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 .
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 5 recite the limitation “wherein the time series condition is estimated using a convolutional neural network layers”. It is unclear if this limitation refers to a singular convolutional neural network layer or multiple convolutional neural network layers. For the purposes of examination, this limitation will be interpreted as “wherein the time series condition is estimated using a plurality of convolutional neural network layers”.
Claims 2-4 and 6 are rejected for their dependency on an indefinite claim.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims
Step 1 – Claim 1 is drawn to a method, claim 5 is drawn to an apparatus, and claim 6 is drawn to a non-transitory computer-readable storage medium. Therefore, each of these claims fall under one of the four categories of statutory subject matter (process/method, machine/product/apparatus, manufacture or composition of matter).
Step 2A Prong 1 – Claims 1, 5 and 6 are directed to a judicially recognized exception of an abstract idea without significantly more. Claims 1, 5 and 6 recite:
estimating a label for the time series data – This limitation is directed towards the abstract idea of a mathematical calculation (see MPEP § 2106.04(a)(2), section I, C). In Paragraph [0030] of the specification, it states “The estimation unit 101 performs a nonlinear transform treating the training time series data Xn (or the time series data X to be estimated) as a matrix (tensor), and thereby estimates the label ^yn (or the label ^y).”. BRI in light of the specification would support that “estimating a label” would encompass a nonlinear matrix transformation and fall under the mathematical concepts grouping.
in which the intermediate output is used to estimate a time series condition – This limitation is directed towards the abstract idea of a mathematical calculation (see MPEP § 2106.04(a)(2), section I, C). In Paragraph [0031] of the specification, it states “In addition, the time series domain adaptation unit 102 treats the frequency cn,1 that is the time series condition Cn included in the training time series data Xn as input, and calculates the error lossc between the frequency cn,1 and the corresponding estimation value, namely the frequency ^cn,1." Similarly, in Paragraph [0032], it states "The time series domain adaptation unit 102 estimates the frequency ^cn,1 by performing a nonlinear transform on the output from the convolutional neural network layers achieving the estimation unit 101.”. BRI in light of the specification would support that “using the intermediate output to estimate a time series condition” would encompass a frequency calculation and fall under the mathematical concepts grouping.
pairing the time series data and the time series condition – This limitation is directed towards the abstract idea of a mental process, or a concept that can be performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2), subsection III, C). In Paragraph [0021] of the specification, it states “the acceleration data is divided up and assigned a ground truth label yn by a sliding window having a fixed time step (such as 3 seconds, for example), and is paired with the time series conditions Cn to obtain the training time series data Xn. During estimation, acceleration data likewise is divided up by a sliding window of fixed time and paired with the time series conditions C to obtain the time series data X to be estimated.” Similarly, in Paragraph [0045], it states “The time series data X to be estimated is similar to the training time series data Xn except that ground truth labels are not assigned, and is expressed as X = ({x1, …, xT}, C}.” BRI in light of the specification would support that “pairing the time series data and the time series condition” would encompass a mental process with or without the assistance of pen and paper of drawing an association between two values.
updating a parameter of the first neural network model and a parameter of the second neural network model by using an error between the estimated label and a ground truth label for the time series and an error between the estimated time series condition and a true time series condition of the time series data – This limitation is directed towards the abstract idea of a mathematical calculation (see MPEP § 2106.04(a)(2), section I, C). In Paragraph [0041] of the specification, it states “Thereafter, the parameter update unit 103 uses the error lossc calculated in step S104 above and the error lossy calculated in step S105 above to calculate the total error lossy + λlossc and updates the parameters by backpropagating the total error so as to minimize the total error according to a known optimization method (step S106). With this arrangement, the parameters of the neural network model achieving the estimation unit 101 (and the neural network model achieving the time series domain adaptation unit 102) are learned." BRI in light of the specification would support that “updating neural network parameters” would encompass a calculating an error loss and fall under the mathematical concepts grouping.
Step 2A Prong 2 – The following additional limitations recited do not integrate the abstract idea into a practical application:
collecting, using a plurality of sensors, time series data, wherein the plurality of sensors further comprises image collecting devices – This limitation recites an insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)) and thus, fails to integrate the exception into a practical application.
inputting the time series data into a first neural network model – This limitation recites an insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)) and thus, fails to integrate the exception into a practical application.
wherein the label representing a real value expressing a continuous quantity is estimated using a deep learning model – This limitation amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). It merely limits the performance of the abstract idea of estimation to a deep learning model and thus, fails to integrate the exception into a practical application.
inputting an intermediate output estimated using the first neural network model into a second neural network model – This limitation recites an insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)) and thus, fails to integrate the exception into a practical application.
wherein the time series condition is estimated using a convolutional neural network layers – This limitation amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). It merely limits the performance of the abstract idea of estimation to convolutional neural network layers and thus, fails to integrate the exception into a practical application.
wherein the time series data is estimated using the convolutional neural network layers – This limitation amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). It merely limits the performance of the abstract idea of estimation to convolutional neural network layers and thus, fails to integrate the exception into a practical application.
Claims 5 and 6:
a processor; and a memory storing program instructions; a non-transitory computer-readable storage medium that stores therein a program – This limitation amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). It recites a generic computer or generic computer components that merely act as a tool on which the method operates.
Step 2B – The additional elements in Step 2A Prong 2, view individually or wholistically, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself.
collecting, using a plurality of sensors, time series data, wherein the plurality of sensors further comprises image collecting devices – This limitation recites an insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)), which is well-understood, routine and conventional activity similar to cases reviewed by the courts involving receiving or transmitting data over a network (see MPEP § 2106.05(d)(II)) and thus, fails to provide significantly more to the judicial exception.
inputting the time series data into a first neural network model – This limitation recites an insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)), which is well-understood, routine and conventional activity similar to cases reviewed by the courts involving receiving or transmitting data over a network (see MPEP § 2106.05(d)(II)) and thus, fails to provide significantly more to the judicial exception.
wherein the label representing a real value expressing a continuous quantity is estimated using a deep learning model – This limitation amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). It merely limits the performance of the abstract idea of estimation to a deep learning model and thus, fails to provide significantly more to the judicial exception.
inputting an intermediate output estimated using the first neural network model into a second neural network model – This limitation recites an insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)), which is well-understood, routine and conventional activity similar to cases reviewed by the courts involving receiving or transmitting data over a network (see MPEP § 2106.05(d)(II)) and thus, fails to provide significantly more to the judicial exception.
wherein the time series condition is estimated using a convolutional neural network layers – This limitation amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). It merely limits the performance of the abstract idea of estimation to convolutional neural network layers and thus, fails to provide significantly more to the judicial exception.
wherein the time series data is estimated using the convolutional neural network layers – This limitation amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). It merely limits the performance of the abstract idea of estimation to convolutional neural network layers and thus, fails to provide significantly more to the judicial exception.
Claims 5 and 6:
a processor; and a memory storing program instructions; a non-transitory computer-readable storage medium that stores therein a program – This limitation amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
As such, claims 1, 5 and 6 are not patent eligible.
Dependent Claims
Claims 2-4 merely narrow the previously cited abstract idea limitations. For the reasons described above with respect to independent claim 1, these judicial exceptions are not meaningfully integrated into a practical application, nor amount to significantly more than the abstract idea itself. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mathematical concepts that are achievable through mathematical computation. Therefore claims 2-4 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. § 101.
Step 1 – Claims 2-4 are drawn to a method. Therefore, each of these claims fall under one of the four categories of statutory subject matter (process/method, machine/product/apparatus, manufacture or composition of matter).
Step 2A Prong 1 – These claims are directed to a judicially recognized exception of an abstract idea without significantly more recited in Claim 1.
Step 2A Prong 2 – These limitations do not recite any additional elements which integrate the abstract idea into a practical application.
Claim 2:
wherein an output from a convolutional neural network layer or an output from a recurrent neural network layer included in the first neural network model when estimating the label is treated as the intermediate output to be inputted into the second neural network model – This limitation amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). It merely limits the use of the abstract idea to ---a neural network with a convolutional or recurrent layer and thus, fails to integrate the exception into a practical application.
Claim 3:
wherein the time series condition includes at least one of a frequency of the time series data or a duration before, after, or before and after a time point treated as a reference when collecting the time series data – This limitation recites the insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated (see MPEP § 2106.05(g)) and thus, fails to integrate the exception into a practical application.
Claim 4:
wherein the second neural network model is a neural network model achieving domain adaptation or domain generalization – This limitation merely recites the idea of achieving domain adaptation/generalization and fails to recite details of how the achieving is accomplished. Reciting the idea of a solution or outcome without detailing how the result is accomplished is equivalent to saying "apply it" (see MPEP § 2106.05(f)) and thus, fails to integrate the exception into a practical application.
Step 2B – These limitations, as a whole, do not amount to significantly more than the judicial exception.
Claim 2:
wherein an output from a convolutional neural network layer or an output from a recurrent neural network layer included in the first neural network model when estimating the label is treated as the intermediate output to be inputted into the second neural network model – This limitation amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). It merely limits the use of the abstract idea to ---a neural network with a convolutional or recurrent layer and thus, fails to provide significantly more to the judicial exception.
Claim 3:
wherein the time series condition includes at least one of a frequency of the time series data or a duration before, after, or before and after a time point treated as a reference when collecting the time series data – This limitation amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). It merely limits the use of the abstract idea to ---time series data frequency/duration and thus, fails to provide significantly more to the judicial exception.
Claim 4:
wherein the second neural network model is a neural network model achieving domain adaptation or domain generalization – This limitation recites the well-understood, routine, conventional activity (see MPEP § 2106.05(d)) of domain adaptation or domain generalization, which are common in the art according to Paragraph [0003] of the specification, which states “there is a problem in that the estimation accuracy may be limited. To address this problem… it is also common practice to use domain adaptation methods (for example, see Non- Patent Literature 1) and domain generalization methods (for example, see Non-Patent Literature 2) and thus, fails to provide significantly more to the judicial exception.
As such, claims 2-4 are not patent eligible.
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)(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 1-2 and 4-6 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Song et al. (US 20210201159 A1, filed 12/31/2019), hereinafter Song. Song was cited in the previous Office Action.
Regarding Claim 1, Song teaches An estimation method characterized by a computer executing:
collecting, using a plurality of sensors, time series data, wherein the plurality of sensors further comprises image collecting devices (Song: “the processing system 110 is configured to obtain the sensor data directly or indirectly from one or more sensors of the sensor system 170.” [0016]; “The sensor system 170 includes one or more sensors. For example, the sensor system 170 includes an image sensor, a camera, a radar sensor, a light detection and ranging (LIDAR) sensor, a thermal sensor, an ultrasonic sensor, an infrared sensor, a motion sensor, an audio sensor, an inertial measurement unit (IMU), any suitable sensor, or any combination thereof.” [0016]; “the teachings disclosed herein may extend to and be applied in advancing general time-series analysis” [0051]);
inputting the time series data into a first neural network model (Song: “Upon receiving the sensor data, the processing system 110 is configured to process this sensor data in connection with the domain adaptation application 130 and the machine learning system 140.” [0016]);
estimating a label for the time series data, wherein the label representing a real value expressing a continuous quantity is estimated used a deep learning model (Song: “At step 304, the processing system 110 is configured to generate label data for the sensor data of the target domain. For example, in the event that the processing system obtains sensor data of the target domain that is unannotated, the processing system 110 is configured to generate and utilize inferred labels (qt) as relatively weak supervision for the target domain.” [0004]; “Upon obtaining the interpolated results (i.e. the mixup formulations), the processing system 110 is configured, via the machine learning system 140, to generate label data that classifies the interpolated results (xs′, ys′). Since samples of the same domain follow the similar distribution, there is no need to apply feature-level linearity.” [0027]; “Deep learning systems typically rely on abundant data and extensive human labeling. As such, during deployment in real-world scenarios, they often face critical challenges when domain shifts occur and the labels under novel distributions are scarce or unavailable. These challenges relate to unsupervised domain adaptation (UDA), which involves utilizing a relevant source domain with abundant labels to build predictive modeling for an unannotated target domain.” [0002]);
inputting an intermediate output estimated using the first neural network model into a second neural network model, in which the intermediate output is used to estimate a time series condition, wherein the time series condition is estimated using a convolutional neural network layers (Song: “xt′ represents intermediary sensor data of the target domain and yt′ represents intermediary virtual label data of the target domain.” [0034]; “the shared embedding encoder 230 (e.g., generator) and the discriminator 250 are trained under the adversarial objective such that the encoder 230 learns to generate domain-invariant features. In this regard, the discriminator 250 is denoted as D: Z → (0,1), where 0/1 annotates the binary domain label.” [0036]; “The method includes generating inter-domain label data by interpolating first label data of the first domain with respect to second label data of the second domain. The method includes generating inter-domain output data via the machine learning system based on the inter-domain sensor data and the inter-domain label data.” [0005]; “In addition, the machine learning system 140 includes a convolutional neural network (CNN), a long short-term memory (LSTM) network, a recurrent neural network (RNN), any suitable artificial neural network, or any number and combination thereof.” [0015]);
pairing the time series data and the time series condition, wherein the time series data is estimated using the convolutional neural network layers (Song: “the shared embedding encoder 230 (e.g., generator) and the discriminator 250 are trained under the adversarial objective such that the encoder 230 learns to generate domain-invariant features. In this regard, the discriminator 250 is denoted as D: Z → (0,1), where 0/1 annotates the binary domain label.” [0036]; “The method includes generating inter-domain label data by interpolating first label data of the first domain with respect to second label data of the second domain. The method includes generating inter-domain output data via the machine learning system based on the inter-domain sensor data and the inter-domain label data.” [0005]; “In addition, the machine learning system 140 includes a convolutional neural network (CNN), a long short-term memory (LSTM) network, a recurrent neural network (RNN), any suitable artificial neural network, or any number and combination thereof.” [0015]); and
updating a parameter of the first neural network model and a parameter of the second neural network model by using an error between the estimated label and a ground truth label for the time series data and an error between the estimated time series condition and a true time series condition of the time series data (Song: “With (xts, qts), the processing system 110 is configured to use the mean square error (MSE) loss as it is more tolerant to false virtual labels in the target domain.” [0032]; “Inter-domain loss data is generated based on the inter-domain output data with respect to the inter-domain label data. Parameters of the machine learning system are updated upon optimizing final loss data that includes at least the inter-domain loss data.” [Abstract]; “At step 314, the processing system 110 is configured to minimize the final loss data, as determined at step 312. Upon minimizing the final loss data, the processing system 110 is configured to update the parameters of the machine learning system 140. Upon updating the parameters and/or training with the updated parameters, the processing system 110 is configured to determine if the machine learning system 140 meets predetermined threshold criteria and make the machine learning system 140 available for step 316 when the machine learning system 140 satisfies the predetermined threshold criteria.” [0045]).
Regarding Claims 5 and 6, they are apparatus and non-transitory computer readable medium claims that correspond to Claim 1. Therefore, they are rejected for the same reasons as Claim 1 above.
Regarding Claim 2, Song teaches the method of Claim 1, wherein an output from a convolutional neural network layer or an output from a recurrent neural network layer included in the first neural network model when estimating the label is treated as the intermediate output to be inputted into the second neural network model (Song: “xt′ represents intermediary sensor data of the target domain and yt′ represents intermediary virtual label data of the target domain.” [0034]; “the shared embedding encoder 230 (e.g., generator) and the discriminator 250 are trained under the adversarial objective such that the encoder 230 learns to generate domain-invariant features. In this regard, the discriminator 250 is denoted as D: Z → (0,1), where 0/1 annotates the binary domain label.” [0036]; “The method includes generating inter-domain label data by interpolating first label data of the first domain with respect to second label data of the second domain. The method includes generating inter-domain output data via the machine learning system based on the inter-domain sensor data and the inter-domain label data.” [0005]; “In addition, the machine learning system 140 includes a convolutional neural network (CNN), a long short-term memory (LSTM) network, a recurrent neural network (RNN), any suitable artificial neural network, or any number and combination thereof.” [0015]).
Regarding Claim 4, Song teaches the method of Claim 1, wherein the second neural network model is a neural network model achieving domain adaptation or domain generalization (Song: “The processing system includes at least one processor that is configured to execute the domain adaptation application to implement a method, which includes generating inter-domain sensor data by interpolating first sensor data of the first domain with respect to second sensor data of the second domain.” [0005]; “FIG. 2 illustrates an inter- and intra-domain mixup training (IIMT) framework 200 for unsupervised domain adaptation according to an example embodiment.” [0018]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Song et al. (US 20210201159 A1, filed 12/31/2019), hereinafter Song; in view of Zhang et al. (“Intelligent Fault Diagnosis under Varying Working Conditions Based on Domain Adaptive Convolutional Neural Networks”, published 10/29/2018), hereinafter Zhang.
Regarding Claim 3, Song teaches the method of Claim 1. However, it fails to expressly disclose wherein the time series condition includes at least one of a frequency of the time series data or a duration before, after, or before and after a time point treated as a reference when collecting the time series data.
In the same field of endeavor, teaches wherein the time series condition includes at least one of a frequency of the time series data or a duration before, after, or before and after a time point treated as a reference when collecting the time series data (Zhang: “Multi-domain statistical features including time domain features, frequency domain features, and time-frequency domain features have also been fed into the SAE model as a way of feature fusion.” [Section I. Introduction]; “In the field of transfer learning, one of the key research directions is domain adaptation (DA). DA aims at minimizing the differences between distributions of different domains in order to minimize the cross-domain prediction error by taking full advantage of information from both source and target domains.” [Section I. Introduction]; “We compare our methods with the convolutional neural networks (CNN) system with frequency features proposed by Jing et al.” [Section VI.B. Experimental Setup]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated wherein the time series condition includes at least one of a frequency of the time series data or a duration before, after, or before and after a time point treated as a reference when collecting the time series data, as taught by Zhang to the method of Song because both of these methods are directed towards utilizing domain adaptation to estimate time-series data conditions. Frequency is a key and well-known feature of time-series data. In making this combination and specifically predicting frequency, it would allow for the method of Song to be readily applied towards industrial monitoring, in which the frequency domain is important for fault diagnosis (Zhang: [Section I. Introduction]).
Response to Arguments
The Examiner acknowledges the Applicant’s amendments to Claims 1 and 5.
Applicant's arguments, filed 10/14/2025, with respect to the rejection of Claims 1-6 under 35 U.S.C. § 101 have been fully considered but are not persuasive.
Applicant alleges, on page 6 of the Remarks, that the amended claim 1, under its broadest interpretation, is directed to a technical solution to solve the technical problem of providing means to accurately estimate labels for time series data. Specifically, the human mind cannot practically perform “estimation using a convolutional neural network layers” and does not fall into the grouping of mental process.
Examiner respectfully disagrees.
As per MPEP § 2106.04(a)(2)(III)(C), “if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept”, “the claim is considered to recite a mental process”. As noted in the 101 analysis above, the described “estimation” is found to be directed more to the abstract idea of a mathematical concept than to a mental process, but regardless, merely reciting “convolutional neural network layers” to as the means to perform an abstract idea does not preclude the claim from reciting an abstract idea.
Applicant alleges, on pages 7-8 of the Remarks, that the amended claim 1 as a whole integrates the alleged judicial exceptions into a practical application. Amended claim 1 recites specific improvements to the technical field of providing means to accurately estimate labels for time series data. For example, amended claim 1 recites limitations of “inputting an intermediate output estimated using the first neural network model into a second neural network model, in which the intermediate output is used to estimate a time series condition, wherein the time series condition is estimated using a convolutional neural network layers [and] pairing the time series data and the time series condition, wherein the time series data is estimated using the convolutional neural network layers”. Such features help to improve the estimation of labels by increasing efficiency while reducing the associated cost. Additionally, these data analysis steps are different from “a claim to ‘collecting information, analyzing it, and displaying certain results of the collection and analysis’ where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind. Contrarily to Electric Power Group, amended claim 1 recites detailed features which are beyond a high-level of generality. Further, MPEP § 2106.05(a) directed examiners to look at the claim ‘as a whole’ and avoid oversimplifying the claims by looking at them generally and failing to account of the specific requirements of the claims.
Examiner respectfully disagrees.
Firstly, the exemplary limitations listed above either recite abstract ideas themselves, merely link the abstract ideas to a particular field of use or technical environment, or recite insignificant extra-solution activity that does not meaningfully add to the claim in such a way to provide integration into a practical application. For example, the limitation of “inputting an intermediate output estimated using the first neural network model into a second neural network model” merely describes the transmission and input of data and does not itself contribute to the alleged improvement of “increasing efficiency while reducing the associated cost” of estimating labels. The limitation of “us[ing] [the intermediate output] to estimate a time series condition, wherein the time series condition is estimated using a convolutional neural network layers”, as noted above, recites an abstract idea being performed in a computer environment and generally links the use of the abstract idea to the technical environment of convolutional neural networks. Similarly, “pairing the time series data and the time series condition” is regularly performed by humans and in the human mind as it simply entails associating data with each other. Even when performed in a computer environment, it merely equates to the idea of concatenating data. And the limitation reciting “wherein the time series data is estimated using the convolutional neural network layers” is, again, directed towards an abstract idea performed in a computer environment, except this estimation is described even more generally than the estimation of the “time series condition” because it is not clear how this limitation is linked to the rest of the claim or contributes to the alleged improvement. Even when all of these limitations are viewed as a whole with each other and with the rest of the claim, it merely amounts to the abstract idea of using a neural network to make an estimation. Second, regarding Electric Power Group, the claim was not found to be recited at a high level of generality because the claim literally recited “collecting information, analyzing it, and displaying certain results of the collection and analysis”, but because the additional limitations and features, detailed as they were in reciting what data was being collected and what information was being analyzed, merely amounted to insignificant extra-solution activity and were insufficient in integrating the abstract concept into a practical application.
Applicant alleges, on pages 8-9 of the Remarks, that the amended claim 1 is patent eligible under Step 2B because it recites additional elements that are “unconventional or otherwise more than what is well-understood, routine, conventional activity in the field”. Specifically, amended claim 1 recites a particular solution to address the computer-centric challenge of providing means to accurately estimate labels for time series data with the limitations of “inputting an intermediate output estimated using the first neural network model into a second neural network model, in which the intermediate output is used to estimate a time series condition, wherein the time series condition is estimated using a convolutional neural network layers [and] pairing the time series data and the time series condition, wherein the time series data is estimated using the convolutional neural network layers”, as these features are neither well-understood, routine nor conventional in the field. Further, similarly to Eibel Process Co., which recites the use of a particular machine, the amended claim 1 recites and uses a particular machine, such as a convolutional neural network and qualifies as significantly more.
Examiner respectfully disagrees.
As noted above, the exemplary limitations above recite abstract ideas performed in a particular technological environment or insignificantly extra-solution activity that the courts have found to be well-understood, routine and conventional. Of the exemplary limitations that are considered additional limitations to the abstract ideas, “inputting an intermediate output estimated using the first neural network into a second neural network” recites the well-understood activity of transmitting data over a network, namely transferring data from one neural network to another, and “estimat[ing] using a convolutional neural network layers” merely recites a computer environment. Unlike Eibel Process Co., in which a specific machine arranged in a novel way was found to be significantly more, merely reciting a convolutional neural network does not qualify as a particular machine because it not only acts merely as an object on which the claimed method operates (see MPEP § 2106.05(b)(II)), but recites a very generic model that is well-known and widely used in the field of AI, and does not constitute patent eligible subject matter. As such, Examiner affirms that a prima facie case of patent ineligibility has been established. The independent claims 5 and 6 are similarly ineligible as they correspond with Claim 1, and dependent claims 2-4 are ineligible for their dependency on an ineligible independent claim as well as for their own deficiencies outlined in the 35 U.S.C. § 101 rejection above.
Applicant’s arguments, filed 10/14/2025, with respect to the rejection of Claims 1-6 under 35 U.S.C. § 103 have been fully considered and are found moot in light of the new grounds of rejection (see rejection above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ardel et al. (US 20210256369 A1) teaches domain-adapted classifier generation for classifying target time series sensor data.
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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/M.E.H./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143