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
This Office Action is in response to applicant’s communication filed 21 July 2025, in response to the Office Action mailed 20 March 2025. The applicant’s remarks and any amendments to the claims or specification have been considered, with the results that follow.
Claim Objections
Claim 15 is objected to because of the following informalities: “wherein the input data received by the trained neural network during the inference of the trained neural network” appears as though it should be “wherein the input data is received by the trained neural network during the inference of the trained neural network” or similar. Appropriate correction is required.
Claims 16-17 depend upon claim 15, and thus include the aforementioned limitation(s).
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 13 and 15-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claims 13 and 15-17 are not exclusively limited to tangible embodiments. While claims 13 and 15-17 purport to be an "apparatus,” in view of applicant's disclosure, specification page 8, paragraph 6, the purported "apparatus" is defined such that it can actually be directed to a combination of purely software modules, thereby lacking the necessary physical articles or objects to actually be an "apparatus" (a machine within the meaning of 35 USC 101); therefore, claims 11-13 fail(s) to fall within a statutory category of invention.
Claim(s) 1-3, 8, 9, and 18-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes and/or mathematical concepts. This judicial exception is not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as described below.
Step 1 for all claims:
Under the first part of the analysis, claims 1-3, 8, and 9 recite a method and claims 18-20 recite a device. Accordingly, these claims fall within the four statutory categories of invention and the analysis proceeds to Step 2A, prongs 1 and 2, and Step 2B, as described below.
As per claim 1:
Under step 2A, prong 1, the claim recites an abstract idea including the following mental process and/or mathematical concept elements:
and generating observations of the latent space – a data scientist generates observations from the latent space of a trained model that has performed inference on some inputs.
generating a probabilistic model of the latent space of the trained neural network using the observations of the latent space – this is a mathematical function, which is calculating a probability distribution of the observations from the latent space of the trained neural network. Alternatively/additionally – the data scientist creates the probability distribution model mentally, or with pen and paper, depending on the number and type of data points.
generating a prediction for a confidence value for an output of the trained neural – the data scientist predicts a confidence value for the output of the network based on a given input.
filtering input data to a trained neural network, to improve an accuracy of the trained neural network – the data scientist decides whether each input should be processed or not (filtering).
wherein filtering the input data includes: determining whether the predicted confidence value exceeds a predetermined confidence threshold and only permitting the trained neural network to process input data that exceeds the predetermined confidence threshold – this is a mathematical function comparing the confidence value to a threshold value. Alternatively/additionally – the data scientist compares the predicted confidence to the threshold and decides whether each input should be processed.
If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. If a claim, under the broadest reasonable interpretation covers concepts that can be performed in the human mind, or by a human using a pen and paper, including observation, evaluation, judgment, or opinion, it will be considered as falling within the “mental processes” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a computer-implemented method of modelling a latent space of a trained neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
comprising the steps of: receiving input data for the trained neural network – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
observing the latent space of the trained neural network during inference of the trained neural network – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein generating the prediction for the confidence value includes using the probabilistic model to generate the prediction of confidence for each of one of more input data to the trained neural network – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
a computer-implemented method of modelling a latent space of a trained neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
comprising the steps of: receiving input data for the trained neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
observing the latent space of the trained neural network during inference of the trained neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
wherein generating the prediction for the confidence value includes using the probabilistic model to generate the prediction of confidence for each of one of more input data to the trained neural network – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 2:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receiving input data for the trained neural network during the inference of the trained neural network – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and wherein the generating of the probabilistic model of the latent space of the trained neural network using the observations of the latent space comprises generating the probabilistic model of the latent space of the trained neural network using the observations of the latent space and the input data received by the trained neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
receiving input data for the trained neural network during the inference of the trained neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
and wherein the generating of the probabilistic model of the latent space of the trained neural network using the observations of the latent space comprises generating the probabilistic model of the latent space of the trained neural network using the observations of the latent space and the input data received by the trained neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 3:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receiving output of the trained neural network during inference of the trained neural network – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and wherein the generating of the probabilistic model of the latent space of the trained neural network using the observations of the latent space comprises generating the probabilistic model of the latent space of the trained neural network using the observations of the latent space and the input data received by the trained neural network during inference and the output of the trained neural network during inference of the trained neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
receiving output of the trained neural network during inference of the trained neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
and wherein the generating of the probabilistic model of the latent space of the trained neural network using the observations of the latent space comprises generating the probabilistic model of the latent space of the trained neural network using the observations of the latent space and the input data received by the trained neural network during inference and the output of the trained neural network during inference of the trained neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 8:
The claim recites the following additional mental process and/or mathematical concept elements:
predicting a confidence value for output of a trained neural network having a given input – the data scientist predicts a confidence value for the output of the network based on a given input.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein predicting the confidence value includes using the probabilistic model to generate a prediction of confidence for each of one or more input data to the trained neural network – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein predicting the confidence value includes using the probabilistic model to generate a prediction of confidence for each of one or more input data to the trained neural network – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 9, see the rejection of claim 8, above.
As per claim 10:
The claim recites the following additional mental process and/or mathematical concept elements:
filtering input data to a trained neural network – the data scientist decides whether each input should be processed.
wherein filtering the input data includes: …predicting the confidence value for the output of the trained neural network – the data scientist predicts a confidence value for the output of the network based on a given input.
determining whether the predicted confidence value exceeds a predetermined confidence threshold and only permitting the trained neural network to process input data that exceeds the predetermined confidence threshold – this is a mathematical calculation comparing the confidence value to a threshold value. Alternatively/additionally – the data scientist compares the predicted confidence to the threshold and decides whether each input should be processed.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receiving input data for the trained neural network – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
receiving input data for the trained neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 18:
See the rejection of claim 1 above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A computer system comprising one or more processors operable to perform operations for modelling a latent space of a trained neural network comprising [the method steps] – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A computer system comprising one or more processors operable to perform operations for modelling a latent space of a trained neural network comprising [the method steps] – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 19, see the rejection of claim 2, above.
As per claim 20, see the rejection of claim 8, above.
[Examiner’s Note: claims 13 and 15-17 are drawn to non-statutory subject matter, as described above, and thus fail the eligibility test at step 1. However, they have been examined under the remaining steps, as follows, for the sake of compact prosecution.]
As per claim 13:
See the rejection of claim 1 above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A computer program product executable to perform operations for modelling a latent space of a trained neural network, the operations comprising [the method steps] – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A computer program product executable to perform operations for modelling a latent space of a trained neural network, the operations comprising [the method steps] – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 15, see the rejection of claim 2, above.
As per claim 16:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein generating of the probabilistic model of the latent space of the trained neural network using the observations of the latent space includes using the output of the trained neural network during inference of the trained neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein generating of the probabilistic model of the latent space of the trained neural network using the observations of the latent space includes using the output of the trained neural network during inference of the trained neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 17, see the rejection of claim 8, above.
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.
Claim(s) 1-3, 8, 9, 13, and 15-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shi et al. (Probabilistic Face Embeddings, April 2019, pgs. 1-12 – cited in an IDS).
As per claim 1, Shi teaches a computer-implemented method of modelling a latent space of a trained neural network [a system that converts existing deterministic face embeddings in a latent space into uncertainty aware Probabilistic Face Embeddings (PFE) which represent each face image as a Gaussian distribution in the latent space including learning a model for the PFEs (abstract; pg. 1, fig. 1; pg. 4, section 4; pg. 5, section 4.3; etc.)] comprising: receiving input data for the trained neural network [To address the above problems, we propose Probabilistic Face Embeddings (PFEs), which give a distributional estimation instead of a point estimation in the latent space for each input face image (pg. 1, section 1; etc.); where the input face images are the received input data]; observing the latent space of the trained neural network during inference of the trained neural network and generating observations of the latent space [given a pre-trained model f which produces a deterministic face embedding in the latent space, we learn an optimized uncertainty module estimate for f(x), where f(x) represents the most likely features of a given input in the latent space (pg. 5, section 4.3; etc.)]; generating a probabilistic model of the latent space of the trained neural network using the observations of the latent space [given a pre-trained model f which produces a deterministic face embedding in the latent space, we learn an optimized uncertainty module estimate for f(x), where f(x) represents the most likely features of a given input in the latent space (pg. 5, section 4.3; etc.), which is a probabilistic model of the latent space]; generating a prediction for a confidence value for an output of the trained neural network [the PFE model can also be used to estimate (predict) the uncertainty/confidence of the model output, for each input (pg. 2, section 1; pg. 4, section 4; etc.)], wherein generating the prediction for the confidence value includes using the probabilistic model to generate the prediction of confidence for each of one or more input data to the trained neural network [the PFE model can also be used to estimate (prediction) the uncertainty/confidence of the model output, for each input (pg. 2, section 1; pg. 4, section 4; etc.), where the uncertainty module is used as part of the PFE to estimate the confidence value (pg. 5, section 4.3; etc.); thus using a probabilistic model to generate the prediction of confidence for each (face image) input]; and filtering input data to a trained neural network to improve an accuracy of the trained neural network [the PFE can be used to filter/reject input images if it is not confident, in order to improve the performance of the system (pg. 8, section 6; etc.)], wherein filtering the input data includes: determining whether the predicted confidence value exceeds a predetermined confidence threshold and only permitting the trained neural network to process input data that exceeds the predetermined confidence threshold [In many scenarios, we may expect a higher performance than our system is able to achieve or we may want to make sure the system’s performance can be controlled when facing complex application scenarios. Therefore, we would expect the model to reject input images if it is not confident. A common solution for this is to filter the images with a quality assessment tool. We show that PFE provides a natural solution for this task. We take all the images from LFW and IJB-A datasets for image-level face verification (We do not follow the original protocols here). The system is allowed to “filter out” a proportion of all images to maintain a better performance. We then report the TAR@FAR= 0:001% against the “Filter Out Rate”. We consider two criteria for filtering: (1) the detection score of MTCNN and (2) a confidence value predicted by our uncertainty module (pg. 8, section 6 and fig. 11; etc.); where using the predicted confidence value to filter input images at a pre-specified “Filter Out Rate” would mean that the predicted confidence exceeds a predetermined confidence threshold (e.g., if the confidence-based “Filter Out Rate” is 20% then the confidence threshold is the 20th percentile confidence)].
As per claim 2, Shi teaches receiving input data for the trained neural network during the inference of the trained neural network [given a pre-trained model f which produces a deterministic face embedding in the latent space, we learn an optimized uncertainty module estimate for f(x), which takes the same inputs as the deterministic model (pg. 5, section 4.3; etc.) To address the above problems, we propose Probabilistic Face Embeddings (PFEs), which give a distributional estimation instead of a point estimation in the latent space for each input face image (pg. 1, section 1; etc.); where the input face images are the received input data during inference for the trained network]; and wherein the generating of the probabilistic model of the latent space of the trained neural network using the observations of the latent space comprises generating the probabilistic model of the latent space of the trained neural network using the observations of the latent space and the input data received by the trained neural network [given a pre-trained model f which produces a deterministic face embedding in the latent space, we learn an optimized uncertainty module estimate for f(x), which takes the same inputs as the deterministic model (pg. 5, section 4.3; etc.)].
As per claim 3, Shi teaches receiving output of the trained neural network during inference of the trained neural network [given a pre-trained model f which produces a deterministic face embedding in the latent space, we learn an optimized uncertainty module estimate for f(x) (the output of the trained neural network), which takes the same inputs as the deterministic model (pg. 5, section 4.3; etc.)]; and wherein the generating of the probabilistic model of the latent space of the trained neural network using the observations of the latent space comprises generating the probabilistic model of the latent space of the trained neural network using the observations of the latent space and the input data received by the trained neural network during inference and the output of the trained neural network during inference of the trained neural network [given a pre-trained model f which produces a deterministic face embedding in the latent space, we learn an optimized uncertainty module estimate for f(x) (the output of the trained neural network), which takes the same inputs as the deterministic model (pg. 5, section 4.3; etc.)].
As per claim 8, Shi teaches predicting a confidence value for output of a trained neural network having a given input, wherein predicting the confidence value includes using the probabilistic model to generate a prediction of confidence for each of one or more input data to the trained neural network [the PFE model can also be used to estimate (prediction) the uncertainty/confidence of the model output, for each input (pg. 2, section 1; pg. 4, section 4; etc.), where the uncertainty module is used as part of the PFE to estimate the confidence value (pg. 5, section 4.3; etc.); thus using a probabilistic model to generate the prediction of confidence for each (face image) input].
As per claim 9, Shi teaches predicting a confidence value for output of a trained neural network having a given input, wherein predicting the confidence value includes using the probabilistic model to generate a prediction of confidence for each of one or more input data to the trained neural network [the PFE model can also be used to estimate (prediction) the uncertainty/confidence of the model output, for each input (pg. 2, section 1; pg. 4, section 4; etc.), where the uncertainty module is used as part of the PFE to estimate the confidence value (pg. 5, section 4.3; etc.); thus using a probabilistic model to generate the prediction of confidence for each (face image) input].
As per claim 13, see the rejection of claim 1, above, wherein Shi further teaches a computer program product executable to perform operations for modelling a latent space of a trained neural network, the operations comprising: [the method steps] [the PFE system is combined with a base model which is a 64-layer network trained with AM-Softmax on the MS-Celeb-1M dataset (pg. 6, section 5.2); which requires program code stored in a memory and executed by at least one processor].
As per claim 15, see the rejection of claim 2, above.
As per claim 16, Shi teaches wherein generating of the probabilistic model of the latent space of the trained neural network using the observations of the latent space includes using the output of the trained neural network during inference of the trained neural network [given a pre-trained model f which produces a deterministic face embedding in the latent space, we learn an optimized uncertainty module estimate for f(x) (the output of the trained neural network), which takes the same inputs as the deterministic model (pg. 5, section 4.3; etc.); which is using the outputs of the trained model (during inference)].
As per claim 17, see the rejection of claim 8, above.
As per claim 18, see the rejection of claim 1, above, wherein Shi further teaches a computer system comprising one or more processors operable to perform operations for modelling a latent space of a trained neural network comprising [the method steps] [the PFE system is combined with a base model which is a 64-layer network trained with AM-Softmax on the MS-Celeb-1M dataset (pg. 6, section 5.2); which requires program code stored in a memory and executed by at least one processor].
As per claim 19, see the rejection of claim 2, above.
As per claim 20, see the rejection of claim 8, above.
Response to Arguments
The prior rejections under 35 U.S.C. 112 have been withdrawn due to the amendments filed.
Applicant's arguments filed 21 July 2025 have been fully considered but they are not persuasive.
Regarding the rejections under 35 U.S.C. 101, applicant argues that the claimed invention is directed to a specific technological solution to a technological problem by filtering out input data to the trained neural network based on confidence values.
However, applicant has described an improvement to the decision about what input data to filter out. As described above, this is a mental process/mathematical calculations. Therefore, (assuming that the invention provides these advantages) this amounts to an improvement to an abstract idea rather than to a computer or technology. See MPEP 2106.05(a). It appears that any benefits to the computer itself are based solely on the use of an improvement to the abstract idea(s), using generic computer components to apply the abstract idea(s). The Federal Circuit has also indicated that mere automation of manual processes or increasing the speed of a process, where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to show an improvement in computer functionality. FairWarning IP, LLC v. latric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017). The Federal Circuit has also indicated that a claim must include more than conventional implementation on generic components or machinery to qualify as an improvement to an existing technology. Affinity Labs of Tex. v. DirecTV, LLC, 838 F.3d 1253, 1264-65, 120 USPQ2d 1201, 1208-09 (Fed. Cir. 2016); TLI Communications LLC v. AVAuto, LLC, 823 F.3d 607, 612-613, 118 USPQ2d 1744, 1747-48 (Fed. Cir. 2016). Claims must also include more than just instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology (MPEP § 2106.05(a)).
Regarding the rejections under 35 U.S.C. 102, over Shi, applicant argues that “Shi does not disclose or suggest modelling a latent space of a trained neural network or generating a probabilistic model of the latent space” and rather “provides for individual images as a distribution in the latent space,” where “representing an image as a distribution in the latent space is not the same as generating a probabilistic model of the latent space itself.”
However, Shi teaches Probabilistic Face Embeddings (PFEs), which give a distributional estimation instead of a point estimation in the latent space for each input face image (pg. 1, section 1; etc.) and, given a pre-trained model f which produces a deterministic face embedding in the latent space, we learn an optimized uncertainty module estimate for f(x), where f(x) represents the most likely features of a given input in the latent space (pg. 5, section 4.3; etc.). This model, that produces estimates for the PFEs based on the observations of the latent space, is a probabilistic model of the latent space.
Conclusion
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i): claims 4-7, 10-12, and 14 are cancelled; claims 1-3, 8, 9, 13, and 15-20 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gonzalez Aguirre (US 2019/0135300) – discloses performing probabilistic modeling of sensor data in a latent space using unsupervised auto-encoding and using the model to predict a confidence based on sensor inputs, with which to perform anomaly detection.
Amores (US 2020/0243069) – discloses determining a confidence score for portions of text, and comparing the confidence to a threshold to determine whether to use that portion of text as training data.
Borland (US 2016/0094567) – discloses storing a confidence level with training data records, and ignoring records for which the confidence level does not meet a minimum threshold.
Braho (US 2006/0178882) – discloses assigning confidence to speech signals, and if the confidence factor is below the acceptance threshold, rejecting/ignoring the speech input.
Northcutt et al. (Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels, Aug 2017, pgs. 1-27) – discloses a rank pruning algorithm that selects confident examples and removes the rest, before training on the pruned set.
The examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c).
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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/GEORGE GIROUX/Primary Examiner, Art Unit 2128