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 Office action is in response to the amendments filed on December 20, 2024. Claims 5-8 are currently pending, with Claims 1-4 being cancelled, and Claims 5-8 being newly added.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application DE 10 2022 002 253.2, filed on June 21, 2022. Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to the declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR.154(b) and 41.202(e). Failure to provide a certified translation may result in no benefit being accorded for the non-English application. No action by the applicant is required at this time.
Acknowledgement is made of Applicant’s claim for priority to the PCT filing of Application No. PCT/EP2023/060701, filed on April 24, 2023. No action by the applicant is required at this time.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on December 20, 2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
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.
Claims 5-8 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 “a minimally permissible acceleration is determined …”. It is unclear if the vehicle is accelerating toward the detected object, or if the vehicle is decelerating/braking upon coming within a threshold distance of the detected object. It is further unclear if the acceleration value relates to the host vehicle or the detected object. The Examiner is interpreting the “minimally permissible acceleration” value as the speed/ acceleration/ deceleration value determined for a trajectory of the host vehicle so as to maintain a vehicle operating condition or avoid hitting another object.
Claim 1 recites “the automatically driving vehicle may be braked onto the respective one of the detected objects …”. It is unclear if the vehicle is driving onto a vehicle, so as to actually crash into a detected object, or if the vehicle is driving towards a detected object and brakes so as to maintain distance or speed from the detected object. The Examiner is interpreting the “braked onto …” language to mean that the vehicle is driving towards a detected vehicle along the path, and determining when to brake so as to not crash the vehicle.
Claim 7 recites “a minimum value of the acceleration …” It is unclear if the minimum value of the acceleration is the same as in Claim 1. The Examiner is interpreting these limitations to be the same.
Claim 8 recites “a trajectory candidate … with the lowest trajectory costs … with a greatest minimum is selected …”. It is unclear if the greatest minimum refers to a speed, reliability, or distance criteria of the host vehicle when selecting a trajectory, or if the greatest minimum refers to the total cost of the trajectory. The Examiner is interpreting this language to mean the trajectory which has the lowest cost is selected.
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 5-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea for adding costs to a trajectory for a vehicle, which can be done mentally. The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, because a driver can mentally determine various trajectories before and during driving along a route, and add a cost to each trajectory, and implement which trajectory works best for the user (i.e. has the lowest or best cost).
101 Analysis – Step 1
Claim 5 is directed to a method for adding costs to a trajectory (i.e., a process). Therefore, Claim 5 is 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 Claim 5 includes limitations that recite abstract ideas that constitutes a mental process (emphasized in bold below) and will be used as a representative claim for the remainder of the 101 rejection, and additional limitations are emphasized with underlined characters.
Claim 5 recites the following:
A method for planning a target trajectory for an automatically driving vehicle, the method comprising:
predetermining a number of trajectory candidates for a predetermined planning horizon, wherein each of the number of trajectory candidates predetermines a path the automatically driving vehicle is to follow upon selecting the trajectory candidate as the target trajectory, and an acceleration with which the vehicle is to follow on the path of the respective one of the number of trajectory candidates;
detecting objects within the predetermined planning horizon;
respectively allocating trajectory costs to each of the number trajectory candidates using a predetermined cost function, wherein the predetermined cost function comprises object costs depending on the detected objects, wherein object costs of an object for a trajectory candidate increase with decreasing distance between the object and the trajectory candidate;
selecting the target trajectory on the trajectory costs of the number of trajectory candidates, wherein for each of the detected objects, a quality value is determined, the quality value specifying a measure for a degree of reliability of the detection of the respective one of the detected objects, wherein for each of the detected objects, depending on the quality value for the respective one of the detected objects, a minimally permissible acceleration is determined with which the automatically driving vehicle may be braked onto the respective one of the detected objects, wherein the trajectory candidates and the object costs determined for the trajectory candidates are evaluated depending on the acceleration predetermined by the respective trajectory candidate and depending on the minimally permissible acceleration when braking onto the object, and wherein the selection of the target trajectory accounts for the evaluation of the trajectory costs and the object costs determined for the trajectory costs.
The Examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, the limitations of “predetermining …”, ”detecting …”, and “allocating …” in the context of this claim encompasses determining potential trajectories, observing the environment, and assigning a weight or cost to the trajectory, which can be done mentally. A driver can mentally determine various trajectories before and during driving along a route based on observing current environmental conditions, and add a cost/ weight to each trajectory, and implement which trajectory works best for the user (i.e. has the lowest or best cost).
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 idea 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”):
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 limitations of “selecting …” the Examiner submits that this limitation consists of insignificant extra solution activity. A person can mentally determine and select the trajectory that is best suited for their needs, such as selecting a trajectory with lowest fuel consumption, or quickest time.
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, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, 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 limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
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, the additional limitation of “selecting …”, the Examiner submits that this limitation consists of extra-solution activity. A person can mentally determine and select the trajectory that is best suited for their needs, such as selecting a trajectory with lowest fuel consumption, or quickest time.
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.
Therefore, Claim 5 is not patent eligible under 35 USC §101. Dependent Claims 6-8 do not recite any further limitations that cause the claims 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 the claims involve further categorizing the objects and the trajectory and further applying costs to each determined option, which can be done mentally. Therefore, dependent Claims 6-8 are not patent eligible under the same rationale as provided for in the rejection of Claim 5.
Adding to the independent claim positive recitation of controlling the vehicle would appear to overcome the current 35 U.S.C. 101 rejection.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 5-7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. 2021/0114617 A1, to Phillips, et al (hereinafter referred to as Phillips).
As per Claim 5, Phillips discloses the features of a method for planning a target trajectory for an automatically driving vehicle (e.g. Paragraphs [0037], [0039]; where the system generates one or more trajectories that can be used to direct an autonomous vehicle from a first position to a second position), the method comprising:
predetermining a number of trajectory candidates for a predetermined planning horizon (e.g. Paragraphs [0039]-[0040]; where the vehicle computing system may generate a plurality of candidate trajectories based on the sensor data and the initial path or over a particular time for which the vehicle may travel along the trajectory), wherein
each of the number of trajectory candidates predetermines a path the automatically driving vehicle is to follow upon selecting the trajectory candidate as the target trajectory (e.g. Paragraphs [0035], [0039]; where the vehicle computing system can generate candidate trajectories for the autonomous vehicle to follow as it traverses the route, by defining the spatial path and steering qualities for the vehicle to follow for each trajectory), and
an acceleration with which the vehicle is to follow on the path of the respective one of the number of trajectory candidates (e.g. Paragraphs [0026], [0041]-[0042]; where each trajectory can include a velocity profile and an offset profile, where the velocity profile describes one or more acceleration values and associated timing information);
detecting objects within the predetermined planning horizon (e.g. Paragraph [0026], [0051], [0102]; where the vehicle computing system can utilize sensor data to detect, classify, and track objects and predict future movement of the detected objects);
respectively allocating trajectory costs to each of the number trajectory candidates using a predetermined cost function (e.g. Paragraphs [0027], [0048]; where the vehicle computing system can determine for each respective trajectory, a cost associated with the respective candidate trajectory), wherein
the predetermined cost function comprises object costs depending on the detected objects (e.g. Paragraphs [0048]-[0049], [0051]; where the vehicle computing system can score each trajectory using one or more cost functions, which consider costs for avoiding object collision, actor caution costs, behavioral blocking costs, costs associated with overtaking another actor, and object collision detection costs), wherein
object costs of an object for a trajectory candidate increase with decreasing distance between the object and the trajectory candidate (e.g. Paragraphs [0053], [0058]-[0059]; where the cost generated for each candidate trajectory may be based on determining whether an object is tracking the same path as the autonomous vehicle, determine if the distance between the autonomous vehicle and the object is between a particular distance, and associate a cost to the candidate trajectory based on the distance, where trajectories that pass too close to other vehicles have an increased cost that increases the longer the distance is too close);
selecting the target trajectory on the trajectory costs of the number of trajectory candidates (e.g. Paragraphs [0027]-[0029]; where the vehicle computing system may select the candidate trajectory with the lowest determined cost, and the selected trajectory is provided to the vehicle controller), wherein
for each of the detected objects, a quality value is determined, the quality value specifying a measure for a degree of reliability of the detection of the respective one of the detected objects (e.g. Paragraph [0067]; where the vehicle computing system can predict one or more actions from other drivers or operators and may generate one or more confidence levels about the motion of other vehicles in the environment, and adjust the determined score for a particular trajectory in response), wherein
for each of the detected objects, depending on the quality value for the respective one of the detected objects, a minimally permissible acceleration is determined with which the automatically driving vehicle may be braked onto the respective one of the detected objects (e.g. Paragraphs [0048], [0054], [0059]-[0060], [0069]; where the behavioral blocking cost function can generate a penalty for trajectories that bring the autonomous vehicle within a certain distance of the object and can limit braking to a certain amount, such that the autonomous vehicle will pass, rather than braking too hard; and where the vehicle computing system an calculate for each position along a path, an acceleration value for that moment, such that the vehicle slows to preserve its ability to stop when conflicting predictions are likely (i.e. determines acceleration values when coming up on a detected object)), wherein
the trajectory candidates and the object costs determined for the trajectory candidates are evaluated depending on the acceleration predetermined by the respective trajectory candidate and depending on the minimally permissible acceleration when braking onto the object (e.g. Paragraphs [0027], [0029], [0048]; where the vehicle computing system can determine a cost for each respective candidate trajectory based on collision avoidance, or costs associated with avoiding potential collision, costs for minimizing the speed or energy of impact, or costs for maintaining speed with gentle acceleration, etc.), and wherein
the selection of the target trajectory accounts for the evaluation of the trajectory costs and the object costs determined for the trajectory costs (e.g. Paragraphs [0027]-[0029]; where the vehicle computing system may select the candidate trajectory with the lowest determined cost, and the selected trajectory is provided to the vehicle controller).
As per Claim 6, Phillips discloses the features of Claim 5, and Phillips further discloses the features of wherein a trajectory candidate of the number of trajectory candidates with a lowest trajectory costs is selected as the target trajectory from the number of trajectory candidates (e.g. Paragraphs [0027]-[0029]; where the vehicle computing system may select the candidate trajectory with the lowest determined cost, and the selected trajectory is provided to the vehicle controller).
As per Claim 7, Phillips discloses the features of Claim 5, and Phillips further discloses the features of wherein
the evaluation of the trajectory candidates and the object costs determined for the trajectory candidates involves a categorization (e.g. Paragraphs [0057], [0064], [0070]; where the costs for a particular trajectory is generated based on the degree to which the trajectory follows established criteria for evaluating trajectories and for costs associated with a spatial relationship between the particular trajectory and other objects in the environment; and where the cost for each trajectory can be based on vehicle motion policies that represent legal rules that govern the area in which the autonomous vehicle is traveling, and may have one or more motion policies not related to illegal restrictions (i.e. categorizes and prioritizes different vehicle policies)),
the categorization categorizes the number of trajectory candidates into an unfiltered category and a filtered category (e.g. Paragraphs [0057], [0070], [0111], [0138]; where the cost function can be safety-critical or not safety-critical, or may be related to legal rules or illegal restrictions; and where the motion planning system can obtain an initial travel path, which represents an ideal travel path from a first position to a second position without regard to any objects that may be in the current environment but are not included in the existing map data (i.e. unfiltered), and candidate trajectories can be removed which are not feasible, such that they will not be selected unless no other trajectories are possible (i.e. filtered)),
all object costs and the corresponding trajectory candidates are allocated to the unfiltered category (e.g. Paragraphs [0057], [0070], [0111]; where the cost function can be safety-critical or not safety-critical, or may be related to legal rules or illegal restrictions; and where the motion planning system can obtain an initial travel path, which represents an ideal travel path from a first position to a second position without regard to any objects that may be in the current environment but are not included in the existing map data (i.e. unfiltered)), and
only those object costs and the corresponding trajectory candidates are allocated to the filtered category for which a minimum value of the acceleration predetermined by the respective trajectory candidate is greater than a minimally permissible acceleration of the respective detected object (e.g. Paragraphs [0051]-[0052], [0138]; where candidate trajectories can be removed such that they will not be selected unless no other trajectories are possible (i.e. filtered); and where the object collision detection cost function can output the relative speed and velocity of the object when predicting a collision to determine the cost of each trajectory).
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 8 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0114617 A1, to Phillips, et al (hereinafter referred to as Phillips), in view of U.S. Patent Publication No. 2022/0219726 A1, to Yadmellat, et al (hereinafter referred to Yadmellat).
As per Claim 8, Phillips discloses the features of Claim 7, and Phillip further discloses the features of wherein,
responsive to selecting the target trajectory, a trajectory candidate of the number of trajectory candidates with the lowest trajectory costs is respectively selected from the unfiltered category and the filtered category (e.g. Paragraphs [0027]-[0029]; where the vehicle computing system may select the candidate trajectory with the lowest determined cost, and the selected trajectory is provided to the vehicle controller),
the minimum of the acceleration predetermined by the respective trajectory candidate is respectively determined for the trajectory candidates with the lowest trajectory costs in the unfiltered category and the filtered category (e.g. Paragraphs [0057], [0070], [0111], [0138]; where the cost function can be safety-critical or not safety-critical, or may be related to legal rules or illegal restrictions; and where the motion planning system can obtain an initial travel path, which represents an ideal travel path from a first position to a second position without regard to any objects that may be in the current environment but are not included in the existing map data (i.e. unfiltered), and candidate trajectories can be removed which are not feasible, such that they will not be selected unless no other trajectories are possible (i.e. filtered); and trajectories with the lowest calculated cost are selected).
Phillips fails to disclose every feature of a trajectory candidate of the trajectory candidates with the lowest trajectory costs in the unfiltered category and the filtered category with a greatest minimum is selected as the target trajectory.
However, Yadmellat, in a similar field of endeavor, teaches a method for evaluating trajectories for a vehicle, where trajectories that fail to satisfy the physical limit criterion may be rejected or filtered if at least one candidate trajectory does not satisfy the physical limit criterion, and the candidate trajectories are then ranked and prioritized into the a higher evaluation value category; and where the trajectory evaluator may perform a secondary trajectory sorting within each category using an overall cost function so that the entire set of candidate trajectories constitutes a sorted plurality of candidate trajectories, which are ordered by their satisfaction of various criteria, such that the trajectory selector may select the first (i.e. lowest-cost) trajectory of the first value category (i.e. the greatest minimum of the filtered or unfiltered categories is selected) (e.g. Paragraphs [0087]-[0088], [0099], [0102]; Tables 1, 3).
It would have been obvious to a person of ordinary skill in the art on or before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the method for optimizing vehicle trajectories in the system of Phillips, with the feature of selecting the trajectory with the minimum value in the system of Yadmellat, in order to look for the best candidate trajectory to follow (see at least Paragraph [0075] of Yadmellat).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Akella, et al (U.S. 2020/0139959 A1), which teaches a method for determining the trajectory of a vehicle using cost functions.
Caldwell, et al (U.S. 2024/0208548 A1), which teaches a method for vehicle trajectory control for a vehicle.
Kindo (U.S. 2022/0066457 A1), which teaches a method for determining a selecting a trajectory for a vehicle.
Wolff, et al (U.S. 2022/0063663 A1), which teaches a method for determining conditional motion predictions for a vehicle.
Zhang, et al (U.S. 2019/0317512 A1), which teaches a method for generating a cost between a reference and target trajectory.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MERRITT E LEVY whose telephone number is (571)270-5595. The examiner can normally be reached Mon-Fri 0630-1600.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Helal Algahaim can be reached at (571) 270-5227. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MERRITT E LEVY/Examiner, Art Unit 3666
/TIFFANY P YOUNG/Primary Examiner, Art Unit 3666