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 .
Election/Restrictions
Applicant’s election without traverse of claims 1 - 18 in the reply filed on 8 June, 2026 is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11 October, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 10, and 18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 16 of copending Application No. 18/787644 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the following:
18/677445
18/787664
Claim 1/10:
generating, via a first execution of a neural network, first feature data associated with a first sensor data frame;
storing the first feature data in a cache;
generating, via a second execution of the neural network, second feature data associated with a second sensor data frame, the second sensor data frame captured subsequent the first sensor data frame;
generating a perception output associated with the second sensor data frame based at least on the first feature data retrieved from the cache and the second feature data.
Claim 10:
generate, using a machine learning model and based at least on one or more first images, state data representative of a recursive combination of one or more first features;
generate, using the machine learning model and based at least on a second image, one or more second features corresponding to the second image;
generate one or more outputs based at least on updating the state data using at least a portion of the one or more second features;
perform one or more operations associated with a machine based at least on the one or more outputs.
Claim 18:
A control system for an autonomous or semi-autonomous machine
a perception system for an autonomous or semi-autonomous machine
a system for performing simulation operations
a system for performing digital twin operations
a system for performing light transport simulation
a system for performing collaborative content creation for 3D assets
a system for performing deep learning operations
a system implemented using an edge device
a system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content
a system implemented using a robot
a system for performing conversational Al operations
a system for performing one or more generative Al operations
a system implementing one or more large language models (LLMs)
a system for generating synthetic data
a system incorporating one or more virtual machines (VMs)
a system implemented at least partially in a data center
a system implemented at least partially using cloud computing resources.
Claim 16:
a control system for an autonomous or semi-autonomous machine;
a perception system for an autonomous or semi-autonomous machine;
a system for performing one or more simulation operations;
a system for performing one or more digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing one or more deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system for performing one or more generative AI operations;
a system for performing operations using one or more large language models (LLMs);
a system for performing operations using one or more vision language models (VLMs);
a system for performing operations using one or more multi-modal language models (VLMs);
a system for performing one or more conversational AI operations;
a system for generating synthetic data;
a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content;
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center;
or a system implemented at least partially using cloud computing resources.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1 and 10 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 2 of copending Application No. 19/669345 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the following:
18/677445
19/668345
Claim 1/10:
generating, via a first execution of a neural network, first feature data associated with a first sensor data frame;
storing the first feature data in a cache;
generating, via a second execution of the neural network, second feature data associated with a second sensor data frame, the second sensor data frame captured subsequent the first sensor data frame;
generating a perception output associated with the second sensor data frame based at least on the first feature data retrieved from the cache and the second feature data.
Claim 1/10:
one or more central processing units (CPUs);
one or more graphics processing units (GPUs);
one or more hardware accelerators;
one or more external sensors having one or more fields of view or one or more sensory fields external to the autonomous or semi-autonomous machine
determine at least a first feature representation associated with a first source and a second feature representation associated with a second source that is different from the first source;
determine, based at least on the first feature representation being related to the second feature representation, a fused feature representation using the first feature representation and the second feature representation;
perform, based at least on the fused feature representation, one or more planning, navigation, or control operations.
Claim 2:
the first source includes at least one of a first machine learning model, a first neural network, a first map, a first object trace, or a first external sensor of the one or more external sensors;
the second source includes at least one of a second machine learning model, a second neural network, a second map, a second object trace, or a second external sensor of the one or more external sensors.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 - 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When reviewing independent claim 1, and based upon consideration of all of the relevant factors with respect to the claim as a whole, claims 1 - 18 are held to claim an abstract idea without reciting elements that amount to significantly more than the abstract idea and is/are therefore rejected as ineligible subject matter under 35 U.S.C. 101. The Examiner will analyze claim 1, and similar rationale applies to independent claim 10. The rationale, under MPEP § 2106, for this finding is explained below:
The claimed invention (1) must be directed to one of the four statutory categories, and (2) must not be wholly directed to subject matter encompassing a judicially recognized exception, as defined below. The following two step analysis is used to evaluate these criteria.
Step 1: Is the claim directed to one of the four patent-eligible subject matter categories: process, machine, manufacture, or composition of matter?
When examining the claim under 35 U.S.C. 101, the Examiner interprets that the claim is related to a process since the claim is directed to a method.
Step 2a, Prong 1: Does the claim wholly embrace a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception?
The Examiner interprets that the judicial exception applies since claim 1 limitation of “generating, via a first execution of a neural network, first feature data associated with a first sensor data frame”, “storing the first feature data in a cache”, “generating, via a second execution of the neural network, second feature data associated with a second sensor data frame, the second sensor data frame captured subsequent the first sensor data frame”, “generating a perception output associated with the second sensor data frame based at least on the first feature data retrieved from the cache and the second feature data” are directed to an abstract idea. The claim is related to a mathematical concept by each of the claim limitations being steps performed by a neural network which merely performs mathematic operations on the various sensor frames. The claim limitation of “storing the first feature data in a cache” is related to a mental process by the step of storing information in a form of memory is something that can be performed easily by a human mind. If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two.
Step 2a, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The Examiner interprets that claim 1 limitations do not provide additional elements or combination of additional elements to a practical application since the claim is generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). See, MPEP §2106.04(a), Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"). OR Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible. In such a case, after making the appropriate rejection (see MPEP § 2106.07 for more information on formulating a rejection for lack of eligibility), it is a best practice for the examiner to recommend an amendment, if possible, that would resolve eligibility of the claim.
Step 2b: If a judicial exception into a practical application is not recited in the claim, the Examiner must interpret if the claim recites additional elements that amount to significantly more than the judicial exception.
The Examiner interprets that the claims do not amount to significantly more since the claim is merely reciting instructions which are mathematical concepts or mental processes in entirety.
Furthermore, the generic computer components of the neural network of claims 1 and 10 and the one or more processors of claim 10 are recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
Claims 2 – 9 and 11 - 18 depending on the independent claims include all the limitations of the independent claim. The Examiner finds that claims 2 – 9 and 11 – 18 do not state significantly more since the claim only recites “wherein the neural network operates on a single frame of sensor data in any given execution” in claim 2; “further comprising concatenating the first feature data with the second feature data to generate combined feature data, wherein the perception output is generated based at least on the combined feature data.” in claim 3; “wherein the first feature data represents one or more features associated with the first sensor data frame and the second feature data represents one or more features associated with the second sensor data frame” in claim 4; “wherein the perception output comprises at least one of path geometry, a path class, a path uncertainty, or one or more path attributes associated with a path within the scene captured in the second sensor data frame.” in claim 5; “wherein the first sensor data frame provides temporal context for the perception output” in claim 6; “wherein the perception output is generated using a temporal model, and further wherein the temporal model includes the neural network as a backbone and one or more additional layers for temporal context.” in claim 7; “wherein the backbone is trained separately from the temporal model to determine a set of weights, and wherein training the temporal model uses the set of weights as initialization.” in claim 8; and “wherein the perception output is generated further based at least on third feature data associated with a third sensor data frame captured at a different time than the first sensor data frame and the second sensor data frame.” in claim 9.
Thus, claims 1 - 9 recite the same abstract idea and therefore are not drawn to the eligible subject matter as they are directed to the abstract idea without significantly more.
Therefore, the Examiner interprets that the claims are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 4 and 13 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Independent claim 1 and 10 states “first feature data associated with a first sensor data frame” and “second feature data associated with a second sensor data frame”, whereas claim 4 and 13 further state “wherein the first feature data represents one or more features associated with the first sensor data frame and the second feature data represents one or more features associated with the second sensor data frame.”. It is understood by an examiner that feature data would inherently comprise “one or more features” associated with the data frame from which the features are extracted/determined. Claims 4 and 13 do not further limit the subject matter of the independent claims in any way by saying feature data represents one or more features.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of pre-AIA 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) the invention was known or used by others in this country, or patented or described in a printed publication in this or a foreign country, before the invention thereof by the applicant for a patent.
(b) the invention was patented or described in a printed publication in this or a foreign country or in public use or on sale in this country, more than one year prior to the date of application for patent in the United States.
Claims 1, 2, 4 – 6, 9 – 11, 13 – 15, and 18 are rejected under pre-AIA 35 U.S.C. 102(a)(2) as being anticipated by Wu et al (U.S. Patent Publication No. 2020/0293064 A1, hereinafter “Wu”).
Regarding claim 1, Wu teaches a method, comprising:
generating, via a first execution of a neural network (¶ 0033: At a high level, the process 100 may include one or more sequential deep neural networks (DNNs) 104 receiving one or more inputs, such as image data 102, and generating one or more outputs 110, such as a time-to-collision (TTC) 112, object two dimensional (2D) motion 114, and/or object three-dimensional (3D) motion 116.), first feature data associated with a first sensor data frame (Figure 5B; Ref. No. B516; ¶ 0061: The method 514, at block B516, includes generating first feature maps each corresponding to one of N frames. For example, feature maps 508B-508D may be generated.);
storing the first feature data in a cache (Figure 5B; Ref. No. B518; ¶ 0062: The method 514, at block B518, includes storing the first feature maps for each of the N frames in a buffer. For example, the feature maps 508B-SOSD may be stored in the buffer 506.);
generating, via a second execution of the neural network (¶ 0033: At a high level, the process 100 may include one or more sequential deep neural networks (DNNs) 104 receiving one or more inputs, such as image data 102, and generating one or more outputs 110, such as a time-to-collision (TTC) 112, object two dimensional (2D) motion 114, and/or object three-dimensional (3D) motion 116.), second feature data associated with a second sensor data frame, the second sensor data frame captured subsequent the first sensor data frame (Figure 5B; Ref. No. B520; ¶ 0063: The method 514, at block B520, includes generating a second feature map for an N+1 frame. For example, the feature map 508A may be generated.);
generating a perception output associated with the second sensor data frame based at least on the first feature data retrieved from the cache and the second feature data (¶ 0041: The sequential DNN 104 may generate or predict output data that may undergo decoding 106 and, in some embodiments, post-processing 108 (e.g., temporal smoothing) to ultimately generate the outputs 110. The TTC (Time to Collision) 112 information may be output, for each detected object in the image data 102, as a value of 1/TTC represented in units of 1/seconds (s). For example, with respect to visualization 200 of FIG. 2, the sequential RNN 104 may predict a value of 3.07 1/s for a bus 202, a value of0.03 1/s for a vehicle 206, and a value of 1.98 1/s for a pedestrian 210. The sequential DNN 104 may also predict a bounding shape).
Regarding claim 2, Wu teaches the method of claim 1.
Additionally, Wu teaches wherein the neural network operates on a single frame of sensor data in any given execution (¶ 0038: In some embodiments, a pre-processing image pipeline may be employed by the image data pre-processor to process a raw image(s) acquired by camera(s) and included in the image data 102 to produce pre-processed image data which may represent an input image(s) to the input layer(s) of the sequential DNN(s) 104.).
Regarding claim 4, Wu teaches the method of claim 1.
Additionally, Wu teaches wherein the first feature data represents one or more features associated with the first sensor data frame and the second feature data represents one or more features associated with the second sensor data frame (Figure 5B; Ref. No. B516 and B520; ¶ 0061: The method 514, at block B516, includes generating first feature maps each corresponding to one of N frames. For example, feature maps 508B-SOSD may be generated.; ¶ 0063: The method 514, at block B520, includes generating a second feature map for an N+1 frame. For example, the feature map 508A may be generated.);
Regarding claim 5, Wu teaches the method of claim 1.
Additionally, Wu teaches wherein the perception output comprises at least one of path geometry, a path class, a path uncertainty, or one or more path attributes associated with a path within the scene captured in the second sensor data frame (¶ 0034: The sequential DNN(s) 104 may be trained to generate the TTC 112, the object 2D motion 114, and/or the object 3D motion 116. These outputs 110 may be used by a world model manager ( e.g., a world model manager layer of an autonomous driving software stack), perception component(s) ( e.g., a perception layer of the autonomous driving software stack), planning component(s) (e.g., a planning layer of the autonomous driving software stack), obstacle avoidance component(s) ( e.g., an obstacle or collision avoidance layer of the autonomous driving software stack), and/or other components or layers of the autonomous driving software stack to aid the autonomous vehicle 1400 in performing one or more operations (e.g., world model management, obstacle avoidance, path planning, etc.) within an environment.).
Regarding claim 6, Wu teaches the method of claim 1.
Additionally, Wu teaches wherein the first sensor data frame provides temporal context for the perception output (¶ 0030: The image data may include timestamps (e.g., corresponding to a frame rate), and the timestamps (or frame rate) may be used by the system in addition to the scale change.).
Regarding claim 9, Wu teaches the method of claim 1.
Additionally, Wu teaches wherein the perception output is generated further based at least on third feature data associated with a third sensor data frame captured at a different time than the first sensor data frame and the second sensor data frame (¶ 0069: As a non-limiting example, the number of feature maps 606 may be four, such that four feature maps 606 are used as input to the sets of sequential layer(s) 608… and a third feature map F{T-(N-2)}… The output of the last set of sequential layers 608 (e.g., the set of sequential layers 608D in the illustration of FIG. 6A) may be provided as input to additional layer(s) 610 of the DNN (e.g., convolutional layers, deconvolutional layers, etc.) to generate a prediction 612 for the image 602.).
The rejection of method claim 1 above applies mutatis mutandis to the corresponding limitations of device claim 10 while noting that the rejection above cites to both method and device disclosures.
For the device limitations of claim 10 see Wu’s teaching on:
One or more processors (¶ 0073: For instance, various functions may be carried out by a processor executing instructions stored in memory.) comprising…
The rejection of method claim 2 above applies mutatis mutandis to the corresponding limitations of device claim 11 while noting that the rejection above cites to both method and device disclosures.
The rejection of method claim 4 above applies mutatis mutandis to the corresponding limitations of device claim 13 while noting that the rejection above cites to both method and device disclosures.
The rejection of method claim 5 above applies mutatis mutandis to the corresponding limitations of device claim 14 while noting that the rejection above cites to both method and device disclosures.
The rejection of method claim 6 above applies mutatis mutandis to the corresponding limitations of device claim 15 while noting that the rejection above cites to both method and device disclosures.
Regarding claim 18, Wu teaches the one or more processors of claim 10.
Additionally, Wu teaches wherein the one or more processors is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine,
a perception system for an autonomous or semi-autonomous machine,
a system for performing simulation operations,
a system for performing digital twin operations,
a system for performing light transport simulation,
a system for performing collaborative content creation for 3D assets,
a system for performing deep learning operations,
a system implemented using an edge device,
a system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content,
a system implemented using a robot,
a system for performing conversational Al operations,
a system for performing one or more generative Al operations,
a system implementing one or more large language models (LLMs),
a system for generating synthetic data,
a system incorporating one or more virtual machines (VMs),
a system implemented at least partially in a data center, or
a system implemented at least partially using cloud computing resources (¶ 0027: Systems and methods disclosed herein relate to temporal information prediction for perception in autonomous machine applications. Although the present disclosure may be described with respect to an example autonomous vehicle 1400 (alternatively referred to herein as "vehicle 1400" or "autonomous vehicle 1400," an example of which is described with respect to FIGS. 14A-14D, this is not intended to be limiting. For example, the systems and methods described herein may be used by, without limitation, non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or more adaptive driver assistance systems (ADAS)), robots, warehouse vehicles, off-road vehicles, flying vessels, boats, shuttles, emergency response vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, underwater craft, drones, and/or other vehicle types. In addition, although the present disclosure may be described with respect to autonomous driving, this is not intended to be limiting. For example, the systems and methods described herein may be used in robotics, aerial systems, boating systems, and/or other technology areas, such as for perception, world model management, path planning, obstacle avoidance, and/or other processes.).
Claim Rejections - 35 USC § 103
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.
Claims 3, 7, 12, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over We et al (U.S. Patent Publication No. 2020/0293064 A1, hereinafter “Wu”) in view of Ulutan et al (U.S. Patent Publication No. 2023/0144745 A1, hereinafter “Ulutan”).
Regarding claim 3, Wu teaches the method of claim 1.
Wu does not explicitly teach concatenating the first feature data with the second feature data to generate combined feature data, wherein the perception output is generated based at least on the combined feature data.
However, Ulutan does teach concatenating the first feature data with the second feature data to generate combined feature data, wherein the perception output is generated based at least on the combined feature data (¶ 0009: The third and fourth outputs may be concatenated and provided as input to a third ML layer, creating a set of reduced features. The set of reduced features may be provided as input to a third ML layer, which may output logit(s), which may be used to determine confidence score(s) associated with a classification task.; ¶ 0058: The feature data output by the ML layer 330 (i.e., first feature data 332 through n-th feature data 336) may be concatenated together and provided as input to ML layer 338, which may be designed to reduce the dimensionality of the concatenated feature data, which may be of the size n q.; ¶ 0059: The reduced features 340 may be provided as input to a final ML layer 342, which may include output heads equal to the number of attributes predicted by the ML architecture.).
Ulutan is considered to be analogous art as it pertains to image processing for vehicle machine vision. Therefore, it would have been obvious to one of ordinary skill in the art to combine the temporal information prediction in autonomous machine applications (as taught by Wu) and the machine-learned architecture for efficient object attribute and/or intention classification (as taught by Ulutan) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Ulutan may reduce the computational load for detecting various object attributes from multiple instances of sensor data received over a time period (See ¶ 0011).
This motivation for the combination of Wu and Ulutan is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim 7, Wu teaches the method of claim 1.
Additionally, Wu teaches wherein the perception output is generated using a temporal model (¶ 0028: Temporal Predictions Using a Sequential Deep Neural Network), and further wherein the temporal model includes the neural network (¶ 0033: Now referring to FIG. 1, FIG. 1 is a data flow diagram illustrating an example process 100 for temporal predictions of objects in an environment, in accordance with some embodiments of the present disclosure. At a high level, the process 100 may include one or more sequential deep neural networks (DNNs) 104) and one or more additional layers for temporal context (¶ 0049: As described herein, because the sequential DNN 104 may be trained by applying cross sensor fusion or another sensor data to an image data correlation technique(s), the sequential DNN 104 may be able to predict temporal information with only the image data 102 as an input.).
Wu does not explicitly teach wherein the neural network is a backbone.
However, Ulutan does teach wherein the perception output is generated using a temporal model (Figure 3; ), and further wherein the temporal model includes the neural network as a backbone (Figure 3, Ref. No. 308; ¶ 0050: 306 may be provided as input to an ML backbone 308, which may comprise one or more ML layers. For example, the ML backbone 308 may be a ResNet or other suitable neural network, e.g., ResNeXt, DenseNet, vision transformer (ViT).) and one or more additional layers for temporal context (Figure 3, Ref. No. 346; ¶ 0060: The ML layers 330, 338, and 342 may each be fully connected layers with different input/output shapes. Collectively ML layers 330, 338, and 342 may make up a model temporal head 346.).
Ulutan is considered to be analogous art as it pertains to image processing for vehicle machine vision. Therefore, it would have been obvious to one of ordinary skill in the art to combine the temporal information prediction in autonomous machine applications (as taught by Wu) and the machine-learned architecture for efficient object attribute and/or intention classification (as taught by Ulutan) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Ulutan may reduce the computational load for detecting various object attributes from multiple instances of sensor data received over a time period (See ¶ 0011).
The rejection of method claim 3 above applies mutatis mutandis to the corresponding limitations of device claim 12 while noting that the rejection above cites to both method and device disclosures.
The rejection of method claim 7 above applies mutatis mutandis to the corresponding limitations of device claim 16 while noting that the rejection above cites to both method and device disclosures.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over We et al (U.S. Patent Publication No. 2020/0293064 A1, hereinafter “Wu”) in view of Ulutan et al (U.S. Patent Publication No. 2023/0144745 A1, hereinafter “Ulutan”) and further in view of Li et al (Z. Li, H. Sun, D. Xiao and H. Xie, "Hybrid Kalman Recurrent Neural Network for Vehicle Trajectory Prediction," in IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-14, 2024, Art no. 2519814, hereinafter “Li”).
Regarding claim 8, Wu teaches the method of claim 7.
Wu does not explicitly teach wherein the backbone is trained separately from the temporal model to determine a set of weights, and wherein training the temporal model uses the set of weights as initialization.
However, Li teaches wherein the backbone is trained separately from the temporal model to determine a set of weights (Page 6, Col. 1, ¶ 1: We fine-tune the ResNet-18 [49] backbone network, which is pretrained on ImageNet, as a feature extractor to obtain feature maps rich in scene information from multichannel stacked semantic maps.; Examiner’s note: The examiner notes that person of ordinary skill in the art would know that a pretrained network would consist of pre-determined weights which are used during the execution of the network.), and wherein training the temporal model uses the set of weights as initialization (Page 7, Col. 2, Section “3) Model Details”: Pretrained models are not used except for the CNN backbone (ResNet-18). The optimizer AdamW was used with a batch size of 128 and a learning rate of 3e - 4. The learning strategy is cosine annealing with a weight decay of 1e - 4. The training schedule is set as 1 epoch, and our model is trained on NVIDIA RTX 3060 GPU, accumulating over 180000 iterations.).
Li is considered to be analogous art as it pertains to image processing for vehicle machine vision. Therefore, it would have been obvious to one of ordinary skill in the art to combine the temporal information prediction in autonomous machine applications (as taught by Wu) and the hybrid Kalman recurrent neural network vehicle trajectory prediction (as taught by Li) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Li has a higher quality performance than other models in predicting the trajectories of surrounding vehicles (See page 8 col. 2).
This motivation for the combination of Wu, Ulutan, and Li is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
The rejection of method claim 8 above applies mutatis mutandis to the corresponding limitations of device claim 17 while noting that the rejection above cites to both method and device disclosures.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW JONES whose telephone number is (703)756-4573. The examiner can normally be reached Monday - Friday 8:00-5:00 EST, off Every Other Friday.
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, Matthew Bella can be reached at (571) 272-7778. 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.
/ANDREW B. JONES/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
Prosecution Insights