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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/9/2026 has been entered.
Response to Amendment
The amendment filed 11/21/2025 has been entered. Claims 1-5 and 6-20 remain pending in the application, claim 21 has been added. Applicant’s amendments to the Claims have overcome each and every rejection under U.S.C. 112(a) and 101 previously set forth in the Office Action mailed 10/14/2025, however new grounds for rejection have been introduced.
Response to Arguments
Applicant's arguments filed 11/21/2025 have been fully considered but they are not persuasive. While the applicant points to the amendments which do overcome the previous rejection under U.S.C 112(a) and 101, regarding the rejection under U.S.C 103 the applicant's simply point to the amended material and an assertion that they are not read on by the Ramamoorthy or Velkey art. In particular, applicant states that neither Ramamoorthy or Velkey disclose “obtaining a third kinematic state distribution of at least the vehicle at a third time from the kinematic state data, the third time occurring during a second time span that is later than the first time and shorter than the first time span, the first and third kinematic state distributions being determined from at least speed measurements, steering wheel angle measurements, and inertial measurements” and instead Ramamoorthy only discloses that speed can be used in determining kinematic state distributions. However, this is not the claim as written, which instead reads “being determined from at least one of speed measurements, steering wheel angle measurements, and inertial measurements” which is read upon by a reference which uses speed measurements Further, the Ramamoorthy reference explicitly discloses the use of accelerometers, ie an inertial measurement, to determine kinematic state distributions. For this and the reasons described in the rejection below, the examiner maintains the rejection under Ramamoorthy in view of Velkey.
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.
Claim 1 is 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 claim recites “capturing kinematic state data of a vehicle and at least one traffic participant”, however the traffic participant may itself be a vehicle. Therefore, it is unclear what is claimed in stating “a vehicle and at least one traffic participant” as this may be the same vehicle, a plurality of vehicle traffic participants, a vehicle and cyclist, an ego vehicle and an external vehicle, or the like.
Claim 2-4 and 5-21 are rejected under U.S.C. 112(b) as indefinite for depending from an indefinite independent claim.
Claim 17 is 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 claim recites “wherein the first time span includes a range of five seconds.” which is not patentably defined as a range is a span between two numbers not a single number. It is therefore unclear as to whether the claim would be infringed by one using a time span of 5 seconds plus or minus X time, a time span of at least 5 seconds, or the like. Further, the claim contradicts the now-amended independent claim in which the time span is “less than or equal to one second”
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.
Claims 1-4, 6, 7 11-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ramamoorthy (US 20210380142), herein after referred to as Ramamoorthy, in view of Velkey (EP 3855120), herein after referred to as Velkey, and Djuric (US 11635764), herein after referred to as Djuric.
Regarding Claim 1, Ramamoorthy discloses:
capturing kinematic state data of a vehicle (see at least [0080] “The data processing component A2 receives sensor data from an on-board sensor system A8 of the AV. The on-board sensor system A8 can take different forms but generally comprises a variety of sensors such as image capture devices (cameras), LiDAR units etc., satellite-positioning sensor(s) (GPS etc.), motion sensor(s) (accelerometers, gyroscopes etc.) etc.”)
and at least one traffic participant using one or more sensors of a vehicle; (see at least [0048] “The parameters of the external agent may be observed parameters, i.e. derived from the sensor inputs.”)
obtaining a first kinematic state distribution of the vehicle (see at least [0080] “motion sensor(s) (accelerometers, gyroscopes etc.) etc., which collectively provide rich sensor data from which it is possible to extract detailed information about the surrounding environment and the state of the AV”)
and the at least one traffic participant at a first time from the kinematic state data; (see at least [0088] “the data processing component A2 provides a comprehensive representation of the ego vehicle's surrounding environment, the current state of any external actors within that environment (location, heading, speed etc. to the extent they are detectable),”)
projecting second kinematic state distributions of the at least one traffic participant at a second time, (see at least [0089] “The prediction component A4 uses this information as a basis for a predictive analysis”)
in a static environment (see at least [0067] “ The generative behaviour model may also be applied to ... one or more parameters of the driving scenario (such as road layout/other driving environment parameters to model the other actor's response to its environment).”)
the second time being a first time span … into the future from the first time; (see at least [0089] “in which it makes predictions about future behavior of the external actors in the vicinity of the AV.” [0103] "The steps of the method are carried out by the inverse planner A5 repeatedly, in real-time or pseudo real-time, so that sufficiently up-to-date predictions are always available to the AV planner A6.)
defining a distribution of trajectories, (see at least [0037] “a distribution of predicted trajectories associated with that maneuver”)
wherein each trajectory of the distribution of trajectories links a kinematic state of the first kinematic state distribution to a kinematic state of the second kinematic state distribution; (see at least [0044] “ the expected trajectory model associated with each goal may comprise a predicted trajectory associated with that goal or a distribution of predicted trajectories associated with that goal”)
the first and third kinematic state distributions being determined from at least one of speed measurements, steering wheel angle measurements, and inertial measurements; (see at least [0080] “motion sensor(s) (accelerometers, gyroscopes etc.) etc., which collectively provide rich sensor data from which it is possible to extract detailed information about the surrounding environment and the state of the AV”)
determining compatibilities between the third kinematic state distribution and a distribution of kinematic states by evaluating a probability of each third kinematic state being positioned on each of the trajectories of the distribution of trajectories at the third time; (see at least [0130] “The above steps are performed repeatedly over time, possibly in real time. For an external agent that is some way off reaching its goal, initially it may not be possible to determine definitively which goal it is executing, because the path distributions for different goals are similar initially, and this will be reflected in the distribution of probabilities of over the set of hypothesized goals. As the path distributions diverge, the probability distribution will generally begin to skew towards a particular goal as the path distributions diverge.”)
predicting behavior of the at least one traffic participant by assigning probabilities to the trajectories of the distribution of trajectories based on the determined compatibilities. (see at least [0280] “If this trajectory is continued to G2, it will involve a sudden braking, resulting in a cost much higher than C2. On the other hand, continuing the trajectory to goal G1 (staying in lane) is essentially the optimal plan for G1 from the initial state. Thus, G2 decreases in posterior probability and G1 increases.”)
and controlling a driving system of the vehicle according to one trajectory of the distribution of trajectories using the predicted traffic participant behavior (see at least [0046] “The control signals may be generated based on the determined likelihood of the at least one goal and the expected trajectory model for that goal..”)
Ramamoorthy does not explicitly disclose:
and based on interactions with the at least one traffic participant
less than or equal to one second into the future
obtaining a third kinematic state distribution of at least the vehicle at a third time from the kinematic state data, the third time occurring during a second time span that is later than the first time and is shorter than the first time span;
While not explicitly disclosing, in view of Ramamoorthy it would be obvious to a person having ordinary skill in the art to:
obtaining a third kinematic state distribution of at least the vehicle at a third time from the kinematic state data, the third time occurring during a second time span that is later than the first time and is shorter than the first time span;
This limitation would be obvious to a person having ordinary skill in the art at the time of the applicant’s claimed invention, as the Ramamoorthy reference already discloses the projecting of a of a kinematic state distribution based on the kinematic state data, and “obtaining” a kinematic state distribution based on kinematic state data. See the cited sections above used in the rejection (see at least [0080] “motion sensor(s) (accelerometers, gyroscopes etc.) etc., which collectively provide rich sensor data from which it is possible to extract detailed information about the surrounding environment and the state of the AV”) as well as the fact that an autonomous vehicle is continuously operating in the environment, it is obvious to any person having ordinary skill in the art at the time of the applicant’s claimed invention to perform the same process at a later point in time again, and it is not patentably distinct to do so. It would not be novel to a person with ordinary skill in the art to repeat a process, or to analyze another time period after a previous time period. Further, the as-written claim specifying that the time period is a smaller time than the second time period is an obvious modification to the prior art, as simply changing the time frame over which a trajectory distribution is determined is an obvious modification to a person having ordinary skill in the art at the time of the applicant’s claimed invention.
In the same field of endeavor, Velkey discloses:
and based on interactions with other traffic participants (see at least [0062] “The interaction model MI predicts dynamic interactions I for the traffic participants between the respective traffic participants.”)
The above pieces of prior art are considered analogous as they both represent inventions in the vehicle prediction field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ramamoorthy to base the projected kinematic state distribution on interactions with other traffic participants, as taught by Velkey to predict interactions with traffic participants between traffic participants [0062].
In the same field of endeavor, Djuric discloses:
a first time span less than or equal to one second into the future (see at least [Col 8, Ln 29-31] “The machine-learned tracking and kinematics model can then determine one or more trajectories for ten subsequent zero point one (0.1) second intervals totaling one second.”)
The above pieces of prior art are considered analogous as they both represent inventions in the vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ramamoorthy to perform the projecting of second kinematic state distributions of the at least one traffic participant at a second time less than or equal to one second int the future, as taught by Djuric to predict the location of surrounding objects at future time intervals [Col 1].
Regarding Claim 2, Ramamoorthy discloses the limitations of Claim 1, and Ramamoorthy further discloses:
wherein the kinematic states of at least one of the first, second or third kinematic state distribution comprise at least one out of a group, the group consisting of a position, a velocity, an acceleration and a jerk. (see at least [0088] “(location, heading, speed etc. to the extent they are detectable),”)
Regarding Claim 3, Ramamoorthy discloses the limitations of Claim 1, and Ramamoorthy further discloses:
wherein the vehicle is at least one of an ego vehicle and the at least one traffic participant is at least one other vehicle or pedestrian. (see at least [0012] “the method may be implemented in an autonomous ego vehicle," [0101] "a particular method of predicting the behaviour of external actors, and other vehicles in particular,”)
Regarding Claim 4, Ramamoorthy discloses the limitations of Claim 1, and Ramamoorthy further discloses:
wherein at least one of the first kinematic state distribution or the third kinematic state distribution of the at least one traffic participant are obtained from tracking the at least one traffic participant. (see at least [0123] “the other vehicle as actually observed over the time period Δt (i.e. between time t and t+Δt)”)
Regarding Claim 6, Ramamoorthy discloses the limitations of Claim 1, but Ramamoorthy does not explicitly disclose:
wherein the first time span is between 3 s and 10 s.
Examiner considers the above limitation to be obvious, however, to a person having ordinary skill in the art at the time of the applicant’s claimed invention, as it is a mere design choice to specify that the time may be anywhere from 3 to 10 seconds.
Regarding Claim 7, Ramamoorthy discloses the limitations of Claim 1, and Ramamoorthy further discloses:
wherein each trajectory of the distribution of trajectories is given using a parametric trajectory representation. (see at least [0066] “The expected trajectory model may be computed by applying a generative behaviour model to one or more observed parameters of the external agent as derived from the AV sensor signals”)
Regarding Claim 11, Ramamoorthy discloses the limitations of Claim 1, and Ramamoorthy further discloses:
predicting fourth kinematic state distributions of the at least one traffic participant at a fourth time, (see at least [Fig. 4] [0130] “The above steps are performed repeatedly over time, possibly in real time.”)(*Examiner interprets the disclosure to repeatedly perform the steps as disclosing such claims as this to repeat the already-performed steps at further times)
the fourth time being, in particular, between the third time and the second time, (see at least [0251] “The best available trajectory has an observed portion between time t and t+Δt that matches the actual observed trajectory (i.e. the black circles in FIG. 4) and additionally includes a future portion for the time after t+ΔT, represented in FIG. 4 using diagonally shaded circles.”)
wherein kinematic states of the fourth kinematic state distribution are obtained from evaluating each trajectory of the distribution of trajectories at the fourth time, (see at least [0247] “ for goal G.sub.2 the optimal trajectory gradually slows as the car approaches a turning point for the exit.”)
using the probabilities of the trajectories given by the distribution of trajectories. (see at least [0126] “the (absolute) likelihood of any given predicted path (trajectory) T after time t+Δt, given the observed trace c, can for example be determined”)
Regarding Claim 12, Ramamoorthy discloses the limitations of Claim 11, and Ramamoorthy further discloses:
wherein the predicted fourth kinematic state distributions are multi-modal distributions. (see at least [Fig. 3A])
Regarding Claim 13, Ramamoorthy discloses the limitations of Claim 11, and Ramamoorthy further discloses:
controlling the ego vehicle based on at least one of the distribution of trajectories or the predicted fourth kinematic state distributions. (see at least [0092] “The AV planner A6 uses the extracted information about the ego's surrounding environment and the external agents within it, together with the behaviour predictions provided by the prediction component A4, as a basis for AV planning. … the AV planner A6 generates control signals, which are input, at least in part, to a drive mechanism A16 of the AV,”)
Regarding Claim 14, Ramamoorthy discloses the limitations of Claim 13, and Ramamoorthy further discloses:
Driving system, in particular driver assistance system and/or autonomous driving system, configured to execute the method according to claim 13. (see at least [0071] “an autonomous vehicle (AV) planner embodied in a computer system”)
Regarding Claim 15, Ramamoorthy discloses the limitations of Claim 14, and Ramamoorthy further discloses:
Vehicle comprising a driving system according to claim 14 (see at least [0036] “The method may be implemented in an autonomous vehicle”)
Regarding Claim 16, Ramamoorthy discloses the limitations of Claim 4, and Ramamoorthy further discloses:
wherein the at least one traffic participant is tracked with a multi-object multi-hypotheses tracker. (see at least [0106] “At step SB2, a set of hypothesised goals is determined for the other vehicle in question" [0108] " observed historical behaviour (such as a trace observed prior to time t) may be taken into account in hypothesising external agent goals, or a combination of map-based and historical behaviour-based inference may be used to hypothesise the goals.”)
Regarding Claim 17, Ramamoorthy discloses the limitations of Claim 6, but Ramamoorthy does not explicitly disclose:
wherein the first time span includes a range of five seconds.
Examiner considers the above limitation to be obvious, however, to a person having ordinary skill in the art at the time of the applicant’s claimed invention, as it is a mere design choice to specify that the time span “includes a range of 5 seconds”.
Regarding Claim 19, Ramamoorthy discloses the limitations of Claim 11, and Ramamoorthy further discloses:
wherein the multi-modal distributions comprise multi object multi modal distributions. (see at least [Fig. 3A] [0089] “predictions about future behaviour of the external actors”)
Claims 8, 9, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ramamoorthy (US 20210380142), herein after referred to as Ramamoorthy, in view of Velkey (EP 3855120), herein after referred to as Velkey, Djuric (US 11635764), herein after referred to as Djuric, and Kanemoto (US 9193070), herein after referred to as Kanemoto.
Regarding Claim 8, Ramamoorthy discloses the limitations of Claim 7, but Ramamoorthy does not explicitly disclose:
wherein each trajectory of the distribution of trajectories is given as a linear combination of a predetermined number of basis functions.
In the same field of endeavor, Kanemoto discloses
wherein each trajectory of the distribution of trajectories is given as a linear combination of a predetermined number of basis functions. (see at least [Col 1, Ln 47-4] “generating a line segment represented by a linear combination of point sequences with a basis function") (*Examiner interprets a single basis function for a single line segment as a predetermined number of basis functions)
The above pieces of prior art are considered analogous as they both represent inventions in the vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ramamoorthy to determine the trajectories of the distribution of trajectories as a linear combination of a predetermined number of basis functions, as taught by Kanemoto to process the control of an object in space by interpolating the path between points [Col 1].
Regarding Claim 9, Ramamoorthy discloses the limitations of Claim 8, but Ramamoorthy does not explicitly disclose:
wherein the basis functions are monomials or Bernstein polynomials
In the same field of endeavor, Kanemoto discloses
wherein the basis functions are monomials or Bernstein polynomials (see at least [Col 8, Ln 29] “or Bernstein basis function”)
The above pieces of prior art are considered analogous as they both represent inventions in the vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ramamoorthy to make use of a Bernstein polynomial as the basis function as taught by Kanemoto to process the control of an object in space by interpolating the path between points [Col 1].
Regarding Claim 18, Ramamoorthy discloses the limitations of Claim 8, but Ramamoorthy does not explicitly disclose:
wherein the predetermined number is between five and eight.
Examiner considers the above limitation to be obvious, however, to a person having ordinary skill in the art at the time of the applicant’s claimed invention, as it is a mere design choice to specify that the a predetermined number between 5 and 8.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Ramamoorthy (US 20210380142), herein after referred to as Ramamoorthy, in view of Velkey (EP 3855120), herein after referred to as Velkey, Djuric (US 11635764), herein after referred to as Djuric, Morales (Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance), herein after referred to as Morales, and Berclaz (Multiple Object Tracking Using K-Shortest Paths Optimization), herein after referred to as Berclaz.
Regarding Claim 10, Ramamoorthy discloses the limitations of Claim 1, and Ramamoorthy further discloses:
setting up a cost function; (see at least [0027] “A defined cost function may be applied to both the expected trajectory model and the best-available trajectory model for each goal, ”)
minimizing the cost function (see at least [0240] “The best available trajectory has an observed portion for the interval [t, t+Δt] which matches the actual observed trajectory and a future portion for a subsequent time interval, chosen so as to minimize an overall cost associated with best available full trajectory”)
with respect to discrete options of the second kinematic state distributions; (see at least [0253] “the lowest-cost path from the car's current location to G.sub.2 must involve sharp braking given the circumstances of the car)—which is penalized by the cost function.”)
Ramamoorthy does not explicitly disclose:
and applying standard multi-object multi-hypotheses tracker algorithms to find at least one of the minimum or a minima of the minimized cost function.
In the same field of endeavor, Morales discloses:
and applying standard multi-object multi-hypotheses tracker algorithms (see at least [Section III] “However, this uncertainty can be modeling by generating multiple hypotheses for each object. This addresses the possible motion options of the objects.”)
The above pieces of prior art are considered analogous as they both represent inventions in the vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ramamoorthy to apply standard multi-object multi-hypotheses tracker algorithms, as taught by Morales to process object tracking data for vehicle safety systems [Section III].
In the same field of endeavor, Berclaz discloses:
to find at least one of the minimum or a minima of the minimized cost function. (see at least [Section 3.3] “Therefore, the global minimum is reached when cost(Pi) changes sign and becomes nonnegative:”)
The above pieces of prior art are considered analogous as they both represent inventions in the vehicle control field. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Ramamoorthy to determine the minimum or minima of the const function, as taught by Berclaz to find the smallest path for object tracking ]Section 3.3].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB D UNDERBAKKE whose telephone number is (571)272-6657. The examiner can normally be reached Monday-Friday 8:00-5:00.
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, Jelani Smith can be reached at 571-270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JACOB DANIEL UNDERBAKKE/Examiner, Art Unit 3662
/MAHMOUD S ISMAIL/Primary Examiner, Art Unit 3662