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 .
Examiner’s Note
Claim 3 appears to have parallel limitations to claim 12 but has not been amended in the same manner. If this was not intended, applicant is advised to amend claim 3 in a similar manner as claim 12.
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: 504(A)-(B). 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.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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:
In claim 20, the "system" (hereinafter simulation system) in the limitation "a system for performing simulation operations" invokes 112(f) as system is a term that does not have definite structure which enables the performance of simulation operations.
In claim 20, the "system" (hereinafter validation/testing simulation system) in the limitation "a system for performing simulation operations to test or validate autonomous machine applications" invokes 112(f) as system is a term that does not have definite structure which enables the performance of simulation operations for testing or validation.
In claim 20, the "system" (hereinafter light simulation system) in the limitation "a system for performing light transport simulation" invokes 112(f) as system is a term that does not have definite structure which enables the simulation of light transport.
In claim 20, the "system" (hereinafter graphical system) in the limitation "a system for rendering graphical output" invokes 112(f) as system is a term that does not have definite structure which enables rendering graphical output.
In claim 20, the "system" (hereinafter deep learning system) in the limitation "a system for performing deep learning operations" invokes 112(f) as system is a term that does not have definite structure which enables the performance of deep learning operations.
In claim 20, the "system" (hereinafter VR system) in the limitation "a system for generating or presenting virtual reality (VR) content" invokes 112(f) as system is a term that does not have definite structure which enables the generation.
In claim 20, the "system" (hereinafter AR system) in the limitation "a system for generating or presenting augmented reality (AR) content" invokes 112(f) as system is a term that does not have definite structure which enables generation/presentation of AR content.
In claim 20, the "system" (hereinafter MR system) in the limitation "a system for generating or presenting mixed reality (MR) content" invokes 112(f) as system is a term that does not have definite structure which enables the generation/presentation of MR content.
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.
Regarding the simulation system, a review of the specification (00145) shows that the following appears to be the corresponding structure to these claim limitations:
"In some examples, the SoC(s) 504 may include a real-time ray-tracing hardware accelerator, such as described in U.S. Patent Application No. 16/101,232, filed on August 10, 2018. The real-time ray-tracing hardware accelerator may be used to quickly and efficiently determine the positions and extents of objects (e.g., within a world model), to generate real-time visualization simulations, for RADAR signal interpretation, for sound propagation synthesis and/or analysis, for simulation of SONAR systems, for general wave propagation simulation, for comparison to LiDAR data for purposes of localization and/or other functions, and/or for other uses. In some embodiments, one or more tree traversal units (TTUs) may be used for executing one or more ray-tracing related operations."
Regard the validation/testing simulation system, a review of the specification does not appear to show corresponding structure to these claim limitations. See 112(b) rejection below.
Regarding the light simulation system, a review of the specification (paragraph) shows that the following appears to be the corresponding structure to these claim limitations:
"In some examples, the SoC(s) 504 may include a real-time ray-tracing hardware accelerator, such as described in U.S. Patent Application No. 16/101,232, filed on August 10, 2018. The real-time ray-tracing hardware accelerator may be used to quickly and efficiently determine the positions and extents of objects (e.g., within a world model), to generate real-time visualization simulations, for RADAR signal interpretation, for sound propagation synthesis and/or analysis, for simulation of SONAR systems, for general wave propagation simulation, for comparison to LiDAR data for purposes of localization and/or other functions, and/or for other uses. In some embodiments, one or more tree traversal units (TTUs) may be used for executing one or more ray-tracing related operations."
Regarding the graphical system, a review of the specification (paragraph 00109) shows that the following appears to be the corresponding structure to these claim limitations:
"One or more of the controller(s) 536 may receive inputs (e.g., represented by input data) from an instrument cluster 532 of the vehicle 500 and provide outputs (e.g., represented by output data, display data, etc.) via a human-machine interface (HMI) display 534, an audible annunciator, a loudspeaker, and/or via other components of the vehicle 500. The outputs may include information such as vehicle velocity, speed, time, map data (e.g., the HD map 522 of FIG. 5C), location data (e.g., the location of the vehicle 500, such as on a map), direction, location of other vehicles (e.g., an occupancy grid), information about objects and status of objects as perceived by the controller(s) 536, etc. For example, the HMI display 534 may display information about the presence of one or more objects (e.g., a street sign, caution sign, traffic light changing, etc.), and/or information about driving maneuvers the vehicle has made, is making, or will make (e.g., changing lanes now, taking exit 34B in two miles, etc.). "
Regarding the deep learning system, a review of the specification (paragraphs 00128, 00135, and 00206) shows that the following appears to be the corresponding structure to these claim limitations:
"The GPU(s) 508 may be power-optimized for best performance in automotive and embedded use cases. For example, the GPU(s) 508 may be fabricated on a Fin field-effect transistor (FinFET). However, this is not intended to be limiting, and the GPU(s) 508 may be fabricated using other semiconductor manufacturing processes. Each streaming microprocessor may incorporate a number of mixed-precision processing cores partitioned into multiple blocks. For example, and without limitation, 64 PF32 cores and 32 PF64 cores may be partitioned into four processing blocks. In such an example, each processing block may be allocated 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs for deep learning matrix arithmetic, an L0 instruction cache, a warp scheduler, a dispatch unit, and/or a 64 KB register file. In addition, the streaming microprocessors may include independent parallel integer and floating-point data paths to provide for efficient execution of workloads with a mix of computation and addressing calculations. The streaming microprocessors may include independent thread-scheduling capability to enable finer-grain synchronization and cooperation between parallel threads. The streaming microprocessors may include a combined L1 data cache and shared memory unit in order to improve performance while simplifying programming."
"The accelerator(s) 514 (e.g., the hardware acceleration cluster) may include a deep learning accelerator(s) (DLA). The DLA(s) may include one or more Tensor processing units (TPUs) that may be configured to provide an additional ten trillion operations per second for deep learning applications and inferencing. The TPUs may be accelerators configured to, and optimized for, performing image processing functions (e.g., for CNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specific set of neural network types and floating-point operations, as well as inferencing. The design of the DLA(s) may provide more performance per millimeter than a general-purpose GPU, and vastly exceeds the performance of a CPU. The TPU(s) may perform several functions, including a single-instance convolution function, supporting, for example, INT8, INT16, and FP16 data types for both features and weights, as well as post-processor functions."
"In some examples, the server(s) 578 may receive data from the vehicles and apply the data to up-to-date real-time neural networks for real-time intelligent inferencing. The server(s) 578 may include deep-learning supercomputers and/or dedicated AI computers powered by GPU(s) 584, such as a DGX and DGX Station machines developed by NVIDIA. However, in some examples, the server(s) 578 may include deep learning infrastructure that use only CPU-powered datacenters."
Examiner will thus interpret the deep learning system as including at least one of two precision mixed-precision NVIDIA TENSOR COREs, DLA(s), TPUs, or deep learning supercomputers including GPU(s).
Regarding the VR system, AR system, and MR system; a review of the specification (paragraph 00138) shows that the following appears to be the corresponding structure to these claim limitations:
"The accelerator(s) 514 (e.g., the hardware acceleration cluster) may include a programmable vision accelerator(s) (PVA), which may alternatively be referred to herein as a computer vision accelerator. The PVA(s) may be designed and configured to accelerate computer vision algorithms for the advanced driver assistance systems (ADAS), autonomous driving, and/or augmented reality (AR) and/or virtual reality (VR) applications. The PVA(s) may provide a balance between performance and flexibility. For example, each PVA(s) may include, for example and without limitation, any number of reduced instruction set computer (RISC) cores, direct memory access (DMA), and/or any number of vector processors."
Examiner will interpret each system of being limited to a PVA including any number of RISC cores, DMA, or vector processors.
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 § 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 system" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim limitation validation/testing simulation “system” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. There is insufficient explicit disclosure for hardware of the system to perform the recited function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-5, 10-15, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson et al. US 20160307447 A1 (hereinafter Johnson) in view of Holtz et al. US 20190248487 A1 (hereinafter Holtz) .
Regarding claims 1-4, 10-14 and 19; Johnson teaches an autonomous or semi-autonomous machine (¶ 0093 describes a UAV autonomously following a flight plan with an optional semi-autonomous function that allows user input to adjust flight plan as desired) comprising:
one or more processors to perform operations (¶ 0103 discloses processors on the UAV to perform the method) comprising:
causing the autonomous or semi-autonomous machine to perform one or more maneuvering control operations related to performance of autonomous or semi-autonomous operation of the autonomous or semi-autonomous machine (¶ 0093 describes a UAV autonomously following a flight plan with an optional semi-autonomous function that allows user input to adjust flight plan as desired); and
in response to determining that the resource data/capacity used to perform the maneuvering operations satisfies a threshold indicating that availability of the one or more resources is in excess of what is required for the autonomous or semi-autonomous machine to be able to continue performing the one or more control operations (¶ 0082 discloses determining that the battery level of a UAV is greater than a required threshold amount for the flight plan), processing, ancillary to performing the one or more maneuvering control operations, sensor data obtained using one or more sensors to identify one or more features of an environment of the machine (¶ 0100 discloses performing image analysis on UAV sensor data to identify environmental features such as damaged areas),
and sending data representative of the one or more features to one or more remote computing devices (¶ 0101 discloses sending results to a user for presentation; ¶ 0002 discloses a user interface displays processed sensor data; see also Figure 1 wherein the user interface 114 is associated with the user device 112 which is external to the UAV);
wherein:
the one or more maneuvering control operations include one or more of: route planning (Abstract discloses flight planning), navigation (¶ 0093 discloses navigating according to the flight plan and operator modifications), perception (¶ 0063 discloses perception of property boundaries), localization (¶ 0102 discloses an example of localization using a GNSS receiver), actuation (¶ 0106 discloses actuators that can be controlled in order to perform flight), or communication (¶ 0093 discloses communication between a UAV and user device to provide flight plan modifications),
the resource data/capacity includes one or more of: power level, battery level (¶ 0082 "battery charge"), fuel level, available memory, disk space, network bandwidth, or system load average, and
the one or more features of the environment include one or more of: pollution, light, graffiti, litter, plants, wildlife, bodies of water, fires, atmospheric conditions (¶ 0044 discloses analysis includes analyzing environment experienced by the UAV such as temperature, humidity, and wind velocity and further determine damage caused by past or current weather), people, or vehicles.
the one or more ancillary operations use at least one of the one or more resources required for performance of the one or more maneuvering control operations (it is inherent that, with the UAV as described by Johnson, at least collection of sensor data during flight would additionally use UAV battery charge);
the one or more ancillary operations are not used for any autonomous or semi- autonomous operations by the autonomous or semi-autonomous machine (¶ 0098 discloses the collected data can be analyzed either after or during the flight plan indicating the survey data is not used by the system for autonomous maneuvering operation),
Johnson does not teach that the resource data/capacity is determined while the autonomous or semi-autonomous machine is performing the one or more maneuvering control operations, and that the one or more ancillary operations include processing using one or more artificial intelligence (AI) models.
Holtz teaches that the resource data/capacity is determined while the autonomous or semi-autonomous machine is performing the one or more maneuvering control operations (claim 36 discloses monitoring a power level during flight), and that the one or more ancillary operations include processing using one or more artificial intelligence (AI) models (¶ 0147 and ¶ 0159 disclose using a machine learning model to process captured images).
It would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have modified Johnson to incorporate the teachings of Holtz such that the battery level determination of Johnson can continue to be performed during flight as taught by Holtz. This modification would be made with a reasonable expectation of success to ensure battery level remains appropriate during the entire flight to prevent the UAV from spontaneously crashing if battery drain is larger than expected. Further, since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function, but in the very combination itself, that is in the substitution of the machine learning model performing image analysis of Holtz for the generically detailed performance of image analysis of Johnson. Thus, the simple substitution of one known element for another producing a predictable result of classifying features in images renders the claim obvious.
Regarding claims 5 and 14, the modified Johnson reference teaches all of claims 1 and 10 as detailed above. Johnson further teaches that the sensor data includes image data (¶ 0066 discloses collecting images), and that the processing includes performing one or more of object localization or image classification with respect to the image data (¶ 0100 discloses using visual classifies to analyze images).
Johnson does not teach the processing using the one or more AI models.
Holtz teaches the processing using the one or more AI models includes (¶ 0147 and ¶ 0159 disclose using a machine learning model to process captured images).
It would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have modified Johnson to incorporate the teachings of Holtz. Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function, but in the very combination itself, that is in the substitution of the machine learning model performing image analysis of Holtz for the generically detailed performance of image analysis of Johnson. Thus, the simple substitution of one known element for another producing a predictable result of classifying features in images renders the claim obvious.
Regarding claim 20, the modified Johnson reference teaches all of claim 19 as detailed above. Johnson further teaches that the system comprises at least one of:
a control system for an autonomous or semi-autonomous machine (Figure 6 processors 635 and 694 and GPUs 636 and 692);
a perception system for an autonomous or semi-autonomous machine (Figure 6 camera 649);
a system for performing simulation operations;
a system for performing simulation operations to test or validate autonomous machine applications;
a system for performing light transport simulation;
a system for rendering graphical output;
a system for performing deep learning operations;
a system for performing generative AI operations using a large language model (LLM);
a system for performing generative AI operations using a vision language model (VLM);
a system implemented using an edge device;
a system for generating or presenting virtual reality (VR) content;
a system for generating or presenting augmented reality (AR) content;
a system for generating or presenting mixed reality (MR) content;
a system incorporating one or more Virtual Machines (VMs);
a system implemented at least partially in a data center;
a collaborative content creation platform for 3D assets; or
a system implemented at least partially using cloud computing resources.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson as modified by Holtz as applied to claims 1 and 10 above, and further in view of Xiong et al. CN 117636169 A (hereinafter Xiong; a translated copy has been provided which the examiner relies upon).
Regarding claims 6 and 16, the modified Johnson reference teaches all of claims 1 and 10 as detailed above.
Johnson does not teach notifying a third party of the one or more features of the environment based at least on the one or more features of the environment being identified as being of interest to the third party.
Xiong teaches notifying a third party of the one or more features of the environment based at least on the one or more features of the environment being identified as being of interest to the third party (translated page 2 last paragraph/page 3 first paragraph discloses, when a fire danger is detected, a warning of the fire danger is sent to fire-fighting department for immediate response).
It would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have further modified Johnson to incorporate the teachings of Xiong such that the processing of sensor data of Johnson can include processing of sensor data to determine occurrence of a fire hazard and further notification to a fire department regarding the hazard. This modification would be made with a reasonable expectation of success to improve safety of the machine and its environment.
Claim(s) 7-8 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson as modified by Holtz as applied to claims 1 and 10, and further in view of Yokoyama et al. US 20230081930 A1 (hereinafter Yokoyama).
Regarding claims 7 and 17, the modified Johnson reference teaches all of claims 1 and 10 as detailed above.
Johnson does not teach at least a portion of the one or more AI models corresponds to a remote server, and the method further comprises sending at least a portion of the sensor data to the remote server for processing by at least the portion of the AI model corresponding to the remote server.
Yokoyama teaches at least a portion of the one or more AI models corresponds to a remote server (paragraph 0045 discloses a machine learning model used in a server), and the method further comprises sending at least a portion of the sensor data to the remote server (paragraph 0049 discloses image data from a camera is sent to a server; paragraph 0048 explicitly discloses a camera can be aboard a vehicle) for processing by at least the portion of the AI model corresponding to the remote server (paragraph 0050 discloses images are processed with the machine learning model).
It would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have further modified Johnson to incorporate the teachings of Yokoyama such that the image data generated by Johnson can be sent to a server for processing by a machine learning model as taught by Yokoyama. This modification would be made with a reasonable expectation of success to reduce local power usage. Furthermore, the rearrangement of the processing of Johnson being incorporated instead on a server as taught by Yokoyama would have been obvious to one having ordinary skill in the art at the time of filing since it has been held that rearranging the location of elements without affecting operation of the elements involves only routine skill in the art. See MPEP 2144.04(VI)(C) and the court cases cited therein.
Regarding claims 8 and 18, the modified Johnson reference teaches all of claims 7 and 17 as detailed above.
Johnson does not teach that the sensor data is sent to the remote server based at least on one or more of: the resource data, a relative location of the autonomous or semi-autonomous machine with respect to the remote server, or network connectivity of the autonomous or semi-autonomous machine.
Yokoyama further teaches that the sensor data is sent to the remote server based at least on one or more of: the resource data, a relative location of the machine with respect to the remote server, or network connectivity of the machine (paragraph 0049 discloses image data is transmitted via a network connection; examiner understands that this means if no network connectivity exists, no communication occurs and therefore communication is based on connectivity).
It would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have further modified Johnson to incorporate the teachings of Yokoyama such that image data is transmitted for remote processing when connected via a network as taught by Yokoyama. This modification would be made with a reasonable expectation of success to reduce local power usage when network connectivity exists.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson as modified by Holtz as applied to claim 1 above, and further in view of Carson US 20160280131 A1.
Regarding claim 9, the modified Johnson reference teaches all of claim 1 as detailed above.
Johnson does not teach that a frame rate corresponding to processing the sensor data is adjusted based at least on the resource data.
Carson teaches that a frame rate corresponding to processing the sensor data is adjusted based at least on the resource data (paragraph 0045 discloses a collected video frame rate can be reduced based on network bandwidth and battery charge level).
It would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have modified Johnson to incorporate the teachings of Yokoyama such that the video captured by the cameras used in processing of Johnson can have a variable frame rate as taught by Carson. This modification would be made with a reasonable expectation of success to maintain charge for a longer amount of time as disclosed by Carson (Abstract).
Response to Amendment
Claim amendments filed 3/31/2026 have been received and fully considered and overcome the claim objections, 101 rejections, 112(a) rejections, and part of the 112(b) rejections of record detailed in the Office Action dated 12/31/2025. These/this objections and rejections have/has been withdrawn.
Specification amendments filed 3/31/2026 have been received and fully considered and overcome part of the drawing objections of record detailed in the Office Action dated 12/31/2025. These/this objection have/has been withdrawn. The remaining drawing objections regarding 504(A) and 504(B) not being in the specification has been maintained. The specification amendments also amend the title necessitating the title objection detailed above.
Response to Arguments
Applicant’s arguments, see page 11, filed 3/31/2026, with respect to the rejection(s) of claim(s) 1, 10, and 19 under 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Johnson and Holtz as detailed above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Tiffany Schoech whose telephone number is (571)272-2937. The examiner can normally be reached 4:45 am - 3:15 pm PT Monday - Thursday.
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, Erin Piateski can be reached at 571-270-7429. 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.
/A.T.S./Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669
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