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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Reference Numeral “460” shown in Figure 4. Paragraph [0070] line 7, “second hardware 165” should read -- second hardware 460 --
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 20 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 20 recites the limitation “the 3D content collaboration platform for 3D assets” in line 1. There is insufficient antecedent basis for this limitation in the claim. However, claim 20 appears to be dependent upon claim 19 and has been treated as such. Affirmation of this is required by the appropriate amendment.
Appropriate correction is required.
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 1-3, 10-14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving”, arXiv:2312.09245v2, 25 Dec. 2023, pp. 1-9) in view of Ding et al. (“HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving”, arXiv:2309.05186v1, 11 Sep. 2023, pp. 1-9)
As to claim 1, Wang et al. teaches one or more processors comprising processing circuitry to (i.e., “We employ a multi-modal LLM (MLLM) to model the behavior planning module of a module AD system”, Abstract, p. 1):
prompt a multi-modal language model to generate one or more subsequent responses (i.e., “At each time”, 3.2. Behavior Planning States Alignment, p. 4) indicating an evaluation of whether one or more conditions associated with the designated task (i.e., “overtake”, 4.6. Case Study & Visualization, pp. 8-9) are present in any of the one or more identified regions of interest (See for example, “Next, these tokens are inputted into the MLLM decoder, which generates the decision state token S along with a corresponding explanation E”, 3.1. System Overview, p. 4; “At each time, one-speed decision and one path decision are mutually inferred and sent to the motion planning framework”, 3.2. Behavior Planning States Alignment, p. 4; and Figure 4, 4.6. Case Study & Visualization, pp. 8-9); and
control one or more operations of the ego-machine (i.e., “Ego Vehicle”, Figure 3, p. 5) based at least on the one or more subsequent responses (i.e., “the decision state S is inputted into a motion planning and control module. This module computes the final trajectory for vehicle control”, 3.1. System Overview, p. 4).
However, Wang et al. does not explicitly disclose prompting a multi-modal language model associated with an ego-machine to generate one or more initial responses associated with a designated task and indicating one or more identified regions of interest in one or more frames of sensor data applied to the multi-modal language model.
Ding et al. teaches prompting a multi-modal language model (i.e., “We propose HiLM-D (Towards High-Resolution Understanding in MLLMs for Autonomous Driving), an efficient method to incorporate HR information into MLLMs for the ROLISP task”, Abstract, p. 1) associated with an ego-machine (i.e., “ego-vehicle”, Abstract, p. 1) to generate one or more initial responses associated with a designated task (See for example, “the generated answer”, Large Language Model (LLM), pp. 3-4) and indicating one or more identified regions of interest (i.e., “generate the bounding box”, Incorporation module, p. 4) in one or more frames of sensor data (i.e., “video”, Figure 1, p. 1) applied to the multi-modal language model.
Wang et al. and Ding et al. are analogous art because they are from the field of digital image processing.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Wang et al. by incorporating the prompting of the multi-modal language model associated with an ego-machine to generate one or more initial responses associated with a designated task and indicating one or more identified regions of interest in one or more frames of sensor data applied to the multi-modal language model, as taught by Ding et al.
The suggestion/motivation for doing so would have been to utilize high-resolution features to enhance vision-specific priors and emphasize potential high-risk object regions.
Therefore, it would have been obvious to combine Ding et al. with Wang et al. to obtain the invention as specified in claim 1.
As to claim 12, Wang et al. teaches a method (i.e., “We introduce DriveMLM, an LLM-based AD framework that can perform close-loop autonomous driving in realistic simulators”, Abstract, p. 1) comprising:
prompting a multi-modal language model to generate one or more subsequent responses (i.e., “At each time”, 3.2. Behavior Planning States Alignment, p. 4) evaluating whether one or more conditions associated with the designated task (i.e., “overtake”, 4.6. Case Study & Visualization, pp. 8-9) are present in one or more identified regions of interest (See for example, “Next, these tokens are inputted into the MLLM decoder, which generates the decision state token S along with a corresponding explanation E”, 3.1. System Overview, p. 4; “At each time, one-speed decision and one path decision are mutually inferred and sent to the motion planning framework”, 3.2. Behavior Planning States Alignment, p. 4; and Figure 4, 4.6. Case Study & Visualization, pp. 8-9); and
controlling one or more operations of the ego-machine (i.e., “Ego Vehicle”, Figure 3, p. 5) based at least on the one or more subsequent responses (i.e., “the decision state S is inputted into a motion planning and control module. This module computes the final trajectory for vehicle control”, 3.1. System Overview, p. 4).
However, Wang et al. does not explicitly disclose based at least on a multi-modal language model generating one or more initial responses associated with a designated task and indicating one or more identified regions of interest in one or more frames of sensor data of an ego-machine.
Ding et al. teaches based at least on a multi-modal language model (i.e., “We propose HiLM-D (Towards High-Resolution Understanding in MLLMs for Autonomous Driving), an efficient method to incorporate HR information into MLLMs for the ROLISP task”, Abstract, p. 1) generating one or more initial responses associated with a designated task (See for example, “the generated answer”, Large Language Model (LLM), pp. 3-4) and indicating one or more identified regions of interest (i.e., “generate the bounding box”, Incorporation module, p. 4) in one or more frames of sensor data (i.e., “video”, Figure 1, p. 1) of an ego-machine (i.e., “ego-vehicle”, Abstract, p. 1).
Therefore, in view of Ding et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang et al. by incorporating the generating of one or more initial responses associated with a designated task and indicating one or more identified regions of interest in one or more frames of sensor data of an ego-machine based at least on a multi-modal language model, as taught by Ding et al., in order to utilize high-resolution features to enhance vision-specific priors and emphasize potential high-risk object regions.
As to claims 2 and 13, Wang et al. does not explicitly disclose wherein the processing circuitry is further to increase/increasing an input resolution of at least one frame of the one or more frames of sensor data applied to the multi-modal language model based at least on the multi-modal language model generating the one or more initial responses indicating the one or more identified regions of interest.
Ding et al. teaches increasing an input resolution of at least one frame of the one or more frames of sensor data applied to the multi-modal language model based at least on the multi-modal language model generating the one or more initial responses indicating the one or more identified regions of interest (See for example, “An intuitive way to enhance the perception ability is to increase the resolution of inputs”, Performance comparison of the baseline and ours across different resolution inputs, pp. 6-7).
Therefore, in view of Ding et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang et al. by incorporating the increasing an input resolution of at least one frame of the one or more frames of sensor data applied to the multi-modal language model based at least on the multi-modal language model generating the one or more initial responses indicating the one or more identified regions of interest, as taught by Ding et al., in order to enhance perception ability, especially for small objects.
As to claims 3 and 14, Wang et al. does not explicitly disclose wherein at least one frame of the one or more frames of sensor data depicting the one or more identified regions of interest identified by the multi-modal language model have a resolution that corresponds to a maximum input resolution supported by the multi-modal language model.
Ding et al. teaches at least one frame of the one or more frames of sensor data depicting the one or more identified regions of interest identified by the multi-modal language model have a resolution that corresponds to a maximum input resolution supported by the multi-modal language model (See for example, “The high-resolution perception branch (HR-PB) is tailored to integrate vision-specific information from the high resolution images and features associated with potential high-risk objects into LR-RB”, High-Resolution Perception Branch, p. 4).
Therefore, in view of Ding et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang et al. by incorporating the at least one frame of the one or more frames of sensor data depicting the one or more identified regions of interest identified by the multi-modal language model have a resolution that corresponds to a maximum input resolution supported by the multi-modal language model, as taught by Ding et al., in order to avoid missing small objects and not overly focus on salient ones.
As to claim 10, Wang et al. teaches wherein the designated task comprises at least one computer vision task (i.e., “At each time, one-speed decision and one path decision are mutually inferred and sent to the motion planning framework”, 3.2. Behavior Planning States Alignment, p. 4), and one or more subsequent responses by the multi-modal language model indicate one or more results of the at least one computer vision task (See for example, Figure 4, Visualization of human interaction with DriveMLM, p. 8),
the at least one computer vision task comprising at least one of: driver drowsiness detection, driver distraction detection, driver out-of-position detection, driver or occupant identification, seatbelt usage detection, occupant presence detection, occupant classification, child presence detection, gesture recognition, sign recognition, context-aware question answering, recognition of one or more objects left behind, or suspicious activity monitoring (See for example, Figure 5, The zero-shot performance of DriveMLM on the real driving scene from nuScenes dataset, p. 8).
As to claims 11 and 17, Wang et al. teaches wherein the one or more processors are comprised in at least one of/the method is performed by 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 for performing remote operations; a system for performing real-time streaming; a system for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system implementing one or more language models; a system implementing one or more large language models (LLMs); a system implementing one or more vision language models (VLMs); a system implementing one or more multi-modal language models; a system for generating synthetic data; a system for generating synthetic data using AI; a system for performing one or more generative AI operations; 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 (See for example, “The DriveMLM framework integrates the world knowledge and reasoning capabilities of large language models (LLMs) into an autonomous driving (AD) system”, 3.1. System Overview, p. 4).
Claims 4-7, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. in view of Ding et al. as applied to claims 1 and 12 above, and further in view of Cruickshank et al. (U.S. Pub. No. 2026/0038141). The teachings of Wang et al. and Ding et al. have been discussed above.
As to claims 4 and 15, Wang et al. and Ding et al. do not explicitly disclose wherein the processing circuitry is further to iteratively prompt/iteratively prompting the multi-modal language model to refine the one or more identified regions of interest within the one or more frames of sensor data.
Cruickshank et al. teaches a processing circuitry to (i.e., “computer system 1900”, Paragraph [0291]) iteratively prompt/iteratively prompting the multi-modal language model to refine the one or more identified regions of interest within the one or more frames of sensor data (See for example, “A shape and pose (i.e. location and orientation) of a given object is refined by providing some initial approximation of the shape and pose (the initialization), and optimising the parameters defining the shape and pose of the object so as to minimise some cost function encoding the prior knowledge about the object as well as the available sensor data in order to generate an improved estimate”, Paragraph [0128]; and Paragraphs [0131] and [0207]).
Wang et al., Ding et al. and Cruickshank et al. are analogous art because they are from the field of digital image processing.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Wang et al. and Ding et al. by incorporating the iteratively prompting of the multi-modal language model to refine the one or more identified regions of interest within the one or more frames of sensor data, as taught by Cruickshank et al.
The suggestion/motivation for doing so would have been to generate a more accurate set of detections.
Therefore, it would have been obvious to combine Cruickshank et al. with Wang et al. and Ding et al. to obtain the invention as specified in claims 4 and 15.
As to claims 5 and 16, Wang et al. and Ding et al. do not explicitly disclose wherein the processing circuitry is further to iteratively prompt/iteratively prompting the multi-modal language model to generate the one or more initial responses indicating the one or more identified regions of interest based at least on applying successive frames of the sensor data representing successive time slices to the multi-modal language model.
Cruickshank et al. teaches a processing circuitry to iteratively prompt/iteratively prompting the multi-modal language model to generate the one or more initial responses indicating the one or more identified regions of interest based at least on applying successive frames of the sensor data representing successive time slices to the multi-modal language model (i.e., “A shape and pose (i.e. location and orientation) of a given object is refined by providing some initial approximation of the shape and pose (the initialization), and optimising the parameters defining the shape and pose of the object so as to minimise some cost function encoding the prior knowledge about the object as well as the available sensor data in order to generate an improved estimate”, Paragraph [0128]; Paragraphs [0131]; and “The refinement techniques described herein use at least two sensor modalities and optimise the pose of the object over a period of multiple timesteps”, Paragraph [0160]).
Therefore, in view of Cruickshank et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Wang et al. and Ding et al. by incorporating the iteratively prompting of the multi-modal language model to generate the one or more initial responses indicating the one or more identified regions of interest based at least on applying successive frames of the sensor data representing successive time slices to the multi-modal language model, as taught by Cruickshank et al., in order to generate a more accurate set of detections.
As to claim 6, Wang et al. and Ding et al. do not explicitly disclose wherein the processing circuitry is further to prompt the multi-modal language model to evaluate whether the one or more conditions are present in the one or more identified regions of interest based at least on the one or more initial responses generated by the multi-modal language model indicating at least a threshold measure of confidence in the one or more identified regions of interest.
Cruickshank et al. teaches a processing circuitry to prompt the multi-modal language model to evaluate whether the one or more conditions are present in the one or more identified regions of interest based at least on the one or more initial responses generated by the multi-modal language model indicating at least a threshold measure of confidence in the one or more identified regions of interest (i.e., “the error and the gradients may be recomputed and the optimisation may continue until convergence to an optimal set of parameters”, Paragraph [0149]; and Paragraph [0151]).
Therefore, in view of Cruickshank et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Wang et al. and Ding et al. by incorporating the prompting if the multi-modal language model to evaluate whether the one or more conditions are present in the one or more identified regions of interest based at least on the one or more initial responses generated by the multi-modal language model indicating at least a threshold measure of confidence in the one or more identified regions of interest, as taught by Cruickshank et al., in order to generate a more accurate set of detections.
As to claim 7, Wang et al. and Ding et al. do not explicitly disclose wherein the processing circuitry is further to apply, based at least on the one or more initial responses generated by the multi-modal language model indicating less than a threshold measure of confidence in the one or more identified regions of interest, a representation of a subsequent frame of the sensor data representing a subsequent time slice to the multi-modal language model.
Cruickshank et al. teaches a processing circuitry to apply, based at least on the one or more initial responses generated by the multi-modal language model indicating less than a threshold measure of confidence in the one or more identified regions of interest, a representation of a subsequent frame of the sensor data representing a subsequent time slice to the multi-modal language model (i.e., when not convergence to optimal set of parameters, see for example, Paragraphs [0149] and [0151]).
Therefore, in view of Cruickshank et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Wang et al. and Ding et al. by incorporating the applying, based at least on the one or more initial responses generated by the multi-modal language model indicating less than a threshold measure of confidence in the one or more identified regions of interest, of a representation of a subsequent frame of the sensor data representing a subsequent time slice to the multi-modal language model, as taught by Cruickshank et al., in order to generate a more accurate set of detections.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving”, arXiv:2312.09245v2, 25 Dec. 2023, pp. 1-9) in view of Ding et al. (“HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving”, arXiv:2309.05186v1, 11 Sep. 2023, pp. 1-9) and Isonni et al. (U.S. Pub. No. 2023/0131458).
As to claim 18, Wang et al. teaches 18. A system comprising one or more processors to (i.e., “We employ a multi-modal LLM (MLLM) to model the behavior planning module of a module AD system”, Abstract, p. 1)
control (i.e., “the decision state S is inputted into a motion planning and control module. This module computes the final trajectory for vehicle control”, 3.1. System Overview, p. 4), within a simulation (i.e., “We introduce DriveMLM, an LLM-based AD framework that can perform close-loop autonomous driving in realistic simulators”, Abstract, p. 1), one or more operations of a simulated ego-machine (i.e., “Ego Vehicle”, Figure 3, p. 5) based at least on one or more outputs of one or more multi-modal language models (i.e., “At each time, one-speed decision and one path decision are mutually inferred and sent to the motion planning framework”, 3.2. Behavior Planning States Alignment, p. 4; and Figure 4, 4.6. Case Study & Visualization, pp. 8-9), the one or more outputs generated based at least on applying one or more frames of simulated sensor data to the multi-modal language model (i.e., “multi-modal data”, 3.1. System Overview, p. 4) and
using the multi-modal language model to evaluate whether one or more conditions associated with a designated task are present in one or more regions of interest of the one or more frames of simulated sensor data (See for example, “Next, these tokens are inputted into the MLLM decoder, which generates the decision state token S along with a corresponding explanation E”, 3.1. System Overview, p. 4; “At each time, one-speed decision and one path decision are mutually inferred and sent to the motion planning framework”, 3.2. Behavior Planning States Alignment, p. 4; and Figure 4, 4.6. Case Study & Visualization, pp. 8-9).
However, Wang et al. does not explicitly disclose the simulation is rendered using one or more light transport simulation algorithms, and the one or more regions of interest identified in one or more initial outputs of the one or more multi-modal language models.
Ding et al. teaches one or more regions of interest identified in one or more initial outputs (i.e., “generate the bounding box”, Incorporation module, p. 4) of one or more multi-modal language models (i.e., “We propose HiLM-D (Towards High-Resolution Understanding in MLLMs for Autonomous Driving), an efficient method to incorporate HR information into MLLMs for the ROLISP task”, Abstract, p. 1).
The combination of Wang et al. and Ding et al. do not explicitly disclose the simulation is rendered using one or more light transport simulation algorithms
Isonni et al. teaches a simulation that is rendered using one or more light transport simulation algorithms (i.e., “The robot simulation server 130 is a server that enables the simulation of robot structure and behavior within a virtual environment”, Paragraph [0049]; “The environment engine 210 can include a graphics or rendering engine (“graphics engine” hereinafter) configured to generate the virtual environment. In some embodiments, the rendering of the environment (and other clients) is specific to the point-of-view of each robot”. Paragraph [0060]).
Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. in view of Ding et al. and Isonni et al. as applied to claim 18 above, and further in view of Tran et al. (“Immersion into 3D Biomedical Data via Holographic AR Interfaces based on the Universal Scene Description (USD) Standard”, 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE), pp. 354-358). The teachings of Wang et al., Ding et al. and Isonni et al. have been discussed above.
As to claim 19, Wang et al., Ding et al. and Isonni et al. do not explicitly disclose wherein the simulation is generated, at least in part, using a three-dimensional (3D) content collaboration platform for 3D assets.
Tran et al. teaches a simulation is generated, at least in part, using a three-dimensional (3D) content collaboration platform for 3D assets (See for example, “real-time multi-user AR immersion into USD data to enhance the potential of USD and take the AR immersion experience to the next level”, Abstract, p. 354; and II. Methods, pp. 355-356).
Wang et al., Ding et al., Isonni et al. and Tran et al. are analogous art because they are from the field of digital image processing.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Wang et al., Ding et al. and Isonni et al. by incorporating the simulation is generated, at least in part, using a three-dimensional (3D) content collaboration platform for 3D assets.
The suggestion/motivation for doing so would have been to provide multi-user, multi-modality, and real-time virtual immersion into USD-generated scenery.
Therefore, it would have been obvious to combine Tran et al. with Wang et al., Ding et al. and Isonni et al. to obtain the invention as specified in claim 19.
As to claim 20, as best understood, Tran et al. teaches wherein the 3D content collaboration platform for 3D assets uses OpenUSD (i.e., “we implement data loading capabilities for the USD file format to render objects defined in the USD file”, II. Methods, pp. 355-356).
Allowable Subject Matter
Claims 8 and 9 are 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: the closest prior art made of record fails to disclose, teach, and/or suggest, inter alia, the one or more processors of claim 1, and further comprising wherein the processing circuitry is further to increase a rate of prompting the multi-modal language model based at least on the multi-modal language model indicating the one or more identified regions of interest; or wherein the processing circuitry is further to prompt the multi-modal language model to generate the one or more initial responses based at least on periodically applying successive frames of the sensor data representing successive time slices to the multi-modal language model until the multi-modal language model identifies the one or more identified regions of interest, as claimed.
Conclusion
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/JOSE M TORRES/Examiner, Art Unit 2664 09/24/2026
