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 .
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.
Claim 29 of the pending application uses the word “means” in two different places. The limitation of “means for applying a machine learning model” indicates a processor that analyzes “training input data” [Specification, ¶¶ 112] and performs other data processing tasks [¶¶ 103-104]. The limitation of “means for performing a driving maneuver” indicates a braking system or a controller that operates a vehicle [Specification, ¶¶ 99-100].
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 1-30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hong, et al., US 11,195,418 B1.
As per Claim 1, Hong teaches an ego vehicle (column 5, lines 57-58; autonomous vehicle 106 of Figure 1), comprising:
one or more memories (column 8, lines 35-37; memory 218 of Figure 2);
one or more transceivers (column 8, lines 57-611); and
one or more processors communicatively coupled to the one or more memories and the one or more transceivers (column 8, lines 35-39; processors 216 of Figure 2), the one or more processors, either alone or in combination, configured to:
apply a machine learning model (column 21, lines 46-51) to one or more agent tensors and one or more map tensors associated with a target vehicle (column 24, lines 57-63) to obtain a lane change classification label representing a predicted lane change intention of the target vehicle on a multi-lane highway (column 30, lines 4-9), wherein the predicted lane change intention includes at least a lane change classification (column 29, lines 60-67); and
perform a driving maneuver based on the predicted lane change intention (column 31, lines 10-15).
As per Claim 2, Hong teaches that the one or more agent tensors represent a plurality of features of at least the target vehicle and one or more neighbor vehicles of the target vehicle over a most recent period of time (column 23, lines 63-67 and column 24, lines 1-14).
As per Claim 3, Hong teaches that the plurality of features comprises: current x coordinates of the target vehicle and the one or more neighbor vehicles, current y coordinates of the target vehicle and the one or more neighbor vehicles (column 23, lines 46-48), previous x coordinates of the target vehicle and the one or more neighbor vehicles, previous y coordinates of the target vehicle and the one or more neighbor vehicles, x-axis velocity values of the target vehicle and the one or more neighbor vehicles, y-axis velocity values of the target vehicle and the one or more neighbor vehicles (column 23, lines 46-48), angular velocity values of the target vehicle and the one or more neighbor vehicles, acceleration values of the target vehicle and the one or more neighbor vehicles, blinker states of the target vehicle and the one or more neighbor vehicles, a time offset between current and previous positions of the target vehicle and the one or more neighbor vehicles, flags indicating a presence of the target vehicle and the one or more neighbor vehicles, or any combination thereof.
As per Claim 4, Hong teaches that a length of the most recent period of time is one second (column 28, lines 24-31).
As per Claim 5, Hong teaches that the one or more map tensors represent map context information around the target vehicle (column 29, lines 36-41).
As per Claim 6, Hong teaches that the map context information comprises: map lane center points for a current lane of the target vehicle, map lane center points for a lane to the left of the target vehicle, map lane center points for a lane to the right of the target vehicle, or any combination thereof (column 22, lines 55-58).
As per Claim 7, Hong teaches that the machine learning model is an encoder-decoder machine learning model (column 28, lines 24-31).
As per Claim 8, Hong teaches that an encoder side of the encoder-decoder machine learning model comprises: an agent encoding module, a map encoding module, and an aggregation module (column 22, lines 22-32).
As per Claim 9, Hong teaches that the agent encoding module comprises a self-attention module (column 10, lines 56-61; the “image generation component 232 can receive data about agents in the environment from the perception component 222 and can receive data about the environment itself from the localization component 220” as in Figure 2).
As per Claim 10, Hong teaches that the aggregation module aggregates results of the agent encoding module and the map encoding module (column 23, lines 63-67 and column 24, lines 1-14).
As per Claim 11, Hong teaches that the aggregation module comprises a map attention module and a cross attention module (column 30, lines 34-40; “the process can include generating, based on the map data and the object data, a top-down view of an environment”).
As per Claim 12, Hong teaches that the aggregation module further comprises a multi-layer perception (MLP) layer, and the MLP layer outputs results of the aggregation module (column 13, lines 16-25).
As per Claim 13, Hong teaches that a decoder side of the encoder-decoder machine learning model comprises one or more MLP layers that output the lane change classification label (column 30, lines 4-14).
As per Claim 14, Hong teaches that the predicted lane change intention further includes one or more predicted lane change trajectories associated with the lane change classification (column 30, lines 7-9; “sophisticated behavior such as maneuvering around obstacles and changing lanes”).
As per Claim 15, Hong the lane change classification label represents one of: a lane change left, a lane change right, or no lane change (column 30, lines 7-9).
As per Claim 16, Hong the driving maneuver comprises: a lane change left, a lane change right, or no lane change.
As per Claim 17, Hong teaches a method of drive trajectory prediction performed by an ego vehicle (column 7, lines 11-1), comprising:
applying a machine learning model (column 21, lines 46-51) to one or more agent tensors and one or more map tensors associated with a target vehicle (column 24, lines 57-63) to obtain a lane change classification label representing a predicted lane change intention of the target vehicle on a multi-lane highway (column 30, lines 4-9), wherein the predicted lane change intention includes at least a lane change classification (column 29, lines 60-67); and
performing a driving maneuver based on the predicted lane change intention (column 31, lines 10-15).
As per Claim 18, Hong teaches that the one or more agent tensors represent a plurality of features of at least the target vehicle and one or more neighbor vehicles of the target vehicle over a most recent period of time (column 23, lines 63-67 and column 24, lines 1-14).
As per Claim 19, Hong the plurality of features comprises: current x coordinates of the target vehicle and the one or more neighbor vehicles, current y coordinates of the target vehicle and the one or more neighbor vehicles, previous x coordinates of the target vehicle and the one or more neighbor vehicles (column 23, lines 46-48), previous y coordinates of the target vehicle and the one or more neighbor vehicles, x-axis velocity values of the target vehicle and the one or more neighbor vehicles, y-axis velocity values of the target vehicle and the one or more neighbor vehicles (column 23, lines 46-48), angular velocity values of the target vehicle and the one or more neighbor vehicles, acceleration values of the target vehicle and the one or more neighbor vehicles, blinker states of the target vehicle and the one or more neighbor vehicles, a time offset between current and previous positions of the target vehicle and the one or more neighbor vehicles, flags indicating a presence of the target vehicle and the one or more neighbor vehicles, or any combination thereof.
As per Claim 20, Hong teaches that the one or more map tensors represent map context information around the target vehicle (column 29, lines 36-41).
As per Claim 21, Hong teaches that the map context information comprises: map lane center points for a current lane of the target vehicle, map lane center points for a lane to the left of the target vehicle, map lane center points for a lane to the right of the target vehicle, or any combination thereof (column 22, lines 55-58).
As per Claim 22, Hong teaches that the machine learning model is an encoder-decoder machine learning model (column 28, lines 24-31).
As per Claim 23, Hong teaches that an encoder side of the encoder-decoder machine learning model comprises: an agent encoding module, a map encoding module, and an aggregation module (column 22, lines 22-32).
As per Claim 24, Hong teaches that the agent encoding module comprises a self-attention module (column 10, lines 56-61; the “image generation component 232 can receive data about agents in the environment from the perception component 222 and can receive data about the environment itself from the localization component 220” as in Figure 2).
As per Claim 25, Hong teaches that the aggregation module aggregates results of the agent encoding module and the map encoding module (column 23, lines 63-67 and column 24, lines 1-14).
As per Claim 26, Hong teaches that the aggregation module comprises a map attention module and a cross attention module (column 30, lines 34-40; “the process can include generating, based on the map data and the object data, a top-down view of an environment”).
As per Claim 27, Hong teaches that a decoder side of the encoder-decoder machine learning model comprises one or more MLP layers that output the lane change classification label (column 30, lines 4-14).
As per Claim 28, Hong teaches that the predicted lane change intention further includes one or more predicted lane change trajectories associated with the lane change classification (column 30, lines 7-9; “sophisticated behavior such as maneuvering around obstacles and changing lanes”).
As per Claim 29, Hong teaches an ego vehicle (column 5, lines 57-58; autonomous vehicle 106 of Figure 1), comprising:
means for applying a machine learning model (column 21, lines 46-51) to one or more agent tensors and one or more map tensors associated with a target vehicle (column 24, lines 57-63) to obtain a lane change classification label representing a predicted lane change intention of the target vehicle on a multi-lane highway (column 30, lines 4-9), wherein the predicted lane change intention includes at least a lane change classification (column 30, lines 4-14); and
means for performing a driving maneuver based on the predicted lane change intention (column 31, lines 10-15).
As per Claim 30, Hong teaches a non-transitory computer-readable medium storing computer-executable instructions (column 10, lines 30-37) that, when executed by an ego vehicle, cause the ego vehicle to:
apply a machine learning model (column 21, lines 46-51) to one or more agent tensors and one or more map tensors associated with a target vehicle (column 24, lines 57-63) to obtain a lane change classification label representing a predicted lane change intention of the target vehicle on a multi-lane highway (column 30, lines 4-9), wherein the predicted lane change intention includes at least a lane change classification (column 30, lines 4-14); and
perform a driving maneuver based on the predicted lane change intention (column 31, lines 10-15).
Conclusion
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ATUL TRIVEDI
Primary Examiner
Art Unit 3661
/ATUL TRIVEDI/Primary Examiner, Art Unit 3661