Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This communication is in response to application No. 18/901,014 filed on September 30, 2024. Claims 1-13 are currently pending and have been examined. Claims 1-13 have been rejected as follows.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on September 30, 2024 and October 31, 2024 are being considered by the examiner.
Priority
Acknowledgment is made of applicant's claim priority for foreign applications DE10 2023 210 179.3, filed on October 18, 2023.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
A device configured to.. , in claim 12.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
. Claim 1 is directed to a method of controlling a vehicle (i.e., a process), claim 12 is directed to apparatus, and claim 13 is directed to a non-transitory computer-readable. Therefore, claims 1, 11 and 20 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claims 1, 12 and 13 include limitations that recite an abstract idea (emphasized below) and claim 1 will be used as a representative claim for the remainder of the 101 rejection.
Claim 1 recites:
A method for detecting a traffic junction, comprising the following steps:
receiving position data describing a respective position history of motor vehicles traveling on roads which include the traffic junction and traveling through the traffic junction;
ascertaining a respective curvature progression of each of the position histories,
ascertaining a respective numerical derivative of each of the curvature progressions to obtain a respective change in the curvature progressions over the respective position history; and
detecting the traffic junction based on the respective ascertained derivatives of the curvature progressions.
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. For example, “ascertaining a curvature progression...” in the context of this claim encompasses a person looking at data collected and forming a simple judgement. Accordingly, the claim recites at least one abstract idea.
101 Analysis – 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”):
A method for detecting a traffic junction, comprising the following steps:
receiving position data describing a respective position history of motor vehicles traveling on roads which include the traffic junction and traveling through the traffic junction;
ascertaining a respective curvature progression of each of the position histories,
ascertaining a respective numerical derivative of each of the curvature progressions to obtain a respective change in the curvature progressions over the respective position history; and
detecting the traffic junction based on the respective ascertained derivatives of the curvature progressions.
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 “receiving position data...,” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer to perform the process. In particular, the receiving steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering vehicle and road condition data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The vehicle control system is recited at a high level of generality and merely automates the evaluating step.
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.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are
sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a vehicle controller to perform the evaluating... amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “receiving position data...,” the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of “receiving position data...,” are well-understood, routine, and conventional activities because the background recites that the sensors are all conventional sensors mounted on the vehicle, and the specification does not provide any indication that the vehicle controller is anything other than a conventional computer within a vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner.
Dependent claim(s) 2-11 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 because it is nothing more than data gathering. Therefore, dependent claims 2-11 are not patent eligible under the same rationale as provided for in the rejection of 1.
Therefore, claim(s) 1-13 is/are ineligible under 35 USC §101.
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.
Claims 1, 5, 9, 10, 11, 12 and 13 are rejected under 35 U.S.C 103 as being unpatentable over Giurgiu (US 20200398856 A1) in view of Rajesh (EP 3287940 A1).
Regarding claim 1, Giurgiu discloses A method for detecting a traffic junction, comprising the following steps:
receiving position data describing a respective position history of motor vehicles traveling on roads which include the traffic junction and traveling through the traffic junction; (see at least [0041]; "To address these technical challenges, the system 100 of FIG. 1 introduces a capability to calculate road curvature data from location trace data (e.g., probe data 107) collected from vehicles 105 and/or user equipment (UE) devices 109a-109n (also collectively referred to as UEs 109) respectively equipped with applications 111a-111n (also collectively referred to as applications 111) and sensors 113a-113n (e.g., positioning sensors capable of determining a location based on signals from satellites 115). In other words, the system 100 introduces an alternative to traditional approaches that calculate curvature based on road geometry (e.g., also referred to as the “spline” method) that instead calculates curvature from large samples of location traces or trajectories (e.g., also referred to the “statistical” method).") Giurgiu describes receiving position data from the history of vehicles traveling on roads which include driving through the traffic junction.
ascertaining a respective curvature progression of each of the position histories, (see at least [0041]; "To address these technical challenges, the system 100 of FIG. 1 introduces a capability to calculate road curvature data from location trace data (e.g., probe data 107) collected from vehicles 105 ") Giurgiu describes ascertaining respective curvature progression of the position histories gathered from vehicles.
ascertaining a respective numerical derivative of each of the curvature progressions to obtain a respective change in the curvature progressions over the respective position history; and (see at least [0060]; "In one embodiment, along the fitted curve, the curvature module 405 can calculate the heading and the curvature of the road as, for instance, the first and second derivatives of the curve. In one embodiment, the curvature can be calculated at fixed intervals of length I. For example, the curvature could be calculated every I=10 meters such that each curvature is computed from a single curve determined from a subset of the collection of curves. For each interval I, the curvature module 405 can collect all the curvatures from all location traces that passed through the corresponding slice. ") Guirgiu describes ascertaining a numerical derivaive of each of the curvature progressions to obtain a respective change in the curvature progressions over the gathered position histories of the vehicles.
Giurgiu does not explicitly teach detecting the traffic junction based on the respective ascertained derivatives of the curvature progressions.
However, Rajesh teaches detecting the traffic junction based on the respective ascertained derivatives of the curvature progressions. (see at least [0007]; "Further, the controller is configured for determining a first boundary and a second boundary of the road in the determined road section, and the controller is configured for determining a first curvature angle of the first boundary and a second curvature angle of the second boundary. Moreover, the controller is configured for detecting an intersection of the road based on a comparison of at least one of the first curvature angle and the second curvature angle with a threshold angle and/or a threshold value.") Rajesh describes detecting a traffic junction based on the respective curvature data, such as the derivative of the curve.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Giurgiu to incorporate teachings of Rajesh which teaches detecting a traffic junction based on the derivatives of the curvature progressions in order to be able to identify the specific traffic junction the vehicle is on for efficient route planning.
Regarding claim 2, Giurgiu and Rajesh, in combination, disclose limitations of claim 1 as discussed above, furthermore, Rajesh discloses The method according to claim 1, wherein road curves are detected based on the respective ascertained derivatives of the curvature progressions, (see at least [13]; " Further, the controller determines, estimates and/or derives the first curvature angle of the first boundary and the second curvature angle of the second boundary…This basically may allow to detect the intersection and/or to improve a reliability of the detection. Finally, in order to reliably determine and/or detect the intersection, at least one of the first curvature angle and the second curvature angle is compared to the threshold angle.")
wherein the traffic junction is detected based on the detected road curves. (see at least [13]; " Further, the controller determines, estimates and/or derives the first curvature angle of the first boundary and the second curvature angle of the second boundary…This basically may allow to detect the intersection and/or to improve a reliability of the detection. Finally, in order to reliably determine and/or detect the intersection, at least one of the first curvature angle and the second curvature angle is compared to the threshold angle.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Giurgiu to incorporate teachings of Rajesh which teaches detecting a traffic junction based on the derivatives of the curvature progressions in order to be able to identify the specific traffic junction the vehicle is on for efficient route planning.
Regarding claim 5, Giurgiu and Rajesh, in combination, disclose limitations of claim 1 as discussed above, furthermore, Giurgiu discloses The method according to claim 2, wherein detected road curves that exhibit an abnormal course are filtered out, wherein the traffic junction is detected based on the road curves that have not been filtered out. (see at least [0060]; "For each interval I, the curvature module 405 can collect all the curvatures from all location traces that passed through the corresponding slice. In addition, the curvature module 405 can apply outlier rejection, for example, by dropping any values more than a designated number of standard deviations (e.g., three standard deviations) away from the mean. ")
Regarding claim 9, Giurgiu and Rajesh, in combination, disclose limitations of claim 1 as discussed above, furthermore, Giurgiu discloses The method according to claim 8, wherein a respective standard deviation of the values of the respectively ascertained derivatives of the curvature progressions is ascertained, (see at least [0065]; "In addition, the curvature module 405 can apply outlier rejection, for example, by dropping any values more than a designated number of standard deviations (e.g., three standard deviations) away from the mean. For example, the curvature module 405 can define the curvature of the interval I as either the mean, median, most probable value, and/or the like of all the curvature values in that interval.")
wherein the respective measure of dispersion is ascertained based on the respective standard deviation. (see at least [0065]; "In addition, the curvature module 405 can apply outlier rejection, for example, by dropping any values more than a designated number of standard deviations (e.g., three standard deviations) away from the mean. For example, the curvature module 405 can define the curvature of the interval I as either the mean, median, most probable value, and/or the like of all the curvature values in that interval.")
Regarding claim 10, Giurgiu and Rajesh, in combination, disclose limitations of claim 1 as discussed above, furthermore, Giurgui discloses The method according to claim 9, wherein the respective standard deviation is scaled with a predetermined scaling factor, which is not equal to 1, to obtain a respective scaled standard deviation, (see at least [0065]; "In addition, the curvature module 405 can apply outlier rejection, for example, by dropping any values more than a designated number of standard deviations (e.g., three standard deviations) away from the mean. For example, the curvature module 405 can define the curvature of the interval I as either the mean, median, most probable value, and/or the like of all the curvature values in that interval.")
wherein the respective measure of dispersion is ascertained based on the respectively scaled standard deviation. (see at least [0065]; "In addition, the curvature module 405 can apply outlier rejection, for example, by dropping any values more than a designated number of standard deviations (e.g., three standard deviations) away from the mean. For example, the curvature module 405 can define the curvature of the interval I as either the mean, median, most probable value, and/or the like of all the curvature values in that interval.")
Regarding claim 11, Giurgiu and Rajesh, in combination, disclose limitations of claim 1 as discussed above, furthermore, Giurgiu discloses The method according to claim 6, wherein values of the position data are clustered within a road curve,(see at least [0060]; "In one embodiment (i.e., Embodiment A1), each of the location traces associated with a given slice is fit with a continuous curve to generate a collection of curves (step 505). ")
wherein the corresponding polygon is created such that it includes all of the values of each cluster. (see at least [0098]; "The HD mapping data records 1111 also include ground truth object models that provide the precise object geometry with polylines or polygonal boundaries, as well as rich attributes of the models. These rich attributes include, but are not limited to, object type, object location, lane traversal information, lane types, lane marking types, lane level speed limit information, and/or the like.")
Regarding claim 12, Giurgiu discloses A device configured to detect a traffic junction, the device configured to: receive position data describing a respective position history of motor vehicles traveling on roads which include the traffic junction and traveling through the traffic junction; (see at least [0041]; "To address these technical challenges, the system 100 of FIG. 1 introduces a capability to calculate road curvature data from location trace data (e.g., probe data 107) collected from vehicles 105 and/or user equipment (UE) devices 109a-109n (also collectively referred to as UEs 109) respectively equipped with applications 111a-111n (also collectively referred to as applications 111) and sensors 113a-113n (e.g., positioning sensors capable of determining a location based on signals from satellites 115). In other words, the system 100 introduces an alternative to traditional approaches that calculate curvature based on road geometry (e.g., also referred to as the “spline” method) that instead calculates curvature from large samples of location traces or trajectories (e.g., also referred to the “statistical” method).") Giurgiu describes receiving position data from the history of vehicles traveling on roads which include driving through the traffic junction.
ascertain a respective curvature progression of each of the position histories, (see at least [0041]; "To address these technical challenges, the system 100 of FIG. 1 introduces a capability to calculate road curvature data from location trace data (e.g., probe data 107) collected from vehicles 105 ") Giurgiu describes ascertaining respective curvature progression of the position histories gathered from vehicles.
ascertain a respective numerical derivative of each of the curvature progressions to obtain a respective change in the curvature progressions over the respective position history; and (see at least [0060]; "In one embodiment, along the fitted curve, the curvature module 405 can calculate the heading and the curvature of the road as, for instance, the first and second derivatives of the curve. In one embodiment, the curvature can be calculated at fixed intervals of length I. For example, the curvature could be calculated every I=10 meters such that each curvature is computed from a single curve determined from a subset of the collection of curves. For each interval I, the curvature module 405 can collect all the curvatures from all location traces that passed through the corresponding slice. ") Guirgiu describes ascertaining a numerical derivaive of each of the curvature progressions to obtain a respective change in the curvature progressions over the gathered position histories of the vehicles.
Giurgiu does not explicitly teach detect the traffic junction based on the respective ascertained derivatives of the curvature progressions.
However, Rajesh teaches detect the traffic junction based on the respective ascertained derivatives of the curvature progressions. (see at least [0007]; "Further, the controller is configured for determining a first boundary and a second boundary of the road in the determined road section, and the controller is configured for determining a first curvature angle of the first boundary and a second curvature angle of the second boundary. Moreover, the controller is configured for detecting an intersection of the road based on a comparison of at least one of the first curvature angle and the second curvature angle with a threshold angle and/or a threshold value.") Rajesh describes detecting a traffic junction based on the respective curvature data, such as the derivative of the curve.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Giurgiu to incorporate teachings of Rajesh which teaches detecting a traffic junction based on the derivatives of the curvature progressions in order to be able to identify the specific traffic junction the vehicle is on for efficient route planning.
Regarding claim 13, Giurgiu discloses A non-transitory machine-readable storage medium on which is stored a computer program for detecting a traffic junction, the computer program, when executed by a computer, causing the computer to perform the following steps: (see at least [0122]; "The software module could reside in RAM memory, flash memory, registers, or any other form of writable computer-readable storage medium known in the art including non-transitory computer-readable storage medium.") Giurgiu
receiving position data describing a respective position history of motor vehicles traveling on roads which include the traffic junction and traveling through the traffic junction; (see at least [0041]; "To address these technical challenges, the system 100 of FIG. 1 introduces a capability to calculate road curvature data from location trace data (e.g., probe data 107) collected from vehicles 105 and/or user equipment (UE) devices 109a-109n (also collectively referred to as UEs 109) respectively equipped with applications 111a-111n (also collectively referred to as applications 111) and sensors 113a-113n (e.g., positioning sensors capable of determining a location based on signals from satellites 115). In other words, the system 100 introduces an alternative to traditional approaches that calculate curvature based on road geometry (e.g., also referred to as the “spline” method) that instead calculates curvature from large samples of location traces or trajectories (e.g., also referred to the “statistical” method).") Giurgiu describes receiving position data from the history of vehicles traveling on roads which include driving through the traffic junction.
ascertaining a respective curvature progression of each of the position histories, (see at least [0041]; "To address these technical challenges, the system 100 of FIG. 1 introduces a capability to calculate road curvature data from location trace data (e.g., probe data 107) collected from vehicles 105 ") Giurgiu describes ascertaining respective curvature progression of the position histories gathered from vehicles.
ascertaining a respective numerical derivative of each of the curvature progressions to obtain a respective change in the curvature progressions over the respective position history; and (see at least [0060]; "In one embodiment, along the fitted curve, the curvature module 405 can calculate the heading and the curvature of the road as, for instance, the first and second derivatives of the curve. In one embodiment, the curvature can be calculated at fixed intervals of length I. For example, the curvature could be calculated every I=10 meters such that each curvature is computed from a single curve determined from a subset of the collection of curves. For each interval I, the curvature module 405 can collect all the curvatures from all location traces that passed through the corresponding slice. ") Guirgiu describes ascertaining a numerical derivaive of each of the curvature progressions to obtain a respective change in the curvature progressions over the gathered position histories of the vehicles.
Giurgiu does not explicitly teach detect the traffic junction based on the respective ascertained derivatives of the curvature progressions.
However, Rajesh teaches detect the traffic junction based on the respective ascertained derivatives of the curvature progressions. (see at least [0007]; "Further, the controller is configured for determining a first boundary and a second boundary of the road in the determined road section, and the controller is configured for determining a first curvature angle of the first boundary and a second curvature angle of the second boundary. Moreover, the controller is configured for detecting an intersection of the road based on a comparison of at least one of the first curvature angle and the second curvature angle with a threshold angle and/or a threshold value.") Rajesh describes detecting a traffic junction based on the respective curvature data, such as the derivative of the curve.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Giurgiu to incorporate teachings of Rajesh which teaches detecting a traffic junction based on the derivatives of the curvature progressions in order to be able to identify the specific traffic junction the vehicle is on for efficient route planning.
Claims 3, 6, and 7 are rejected under 35 U.S.C 103 as being unpatentable over Giurgiu (US 20200398856 A1) in view of Rajesh (EP 3287940 A1), in further view of Desai (US 5515283 A) and Thompson (US 11192558 B2).
Regarding claim 3, Giurgiu and Rajesh, in combination, disclose limitations of claim 1 as discussed above. However, Giurgiu does not explicitly disclose The method according to claim 2, wherein a number of entrances and exits into or out of the road curve is ascertained for each road curve, wherein the traffic junction is detected based on the ascertained numbers of entrances and exits.
However, Desai teaches The method according to claim 2, wherein a number of entrances and exits into or out of the road curve is ascertained for each road curve, (see at least [9]; "The method of the invention identifies the highways of interest and their associated access points (e.g., on-ramps and off-ramps, intersections, etc.). ")
wherein the traffic junction is detected based on the ascertained numbers of entrances and exits. (see at least [9]; " The method of the invention identifies the highways of interest and their associated access points (e.g., on-ramps and off-ramps, intersections, etc.). The access ramps are then identified by the roads from which access to the highway is gained or to which access from the highway is provided")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Giurgiu to incorporate teachings of Desai which teaches ascertaining the number of exits and entrances on a traffic junction and detecting a traffic junction based on the number of exits and entrances in order to be able to identify the specific traffic junction the vehicle is on for efficient route planning.
Regarding claim 6, Giurgiu and Rajesh, in combination, disclose limitations of claim 1 as discussed above. However, Giurgiu does not explicitly disclose The method according to claim 5, wherein a polygon which represents the respective road curve is ascertained for each road curve that has not been filtered out, wherein the traffic junction is detected based on the ascertained polygons.
Thompson teaches The method according to claim 5, wherein a polygon which represents the respective road curve is ascertained for each road curve that has not been filtered out, wherein the traffic junction is detected based on the ascertained polygons. (see at least [62]; " In one embodiment, geographic features (e.g., two-dimensional or three-dimensional features) are represented using polylines and/or polygons (e.g., two-dimensional features) or polygon extrusions (e.g., three-dimensional features). In one embodiment, these polylines/polygons can also represent ground truth or reference features or objects (e.g., signs, road markings, lane lines, landmarks, etc.) used for visual odometry")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Giurgiu to incorporate teachings of Thompson which teaches representing the road curvatures as polygons, in order to calculate, interpret and locate the curvature and the associated traffic junction in a timely fashion for use in immediate route planning.
Regarding claim 7, Giurgiu and Rajesh, in combination, disclose limitations of claim 1 as discussed above. However, Giurgiu does not explicitly disclose The method according to claim 6, wherein a number of entrances and exits into or out of the road curve is ascertained for each road curve, wherein the traffic junction is detected based on the ascertained numbers of entrances and exits, and wherein, to respectively ascertain the number of entrances and exits into or out of the road curve, a number of entrances and exits into or out of the respective polygon is ascertained, wherein the traffic junction is detected based on the ascertained numbers of entrances and exits into or out of the respective polygon.
However, Desai teaches The method according to claim 6, wherein a number of entrances and exits into or out of the road curve is ascertained for each road curve, (see at least [9]; "The method of the invention identifies the highways of interest and their associated access points (e.g., on-ramps and off-ramps, intersections, etc.). ")
wherein the traffic junction is detected based on the ascertained numbers of entrances and exits, (see at least [9]; " The method of the invention identifies the highways of interest and their associated access points (e.g., on-ramps and off-ramps, intersections, etc.). The access ramps are then identified by the roads from which access to the highway is gained or to which access from the highway is provided")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Giurgiu to incorporate teachings of Desai which teaches ascertaining the number of exits and entrances on a traffic junction and detecting a traffic junction based on the number of exits and entrances in order to be able to identify the specific traffic junction the vehicle is on for efficient route planning.
Desai does not explicitly teach wherein, to respectively ascertain the number of entrances and exits into or out of the road curve, a number of entrances and exits into or out of the respective polygon is ascertained, wherein the traffic junction is detected based on the ascertained numbers of entrances and exits into or out of the respective polygon.
However, Thompson teaches and wherein, to respectively ascertain the number of entrances and exits into or out of the road curve, a number of entrances and exits into or out of the respective polygon is ascertained, (see at least [62]; " In one embodiment, geographic features (e.g., two-dimensional or three-dimensional features) are represented using polylines and/or polygons (e.g., two-dimensional features) or polygon extrusions (e.g., three-dimensional features). In one embodiment, these polylines/polygons can also represent ground truth or reference features or objects (e.g., signs, road markings, lane lines, landmarks, etc.) used for visual odometry") Thompson
wherein the traffic junction is detected based on the ascertained numbers of entrances and exits into or out of the respective polygon. (see at least [62]; " In one embodiment, geographic features (e.g., two-dimensional or three-dimensional features) are represented using polylines and/or polygons (e.g., two-dimensional features) or polygon extrusions (e.g., three-dimensional features). In one embodiment, these polylines/polygons can also represent ground truth or reference features or objects (e.g., signs, road markings, lane lines, landmarks, etc.) used for visual odometry")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Giurgiu to incorporate teachings of Thompson which teaches representing the road curvatures as polygons, in order to calculate, interpret and locate the curvature and the associated traffic junction in a timely fashion for use in immediate route planning.
Claim 4 is rejected under 35 U.S.C 103 as being unpatentable over Giurgiu (US 20200398856 A1) in view of Rajesh (EP 3287940 A1), in further view of BUTELIAUSKAS (WO-2006004384-A1).
Giurgiu does not explicitly teach The method according to claim 3, wherein a road curve is specified as belonging to a traffic junction when the number of entrances and exits is greater than or equal to three.
However, Buteliauskas teaches The method according to claim 3, wherein a road curve is specified as belonging to a traffic junction when the number of entrances and exits is greater than or equal to three. (see at least [14]; "The essence of the invention lies in the fact that in order to increase traffic safety and junction capacity as well as expand its application to junctions of three road directions, the roadway of each road intended for driving through the junction before the entry into junction must make a large-radius right curve with a possible slope before the overpass and under it; behind the overpass, it must make an ascending large-radius left curve leading to the overpass, along it and then turn to the right. This provision must be applied to junctions of any number of road directions, but is sufficient for a junction only three road directions…The claimed invention is suitable for three, four and five road direction junctions and could easily be applied for junctions of still more road directions.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Giurgiu to incorporate teachings of Buteliauskas which teaches identifying a road curve belonging to a traffic junction when the number of entrances and exits is greater than three in order to easily eliminate curved roads that do not fall into the desired traffic junction in order calculate, interpret and locate the curvature and the appropriate traffic junction in a timely fashion for use in immediate route planning.
Claim 8 is rejected under 35 U.S.C 103 as being unpatentable over Giurgiu (US 20200398856 A1) in view of Rajesh (EP 3287940 A1), in further view of Takayuki (JP 5402335 B2).
Regarding claim 8, Giurgiu and Rajesh, in combination, disclose limitations of claim 1 as discussed above. However, Giurgiu does not explicitly disclose The method according to claim 2, wherein a respective measure of dispersion of values of the respectively ascertained derivatives of the curvature progressions is ascertained, wherein those of the values of the respectively ascertained derivatives of the curvature progressions which exceed the respective measure of dispersion are ascertained, wherein those of the values which exceed the respective measure of dispersion are respectively grouped in intervals a positive change of curvature and a negative change of curvature, wherein a road curve is specified as being a pair of immediately successive intervals with a respective positive and negative change of curvature.
However, Takayuki teaches The method according to claim 2, wherein a respective measure of dispersion of values of the respectively ascertained derivatives of the curvature progressions is ascertained, (see at least [0039]; "For this reason, the variance of the curvature determined from the lateral acceleration Gt is small, and the variance of the start position of the curve section, which is directly affected by the steering operation timing, is large compared to the variance of the curvature. Therefore, in this embodiment, the control unit 20 considers the variance of the measurement values to be small if the sensors acquiring the measurement values do not include sensors that directly measure the operation of the vehicle driver, and considers the variance of the measurement values to be large if the sensors acquiring the measurement values include sensors that directly measure the operation of the vehicle driver.")
wherein those of the values of the respectively ascertained derivatives of the curvature progressions which exceed the respective measure of dispersion are ascertained, (see at least [0033, 0017]; "Then, if it is determined that a curve section exists within the specified range, the control unit 20 evaluates the curvature related to vehicle control in the curve section and the start position of the curve section through processing by the evaluation target value acquisition unit 21a, and extracts the values of the curvature and the start position of the curve section from the map information 30a and sets them as evaluation target values….
Since the difference between the measurement value that is most likely to be measured and the value to be evaluated corresponds to the reliability, various configurations can be adopted, such as a configuration in which the difference is compared with a threshold and the reliability is evaluated as low if the difference exceeds the threshold, or a configuration in which the magnitude of the difference is associated with the reliability and the reliability is evaluated")
wherein those of the values which exceed the respective measure of dispersion are respectively grouped in intervals a positive change of curvature and a negative change of curvature, (see at least [0038]; "2A and 2B are diagrams showing frequency distributions for two types of measurement values, with the horizontal axis representing the measurement value and the vertical axis representing the frequency.
FIG. 2A shows the frequency distribution of the measured values of curvature, and FIG. 2B shows the frequency distribution of the measured values at the start position of the curve section. The frequency distribution is calculated by using a predetermined interval as a unit and the number of times a value is measured within each interval as a frequency.
As shown in FIGS. 2A and 2B, the frequency distribution in FIG. 2A is steeper than the frequency distribution in FIG. 2B, and the variance of the former is smaller than the variance of the latter. The tendency of such dispersion depends on the properties of the object being measured by the sensor")
wherein a road curve is specified as being a pair of immediately successive intervals with a respective positive and negative change of curvature. (see at least [0038]; "2A and 2B are diagrams showing frequency distributions for two types of measurement values, with the horizontal axis representing the measurement value and the vertical axis representing the frequency.
FIG. 2A shows the frequency distribution of the measured values of curvature, and FIG. 2B shows the frequency distribution of the measured values at the start position of the curve section. The frequency distribution is calculated by using a predetermined interval as a unit and the number of times a value is measured within each interval as a frequency.
As shown in FIGS. 2A and 2B, the frequency distribution in FIG. 2A is steeper than the frequency distribution in FIG. 2B, and the variance of the former is smaller than the variance of the latter.
The tendency of such dispersion depends on the properties of the object being measured by the sensor")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Giurgiu to incorporate teachings of Takayuki which teaches the measure of dispersion values of the derivatives are grouped together to gather curvature changes to gain an accurate measure of the road curvatures for safety and route planning.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HANA VICTORIA HALL whose telephone number is (571)272-5289. The examiner can normally be reached M-F 9-5.
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/HANA VICTORIA HALL/ Examiner, Art Unit 3664
/RACHID BENDIDI/Supervisory Patent Examiner, Art Unit 3664