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 .
Response to Amendment
Amendment to claims 1-20 on 04/02/2026 has been entered and acknowledged.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 8-12 and 14-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 8 - A non-transitory computer readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to: select a plurality of samples, wherein the plurality of samples are identified based on a predicted sample, predicted by one or more neural networks, and selecting the plurality of samples from a calculated distribution of samples around the predicted sample; determine a combined loss value based on applying one or more loss functions to the plurality of samples; and generate updated samples by applying the combined loss value as guidance to the one or more neural networks
Step 1: Statutory category – Yes
The claim recites a non-transitory computer readable storage medium. The claim falls within one of the four statutory categories. MPEP 2106.03.
Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
Claim(s) is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the claim covers performance using mental processes.
The claim recites the limitations “predicted sample “, “identified based on a predicted sample”, “calculated distribution of samples around the predicted sample”, “selecting the plurality of samples from a calculated distribution of samples around the predicted sample”, “determine a combined loss value based on applying one or more loss functions to the plurality of samples “and “generate updated samples by applying the combined loss value as guidance”. These limitations, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind with possible aid of pen and paper. For example, using pen and paper one could mentally predict and select samples by perform matrix multiplication and addition (multiplying input numbers by “weights” and adding “biases”), and then apply a mathematical function (like a sigmoid or ReLU) to get a predicted number. To sample from a distribution, one would calculate a probability curve (like a bell curve/Gaussian distribution) and use a random number table to pick samples from that curve. One could mentally perform using pen and paper loss evaluation by taking the sampled numbers and plug them into a loss function equation (such as Mean Squared Error, which just involves subtracting the prediction from the target, squaring the result, and finding the average). For guidance, one could utilize calculus - specifically, the chain rule (backpropagation) to calculate the derivatives of the loss function with respect to the weights. One would then use basic subtraction to update the weights to generate better samples for next iteration. Thus, the claim recites mental processes.
Step 2A Prong Two evaluation: Practical Application - No
Claim(s) is evaluated whether as a whole it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”).
The claim recites additional element of non-transitory computer readable storage medium (NCRM) and processors of a computer system that perform the identified abstraction. The NCRM and computer system is recited at high level of generality and merely automates the identified mental steps. The computer components are described within the specification and claimed generically for operating in their ordinary capacity and do not use the judicial exception in a manner that impose meaningful limit on the judicial exception. Hence, the claim is no more than a drafting effort designed to monopolize the exception. The additional limitations are no more than mere instructions to apply the exception using a computer, MPEP 2106.05(f). The claim is directed to the abstract idea.
101 Analysis - Step 2B evaluation: Inventive concept - No
The claim(s) is evaluated whether the claim as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claim is therefore patent ineligible.
In regards to claim 9- The step of using loss value to specify one condition to apply to the samples is directed to mental step using pen and paper as indicated above. The claim is patent ineligible.
In regards to claim 10- small sample set can be generated using simple basic mathematical diffusion process using pen and paper. The claim is patent ineligible.
In regards to claim 11 – the step of determine one motion of one object based, at least in part, on the updated samples could be done via pen and paper as indicated above. The claim is patent ineligible.
In regards to claim 12 – the step of entering loss using text input could be done mentally using pen and paper. The claim is patent ineligible.
In regards to claim 14 – the claim is directed to an abstract idea without significantly more. The claim analysis is the same per claims 8 and 11 above. The claim is patent ineligible.
In regards to claim 15 – the step of combine loss value based on a path following loss and an obstacle avoidance loss could be done mentally using pen and paper. The claim is patent ineligible.
In regards to claim 16 – the step of estimate a loss guidance term could be done mentally using pen and paper. The claim is patent ineligible.
In regards to claim 17 – the claim analysis is the same per claim 9 above. The claim is patent ineligible.
In regards to claim 18 – the claim analysis is the same per claim 10 above. The claim is patent ineligible.
In regards to claim 19 – the claim analysis is the same per claim 12 above. The claim is patent ineligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 8-10 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Dhariwal et al. “Diffusion Models Beat GANs on Image Synthesis”.
Dhariwal discloses a non-transitory computer readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to: select a plurality of samples, wherein the plurality of samples is identified based on a predicted sample (Section 2 & Section 4 - diffusion model/neural network predicts the noise in images/samples at a specific timestep and denoises the samples), predicted by one or more neural networks, and selecting the plurality of samples from a calculated distribution of samples around the predicted sample (Section 2 & Section 4 - The system calculates via Gaussian distribution around the prediction and draws samples from it); determine a combined loss value based on applying one or more loss functions to the plurality of samples (Section 4.3, algorithm 1 – “gradient of the log probability,” in machine learning, maximizing the log probability is mathematically identical to minimizing the cross-entropy loss. Therefore, evaluating the sample with the classifier and calculating this gradient is the exact equivalent of applying a loss function to determine a loss value or error gradient for the selected samples; and generate updated samples by applying the combined loss value as guidance to the one or more neural networks (Section 4.1, equation 10 - shows the mean of the distribution being shifted by the gradient).
In regards to claim 9 - Dhariwal discloses wherein the combined loss value is to be used to specify one or more conditions to apply to the plurality of samples (algorithm 1 and equation 10 where the gradient of the specified label y is added to the mean of the sample distribution to guide the generation process).
In regards to claim 10 - Dhariwal discloses plurality of samples are generated using one or more diffusion models (Abstract, Section 4.1 “…We start with a diffusion model with an unconditional reverse noising process…”).
Claims 1-4, 6-8-11, 13-18 and 20 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Jiang et al. (US2024/0157979).
In regards to claim 1 - Jiang ‘979 discloses a method of controlling an autonomous machine comprising: selecting a plurality of samples of a sequence of motion of the autonomous machine, wherein the plurality of samples are identified based on a predicted sample, predicted by one or more neural networks (neural network 212 Fig. 2), and selecting the plurality of samples from a calculated distribution of samples around the predicted sample (P. 0042, P.0088 Fig. 2 samples selected using diffusion model 214); determining a combined loss value based on applying one or more loss functions to the plurality of samples; generating updated samples by applying the combined loss value as guidance to the one or more neural networks (P.0063 equation 2); determining one or more motions of the autonomous machine based on the updated samples (P.0002 trajectory prediction, P.0054, “… generates the respective trajectory prediction output for each of the one or more target agents …”, P. 0123, P. 0087); and providing the one or more motions to the autonomous machine to cause causing the autonomous machine to move based on the one or more motions (P.0022 “…assisting an on-board system of a vehicle to make more informed planning decisions that cause the vehicle to travel along a safe, smooth, and comfortable trajectory…”.
In regards to claim 8 and 14 - Jiang ‘979 discloses a system comprising: one or more processors (Fig. -4) to: select a plurality of samples, wherein the plurality of samples are identified based on a predicted sample, predicted by one or more neural networks (neural network 212 Fig. 2), and selecting the plurality of samples from a calculated distribution of samples around the predicted sample (P. 0042, P.0088 Fig. 2 samples selected using diffusion model 214); determine a combined loss value based on applying one or more loss functions to the plurality of samples; generate updated samples by applying the combined loss values value as guidance to the one or more neural networks (P.0063 equation 2); and determine one or more motions of one or more objects based, at least in part, on the updated samples (P.0002 trajectory prediction, P.0054, “… generates the respective trajectory prediction output for each of the one or more target agents …”, P. 0123, P. 0087).
In regards to claims 2, 9 and 17 - Jiang ‘979 discloses the combined loss value is to be used to specify one or more conditions to apply to the plurality of samples ((P. 0015 & P. 0063).
In regards to claims 3, 10 and 18 - Jiang ‘979 discloses the plurality of samples are generated using one or more diffusion models (Fig. 2).
In regards to claims 4 and 16 - Jiang ‘979 discloses the one or more loss functions are to estimate a loss guidance term (P. 0021).
In regards to claims 6 and 15 - Jiang ‘979 discloses the combined loss value is based on a path following loss and an obstacle avoidance loss (P.0062 - a constraint can be added so that the predicted trajectories of a target agent avoid collision with other target or context agents. This indicates that the calculated loss value for the samples are based on path following and obstacle avoidance constraints).
In regards to claim 7 Jiang ‘979 discloses the plurality of samples is identified based on a distribution around one or more samples of the plurality of samples ((P. 0042, P.0088 Fig. 2 samples selected using diffusion model 214).
In regards to claim 11 - Jiang ‘979 discloses the computer system is further caused to determine one or more motions of one or more objects based, at least in part, on the updated samples ((P.0002 trajectory prediction, P.0054, “… generates the respective trajectory prediction output for each of the one or more target agents …”, P. 0123, P. 0087).
In regards to claim 13 - Jiang ‘979 discloses determining generating one or more images, at least in part, on the updated samples (P.005, P0028 – scene in an environment).
In regards to claim 20 - Jiang ‘979 discloses on of: a control system for an autonomous machine (P.0012); a perception system for an autonomous or semi-autonomous machine a first system for performing simulation operations (P. 0013).
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.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang et al. (US2024/0157979) in view of Grouiex et al. (US 12,633,006).
Jiang ‘979 discloses all elements per claims 1, 8 and 14 above. However, it is silent as to the usage of text prompts for the loss functions.
Grouiex ‘006 is directed to training image neural network with diffusion model. Grouiex ‘979 teaches that text input or text prompt can be used as means for input sample description.
It would have been obvious for one of ordinary skill in the art prior the filing of the claimed invention to provide Jiang ‘979 samples via text prompt as means for condition the plurality of samples as taught by Grouiex. The modification yields predictable result of providing sample description/condition via textual input.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Dhariwal et al. “Diffusion Models Beat GANs on Image Synthesis” per claim 8 above and in view of Grouiex et al. (US 12,633,006).
Dhariwal discloses all elements per claim 8 above. However, it is silent as to the usage of text prompts for the loss functions.
Grouiex ‘006 is directed to training image neural network with diffusion model. Grouiex ‘979 teaches that text input or text prompt can be used as means for input sample description.
It would have been obvious for one of ordinary skill in the art prior the filing of the claimed invention to provide Dhariwal samples via text prompt as means for condition the plurality of samples as taught by Grouiex. The modification yields predictable result of providing sample description/condition via textual input.
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.
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/KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656
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