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 the Claims
Claims 1-20 filed on 13 SEP 2024 are currently pending and have been examined.
Priority
The pending application 18/885,049, filed on 13 SEP 2024, is a continuation of U.S. patent application 17/370,285, filed on 8 JUL 2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 13 SEP 2024 has been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a computing system” in claims 1 and 11: support found in ¶ [0007]
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 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.
Claim(s) 1, 5-8, 11-13, 16-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Petousis et al. (US 2020/0097841 A1, cited by applicant in IDS dated 13 SEP 2024).
Regarding claim 1, Petousis et al. discloses:
[Note: what is not explicitly taught by Petousis et al. has been struck-through]
A method comprising:
receiving, at a computing system (Petousis et al. “The vehicle system includes a processing system (e.g., graphical processing unit or GPU, central processing unit or CPU) as well as memory.” - ¶ [0047]), first radar data and motion data (Petousis et al. receiving sensor data S210, Fig. 2; “Such sensors include embedded sensors, GPS systems, LiDAR, radar, cameras, audio sensors, temperature sensors, pressure sensors, position sensors, velocity sensors, timers, clocks, and any other suitable sensors. The data can be of any suitable type, including, but not limited to: image data, audio data, one-dimensional data (e.g., voltage, current), multi-dimensional data (e.g., point cloud, heatmap, functional surface, other 2D or 3D data, etc.), and/or time series.” - ¶ [0051]) captured during navigation by a vehicle (Petousis et al. vehicle 110, Fig. 1) in an environment (Petousis et al. “Further, the machine learning model may be situationally trained or designed to operate in any type of situation (e.g., while vehicle is driving in the city versus the highway).” - ¶ [0030]);
(Petousis et al. “The data can be of any suitable type, including, but not limited to: image data, audio data, one-dimensional data (e.g., voltage, current), multi-dimensional data (e.g., point cloud, heatmap, functional surface, other 2D or 3D data, etc.), and/or time series.” - ¶ [0051]);
estimating, by the computing system using the first radar (Petousis et al. “The model attempts to predict the value of the message (its actual value or the change in value) at time t based on its value at times t−n (where n>=1 and n is an integer)…” - ¶ [0031]; “For example, with respect to acceleration and speed, the remote computing platform may receive a sequence of acceleration values of the vehicle at known intervals, the speed of the vehicle can be predicted or calculated (v(t2)=a*δt+v(t1)).” - ¶ [0082]);
performing a comparison between the radar (Petousis et al. “The error between the predicted values and the actual data values at the encoder may be calculated.” - ¶ [0034]); and
storing, by the computing system, a radar data file in memory, wherein the radar data file represents a difference between the radar (Petousis et al. “The prediction for time t is compared to the actual value of the message at time t and only the errors (subject to a minimum error value threshold if necessary) are transmitted (or stored).” - ¶ [0031]).
Although Petousis et al. does not explicitly disclose a first radar representation, a radar representation prediction and a second radar representation, Petousis et al. does disclose that the vehicle sensors include radar and the sensor data can be of any suitable type, including image data (Petousis et al. ¶ [0051]). Petousis et al. further discloses that “the trained machine learning units at the device and at the cloud are given as input a sample of video frames (e.g., Frame 1 – Frame 5) to predict a future frame (Frame 6).” (Petousis et al. ¶ [0083]) and “video frame values may be obtained from a camera or other sensor of the vehicle.” (Petousis et al. ¶ [0084]). Therefore, it would be obvious to one of ordinary skill in the art at the time of the applicant’s filing to use the system taught by Petousis et al. for video radar data in order to achieve a “maximum compression ratio to reduce bandwidth usage or to achieve minimal computational power/time to reduce energy usage and the like” (Petousis et al. ¶ [0036]) when transferring data from autonomous vehicle to a remote computing platform (Petousis et al. ¶ [0029]).
Regarding claim 5, Petousis et al. as modified above discloses:
The method of claim 1, wherein the first radar representation is a first radar image having a first plurality of pixels (Petousis et al. Frame 5, Fig. 3; ¶ [0083]-[0084]; where the video frame values are obtained from a camera or other sensor on the vehicle, ¶ [0085]; the vehicle sensors include radar and radar data can in the form of image data, ¶ [0051]) and the second radar representation is a second radar image having a second plurality of pixels (Petousis et al. Frame 6 (actual), Fig. 3; ¶ [0083]-[0084]; where the video frame values are obtained from a camera or other sensor on the vehicle, ¶ [0085]; the vehicle sensors include radar and radar data can in the form of image data, ¶ [0051]).
Regarding claim 6, Petousis et al. as modified above discloses:
The method of claim 5, wherein the radar representation prediction is a prediction image having a third plurality of pixels (Petousis et al. Frame 6 (predicted), Fig. 3; ¶ [0083]-[0084]; where the video frame values are obtained from a camera or other sensor on the vehicle, ¶ [0085]; the vehicle sensors include radar and radar data can in the form of image data, ¶ [0051]), and wherein performing the comparison between the radar representation prediction and the second radar representation comprises:
performing the comparison between the third plurality of pixels in the prediction image and the second plurality of pixels in the second radar image (Petousis et al. “An error determination is made in schematic 300 based on finding a difference between the predictive value for Frame 6 and an actual value for Frame 6.” - ¶ [0084]).
Regarding claim 7, Petousis et al. as modified above discloses:
The method of claim 6, wherein storing the radar data file in memory comprises:
generating the radar data file based on respective differences between the third plurality of pixels in the predication image and the second plurality of pixels in the second radar image (Petousis et al. “The prediction for time t is compared to the actual value of the message at time t and only the errors (subject to a minimum error value threshold if necessary) are transmitted (or stored).” - ¶ [0031]);
compressing the radar data file (Petousis et al. “In essence, using standard compression language, the machine learning model (which is shared between the car and the cloud) becomes the encoder/decoder and the transmitted errors represent the encoded message.” - ¶ [0031]); and
storing the radar data file in memory after compressing the radar data file (Petousis et al. “The prediction for time t is compared to the actual value of the message at time t and only the errors (subject to a minimum error value threshold if necessary) are transmitted (or stored).” - ¶ [0031]).
Regarding claim 8, Petousis et al. as modified above discloses:
The method of claim 7, further comprising:
decompressing the compressed radar data file (Petousis et al. adding errors to reconstruct the frames, Fig. 3); and
performing machine learning using the decompressed radar data file to train a model (Petousis et al. errors from the car are sent to the cloud, where the errors are used to reconstruct and input the frames to the machine learning model in the cloud, Fig. 3), wherein the model is configured for use during subsequent navigation by the vehicle (Petousis et al. the machine learning model is periodically re-trained and pushed to the autonomous vehicle, Fig. 3; “Vehicle sensor data can originate from any suitable vehicle sensor (e.g., interior or exterior on-board sensors, external sensors, etc.). Such sensors include embedded sensors, GPS systems, LiDAR, radar, cameras, audio sensors, temperature sensors, pressure sensors, position sensors, velocity sensors, timers, clocks, and any other suitable sensors.” - ¶ [0051]).
Regarding claim 11, Petousis et al. discloses:
[Note: what is not explicitly taught by Petousis et al. has been struck-through]
A system comprising:
a memory (Petousis et al. “The vehicle system includes a processing system (e.g., graphical processing unit or GPU, central processing unit or CPU) as well as memory.” - ¶ [0047]); and
a computing system (Petousis et al. “The vehicle system includes a processing system (e.g., graphical processing unit or GPU, central processing unit or CPU) as well as memory.” - ¶ [0047]) configured to:
receive first radar data and motion data (Petousis et al. receiving sensor data S210, Fig. 2; “Such sensors include embedded sensors, GPS systems, LiDAR, radar, cameras, audio sensors, temperature sensors, pressure sensors, position sensors, velocity sensors, timers, clocks, and any other suitable sensors. The data can be of any suitable type, including, but not limited to: image data, audio data, one-dimensional data (e.g., voltage, current), multi-dimensional data (e.g., point cloud, heatmap, functional surface, other 2D or 3D data, etc.), and/or time series.” - ¶ [0051]) captured during navigation by a vehicle (Petousis et al. vehicle 110, Fig. 1) in an environment (Petousis et al. “Further, the machine learning model may be situationally trained or designed to operate in any type of situation (e.g., while vehicle is driving in the city versus the highway).” - ¶ [0030]);
(Petousis et al. “The data can be of any suitable type, including, but not limited to: image data, audio data, one-dimensional data (e.g., voltage, current), multi-dimensional data (e.g., point cloud, heatmap, functional surface, other 2D or 3D data, etc.), and/or time series.” - ¶ [0051]);
estimate, using the first radar r(Petousis et al. “The model attempts to predict the value of the message (its actual value or the change in value) at time t based on its value at times t−n (where n>=1 and n is an integer)…” - ¶ [0031]; “For example, with respect to acceleration and speed, the remote computing platform may receive a sequence of acceleration values of the vehicle at known intervals, the speed of the vehicle can be predicted or calculated (v(t2)=a*δt+v(t1)).” - ¶ [0082]);
perform a comparison between the radar (Petousis et al. “The error between the predicted values and the actual data values at the encoder may be calculated.” - ¶ [0034]); and
store a radar data file in memory, wherein the radar data file represents a difference between the radar (Petousis et al. “The prediction for time t is compared to the actual value of the message at time t and only the errors (subject to a minimum error value threshold if necessary) are transmitted (or stored).” - ¶ [0031]).
Although Petousis et al. does not explicitly disclose a first radar representation, a radar representation prediction and a second radar representation, Petousis et al. does disclose that the vehicle sensors include radar and the sensor data can be of any suitable type, including image data (Petousis et al. ¶ [0051]). Petousis et al. further discloses that “the trained machine learning units at the device and at the cloud are given as input a sample of video frames (e.g., Frame 1 – Frame 5) to predict a future frame (Frame 6).” (Petousis et al. ¶ [0083]). Therefore, it would be obvious to one of ordinary skill in the art at the time of the applicant’s filing to use the system taught by Petousis et al. for video radar data in order to achieve a “maximum compression ratio to reduce bandwidth usage or to achieve minimal computational power/time to reduce energy usage and the like” (Petousis et al. ¶ [0036]) when transferring data from autonomous vehicle to a remote computing platform (Petousis et al. ¶ [0029]).
Regarding claim 12, Petousis et al. as modified above discloses:
The system of claim 11
Although Petousis et al. does not explicitly disclose that the first radar representation and the second radar representation are part of a radar video stream, Petousis et al. does disclose that the vehicle sensors include radar and the sensor data can be of any suitable type, including image data (Petousis et al. ¶ [0051]). Petousis et al. further discloses that the “video frame values may be obtained from a camera or other sensor of the vehicle.” (Petousis et al. ¶ [0084]). Therefore, it would be obvious to one of ordinary skill in the art at the time of the applicant’s filing to use the system taught by Petousis et al. for video radar data in order to achieve a “maximum compression ratio to reduce bandwidth usage or to achieve minimal computational power/time to reduce energy usage and the like” (Petousis et al. ¶ [0036]) when transferring data from autonomous vehicle to a remote computing platform (Petousis et al. ¶ [0029]).
Regarding claim 13, Petousis et al. as modified above discloses:
The system of claim 11, wherein the first radar representation indicates respective ranges for the surfaces in the environment (Petousis et al. radar data obviously indicates ranges for surfaces in the environment), and wherein the radar representation prediction further depends on the respective ranges for the surfaces in the environment (Petousis et al. “The model attempts to predict the value of the message (its actual value or the change in value) at time t based on its value at times t−n (where n>=1 and n is an integer)…” - ¶ [0031]; where the range at time t depends on the range at time t-n).
Regarding claim 16, Petousis et al. as modified above discloses:
The system of claim 11, wherein the first radar representation is a first radar image having a first plurality of pixels (Petousis et al. Frame 5, Fig. 3; ¶ [0083]-[0084]; where the video frame values are obtained from a camera or other sensor on the vehicle, ¶ [0085]; the vehicle sensors include radar and radar data can in the form of image data, ¶ [0051]), the second radar representation is a second radar image having a second plurality of pixels (Petousis et al. Frame 6 (actual), Fig. 3; ¶ [0083]-[0084]; where the video frame values are obtained from a camera or other sensor on the vehicle, ¶ [0085]; the vehicle sensors include radar and radar data can in the form of image data, ¶ [0051]), and the radar representation prediction is a prediction image having a third plurality of pixels (Petousis et al. Frame 6 (predicted), Fig. 3; ¶ [0083]-[0084]; where the video frame values are obtained from a camera or other sensor on the vehicle, ¶ [0085]; the vehicle sensors include radar and radar data can in the form of image data, ¶ [0051]).
Regarding claim 17, Petousis et al. as modified above discloses:
The system of claim 16, wherein the comparison is used to identify respective differences between the third plurality of pixels in the prediction image and the second plurality of pixels in the second radar image (Petousis et al. “An error determination is made in schematic 300 based on finding a difference between the predictive value for Frame 6 and an actual value for Frame 6.” - ¶ [0084]).
Regarding claim 18, Petousis et al. as modified above discloses:
The system of claim 17, wherein the computing system is further configured to:
compress the identified respective differences to generate the radar data file (Petousis et al. “The prediction for time t is compared to the actual value of the message at time t and only the errors (subject to a minimum error value threshold if necessary) are transmitted (or stored).” - ¶ [0031]).
Regarding claim 20, Petousis et al. discloses:
[Note: what is not explicitly taught by Petousis et al. has been struck-through]
A non-transitory computer-readable medium configured to store instructions (Petousis et al. “a computer-readable medium storing computer-readable instructions” - ¶ [0087]), that when executed by a computing system comprising one or more processors (Petousis et al. “The vehicle system includes a processing system (e.g., graphical processing unit or GPU, central processing unit or CPU) as well as memory.” - ¶ [0047]), causes the computing system to perform operations comprising:
receiving first radar data and motion data (Petousis et al. receiving sensor data S210, Fig. 2; “Such sensors include embedded sensors, GPS systems, LiDAR, radar, cameras, audio sensors, temperature sensors, pressure sensors, position sensors, velocity sensors, timers, clocks, and any other suitable sensors. The data can be of any suitable type, including, but not limited to: image data, audio data, one-dimensional data (e.g., voltage, current), multi-dimensional data (e.g., point cloud, heatmap, functional surface, other 2D or 3D data, etc.), and/or time series.” - ¶ [0051]) captured during navigation by a vehicle (Petousis et al. vehicle 110, Fig. 1) in an environment (Petousis et al. “Further, the machine learning model may be situationally trained or designed to operate in any type of situation (e.g., while vehicle is driving in the city versus the highway).” - ¶ [0030]);
(Petousis et al. “The data can be of any suitable type, including, but not limited to: image data, audio data, one-dimensional data (e.g., voltage, current), multi-dimensional data (e.g., point cloud, heatmap, functional surface, other 2D or 3D data, etc.), and/or time series.” - ¶ [0051]; where the first radar representation conveying respective positions of surfaces in the environment is inherent to the operating principles of radar detecting the distance to an object based on the time for a signal to be transmitted to and reflected from the surface of that object in the environment);
estimating, using the first radar (Petousis et al. “The model attempts to predict the value of the message (its actual value or the change in value) at time t based on its value at times t−n (where n>=1 and n is an integer)…” - ¶ [0031]; “For example, with respect to acceleration and speed, the remote computing platform may receive a sequence of acceleration values of the vehicle at known intervals, the speed of the vehicle can be predicted or calculated (v(t2)=a*δt+v(t1)).” - ¶ [0082]);
performing a comparison between the radar (Petousis et al. “The error between the predicted values and the actual data values at the encoder may be calculated.” - ¶ [0034]); and
storing a radar data file in memory, wherein the radar data file represents a difference between the radar (Petousis et al. “The prediction for time t is compared to the actual value of the message at time t and only the errors (subject to a minimum error value threshold if necessary) are transmitted (or stored).” - ¶ [0031]).
Although Petousis et al. does not explicitly disclose a first radar representation, a radar representation prediction and a second radar representation, Petousis et al. does disclose that the vehicle sensors include radar and the sensor data can be of any suitable type, including image data (Petousis et al. ¶ [0051]). Petousis et al. further discloses that “the trained machine learning units at the device and at the cloud are given as input a sample of video frames (e.g., Frame 1 – Frame 5) to predict a future frame (Frame 6).” (Petousis et al. ¶ [0083]). Therefore, it would be obvious to one of ordinary skill in the art at the time of the applicant’s filing to use the system taught by Petousis et al. for video radar data in order to achieve a “maximum compression ratio to reduce bandwidth usage or to achieve minimal computational power/time to reduce energy usage and the like” (Petousis et al. ¶ [0036]) when transferring data from autonomous vehicle to a remote computing platform (Petousis et al. ¶ [0029]).
Claim(s) 2, 4 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Petousis et al. (US 2020/0097841 A1, cited by applicant in IDS dated 13 SEP 2024) in view of Liang et al. (WO 2020/0198121 A1, cited by applicant in IDS dated 13 SEP 2024).
Regarding claim 2, Petousis et al. as modified above discloses:
[Note: what is not explicitly taught by Petousis et al. has been struck-through]
The method of claim 1, wherein receiving motion data captured during navigation by the vehicle comprises:
receiving velocity (Petousis et al. receiving sensor data S210, Fig. 2; “Such sensors include embedded sensors, GPS systems, LiDAR, radar, cameras, audio sensors, temperature sensors, pressure sensors, position sensors, velocity sensors, timers, clocks, and any other suitable sensors. The data can be of any suitable type, including, but not limited to: image data, audio data, one-dimensional data (e.g., voltage, current), multi-dimensional data (e.g., point cloud, heatmap, functional surface, other 2D or 3D data, etc.), and/or time series.” - ¶ [0051]).
Liang et al. discloses:
wherein receiving motion data captured during navigation by the vehicle comprises:
receiving velocity and yaw rate information for the vehicle (Liang et al. “the positioning system 118 can determine a position by using one or more of inertial sensors… The vehicle 108 can identify its position within the surrounding environment (e.g., across six axes) based at least in part on the data described herein.” ¶ [0097]; where the six axes includes yaw).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Liang et al. into the invention of Petousis et al. to yield the invention of claim 2 above. Petousis et al. and Liang et al. are considered analogous arts to the claimed invention as they disclose receiving and processing radar data. Petousis et al. discloses the method of claim 1. However, Petousis et al. fails to explicitly disclose receiving yaw rate information for the vehicle. This feature is disclosed by Liang et al. where “the positioning system 118 can determine a position by using one or more of inertial sensors… The vehicle 108 can identify its position within the surrounding environment (e.g., across six axes) based at least in part on the data described herein.” (Liang et al. ¶ [0097]). The combination of Petousis et al. and Liang et al. would be obvious with a reasonable expectation of success to “increase the accuracy and precision with which the motion of objects can be predicted.” (Liang et al. ¶ [0026]).
Regarding claim 4, Petousis et al. as modified above discloses:
[Note: what is not explicitly taught by Petousis et al. has been struck-through]
The method of claim 2, wherein the velocity (Petousis et al. receiving sensor data S210, Fig. 2; “Such sensors include embedded sensors, GPS systems, LiDAR, radar, cameras, audio sensors, temperature sensors, pressure sensors, position sensors, velocity sensors, timers, clocks, and any other suitable sensors. ” - ¶ [0051]).
Liang et al. discloses:
wherein the velocity and yaw rate information is received from at least one inertial measurement unit (IMU) (Liang et al. “the positioning system 118 can determine a position by using one or more of inertial sensors… The vehicle 108 can identify its position within the surrounding environment (e.g., across six axes) based at least in part on the data described herein.” ¶ [0097]).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Liang et al. into the invention of Petousis et al. to yield the invention of claim 4 above. Petousis et al. and Liang et al. are considered analogous arts to the claimed invention as they disclose receiving and processing radar data. Petousis et al. as modified above discloses the method of claim 2. However, Petousis et al. fails to explicitly disclose wherein the velocity and yaw rate information is received from at least one inertial measurement unit (IMU). This feature is disclosed by Liang et al. where “the positioning system 118 can determine a position by using one or more of inertial sensors… The vehicle 108 can identify its position within the surrounding environment (e.g., across six axes) based at least in part on the data described herein.” (Liang et al. ¶ [0097]). The combination of Petousis et al. and Liang et al. would be obvious with a reasonable expectation of success to “increase the accuracy and precision with which the motion of objects can be predicted.” (Liang et al. ¶ [0026]).
Regarding claim 15, Petousis et al. as modified above discloses:
[Note: what is not explicitly taught by Petousis et al. has been struck-through]
The system of claim 11, wherein the motion data comprises:
velocity (Petousis et al. receiving sensor data S210, Fig. 2; “Such sensors include embedded sensors, GPS systems, LiDAR, radar, cameras, audio sensors, temperature sensors, pressure sensors, position sensors, velocity sensors, timers, clocks, and any other suitable sensors. The data can be of any suitable type, including, but not limited to: image data, audio data, one-dimensional data (e.g., voltage, current), multi-dimensional data (e.g., point cloud, heatmap, functional surface, other 2D or 3D data, etc.), and/or time series.” - ¶ [0051]).
Liang et al. discloses:
wherein the motion data comprises:
velocity and yaw rate information for the vehicle (Liang et al. “the positioning system 118 can determine a position by using one or more of inertial sensors… The vehicle 108 can identify its position within the surrounding environment (e.g., across six axes) based at least in part on the data described herein.” ¶ [0097]).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Liang et al. into the invention of Petousis et al. to yield the invention of claim 15 above. Petousis et al. and Liang et al. are considered analogous arts to the claimed invention as they disclose receiving and processing radar data. Petousis et al. as modified above discloses the system of claim 11. However, Petousis et al. fails to explicitly disclose the motion data comprises yaw rate information for the vehicle. This feature is disclosed by Liang et al. where “the positioning system 118 can determine a position by using one or more of inertial sensors… The vehicle 108 can identify its position within the surrounding environment (e.g., across six axes) based at least in part on the data described herein.” (Liang et al. ¶ [0097]). The combination of Petousis et al. and Liang et al. would be obvious with a reasonable expectation of success to “increase the accuracy and precision with which the motion of objects can be predicted.” (Liang et al. ¶ [0026]).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Petousis et al. (US 2020/0097841 A1, cited by applicant in IDS dated 13 SEP 2024) in view of Tran (US 10609148 B1, cited by applicant in IDS dated 13 SEP 2024).
Regarding claim 9, Petousis et al. as modified above discloses:
The method of claim 1, further comprising:
compressing the radar data file (Petousis et al. “In essence, using standard compression language, the machine learning model (which is shared between the car and the cloud) becomes the encoder/decoder and the transmitted errors represent the encoded message.” - ¶ [0031]); and
transmitting the compressed radar data file to a remote computing device (Petousis et al. “The prediction for time t is compared to the actual value of the message at time t and only the errors (subject to a minimum error value threshold if necessary) are transmitted (or stored).” - ¶ [0031]; “transmitting the error data to the remote computing platform” - ¶ [0053]; S270, Fig. 2),
Although Petousis et al. does not explicitly disclose that the remote computing device is configured to compile compressed radar files froma plurality of vehicles, Petousis et al. does disclose that “vehicles are typically connected to a network (e.g., the Internet or mobile broadband network) by one or more uplinks of various types…” (Petousis et al. ¶ [0003]).
Tran discloses:
wherein the remote computing device is configured to compile compressed radar data files from a plurality of vehicles (Tran “The system includes a crowdsourcing server in communication with a plurality of vehicles 1 . . . n.” – Col. 22, lines 7-9; “Next, a system to crowd-source the updates of precision maps with data from smart vehicles is detailed. In embodiments, crowd-sourced obstacle data can be used to update a map with precision. The obstacles can be rocks, boulders, pot-holes, manhole, utility hole, cable chamber, maintenance hole, inspection chamber, access chamber, sewer hole, confined space or can be water pool or rising tidal waves that affect the road as detected by a plurality of vehicles.” – Col. 22, lines 20-27).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Tran into the invention of Petousis et al. to yield the invention of claim 9 above. Petousis et al. and Tran are considered analogous arts to the claimed invention as they disclose receiving and processing radar data. Petousis et al. discloses the method of claim 1. However, Petousis et al. fails to explicitly disclose wherein the remote computing device is configured to compile compressed radar data files from a plurality of vehicles. This feature is disclosed by Tran where “The system includes a crowdsourcing server in communication with a plurality of vehicles 1 . . . n.” (Tran Col. 22, lines 7-9. The combination of Petousis et al. and Tran would be obvious with a reasonable expectation of success to use crowd-sourced obstacle data to update a map with precision (Tran Col. 22, lines 20-27).
Claim(s) 10 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Petousis et al. (US 2020/0097841 A1, cited by applicant in IDS dated 13 SEP 2024) in view of Jansen et al. (US 9,448,300 B2, cited by applicant in IDS dated 13 SEP 2024).
Regarding claim 10, Petousis et al. as modified above discloses:
[Note: what is not explicitly taught by Petousis et al. has been struck-through]
The method of claim 1, further comprising:
Jansen et al. discloses:
controlling the vehicle based on the radar data file (Jansen et al. “the transmitted compressed signal may be used for a variety of safety operations, such as with automotive safety systems 150 and 152. Such an approach may be implemented, for example, to present distance information to an alarm system indicative of an object in the path of a vehicle, or to a braking system for automatic braking.” – Col. 6, lines 37-43).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Jansen et al. into the invention of Petousis et al. to yield the invention of claim 10 above. Petousis et al. and Jansen et al. are considered analogous arts to the claimed invention as they disclose receiving and processing radar data. Petousis et al. discloses the method of claim 1. However, Petousis et al. fails to explicitly disclose controlling the vehicle based on the radar data file. This feature is disclosed by Jansen et al. where a braking system for automatic braking is controlled based on the radar data file (Jansen et al. Col. 6, lines 37-43). The combination of Petousis et al. and Jansen et al. would be obvious with a reasonable expectation of success to enhance vehicle safety (Jansen et al. Col. 6, lines 37-43).
Regarding claim 19, Petousis et al. as modified above discloses:
[Note: what is not explicitly taught by Petousis et al. has been struck-through]
The system of claim 18
Jansen et al. discloses:
wherein the computing system is further configured to: transmit the radar data file to a control system of the vehicle, wherein the control system is configured to control the vehicle based on the radar data file (Jansen et al. “the transmitted compressed signal may be used for a variety of safety operations, such as with automotive safety systems 150 and 152. Such an approach may be implemented, for example, to present distance information to an alarm system indicative of an object in the path of a vehicle, or to a braking system for automatic braking.” – Col. 6, lines 37-43).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Jansen et al. into the invention of Petousis et al. to yield the invention of claim 19 above. Petousis et al. and Jansen et al. are considered analogous arts to the claimed invention as they disclose receiving and processing radar data. Petousis et al. discloses the system of claim 18. However, Petousis et al. fails to explicitly disclose controlling the vehicle based on the radar data file. This feature is disclosed by Jansen et al. where a braking system for automatic braking is controlled based on the radar data file (Jansen et al. Col. 6, lines 37-43). The combination of Petousis et al. and Jansen et al. would be obvious with a reasonable expectation of success to enhance vehicle safety (Jansen et al. Col. 6, lines 37-43).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Petousis et al. (US 2020/0097841 A1, cited by applicant in IDS dated 13 SEP 2024) in view of Jansen et al. (US 9,48,300 B2, cited by applicant in IDS dated 13 SEP 2024) and Tran (US 10609148 B1, cited by applicant in IDS dated 13 SEP 2024).
Regarding claim 14, Petousis et al. as modified above discloses:
[Note: what is not explicitly taught by Petousis et al. has been struck-through]
The system of claim 11
Jansen et al. discloses:
wherein the computing system is further configured to determine a control strategy for controlling the vehicle based on the radar data file and a plurality of additional radar data files (Jansen et al. “the transmitted compressed signal may be used for a variety of safety operations, such as with automotive safety systems 150 and 152. Such an approach may be implemented, for example, to present distance information to an alarm system indicative of an object in the path of a vehicle, or to a braking system for automatic braking.” – Col. 6, lines 37-43).
Tran discloses:
a plurality of additional radar data files (Tran “The system includes a crowdsourcing server in communication with a plurality of vehicles 1 . . . n.” – Col. 22, lines 7-9; “Next, a system to crowd-source the updates of precision maps with data from smart vehicles is detailed. In embodiments, crowd-sourced obstacle data can be used to update a map with precision. The obstacles can be rocks, boulders, pot-holes, manhole, utility hole, cable chamber, maintenance hole, inspection chamber, access chamber, sewer hole, confined space or can be water pool or rising tidal waves that affect the road as detected by a plurality of vehicles.” – Col. 22, lines 20-27)
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Jansen et al. and Tran into the invention of Petousis et al. to yield the invention of claim 14 above. Petousis et al., Jansen et al., and Tran are considered analogous arts to the claimed invention as they disclose receiving and processing radar data. Petousis et al. discloses the system of claim 11. However, Petousis et al. fails to explicitly disclose controlling the vehicle based on the radar data file. This feature is disclosed by Jansen et al. where a braking system for automatic braking is controlled based on the radar data file (Jansen et al. Col. 6, lines 37-43) and Tran where a plurality of radar files are acquired through crowd-sourcing (Tran Col. 22, lines 20-27). The combination of Petousis et al., Jansen et al., and Tran would be obvious with a reasonable expectation of success to enhance vehicle safety (Jansen et al. Col. 6, lines 37-43) and use crowd-sourced obstacle data to update a map with precision (Tran Col. 22, lines 20-27).
Allowable Subject Matter
Claim 3 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding dependent claim 3, the prior art of record fails to explicitly teach or render obvious, either alone or in combination, the method of claim 2, wherein the yaw rate information is generated by laser matching.
Conclusion
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NAOMI M. WOLFORD
Examiner
Art Unit 3648
/N.M.W./ Examiner, Art Unit 3648
11 JUN 2026
/RESHA DESAI/ Supervisory Patent Examiner, Art Unit 3648
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