DETAILED ACTION
This is a response to Applicant’s submissions filed on 2/25/2026. Claims 1-20 are pending.
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 .
Information Disclosure Statement
The listing of references in paragraph 97 of the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered. It is further unclear how the water disinfection apparatus disclosed in US application 16/101,432 discloses a real-time ray-tracing hardware accelerator.
Response to Arguments
Applicant's arguments filed 2/25/2026 have been fully considered but they are not persuasive.
In response to Applicant’s argument that the term module is a structural term that clearly refers to a neural network to a person of ordinary skill in the art (Applicant’s Remarks; pp. 13-14), the Examiner respectfully disagrees. Paragraph 208 discloses aspects of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “module”. The Applicant’s specification explicitly discloses the modules may be either hardware or software, therefore, their claimed structure is indefinite. See interpretation and rejection below.
In response to Applicant’s argument that the data dependencies between the compute nodes of Donderici do not disclose cross-attention features (Applicant’s Remarks; pp. 14-15), the Examiner respectfully disagrees. The Applicant’s disclosure does not explicitly define cross-attention features, however, paragraph 164 discloses cross-attention features are essentially updates to learned tokens that combine prior information from a learned token with new information from tokens generated from features extracted from sensor data. Donderici, in paragraph 47, discloses the perception node may run a deep learning model that provides perceptions based on sensor data provided by a data collection node. Donderici, in figure 10, further discloses the training process for the deep learning models wherein the tokens are iteratively learned based on propagating training data through the AI model. Therefore, processing sensor data and information via a machine learning model based on one or more cross-attention features, under its broadest reasonable interpretation, includes Donderici’s generation of output values by a trained deep learning model based on sensor data and the outputs of other parallel deep learning models. See rejection below.
Drawings
The amended drawings were received on 2/25/2026.
The drawings are objected to because:
Figure 6 appears to include different views of exemplary inputs (620-630) and exemplary outputs (622) of the parallelized model. Each different view must be numbered (see CFR § 1.84(u)(1)), such as by indicating the top collection of inputs 620-630 as figure 6A, and the bottom exemplary outputs as figure 6B.
In the top view of figure 6, it is unclear how reference character 604 illustrates planned outputs generated by the parallelized model as disclosed in paragraph 173.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) 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. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. 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.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: computer-based system 100 (para. 27, l. 1). Corrected drawing sheets in compliance with 37 CFR 1.121(d) 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 amendments to the specification were received on 2/25/2026.
Claim Objections
Claims 6 and 20 are objected to because of the following informalities:
In claim 6, lines 3-4, “predict occupancy of objects” should read “predict occupancy of the objects” to make it clear that the motion and occupancy are predicted for the same objects.
In claim 20, line 5, “information that is associated with the vehicle” should read “information that is associated with a vehicle” to provide sufficient antecedent basis for the vehicle in the claim.
Claim 20, line 5, contains replaced text that is not shown by underlining, and strike-through or double brackets, in accordance with 37 CFR § 1.121(c)(2).
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a module configured to generate maps”, “a module configured to predict motions of objects”, “a module configured to predict occupancy of objects”, and “a module configured to generate planned motion” in claim 14, lines 2-5.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 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 14 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.
Regarding claim 14, lines 2-5, the limitations “a module configured to generate maps”, “a module configured to predict motions of objects”, “a module configured to predict occupancy of objects”, and “a module configured to generate planned motion” invoke 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. Although paragraph 208 generally discloses a module may refer to an entirely hardware embodiment, and entirely software embodiment, or a combination thereof, the structure of a hardware module of a parallelized machine learning module is unclear. Therefore, the claims are 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 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.
Claim(s) 1, 6, 8-11, 13-14 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Musk et al. (US 2020/0265247), hereinafter Musk, in view of Donderici (US 2023/0406352).
Regarding claims 1, 11 and 20, as best understood, Musk discloses a system, comprising: one or more memories storing instructions; and one or more processors that are coupled to the one or more memories (Musk; para. 9: a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor) and, when executing the instructions, are configured to: receive sensor data (Musk; fig. 4, step 401: receive sensor data) and information that is associated with a vehicle and distinct from the sensor data (Musk; para. 48: the captured data from different sensors is associated with captured metadata to allow the data captured from different sensors to be associated together) and process the sensor data and the information via a machine learning model (Musk; fig. 4, step 405: initiate deep learning analysis) to generate a planned motion for the vehicle (Musk; para. 52: At 407, the results of deep learning analysis are provided to vehicle control … identified objects and their properties (e.g., distance, direction, etc.) may be used to determine drivable space. The drivable space is then used to determine a drivable path for the vehicle.).
Musk does not explicitly disclose a plurality of modules execute in parallel based on one or more cross-attention features.
Donderici, in the same field of endeavor (autonomous vehicle controls), discloses a plurality of modules execute in parallel (Donderici; para. 21: rather than serializing the various compute nodes of the AV controller, the nodes may operate in parallel; para. 22: the perception node and planning node may both execute a DL model) based on one or more cross-attention features (Donderici; para. 20: compute nodes may compete with one another for available resources and may also have data dependencies. For example, a planning node may require data from a perception node. The perception node may, in its turn, require data from a data collection node).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified the machine learning model of Musk to include a plurality of task-specific, compute nodes that include deep-learning models, as disclosed by Donderici, with the motivation of reducing the lag and response time of the vehicle controller (Donderici; para. 21).
Regarding claims 6 and 14, as best understood, Musk, as modified, discloses the plurality of modules includes at least one of a module configured to generate maps, a module configured to predict motions of objects, a module configured to predict occupancy of the objects within an environment, or a module configured to generate planned motions of the vehicle (Donderici; para. 49: Planning node 320 may be responsible for planning vehicle activity based on predictions. Planning node 320 may plan actions to take after selecting a preferred or optimal predicted action from predictions 344.).
Regarding claim 8, as best understood, Musk, as modified, discloses the information associated with the vehicle includes at least one of one or more commands used to control the vehicle, controller area network (CAN) bus information, or a history of one or more trajectories of the vehicle (Musk; para. 48: Using the metadata, different formats of sensor data can be associated together to better capture the environment surrounding the vehicle. In some embodiments, the sensor data includes odometry data including the location, orientation, change in location, and/or change in orientation, etc. of the vehicle. For example, location data is captured and associated with other sensor data captured during the same time frame.).
Regarding claims 9 and 17, Musk, as modified, discloses processing the sensor data and the information via the machine learning model further generates at least one of a map of an environment, a predicted motion of one or more objects, or a predicted occupancy of the one or more objects within the environment (Musk; para. 52: the various outputs of deep learning are used to construct a three-dimensional representation of the vehicle's environment for autonomous driving which includes identified objects, the distance and direction of identified objects, predicted paths of vehicles).
Regarding claims 10 and 19, Musk, as modified, discloses performing one or more operations to control the vehicle based on the planned motion (Musk; para. 53: using the results of deep learning analysis, a vehicle control module determines the appropriate manner to operate the vehicle, for example, along a determined path with the appropriate speed).
Regarding claim 13, Musk, as modified, discloses each module included in the plurality of modules comprises a multi-layer perceptron (Donderici; para. 126: Output layer 920 includes a number of neurons known as perceptrons that compute an activation value based on their weighted connections to each neuron in the last hidden layer 916.).
Regarding claim 18, Musk, as modified, discloses the sensor data includes at least one of image data, light detection and ranging (LIDAR) data, or radio detection and ranging (RADAR) data (Musk; para. 13: a vehicle is equipped with sensors to capture the environment of the vehicle and vehicle operating parameters. The captured data includes vision data (such as video and/or still images) and additional auxiliary data such as radar, lidar, inertia, audio, odometry, location, and/or other forms of sensor data.).
Claim(s) 2, 7, 12 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Musk in view of Donderici as applied to claims 1 and 11 above, and further in view of Nir et al. (US 2024/0419944), hereinafter, Nir.
Regarding claims 2 and 12, Musk, as modified, discloses generating one or more tokens based on the sensor data; processing the one or more tokens (Donderici; para. 121: input layer 912 receives an input such as input image 904, and at output layer 920, neural network 900 “lights up” a perceptron that indicates which character neural network 900 thinks is represented by input image 904) and one or more learned tokens (Donderici; para. 139: the network is refined by providing a “training” set, which includes objects with known results. Because the correct answer for each object is known, training sets can be used to iteratively move the weights and biases away from garbage values, and toward more useful values) via one or more cross-attention layers (Donderici; para. 45: Compute nodes 302 compete for access to various shared resources 570. These may include hardware and/or software resources, access to data locations, access to buses, access to memory, access to processor or GPU time, or other resource contentions.); and processing each cross-attention feature included in the one or more cross-attention features via one of the modules included in the plurality of modules (Donderici; para. 20: compute nodes may compete with one another for available resources and may also have data dependencies. For example, a planning node may require data from a perception node. The perception node may, in its turn, require data from a data collection node).
Musk, as modified, does not appear to explicitly disclose generating the cross-attention features in the cross-attention layer.
Nir, in a reasonably pertinent field of endeavor (computer vision), discloses generating cross-attention feature in a cross-attention layer (Nir; para. 88: the cross-attention layer (340) fuses results from the machine learning models (315, 325, 335) in the different channels … a cross-attention layer is any mechanism that aggregates results from machine learning models that process diverse inputs, which can be different types or modalities of inputs or inputs from different sources).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, with a reasonable expectation of success, to modify the shared resources that distribute data between the compute nodes that include deep-learning models of Musk, as modified, to aggregate results of the models by processing them in a cross-attention layer, as disclosed by Nir, with the motivation of sharing spatial information between models thereby improving precision (Nir; para. 88).
Regarding claim 7, as best understood, Musk, as modified, discloses performing one or more operations to train the machine learning model (Musk; fig. 3, block 303: train machine learning model), wherein the one or more operations cause one or more parameters of the plurality of modules and one or more values of one or more learned tokens to be updated (Donderici; para. 139: the network is refined by providing a “training” set, which includes objects with known results. Because the correct answer for each object is known, training sets can be used to iteratively move the weights and biases away from garbage values, and toward more useful values).
Regarding claim 16, as best understood, Musk, as modified, discloses a first cross-attention layer included in the one or more cross-attention layers distributes first cross-attention features included in the one or more cross-attention features (Donderici; para. 45: Compute nodes 302 compete for access to various shared resources 570. These may include hardware and/or software resources, access to data locations, access to buses, access to memory, access to processor or GPU time, or other resource contentions.; para. 20: compute nodes may compete with one another for available resources and may also have data dependencies. For example, a planning node may require data from a perception node. The perception node may, in its turn, require data from a data collection node) based on the information associated with the vehicle (Musk; para. 48: the captured data from different sensors is associated with captured metadata to allow the data captured from different sensors to be associated together), the one or more tokens (Donderici; para. 121: input layer 912 receives an input such as input image 904, and at output layer 920, neural network 900 “lights up” a perceptron that indicates which character neural network 900 thinks is represented by input image 904), and a first learned token included in the one or more learned tokens (Donderici; para. 139: the network is refined by providing a “training” set, which includes objects with known results. Because the correct answer for each object is known, training sets can be used to iteratively move the weights and biases away from garbage values, and toward more useful values).
Musk, as modified, does not appear to explicitly disclose generating the cross-attention features in the cross-attention layer.
Nir, in a reasonably pertinent field of endeavor (computer vision), discloses generating cross-attention feature in a cross-attention layer (Nir; para. 88: the cross-attention layer (340) fuses results from the machine learning models (315, 325, 335) in the different channels … a cross-attention layer is any mechanism that aggregates results from machine learning models that process diverse inputs, which can be different types or modalities of inputs or inputs from different sources).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, with a reasonable expectation of success, to modify the shared resources that distribute data between the computer nodes that include deep-learning models of Musk, as modified, to aggregate results of the models by processing them in a cross-attention layer, as disclosed by Nir, with the motivation of sharing spatial information between models thereby improving precision (Nir; para. 88).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Musk in view of Donderici and Nir as applied to claim 2 above, and further in view of Cherian et al. (US 2025/0187198), hereinafter Cherian.
Musk, as modified, discloses the invention substantially as claimed as described above.
Musk, as modified, does not explicitly disclose generating the one or more tokens comprises processing the sensor data via one of a spatiotemporal transformer model, an autoregressive transformer model, or a QueryTransformer (QFormer) model.
Cherian, in the same field of endeavor (autonomous vehicle controls), discloses generating one or more tokens comprises processing sensor data via one of a spatiotemporal transformer model (Cherian; para. 83: The pre-processed sequence of video frames 402a may then be inputted to a spatio-temporal transformer 404. The spatio-temporal transformer 404 transforms each of the pre-processed sequence of video frames 402a frames into a spatio-temporal scene graph 406 (G) of the sequence of video frames 102 to capture spatio-temporal information of the sequence of video frames 102.), an autoregressive transformer model, or a QueryTransformer (QFormer) model.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified the compute nodes that process video data of Musk, as modified, to include a spatiotemporal transformer, as disclosed by Cherian, to yield the predictable result of accurately determining the relative positions of static and dynamic objects surrounding the vehicle.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Musk in view of Donderici as applied to claim 1 above, and further in view of Nir.
Musk, as modified, discloses the invention substantially as claimed as described above.
Musk, as modified, does not explicitly disclose each module included in the plurality of modules comprises a decoder model.
Nir discloses each module included in a plurality of modules comprises a decoder model (Nir; fig. 3: frame decoding operations (310); motion vector decoding and derivation operations (320); and residual decoding and derivation operations (330)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified the compute nodes that include deep-learning models of Musk, as modified, to include decoding operations, as disclosed by Nir, to yield the predictable result of avoiding processing delays caused by using a single decoder to process the outputs of the multiple machine learning models.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Musk in view of Donderici as applied to claim 1 above, and further in view of Nir and Recasens Continente et al. (US 2025/0181887), hereinafter Continente.
Musk, as modified, discloses the invention substantially as claimed as described above.
Musk, as modified, does not explicitly disclose each module included in the plurality of modules comprises an encoder model and a decoder model.
Continente, in the same field of endeavor (machine learning), discloses each module included in a plurality of modules comprises an encoder model (Continente; para. 38: For each partition 104, the system 100 generates, from the partition, a respective set of latent tokens 106 for the partition. For each partition 104, the system 100 generates, from the partition, a respective set of latent tokens 106 for the partition. For example, the system 100 can process the partition using a corresponding encoder neural network to generate the latent tokens.).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified the compute nodes that include deep-learning models of Musk, as modified, to include an encoder neural network, as disclosed by Continente, to yield the predictable result of avoiding processing delays caused by using a single encoder to provide inputs to multiple machine learning models.
Musk, as modified, does not explicitly disclose each module included in the plurality of modules comprises a decoder model.
Nir discloses each module included in a plurality of modules comprises a decoder model (Nir; fig. 3: frame decoding operations (310); motion vector decoding and derivation operations (320); and residual decoding and derivation operations (330)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified the compute nodes that include deep-learning models of Musk, as modified, to include decoding operations, as disclosed by Nir, to yield the predictable result of avoiding processing delays caused by using a single decoder to process the outputs of the multiple machine learning models.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Musk in view of Donderici as applied to claim 11 above, and further in view of Seff et al. (US 2024/0300542), hereinafter Seff.
Musk, as modified, discloses the instructions, when executed by the at least one processor, further cause the at least one processor to perform the step of performing one or more operations to train the machine learning model (Musk; fig. 3, block 303: train machine learning model).
Musk, as modified, does not explicitly disclose the one or more operations cause one or more parameters of the plurality of modules to be updated in parallel based on a computed loss.
Seff, in the same field of endeavor (vehicle controls), discloses one or more operations cause one or more parameters of a plurality of modules to be updated in parallel (Seff; para. 22: The described systems can therefore process multiple trajectory predictions in parallel, both for training and for inference.) based on a computed loss (Seff; para. 98: the scene encoder neural network 402 can be pre-trained (e.g., with an appropriate reconstruction loss) to produce scene encodings).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified the training of the parallelized deep-learning models of Musk, as modified, to be executed in parallel, as disclosed by Seff, to yield the predictable result of reducing the required training time.
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 JOSEPH THOMPSON whose telephone number is (571)272-3660. The examiner can normally be reached Mon-Thurs 9:00AM-3:00PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin Bishop can be reached at (571)270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSEPH THOMPSON/Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665
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