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 20 January 2026.
Claims 1, 11, 16, 14, and 19 have been amended.
Claims 1-20 are pending and have been examined.
This action is FINAL.
Response to Amendments and Remarks
Drawings
The drawings were objected to because of informalities. Applicant has amended the specification to overcome or render moot each of the objections to the drawings. Accordingly, the objection to the drawings has been withdrawn.
Specification
The specification was objected to because of informalities. Applicant has amended the specification to overcome the objections to the specification. Accordingly, the objection to the specification has been withdrawn.
Claim Objections
Claims 16-18 were objected to because of informalities. Applicant has not amended nor provided arguments regarding the objection. Accordingly, the objection of claims 16-18 has been maintained and repeated below.
Claim Rejections - 35 USC § 112
Claims 1-20 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 most of the rejections under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph,. Accordingly, most of the rejections of claims 1-20 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, has been withdrawn. However, the rejection of claim 1, 11, and 19 is maintained, and updated, based on the amendment below.
Claim Rejections - 35 USC § 101
Claims 1-19 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 10, filed 20 January 2026 with respect to the rejection(s) of claim(s) 1-19 under 35 U.S.C. 101 have been fully considered, but they are not persuasive. Applicant’s arguments amount to a general allegation that the claims are patent eligible in light of the amended claims. The examiner has provided an updated rejection below addressing the amended claims.
Claim Rejections – Double Patenting
Claims 1-20 were rejected under the ground of nonstatutory double patenting as being unpatentable over claims 1-10 and 14-19 of U.S. Patent No. 12,051,247. Applicant's arguments have been fully considered but they are not persuasive. Applicant’s arguments amount to a general allegation that the claims are not obvious in light of the foregoing patent application without providing any explanation regarding how the claims distinguish from the parent patent. The examiner has provided an updated rejection below addressing the amended claims.
Claim Rejections - 35 USC § 103
Claims 1-8, 11-12, and 16-20 were rejected under 35 U.S.C. § 103 as being unpatentable over Golov in view of Pipe. Claims 9-10 were rejected under 35 U.S.C. § 103 as being unpatentable over Golov and Pipe in further view of Kundu. Claims 13-15 were rejected under 35 U.S.C. § 103 as being unpatentable over Golov and Pipe in further view of Slusar.
Applicant’s arguments, see pages11-12, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant argues:
Without conceding the merits of the Section 103 rejections, independent claims 1, 11, and 19 have been amended to clarify the features thereof. For example, claim 1, as amended, now recites "comparing the roadway item risk value to a risk threshold;" and "in response to a result of comparing the roadway item risk value to the risk threshold, proposing an alternative path for the vehicle, wherein the alternative path is generated at least based on the operational data and the positional data." (Emphasis added.)
Applicant respectfully submits that the cited references fail to disclose or suggest the foregoing features. As a result, the combination of Golov, Pipe, Kundu, and Slusar fails to support a Section 103 rejection of independent claims 1, 11, and 19 and their dependent claims. Accordingly, the Section 103 rejection of these claims should be withdrawn.
The examiner respectfully disagrees. The rejection below addresses the new claim limitations. Specifically, Golov teaches wherein the alternative path is generated at least based on the operational data and the positional data (see at least Golov [0062-0063] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle). For example, server 101 may determine that a set of braking events corresponds to a pattern and a corresponding location is identified based on this determination. In one example, a location can be determined as being unsafe based on numerous emergency braking activations on vehicles at that location or within a predetermined distance of the identified location…In response to identifying the location, at least one action is performed. For example, server 101 can transmit a communication to current vehicle 111 that causes the vehicle to change a navigation path and/or activate a braking system when within a predetermined distance of the identified unsafe location.”)
Further, while Golov teaches determining a roadway item risk value (e.g. unsafe location as cited above), Golov does not explicitly teach calculating a roadway item risk value or comparing the roadway item risk value to a risk threshold and in response to a result of comparing the roadway item risk value to the risk threshold, proposing an alternative path for the vehicle.
Pipe teaches
calculating a roadway item risk value and comparing the roadway item risk value to a risk threshold (see at least Pipe [0079-0083] “[0079] The hazard detection system 100 may determine a severity score (406). …The severity score may be based on the differences between the driver behavior patterns and corresponding thresholds for the driver behavior patterns and between the moving patterns of objects and corresponding thresholds for the moving patterns of the objects. The hazard detection system 100 may use a weighted combination of the differences between the driver behavior patterns and moving patterns of objects and their corresponding thresholds to determine the severity score… [0083] When the hazard detection system 100 determines that the score is greater than or equal to the high threshold score, the hazard detection system 100 may re-route the vehicle 102 to avoid the hazard (414). The hazard detection system 100 may perform other operations to the vehicle 102, such as activating the brakes, shifting lanes or otherwise actively avoiding the hazardous condition.”) and
in response to a result of comparing the roadway item risk value to the risk threshold, proposing an alternative path for the vehicle (see at least Pipe [0020] “Additionally, the hazard detection system may perform different operations to alert, avoid or otherwise mitigate consequences of the hazardous object or situation. For example, the hazard detection system may alert the driver of the presence of the hazardous object or situation… In another example, the hazard detection system may propose an alternative route and/or re-route the vehicle.” See also [0030] “The hazard detection system 100 may include a user interface 120. The hazard detection system 100 may display one or more notifications on the user interface 120. The one or more notifications on the user interface 120 may notify occupants of the vehicle when the hazard detection system 100 is initialized or activated or when a hazardous condition is detected. Moreover, the user interface 120 may display a route or an updated route of a path of the vehicle 102.” See also [0056] “The hazard detection system 100 may perform different operations for different severities of the hazardous condition, which is further described in FIG. 4. The hazard detection system 100 may perform operations, such as notify or alert the driver or occupants of the vehicle 102, other devices 106 and/or third-parties. The hazard detection system 100 may perform other operations including braking, changing lanes, re-routing the path of the vehicle 102 on the user interface 120, and/or autonomously steering the vehicle 102 onto the re-routed path or otherwise changing the path of the vehicle 102.” See also Pipe [0081] [0062]).
Further, the examiner notes that Pipe also teaches wherein the alternative path is generated at least based on the operational data and the positional data (as seen in [0059], [0067-0068], [0051] [0053] as cited above. For example, [0051] teaches “The hazard detection system 100 may determine the baseline based on a frequency or pattern of the behavior or movement of one or more objects at the current location…In another example, when one or more drivers speed, change speeds, control the steering wheel or otherwise control a vehicle in a certain manner at a location a threshold amount of times, the hazard detection system 100 may determine that those driver behavior patterns are normal and part of the baseline for that location..)”
Claim Objections
Claims 16-18 are objected to because of the following informalities: Claim 16-17 depend from claim 11. Claim 11 is a system claim, however claims 16-17 recite “the method of”. Therefore, claims 16-17 recite both an apparatus and a process of using the apparatus and have indefinite scope. The examiner believes this was a typographical error and will examine claims 16-17 as system claims. Appropriate correction is required.
Claim 18 appears to be dependent on claim 1, however the examiner notes that claim 18 is a substantial duplicate of claim 8, also dependent on claim 1. The examiner believes Applicant intended to have claim 18 depend from claim 11 and has examined accordingly. The examiner recommends amending claim 18 to be dependent on claim 11 and to amend the claim to recite “the system of claim 11”. 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 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 “calculating a roadway item risk value for the vehicle”. It is not clear if the roadway item risk value is for the roadway item or for the vehicle. As written, it appears Applicant is reciting a roadway item risk value for the vehicle. However, the examiner notes that the specification appears to support a roadway item risk value is calculated or determined for the roadway item not for the vehicle. Specifically, the specification at [0041-0042] and [0057] teaches comparing a trained machine learning model to the received features to generate comparison results to determine or recognize the roadway entity. This is further supported by claim 9 which depends from claim 1. Claim 9 further defines a roadway item for which the roadway item risk value is calculated. The examiner recommends replacing “calculating a roadway item risk value for the vehicle” with “calculating a roadway item risk value”. Claim 11 and claim 19 have a similar recitation and are rejected for the same reason.
Claim 2 recites “transmitting location information of the roadway hazard”. There is insufficient antecedent basis for “the roadway hazard” in the claim. The examiner notes that claim 2 depends from claim 1 which recites in the preamble “for detecting and avoiding at least one roadway hazard”. However, this language is intended use and claim does not positively recite actually detecting a roadway hazard in the body of the claim. The examiner recommends amending claim 1 to positively recite detecting a roadway hazard.
Claims 4 recites “the at least one roadway hazard”. There is insufficient antecedent basis for “the roadway hazard” in the claim. Claim 5 has a similar recitation and is rejected for the same reason.
Claim 12 recites “the at least one roadway hazard”. There is insufficient antecedent basis for “the roadway hazard” in the claim.
Claim 13 recites “an alternative path”. Claim 13 depends from claim 11 which recites “an alternative path”. It is not clear if the alternative path of claim 13 is the same or different than that recited in claim 11.
Claim 19 recites “the vehicle” in line 18. There is insufficient antecedent basis for this limitation in the claim. Claim 19 previously recited “a first vehicle”.
Claim 20 recites “an alternative path” in line 2. Claim 20 depends from claim 19 which recites “proposing an alternative path” in line 18. It is not clear if the alternative path of claim 20 is the same or different than that recited in claim 19. Further claim 20 recites “the calculated alternative path” in line 3. It is not clear if the calculated alternative path is the same or different than the alternative path. The examiner recommends reciting “the alternative path” in claim 20 consistently throughout the claim.
Claims 2-10 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 12-18 depend from claim 11 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. Claim 20 depends from claim 19 and is similarly rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, based on its dependency on claim 19.
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 1-19 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 a computer-implemented method and is therefore eligible for further analysis.
Claim 11 is directed toward system and is therefore eligible for further analysis.
Claim 19 is directed toward vehicular system 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 11 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 11 recites:
A system comprising:
at least one processor; and
a memory coupled to the at least one processor, the memory comprising computer executable instructions that, when executed by the at least one processor, performs the steps of:
receiving positional data of a vehicle indicating a current position of the vehicle;
receiving, from at least one sensor connected to the vehicle, environmental data external to the vehicle;
receiving operational data associated with the vehicle;
based on the positional data, environmental data, and operational data associated with the vehicle, calculating a roadway item risk value for the vehicle, wherein the roadway item risk value is calculated by considering a recognized pattern, wherein the recognized pattern is identified at least based on a comparison between the operational data associated with the vehicle with historical operational data associated with the recognized pattern, wherein the operational data indicates a change of braking data associated with the vehicle;
comparing the roadway item risk value to a risk threshold; and
in response to a result of comparing the roadway item risk value to the risk threshold, proposing an alternative path for the vehicle, wherein the alternative path is generated at least based on the operational data and the positional data.
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 steps of “based on the positional data, environmental data, and operational data associated with the vehicle, calculating a roadway item risk value for the vehicle, wherein the roadway item risk value is calculated by considering a recognized pattern, wherein the recognized pattern is identified at least based on a comparison between the operational data associated with the vehicle with historical operational data associated with the recognized pattern, wherein the operational data indicates a change of braking data associated with the vehicle” and “comparing the roadway item risk value to a risk threshold” encompass a human viewing data on paper and determining that based on the location of the vehicle and history of vehicles that an object poses a risk to the vehicle, and determining the level of risk, for example based on the size of the object (high, medium or low risk) and comparing that to a threshold to determine if the vehicle should modify its path. For example the threshold could be set such that the vehicle should modify its path for all high risk objects.
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 11 recites:
A system comprising:
at least one processor; and
a memory coupled to the at least one processor, the memory comprising computer executable instructions that, when executed by the at least one processor, performs the steps of:
receiving positional data of a vehicle indicating a current position of the vehicle;
receiving, from at least one sensor connected to the vehicle, environmental data external to the vehicle;
receiving operational data associated with the vehicle;
based on the positional data, environmental data, and operational data associated with the vehicle, calculating a roadway item risk value for the vehicle, wherein the roadway item risk value is calculated by considering a recognized pattern, wherein the recognized pattern is identified at least based on a comparison between the operational data associated with the vehicle with historical operational data associated with the recognized pattern, wherein the operational data indicates a change of braking data associated with the vehicle;
comparing the roadway item risk value to a risk threshold; and
in response to a result of comparing the roadway item risk value to the risk threshold, proposing an alternative path for the vehicle, wherein the alternative path is generated at least based on the operational data and the positional data.
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 “at least one processor” “a memory coupled to the at least one processor, the memory comprising computer executable instructions that, when executed by the at least one processor, performs the steps”, “receiving positional data of a vehicle indicating a current position of the vehicle” “receiving, from at least one sensor connected to the first vehicle, environmental data external to the first vehicle”, “receiving operational data associated with the vehicle” and “in response to a result of comparing the roadway item risk value to the risk threshold, proposing an alternative path for the vehicle, wherein the alternative path is generated at least based on the operational data and the positional data” 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”.
Specifically, 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). The additional limitations of “at least one processor” and “a memory coupled to the at least one processor, the memory comprising computer executable instructions that, when executed by the at least one processor, performs the steps”, and “receiving, from at least one sensor …” are recited at a high level of generality that merely automates the receiving, calculating, and comparing steps, therefore acting as a generic computer or generic components such as processors, memory and sensors that are simply employed as a tool to perform the abstract idea (see instant application [0031] and [0032] for the processor and memory and at least [0029-0030] and [0033] for sensors).
Further, the limitations of “receiving positional data of a vehicle indicating a current position of the vehicle;”, “ receiving, …environmental data external to the first vehicle” and “receiving operational data associated with the vehicle” and “receiving operational data associated with the vehicle” and “in response to a result of comparing the roadway item risk value to the risk threshold, proposing an alternative path for the vehicle, wherein the alternative path is generated at least based on the operational data and the positional data 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 and data output, which is a form of insignificant extra-solution activity. See at least MPEP 2106.05(g). The examiner notes that the claim only requires alternative path generation and proposal, the claim does not require dynamically modifying the path of the vehicle. Rather, thee examiner notes that the term “propose” or “proposal” has not been used in the specification, however, in view of [0051] the examiner interprets proposal to include merely displaying the alternative route. For example, see the claim differentiation in claim 13 which teaches displaying an alternate path in a map on a display. Thus, these additional elements merely reflect insignificant extra-solution activity.
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 11 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.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “at least one processor” and “a memory coupled to the at least one processor, the memory comprising computer executable instructions that, when executed by the at least one processor, performs the steps”, and “receiving, from at least one sensor …” amounts to nothing more than mere instructions to apply the exception using a generic computer or generic components (see [0029-0033] of the instant application). Mere instructions to apply an exception using a generic computer or generic components that are simply employed as a tool cannot provide an inventive concept.
Further, as discussed above, the additional limitations of “receiving positional data of a vehicle indicating a current position of the vehicle;”, “ receiving, from at least one sensor connected to the first vehicle, environmental data external to the first vehicle” and “receiving operational data associated with the vehicle” and “in response to a result of comparing the roadway item risk value to the risk threshold, proposing an alternative path for the vehicle, wherein the alternative path is generated at least based on the operational data and the positional data” the examiner submits are insignificant extra-solution activity. Hence, the claim is not patent eligible.
Claim 1 and 19 have similar recitations to claim 11 and the analysis above with respect to claim 11 also applies to claims 1 and 19.
Dependent claim(s) 2-10, and 12-18 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 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 2-10, and 12-18 are not patent eligible.
The examiner notes that claims 13, 14, and 15 recite displaying information on a display. However, these limitations are “recited at a high level of generality (i.e. as a general means 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.
Accordingly, claims 1-19 are not patent eligible.
The examiner notes that claim 20 recites “dynamically modifying a path of the first vehicle” (support in instant application [0036]) which is a practical application of the abstract idea and thus, is eligible subject matter.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 and 14-19 of U.S. Patent No. 12,051,247. Although the claims at issue are not identical, they are not patentably distinct from each other because they are not patentably distinct from each other as noted below in the following claim chart.
The examiner notes that the currently amended independent claims recite “wherein the alternative path is generated at least based on the operational data and the positional data”. This recitation corresponds to the reference patent recitation of “based on….positional data….and operational data….calculating a roadway item risk value” and “when the roadway risk item value exceeds the risk threshold, calculating an alternative path” Thus, in the reference patent, the alternative path is generated based on the risk value which is, in turn, based on the operational data and the positional data. Accordingly, the alternative path is generated at least based on the operational data and the positional data in the reference patent and thus, it is not patentably distinct.
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) 1-8, 11-13, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Golov (US Pub. No. 2019/0382029, hereinafter “Golov”) in view of Pipe et al. (US Pub. No. 2021/0049908, hereinafter “Pipe”).
Regarding claim 1, Golov teaches a computer-implemented method associated with a vehicle being in operation on a roadway for detecting and avoiding at least one roadway hazard comprising:
receiving positional data of the vehicle indicating a current position of the vehicle (see at least Golov [0059] “In one embodiment, data regarding braking events occurring on vehicles (e.g., other or prior vehicle 113) is received by server 101 via communication network 102. The received data includes a location for each of the braking events. For example, the received data can include an event location 163 for each braking event 161. Braking event 161 can include data such as, for example, an identifier, a type of braking event, etc. The received braking event data can be stored as part of map data 160” See also [0092] regarding positional data being received. See also [0035] and [00146] For example [0035] teaches “ The location itself may be determined, for example, based on location data (e.g., geographic coordinates) provided from the vehicle itself (e.g., by a GPS location system) and/or location data otherwise associated with or known about the vehicle”);
receiving, from at least one sensor connected to the vehicle, environmental data external to the vehicle (see at least Golov [0064] “In some embodiments, in addition to sending data regarding braking events, vehicle 113 and/or other prior vehicles send data regarding objects detected during travel (e.g. vehicle 113 can be traveling prior to current vehicle 111, which arrives later at the same location where an object has been detected by vehicle 113). These objects can include, for example, object 155 and object 157. Sensors of vehicle 113 and the other prior vehicles collect and/or generate data regarding the objects that have been detected. Data regarding detected objects can be analyzed in conjunction with braking event data in order to identify a location that prompts an action.)
receiving operational data associated with the vehicle (see at least Golov [0059] “In one embodiment, data regarding braking events occurring on vehicles (e.g., other or prior vehicle 113) is received by server 101 via communication network 102. The received data includes a location for each of the braking events. For example, the received data can include an event location 163 for each braking event 161. Braking event 161 can include data such as, for example, an identifier, a type of braking event, etc. The received braking event data can be stored as part of map data 160” The examiner interprets the braking data as the operational data.);
based on the positional data, environmental data, and operational data associated with the vehicle, [[calculating]] a roadway item risk value of the vehicle (see at least Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle). For example, server 101 may determine that a set of braking events corresponds to a pattern and a corresponding location is identified based on this determination. In one example, a location can be determined as being unsafe based on numerous emergency braking activations on vehicles at that location or within a predetermined distance of the identified location.) wherein the [[roadway item risk value is calculated]] by considering a recognized pattern, wherein the recognized pattern is identified at least based on a comparison between the operational data associated with the vehicle with historical operational data associated with the recognized pattern, wherein the operational data indicates [a sudden change of]] braking data associated with the vehicle (see at least Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle). For example, server 101 may determine that a set of braking events corresponds to a pattern and a corresponding location is identified based on this determination. In one example, a location can be determined as being unsafe based on numerous emergency braking activations on vehicles at that location or within a predetermined distance of the identified location. See also [0059] and [0064]) ;
wherein the alternative path is generated at least based on the operational data and the positional data (see at least Golov [0062-0063] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle). For example, server 101 may determine that a set of braking events corresponds to a pattern and a corresponding location is identified based on this determination. In one example, a location can be determined as being unsafe based on numerous emergency braking activations on vehicles at that location or within a predetermined distance of the identified location…In response to identifying the location, at least one action is performed. For example, server 101 can transmit a communication to current vehicle 111 that causes the vehicle to change a navigation path and/or activate a braking system when within a predetermined distance of the identified unsafe location.”)
The examiner notes that Golov teaches that the data may be collected by the current or other prior vehicles (see at least [0054] In some embodiments, the analysis of braking event and/or sensor data collected by the current or other prior vehicles includes providing the data as an input to a machine learning model.” And [0072] “ In one embodiment, data from vehicle 111 (or from vehicle 113) can be collected by sensors located in the vehicle. The collected data is analyzed, for example, using a computer model such as an artificial neural network (ANN) model.”) Golov further teaches that the artificial neural network model can be implemented on the current vehicle or the other vehicle (e.g. the probe vehicle, see for example [0071] “In some embodiments, artificial neural network model 119 itself and/or associated data can be transmitted to and implemented on vehicle 111 and/or other vehicles.”). Thus, the examiner notes that the data to determining the pattern and controlling of the vehicle can occur on the own vehicle (“the vehicle”) as taught by Golov.
The examiner notes that while Golov teaches determining a roadway item risk value (e.g. unsafe location as cited above), Golov does not explicitly teach calculating a roadway item risk value or comparing the roadway item risk value to a risk threshold and in response to a result of comparing the roadway item risk value to the risk threshold, proposing an alternative path for the vehicle. Further while Golov teaches wherein the operational data is braking data, Golov does not explicitly teach the data indicates “a change of braking data.
Pipe teaches
calculating a roadway item risk value and comparing the roadway item risk value to a risk threshold (see at least Pipe [0079-0083] “[0079] The hazard detection system 100 may determine a severity score (406). …The severity score may be based on the differences between the driver behavior patterns and corresponding thresholds for the driver behavior patterns and between the moving patterns of objects and corresponding thresholds for the moving patterns of the objects. The hazard detection system 100 may use a weighted combination of the differences between the driver behavior patterns and moving patterns of objects and their corresponding thresholds to determine the severity score… [0083] When the hazard detection system 100 determines that the score is greater than or equal to the high threshold score, the hazard detection system 100 may re-route the vehicle 102 to avoid the hazard (414). The hazard detection system 100 may perform other operations to the vehicle 102, such as activating the brakes, shifting lanes or otherwise actively avoiding the hazardous condition.”) and
in response to a result of comparing the roadway item risk value to the risk threshold, proposing an alternative path for the vehicle (see at least Pipe [0020] “Additionally, the hazard detection system may perform different operations to alert, avoid or otherwise mitigate consequences of the hazardous object or situation. For example, the hazard detection system may alert the driver of the presence of the hazardous object or situation… In another example, the hazard detection system may propose an alternative route and/or re-route the vehicle.” See also [0030] “The hazard detection system 100 may include a user interface 120. The hazard detection system 100 may display one or more notifications on the user interface 120. The one or more notifications on the user interface 120 may notify occupants of the vehicle when the hazard detection system 100 is initialized or activated or when a hazardous condition is detected. Moreover, the user interface 120 may display a route or an updated route of a path of the vehicle 102.” See also [0056] “The hazard detection system 100 may perform different operations for different severities of the hazardous condition, which is further described in FIG. 4. The hazard detection system 100 may perform operations, such as notify or alert the driver or occupants of the vehicle 102, other devices 106 and/or third-parties. The hazard detection system 100 may perform other operations including braking, changing lanes, re-routing the path of the vehicle 102 on the user interface 120, and/or autonomously steering the vehicle 102 onto the re-routed path or otherwise changing the path of the vehicle 102.” See also Pipe [0081] [0062]).
Further Pipe teaches wherein the pattern includes a change of braking data (see at least Pipe [0059] “The hazard detection system 100 obtains, extracts or determines the driver behavior patterns from the vehicle sensor data (302). The hazard detection system 100 may use one or more sensors 116 to obtain the sensor data that includes the driver behavior patterns, as described above. The driver behavior patterns include the speed, the rate of change of the speed, the angle of the steering wheel, the rate of change of the angle of the steering wheel, the amount or rate of braking or acceleration, …” See also [0067-0068]. See also [0051] for establishing a baseline and [0053] for comparing sensor data versus the baseline for determining a hazardous condition. See also [0061]).
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 Golov with the teaching of Pipe, with a reasonable expectation of success, because as Pipe teaches this allows the hazard detection system to control an operation of the vehicle 102 to mitigate, reduce, alert or otherwise notify that there is a hazard condition (see at least Pipe [0054]).
Further, the examiner notes that Pipe also teaches wherein the alternative path is generated at least based on the operational data and the positional data (as seen in [0059], [0067-0068], [0051] [0053] as cited above. For example, [0051] teaches “The hazard detection system 100 may determine the baseline based on a frequency or pattern of the behavior or movement of one or more objects at the current location…In another example, when one or more drivers speed, change speeds, control the steering wheel or otherwise control a vehicle in a certain manner at a location a threshold amount of times, the hazard detection system 100 may determine that those driver behavior patterns are normal and part of the baseline for that location..)”
Regarding claim 2, the combination of Golov and Pipe teach the method of claim 1, further comprising:
transmitting location information of the roadway hazard to at least one remote device (see at least Golov [0034] “At least some embodiments disclosed herein relate to monitoring data regarding braking events occurring on vehicles. The data is received from each of the vehicles (e.g., received by a server).” See also Golov [0035] “For example, when a driver performs an emergency braking, or the automatic emergency braking system of a vehicle is activated, the location of that braking event is transmitted to, for example, a server or other computing device in the cloud service. The location itself may be determined, for example, based on location data (e.g., geographic coordinates) provided from the vehicle itself (e.g., by a GPS location system)” See also Golov [0060] “In some embodiments, additional data is received by server 101 from the vehicles. This can include, for example, data regarding detected objects such as object type 162 and object location 164. This additional data can be stored as part of map data 160. Also, additional data such as sensor data 103 can be received from the vehicles” See also at least Pipe [0054] “If the hazard detection system 100 determines that there is a hazard condition present, the hazard detection system 100 controls an operation of the vehicle 102 to mitigate, reduce, alert or otherwise notify that there is a hazard condition. For example, the hazard detection system 100 may send the alert to a server or external database along with a location to disseminate the location of the hazardous condition to other vehicles.” ). ; and
transmitting the environmental data external to the vehicle to the at least one remote device (see at least Golov [0040-0046] “[0042] In one embodiment, additional data from other vehicles can be received and/or stored that relates to objects detected by the other vehicles. For example, a fallen tree may be detected within a predetermined time of the occurrence of a braking event. For example, the map above can store data regarding the stop sign detected by one or more prior vehicles. The map includes a location of the stop sign along with data regarding an associated braking event.” Wherein Golov teaches the map is stored in a server 101 which may be a cloud service (see [0046]). See also Golov [0060] “In some embodiments, additional data is received by server 101 from the vehicles. This can include, for example, data regarding detected objects such as object type 162 and object location 164. This additional data can be stored as part of map data 160. Also, additional data such as sensor data 103 can be received from the vehicles” See also Pipe [0053] “Once the vehicle sensor data and the environmental sensor data are obtained or detected, the hazard detection system 100 determines whether the hazardous condition is present (210). … Moreover, the hazard detection system 100 may analyze the weather information to determine whether there is a hazardous condition present. For example, if the weather information indicates that there is heavy snowfall and the one or more sensors 116 detect environmental data that includes snowfall on the roadway, the hazard detection system 100 may determine that the snowfall is a hazardous condition….[0054] “If the hazard detection system 100 determines that there is a hazard condition present, the hazard detection system 100 controls an operation of the vehicle 102 to mitigate, reduce, alert or otherwise notify that there is a hazard condition. For example, the hazard detection system 100 may send the alert to a server or external database along with a location to disseminate the location of the hazardous condition to other vehicles.”)
Regarding claim 3, the combination of Golov and Pipe teach method of claim 2, wherein the at least one remote device comprises at least one of: a mobile device, a vehicular computer, a personal computer, an electronic stop sign, a satellite, a central hub, and a server (See also Golov [0060] “In some embodiments, additional data is received by server 101 from the vehicles. This can include, for example, data regarding detected objects such as object type 162 and object location 164. This additional data can be stored as part of map data 160. Also, additional data such as sensor data 103 can be received from the vehicles”)
Regarding claim 4, the combination of Golov and Pipe teach method of claim 1, wherein the at least one roadway hazard comprises at least one of: ruts, potholes, bumps, dips, cracks, stopped vehicles, pedestrians, bicyclists, malfunctioning traffic lights, weather hazards, road debris, and reckless drivers (see at least Golov [0042] In one embodiment, additional data from other vehicles can be received and/or stored that relates to objects detected by the other vehicles. For example, a fallen tree may be detected within a predetermined time of the occurrence of a braking event. For example, the map above can store data regarding the stop sign detected by one or more prior vehicles. The map includes a location of the stop sign along with data regarding an associated braking event.” See also Pipe [0046] “Moreover, the other information may include a location and/or a position of one or more other vehicles and/or the location of one or more known hazardous conditions, such as a pothole, black ice, a road construction area or other road hazard. In some implementations, the hazard detection system 100 may provide the current location of the vehicle 102 to the external database 104, and in response, receive other information that is specific to the current location of the vehicle, such as the traffic, density or position of one or more surrounding vehicles or the location of the one or more known hazardous conditions.).
Regarding claim 5, the combination of Golov and Pipe teach method of claim 2, wherein the location information of the at least one roadway hazard is received from one or more position sensors connected to the vehicle, a remote server communicably coupled to the vehicle, or another vehicle communicably coupled to the vehicle and traveling on the portion of the roadway (see a least Golov [0146] “The system includes an autonomous vehicle subsystem 402. In the illustrated embodiment, autonomous vehicle subsystem 402 includes map database 402A, radar devices 402B, Lidar devices 402C, digital cameras 402D, sonar devices 402E, GPS receivers 402F, and inertial measurement units 402G. Each of the components of autonomous vehicle subsystem 402 comprise standard components provided in most current autonomous vehicles. In one embodiment, map database 402A stores a plurality of high-definition three-dimensional maps used for routing and navigation. Radar devices 402B, Lidar devices 402C, digital cameras 402D, sonar devices 402E, GPS receivers 402F, and inertial measurement units 402G may comprise various respective devices installed at various positions throughout the autonomous vehicle as known in the art. For example, these devices may be installed along the perimeter of an autonomous vehicle to provide location awareness, collision avoidance, and other standard autonomous vehicle functionality.” See also Pipe [0032] “The hazard detection system 100 may include a navigation unit 114 and/or one or more sensors 116. The navigation unit 114 may be integral to the vehicle 102 or a separate unit coupled to the vehicle 102, such as a personal device with navigation capabilities. When the navigation unit 114 is separate from the vehicle 102, the navigation unit 114 may communicate with the vehicle 102 via the network access device 118. The vehicle 102 may include a Global Positioning System (GPS) unit (not shown) for detecting location data including a current location of the vehicle 102”.)
Regarding claim 6, the combination of Golov and Pipe teach method of claim 1, wherein the positional data of the vehicle is received from at least one of: a LiDAR unit associated with the vehicle, a radar unit associated with the vehicle, a camera unit associated with the vehicle, or a GPS unit associated with the vehicle see a least Golov [0146] “The system includes an autonomous vehicle subsystem 402. In the illustrated embodiment, autonomous vehicle subsystem 402 includes map database 402A, radar devices 402B, Lidar devices 402C, digital cameras 402D, sonar devices 402E, GPS receivers 402F, and inertial measurement units 402G. Each of the components of autonomous vehicle subsystem 402 comprise standard components provided in most current autonomous vehicles. In one embodiment, map database 402A stores a plurality of high-definition three-dimensional maps used for routing and navigation. Radar devices 402B, Lidar devices 402C, digital cameras 402D, sonar devices 402E, GPS receivers 402F, and inertial measurement units 402G may comprise various respective devices installed at various positions throughout the autonomous vehicle as known in the art. For example, these devices may be installed along the perimeter of an autonomous vehicle to provide location awareness, collision avoidance, and other standard autonomous vehicle functionality.” See also Pipe [0032] “The hazard detection system 100 may include a navigation unit 114 and/or one or more sensors 116. The navigation unit 114 may be integral to the vehicle 102 or a separate unit coupled to the vehicle 102, such as a personal device with navigation capabilities. When the navigation unit 114 is separate from the vehicle 102, the navigation unit 114 may communicate with the vehicle 102 via the network access device 118. The vehicle 102 may include a Global Positioning System (GPS) unit (not shown) for detecting location data including a current location of the vehicle 102”.)
Regarding claim 7, the combination of Golov and Pipe teach method of claim 1, wherein the operational data associated with the at least one vehicle includes at least one of: a speed indication, a gyroscope indication, an axle angle indication, and fuel usage data (see at least Golov [0050] In one embodiment, the determination whether a vehicle has experienced a braking event and/or been involved in an accident can be based on data from one or more sensors of the vehicle. For example, data from an accelerometer of the vehicle can indicate a rapid deceleration of the vehicle (e.g., deceleration exceeding a threshold). See also at least Pipe [0033] “The navigation unit 114 may provide and obtain navigational map information including location data, which may include a current location, a starting location, a destination location and/or a route between the starting location or current location and the destination location of the vehicle 102. The navigation unit 114 may include a memory (not shown) for storing the route data. The navigation unit 114 may receive data from other sensors capable of detecting data corresponding to location information. For example, the other sensors may include a gyroscope or an accelerometer.” ).
Regarding claim 8, the combination of Golov and Pipe teach method of claim 1, wherein the environmental data external to the vehicle includes at least one of: an ambient temperature, an ambient pressure, an ambient humidity, a condition of the roadway, a wind speed, an amount of rainfall, an amount of snow, and an amount of ambient light (see at least Golov which describes an unsafe condition of the roadway. For example [0007] “In some cases, an object may be positioned in a way that creates an unsafe driving condition (e.g., a deep pothole in the center of a road). Failure by a driver or an autonomous vehicle navigation system to detect the unsafe condition may create a physical danger of injury to the driver and/or other passengers of a vehicle (e.g., a vehicle that suddenly encounters a deep pothole or other unsafe road condition without warning)”. See also Golov [0034] “In one example, an unsafe road condition may include alien objects that are unsafely positioned on a road. For example, a tree may have unexpectedly fallen on a road due to a recent storm, and the tree is blocking safe travel on the road..” See also Pipe [0053] “Once the vehicle sensor data and the environmental sensor data are obtained or detected, the hazard detection system 100 determines whether the hazardous condition is present (210). … Moreover, the hazard detection system 100 may analyze the weather information to determine whether there is a hazardous condition present. For example, if the weather information indicates that there is heavy snowfall and the one or more sensors 116 detect environmental data that includes snowfall on the roadway, the hazard detection system 100 may determine that the snowfall is a hazardous condition….[0054] “If the hazard detection system 100 determines that there is a hazard condition present, the hazard detection system 100 controls an operation of the vehicle 102 to mitigate, reduce, alert or otherwise notify that there is a hazard condition. For example, the hazard detection system 100 may send the alert to a server or external database along with a location to disseminate the location of the hazardous condition to other vehicles.”)
Regarding claim 11, Golov teaches a system comprising:
at least one processor (see at least Golov [0084] The computer 131 of the vehicle 111 includes one or more processors 133, memory 135 storing firmware (or software) 127, the ANN model 119 (e.g., as illustrated in FIG. 1), and other data 129. See also Golov [0096] “ In one embodiment, a system includes: at least one processor; and memory storing instructions configured to instruct the at least one processor to: receive data regarding braking events, each event occurring on one of a plurality of vehicles, and each event associated with a location; determine that the braking events correspond to a pattern; identify, based on determining that the braking events correspond to the pattern, a first location; and in response to identifying the first location, perform at least one action.” ) ; and
a memory coupled to the at least one processor, the memory comprising computer executable instructions that, when executed by the at least one processor, performs the steps of see at least Golov [0084] The computer 131 of the vehicle 111 includes one or more processors 133, memory 135 storing firmware (or software) 127, the ANN model 119 (e.g., as illustrated in FIG. 1), and other data 129. See also Golov [0096] “ In one embodiment, a system includes: at least one processor; and memory storing instructions configured to instruct the at least one processor to: receive data regarding braking events, each event occurring on one of a plurality of vehicles, and each event associated with a location; determine that the braking events correspond to a pattern; identify, based on determining that the braking events correspond to the pattern, a first location; and in response to identifying the first location, perform at least one action.” ):
receiving positional data of the vehicle indicating a current position of the vehicle (see at least Golov [0059] “In one embodiment, data regarding braking events occurring on vehicles (e.g., other or prior vehicle 113) is received by server 101 via communication network 102. The received data includes a location for each of the braking events. For example, the received data can include an event location 163 for each braking event 161. Braking event 161 can include data such as, for example, an identifier, a type of braking event, etc. The received braking event data can be stored as part of map data 160” See also [0092] regarding positional data being received. See also [0035] and [00146] For example [0035] teaches “ The location itself may be determined, for example, based on location data (e.g., geographic coordinates) provided from the vehicle itself (e.g., by a GPS location system) and/or location data otherwise associated with or known about the vehicle”);
receiving, from at least one sensor connected to the vehicle, environmental data external to the vehicle (see at least Golov [0064] “In some embodiments, in addition to sending data regarding braking events, vehicle 113 and/or other prior vehicles send data regarding objects detected during travel (e.g. vehicle 113 can be traveling prior to current vehicle 111, which arrives later at the same location where an object has been detected by vehicle 113). These objects can include, for example, object 155 and object 157. Sensors of vehicle 113 and the other prior vehicles collect and/or generate data regarding the objects that have been detected. Data regarding detected objects can be analyzed in conjunction with braking event data in order to identify a location that prompts an action.)
receiving operational data associated with the vehicle (see at least Golov [0059] “In one embodiment, data regarding braking events occurring on vehicles (e.g., other or prior vehicle 113) is received by server 101 via communication network 102. The received data includes a location for each of the braking events. For example, the received data can include an event location 163 for each braking event 161. Braking event 161 can include data such as, for example, an identifier, a type of braking event, etc. The received braking event data can be stored as part of map data 160” The examiner interprets the braking data as the operational data.);
based on the positional data, environmental data, and operational data associated with the vehicle, [[calculating]] a roadway item risk value of the vehicle (see at least Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle). For example, server 101 may determine that a set of braking events corresponds to a pattern and a corresponding location is identified based on this determination. In one example, a location can be determined as being unsafe based on numerous emergency braking activations on vehicles at that location or within a predetermined distance of the identified location.) wherein the [[roadway item risk value is calculated]] by considering a recognized pattern, wherein the recognized pattern is identified at least based on a comparison between the operational data associated with the vehicle with historical operational data associated with the recognized pattern, wherein the operational data indicates [a sudden change of]] braking data associated with the vehicle (see at least Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle). For example, server 101 may determine that a set of braking events corresponds to a pattern and a corresponding location is identified based on this determination. In one example, a location can be determined as being unsafe based on numerous emergency braking activations on vehicles at that location or within a predetermined distance of the identified location. See also [0059] and [0064]); and
wherein the alternative path is generated at least based on the operational data and the positional data (see at least Golov [0062-0063] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle). For example, server 101 may determine that a set of braking events corresponds to a pattern and a corresponding location is identified based on this determination. In one example, a location can be determined as being unsafe based on numerous emergency braking activations on vehicles at that location or within a predetermined distance of the identified location…In response to identifying the location, at least one action is performed. For example, server 101 can transmit a communication to current vehicle 111 that causes the vehicle to change a navigation path and/or activate a braking system when within a predetermined distance of the identified unsafe location.”)
The examiner notes that Golov teaches that the data may be collected by the current or other prior vehicles (see at least [0054] In some embodiments, the analysis of braking event and/or sensor data collected by the current or other prior vehicles includes providing the data as an input to a machine learning model.” And [0072] “ In one embodiment, data from vehicle 111 (or from vehicle 113) can be collected by sensors located in the vehicle. The collected data is analyzed, for example, using a computer model such as an artificial neural network (ANN) model.”) Golov further teaches that the artificial neural network model can be implemented on the current vehicle or the other vehicle (e.g. the probe vehicle, see for example [0071] “In some embodiments, artificial neural network model 119 itself and/or associated data can be transmitted to and implemented on vehicle 111 and/or other vehicles.”). Thus, the examiner notes that the data to determining the pattern and controlling of the vehicle can occur on the own vehicle (“the vehicle”) as taught by Golov.
The examiner notes that while Golov teaches determining a roadway item risk value (e.g. unsafe location as cited above), Golov does not explicitly teach calculating a roadway item risk value or comparing the roadway item risk value to a risk threshold and in response to a result of comparing the roadway item risk value to the risk threshold, proposing an alternative path for the vehicle to avoid at least one roadway hazard. Further while Golov teaches wherein the operational data is braking data, Golov does not explicitly teach the data indicates a change of braking data.
Pipe teaches
calculating a roadway item risk value and comparing the roadway item risk value to a risk threshold (see at least Pipe [0079-0083] “[0079] The hazard detection system 100 may determine a severity score (406). …The severity score may be based on the differences between the driver behavior patterns and corresponding thresholds for the driver behavior patterns and between the moving patterns of objects and corresponding thresholds for the moving patterns of the objects. The hazard detection system 100 may use a weighted combination of the differences between the driver behavior patterns and moving patterns of objects and their corresponding thresholds to determine the severity score… [0083] When the hazard detection system 100 determines that the score is greater than or equal to the high threshold score, the hazard detection system 100 may re-route the vehicle 102 to avoid the hazard (414). The hazard detection system 100 may perform other operations to the vehicle 102, such as activating the brakes, shifting lanes or otherwise actively avoiding the hazardous condition.”) and
in response to a result of comparing the roadway item risk value to the risk threshold, proposing an alternative path for the vehicle to avoid at least one roadway hazard (see at least Pipe [0020] “Additionally, the hazard detection system may perform different operations to alert, avoid or otherwise mitigate consequences of the hazardous object or situation. For example, the hazard detection system may alert the driver of the presence of the hazardous object or situation… In another example, the hazard detection system may propose an alternative route and/or re-route the vehicle.” See also [0030] “The hazard detection system 100 may include a user interface 120. The hazard detection system 100 may display one or more notifications on the user interface 120. The one or more notifications on the user interface 120 may notify occupants of the vehicle when the hazard detection system 100 is initialized or activated or when a hazardous condition is detected. Moreover, the user interface 120 may display a route or an updated route of a path of the vehicle 102.” See also [0056] “The hazard detection system 100 may perform different operations for different severities of the hazardous condition, which is further described in FIG. 4. The hazard detection system 100 may perform operations, such as notify or alert the driver or occupants of the vehicle 102, other devices 106 and/or third-parties. The hazard detection system 100 may perform other operations including braking, changing lanes, re-routing the path of the vehicle 102 on the user interface 120, and/or autonomously steering the vehicle 102 onto the re-routed path or otherwise changing the path of the vehicle 102.” See also Pipe [0081] [0062]).
Further Pipe teaches wherein the pattern includes a change of braking data (see at least Pipe [0059] “The hazard detection system 100 obtains, extracts or determines the driver behavior patterns from the vehicle sensor data (302). The hazard detection system 100 may use one or more sensors 116 to obtain the sensor data that includes the driver behavior patterns, as described above. The driver behavior patterns include the speed, the rate of change of the speed, the angle of the steering wheel, the rate of change of the angle of the steering wheel, the amount or rate of braking or acceleration, …” See also [0067-0068]. See also [0051] for establishing a baseline and [0053] for comparing sensor data versus the baseline for determining a hazardous condition. See also [0061]).
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 Golov with the teaching of Pipe, with a reasonable expectation of success, because as Pipe teaches this allows the hazard detection system to control an operation of the vehicle 102 to mitigate, reduce, alert or otherwise notify that there is a hazard condition (see at least Pipe [0054]).
Further, the examiner notes that Pipe also teaches wherein the alternative path is generated at least based on the operational data and the positional data (as seen in [0059], [0067-0068], [0051] [0053] as cited above. For example, [0051] teaches “The hazard detection system 100 may determine the baseline based on a frequency or pattern of the behavior or movement of one or more objects at the current location…In another example, when one or more drivers speed, change speeds, control the steering wheel or otherwise control a vehicle in a certain manner at a location a threshold amount of times, the hazard detection system 100 may determine that those driver behavior patterns are normal and part of the baseline for that location..)”
Claim 12 is rejected under the same rationale, mutatis mutandis, as claim 4, above.
Regarding claim 13, the combination of Golov and Pipe teach system of claim 11, wherein the steps further includes displaying an alternative path in a map on a display, (see at least Pipe [0030] “The hazard detection system 100 may include a user interface 120. The hazard detection system 100 may display one or more notifications on the user interface 120. The one or more notifications on the user interface 120 may notify occupants of the vehicle when the hazard detection system 100 is initialized or activated or when a hazardous condition is detected. Moreover, the user interface 120 may display a route or an updated route of a path of the vehicle 102”.).
Claim 16 is rejected under the same rationale, mutatis mutandis, as claim 6, above.
Claim 17 is rejected under the same rationale, mutatis mutandis, as claim 7, above.
Claim 18 is rejected under the same rationale, mutatis mutandis, as claim 8, above.
Regarding claim 19, Golov teaches a vehicular system comprising:
a non-transitory memory see at least Golov [0084] The computer 131 of the vehicle 111 includes one or more processors 133, memory 135 storing firmware (or software) 127, the ANN model 119 (e.g., as illustrated in FIG. 1), and other data 129. See also Golov [0096] “ In one embodiment, a system includes: at least one processor; and memory storing instructions configured to instruct the at least one processor to: receive data regarding braking events, each event occurring on one of a plurality of vehicles, and each event associated with a location; determine that the braking events correspond to a pattern; identify, based on determining that the braking events correspond to the pattern, a first location; and in response to identifying the first location, perform at least one action.” ) ;
a processor coupled to the memory see at least Golov [0084] The computer 131 of the vehicle 111 includes one or more processors 133, memory 135 storing firmware (or software) 127, the ANN model 119 (e.g., as illustrated in FIG. 1), and other data 129. See also Golov [0096] “ In one embodiment, a system includes: at least one processor; and memory storing instructions configured to instruct the at least one processor to: receive data regarding braking events, each event occurring on one of a plurality of vehicles, and each event associated with a location; determine that the braking events correspond to a pattern; identify, based on determining that the braking events correspond to the pattern, a first location; and in response to identifying the first location, perform at least one action.” ) , wherein the processor is configured to:
receive positional data of a first vehicle indicating a current position of the first vehicle (see at least Golov [0059] “In one embodiment, data regarding braking events occurring on vehicles (e.g., other or prior vehicle 113) is received by server 101 via communication network 102. The received data includes a location for each of the braking events. For example, the received data can include an event location 163 for each braking event 161. Braking event 161 can include data such as, for example, an identifier, a type of braking event, etc. The received braking event data can be stored as part of map data 160” See also [0092] regarding positional data being received. See also [0035] and [00146] For example [0035] teaches “ The location itself may be determined, for example, based on location data (e.g., geographic coordinates) provided from the vehicle itself (e.g., by a GPS location system) and/or location data otherwise associated with or known about the vehicle”);
receive, from at least one sensor connected to the first vehicle, environmental data external to the first vehicle (see at least Golov [0064] “In some embodiments, in addition to sending data regarding braking events, vehicle 113 and/or other prior vehicles send data regarding objects detected during travel (e.g. vehicle 113 can be traveling prior to current vehicle 111, which arrives later at the same location where an object has been detected by vehicle 113). These objects can include, for example, object 155 and object 157. Sensors of vehicle 113 and the other prior vehicles collect and/or generate data regarding the objects that have been detected. Data regarding detected objects can be analyzed in conjunction with braking event data in order to identify a location that prompts an action.);
receive operational data associated with the first vehicle (see at least Golov [0059] “In one embodiment, data regarding braking events occurring on vehicles (e.g., other or prior vehicle 113) is received by server 101 via communication network 102. The received data includes a location for each of the braking events. For example, the received data can include an event location 163 for each braking event 161. Braking event 161 can include data such as, for example, an identifier, a type of braking event, etc. The received braking event data can be stored as part of map data 160” The examiner interprets the braking data as the operational data.);
based on the positional data, environmental data, and operational data associated with the vehicle, [[calculate a roadway item risk value]] of the vehicle (see at least Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle). For example, server 101 may determine that a set of braking events corresponds to a pattern and a corresponding location is identified based on this determination. In one example, a location can be determined as being unsafe based on numerous emergency braking activations on vehicles at that location or within a predetermined distance of the identified location.) wherein the [[roadway item risk value is calculated]] by considering a recognized pattern, wherein the recognized pattern is identified at least based on a comparison between the operational data associated with the vehicle with historical operational data associated with the recognized pattern, wherein the operational data indicates [a sudden change of]] braking data associated with the vehicle (see at least Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle). For example, server 101 may determine that a set of braking events corresponds to a pattern and a corresponding location is identified based on this determination. In one example, a location can be determined as being unsafe based on numerous emergency braking activations on vehicles at that location or within a predetermined distance of the identified location. See also [0059] and [0064]); and
wherein the alternative path is generated at least based on the operational data and the positional data (see at least Golov [0062-0063] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle). For example, server 101 may determine that a set of braking events corresponds to a pattern and a corresponding location is identified based on this determination. In one example, a location can be determined as being unsafe based on numerous emergency braking activations on vehicles at that location or within a predetermined distance of the identified location…In response to identifying the location, at least one action is performed. For example, server 101 can transmit a communication to current vehicle 111 that causes the vehicle to change a navigation path and/or activate a braking system when within a predetermined distance of the identified unsafe location.”)
The examiner notes that Golov teaches that the data may be collected by the current or other prior vehicles (see at least [0054] In some embodiments, the analysis of braking event and/or sensor data collected by the current or other prior vehicles includes providing the data as an input to a machine learning model.” And [0072] “ In one embodiment, data from vehicle 111 (or from vehicle 113) can be collected by sensors located in the vehicle. The collected data is analyzed, for example, using a computer model such as an artificial neural network (ANN) model.”) Golov further teaches that the artificial neural network model can be implemented on the current vehicle or the other vehicle (e.g. the probe vehicle, see for example [0071] “In some embodiments, artificial neural network model 119 itself and/or associated data can be transmitted to and implemented on vehicle 111 and/or other vehicles.”). Thus, the examiner notes that the data to determining the pattern and controlling of the vehicle can occur on the own vehicle (“the vehicle”) as taught by Golov.
The examiner notes that while Golov teaches determining a roadway item risk value (e.g. unsafe location as cited above), Golov does not explicitly teach calculating a roadway item risk value or comparing the roadway item risk value to a risk threshold and in response to a result of comparing the roadway item risk value to the risk threshold, proposing an alternative path for the vehicle. Further while Golov teaches wherein the operational data is braking data, Golov does not explicitly teach the data indicates a change of braking data.
Pipe teaches
calculating a roadway item risk value and comparing the roadway item risk value to a risk threshold (see at least Pipe [0079-0083] “[0079] The hazard detection system 100 may determine a severity score (406). …The severity score may be based on the differences between the driver behavior patterns and corresponding thresholds for the driver behavior patterns and between the moving patterns of objects and corresponding thresholds for the moving patterns of the objects. The hazard detection system 100 may use a weighted combination of the differences between the driver behavior patterns and moving patterns of objects and their corresponding thresholds to determine the severity score… [0083] When the hazard detection system 100 determines that the score is greater than or equal to the high threshold score, the hazard detection system 100 may re-route the vehicle 102 to avoid the hazard (414). The hazard detection system 100 may perform other operations to the vehicle 102, such as activating the brakes, shifting lanes or otherwise actively avoiding the hazardous condition.”) and
in response to a result of comparing the roadway item risk value to the risk threshold, proposing an alternative path for the vehicle (see at least Pipe [0020] “Additionally, the hazard detection system may perform different operations to alert, avoid or otherwise mitigate consequences of the hazardous object or situation. For example, the hazard detection system may alert the driver of the presence of the hazardous object or situation… In another example, the hazard detection system may propose an alternative route and/or re-route the vehicle.” See also [0030] “The hazard detection system 100 may include a user interface 120. The hazard detection system 100 may display one or more notifications on the user interface 120. The one or more notifications on the user interface 120 may notify occupants of the vehicle when the hazard detection system 100 is initialized or activated or when a hazardous condition is detected. Moreover, the user interface 120 may display a route or an updated route of a path of the vehicle 102.” See also [0056] “The hazard detection system 100 may perform different operations for different severities of the hazardous condition, which is further described in FIG. 4. The hazard detection system 100 may perform operations, such as notify or alert the driver or occupants of the vehicle 102, other devices 106 and/or third-parties. The hazard detection system 100 may perform other operations including braking, changing lanes, re-routing the path of the vehicle 102 on the user interface 120, and/or autonomously steering the vehicle 102 onto the re-routed path or otherwise changing the path of the vehicle 102.” See also Pipe [0081] [0062]).
Further Pipe teaches wherein the pattern includes a change of braking data (see at least Pipe [0059] “The hazard detection system 100 obtains, extracts or determines the driver behavior patterns from the vehicle sensor data (302). The hazard detection system 100 may use one or more sensors 116 to obtain the sensor data that includes the driver behavior patterns, as described above. The driver behavior patterns include the speed, the rate of change of the speed, the angle of the steering wheel, the rate of change of the angle of the steering wheel, the amount or rate of braking or acceleration, …” See also [0067-0068]. See also [0051] for establishing a baseline and [0053] for comparing sensor data versus the baseline for determining a hazardous condition. See also [0061]).
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 Golov with the teaching of Pipe, with a reasonable expectation of success, because as Pipe teaches this allows the hazard detection system to control an operation of the vehicle 102 to mitigate, reduce, alert or otherwise notify that there is a hazard condition (see at least Pipe [0054]).
Further, the examiner notes that Pipe also teaches wherein the alternative path is generated at least based on the operational data and the positional data (as seen in [0059], [0067-0068], [0051] [0053] as cited above. For example, [0051] teaches “The hazard detection system 100 may determine the baseline based on a frequency or pattern of the behavior or movement of one or more objects at the current location…In another example, when one or more drivers speed, change speeds, control the steering wheel or otherwise control a vehicle in a certain manner at a location a threshold amount of times, the hazard detection system 100 may determine that those driver behavior patterns are normal and part of the baseline for that location..)”
Regarding claim 20, the combination of Golov and Pipe teach system of claim 19, wherein the processor is configured to:
calculate an alternative path (see at least Pipe [0083] When the hazard detection system 100 determines that the score is greater than or equal to the high threshold score, the hazard detection system 100 may re-route the vehicle 102 to avoid the hazard (414). The hazard detection system 100 may perform other operations to the vehicle 102, such as activating the brakes, shifting lanes or otherwise actively avoiding the hazardous condition.”) ;
based on the calculated alternative path, dynamically modifying a path of the first vehicle (see at least Pipe [0083] When the hazard detection system 100 determines that the score is greater than or equal to the high threshold score, the hazard detection system 100 may re-route the vehicle 102 to avoid the hazard (414). The hazard detection system 100 may perform other operations to the vehicle 102, such as activating the brakes, shifting lanes or otherwise actively avoiding the hazardous condition.”) ;
display the alternative path on a display within the first vehicle (see at least Pipe [0081] In these instances when the hazard detection system 100 determines that the severity score is greater than the low threshold score, the hazard detection system 100 may alert the user via the user interface 120, alert one or more other devices 106 via the network access device 118, and/or provide the alert to the third party (410). The hazard detection system 100 may include information, such as environmental sensor data and/or vehicle sensor data, and/or a suggestion or recommendation, such as to accelerate, brake, decelerate or change lanes, in the alert. The environmental sensor data and/or vehicle sensor data may include image data, such as an image of the driver when the driver is distracted or an image of the license plate of the vehicle in front that is swerving.”); and
transmit the alternative path to a second vehicle proximal to the first vehicle (see at least Pipe [0081] In these instances when the hazard detection system 100 determines that the severity score is greater than the low threshold score, the hazard detection system 100 may alert the user via the user interface 120, alert one or more other devices 106 via the network access device 118, and/or provide the alert to the third party (410). The hazard detection system 100 may include information, such as environmental sensor data and/or vehicle sensor data, and/or a suggestion or recommendation, such as to accelerate, brake, decelerate or change lanes, in the alert. The environmental sensor data and/or vehicle sensor data may include image data, such as an image of the driver when the driver is distracted or an image of the license plate of the vehicle in front that is swerving.”).
Claims 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Golov and Pipe in further view of in view of Kundu et al. (US Pub. No. 2020/0250984, hereinafter “Kundu”).
Regarding claim 9, the combination of Golov and Pipe teach method of claim 1, further comprising:
collecting data associated with a roadway item (see at least Golov [0070] and [0077]. For example in [0072] “In one embodiment, data from vehicle 111 (or from vehicle 113) can be collected by sensors located in the vehicle. The collected data is analyzed, for example, using a computer model such as an artificial neural network (ANN) model. In one embodiment, the collected data is provided as an input to the ANN model.”… [0077] “During the operations of the vehicles 111, . . . , 113 in their respective service environments, the vehicles 111, . . . , 113 encounter items, such as events or objects, that are captured in the sensor data. The ANN model 119 is used by the vehicles 111, . . . , 113 to provide the identifications of the items to facilitate the generation of commands for the operations of the vehicles 111, . . . , 113, such as for autonomous driving and/or for advanced driver assistance.” See also Pipe [0027] and [0046]);
extracting a set of [features] from the data associated with the roadway item (see at least Golov [0077] “During the operations of the vehicles 111, . . . , 113 in their respective service environments, the vehicles 111, . . . , 113 encounter items, such as events or objects, that are captured in the sensor data. The ANN model 119 is used by the vehicles 111, . . . , 113 to provide the identifications of the items to facilitate the generation of commands for the operations of the vehicles 111, . . . , 113, such as for autonomous driving and/or for advanced driver assistance.” );
evaluating the set of [features] using at least one machine learning model (see at least Golov [ [0077] “During the operations of the vehicles 111, . . . , 113 in their respective service environments, the vehicles 111, . . . , 113 encounter items, such as events or objects, that are captured in the sensor data. The ANN model 119 is used by the vehicles 111, . . . , 113 to provide the identifications of the items to facilitate the generation of commands for the operations of the vehicles 111, . . . , 113, such as for autonomous driving and/or for advanced driver assistance.) ;
generating the roadway item risk value based on the evaluation of the set of [[features]] (see at least Golov [0087] “Alternatively, and/or additionally, the identification of an unsafe location and/or classification of a braking event or object generated by the ANN model 119 can be used by an autonomous driving module of the firmware (or software) 127, or an advanced driver assistance system, to generate a response”).;and
when the roadway item risk value exceeds the risk threshold, classifying the roadway item as a roadway hazard ([0087] “Alternatively, and/or additionally, the identification of an unsafe location and/or classification of a braking event or object generated by the ANN model 119 can be used by an autonomous driving module of the firmware (or software) 127, or an advanced driver assistance system, to generate a response.” The examiner interprets the classification of an unsafe location or object as classification of the object as a hazard.) .
While the combination of Golov and Pipe teach evaluating the risk item with a machine learning model and generating the roadway item risk value, the combination does not explicitly teach evaluating the risk item based on features and generating the roadway item risk value based on an evaluation of features.
Kundu teaches these features including evaluating the risk item based on features (see at least Kundu “the disparity image 401-1 is generated by using a machine learning process received from a cloud system…” [0055] and “camera systems may be a single camera coupled with a machine learning process configured to generate difference images based on the images received from the single camera or multiple cameras [0143]); and generating the roadway item risk value based on an evaluation of features (see at least Kundu [0083] and [0086] “the distribution of intensity is analyzed” [0083] and [0086] For example see [0083] “Based on the specified threshold conditions, intensity classifier results will be used along with depth classifier results…” and “if the calculated disparity values are correct (verified using previously calculated thresholds), then they are designated as ‘valid pixels’…and if there an insufficient number of valid pixels…” leading “to false depth measurement and false detection.”[0086]. In addition, see [0109], [0123], specifically in context to thresholds as described in [0083] wherein the specific threshold is calculated using previously analyzed data by statistical analysis, e.g. similar in size and depth indicates classifying as pothole, see also Figure 33).
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 Golov and Pipe with Kundu, with a reasonable expectation of success, because as Kundu teaches the analysis of the features of the pothole allows the system to determine the depth of the pothole and control the vehicle based on the depth of the pothole to mitigate and danger (see at least Kundu [0134-0135] and Figure 33).
The examiner notes that Kundu further teaches additional elements of claim 9 including:collecting data associated with a roadway item (See at least Kundu, [0051] images from camera system 301);
extracting a set of features from the data associated with the roadway item (See at least Kundu, [0051] “From the ROI, the roadway depression candidates such as potholes are extracted” and “a feature extraction module is utilized to conduct feature extraction and classification” [0107-0109]);
evaluating the set of features using at least one machine learning model (See at least Kundu “the disparity image 401-1 is generated by using a machine learning process received from a cloud system…” [0055] and “camera systems may be a single camera coupled with a machine learning process configured to generate difference images based on the images received from the single camera or multiple cameras [0143]);
generating the roadway item risk value based on the evaluation of the set of features wherein the roadway item risk value is generated by considering a recognized pattern determined by the set of features (“the distribution of intensity is analyzed” [0083] and [0086]);
comparing the risk value to a risk threshold (see [0083] “each of these features are compared with a specific threshold (e.g. calculated using previously analyzed data by statistical analysis”));
when the risk value exceeds the risk threshold, classifying the roadway item as a roadway hazard (see [0083] “Based on the specified threshold conditions, intensity classifier results will be used along with depth classifier results…” and “if the calculated disparity values are correct (verified using previously calculated thresholds), then they are designated as ‘valid pixels’…and if there an insufficient number of valid pixels…” leading “to false depth measurement and false detection.”[0086]. In addition, see [0109], [0123], specifically in context to thresholds as described in [0083] wherein the specific threshold is calculated using previously analyzed data by statistical analysis, e.g. similar in size and depth indicates classifying as pothole, see also Figure 33).
Regarding claim 10, the combination of Golov, Pipe, and Kundu discloses the method wherein the risk value indicates a degree of similarity between the roadway item and a previously identified roadway hazard (See at least Kundu, [0109], [0123], specifically in context to thresholds as described in [0083] wherein the specific threshold is calculated using previously analyzed data by statistical analysis, e.g. similar in size and depth indicates classifying as pothole, see also Figure 33).
Claims 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Golov and Pipe in further view of in view of Slusar et al. (US PG Pub. 2017/0089710 hereinafter “Slusar”).
Regarding claim 14, the combination of Golov and Pipe teach system of claim 11, however the combination of Golov and Pipe do not teach wherein the map further displays traffic density information.
Slusar discloses wherein the map displays traffic density information (see at least Slusar “As another example, the environmental information may include data detailing foot traffic and other types of traffic (e.g. pedestrians, cyclists, motorcyclists, and the like)…” [0034], “number of lanes, width of roads/lanes, population density” and “The risk map generation system 302 may be able to provide first responders with a plurality of possible routes to a predetermined destination and rank them based on risk value, time, distance, traffic, and other safety and travel factors.” [0075]. The examiner interprets traffic and population density to include traffic density.)
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 Golov, and Pipe with the aforementioned feature of Slusar with a reasonable expectation of success in order to provide first responders with a plurality of possible routes to a predetermined destination and rank them based on risk value, time, distance, traffic and other safety travel factors (Slusar [0075]).
Regarding claim 15, the combination of Golov, Pipe and Slusar teach the system of claim 14, wherein the map further displays a location of at least one of: a traffic light, a stop sign, and a roadway shoulder (see at least Golov “For example, the map above can store data regarding the stop sign detected by one or more prior vehicles. The map includes a location of the stop sign along with data regarding an associated braking event.” See also [0125] “] In one embodiment, memory 309 stores a database 310, which may include data collected by sensors 306 and/or data received by a communication interface 305 from computing device, such as, for example, a server 301 (server 301 can be, for example, server 101 of FIG. 1 in some embodiments). In one example, this communication may be used to wirelessly transmit collected data from the sensors 306 to the server 301. The received data may include configuration, training, and other data used to configure control of the display devices 308 or other components by controller 307.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US-20180309592-A1 to Stolfus is cited for showing receiving positional data (GPS [0102]) detecting operation conditions (such as braking events [0175]), determining if a risk of a risk item (incident) is above a threshold value determining an alternative path. See at least Figure 16, [0139], [0144], [0158].
US-20180276485-A1 to Heck et al. is cited for showing detecting operating parameters of a vehicle (see at least [0034]) and determining a risk score for an object comparing the risk score to determine if the risk score exceeds a threshold (see at least [0036]).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENNIFER M. ANDA whose telephone number is (571)272-5042. The examiner can normally be reached Monday-Friday 8:30 am-5pm MST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aniss Chad can be reached on (571)270-3832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JENNIFER M ANDA/Examiner, Art Unit 3662