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 .
This action is responsive to the Amendment filed on 7/3/2025.
Claims 1-45 are pending in the case.
Claims 4, 5, 9, 10, 13-15, 17-30, 32-45 have each
(i) been amended to depend on claim 0 (which does not exist),
(ii) been marked as Original, and
(iii) been presented without any markup to indicate amendments to the claims.
The markup to the amendment(s) to claim 15, required under 37 C.F.R. § 1.121(c)(2) is absent in several places, including one or more of (a) new text that is not underlined, (b) strikethrough text that was not in the original claims, and (c) text that is both underlined and strikethrough.
Currently there does not appear to be any deceptive intent, however future arguments that rely upon past inaccurate markup may be considered deceptive in violation of MPEP § 2001 and 37 C.F.R. § 1.56.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 4, 5, 9, 10, 13-15, 17-30, 32-45 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claims 4, 5, 9, 10, 13-15, 17-30, 32-45 have each been amended to depend on claim 0, which does not exist.
Examiner believes that the amendments were unintended, and has interpreted each of the claims as not being amended and with dependencies indicated in the original claims filed on 12/9/2021.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 4, 5, 9, 10, 12-15, 17-30, 32-45, 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 pre-AIA the applicant regards as the invention.
Claims 4, 5, 9, 10, 13-15, 17-30, 32-45 have each been amended to depend on claim 0. However claim 0 does not exist, rendering the claims indefinite.
Examiner has interpreted each of the claims 4, 5, 9, 10, 13-15, 17-30, 32-45, as not being amended and with dependencies indicated in the original claims filed on 12/9/2021.
Claim(s) 12 recite(s) “separately provide the dimensionally reduced encoding to a latent-space feature model and to the trained decoder network”.
It is unclear what provide “separately” entails that is different from “provide the dimensionally reduced encoding to a latent-space feature model and to the trained decoder network”, rendering the claim(s) indefinite.
For examination purposes the examiner has interpreted “separately provide the dimensionally reduced encoding to a latent-space feature model and to the trained decoder network” to be “provide the dimensionally reduced encoding to a latent-space feature model and to the trained decoder network.
Claim(s) 5, 14, 15, 19, 20, 24, 28, 29, 30, 34, 35, 39, 43, 44 and 45 do not contain claim limitations that cure the indefiniteness of claim(s) 4, 13, 13, 18, 18, 23, 27, 28, 28, 33, 33, 38, 42, 43 and 43 respectively, and therefore are also indefinite under 35 U.S.C. 112(b).
Response to Arguments
Applicant's arguments and amendments with regards to the 35 U.S.C. § 102 and 103 rejection of claim(s) 1-45 have been considered, but are not persuasive.
Applicant argues that the amended claims are allowable due to the following:
Regarding claim(s) 1, applicant argues
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Examiner respectfully disagrees. Ryan [118-125] discloses dimensionality of time-series data may be reduced (encoding) and transformed (decoded), Ryan [203, 204, 209] encoding and decoding may be performed after training and before retraining,
and therefore sufficiently teaches process a portion of time-series data using a trained encoder network to generate a dimensionally reduced encoding of the portion of the time-series data; process the dimensionally reduced encoding using a trained decoder network to determine decoder output data.
Applicant further argues
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Examiner respectfully disagrees. Hussain [119, 122, 124, 125, 138] discloses neural networks may have set parameters based on already completed training, second neural network may include encoder and decoder networks, encoder may reduce dimensions, decoder may decode encoder output, and
Hussain [30, 41, 119, 126, 136, 138] discloses loss is determined based on decoder output (in combination with other network outputs), parameters for neural networks may be adjusted based on loss, networks may be used to predict future values of time series.
Therefore Hussain sufficiently teaches process the dimensionally reduced encoding using a trained decoder network to determine decoder output data that represents parameters for a predictive machine-learning model; and configure the predictive machine-learning model based on the parameters, wherein the predictive machine-learning model is configured to, based on the parameters, determine a predicted future value of the time-series data.
The combination of Ryan and Hussain therefore sufficient teaches the above limitation(s) and Examiner asserts that the combination of Ryan and Hussain sufficient teaches the limitations of amended claim 1.
Regarding claim(s) 2-45 applicant argues that the claims are allowable for reasons similar to those references with regards to claim 1.
For reasons similar to those discussed above with regards to claim 1, examiner asserts that the previously cited art sufficiently teaches the amended claims 2-45
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-13, 16-28, 31-43 are rejected under 35 U.S.C. 103 as being unpatentable over Ryan (US 20200387797 A1), in view of Husain (US 20200023846 A1).
Regarding claim 1, Ryan teaches a device comprising: one or more processors configured to (Ryan [175-180] device processor executes instructions in memory to perform steps):
process a portion of time-series data using a trained encoder network to generate a dimensionally reduced encoding of the portion of the time-series data; process the dimensionally reduced encoding using a trained decoder network to determine decoder output data (Ryan [118-125, 203, 204, 209] dimensionality of time-series data may be reduced (encoding) and transformed (decoded), encoding and decoding may be performed after training); and
...set parameters of a... predictive machine-learning model based on the ...decoder output data..., wherein the predictive machine-learning model is configured to, based on the parameters, determine a predicted future value of the time-series data (Ryan [125-127, 209, 215] transformed data is determine score difference, based on score difference and threshold,, model may be retrained to set parameters of model that may be used for prediction).
Ryan does not specifically teach ... decoder output data that represents parameters for a predictive machine-learning model ...; and configure the predictive machine-learning model based on the parameters.
However Hussain teaches process the dimensionally reduced encoding using a trained decoder network to determine decoder output data that represents parameters for a predictive machine-learning model; and configure the predictive machine-learning model based on the parameters, wherein the predictive machine-learning model is configured to, based on the parameters, determine a predicted future value of the time-series data (Hussain [119, 122, 124, 125, 138] neural networks may have set parameters based on already completed training, second neural network may include encoder and decoder networks, encoder may reduce dimensions, decoder may decode encoder output, Hussain [30, 41, 119, 126, 136, 138] loss is determined based on decoder output (in combination with other network outputs), parameters for neural networks may be adjusted based on loss, networks may be used to predict future values of time series).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Husain of process the dimensionally reduced encoding using a trained decoder network to determine decoder output data that represents parameters for a predictive machine-learning model; and configure the predictive machine-learning model based on the parameters, wherein the predictive machine-learning model is configured to, based on the parameters, determine a predicted future value of the time-series data, into the invention suggested by Ryan; since both inventions are directed towards predicting operational states using trained models on time series data, and incorporating the teaching of Husain into the invention suggested by Ryan would provide the added advantage of using loss to determine adjustments to network parameters, and the combination would perform with a reasonable expectation of success ((Husain [119, 122, 124, 125, 138, 30, 41, 119, 136, 138]).
Regarding claim 2, Ryan and Hussain teach the invention as claimed in claim 1 above. Ryan further teaches after setting the parameters of the predictive machine-learning model, provide input data based on the portion of the time-series data as input to the predictive machine-learning model to generate the predicted future value of the time-series data (Ryan [65, 90-96] sliding window is used on time series data to train and then use data as input to trained model).
Regarding claim 3, Ryan and Hussain teach the invention as claimed in claim 1 above. Ryan further teaches receive a subsequent portion of the time-series data; and
determine, based on a comparison of the predicted future value to a corresponding future value of the subsequent portion of the time-series data, whether a monitored system associated with the time-series data has deviated from a particular operational state (Ryan [65, 71, 90-96, 116] sliding window is used on time series data to train and then use data as input to trained model, model provides anomaly (which can include failure prediction) and probability of correct prediction).
Regarding claim 4, Ryan and Hussain teach the invention as claimed in claim 3 above. Ryan does not specifically teach determining an error value based on the comparison; and determining whether the error value satisfies a detection criterion that indicates that the monitored system has deviated from the particular operational state.
However Husain teaches determining an error value based on the comparison; and determining whether the error value satisfies a detection criterion that indicates that the monitored system has deviated from the particular operational state (Husain [81, 112, 114, 137] determination may be made whether the fitness value satisfies certain criteria- in order to determine the fitness of a model).
Regarding claim 5, Ryan and Hussain teach the invention as claimed in claim 3 above. Ryan further teaches wherein the one or more processors are further configured to determine whether to generate an alert based on the comparison (Ryan [73, 74] alerts may be provided based on anomaly detection)
Regarding claim 6, Ryan and Hussain teach the invention as claimed in claim 1 above. Ryan further teaches wherein the predictive machine-learning model includes a neural network, and wherein setting the parameters of the predictive machine-learning model includes setting a link weight of the neural network to a value indicated by the decoder output data (Ryan [138] weights of neural network may be set based on recency of data).
Regarding claim 7, Ryan and Hussain teach the invention as claimed in claim 1 above. Ryan further teaches wherein the trained encoder network, the trained decoder network, and the predictive machine-learning model are trained together based on training data associated with a monitored system (Ryan [117, 118, 201] models may be trained in combination using training data).
Regarding claim 8, Ryan and Hussain teach the invention as claimed in claim 1 above. Ryan further teaches wherein the one or more processors are further configured to generate an output to a control system based on the predicted future value of the time-series data (Ryan [70, 74, 88] trends and alerts may be provided based on predictions).
Regarding claim 9, Ryan and Hussain teach the invention as claimed in claim 8 above. Ryan further teaches wherein the output includes a control signal to modify operation associated with a monitored system (Ryan [70] output may be changing network configuration).
Regarding claim 10, Ryan and Hussain teach the invention as claimed in claim 8 above. Ryan does not specifically teach wherein the output includes a display including an indication of the predicted future value of the time-series data, an indication of an inferred operating state of a monitored system, or both.
However Husain teaches wherein the output includes a display including an indication of the predicted future value of the time-series data, an indication of an inferred operating state of a monitored system, or both (Husain [30, 41, 47, 81] display may show when and what problem could occur, output may be determining or predicting operating state of a system).
Regarding claim 11, Ryan and Hussain teach the invention as claimed in claim 1 above. Ryan does not specifically teach determining a value of a particular latent-space feature based, at least in part, on a probability distribution associated with the particular latent-space feature to generate a value of the dimensionally reduced encoding
However Husain teaches determining a value of a particular latent-space feature based, at least in part, on a probability distribution associated with the particular latent-space feature to generate a value of the dimensionally reduced encoding (Husain [121-124, 30, 41, 47] probability distribution associated with latent space feature(s) may be used to generate reduced dimension encoded input data, by clustering data and may represent cluster identification and latent space location along with the input features in a “compressed” fashion, output may be determining or predicting operating state of a system).
Regarding claim 12, Ryan teaches the invention as claimed in claim 1 above.
Ryan further teaches determine an inferred operating state of a monitored system based on the dimensionally reduced encoding; based on the inferred operating state, select a behavior model from among a plurality of behavior models associated with the monitored system; and provide input data based on the time-series data to the behavior model to generate an output indicating whether the monitored system has deviated from the inferred operating state (Ryan [64, 65, 95, 126, 127] patterns may be detected, and model may be selected based on current context, models are evaluated for performance).
Ryan does not specifically teach separately provide the dimensionally reduced encoding to a latent-space feature model and to the trained decoder network: process the dimensionally reduced encoding using the latent-space feature model.
However Hussain teaches Ryan does not specifically teach separately provide the dimensionally reduced encoding to a latent-space feature model and to the trained decoder network: process the dimensionally reduced encoding using the latent-space feature model determine an inferred operating state of a monitored system based on the dimensionally reduced encoding (Husain [121-124, 30, 41, 47] reduced dimension data may be provided to latent space feature layer and latent space feature layer output may be provided to decoder, reduced dimension encoded input data may represent cluster identification and latent space location along with the input features in a “compressed” fashion, output may be determining or predicting operating state of a system).
Regarding claim 13, Ryan and Hussain teach the invention as claimed in claim 12 above. Ryan does not specifically teach wherein determining the inferred operating state of the monitored system includes comparing a location of the dimensionally reduced encoding in a latent space to a location in the latent space associated with a detectable operating state.
However Husain teaches wherein determining the inferred operating state of the monitored system includes comparing a location of the dimensionally reduced encoding in a latent space to a location in the latent space associated with a detectable operating state (Husain [121-124, 30, 41, 47] reduced dimension encoded input data may represent cluster identification and latent space location along with the input features in a “compressed” fashion, output may be determining or predicting operating state of a system).
Claim 16 is directed towards a method performing instructions similar in scope to the instructions executed by the device of claim 1, and is rejected under the same rationale.
Claim(s) 17-26, 28, is/are dependent on claim 16 above, is/are directed towards a method performing instructions similar in scope to the instructions executed by the device of claim(s) 2-11, 13 respectively, and is/are rejected under the same rationale.
Regarding claim 27, Ryan and Hussain teach the invention as claimed in claim 1 above. Ryan further teaches determining an inferred operating state of a monitored system based on the dimensionally reduced encoding; based on the inferred operating state, selecting a behavior model from among a plurality of behavior models associated with the monitored system; and providing input data based on the time-series data to the behavior model to generate an output indicating whether the monitored system has deviated from the inferred operating state (Ryan [64, 65, 95, 126, 127] patterns may be detected, and model may be selected based on current context, models are evaluated for performance).
Claim 31 is directed towards a storage device storing instructions similar in scope to the instructions executed by the device of claim 1, and is rejected under the same rationale. Ryan further teaches a computer-readable storage device storing instructions that are executable by one or more processors to cause the one or more processors to perform operations (Ryan [175-180]).
Claim(s) 32-41, 43 is/are dependent on claim 31 above, is/are directed towards a storage device storing instructions similar in scope to the instructions executed by the device of claim(s) 2-11, 13 respectively, and is/are rejected under the same rationale.
Regarding claim 42, Ryan and Hussain teach the invention as claimed in claim 1 above. Ryan further teaches determining an inferred operating state of a monitored system based on the dimensionally reduced encoding; based on the inferred operating state, selecting a behavior model from among a plurality of behavior models associated with the monitored system; and providing input data based on the time-series data to the behavior model to generate an output indicating whether the monitored system has deviated from the inferred operating state (Ryan [64, 65, 95, 126, 127] patterns may be detected, and model may be selected based on current context, models are evaluated for performance).
Claims 14, 29, 44, are rejected under 35 U.S.C. 103 as being unpatentable over Ryan (US 20200387797 A1) in view of Husain (US 20200023846 A1), and further in view of Ma (US 20210303970 A1).
Regarding claim 14, Ryan and Hussain teach the invention as claimed in claim 13 above. Ryan does not specifically teach wherein the location in the latent space associated with the detectable operating state corresponds to a boundary of a cluster of points representing the detectable operating state or to a representative location of the cluster of points
However Ma teaches wherein the location in the latent space associated with the detectable operating state corresponds to a boundary of a cluster of points representing the detectable operating state or to a representative location of the cluster of points (Ma [17, 49, 55] clustering of input data may be based on producing the clearest bounds for clusters of data).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Ma of wherein the location in the latent space associated with the detectable operating state corresponds to a boundary of a cluster of points representing the detectable operating state or to a representative location of the cluster of points, into the invention suggested by Ryan and Hussain; since both inventions are directed towards clustering and analyzing time series data using models, and incorporating the teaching of Ma into the invention suggested by Ryan and Hussain would provide the added advantage of allowing data to be clustered based on producing the clearest bounds for clusters of data, and the combination would perform with a reasonable expectation of success (Ma [17, 49, 55]).
Claim(s) 29, is/are dependent on claim 16 above, is/are directed towards a method performing instructions similar in scope to the instructions executed by the device of claim(s) 14, and is/are rejected under the same rationale.
Claim(s) 44, is/are dependent on claim 31 above, is/are directed towards a storage device storing instructions similar in scope to the instructions executed by the device of claim(s) 14, and is/are rejected under the same rationale.
Claims 15, 30, 45, are rejected under 35 U.S.C. 103 as being unpatentable over Ryan (US 20200387797 A1) in view of Husain (US 20200023846 A1), and further in view of Etkin (US 20210353224 A1).
Regarding claim 15 Ryan and Hussain teach the invention as claimed in claim 13 above. Ryan does not specifically teach wherein comparing the location of the dimensionally reduced encoding to the location in the latent space associated with the detectable operating state comprises determining whether a distance between the location of the dimensionally reduced encoding and the location in the latent space associated with the detectable operating state satisfies a distance threshold
However Etkin teaches wherein comparing the location of the dimensionally reduced encoding to the location in the latent space associated with the detectable operating state comprises determining whether a distance between the location of the dimensionally reduced encoding and the location in the latent space associated with the detectable operating state satisfies a distance threshold (Etkin [102, 267] operating state (disease) is based on based at least on the band power of the patient's latent signals being closest to and/or within a threshold distance of a cluster corresponding to that type and/or subtype of disease).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Etkin of wherein comparing the location of the dimensionally reduced encoding to the location in the latent space associated with the detectable operating state comprises determining whether a distance between the location of the dimensionally reduced encoding and the location in the latent space associated with the detectable operating state satisfies a distance threshold, into the invention suggested by Ryan and Hussain; since both inventions are directed towards generating reduced dimension encoded input data, and incorporating the teaching of Etkin into the invention suggested by Ryan and Hussain would provide the added advantage of allowing for threshold cluster distances in a latent space for feature detection, and the combination would perform with a reasonable expectation of success (Etkin [102, 267]).
Claim(s) 30 is/are dependent on claim 16 above, is/are directed towards a method performing instructions similar in scope to the instructions executed by the device of claim(s) 15, and is/are rejected under the same rationale.
Claim(s) 45 is/are dependent on claim 31 above, is/are directed towards a storage device storing instructions similar in scope to the instructions executed by the device of claim(s) 15, and is/are rejected under the same rationale.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANCHITA ROY whose telephone number is (571)272-5310. The examiner can normally be reached Monday-Friday 12-8.
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, Usmaan Saeed can be reached at (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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SANCHITA . ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146