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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 22 April 2026 has been entered.
Claims 1, 11, 13, 16, and 17-20 have been amended.
Claims 1-20 are pending and have been examined.
Response to Amendments and Remarks
Claim Objections
Claims 16-18 were objected to because of informalities. The Applicant has amended the claims to overcome the objection. Accordingly, the objection of claims 16-18 has been withdrawn. .
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most of the rejections of claims 1-20 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, has been withdrawn. The examiner notes that the rejection of claim 2 remains as noted 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 19, filed 21 April 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 persuasive in view of the amendment (i.e. identifying a type of the at least one roadway hazard based on at least one physical characteristic). Accordingly the double patenting rejection is withdrawn. 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 pages 9-10, 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 "detecting at least one roadway hazard; identifying a type of the at least one roadway hazard based on at least one physical characteristic; based on the positional data, environmental data, the type of the at least one roadway hazard, and operational data associated with the vehicle, calculating a roadway item risk value." (Emphasis added.) Support of the foregoing feature can be found at least in paragraph [0026] of the specification as published.
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:
detecting at least one roadway hazard (see at least 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 Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle).” See also Golov [0066] “Subsequent to receiving the data regarding detected objects from the prior vehicles, a current vehicle 111 also may transmit data regarding new objects that are being detected during travel. For example, object 155 can be a new object from the perspective of vehicle 111.”);
identifying a type of the at least one roadway hazard based on at least one physical characteristic (see at least 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 Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle).” See also Golov [0066] “Subsequent to receiving the data regarding detected objects from the prior vehicles, a current vehicle 111 also may transmit data regarding new objects that are being detected during travel. For example, object 155 can be a new object from the perspective of vehicle 111.”).
The examiner notes the 112 rejection below wherein it is not clear what the physical characteristic refers back to. As indicated above, Golov does teach identifying a type of the roadway hazard based on at least one physical characteristic (e.g. surrounding vehicles braking as cited above and the location of the braking). Assuming arguendo, “identifying a type of the roadway hazard based on at least one physical characteristic of the roadway hazard”, then Golov also teaches this limitation. The instant specification describes the type and/or physical characteristic to include the location of the roadway item (see [0041]). Golov further teaches the location of the roadway hazard. Thus, Golov does teach identifying a type of the roadway hazard based on at least one physical characteristic of the roadway hazard”.
Further, 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:
identifying a type of the roadway hazard based on at least one physical characteristic of the roadway hazard (see at least Pipe [0022] “The hazard detection system 100 uses the data to identify, detect or otherwise determine hazardous objects and/or hazardous situations (“hazardous conditions”) that are present in the environment. For example, the hazard detection system 100 may identify when there is an obstacle in the road, a surrounding vehicle is driving dangerously, a driver of the vehicle 102 is driving erratically or other hazardous condition. See also Pipe [0060] “The hazard detection system 100 obtains, extracts or determines moving patterns of objects in the environment from the environmental sensor data (304). The hazard detection system 100 may use one or more sensors 116 to obtain the environmental sensor data that includes the driving patterns of one or more surrounding vehicles, as described above, or one or more moving patterns of other objects. The moving patterns include patterns of movement of different objects including driving patterns of other surrounding vehicles. The patterns of movement include the speed, the rate of change of the speed, the relative distance between the other surrounding vehicle and the vehicle 102, the change in the relative distance, direction of travel of the surrounding vehicle and/or other factors of objects including vehicles in the surrounding environment of the vehicle 102.” See also [0063] “For example, the hazard detection system 100 may measure the change in the angle of the steering wheel or rate of change in the angle of the steering wheel. When the change in the angle of the steering wheel or the rate of change in the angle of the steering wheel exceeds a threshold amount, the hazard detection system 100 may determine that the vehicle 102 is swerving in and out of the lane of the roadway, which may indicate that the driver of the vehicle 102 is distracted, inebriated or otherwise not in control of the vehicle 102.”and [0067] “The value may be a relative speed of another vehicle, the relative amount of movement of another vehicle inside and/or outside a lane and/or the relative distance between the other vehicle and the vehicle 102. The object may be a pothole, a tire, an accident, a branch or other object on or in proximity to the roadway.”) .
wherein the alternative path is generated at least based on the operational data, the type of the at least one roadway hazard 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..)”
Finally, the examiner notes, Kundu, already of record but not relied upon for the independent claims, also teaches identifying the obstacle based on physical characteristics (e.g. depth of pothole) and determines the risk value based on the depth of the pothole.
Claim 19 has not been amended to recite “the type of the at least one roadway hazard, and operational data associated with the vehicle,” as asserted by the response.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “the type of the at least one roadway hazard, and operational data associated with the vehicle”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant does not provide separate arguments for the dependent claims, relying upon the arguments provided for the independent claims and addressed above.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “identifying the type of the at least one roadway hazard based on at least one physical characteristic”. It is not clear what the physical characteristic corresponds to. Is it the physical characteristic of the surrounding vehicles in their reaction of the road hazard (e.g. “hard braking”) or the physical characteristic of the actual roadway hazard. Claim 11 and 19 have similar recitations and are rejected to for the same reasons.
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 “detecting at least one roadway hazard”.
Claim 11 recites “avoid at least one roadway hazard: in line 23 of the claim. Claim 11 recites “the at least one roadway hazard” (amended claim, line 10). It is not clear if the at least one roadway hazard in line 23 is the same roadway hazard referred to in cline 10. The examiner recommends amending line 23 to recite “avoid the at least one roadway hazard”.
Claims 1-10 depend from claim 2 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 11. 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 20.
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;
detecting at least one roadway hazard;
Identifying a type of the at least one roadway hazard based on at least one physical characteristic;
based on the positional data, environmental data, the type of the at least one roadway hazard, and operational data associated with the vehicle, calculating a roadway item risk value, 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 “detecting at least one roadway hazard, Identifying a type of the at least one roadway hazard based on at least one physical characteristic; based on the positional data, environmental data, the type of the at least one roadway hazard and operational data associated with the vehicle, calculating a roadway item risk value, 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 a picture or a scene outside a vehicle and determining there is a hazard and identifying they type of hazard based on the shape and color of the hazard (e.g. a tire piece) and further 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;
detecting at least one roadway hazard;
Identifying a type of the at least one roadway hazard based on at least one physical characteristic;
based on the positional data, environmental data, the type of the at least one roadway hazard, and operational data associated with the vehicle, calculating a roadway item risk value, 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.
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.);
detecting at least one roadway hazard (see at least 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 Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle).” See also Golov [0066] “Subsequent to receiving the data regarding detected objects from the prior vehicles, a current vehicle 111 also may transmit data regarding new objects that are being detected during travel. For example, object 155 can be a new object from the perspective of vehicle 111.”);
identifying a type of the at least one roadway hazard based on at least one physical characteristic (see at least 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 Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle).” See also Golov [0066] “Subsequent to receiving the data regarding detected objects from the prior vehicles, a current vehicle 111 also may transmit data regarding new objects that are being detected during travel. For example, object 155 can be a new object from the perspective of vehicle 111.”);
based on the positional data, environmental data, the type of the at least one roadway hazard and operational data associated with the vehicle, [[calculating]] a roadway item risk value (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] The examiner notes that the claim limitation as written includes an interpretation that the identity of the object is determined based on a physical characteristic of other vehicles, such as braking or swerving. Further as discussed below, the instant application indicates that a location of the object is a physical characteristic. Golov teaches determining an unsafe road obstacle (an identity) based on the physical characteristic of other vehicles braking and further based on the location in the road.) ;
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] and [0066] 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 “ 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 detecting at least one roadway hazard and identifying a type of the at least one roadway hazard (see at least 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 Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle).” See also Golov [0066] “Subsequent to receiving the data regarding detected objects from the prior vehicles, a current vehicle 111 also may transmit data regarding new objects that are being detected during travel. For example, object 155 can be a new object from the perspective of vehicle 111.”).
The examiner notes the 112 rejection above wherein it is not clear what the physical characteristic refers back to. As indicated above, Golov does teach identifying a type of the roadway hazard based on at least one physical characteristic (e.g. surrounding vehicles braking as cited above and the location of the braking). Assuming arguendo, “identifying a type of the roadway hazard based on at least one physical characteristic of the roadway hazard”, then Golov also teaches this limitation. The instant specification describes the type and/or physical characteristic to include the location of the roadway item (see [0041]). Golov further teaches the location of the roadway hazard. Thus, Golov does teach identifying a type of the roadway hazard based on at least one physical characteristic of the roadway hazard”.
Further, 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:
identifying a type of the roadway hazard based on at least one physical characteristic of the roadway hazard (see at least Pipe [0022] “The hazard detection system 100 uses the data to identify, detect or otherwise determine hazardous objects and/or hazardous situations (“hazardous conditions”) that are present in the environment. For example, the hazard detection system 100 may identify when there is an obstacle in the road, a surrounding vehicle is driving dangerously, a driver of the vehicle 102 is driving erratically or other hazardous condition. See also Pipe [0060] “The hazard detection system 100 obtains, extracts or determines moving patterns of objects in the environment from the environmental sensor data (304). The hazard detection system 100 may use one or more sensors 116 to obtain the environmental sensor data that includes the driving patterns of one or more surrounding vehicles, as described above, or one or more moving patterns of other objects. The moving patterns include patterns of movement of different objects including driving patterns of other surrounding vehicles. The patterns of movement include the speed, the rate of change of the speed, the relative distance between the other surrounding vehicle and the vehicle 102, the change in the relative distance, direction of travel of the surrounding vehicle and/or other factors of objects including vehicles in the surrounding environment of the vehicle 102.” See also [0063] “For example, the hazard detection system 100 may measure the change in the angle of the steering wheel or rate of change in the angle of the steering wheel. When the change in the angle of the steering wheel or the rate of change in the angle of the steering wheel exceeds a threshold amount, the hazard detection system 100 may determine that the vehicle 102 is swerving in and out of the lane of the roadway, which may indicate that the driver of the vehicle 102 is distracted, inebriated or otherwise not in control of the vehicle 102.”and [0067] “The value may be a relative speed of another vehicle, the relative amount of movement of another vehicle inside and/or outside a lane and/or the relative distance between the other vehicle and the vehicle 102. The object may be a pothole, a tire, an accident, a branch or other object on or in proximity to the roadway.”) .
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, the type of the at least one roadway hazard 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.);
detecting at least one roadway hazard (see at least 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 Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle).” See also Golov [0066] “Subsequent to receiving the data regarding detected objects from the prior vehicles, a current vehicle 111 also may transmit data regarding new objects that are being detected during travel. For example, object 155 can be a new object from the perspective of vehicle 111.”);
identifying a type of the at least one roadway hazard based on at least one physical characteristic (see at least 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 Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle).” See also Golov [0066] “Subsequent to receiving the data regarding detected objects from the prior vehicles, a current vehicle 111 also may transmit data regarding new objects that are being detected during travel. For example, object 155 can be a new object from the perspective of vehicle 111.”);
based on the positional data, environmental data, the type of the at least one roadway hazard and operational data associated with the vehicle, [[calculating]] a roadway item risk value (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] The examiner notes that the claim limitation as written includes an interpretation that the identity of the object is determined based on a physical characteristic of other vehicles, such as braking or swerving. Further as discussed below, the instant application indicates that a location of the object is a physical characteristic. Golov teaches determining an unsafe road obstacle (an identity) based on the physical characteristic of other vehicles braking and further based on the location in the road.) ;
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] and [0066] 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 “ 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 detecting at least one roadway hazard and identifying a type of the at least one roadway hazard (see at least 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 Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle).” See also Golov [0066] “Subsequent to receiving the data regarding detected objects from the prior vehicles, a current vehicle 111 also may transmit data regarding new objects that are being detected during travel. For example, object 155 can be a new object from the perspective of vehicle 111.”).
The examiner notes the 112 rejection above wherein it is not clear what the physical characteristic refers back to. As indicated above, Golov does teach identifying a type of the roadway hazard based on at least one physical characteristic (e.g. surrounding vehicles braking as cited above and the location of the braking). Assuming arguendo, “identifying a type of the roadway hazard based on at least one physical characteristic of the roadway hazard”, then Golov also teaches this limitation. The instant specification describes the type and/or physical characteristic to include the location of the roadway item (see [0041]). Golov further teaches the location of the roadway hazard. Thus, Golov does teach identifying a type of the roadway hazard based on at least one physical characteristic of the roadway hazard”.
Further, 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
identifying a type of the roadway hazard based on at least one physical characteristic of the roadway hazard (see at least Pipe [0022] “The hazard detection system 100 uses the data to identify, detect or otherwise determine hazardous objects and/or hazardous situations (“hazardous conditions”) that are present in the environment. For example, the hazard detection system 100 may identify when there is an obstacle in the road, a surrounding vehicle is driving dangerously, a driver of the vehicle 102 is driving erratically or other hazardous condition. See also Pipe [0060] “The hazard detection system 100 obtains, extracts or determines moving patterns of objects in the environment from the environmental sensor data (304). The hazard detection system 100 may use one or more sensors 116 to obtain the environmental sensor data that includes the driving patterns of one or more surrounding vehicles, as described above, or one or more moving patterns of other objects. The moving patterns include patterns of movement of different objects including driving patterns of other surrounding vehicles. The patterns of movement include the speed, the rate of change of the speed, the relative distance between the other surrounding vehicle and the vehicle 102, the change in the relative distance, direction of travel of the surrounding vehicle and/or other factors of objects including vehicles in the surrounding environment of the vehicle 102.” See also [0063] “For example, the hazard detection system 100 may measure the change in the angle of the steering wheel or rate of change in the angle of the steering wheel. When the change in the angle of the steering wheel or the rate of change in the angle of the steering wheel exceeds a threshold amount, the hazard detection system 100 may determine that the vehicle 102 is swerving in and out of the lane of the roadway, which may indicate that the driver of the vehicle 102 is distracted, inebriated or otherwise not in control of the vehicle 102.”and [0067] “The value may be a relative speed of another vehicle, the relative amount of movement of another vehicle inside and/or outside a lane and/or the relative distance between the other vehicle and the vehicle 102. The object may be a pothole, a tire, an accident, a branch or other object on or in proximity to the roadway.”) .
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, the type of the at least one roadway hazard 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.);
detect at least one roadway hazard (see at least 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 Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle).” See also Golov [0066] “Subsequent to receiving the data regarding detected objects from the prior vehicles, a current vehicle 111 also may transmit data regarding new objects that are being detected during travel. For example, object 155 can be a new object from the perspective of vehicle 111.”);
identifying a type of the at least one roadway hazard based on at least one physical characteristic (see at least 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 Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle).” See also Golov [0066] “Subsequent to receiving the data regarding detected objects from the prior vehicles, a current vehicle 111 also may transmit data regarding new objects that are being detected during travel. For example, object 155 can be a new object from the perspective of vehicle 111.”);
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 detecting at least one roadway hazard and identifying a type of the at least one roadway hazard (see at least 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 Golov [0062] “Based on analysis of the received braking event data, a location is identified (e.g., an unsafe road obstacle).” See also Golov [0066] “Subsequent to receiving the data regarding detected objects from the prior vehicles, a current vehicle 111 also may transmit data regarding new objects that are being detected during travel. For example, object 155 can be a new object from the perspective of vehicle 111.”).
The examiner notes the 112 rejection above wherein it is not clear what the physical characteristic refers back to. As indicated above, Golov does teach identifying a type of the roadway hazard based on at least one physical characteristic (e.g. surrounding vehicles braking as cited above and the location of the braking). Assuming arguendo, “identifying a type of the roadway hazard based on at least one physical characteristic of the roadway hazard”, then Golov also teaches this limitation. The instant specification describes the type and/or physical characteristic to include the location of the roadway item (see [0041]). Golov further teaches the location of the roadway hazard. Thus, Golov does teach identifying a type of the roadway hazard based on at least one physical characteristic of the roadway hazard”.
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
identifying a type of the roadway hazard based on at least one physical characteristic of the roadway hazard (see at least Pipe [0022] “The hazard detection system 100 uses the data to identify, detect or otherwise determine hazardous objects and/or hazardous situations (“hazardous conditions”) that are present in the environment. For example, the hazard detection system 100 may identify when there is an obstacle in the road, a surrounding vehicle is driving dangerously, a driver of the vehicle 102 is driving erratically or other hazardous condition. See also Pipe [0060] “The hazard detection system 100 obtains, extracts or determines moving patterns of objects in the environment from the environmental sensor data (304). The hazard detection system 100 may use one or more sensors 116 to obtain the environmental sensor data that includes the driving patterns of one or more surrounding vehicles, as described above, or one or more moving patterns of other objects. The moving patterns include patterns of movement of different objects including driving patterns of other surrounding vehicles. The patterns of movement include the speed, the rate of change of the speed, the relative distance between the other surrounding vehicle and the vehicle 102, the change in the relative distance, direction of travel of the surrounding vehicle and/or other factors of objects including vehicles in the surrounding environment of the vehicle 102.” See also [0063] “For example, the hazard detection system 100 may measure the change in the angle of the steering wheel or rate of change in the angle of the steering wheel. When the change in the angle of the steering wheel or the rate of change in the angle of the steering wheel exceeds a threshold amount, the hazard detection system 100 may determine that the vehicle 102 is swerving in and out of the lane of the roadway, which may indicate that the driver of the vehicle 102 is distracted, inebriated or otherwise not in control of the vehicle 102.”and [0067] “The value may be a relative speed of another vehicle, the relative amount of movement of another vehicle inside and/or outside a lane and/or the relative distance between the other vehicle and the vehicle 102. The object may be a pothole, a tire, an accident, a branch or other object on or in proximity to the roadway.”) .
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-10019903-B2 to Lee is cited for showing an abnormal vehicle information based on historical data, including braking (see at least col. 1, lines 50-55) and determining a risk value and determining whether the risk value is below a risk threshold (see at least column 2, lines 19-26).
US-20210089938-A1 to Ariannezhad et al. is cited for determining risk value and comparing the risk value against a threshold value (see at least 0020] and [0022]).
Further, the examiner notes, Kundu, already of record, teaches identifying the obstacle based on physical characteristics (e.g. depth of pothole) and determines the risk value based on the depth of the pothole.
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/Primary Examiner, Art Unit 3662
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