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 .
Response to Amendment
This action is in response to the amendment and remarks filed 5-19-2025. In the amendment, claims 1-3, 5, 7-9, and 12-14 were amended, claim 15 was canceled, and no claims were added. Thus, claims 1-14 are pending.
The objections of claims 2-9, set forth in the Office Action issued 1-23-2025 (hereinafter “the previous Office Action”), have been withdrawn in view of Applicant’s amendments and remarks.
The interpretations of claims 12-14 under 35 U.S.C. § 112(f), set forth in the previous Office Action, have been withdrawn in view of Applicant’s amendments and remarks.
The rejections of claims 12-14 under 35 U.S.C. § 112(a) and 112(b), set forth in the previous Office Action, have been withdrawn in view of Applicant’s amendments and remarks.
The rejections of claims 12-13 under 35 U.S.C. § 101, software per se, set forth in the previous Office Action, have been withdrawn in view of Applicant’s amendments and remarks.
The rejections of claims 1-14 under 35 U.S.C. § 101, abstract idea, set forth in the previous Office Action, have been withdrawn in view of Applicant’s amendments and remarks.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7-7-2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 10 is objected to because of the following informalities: “wherein the distance ranging device is one of an ultrasonic sensor, a laser scanner, a lidar sensor, a radar sensor, and a camera” should read, “wherein the distance ranging device is one of: an ultrasonic sensor, a laser scanner, a lidar sensor, a radar sensor, and a camera”. Appropriate correction is required.
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.
Claims 1 and 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over Badia et al. (WO 2018211141), hereinafter Badia, in view of Houston et al. (US 11354913), hereinafter Houston.
Regarding Claim 1:
Badia discloses:
A method for predicting future sensory observations
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of a distance ranging device of an autonomous or semi-autonomous vehicle as an input for a path planning module, the method comprising:
Badia, [0012], “The neural environment model receives the current observation and/or a history of observations, and also a current action, and predicts a subsequent observation in response…The policy module defines a policy to rollout a sequence of actions and states using the environment model, defining a trajectory. The trajectory may be defined by one or more of predicted observations, predicted actions, predicted rewards, and a predicted sequence termination signal.”
[0033], “For example, the agent may be a robot interacting with the environment to accomplish a specific task or an autonomous or semi-autonomous vehicle navigating through the environment. In these cases the observation can be data captured by one or more sensors of the agent as it interacts with the environment, e.g., a camera, a LIDAR sensor, a temperature sensor, and so forth.”
In 12, Badia discloses one or more predicted observations [A method for predicting future sensory observations
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+
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], and the predicted observations are used to define a trajectory which is defined by the policy module [as an input for a path planning module]. 33 discloses using a LIDAR sensor [distance ranging device] of an autonomous or semi-autonomous vehicle.
receiving a sequence of previous sensory observations
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and a sequence of control actions
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Badia, [0012], “The neural environment model receives the current observation and/or a history of observations, and also a current action, and predicts a subsequent observation in response.
[0047], “A second input to the environmental model 12 is an action at. This may be provided in the form of a vector 42, which has a number of components equal to the number of possible actions.”
In 12, Badia discloses receiving a current and a history of observations [a sequence of previous sensory observations
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] as well as a current action. 47 further specifies that the current action is a number of possible actions [a sequence of control actions
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].
processing the sequence of previous sensory observations
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and the sequence of control actions
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with a self-supervised temporal neural network to generate a sequence of predicted future sensory observations
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Badia, [0038], “The environment model 12 can be any recurrent architecture which can be trained in an unsupervised fashion from agent trajectories: given a past state and a corresponding action, the environment model 12 predicts the next state and any number of signals from the environment.”
Badia discloses that the environment model can be any recurrent architecture, this corresponds to a temporal neural network because a recurrent model processes sequential data such as time series data. Furthermore, the environment model is unsupervised, this corresponds to self-supervised because both refer training a model without human guidance as well as unlabeled data. The environment model is also disclosed to be given a past state and a corresponding action [the sequence of previous sensory observations
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<
t
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N
,
t
>
and the sequence of control actions
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<
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]. As discussed above, there are one or more previous observations and actions. The environment model processes this information in order to predict the next state and any number of signals from the environment [generate a sequence of predicted future sensory observations
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].
outputting the sequence of predicted future sensory observations
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to the path planning module
Badia, [0039], “Turning to FIG. 2, a prediction-and encoding unit 2 is illustrated which uses the IC [Imagination Core] 1. …It is assumed in this example that the IC 1 is of the form which outputs both the next observation
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1
and the next reward
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1
.”
[0034], “Fig. 1 shows an imagination core (IC) 1 proposed by the present disclosure, which is a component of a neural network system (illustrated in Fig. 3) for controlling the agent. The IC 1 includes a policy module 11 and a neural environment model 12, that is a model which, given information at a time t, is able to make a prediction about at least one later time. As described below, the environment model 12 is used in the neural network to make a prediction about multiple times after time t (discretized as time steps). This is referred to as a rollout. It represents an imagined trajectory of the environment at times after time t, assuming that the agent performs certain actions. The results are interpreted by a neural network (encoder), and used as additional content for a policy module of the neural network system which generates data representing a policy for the agent.”
In 39, Badia discloses that the IC outputs the next observations [outputting the sequence of predicted future sensory observations
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]. 34 further specifies that the IC is a component of the neural network, and that the results of the neural network are used as input for the policy module [outputting the sequence…to the path planning module].
wherein
Ω
^
represents future sensory observations,
Ω
represents previous sensory observations,
u
represents a sequence of control actions,
t
represents time,
P
specifies a length of a sequence for future sensory observations, and
N
specifies a length of a sequence for previous sensory observations
Badia, [0035], “In FIG. 1 an observation of the environment at any time t is denoted by ‘ot’, while an action at any time t is denoted by ‘at’, and a reward at any time t is denoted by ‘rt’.”
[0040], “To generate a trajectory, a current (actual) observation ot is input to the unit 2, and input into the IC 1, to generate
O
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+
1
and
r
^
t
+
1
. The predicition is input into the IC 1 again, to generate
O
^
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+
2
and
r
^
t
+
2
. This process is carried out in total r times, to produce a rollout trajectory of
τ
rollout time-steps.”
In 35, Badia discloses the observations o [
Ω
represents previous sensory observations] at time step t [t represents time] are denoted as ot. Actions a [
u
represents a sequence of control actions] at time step t are denoted as at. The total number of times the observations are processed and a prediction is generated is r times. This corresponds to both P and N because r refers to both the sequence length of the observations and the predictions. Lastly,
τ
is disclosed to be the trajectory [
Ω
^
represents future sensory observations].
Badia does not explicitly disclose:
wherein the sequence of predicted future sensory observations
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t
,
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+
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>
cause the autonomous or semi-autonomous vehicle to perform at least one of: a collision-avoidance maneuver, pre-charging brakes of the vehicle, and pre-tensioning seatbelts of the vehicle
However, in the same field, analogous art Houston teaches:
wherein the sequence of predicted future sensory observations
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+
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>
cause the autonomous or semi-autonomous vehicle to perform at least one of: a collision-avoidance maneuver, pre-charging brakes of the vehicle, and pre-tensioning seatbelts of the vehicle
Houston, [0033], “As an example and not by way of limitation, based on the predicted behavior of the agents surrounding the vehicle and the traffic data to a particular destination, planning module 120 may determine a particular navigation path and associated driving operations for the vehicle to avoid possible collisions with one or more agents.”
Houston discloses determining a particular navigation path and associated driving operations for a vehicle to avoid possible collisions [cause the autonomous or semi-autonomous vehicle to perform at least one of: a collision-avoidance maneuver…] based on predicted behavior of the agents [the sequence of predicted future sensory observations
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^
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t
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+
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].
Badia, Houston, and the instant application are analogous art because they are all directed to using observations to make predictions for autonomous vehicles.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Badia with Houston to take an action in order to ensure the safety of the autonomous or semi-autonomous vehicle. “Based on the plan generated by planning module 120, which may include one or more navigation path or associated driving operations, control module 125 may determine the specific commands to be issued to the actuators of the vehicle. The actuators of the vehicle are components that are responsible for moving and controlling the vehicle. The actuators control driving functions of the vehicle, such as for example, steering, turn signals, deceleration (braking), acceleration, gear shift, etc. As an example and not by way of limitation, control module 125 may transmit commands to a steering actuator to maintain a particular steering angle for a particular amount of time to move a vehicle on a particular trajectory to avoid agents predicted to encroach into the area of the vehicle” (Houston, [0034]). As disclosed by Houston, actuating actuators of the vehicle to avoid a possible collision ensures the safety of the vehicle.
Regarding Claim 10:
As discussed above, Badia in view of Houston teach [the] method according to claim 1, and Badia further discloses:
wherein the distance ranging device is one of an ultrasonic sensor, a laser scanner, a lidar sensor, a radar sensor, and a camera
Badia, [0033], “In these cases the observation can be data captured by one or more sensors of the agent as it interacts with the environment, e.g., a camera, a LIDAR sensor, a temperature sensor, and so forth.”
Badia discloses a LIDAR sensor.
Regarding Claim 11:
As discussed above, Badia in view of Houston teach [the] method according to claim 1, and Badia further discloses:
A non-transitory computer-readable medium having stored thereon computer-executable instructions, which, when executed by at least one processor, cause the at least one processor to perform the method according to claim 1 for predicting future sensory observations
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t
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+
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of a distance ranging device of an autonomous or semi-autonomous vehicle
Badia, [0054], “Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non transitory program carrier for execution by, or to control the operation of, data processing apparatus.”
[0055], “The term ‘data processing apparatus’ encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers.”
Badia discloses that the embodiments of the subject matter [a method according to claim 1] are implemented as computer program instructions on a non-transitory program carrier [non-transitory computer-readable medium having stored thereon computer-executable instructions] to be executed on a data processing apparatus, which is disclosed to be a computer [at least one processor].
Regarding Claim 12:
Badia discloses:
An apparatus for predicting future sensory observations
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^
<
t
,
t
+
P
>
of a distance ranging device of an autonomous or semi-autonomous vehicle as an input for a path planning module, the apparatus comprising
Badia, [0054], “Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non transitory program carrier for execution by, or to control the operation of, data processing apparatus.”
[0012], “The neural environment model receives the current observation and/or a history of observations, and also a current action, and predicts a subsequent observation in response…The policy module defines a policy to rollout a sequence of actions and states using the environment model, defining a trajectory. The trajectory may be defined by one or more of predicted observations, predicted actions, predicted rewards, and a predicted sequence termination signal.”
[0033], “For example, the agent may be a robot interacting with the environment to accomplish a specific task or an autonomous or semi-autonomous vehicle navigating through the environment. In these cases the observation can be data captured by one or more sensors of the agent as it interacts with the environment, e.g., a camera, a LIDAR sensor, a temperature sensor, and so forth.”
In 54, Badia discloses a data processing apparatus for performing the embodiments. 12 discloses one or more predicted observations [predicting future sensory observations
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^
<
t
,
t
+
P
>
], and the predicted observations are used to define a trajectory which is defined by the policy module [as an input for a path planning module]. 33 discloses using a LIDAR sensor [distance ranging device] of an autonomous or semi-autonomous vehicle.
a non-transitory computer-readable medium having stored thereon computer-executable instructions, which, when executed by at least one processor, cause the at least one processor to implement:
Badia, [0054], “Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non transitory program carrier for execution by, or to control the operation of, data processing apparatus.”
[0055], “The term ‘data processing apparatus’ encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers.”
Badia discloses that the embodiments of the subject matter are implemented as computer program instructions on a non-transitory program carrier [non-transitory computer-readable medium having stored thereon computer-executable instructions] to be executed on a data processing apparatus, which is disclosed to be a computer [at least one processor].
a self-supervised temporal neural network configured to process a received sequence of previous sensory observations
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<
t
-
N
,
t
>
and a received sequence of control actions
u
<
t
,
t
+
P
>
to generate a sequence of predicted future sensory observations
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^
<
t
,
t
+
P
>
Badia, [0038], “The environment model 12 can be any recurrent architecture which can be trained in an unsupervised fashion from agent trajectories: given a past state and a corresponding action, the environment model 12 predicts the next state and any number of signals from the environment.”
In 38, Badia discloses that the environment model can be any recurrent architecture, this corresponds to a temporal neural network because a recurrent model processes sequential data such as time series data. Furthermore, the environment model is unsupervised, this corresponds to self-supervised because both refer training a model without human guidance as well as unlabeled data. The environment model is also disclosed to be given a past state and a corresponding action [the sequence of previous sensory observations
Ω
<
t
-
N
,
t
>
and the sequence of control actions
u