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 .
DETAILED ACTION
2. This office action is in response to the amendment filed on 06/27/2025. Claims 2, 4-10, 12, 17 and 19-20 are canceled and claims 1, 3, 11, 13-16 and 18 are pending and have been considered below.
Claim Rejections - 35 USC § 101
3. 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, 3, 11, 13-16 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 18 recite the limitations “comparing the filtered data with the measured data by the comparison subcircuit using a first cost function, wherein the first cost function yields a first cost value representing a magnitude of the anomalies removed by the first subcircuit”; “comparing the predicted data with at least one of the measured data and the filtered data using a second cost function, wherein the second cost function yields a second cost value representing a magnitude of differences between the predicted data and the at least one of the measured data and the filtered data” (2106.04(a)(2)(I)(C).
2106.04(a)(2)(I)(C) “Mathematical Calculations A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation. In this case, cost function is calculation of sum of squared differences of filtered and measured data [0085]”
The limitations “generating filtered data based on the measured data by the first subcircuit, wherein the filtered data is a denoised version of the measured data”; generating predicted data based on the filtered data by the second subcircuit, wherein the predicted data is a predicted version of the measured data and/or filtered data for a predetermined point in the future”; “determining that the graphical representation of the spectrogram comprises an anomaly if the first cost value exceeds a first predetermined threshold and/or the second cost value exceeds a second predetermined threshold” in claims 1 and 18 as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting generic computing elements “circuit” and “subcircuit”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “circuit” and “subcircuit” language, in the context of these claims encompasses the user mentally, or manually with the aid of pen and paper, using known preference data, and additional information that can include user data related evaluation data. If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Under Prong 2, the claims recite additional element “receiving, by an antenna, a radio signal; generating, by a processing circuit, a graphical representation of a spectrogram of the radio signal received; receiving the graphical representation of the spectrogram, as measured data by a detection circuit having a first machine learning subcircuit, a second machine learning subcircuit and a comparison subcircuit, wherein the first sub-circuit comprises a pre-trained artificial neural network and the second sub-circuit comprises a pre-trained artificial neural network, wherein the first sub-circuit and the second sub-circuit have been pre-trained with a training data set free of anomalies, wherein the first sub-circuit is pre-trained to remove noise or anomalies from measured data;”. However, these limitations do nothing more than add insignificant extra solution activity to the judicial exception, such as receiving, and generating spectrogram representation. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activities identified above, which include the “ circuit” and “subcircuit”, are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g.,at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); (v) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93). The claims are not patent eligible.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim recites the additional limitation of “wherein at least one of the measured data, the filtered data, and the predicted data are a graphical representation of the spectrogram at a single point in time or a predefined time interval.”, which amounts to data-gathering steps, and which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)). The data-gathering elements are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d) (II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)), therefore, do not amount to significantly more than the abstract idea.
Claim 11 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 11 recites the same abstract idea of claim 1. The claim recites the additional limitation of “wherein the predefined amount is set manually or has been determined by the detection circuit during training” which further elaborates on the abstract idea by emphasizing on predefined amount, and therefore, does not amount to significantly more
Claim 13 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 13 recites the same abstract idea of claim 1. The claim recites the additional limitation of “wherein the first subcircuit comprises or is an autoencoder” which further elaborates on the abstract idea by emphasizing on the first sub-circuit. Mere instructions to apply an exception using a generic computer does not amount to significantly more and therefore, does not amount to significantly more.
Claim 14 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 14 recites the same abstract idea of claim 1. The claim recites the additional limitation of “wherein the second subcircuit is a recurrent artificial neural network” which further elaborates on the abstract idea by emphasizing on the second sub-circuit. Mere instructions to apply an exception using a generic computer does not amount to significantly more
Claim 15 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 15 recites the same abstract idea of claim 1. The claim recites the additional limitation of “wherein the data of the training data set is at least one of a recorded data set, and live data.” which further elaborates on the abstract idea by emphasizing on the training data, and therefore, does not amount to significantly more.
Claim 16. is dependent on claim 15 and includes all the limitations of claim 15. Therefore, claim 16 recites the same abstract idea of claim 1. The claim recites the additional limitation of “wherein the data of the training data set is at least one of a recorded data set, and live data.” which further elaborates on the abstract idea by emphasizing on the training data, and therefore, does not amount to significantly more., wherein the data of the training data set is a recorded data set that has been pre-processed to be free of anomalies.
Additionally, the claims do not include a requirement of anything other than conventional, generic computer technology for executing the abstract idea, and therefore, do not amount to significantly more than the abstract idea.
Claims 1, 3, 11, 13-16 and 18 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
4. 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 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.
5. Claims 1, 3, 11, 13-16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Niculescu-Mizil et al. (US 2019/0197236) in view of Goodman (US 2012/0026031).
Claim 1. Niculescu-Mizil discloses a method for detecting anomalies in a a graphical representation of a spectrogram, the method comprising:
receiving the spectrogram, as measured data by a detection circuit (fig. 1, item 204, fig. 2) having a first machine learning subcircuit (autoencoder 308), a second machine learning subcircuit (Anomaly module 312) and a comparison subcircuit (claim 7), wherein the first sub-circuit comprises a pre- trained artificial neural network and the second sub-circuit comprises a pre-trained artificial neural network ([0012],[0024],[0031]), wherein the first sub-circuit and the second sub-circuit have been pre-trained with a training data set free of anomalies (training data that includes only sensor data collected during normal behavior of the monitored system) ([0004]), wherein the first sub-circuit is pre-trained to remove noise or anomalies from measured data (The autoencoder model is then trained using backpropagation with the objective to reconstruct the input, using a loss function that reflects the difference between the input sensor measurements and the reconstructed sensor measurements) ([0021]) [0031]-[0032])
generating filtered data based on the measured data by the first subcircuit ([0019]), wherein the filtered data is a denoised version of the measured data (The autoencoder model is then trained using backpropagation with the objective to reconstruct the input, using a loss function that reflects the difference between the input sensor measurements and the reconstructed sensor measurements) ([0021])…(The anomaly module 312 flags any deviations between the actual sensor information and the predicted behavior that are greater than a threshold value as an anomaly. The control module 314 automatically responds to flagged anomalies by sending instructions to the system control 108 using the network interface 306.) ([0032]);
comparing the filtered data with the measured data by the comparison subcircuit using a first cost function, wherein the first cost function yields a first cost value representing a magnitude of the anomalies removed by the first subcircuit ([0019], item 208, fig. 2, [0024], claim 16);
generating predicted data based on at least on the filtered data by the second subcircuit, wherein the predicted data is a predicted version of the measured data and/or filtered data for a predetermined point in the future ([0032]), abstract, [0005]) [0021]);
comparing the predicted data with at least one of the measured data and the filtered data using a second cost function, wherein the second cost function yields a second cost value representing a magnitude of differences between the predicted data and the at least one of the measured data and the filtered data (Block 208 then compares the measured sensor information that characterizes the actual system behavior against the predicted behavior. If the actual behavior of the monitored system 102 deviates from the predicted behavior by more than a threshold value, then an anomaly has been discovered and block 210 performs a corrective action) ([0019]-[0020], [0024], claim 8); and
determining that the graphical representation of the spectrogram comprises an anomaly if the first cost value exceeds a first predetermined threshold and/or the second cost value exceeds a second predetermined threshold ([0019]-[0020], [0032], claim 7).
Niculescu-Mizil does not explicitly disclose receiving, by an antenna, a radio signal; generating, by a processing circuit, a graphical representation of a spectrogram of the radio signal received.
However, Goodman disclose receiving, by an antenna, a radio signal ([0024]); generating, by a processing circuit, a graphical representation of a spectrogram of the radio signal received ([0031]); the graphical representation of the spectrogram ([0031], figs. 2,3). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Niculescu-Mizil further in view of Goodman to incorporate the above cited features. One would have been motivated to do so in order to clearly identify the source of errors/anomalies.
Claim 3. Niculescu-Mizil and Goodman disclose the disclose method according to claim1, Goodman further discloses the graphical representation of the spectrogram is at a single point in time or a predefined time interval ([0031], figs. 2-3). One would have been motivated to do so in order to clearly detect anomalies from such collected data.
Claim 11. Niculescu-Mizil and Goodman disclose the method according to claim 1, Niculescu-Mizil further discloses wherein the predefined amount is set manually or has been determined by the detection module during training ([0019]).
Claim 13. Niculescu-Mizil and Goodman disclose the method according to claim 1, Niculescu-Mizil further discloses wherein the first submodule comprises or is an autoencoder ([0021], fig. 3 item 308).
Claim 14. Niculescu-Mizil and Goodman disclose the method according to claim 1, Niculescu-Mizil further discloses wherein the second submodule is a recurrent artificial neural network ([0031]).
Claim 15. Niculescu-Mizil and Goodman disclose the method according to claim 1, Niculescu-Mizil further discloses wherein the data of the training data set is at least one of a recorded data set (training data that includes only sensor data collected during normal behavior of the monitored system), and live data (abstract).
Claim 16. Niculescu-Mizil and Goodman disclose the method according to claim 15, Niculescu-Mizil further discloses wherein the data of the training data set is a recorded data set that has been pre-processed to be free of anomalies (training data that includes only sensor data collected during normal behavior of the monitored system) ([0004]).
Claim 18 represents the system of claim 1 and is rejected along the same rationale.
Response to Arguments
6. Applicant’s arguments and amendments filed on 06/27/2025 have been fully considered but are not persuasive.
Applicant argues that, consistent with Example 47 of the USPTO’s Subject Matter Eligibility Examples, claims relating to artificial neural networks and anomaly detection are deemed eligible when integrated into a practical application, such as generating and processing a graphical representation of a spectrogram of a radio signal received via an antenna.
The Examiner respectfully disagrees.
While Example 47 illustrates an instance where claims reciting an artificial neural network were found eligible, that determination was based on the recitation of a specific technological improvement—namely, a particular manner of processing data (a spectrogram) received from a physical antenna using a defined neural network architecture that improved the functioning of a signal-processing system. In contrast, the present claims do not recite any comparable specific technological implementation or improvement to computer functionality or another field of technology. Rather, the claims recite abstract operations for processing data using a mathematical model without a concrete link to a particular physical device or non-generic computing environment.
As discussed above, the broadest reasonable interpretation of the “comparing” steps encompass mathematical concepts which require specific mathematical calculations (sum of the squared differences…) as evidenced at paragraph 85 of the published application.
The claimed invention differs materially from Example 47 because it does not specify how the alleged neural network or processing steps are implemented in a manner that improves the functioning of a computer or signal processing technology itself. Instead, the claims are drafted at a high level of generality, describing only desired results (e.g., processing features or detecting patterns) without defining any particular algorithmic structure, data transformation, or specialized hardware configuration. See MPEP §2106.05(a) (improvements to computer technology) and MPEP §2106.05(f) (mere data gathering or output as extra-solution activity).
Accordingly, Example 47 is not controlling in this case. The instant claims, when properly analyzed under Step 2A, Prong Two, do not integrate the abstract idea into a practical application, and thus remain directed to a judicial exception without significantly more. The rejection under 35 U.S.C. §101 is therefore maintained.
As per the remaining arguments, there are moot in view of new ground of rejection(s). Applicants are being referred to the above rejection which is very explicit.
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 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.
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al. (US 2021/0256392) relates to neural architecture search processes and systems, and more particularly to automated design of neural networks for anomaly detection.
Bhardwaj et al. (US 10,956,808) relates to systems and methods in the field of computer science, and in particular to the deep learning unsupervised anomaly detection in Internet of Things sensor networks.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Phenuel S. Salomon whose telephone number is (571) 270-1699. The examiner can normally be reached on Mon-Fri 7:00 A.M. to 4:00 P.M. (Alternate Friday Off) EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew J. Jung can be reached on (571) 270-3779. The fax phone number for the organization where this application or proceeding is assigned is 571-273-3800.
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/PHENUEL S SALOMON/Primary Examiner, Art Unit 2146