DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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 Arguments
2. Applicant's arguments received 6/11/2026 have been considered but are moot in view of the new ground(s) of rejection. Detailed response is given in sections 3-6 as set forth below in this Office action.
Regarding the claim eligibility, Applicant argues that:
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Examiner respectfully disagrees. Applicant is advised that, according to MPEP 2106 and the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), the USPTO determines claim eligibility under 35 U.S.C. § 101 using the Alice framework. The analysis under Step 2A - Prong 1 evaluates whether the claim recites a judicial exception. Step 2A - Prong 2 asks does the claim recite additional elements that integrate the judicial exception into a practical application, and, if necessary, Step 2B further analyzes whether or not the claim provides an Inventive Concept. That is, the claim needs to be analyzed limitation by limitation, and/or element by element, following the MPEP/2019 PEG guidelines. Applicant is particularly advised that, under the 2019 PEG, when assessing subject matter eligibility for a patent, examples of “determining or calculating parameters" that might be considered a judicial exception include claims that simply involve basic data manipulation and/or mathematical calculations that can be performed in mind or the aid of a general-purpose computer, without any inventive application of that calculation to a specific technological problem.
In the instant case, focusing on what the inventors have invented exactly and giving the broadest reasonable interpretation (BRI) to the claims, Examiner asserts that the pending claims 1-16 are directed to an abstract idea of diagnosing faults of a rotating machinery using a trained machine/deep learning model, but without reciting any additional elements that amount to “significantly more” than the judicial exception.
Specifically, claim 1 recites:
A method of establishing a fault diagnosis model for a sliding bearing in rotating machinery, the method comprising:
generating a set of simulated data based on data obtained by virtual measurements with simulated measurement directions that are orthogonal to each other; (the “generating a set of simulated data” is done using a mathematical calculation in light of the supporting disclosure [0035] which gives equation 1 for generating that simulated data)
obtaining a signal data set, the signal data set comprising a plurality of displacement signals of shaft vibration of the sliding bearing in the rotating machinery and the set of simulated data; (high level data gathering, without any description of how the data is obtained, just what the data represents however mere data characterization does no more than specify the field of use, e.g. vibrations of a sliding bearing in a rotating machinery)
classifying and archiving the displacement signals of the shaft vibration in the signal data set according to fault types; (classifying data according to fault types is abstract, such as a mental evaluation and judgement, archiving in light of the spec. is “sorting and summarizing data according to certain standards.” See [0034] therefore it is also abstract as sorting and summarizing data are mental processes)
synthesizing the displacement signals in the archived signal data set to obtain a plot of shaft center orbit of the sliding bearing in the rotating machinery, so as to obtain a plot data set of the shaft center orbit; (the “synthesizing” the only description of how the synthesizing is done seems to just include generating the data plot as represented by Fig. 3 and represented by expression 2 see [0046], therefore the “synthesizing” appears to just be the plotting of data using mathematical relationships) and
training the fault diagnosis model based on the plot data set of the shaft center orbit. (the “training” is recited at a high level of generality without any description of how the training is performed such that this limitation is merely the use of generic machine learning/artificial intelligence technology [although no actual AI/ML technology is recited in the independent claim, just suggested through the use of the word “train” and when read in light of the spec.] used as a tool to apply the abstract idea)
Claim 9 recites the step of “utilizing a fault diagnosis model to diagnose the fault type…based on the plot” which is also abstract, i.e. data observation, evaluation, or judgement ; the use of a model which includes a ML/AI model (e.g. spec. [0012] and claim 10) recited at a high level of generality is just the use of general purpose computer technology as a tool to apply the abstract idea.
Under the broadest reasonable interpretation (BRI), Examiner asserts that each of the pending claims 1 and 9 recites an abstract idea and the additional elements do not appear sufficient to integrate into a practical application.
While arguing about “[t]he improved process for training a fault diagnosis model”, Applicant fails to show enough detail on the model or how it is trained to amount to any improvement in AI/ML technology. No particular machine is claimed, no significant details of the data gathering steps seem to be recited, no particular transformation is claimed. Also looking to the Spec. [0035] recites the problem is that its difficult to obtain actual condition data onsite and the solution to this is to simulate the data instead using the orbit generation mechanism equation 1, therefore it appears the asserted improvements come from the abstract simulation of the data and not from the additional elements. In other words, it appears the claims recite a mathematical solution, not a technological one. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.”
As such, Examiner affirmed that none of the additional limitations is qualified to be “significantly more”, such that it improves the functioning of a computer or the relevant technology by using the judicial exception in conjunction with a particular machine or manufacture that is integral to the claim.
It is held that simply setting forth advantages (i.e. benefits) of use without providing any rational/evidence to how/why the claimed elements amount to significantly more than the judicial exception could be treated as mere instructions to apply the judicial exception on a computer component (MPEP 2106.05(f)), but not qualified for an improvement (i.e. enhancement) in the functioning of a computer or an improvement to another technology or technical field. The key is to show that the claim goes beyond just performing a calculation and provides a practical application or significant improvement through the use of that calculation. See MPEP 2106.04(d)(I) and 2106.05(a). Applicant’s arguments in this regard are therefore not persuasive.
Applicant's arguments regarding the rejection under 35 USC 102/103 have been considered are moot in view of the new ground(s) of rejection. Detailed response is given in sections 5-6 as set forth below in this Office action.
Applicant’s argument regarding the claim interpretation is deemed persuasive. As such, the Examiner re‑interprets the claims without invoking 35 USC 112(f).
Claim Rejections - 35 USC § 101
3. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 101 that form the basis for the rejections under this section made in this Office action:
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.
4. Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the 2019 PEG (now been incorporated into MPEP 2106), the revised procedure for determining whether a claim is "directed to" a judicial exception requires a two-prong inquiry into whether the claim recites: (1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human interactions such as a fundamental economic practice, or mental processes); and (2) additional elements that integrate the judicial exception into a practical application (see MPEP § 2106.05(a)-(c), (e)-(h)).
Only if a claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look to whether the claim: (3) adds a specific limitation beyond the judicial exception that is not "well-understood, routine, conventional" in the field (see MPEP § 2106.0S(d)); or (4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Claims 1-16 are directed to an abstract idea of diagnosing faults of a rotating machinery using a machine/deep learning model.
Specifically, representative claim 1 recites:
A method of establishing a fault diagnosis model for a sliding bearing in rotating machinery, the method comprising:
(S1) generating a set of simulated data based on data obtained by virtual measurements with simulated measurement directions that are orthogonal to each other;
(S2) obtaining a signal data set, the signal data set comprising a plurality of displacement signals of shaft vibration of the sliding bearing in the rotating machinery and the set of simulated data;
(S3) classifying and archiving the displacement signals of the shaft vibration in the signal data set according to fault types;
(S4) synthesizing the displacement signals in the archived signal data set to obtain a plot of shaft center orbit of the sliding bearing in the rotating machinery, so as to obtain a plot data set of the shaft center orbit; and
(S5) training the fault diagnosis model based on the plot data set of the shaft center orbit.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
The highlighted portion of the claim constitutes an abstract idea under the 2019 Revised Patent Subject Matter Eligibility Guidance and the additional elements are NOT sufficient to amount to significantly more than the judicial exceptions, as analyzed below:
Step
Analysis
1. Statutory Category ?
Yes.
Method
2A - Prong 1: Judicial Exception Recited?
Yes.
See the bolded portion listed above.
Under its broadest reasonable interpretation (BRI), the limitation S1 encompasses mathematical concepts, namely a series of calculations leading to one or more numerical results or answers. The “generating a set of simulated data” is done using a mathematical calculation in light of the supporting disclosure (Spec. [0035]) which gives equation 1 for generating that simulated data.
Under its BRI, each of the limitations S3 and S4 encompasses mental processes, i.e., data analysis, evaluation, judgement and/or concepts that can be performed in the human mind with the aid of pen and paper. In particular, a general method of classifying data is considered a mental process as it is similar to how a human would categorize or filter information, and “synthesizing” appears to just be the plotting of data using mathematical relationships (see Spec. [0046]). The recited attributes of signals and the derived plot data set are merely characterization which can be viewed as to generally link the use of the mental processes to the relevant technological environment or field of use.
The limitation S5 is recited at a high level of generality without any description of how the training is performed such that this limitation is merely the use of generic machine learning/artificial intelligence technology used as a tool to apply the abstract idea. Under its BRI and in light of the USPTO’s July 2024 Subject Matter Eligibility Examples (e.g., Example 47, claim 2), a generic recitation of training an AI model, by a computer, based on input of existing training data involve optimizing the AI model using a series of mathematical calculations to iteratively adjust the algorithms and/or parameter values of the AI model, therefore encompasses mathematical concepts (see Applicant’s Spec. [0061]-[0064]).
Nothing in the bolded portion precludes the limitations S1, S3, S4 and S5 from practically being performed in the mind and/or using a pen and paper. As such, the bolded portion of instant claim 1 falls within a combination of the “Mental Process” and “Mathematical Concepts” groupings of Abstract Ideas defined by the 2019 PEG.
2A - Prong 2: Integrated into a Practical Application?
No.
Under the BRI, the limitation S2 encompasses an insignificant pre-solution activity (i.e., necessary data gathering). It is held that high level data gathering, without any description of how the data is obtained, just what the data represents however mere data characterization does no more than specify the field of use, e.g. vibrations of a sliding bearing in a rotating machinery. According to MPEP 2106.05(g)(3): … that were described as mere data gathering in conjunction with a law of nature or abstract idea. See also Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 13863, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
In general, the claim as a whole does not meet any of the following criteria to integrate the abstract idea into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. However, in all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the algorithm across a wide range of applications.
2B: Claim provides an Inventive Concept?
No.
See analysis given in 2A - Prong 2 above.
Focusing on what the inventors have invented exactly, it is considered that the “heart” of pending claim 1 is directed to an algorithm of processing observation data values to extract plot data set for training an AI model.
The claim does not recite any additional element that is qualified for “significantly more” or reflects an “inventive concept” (See discussion of prior art as set forth in sections 7-8 below; see also MPEP 2106.05).
The claim is therefore ineligible under 35 USC 101.
The dependent claims 2-8 inherit attributes of the independent claim 1, but do not add anything which would render the claimed invention a patent eligible application of the abstract idea. These claims merely extend (or narrow) the abstract idea which do not amount for "significant more" because they merely add details to the algorithm which forms the abstract idea as discussed above.
Claim 2 recites: wherein the signal data set includes at least one of a set of simulated data and a set of condition monitoring data, the set of simulated data includes data obtained by virtual measurements with simulated measurement directions that are orthogonal to each other, and the set of the condition monitoring data include data obtained by two sensors with measurement directions that are orthogonal to each other. Under the BRI, each of the limitations of “a set of simulated data” which “includes data obtained by virtual measurements with simulated measurement directions that are orthogonal to each other” and “the set of the condition monitoring data include data obtained by two sensors with measurement directions that are orthogonal to each other” reads on an insignificant pre-solution activity (i.e., necessary data gathering). Further, the limitation of “virtual measurements” covers concepts that can be performed in the human mind, including observation, evaluation, judgment and opinion; while the lack of particular type of sensor and/or deployment of the sensors in particular locations of the rotating machinery would monopolize the judicial exception across a wide range of applications.
Claims 5-8 recite additional elements of a general-purpose computer adapted to perform computing activities via basic function of the computer for practicing an abstract idea. According to MPEP 2106.04(a)(2), if a claim limitation, under its broadest reasonable interpretation, covers mental processes except for the mention of generic computer components performing computing activities via basic function of the computer, then the claim is likely considered to be directed to an ineligible abstract idea, as it essentially describes a mental process that could be performed by a human without the computer components adding any significant practical application beyond the abstract concept itself. As such, none of the limitations recited in claims 5-8 amounts to be “significantly more” than the abstract idea itself.
Claims 9-16 are rejected for the same reasons as set forth above for claims 1-8.
Particular consideration is given to the limitation “utilizing a fault diagnosis model to diagnose the fault type of the sliding bearing in the rotating machinery to be diagnosed, based on the plot of the shaft center orbit of the sliding bearing in the rotating machinery to be diagnosed” recited in claim 9. Under its BRI, this limitation is also abstract, i.e. data observation, evaluation, or judgement. The use of a model which includes a ML/AI model (e.g. Spec. [0012] and claim 10) recited at a high level of generality is just the use of general purpose computer technology as a tool to apply the abstract idea. In view of the USPTO’s July 17, 2024 Subject Matter Eligibility Examples (e.g., Examples 47-49), a prediction using a machine learning model is considered an abstract idea if the claim focuses solely on the concept of making predictions and/or classification utilizing the machine learning model, but without any specific technical improvements or applications that go beyond the basic idea of using a computer to analyze data and generate predictions/classifications; essentially, if the claim is too high-level and does not describe a concrete, inventive implementation of the machine learning process. In the instant case, the recited “utilizing a fault diagnosis model to diagnose the fault type of …” generally applies the abstract idea without placing any limits on how the “fault diagnosis model”, which comprises “a machine learning model, a deep learning model or a fusion model thereof”, functions in a way that reflects an inventive concept. Rather, the claims only recite the outcome of the AI model but does not include any details about how the “diagnose” is accomplished. See MPEP 2106.05(f).
Hence instant claims 1-16 are treated as ineligible subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
5. 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; or
(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.
6. Claims 1-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jeong et al. (Rotating Machinery Diagnostics using Deep Learning on Orbit Plot Images, Procedia Manufacturing, Volume 5, 2016, Pages 1107–1118).
Regarding claims 1 and 13, Jeong discloses a method of establishing a fault diagnosis model (Fig. 2) for a sliding bearing in rotating machinery and a device (e.g., a general-purpose computer performing a generic computer function of data processing) for implementing the method (Abstract; Fig. 7; Section 4.1: “The testbed consists of a shaft with length of 470 mm coupled with a flexible coupling to reduce the effect of the high frequency vibration, two discs and three bearing housings”), the method comprising:
generating (e.g., via optimization method based on the mathematical orbit model discussed in section 2.3) a set of simulated data (e.g., the approximated orbit trajectory or the binary image converted from the approximated orbit trajectory) based on data obtained by virtual measurements (Section 3.4) with simulated measurement directions that are orthogonal to each other (Section 4.1: “Two accelerometers are mounted at bearing housing along x and y directions”; the raw orbit image is generated from the data measured by the two accelerometers; as such, the set of simulated data generated by optimizing the original orbit image based on the mathematical orbit model as discussed in Section 3.4 encompasses simulated measurement directions that are orthogonal to each other);
obtaining a signal data set (i.e., the dataset of orbit images fed into the CNN to train a classification model for image pattern recognition), the signal data set comprising a plurality of displacement signals of shaft vibration of the sliding bearing in the rotating machinery (Section 1: “ … monitoring vibration signals collected by accelerometer or proximity sensors in various locations”; Section 2.5: “ … are the vibration signal”; by inherency, the data set of orbit images fed into the CNN must comprises or be made up of the raw orbit image data from which the simulated data is derived) and the set of simulated data (Section 3: “Before we feed orbit images to CNN to train a classification model for image pattern recognition, it is necessary to conduct a pre-processing step”; Section 4.1: “We apply the proposed orbit image pattern recognition algorithm to the orbit images collected …”);
classifying and archiving the displacement signals of the shaft vibration in the signal data set according to fault types (Section 4.1: “We apply the proposed orbit image pattern recognition algorithm to the orbit images collected from the rotor kit, shown in Figure 7. There are pre-defined five classes of orbits: circle (C), ellipse (E), eight (8), heart (H), and tornado (T) shapes according to the rotor status. Different orbit shapes are produced, depending on the rotor status such as normal, unbalance, misalignment, rubbing, etc.”);
synthesizing the displacement signals in the archived signal data set to obtain a plot of shaft center orbit of the sliding bearing in the rotating machinery, so as to obtain a plot data set of the shaft center orbit (Section 4.1: “The training set of 150 orbit images are acquired by each pattern (normal, unbalance, misalignment in Table1”; see also Table 2 and related text); and
training the fault diagnosis model (Section 2.3) based on the plot data set of the shaft center orbit (Section 4.1: “These data sets are used to train weight parameters for CNN. Total 3 layers structure (convolution and sub-sampling layer, and fully connected to 5 output neurons) is used for CNN in our experiment. Table 3 and Figure 8 show the detailed training constraints and information”).
Regarding claim 2, Jeong discloses: wherein the signal data set (by inherency, the data set of orbit images fed into the CNN must comprises or be made up of the raw orbit image data from which the simulated data is derived) further includes a set of condition monitoring data (i.e., the raw orbit image data), the set of the condition monitoring data include data obtained by two sensors with measurement directions that are orthogonal to each other (Section 4.1: “Two accelerometers are mounted at bearing housing along x and y directions”).
Regarding claim 3, Jeong discloses: wherein training fault diagnosis model based on the plot data set of the shaft center orbit comprises: extracting a set of signal features based on the archived signal data set (see Section 3: Pre-processing), and extracting a set of image features (e.g., Section 4.1: “apply the proposed orbit image pattern recognition algorithm to the orbit images collected from the rotor kit, shown in Figure 7. … Different orbit shapes are produced, depending on the rotor status such as normal, unbalance, misalignment, rubbing, etc.”) based on the plot data set of the shaft center orbit; and training the fault diagnosis model based on the extracted set of the signal features and the extracted set of the image features (Section 4.1: “The training set of 150 orbit images are acquired by each pattern (normal, unbalance, misalignment in Table1). The orbit images are changed x and y axial symmetry images to maximize the effect of training. These data sets are used to train weight parameters for CNN”).
Regarding claim 4, Jeong discloses: wherein training the fault diagnosis model based on the extracted set of the signal features and the extracted set of the image features comprises: performing machine learning modeling and deep learning modeling based on the extracted set of the signal features and the extracted set of the image features (Abstract; Section 4.1; see also Fig. 2 and related text); fusing a machine learning model and a deep learning mode to obtain the fault diagnosis model (Section 2.3: Jeong’s Convolutional Neural Network (CNN) model represents a hybrid approach which leverages traditional ML for structured data (e.g., tabular data) and deep learning for unstructured data (e.g., images), this hybrid approach integrates model architectures of traditional machine learning and deep learning using the CNN for feature extraction and classification).
Regarding claims 5-8, Jeong discloses: a device (e.g., a general-purpose computer performing a generic computer function of data processing) for fault diagnosis of a sliding bearing in rotating machinery (Fig. 7), the device comprising: a non-transitory computer-readable storage medium having computer instructions stored thereon, and a processor (inherent to a general-purpose computer), wherein the instructions, when executed by the processor, cause the processor to perform a method of claim 1 (Abstract; Section 2.2: “deep learning is a computational model which is composed of multiple processing layers that perform non-linear input-output mappings to learn representations of data with multiple levels of abstraction. Then, deep learning can find complicated hidden patterns in large data sets by using the backpropagation algorithm to calculate its internal parameters that are used to compute the representation in each layer from the representation in the previous layer”; see also discussion for claim 1 above).
Regarding claims 9 and 14, Jeong discloses a method for fault diagnosis of a sliding bearing in rotating machinery and a device (e.g., a general-purpose computer performing a generic computer function of data processing) for implementing the method (Abstract; Fig. 7; Section 4.1: “The testbed consists of a shaft with length of 470 mm coupled with a flexible coupling to reduce the effect of the high frequency vibration, two discs and three bearing housings”), the method comprising: obtaining displacement signals of shaft vibration of the sliding bearing in the rotating machinery to be diagnosed (Section 1: “ … monitoring vibration signals collected by accelerometer or proximity sensors in various locations”; Section 2.5: “ … are the vibration signal”); synthesizing the displacement signals of the shaft vibration to obtain a plot of shaft center orbit of the sliding bearing in the rotating machinery to be diagnosed (Section 4.1: “The training set of 150 orbit images are acquired by each pattern (normal, unbalance, misalignment in Table1”; see also Table 2 and related text); utilizing a fault diagnosis model (Section 2.3: the CNN model) to diagnose the fault type (classification) of the sliding bearing in the rotating machinery to be diagnosed, based on the plot of the shaft center orbit of the sliding bearing in the rotating machinery to be diagnosed (Section 4.2); wherein the fault diagnosis model is trained based on a signal data set (i.e., the dataset of orbit images fed into the CNN to train a classification model) comprising a plurality of displacement signals of shaft vibration obtained by two sensors with measurement directions that are orthogonal to each other (Section 4.1: “Two accelerometers are mounted at bearing housing along x and y directions”) and a set of simulated data generated based on data obtained by virtual measurements with simulated measurement directions that are orthogonal to each other (see discussion of the simulated data for claims 1 and 13 above).
Regarding claim 10, Jeong discloses: wherein in case the fault diagnosis model is a machine learning model, a deep learning model or a fusion model thereof (see discussion of claim 4 above), the method further includes: extracting signal features based on the displacement signals of the shaft vibration, and extracting image features based on the plot of the shaft center orbit (Section 2.3: “Many techniques are performed to solve image pattern recognition problem as extracting features or developing matching algorithm. We will briefly describe how CNN works since it is used as a key algorithm for the orbit image pattern recognition in this paper. … This hierarchical organization is able to extract proper features in image classification tasks”); and inputting the extracted signal features and image features into the fault diagnosis model to diagnose the fault type of the sliding bearing in the rotating machinery to be diagnosed (Section 4.2; see also Section 4.3: “Because deep learning can autonomously extract abstract features which, in general, cannot be seen in a training data set, the deep learning algorithm with CNN can provide robust classification results even with subtle difference of shape, orientation and position”).
Regarding claims 11-12 and 15-16, Jeong discloses the claimed invention (see discussion for claims 5-8 above).
Conclusion
7. 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 extension fee 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 date of this final action.
Citation of Relevant Prior Art
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Orvalho et al. (US 20230237840 A1) discloses technique of training a neural network model for facial recognition using synthetically rendered facial images. The model is able to learn relevant features from the synthetic training data that also apply to real test data.
Contact Information
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIUQIN SUN whose telephone number is (571)272-2280. The examiner can normally be reached 9:30am-6:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/X.S/Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857
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