to avoid the situation in which a sudden change in the target travelling scenario, e.g., a closed road section or being cut-in, causes the driver to perceive the presence of a security threat. At this time, the target time headway and speed of reaching the target time headway can be configured further, to improve the user experience of the automatic driving.” See also [0071] (see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.”).
The examiner notes that while Wang is teaching using a machine learning model to determine the second safety distance, as defined by the claims, Wang teaches that the process is performed continuously and iteratively as the scenarios change in time. The “first safety distance” and “second safety distance” are simply labels for a distance between vehicles. It would be obvious to use the same process to determine one distance to also determine another distance as the scenario changes. Further, the examiner notes that claim is an apparatus claim and thus, the apparatus is capable of performing the determination of the first distance with machine learning just as it is taught to perform the determination for the second distance.
Regarding claim 5, the combination of Wang, Luo, and Ping Wang teach the apparatus of claim 1, wherein the set of features is associated with: (i) vehicle information associated with the one or more of the set of vehicles; (ii) driving information associated with the one or more of the set of vehicles; (iii) traffic information associated with a road on which the subject vehicle and the set of vehicles are being driven; (iv) user information associated with the one or more of the set of users; (v) temporal information; (vi) distance information indicating a distance between two vehicles of the set of vehicles; or (vii) a combination thereof (see at least Wang, which teaches the features can be traffic information such as traffic speed, traffic congestion, frequency of cut-ions [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.”).
Regarding claim 6, the combination of Wang, Luo, and Ping Wang teach the apparatus of claim 1, wherein, to determine the second safety distance, the computer program code instructions are configured to, when executed, cause the apparatus to:
apply a machine learning (ML) model on the set of features, wherein the ML model is trained to output a prediction result based on the set of features (see at least Wang [0049-0050] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions.” See [0095-0102] regarding calculation of base time headway using the scenario perception coefficient that is determined using the machine learning. Specifically, [0097] teaches “There are many ways to implement the time headway adjustment model, which can be statistical model of historical data, or self-learning model such as neural network.”, “See also [0084] “In this step, the scenario perception coefficient provides a medium for connecting the target travelling scenario with the adjustment of the time headway. The setting of the scenario perception coefficient can be obtained according to the target travelling scenario using big data statistics. Neural network models may also be used to implement automatic intelligent learning. For different regions and different users, different scenario perception coefficient settings can be applied.”);
determine the second safety distance at least in part based on the prediction result output by the ML model, wherein the second safety distance is indicative of a gap to be maintained between the subject vehicle and the preceding vehicle, and wherein the second distance is a distance to be maintained between the subject vehicle and the preceding vehicle to avoid an occupation of the gap by at least one vehicle of the set of vehicles (see at least Wang [0049-0054] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions… S103, control the vehicle according to the target time headway..[0053] In this step, the target time headway can be reached by setting to control the vehicle to go through different gradual processes based on different target travelling scenarios….[0054] For example, when the vehicle is in a frequent-cut-in scenario such as a congested road, and the current vehicle speed of the vehicle is low, the vehicle can be controlled to quickly reach the target time headway in order to prevent from being cut-in any further. When the vehicle is in a frequent-cut-in scenario with multiple vehicles moving in parallel on a highway, in order to avoid a traffic accident caused by an abrupt speed change of the vehicle, a smoother change process of the time headway can be set, so as to make the vehicle reach the target time headway more slowly. In this process, the target time headway can be modified further according to a change in the target travelling scenario to avoid the situation in which a sudden change in the target travelling scenario, e.g., a closed road section or being cut-in, causes the driver to perceive the presence of a security threat. At this time, the target time headway and speed of reaching the target time headway can be configured further, to improve the user experience of the automatic driving.” See also [0071] (see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.”).
Regarding claim 9, the combination of Wang, Luo, and Ping Wang teach the apparatus of claim 1, wherein, to obtain the set of features, the computer program code instructions are configured to, when executed, cause the apparatus to:
receive sensor data from one or more sensors of the subject vehicle, one or more sensors of the set of vehicles, or a combination thereof (see at least Wang 0043] “In a possible design, the real-time monitoring data also includes a real-time road condition image, which is captured by a vehicle-mounted camera. By performing image recognition on the real-time road condition image, the presence of special vehicle around can be detected, and the frequent-cut-in scenario can be recognized.”); and
obtain at least one of the set of features from the sensor data (see at least Wang [0043] wherein the feature can be traffic information “In a possible design, the real-time monitoring data also includes a real-time road condition image, which is captured by a vehicle-mounted camera. By performing image recognition on the real-time road condition image, the presence of special vehicle around can be detected, and the frequent-cut-in scenario can be recognized.”).
Regarding claim 11, the combination of Wang, Luo, and Ping Wang teach the apparatus of claim 1, wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
generate a virtual object indicating the first safety distance, the second safety distance, or both the first safety distance and second safety distance (see at least Luo, Figures 7-9, wherein the virtual object is the “parking spot” of Luo where the vehicle should be if following the safe distance, as described in Luo, lines 342-372 and “The on-board controller controls the instrument of the target vehicle to display a target image, wherein the target vehicle is a vehicle driven by the driver, and the target image is used to display the virtual image of the target vehicle and the first a virtual image of a following target, the first following target is a vehicle located in front of the target vehicle;…The adaptive cruise system is used to control the instrument of the target vehicle to display a real-time image through the on-board controller after acquiring the target following distance input by the user, wherein the real-time image is used to simulate the The process in which the distance between the target vehicle and the first following target is changed from the preset following distance to the target following distance. The real-time image includes: a virtual image of the target vehicle, the The virtual image and virtual parking space of the first following target, where the virtual parking space is where the virtual image of the target vehicle is when the distance between the target vehicle and the first following target is the target following distance. describe the location in the live image); and
output the virtual object on an infotainment system of the subject vehicle (see at least Luo, lines 342-372 and Figures 7-9 “The on-board controller controls the instrument of the target vehicle to display a target image, wherein the target vehicle is a vehicle driven by the driver, and the target image is used to display the virtual image of the target vehicle and the first a virtual image of a following target, the first following target is a vehicle located in front of the target vehicle;…The adaptive cruise system is used to control the instrument of the target vehicle to display a real-time image through the on-board controller after acquiring the target following distance input by the user, wherein the real-time image is used to simulate the The process in which the distance between the target vehicle and the first following target is changed from the preset following distance to the target following distance. The real-time image includes: a virtual image of the target vehicle, the The virtual image and virtual parking space of the first following target, where the virtual parking space is where the virtual image of the target vehicle is when the distance between the target vehicle and the first following target is the target following distance. describe the location in the live image”).
Regarding claim 14, the combination of Wang, Luo, and Ping Wang teach the apparatus of claim 1, wherein the computer program code instructions are configured to, when executed, cause the apparatus to control maneuver of the subject vehicle to maintain the first safety distance or the second safety distance (see at least Wang Figure 1, S103, control vehicle according to the target time headway, see also at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.”)
Claims 15 and 18 are rejected under the same rationale, mutatis mutandis, as claim 1, above.
Regarding claims 16 and 19, the combination of Wang, Luo, and Ping Wang teach the method and non-transitory computer readable storage medium as rejected in claims 15 and 18 and further teach, wherein the set of features is a second set of features, and wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
apply a first machine learning (ML) model on the first set of features, wherein the ML model is trained to output a prediction result based on the first set of features (see at least Wang [0049-0050] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions.” See [0095-0102] regarding calculation of base time headway using the scenario perception coefficient that is determined using the machine learning. Specifically, [0097] teaches “There are many ways to implement the time headway adjustment model, which can be statistical model of historical data, or self-learning model such as neural network.”, “See also [0084] “In this step, the scenario perception coefficient provides a medium for connecting the target travelling scenario with the adjustment of the time headway. The setting of the scenario perception coefficient can be obtained according to the target travelling scenario using big data statistics. Neural network models may also be used to implement automatic intelligent learning. For different regions and different users, different scenario perception coefficient settings can be applied.”);
wherein the first set of features are associated with: (i) information associated with the subject vehicle; (ii) vehicle information associated with the one or more of the set of vehicles; (iii) road information associated with a road on which the subject vehicle and the set of vehicles are being driven; (iv) traffic information associated with the road; (v) environmental information; (vi) distance information associated with a distance between the subject vehicle and a first lane of a set of lanes on the road; (vii) temporal information; or (viii) a combination thereof (see at least Wang, which teaches the features can be traffic information such as traffic speed, traffic congestion, frequency of cut-ions [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.”);and
determine the first safety distance at least in part based on the prediction result output by the first ML model (see at least Wang [0049-0054] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions… S103, control the vehicle according to the target time headway..[0053] In this step, the target time headway can be reached by setting to control the vehicle to go through different gradual processes based on different target travelling scenarios….[0054] For example, when the vehicle is in a frequent-cut-in scenario such as a congested road, and the current vehicle speed of the vehicle is low, the vehicle can be controlled to quickly reach the target time headway in order to prevent from being cut-in any further. When the vehicle is in a frequent-cut-in scenario with multiple vehicles moving in parallel on a highway, in order to avoid a traffic accident caused by an abrupt speed change of the vehicle, a smoother change process of the time headway can be set, so as to make the vehicle reach the target time headway more slowly. In this process, the target time headway can be modified further according to a change in the target travelling scenario to avoid the situation in which a sudden change in the target travelling scenario, e.g., a closed road section or being cut-in, causes the driver to perceive the presence of a security threat. At this time, the target time headway and speed of reaching the target time headway can be configured further, to improve the user experience of the automatic driving.” See also [0071] (see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.”).
The examiner notes that while Wang is teaching using a machine learning model to determine the second safety distance, as defined by the claims, Wang teaches that the process is performed continuously and iteratively as the scenarios change in time. The “first safety distance” and “second safety distance” are simply labels for a distance between vehicles. It would be obvious to use the same process to determine one distance to also determine another distance as the scenario changes. Further, the examiner notes that claim is an apparatus claim and thus, the apparatus is capable of performing the determination of the first distance with machine learning just as it is taught to perform the determination for the second distance.
Regarding claims 17 and 20, the combination of Wang, Luo, and Ping Wang teach the method and non-transitory computer readable storage medium as rejected in claims 16 and 19, respectively, and further teach:
apply a [second] machine learning (ML) model on the set of features, wherein the ML model is trained to output the second safety distance based on the set of features (see at least Wang [0049-0050] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions.” See [0095-0102] regarding calculation of base time headway using the scenario perception coefficient that is determined using the machine learning. Specifically, [0097] teaches “There are many ways to implement the time headway adjustment model, which can be statistical model of historical data, or self-learning model such as neural network.”, “See also [0084] “In this step, the scenario perception coefficient provides a medium for connecting the target travelling scenario with the adjustment of the time headway. The setting of the scenario perception coefficient can be obtained according to the target travelling scenario using big data statistics. Neural network models may also be used to implement automatic intelligent learning. For different regions and different users, different scenario perception coefficient settings can be applied.”);
determine the second safety distance at least in part based on the prediction result output by the second ML model, wherein the second safety distance is indicative of a gap to be maintained between the subject vehicle and the preceding vehicle, and wherein the second distance is a distance to be maintained between the subject vehicle and the preceding vehicle to avoid an occupation of the gap by at least one vehicle of the set of vehicles (see at least Wang [0049-0054] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions… S103, control the vehicle according to the target time headway..[0053] In this step, the target time headway can be reached by setting to control the vehicle to go through different gradual processes based on different target travelling scenarios….[0054] For example, when the vehicle is in a frequent-cut-in scenario such as a congested road, and the current vehicle speed of the vehicle is low, the vehicle can be controlled to quickly reach the target time headway in order to prevent from being cut-in any further. When the vehicle is in a frequent-cut-in scenario with multiple vehicles moving in parallel on a highway, in order to avoid a traffic accident caused by an abrupt speed change of the vehicle, a smoother change process of the time headway can be set, so as to make the vehicle reach the target time headway more slowly. In this process, the target time headway can be modified further according to a change in the target travelling scenario to avoid the situation in which a sudden change in the target travelling scenario, e.g., a closed road section or being cut-in, causes the driver to perceive the presence of a security threat. At this time, the target time headway and speed of reaching the target time headway can be configured further, to improve the user experience of the automatic driving.” See also [0071] (see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.”).
While Wang does not explicitly state that there is a first model for the first set of features and a second model for the second set of features, Wang does teach that there are plural neural network models which can be used for different regions, users, and scenarios such that different scenario perception coefficient settings can be applied (see Wang [0084]). Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to utilize a first model for the first features (a first scenario) and use a second model for the second features (a second scenario), with a reasonable expectation of success, because as Wang teaches it allows for different coefficient settings to be applied to match the current scenario.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Luo and Ping Wang in view of Peterson (US Pub. No. 2019/0248379, hereinafter “Peterson”) and further in view of Gesang et al. ( US Pub. No. 2023/0347872, hereinafter “Gesang”)
Regarding claim 7, the combination of Wang, Luo, and Ping Wang teach the apparatus of claim 1, but do not explicitly discloses wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
receive a user input associated with a determination of a navigation route from a first location to a second location;
determine, from a map database, a set of navigation routes from the first location to the second location based on the received user input, wherein each navigation route of the set of navigation routes comprises information indicating the first safety distance, the second safety distance, or both the first safety distance and the second safety distance;
select a first navigation route from the determined set of navigation routes; and
output the selected first navigation route on the user interface.
Peterson teaches wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
receive a user input associated with a determination of a navigation route from a first location to a second location (see at least Peterson Figure 3 and 4, and [0040] “0040] Beginning with FIG. 3, a GUI 300 is shown. A user may provide text input to input box 302 of the GUI 300 by selecting it using touch input and inputting text using a hard or soft keyboard to indicate a destination to which the user would like to travel via an autonomous driving vehicle such as the vehicle 216.”);
determine, from a map database, a set of navigation routes from the first location to the second location based on the received user input (see at least Peterson Figure 3 and 4, and [0044] “Now describing FIG. 4, it shows a GUI 400 that may also be presented on a display controlled by the user's device responsive to receipt of the response that was output by the server (or user's device) that indicates a map and directions for the user and/or autonomous vehicle to follow along one or more potential routes to the user's destination. In turn, the directions (and also the notifications described below) may then be output by the user's own device via a display and/or speakers. Additionally, or alternatively, the directions may then be output to the autonomous vehicle for the vehicle to control its steering, power, and braking mechanisms to autonomously follow the directions to the destination…[0045] As may be appreciated from FIG. 4, the GUI 400 indicates a first route 402 and a second route 404 from an origination point “A” to a destination point “B”. Route 402 as presented on the GUI 400 also indicates that a section 406 of the route 402 involves manual driving rather than autonomous driving..”); [[wherein each navigation route of the set of navigation routes comprises information indicating the first safety distance, the second safety distance, or a combination thereof]];
select a first navigation route from the determined set of navigation routes (see at least Peterson Figure 3 and 4, [0046] “The GUI 400 also includes a section 410 listing the routes 402 and 404. Selector 412 may be selected (e.g., based on touch or cursor input to the selector 412) to provide user input selecting route 402 as the route for the autonomous vehicle to follow even though the user will have to take over and drive manually for part of the route 402. …Selector 414 may be selected to provide user input selecting route 404 as the route for the autonomous vehicle to follow.”); and
output the selected first navigation route on the user interface (see at least Peterson Figure 4 and 5).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, and Ping Wang with the teaching of Peterson to allow for entering a destination and presenting of routes, with a reasonable expectation of success, because it enhances the user experience to obtain turn by turn navigation instructions to their desired destination.
The combination of Wang, Luo, Ping Wang and Peterson teach providing information regarding each navigation route of the set of navigation routes, but do not explicitly teach the information indicates the first safety distance, the second safety distance, or both the first safety distance and the second safety distance.
However, Gesang teaches providing information indicating the first safety distance, the second safety distance, or both the first safety distance and the second safety distance (see at least Gesang [0184] and [0201] which teaches slope information is stored in the map and retrieved to make a determination regarding safe following distance and thus “indicates a following distance. [0184] “For example, the memory of the vehicle-mounted map unit 240 shown in FIG. 1 can comprise the 3D map with the road meter-level positioning precision (longitude and latitude) and longitudinal slope precision with 0.1 degree accuracy. Various advanced driver assistance system (ADAS) maps containing the above road 3D information have already been commercialized in batches in major global automotive markets.” [0201] “ The VCU 201 dynamically adjusts the safe following distance L.sub.s of adaptive cruise control according to the operation information including the gross weight, vehicle configuration and vehicle speed, and combining with the current 3D road information (longitude, latitude and longitudinal slope) of the vehicle as well as the longitudinal slope distribution function, bend curvature and other three-dimensional information of roads within the vehicle electronic horizon stored in the map unit 240…L.sub.s is very important for the safety of ACE heavy duty truck in the above PAC mode. The safe following distance L.sub.s can be subdivided into three specific distances: L1 is the early warning distance, L2 is the warning distance, L3 is the dangerous distance, where L1>L2>L3. The VCU 201 can dynamically calculate the above three following distances (L1, L2, L3) according to the vehicle configuration parameter and operating condition data (e.g., vehicle gross weight and vehicle speed), real-time weather (wind, rain, snow, ice, temperature, etc.) and the 3D road data (longitude, latitude and longitudinal slope, etc.) within a kilometer-level range ahead of the vehicle.”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, Ping Wang and Peterson with the teaching of Gesang to provide information indicating the safety distances, with a reasonable expectation of success, because as Gesang teaches the longitudinal slope can be used to determine safety distances as well as fuel economy and thus, are important data points that can be used to make a selection of the preferred route.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Luo, Ping Wang in view of Gesang.
Regarding claim 8, the combination of Wang, Luo, and Ping Wang teach the apparatus of claim 1, but do not disclose wherein, to obtain the set of features, the computer program code instructions are configured to, when executed, cause the apparatus to:
transmit a command to a map database, wherein the command is associated with retrieval of at least one feature of the set of features from the map database; and
obtain the at least one feature of the set of features from the map database.
Gesang teaches wherein, to obtain the set of features, the computer program code instructions are configured to, when executed, cause the apparatus to:
transmit a command to a map database, wherein the command is associated with retrieval of at least one feature of the set of features from the map database (see at least Gesang [0184] and [0201] which teaches slope information is stored in the map and retrieved to make a determination regarding safe following distance [0184] “For example, the memory of the vehicle-mounted map unit 240 shown in FIG. 1 can comprise the 3D map with the road meter-level positioning precision (longitude and latitude) and longitudinal slope precision with 0.1 degree accuracy. Various advanced driver assistance system (ADAS) maps containing the above road 3D information have already been commercialized in batches in major global automotive markets.” [0201] “ The VCU 201 dynamically adjusts the safe following distance L.sub.s of adaptive cruise control according to the operation information including the gross weight, vehicle configuration and vehicle speed, and combining with the current 3D road information (longitude, latitude and longitudinal slope) of the vehicle as well as the longitudinal slope distribution function, bend curvature and other three-dimensional information of roads within the vehicle electronic horizon stored in the map unit 240…L.sub.s is very important for the safety of ACE heavy duty truck in the above PAC mode. The safe following distance L.sub.s can be subdivided into three specific distances: L1 is the early warning distance, L2 is the warning distance, L3 is the dangerous distance, where L1>L2>L3. The VCU 201 can dynamically calculate the above three following distances (L1, L2, L3) according to the vehicle configuration parameter and operating condition data (e.g., vehicle gross weight and vehicle speed), real-time weather (wind, rain, snow, ice, temperature, etc.) and the 3D road data (longitude, latitude and longitudinal slope, etc.) within a kilometer-level range ahead of the vehicle.”); and
obtain the at least one feature of the set of features from the map database (see at least Gesang [0184] and [0201] which teaches slope information is stored in the map and retrieved to make a determination regarding safe following distance and thus “indicates a following distance. [0184] “For example, the memory of the vehicle-mounted map unit 240 shown in FIG. 1 can comprise the 3D map with the road meter-level positioning precision (longitude and latitude) and longitudinal slope precision with 0.1 degree accuracy. Various advanced driver assistance system (ADAS) maps containing the above road 3D information have already been commercialized in batches in major global automotive markets.” [0201] “ The VCU 201 dynamically adjusts the safe following distance L.sub.s of adaptive cruise control according to the operation information including the gross weight, vehicle configuration and vehicle speed, and combining with the current 3D road information (longitude, latitude and longitudinal slope) of the vehicle as well as the longitudinal slope distribution function, bend curvature and other three-dimensional information of roads within the vehicle electronic horizon stored in the map unit 240…L.sub.s is very important for the safety of ACE heavy duty truck in the above PAC mode. The safe following distance L.sub.s can be subdivided into three specific distances: L1 is the early warning distance, L2 is the warning distance, L3 is the dangerous distance, where L1>L2>L3. The VCU 201 can dynamically calculate the above three following distances (L1, L2, L3) according to the vehicle configuration parameter and operating condition data (e.g., vehicle gross weight and vehicle speed), real-time weather (wind, rain, snow, ice, temperature, etc.) and the 3D road data (longitude, latitude and longitudinal slope, etc.) within a kilometer-level range ahead of the vehicle.”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, and Ping Wang with the teaching of Gesang to obtain the at least one feature from the map database, with a reasonable expectation of success, because as Gesang teaches the longitudinal slope can be used to determine safety distances as well as fuel economy and thus, are important data points that can be used to make a selection of the preferred route.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Luo and Ping Wang in view of Wells et al. (US Patent 11,702,076, hereinafter “Wells”) and further in view of Polisson et al. ( US Pub. No. 2018/0050698, hereinafter “Polisson”).
Regarding claim 10, Wang, Luo and Ping Wang teach the apparatus of claim 1, but do not teach wherein the subject vehicle is an electric vehicle, and wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
compute a driving range of the subject vehicle based on the first safety distance, the second safety distance, or both the first safety distance and the second safety distance; and
output the computed driving range of the subject vehicle on the user interface.
Wells teaches wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
compute [a driving range] a fuel consumption efficiency of the subject vehicle based on the first safety distance, the second safety distance, or both the first safety distance and the second safety distance (see at least Wells throughout and col. 1, lines 45-60 “(4) In an embodiment, a system for providing following a graphical representation of following distances to an augmented reality (AR) vehicle heads-up-display (HUD) system of a trailing vehicle includes a processor, a vehicle type database, and a memory. The vehicle type database includes a plurality of vehicle type profiles, each of the plurality of vehicle type profiles being associated with a vehicle type having a vehicle type specific aerodynamic profile and including optimal following ranges associated with the vehicle type, each of the optimal following distance ranges being based on the vehicle type specific aerodynamic profile of the vehicle type and a vehicle speed of the vehicle type wherein a trailing vehicle disposed in the optimal following range is configured to operate at an optimal fuel efficiency.”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, and Ping Wang with the teaching of Wells, with a reasonable expectation of success, because as Wells teaches this informs the driver of the most efficient safe following distances.
The examiner notes that while Wells teaches computing the most optimized following distances based on fuel efficiency, and displaying the most optimized following distances, Wells does not teach outputting the computed driving range of the subject vehicle on the user interface. The examiner notes that computed driving range is a common calculation that is based on the fuel efficiency. The computed driving range is the product of fuel efficiency (e.g. in miles per gallon) and the tank size of the vehicle (e.g. in gallons).
Polisson teaches computing a driving range based on fuel consumption and output the computed driving range of the subject vehicle on the user interface (see at least Polisson [0054] “The HUD may also display other operation data of the vehicle, such as the current speed, whether the vehicle is travelling above the speed limit for the road the driver is on, remaining fuel range, distance to destination, etc.”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, Ping Wang and Wells with the teaching of Polisson to display the remaining fuel range, with a reasonable expectation of success, because it allows for drivers to be informed of vital information without taking their eyes off the road.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Luo and Ping Wang in view of Broll et. al. (US Pub. No. 2019/0232962, hereinafter “Broll”).
Regarding claim 12, the combination of Wang, Luo, and Ping Wang teach the apparatus of claim 1, including receive at least one notification from the one or more of the set of vehicles, wherein the at least one notification is associated with an overtaking of the subject vehicle by the one or more of the set of vehicles (see at least Ping Wang, abstract “A rear car overtaking reminding system based on distance measuring is mainly composed of a distance measuring system, an overtaking identification system and an overtaking reminding system. The distance measuring system measures the distance between the left adjacent lane and a car, the distance between the right adjacent lane and the car and the distance between a car behind the lane where the car is located and the car; the overtaking identification system judges an overtaking intention of the rear car according to distance change information of the adjacent moments and the safety distance; the overtaking reminding system drives corresponding overtaking indicator lights and can give out voice prompt to remind a driver that the rear car is about to overtake from the left side or the right side according to the overtaking intention. By means of the rear car overtaking reminding system based on distance measuring, it can be guaranteed that the driver perceives approaching of the rear car and the overtaking intention of the rear car, the condition that lane changing is conducted when the rear car overtakes is avoided, and safe driving of the car is guaranteed.” And lines 81-84 “2，The present invention may indicate the overtaking intention of the rear vehicle by the overtaking indicator light or may also be broadcast by the driver to select the overtaking voice prompt system to broadcast the content of the overtaking prompt message of the overtaking vehicle, so as to increase driving pleasure and be conducive to safe driving.”). However the combination does not but do not disclose wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
transmit the first safety distance, the second safety distance, or both the first safety distance and the second safety distance to the one or more of the set of vehicles; and
Broll teaches wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
transmit the first safety distance, the second safety distance, or both the first safety distance and the second safety distance to the one or more of the set of vehicles (see at least Broll [0032-0033] “[0032] The dynamic vehicle distance Adyn is selected in such a manner that a collision between the vehicles 10, 20, 30, 40 may also be prevented in the case of an emergency braking procedure N of the preceding vehicle VF in a dangerous situation. Furthermore, the dynamic vehicle distance Adyn is selected in such a manner that it is possible to optimize fuel consumption and road capacity utilization…[0033] A V2V signal S1 is constantly transmitted between the preceding vehicle VF and the respective following vehicle FF via a wireless data communication 50 (vehicle-to-vehicle communication, V2V) so as to be able to coordinate or rather monitor the platoon 100. The V2V signal S1 transmits in this case in particular a vehicle velocity v_VF, vFF of the respective vehicles VF, FF, the dynamic vehicle distance Adyn and also the information regarding whether an emergency braking procedure N has been initiated. A V2V signal S1 is transmitted within a first transmission time t1 exclusively in a wireless manner.”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, and Ping Wang with the teaching of Broll, with a reasonable expectation of success, because as Broll teaches this allows the vehicles to coordinate the platoon.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Luo and Ping Wang in view of Eguchi et al. (US Pub. No. 2020/0380870, hereinafter “Eguchi”).
Regarding claim 13, the combination of Wang, Luo, and Ping Wang teach the apparatus of claim 1, including outputting a likelihood value on the user interface (see at least Ping Wang, abstract “A rear car overtaking reminding system based on distance measuring is mainly composed of a distance measuring system, an overtaking identification system and an overtaking reminding system. The distance measuring system measures the distance between the left adjacent lane and a car, the distance between the right adjacent lane and the car and the distance between a car behind the lane where the car is located and the car; the overtaking identification system judges an overtaking intention of the rear car according to distance change information of the adjacent moments and the safety distance; the overtaking reminding system drives corresponding overtaking indicator lights and can give out voice prompt to remind a driver that the rear car is about to overtake from the left side or the right side according to the overtaking intention. By means of the rear car overtaking reminding system based on distance measuring, it can be guaranteed that the driver perceives approaching of the rear car and the overtaking intention of the rear car, the condition that lane changing is conducted when the rear car overtakes is avoided, and safe driving of the car is guaranteed.” And lines 81-84 “2，The present invention may indicate the overtaking intention of the rear vehicle by the overtaking indicator light or may also be broadcast by the driver to select the overtaking voice prompt system to broadcast the content of the overtaking prompt message of the overtaking vehicle, so as to increase driving pleasure and be conducive to safe driving.”). However, the combination does not explicitly teach wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
calculate a likelihood value indicative of a likelihood of the one or more of the set of vehicles overtaking the subject vehicle and occupying a gap between the subject vehicle and the preceding vehicle; and
Eguchi teaches:
calculating a likelihood value indicative of a likelihood of the one or more of the set of vehicles overtaking the subject vehicle and occupying a gap between the subject vehicle and the preceding vehicle (see at least Eguchi Fig. 1 and [0018] “In the example shown in FIG. 1, the drone 100 moves to a junction (exit) of a highway positioned in front of the platoon Co in accordance with the instruction from the ECU 10 (refer to FIG. 2). The junction of the highway is a location where other vehicles (surrounding vehicles SM that travel on the merging lane) are likely to enter between the plurality of vehicles M1 to M4 that form the platoon Co, that is, the platoon Co is disturbed and the platooning is likely to become impossible. In the vehicle control system according to the present embodiment, the ECU 10 (refer to FIG. 2) acquires the situation of the surrounding vehicle SM from the camera 101 of the drone 100, the control to shorten the distance (inter-vehicle distance) between the plurality of vehicles M1 to M4 is performed in accordance with the situation of the surrounding vehicle, and accordingly, the disturbance of the platoon Co is avoided, for example, even at the location where the platoon Co is likely to be disturbed, such as the junction of the highway, and continuation of the platooning is realized.” The examiner interprets the teaching of Eguchi of the determination that vehicles are “likely to enter between the plurality of vehicles M1 to M4” to correspond to a likelihood value. See also [0048-0049] ) and
outputting the [likelihood value] on the user interface (see also Eguchi [0030] “Further, the acquisition unit 11 may output the imaging result of the camera 101 to an on-vehicle monitor (not shown) and notify the driver of the situation in the vicinity of the exit to the highway” and [0036] “In addition, for the vehicle M where the driver is located, the control of the vehicle control unit 13 does not necessarily have to be the control of the actuator 60, and the vehicle control unit 13 may notify an on-vehicle monitor (not shown) of the shortening of the distance between the plurality of vehicles M1 to M4, deceleration of the vehicles M1 to M4, and the like. In other words, for a vehicle where the driver is located, control of the inter-vehicle distance and the like may not be performed automatically.”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, and Ping Wang with the teaching Eguchi, with a reasonable expectation of success, because as Eguchi teaches, awareness of the cutting in allows the vehicles to act appropriately.
Claim(s) 1-6, 9, 11 and 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US Pub. No. 2022/0097697, hereinafter “Wang”) in view of Luo (CN 115123226A, hereinafter “Luo”, citations correspond to the machine translation provided in the Non-Final Office Action) and in further view of Nagai (JP-6977633-B2, citations correspond to the machine translation provided herewith).
Regarding claim 1, Wang discloses an apparatus comprising at least one processor and at least one non-transitory memory including computer program code instructions, the computer program code instructions (see at least Wang Figure 8 processor 801, memory 802, and [0169-0173] “As shown in FIG. 8, the electronic device includes: one or more processors 801, a memory 802, and interfaces for connecting various components, including a high-speed interface and a low-speed interface…The processor may process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of the GUI on an external input/output apparatus (such as a display apparatus coupled to an interface)…[0170] The memory 802 is a non-transitory computer-readable storage medium provided in the present disclosure. The memory stores instructions executable by at least one processor to enable the at least one processor to execute the vehicle control method provided in the present disclosure. The non-transitory computer-readable storage medium of the present disclosure stores computer instructions, and the computer instructions are used to make a computer execute the vehicle control method provided in the present disclosure.”) configured to, when executed, cause apparatus to:
determine or retrieve a first safety distance between a subject vehicle and a preceding vehicle among a set of vehicles within a pre-determined distance of the subject vehicle, wherein the preceding vehicle is traveling directly ahead of the subject vehicle (see at least Wang “safe distance” as described as a theoretical value that is known [0004] “ Existing automatic driving typically takes a safe distance derived from theoretical calculations of physics as the core in designing vehicle control solutions on active safety. For example, in adaptive cruise control, stop-and-go is performed according to a safety braking distance between a front and a rear vehicle. In an automatic overtaking, corresponding measures are taken according to a safe distance set between two vehicles.” See also [0049] “In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway.” See also Wang [0032] “However, for automatic driving in the prior art, control strategies are typically designed from the perspective of the vehicle. For example, an operation scheme of an adaptive cruise system is as follows: when there is no vehicle ahead, the adaptive cruise system will make the vehicle travel at a speed set by a user through controlling the throttle and the brake; when following a vehicle ahead, a control unit can keep a distance from the vehicle by braking until a safe distance is restored if the distance to the vehicle ahead is less than a safe vehicle-following distance; when the distance to the vehicle ahead is less than the safe vehicle-following distance, the host vehicle may still drive at a set speed. The safe distance is typically defined in terms of the speed of the host vehicle and the speed of the vehicle ahead, as well as the vehicle-following strategy adopted, such as time headway and so on.” See also [0063] and [0071] The examiner notes that while the majority of Wang discusses time headway, the time headway is based on the distance between the two vehicles and the speed of the vehicles, and thus is intrinsically linked to the distance between the vehicles, though, where possible, the examiner cites to the disclosure of safe distance rather than the time headway.);
obtain a set of features associated with one or more of the set of vehicles, one or more of a set of users driving the set of vehicles, or a combination thereof (see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.”);
monitor a trigger event while maintaining the first safety distance, see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.” See also Figure 1, S102 and S103 which controls the vehicle to a target headway, the examiner notes that to determine a target headway, the system must necessarily determine a target distance, for example see [0048] “It should be noted that the time headway is an important technical parameter index in automatic driving, which is calculated based on the distance between two vehicles and a current vehicle speed of the host vehicle.”).
determine a second safety distance to be maintained between the subject vehicle and the preceding vehicle based on the first safety distance and the set of features, wherein the second safety distance is less than the first safety distance and is maintained to avoid an occupation of the gap by at least one vehicle of the set of vehicles (see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.” See also Figure 1, S102 and S103 which controls the vehicle to a target headway, the examiner notes that to determine a target headway, the system must necessarily determine a target distance, for example see [0048] “It should be noted that the time headway is an important technical parameter index in automatic driving, which is calculated based on the distance between two vehicles and a current vehicle speed of the host vehicle.”)
cause the apparatus to control maneuver of the subject vehicle or inform, via vehicle-to-vehicle communication, at least one vehicle of the set of vehicles (see at least Wang [0071] “Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.” See also Figure 1, S102 and S103 which controls the vehicle to a target headway, the examiner notes that to determine a target headway, the system must necessarily determine a target distance, for example see [0048] “It should be noted that the time headway is an important technical parameter index in automatic driving, which is calculated based on the distance between two vehicles and a current vehicle speed of the host vehicle.”)
Wang discloses controlling the vehicle to the second safety distance, but does not teach outputting the second safety distances on a user interface.
Luo teaches outputting the second safety distances on a user interface (see at least Luo, lines 342-372 and Figures 7-9 “The on-board controller controls the instrument of the target vehicle to display a target image, wherein the target vehicle is a vehicle driven by the driver, and the target image is used to display the virtual image of the target vehicle and the first a virtual image of a following target, the first following target is a vehicle located in front of the target vehicle;…The adaptive cruise system is used to control the instrument of the target vehicle to display a real-time image through the on-board controller after acquiring the target following distance input by the user, wherein the real-time image is used to simulate the The process in which the distance between the target vehicle and the first following target is changed from the preset following distance to the target following distance. The real-time image includes: a virtual image of the target vehicle, the The virtual image and virtual parking space of the first following target, where the virtual parking space is where the virtual image of the target vehicle is when the distance between the target vehicle and the first following target is the target following distance. describe the location in the live image).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang with the teaching of Luo to display the second safety distance, with a reasonable expectation of success, because as Luo teaches, providing the driving scene improves the user experience (see Luo, lines 14-34).
The combination of Wang and Luo teach the method as claimed including monitoring a trigger event of vehicles overtaking the subject vehicle, but the combination does not explicitly teach wherein the trigger event being indicative of at least one vehicle of the set of vehicles trying to overtake the subject vehicle and to occupy a gap between the subject vehicle and the preceding vehicle.
Nagai teaches wherein the trigger event being indicative of at least one vehicle of the set of vehicles trying to overtake the subject vehicle and to occupy a gap between the subject vehicle and the preceding vehicle (see at least Nagai Figure 2 wherein Vehicle B2 is attempting to overtake the subject vehicle A, see also Nagai [0021] “Further, the safe inter-vehicle distance from the other vehicle traveling in such an adjacent lane is secured when the presence of the second other vehicle B2 traveling behind the adjacent lane of the own vehicle A is detected from the sensor information. It may be a thing. In this case, when the second other vehicle B2 approaches the own vehicle A, the speed of the own vehicle A is reduced to increase the distance between the second other vehicle B1.” [0016] “Overtaking means that a following vehicle traveling in the second lane adjacent to the first lane changes lanes and exits in front of the vehicle traveling in the first lane. In the present embodiment, overtaking means that the second other vehicle B2 traveling behind the adjacent lane L1 changes lanes and exits in front of the own vehicle A traveling in the own lane L.”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Want and Luo with the teaching of Nagai, with a reasonable expectation of success to use the trigger event indicating a vehicle will overtake the subject vehicle, because as Nagai teaches this allows the driver to be aware of vehicles that will likely be cutting in and further allows the vehicle to be controlled as appropriate (see at least Nagai [0008], [0006] [0021]).
Regarding claim 2, the combination of Wang, Luo, and Nagai teach the apparatus of claim 1, wherein, to determine the second safety distance, the computer program code instructions are configured to, when executed, cause the apparatus to:
monitor the trigger event indicative of a change in a value of at least one feature associated with the set of vehicles or the set of users (see at least Wang 0043] “In a possible design, the real-time monitoring data also includes a real-time road condition image, which is captured by a vehicle-mounted camera. By performing image recognition on the real-time road condition image, the presence of special vehicle around can be detected, and the frequent-cut-in scenario can be recognized.”) ; and
responsive to detecting the trigger event, determine the second safety distance,
wherein the trigger event is detected based on at least one of:
(i) whether any vehicle of the set of vehicles within the pre-determined distance of the subject vehicle is changing lanes (see at least Nagai Figure 2 wherein Vehicle B2 is attempting to overtake the subject vehicle A, see also Nagai [0021] “Further, the safe inter-vehicle distance from the other vehicle traveling in such an adjacent lane is secured when the presence of the second other vehicle B2 traveling behind the adjacent lane of the own vehicle A is detected from the sensor information. It may be a thing. In this case, when the second other vehicle B2 approaches the own vehicle A, the speed of the own vehicle A is reduced to increase the distance between the second other vehicle B1.” [0016] “Overtaking means that a following vehicle traveling in the second lane adjacent to the first lane changes lanes and exits in front of the vehicle traveling in the first lane. In the present embodiment, overtaking means that the second other vehicle B2 traveling behind the adjacent lane L1 changes lanes and exits in front of the own vehicle A traveling in the own lane L.” See also at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.” See also Figure 1, S102 and S103 which controls the vehicle to a target headway, the examiner notes that to determine a target headway, the system must necessarily determine a target distance, for example see [0048] “It should be noted that the time headway is an important technical parameter index in automatic driving, which is calculated based on the distance between two vehicles and a current vehicle speed of the host vehicle.”);
(ii) whether any vehicle of the set of vehicles is driving aggressively at high speed
(iii) whether any vehicle of the set of vehicles within the pre-determined distance of the subject vehicle is driving at uneven speed; or
(iv) whether at least one driver of the set of drivers driving the set of vehicles is looking at a side or a rear-view mirror.
Regarding claim 3, the combination of Wang, Luo, and Nagai teach the apparatus of claim 1, wherein the set of features is a second set of features, and wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
obtain a first set of features associated with: (i) information associated with the subject vehicle; (ii) vehicle information associated with the one or more of the set of vehicles; (iii) road information associated with a road on which the subject vehicle and the set of vehicles are being driven; (iv) traffic information associated with the road; (v) environmental information; (vi) distance information associated with a distance between the subject vehicle and a first lane of a set of lanes on the road; (vii) temporal information; or (viii) a combination thereof (see at least Wang, which teaches the features can be traffic information such as traffic speed, traffic congestion, frequency of cut-ions [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.”)
determine the first safety distance to be maintained between the subject vehicle and the preceding vehicle based on the first set of features (see at least Wang, which continuously monitors the road conditions and updates the safe distance as the target scenario changes, see for example [0055] “The target time headway is determined based on the monitored target travelling scenario. Furthermore, the state of the host vehicle is controlled dynamically, so as to reach the target time headway. Moreover, correction can be made in time if the target scenario changes during this process…[0056] Specifically, a target travelling scenario is determined according to a real-time monitoring data when a preset update condition has been fulfilled. Then, the target time headway is determined according to the target travelling scenario, and finally the vehicle is controlled according to the target time headway” See also [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.”); and
output the first safety distance on the user interface (see at least Luo, lines 342-372 and Figures 7-9 “The on-board controller controls the instrument of the target vehicle to display a target image, wherein the target vehicle is a vehicle driven by the driver, and the target image is used to display the virtual image of the target vehicle and the first a virtual image of a following target, the first following target is a vehicle located in front of the target vehicle;…The adaptive cruise system is used to control the instrument of the target vehicle to display a real-time image through the on-board controller after acquiring the target following distance input by the user, wherein the real-time image is used to simulate the The process in which the distance between the target vehicle and the first following target is changed from the preset following distance to the target following distance. The real-time image includes: a virtual image of the target vehicle, the The virtual image and virtual parking space of the first following target, where the virtual parking space is where the virtual image of the target vehicle is when the distance between the target vehicle and the first following target is the target following distance. describe the location in the live image).
Regarding claim 4, the combination of Wang, Luo, and Nagai teach the apparatus of claim 3, wherein, to determine the first safety distance, the computer program code instructions are configured to, when executed, cause the apparatus to:
apply a machine learning (ML) model on the first set of features, wherein the ML model is trained to output a prediction result based on the first set of features (see at least Wang [0049-0050] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions.” See [0095-0102] regarding calculation of base time headway using the scenario perception coefficient that is determined using the machine learning. Specifically, [0097] teaches “There are many ways to implement the time headway adjustment model, which can be statistical model of historical data, or self-learning model such as neural network.”, “See also [0084] “In this step, the scenario perception coefficient provides a medium for connecting the target travelling scenario with the adjustment of the time headway. The setting of the scenario perception coefficient can be obtained according to the target travelling scenario using big data statistics. Neural network models may also be used to implement automatic intelligent learning. For different regions and different users, different scenario perception coefficient settings can be applied.”); and
determine the first safety distance at least in part based on the prediction result output by the ML model (see at least Wang [0049-0054] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions… S103, control the vehicle according to the target time headway..[0053] In this step, the target time headway can be reached by setting to control the vehicle to go through different gradual processes based on different target travelling scenarios….[0054] For example, when the vehicle is in a frequent-cut-in scenario such as a congested road, and the current vehicle speed of the vehicle is low, the vehicle can be controlled to quickly reach the target time headway in order to prevent from being cut-in any further. When the vehicle is in a frequent-cut-in scenario with multiple vehicles moving in parallel on a highway, in order to avoid a traffic accident caused by an abrupt speed change of the vehicle, a smoother change process of the time headway can be set, so as to make the vehicle reach the target time headway more slowly. In this process, the target time headway can be modified further according to a change in the target travelling scenario to avoid the situation in which a sudden change in the target travelling scenario, e.g., a closed road section or being cut-in, causes the driver to perceive the presence of a security threat. At this time, the target time headway and speed of reaching the target time headway can be configured further, to improve the user experience of the automatic driving.” See also [0071] (see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.”).
The examiner notes that while Wang is teaching using a machine learning model to determine the second safety distance, as defined by the claims, Wang teaches that the process is performed continuously and iteratively as the scenarios change in time. The “first safety distance” and “second safety distance” are simply labels for a distance between vehicles. It would be obvious to use the same process to determine one distance to also determine another distance as the scenario changes. Further, the examiner notes that claim is an apparatus claim and thus, the apparatus is capable of performing the determination of the first distance with machine learning just as it is taught to perform the determination for the second distance.
Regarding claim 5, the combination of Wang, Luo, and Nagai teach the apparatus of claim 1, wherein the set of features is associated with: (i) vehicle information associated with the one or more of the set of vehicles; (ii) driving information associated with the one or more of the set of vehicles; (iii) traffic information associated with a road on which the subject vehicle and the set of vehicles are being driven; (iv) user information associated with the one or more of the set of users; (v) temporal information; (vi) distance information indicating a distance between two vehicles of the set of vehicles; or (vii) a combination thereof (see at least Wang, which teaches the features can be traffic information such as traffic speed, traffic congestion, frequency of cut-ions [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.”).
Regarding claim 6, the combination of Wang, Luo, and Nagai teach the apparatus of claim 1, wherein, to determine the second safety distance, the computer program code instructions are configured to, when executed, cause the apparatus to:
apply a machine learning (ML) model on the set of features, wherein the ML model is trained to output a prediction result based on the set of features (see at least Wang [0049-0050] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions.” See [0095-0102] regarding calculation of base time headway using the scenario perception coefficient that is determined using the machine learning. Specifically, [0097] teaches “There are many ways to implement the time headway adjustment model, which can be statistical model of historical data, or self-learning model such as neural network.”, “See also [0084] “In this step, the scenario perception coefficient provides a medium for connecting the target travelling scenario with the adjustment of the time headway. The setting of the scenario perception coefficient can be obtained according to the target travelling scenario using big data statistics. Neural network models may also be used to implement automatic intelligent learning. For different regions and different users, different scenario perception coefficient settings can be applied.”);
determine the second safety distance at least in part based on the prediction result output by the ML model, wherein the second safety distance is indicative of a gap to be maintained between the subject vehicle and the preceding vehicle, and wherein the second distance is a distance to be maintained between the subject vehicle and the preceding vehicle to avoid an occupation of the gap by at least one vehicle of the set of vehicles (see at least Wang [0049-0054] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions… S103, control the vehicle according to the target time headway..[0053] In this step, the target time headway can be reached by setting to control the vehicle to go through different gradual processes based on different target travelling scenarios….[0054] For example, when the vehicle is in a frequent-cut-in scenario such as a congested road, and the current vehicle speed of the vehicle is low, the vehicle can be controlled to quickly reach the target time headway in order to prevent from being cut-in any further. When the vehicle is in a frequent-cut-in scenario with multiple vehicles moving in parallel on a highway, in order to avoid a traffic accident caused by an abrupt speed change of the vehicle, a smoother change process of the time headway can be set, so as to make the vehicle reach the target time headway more slowly. In this process, the target time headway can be modified further according to a change in the target travelling scenario to avoid the situation in which a sudden change in the target travelling scenario, e.g., a closed road section or being cut-in, causes the driver to perceive the presence of a security threat. At this time, the target time headway and speed of reaching the target time headway can be configured further, to improve the user experience of the automatic driving.” See also [0071] (see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.”).
Regarding claim 9, the combination of Wang, Luo, and Nagai teach the apparatus of claim 1, wherein, to obtain the set of features, the computer program code instructions are configured to, when executed, cause the apparatus to:
receive sensor data from one or more sensors of the subject vehicle, one or more sensors of the set of vehicles, or a combination thereof (see at least Wang 0043] “In a possible design, the real-time monitoring data also includes a real-time road condition image, which is captured by a vehicle-mounted camera. By performing image recognition on the real-time road condition image, the presence of special vehicle around can be detected, and the frequent-cut-in scenario can be recognized.”); and
obtain at least one of the set of features from the sensor data (see at least Wang [0043] wherein the feature can be traffic information “In a possible design, the real-time monitoring data also includes a real-time road condition image, which is captured by a vehicle-mounted camera. By performing image recognition on the real-time road condition image, the presence of special vehicle around can be detected, and the frequent-cut-in scenario can be recognized.”).
Regarding claim 11, the combination of Wang, Luo, and Nagai teach the apparatus of claim 1, wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
generate a virtual object indicating the first safety distance, the second safety distance, or both the first safety distance and second safety distance (see at least Luo, Figures 7-9, wherein the virtual object is the “parking spot” of Luo where the vehicle should be if following the safe distance, as described in Luo, lines 342-372 and “The on-board controller controls the instrument of the target vehicle to display a target image, wherein the target vehicle is a vehicle driven by the driver, and the target image is used to display the virtual image of the target vehicle and the first a virtual image of a following target, the first following target is a vehicle located in front of the target vehicle;…The adaptive cruise system is used to control the instrument of the target vehicle to display a real-time image through the on-board controller after acquiring the target following distance input by the user, wherein the real-time image is used to simulate the The process in which the distance between the target vehicle and the first following target is changed from the preset following distance to the target following distance. The real-time image includes: a virtual image of the target vehicle, the The virtual image and virtual parking space of the first following target, where the virtual parking space is where the virtual image of the target vehicle is when the distance between the target vehicle and the first following target is the target following distance. describe the location in the live image); and
output the virtual object on an infotainment system of the subject vehicle (see at least Luo, lines 342-372 and Figures 7-9 “The on-board controller controls the instrument of the target vehicle to display a target image, wherein the target vehicle is a vehicle driven by the driver, and the target image is used to display the virtual image of the target vehicle and the first a virtual image of a following target, the first following target is a vehicle located in front of the target vehicle;…The adaptive cruise system is used to control the instrument of the target vehicle to display a real-time image through the on-board controller after acquiring the target following distance input by the user, wherein the real-time image is used to simulate the The process in which the distance between the target vehicle and the first following target is changed from the preset following distance to the target following distance. The real-time image includes: a virtual image of the target vehicle, the The virtual image and virtual parking space of the first following target, where the virtual parking space is where the virtual image of the target vehicle is when the distance between the target vehicle and the first following target is the target following distance. describe the location in the live image).
Regarding claim 14, the combination of Wang, Luo, and Nagai teach the apparatus of claim 1, wherein the computer program code instructions are configured to, when executed, cause the apparatus to control maneuver of the subject vehicle to maintain the first safety distance or the second safety distance (see at least Wang Figure 1, S103, control vehicle according to the target time headway, see also at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.”)
Claims 15 and 18 are rejected under the same rationale, mutatis mutandis, as claim 1, above.
Regarding claims 16 and 19, the combination of Wang, Luo, and Nagai teach the method and non-transitory computer readable storage medium as rejected in claims 15 and 18 and further teach, wherein the set of features is a second set of features, and wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
apply a first machine learning (ML) model on the first set of features, wherein the ML model is trained to output a prediction result based on the first set of features (see at least Wang [0049-0050] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions.” See [0095-0102] regarding calculation of base time headway using the scenario perception coefficient that is determined using the machine learning. Specifically, [0097] teaches “There are many ways to implement the time headway adjustment model, which can be statistical model of historical data, or self-learning model such as neural network.”, “See also [0084] “In this step, the scenario perception coefficient provides a medium for connecting the target travelling scenario with the adjustment of the time headway. The setting of the scenario perception coefficient can be obtained according to the target travelling scenario using big data statistics. Neural network models may also be used to implement automatic intelligent learning. For different regions and different users, different scenario perception coefficient settings can be applied.”);
wherein the first set of features are associated with: (i) information associated with the subject vehicle; (ii) vehicle information associated with the one or more of the set of vehicles; (iii) road information associated with a road on which the subject vehicle and the set of vehicles are being driven; (iv) traffic information associated with the road; (v) environmental information; (vi) distance information associated with a distance between the subject vehicle and a first lane of a set of lanes on the road; (vii) temporal information; or (viii) a combination thereof (see at least Wang, which teaches the features can be traffic information such as traffic speed, traffic congestion, frequency of cut-ions [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.” See also [0063] “Therefore, the present disclosure takes the high-speed scenario and the low-speed scenario as two base scenarios to make a base decision. In the high-speed scenario, the vehicle control strategy is more conservative and stable, while in the low-speed scenario, the vehicle control strategy can be set to be more dynamic or volatile to quickly change the travelling state of the vehicle. In addition, the distance to the vehicle ahead needs to be widened to ensure a safe braking distance in the high-speed scenario, while the distance to the vehicle ahead can be appropriately reduced in the low-speed scenario.”);and
determine the first safety distance at least in part based on the prediction result output by the first ML model (see at least Wang [0049-0054] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions… S103, control the vehicle according to the target time headway..[0053] In this step, the target time headway can be reached by setting to control the vehicle to go through different gradual processes based on different target travelling scenarios….[0054] For example, when the vehicle is in a frequent-cut-in scenario such as a congested road, and the current vehicle speed of the vehicle is low, the vehicle can be controlled to quickly reach the target time headway in order to prevent from being cut-in any further. When the vehicle is in a frequent-cut-in scenario with multiple vehicles moving in parallel on a highway, in order to avoid a traffic accident caused by an abrupt speed change of the vehicle, a smoother change process of the time headway can be set, so as to make the vehicle reach the target time headway more slowly. In this process, the target time headway can be modified further according to a change in the target travelling scenario to avoid the situation in which a sudden change in the target travelling scenario, e.g., a closed road section or being cut-in, causes the driver to perceive the presence of a security threat. At this time, the target time headway and speed of reaching the target time headway can be configured further, to improve the user experience of the automatic driving.” See also [0071] (see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.”).
The examiner notes that while Wang is teaching using a machine learning model to determine the second safety distance, as defined by the claims, Wang teaches that the process is performed continuously and iteratively as the scenarios change in time. The “first safety distance” and “second safety distance” are simply labels for a distance between vehicles. It would be obvious to use the same process to determine one distance to also determine another distance as the scenario changes. Further, the examiner notes that claim is an apparatus claim and thus, the apparatus is capable of performing the determination of the first distance with machine learning just as it is taught to perform the determination for the second distance.
Regarding claims 17 and 20, the combination of Wang, Luo, and Nagai teach the method and non-transitory computer readable storage medium as rejected in claims 16 and 19, respectively, and further teach:
apply a [second] machine learning (ML) model on the set of features, wherein the ML model is trained to output the second safety distance based on the set of features (see at least Wang [0049-0050] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions.” See [0095-0102] regarding calculation of base time headway using the scenario perception coefficient that is determined using the machine learning. Specifically, [0097] teaches “There are many ways to implement the time headway adjustment model, which can be statistical model of historical data, or self-learning model such as neural network.”, “See also [0084] “In this step, the scenario perception coefficient provides a medium for connecting the target travelling scenario with the adjustment of the time headway. The setting of the scenario perception coefficient can be obtained according to the target travelling scenario using big data statistics. Neural network models may also be used to implement automatic intelligent learning. For different regions and different users, different scenario perception coefficient settings can be applied.”);
determine the second safety distance at least in part based on the prediction result output by the second ML model, wherein the second safety distance is indicative of a gap to be maintained between the subject vehicle and the preceding vehicle, and wherein the second distance is a distance to be maintained between the subject vehicle and the preceding vehicle to avoid an occupation of the gap by at least one vehicle of the set of vehicles (see at least Wang [0049-0054] “[0049] In different target travelling scenarios, on the basis of establishing base time headway related to a safe distance according to a travelling state of the vehicle itself, a perception correction model can be provided for different scenarios, to correct the base time headway, obtaining the target time headway…[0050] In a possible design, the perception correction model can be a neural network model which, after being trained using big data, adopts various correction manners for different regions to obtain an optimal correction effects suited to distinct regions, such as a urban or rural regions… S103, control the vehicle according to the target time headway..[0053] In this step, the target time headway can be reached by setting to control the vehicle to go through different gradual processes based on different target travelling scenarios….[0054] For example, when the vehicle is in a frequent-cut-in scenario such as a congested road, and the current vehicle speed of the vehicle is low, the vehicle can be controlled to quickly reach the target time headway in order to prevent from being cut-in any further. When the vehicle is in a frequent-cut-in scenario with multiple vehicles moving in parallel on a highway, in order to avoid a traffic accident caused by an abrupt speed change of the vehicle, a smoother change process of the time headway can be set, so as to make the vehicle reach the target time headway more slowly. In this process, the target time headway can be modified further according to a change in the target travelling scenario to avoid the situation in which a sudden change in the target travelling scenario, e.g., a closed road section or being cut-in, causes the driver to perceive the presence of a security threat. At this time, the target time headway and speed of reaching the target time headway can be configured further, to improve the user experience of the automatic driving.” See also [0071] (see at least Wang [0071] “If the number of cut-in is greater than a preset number of, e.g., 3, the target travelling scenario is determined to be the frequent-cut-in scenario. In this scenario, it is easy to cause the driver to be discontent with the adaptive cruise and switch to manual control if the vehicle is cut-in for too many times. This can easily cause scratching and collision accidents. Therefore, when such scenario is recognized, the prior art approach of keeping a safe distance no longer applies. Instead, the distance to the vehicle ahead should be kept as close as possible.”).
While Wang does not explicitly state that there is a first model for the first set of features and a second model for the second set of features, Wang does teach that there are plural neural network models which can be used for different regions, users, and scenarios such that different scenario perception coefficient settings can be applied (see Wang [0084]). Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to utilize a first model for the first features (a first scenario) and use a second model for the second features (a second scenario), with a reasonable expectation of success, because as Wang teaches it allows for different coefficient settings to be applied to match the current scenario.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Luo and Nagai in view of Peterson (US Pub. No. 2019/0248379, hereinafter “Peterson”) and further in view of Gesang et al. ( US Pub. No. 2023/0347872, hereinafter “Gesang”)
Regarding claim 7, the combination of Wang, Luo, and Nagai teach the apparatus of claim 1, but do not explicitly discloses wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
receive a user input associated with a determination of a navigation route from a first location to a second location;
determine, from a map database, a set of navigation routes from the first location to the second location based on the received user input, wherein each navigation route of the set of navigation routes comprises information indicating the first safety distance, the second safety distance, or both the first safety distance and the second safety distance;
select a first navigation route from the determined set of navigation routes; and
output the selected first navigation route on the user interface.
Peterson teaches wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
receive a user input associated with a determination of a navigation route from a first location to a second location (see at least Peterson Figure 3 and 4, and [0040] “0040] Beginning with FIG. 3, a GUI 300 is shown. A user may provide text input to input box 302 of the GUI 300 by selecting it using touch input and inputting text using a hard or soft keyboard to indicate a destination to which the user would like to travel via an autonomous driving vehicle such as the vehicle 216.”);
determine, from a map database, a set of navigation routes from the first location to the second location based on the received user input (see at least Peterson Figure 3 and 4, and [0044] “Now describing FIG. 4, it shows a GUI 400 that may also be presented on a display controlled by the user's device responsive to receipt of the response that was output by the server (or user's device) that indicates a map and directions for the user and/or autonomous vehicle to follow along one or more potential routes to the user's destination. In turn, the directions (and also the notifications described below) may then be output by the user's own device via a display and/or speakers. Additionally, or alternatively, the directions may then be output to the autonomous vehicle for the vehicle to control its steering, power, and braking mechanisms to autonomously follow the directions to the destination…[0045] As may be appreciated from FIG. 4, the GUI 400 indicates a first route 402 and a second route 404 from an origination point “A” to a destination point “B”. Route 402 as presented on the GUI 400 also indicates that a section 406 of the route 402 involves manual driving rather than autonomous driving..”); [[wherein each navigation route of the set of navigation routes comprises information indicating the first safety distance, the second safety distance, or a combination thereof]];
select a first navigation route from the determined set of navigation routes (see at least Peterson Figure 3 and 4, [0046] “The GUI 400 also includes a section 410 listing the routes 402 and 404. Selector 412 may be selected (e.g., based on touch or cursor input to the selector 412) to provide user input selecting route 402 as the route for the autonomous vehicle to follow even though the user will have to take over and drive manually for part of the route 402. …Selector 414 may be selected to provide user input selecting route 404 as the route for the autonomous vehicle to follow.”); and
output the selected first navigation route on the user interface (see at least Peterson Figure 4 and 5).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, and Nagai with the teaching of Peterson to allow for entering a destination and presenting of routes, with a reasonable expectation of success, because it enhances the user experience to obtain turn by turn navigation instructions to their desired destination.
The combination of Wang, Luo, Nagai and Peterson teach providing information regarding each navigation route of the set of navigation routes, but do not explicitly teach the information indicates the first safety distance, the second safety distance, or both the first safety distance and the second safety distance.
However, Gesang teaches providing information indicating the first safety distance, the second safety distance, or both the first safety distance and the second safety distance (see at least Gesang [0184] and [0201] which teaches slope information is stored in the map and retrieved to make a determination regarding safe following distance and thus “indicates a following distance. [0184] “For example, the memory of the vehicle-mounted map unit 240 shown in FIG. 1 can comprise the 3D map with the road meter-level positioning precision (longitude and latitude) and longitudinal slope precision with 0.1 degree accuracy. Various advanced driver assistance system (ADAS) maps containing the above road 3D information have already been commercialized in batches in major global automotive markets.” [0201] “ The VCU 201 dynamically adjusts the safe following distance L.sub.s of adaptive cruise control according to the operation information including the gross weight, vehicle configuration and vehicle speed, and combining with the current 3D road information (longitude, latitude and longitudinal slope) of the vehicle as well as the longitudinal slope distribution function, bend curvature and other three-dimensional information of roads within the vehicle electronic horizon stored in the map unit 240…L.sub.s is very important for the safety of ACE heavy duty truck in the above PAC mode. The safe following distance L.sub.s can be subdivided into three specific distances: L1 is the early warning distance, L2 is the warning distance, L3 is the dangerous distance, where L1>L2>L3. The VCU 201 can dynamically calculate the above three following distances (L1, L2, L3) according to the vehicle configuration parameter and operating condition data (e.g., vehicle gross weight and vehicle speed), real-time weather (wind, rain, snow, ice, temperature, etc.) and the 3D road data (longitude, latitude and longitudinal slope, etc.) within a kilometer-level range ahead of the vehicle.”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, Nagai and Peterson with the teaching of Gesang to provide information indicating the safety distances, with a reasonable expectation of success, because as Gesang teaches the longitudinal slope can be used to determine safety distances as well as fuel economy and thus, are important data points that can be used to make a selection of the preferred route.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Luo, Nagai in view of Gesang.
Regarding claim 8, the combination of Wang, Luo, and Nagai teach the apparatus of claim 1, but do not disclose wherein, to obtain the set of features, the computer program code instructions are configured to, when executed, cause the apparatus to:
transmit a command to a map database, wherein the command is associated with retrieval of at least one feature of the set of features from the map database; and
obtain the at least one feature of the set of features from the map database.
Gesang teaches wherein, to obtain the set of features, the computer program code instructions are configured to, when executed, cause the apparatus to:
transmit a command to a map database, wherein the command is associated with retrieval of at least one feature of the set of features from the map database (see at least Gesang [0184] and [0201] which teaches slope information is stored in the map and retrieved to make a determination regarding safe following distance [0184] “For example, the memory of the vehicle-mounted map unit 240 shown in FIG. 1 can comprise the 3D map with the road meter-level positioning precision (longitude and latitude) and longitudinal slope precision with 0.1 degree accuracy. Various advanced driver assistance system (ADAS) maps containing the above road 3D information have already been commercialized in batches in major global automotive markets.” [0201] “ The VCU 201 dynamically adjusts the safe following distance L.sub.s of adaptive cruise control according to the operation information including the gross weight, vehicle configuration and vehicle speed, and combining with the current 3D road information (longitude, latitude and longitudinal slope) of the vehicle as well as the longitudinal slope distribution function, bend curvature and other three-dimensional information of roads within the vehicle electronic horizon stored in the map unit 240…L.sub.s is very important for the safety of ACE heavy duty truck in the above PAC mode. The safe following distance L.sub.s can be subdivided into three specific distances: L1 is the early warning distance, L2 is the warning distance, L3 is the dangerous distance, where L1>L2>L3. The VCU 201 can dynamically calculate the above three following distances (L1, L2, L3) according to the vehicle configuration parameter and operating condition data (e.g., vehicle gross weight and vehicle speed), real-time weather (wind, rain, snow, ice, temperature, etc.) and the 3D road data (longitude, latitude and longitudinal slope, etc.) within a kilometer-level range ahead of the vehicle.”); and
obtain the at least one feature of the set of features from the map database (see at least Gesang [0184] and [0201] which teaches slope information is stored in the map and retrieved to make a determination regarding safe following distance and thus “indicates a following distance. [0184] “For example, the memory of the vehicle-mounted map unit 240 shown in FIG. 1 can comprise the 3D map with the road meter-level positioning precision (longitude and latitude) and longitudinal slope precision with 0.1 degree accuracy. Various advanced driver assistance system (ADAS) maps containing the above road 3D information have already been commercialized in batches in major global automotive markets.” [0201] “ The VCU 201 dynamically adjusts the safe following distance L.sub.s of adaptive cruise control according to the operation information including the gross weight, vehicle configuration and vehicle speed, and combining with the current 3D road information (longitude, latitude and longitudinal slope) of the vehicle as well as the longitudinal slope distribution function, bend curvature and other three-dimensional information of roads within the vehicle electronic horizon stored in the map unit 240…L.sub.s is very important for the safety of ACE heavy duty truck in the above PAC mode. The safe following distance L.sub.s can be subdivided into three specific distances: L1 is the early warning distance, L2 is the warning distance, L3 is the dangerous distance, where L1>L2>L3. The VCU 201 can dynamically calculate the above three following distances (L1, L2, L3) according to the vehicle configuration parameter and operating condition data (e.g., vehicle gross weight and vehicle speed), real-time weather (wind, rain, snow, ice, temperature, etc.) and the 3D road data (longitude, latitude and longitudinal slope, etc.) within a kilometer-level range ahead of the vehicle.”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, and Nagai with the teaching of Gesang to obtain the at least one feature from the map database, with a reasonable expectation of success, because as Gesang teaches the longitudinal slope can be used to determine safety distances as well as fuel economy and thus, are important data points that can be used to make a selection of the preferred route.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Luo and Nagai in view of Wells et al. (US Patent 11,702,076, hereinafter “Wells”) and further in view of Polisson et al. ( US Pub. No. 2018/0050698, hereinafter “Polisson”).
Regarding claim 10, Wang, Luo and Nagai teach the apparatus of claim 1, but do not teach wherein the subject vehicle is an electric vehicle, and wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
compute a driving range of the subject vehicle based on the first safety distance, the second safety distance, or both the first safety distance and the second safety distance; and
output the computed driving range of the subject vehicle on the user interface.
Wells teaches wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
compute [a driving range] a fuel consumption efficiency of the subject vehicle based on the first safety distance, the second safety distance, or both the first safety distance and the second safety distance (see at least Wells throughout and col. 1, lines 45-60 “(4) In an embodiment, a system for providing following a graphical representation of following distances to an augmented reality (AR) vehicle heads-up-display (HUD) system of a trailing vehicle includes a processor, a vehicle type database, and a memory. The vehicle type database includes a plurality of vehicle type profiles, each of the plurality of vehicle type profiles being associated with a vehicle type having a vehicle type specific aerodynamic profile and including optimal following ranges associated with the vehicle type, each of the optimal following distance ranges being based on the vehicle type specific aerodynamic profile of the vehicle type and a vehicle speed of the vehicle type wherein a trailing vehicle disposed in the optimal following range is configured to operate at an optimal fuel efficiency.”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, and Nagai with the teaching of Wells, with a reasonable expectation of success, because as Wells teaches this informs the driver of the most efficient safe following distances.
The examiner notes that while Wells teaches computing the most optimized following distances based on fuel efficiency, and displaying the most optimized following distances, Wells does not teach outputting the computed driving range of the subject vehicle on the user interface. The examiner notes that computed driving range is a common calculation that is based on the fuel efficiency. The computed driving range is the product of fuel efficiency (e.g. in miles per gallon) and the tank size of the vehicle (e.g. in gallons).
Polisson teaches computing a driving range based on fuel consumption and output the computed driving range of the subject vehicle on the user interface (see at least Polisson [0054] “The HUD may also display other operation data of the vehicle, such as the current speed, whether the vehicle is travelling above the speed limit for the road the driver is on, remaining fuel range, distance to destination, etc.”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, Nagai and Wells with the teaching of Polisson to display the remaining fuel range, with a reasonable expectation of success, because it allows for drivers to be informed of vital information without taking their eyes off the road.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Luo and Nagai in view of Broll et. al. (US Pub. No. 2019/0232962, hereinafter “Broll”) and in further view of Ping Wang et al. (CN 106314276A, provided in the IDS, hereinafter “Ping Wang”, the citations include line numbers of the machine translation).
Regarding claim 12, the combination of Wang, Luo, and Nagai teach the apparatus of claim 1, but do not disclose wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
transmit the first safety distance, the second safety distance, or both the first safety distance and the second safety distance to the one or more of the set of vehicles; and
receive at least one notification from the one or more of the set of vehicles, wherein the at least one notification is associated with an overtaking of the subject vehicle by the one or more of the set of vehicles.
Broll teaches wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
transmit the first safety distance, the second safety distance, or both the first safety distance and the second safety distance to the one or more of the set of vehicles (see at least Broll [0032-0033] “[0032] The dynamic vehicle distance Adyn is selected in such a manner that a collision between the vehicles 10, 20, 30, 40 may also be prevented in the case of an emergency braking procedure N of the preceding vehicle VF in a dangerous situation. Furthermore, the dynamic vehicle distance Adyn is selected in such a manner that it is possible to optimize fuel consumption and road capacity utilization…[0033] A V2V signal S1 is constantly transmitted between the preceding vehicle VF and the respective following vehicle FF via a wireless data communication 50 (vehicle-to-vehicle communication, V2V) so as to be able to coordinate or rather monitor the platoon 100. The V2V signal S1 transmits in this case in particular a vehicle velocity v_VF, vFF of the respective vehicles VF, FF, the dynamic vehicle distance Adyn and also the information regarding whether an emergency braking procedure N has been initiated. A V2V signal S1 is transmitted within a first transmission time t1 exclusively in a wireless manner.”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, and Nagai with the teaching of Broll, with a reasonable expectation of success, because as Broll teaches this allows the vehicles to coordinate the platoon.
The combination of Wang, Luo, Nagai and Broll does not teach receive at least one notification from the one or more of the set of vehicles, wherein the at least one notification is associated with an overtaking of the subject vehicle by the one or more of the set of vehicles.
Ping Wang teaches receive at least one notification from the one or more of the set of vehicles, wherein the at least one notification is associated with an overtaking of the subject vehicle by the one or more of the set of vehicles (see at least Ping Wang, abstract “A rear car overtaking reminding system based on distance measuring is mainly composed of a distance measuring system, an overtaking identification system and an overtaking reminding system. The distance measuring system measures the distance between the left adjacent lane and a car, the distance between the right adjacent lane and the car and the distance between a car behind the lane where the car is located and the car; the overtaking identification system judges an overtaking intention of the rear car according to distance change information of the adjacent moments and the safety distance; the overtaking reminding system drives corresponding overtaking indicator lights and can give out voice prompt to remind a driver that the rear car is about to overtake from the left side or the right side according to the overtaking intention. By means of the rear car overtaking reminding system based on distance measuring, it can be guaranteed that the driver perceives approaching of the rear car and the overtaking intention of the rear car, the condition that lane changing is conducted when the rear car overtakes is avoided, and safe driving of the car is guaranteed.” And lines 81-84 “2，The present invention may indicate the overtaking intention of the rear vehicle by the overtaking indicator light or may also be broadcast by the driver to select the overtaking voice prompt system to broadcast the content of the overtaking prompt message of the overtaking vehicle, so as to increase driving pleasure and be conducive to safe driving.”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, Nagai and Broll with the teaching of Ping Wang, with a reasonable expectation of success, because as Ping Wang teaches, this improves safety and driver enjoyment.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Luo and Nagai in view of Eguchi et al. (US Pub. No. 2020/0380870, hereinafter “Eguchi”) and in further view of Ping Wang et al. (CN 106314276A, provided in the IDS, hereinafter “Ping Wang”, the citations include line numbers of the machine translation).
Regarding claim 13, the combination of Wang, Luo, and Nagai teach the apparatus of claim 1, but do not teach wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
calculate a likelihood value indicative of a likelihood of the one or more of the set of vehicles overtaking the subject vehicle and occupying a gap between the subject vehicle and the preceding vehicle; and
output the likelihood value on the user interface.
Eguchi teaches:
calculating a likelihood value indicative of a likelihood of the one or more of the set of vehicles overtaking the subject vehicle and occupying a gap between the subject vehicle and the preceding vehicle (see at least Eguchi Fig. 1 and [0018] “In the example shown in FIG. 1, the drone 100 moves to a junction (exit) of a highway positioned in front of the platoon Co in accordance with the instruction from the ECU 10 (refer to FIG. 2). The junction of the highway is a location where other vehicles (surrounding vehicles SM that travel on the merging lane) are likely to enter between the plurality of vehicles M1 to M4 that form the platoon Co, that is, the platoon Co is disturbed and the platooning is likely to become impossible. In the vehicle control system according to the present embodiment, the ECU 10 (refer to FIG. 2) acquires the situation of the surrounding vehicle SM from the camera 101 of the drone 100, the control to shorten the distance (inter-vehicle distance) between the plurality of vehicles M1 to M4 is performed in accordance with the situation of the surrounding vehicle, and accordingly, the disturbance of the platoon Co is avoided, for example, even at the location where the platoon Co is likely to be disturbed, such as the junction of the highway, and continuation of the platooning is realized.” The examiner interprets the teaching of Eguchi of the determination that vehicles are “likely to enter between the plurality of vehicles M1 to M4” to correspond to a likelihood value. See also [0048-0049] ) and
outputting the [likelihood value] on the user interface (see also Eguchi [0030] “Further, the acquisition unit 11 may output the imaging result of the camera 101 to an on-vehicle monitor (not shown) and notify the driver of the situation in the vicinity of the exit to the highway” and [0036] “In addition, for the vehicle M where the driver is located, the control of the vehicle control unit 13 does not necessarily have to be the control of the actuator 60, and the vehicle control unit 13 may notify an on-vehicle monitor (not shown) of the shortening of the distance between the plurality of vehicles M1 to M4, deceleration of the vehicles M1 to M4, and the like. In other words, for a vehicle where the driver is located, control of the inter-vehicle distance and the like may not be performed automatically.”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, and Nagai with the teaching Eguchi , with a reasonable expectation of success, because as Eguchi teaches, awareness of the cutting in allows the vehicles to act appropriately.
While Eguchi teaches determining a likelihood value and notifying the vehicle on a monitor within the vehicle regarding the driving situation (see Eguchi [0030], [0036] , Eguchi does not explicitly teach outputting the likelihood value.
Ping Wang teaches outputting a likelihood value of being overtaken (see at least Ping Wang, abstract “A rear car overtaking reminding system based on distance measuring is mainly composed of a distance measuring system, an overtaking identification system and an overtaking reminding system. The distance measuring system measures the distance between the left adjacent lane and a car, the distance between the right adjacent lane and the car and the distance between a car behind the lane where the car is located and the car; the overtaking identification system judges an overtaking intention of the rear car according to distance change information of the adjacent moments and the safety distance; the overtaking reminding system drives corresponding overtaking indicator lights and can give out voice prompt to remind a driver that the rear car is about to overtake from the left side or the right side according to the overtaking intention. By means of the rear car overtaking reminding system based on distance measuring, it can be guaranteed that the driver perceives approaching of the rear car and the overtaking intention of the rear car, the condition that lane changing is conducted when the rear car overtakes is avoided, and safe driving of the car is guaranteed.” And lines 81-84 “2，The present invention may indicate the overtaking intention of the rear vehicle by the overtaking indicator light or may also be broadcast by the driver to select the overtaking voice prompt system to broadcast the content of the overtaking prompt message of the overtaking vehicle, so as to increase driving pleasure and be conducive to safe driving.”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Wang, Luo, Nagai and Eguchi with the teaching of Ping Wang, with a reasonable expectation of success, because as Ping Wang teaches, this improves safety and driver enjoyment.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20200012295-A1 to Kim1 is cited for describing changing the safety distance to narrower when an object is approaching from behind to prevent cutting in (see [0292-0293]).
US-20190339716-A1 to Kopischke is cited to selecting a short distance to prevent a vehicle from cutting in (see at least [0052] and [0100]).
US-20200216069-A1 to Elflein is cited for teaching closing gaps between vehicles to prevent passing [0037].
US-20220379888-A1 to Kim2 is cited for teaching a target distance to prevent cutting in ([0079]).
US-20190346862-A1 to Switkes is cited for teaching determining a following distance based on features including detecting that a vehicle is trying to cut in or an aggressive driver (see [0061]).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/JENNIFER M ANDA/Primary Examiner, Art Unit 3662
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