DETAILED ACTION
The current office action is in response to the communication filed on 3/4/25.
The applicant amended claims 6 and 18 and cancelled claim 17 in the Preliminary Amendment received on 3/4/25.
Claims 1-16 and 18 are pending.
The Examiner recommends filing a written authorization for Internet communication in response to the present action. Doing so permits the USPTO to communicate with Applicant using Internet email to schedule interviews or discuss other aspects of the application. Without a written authorization in place, the USPTO cannot respond to Internet correspondence received from Applicant. The preferred method of providing authorization is by filing form PTO/SB/439, available at: https://www.uspto.gov/patent/forms/forms. See MPEP § 502.03 for other methods of providing written authorization.
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 .
Paper Submitted
It is hereby acknowledged that the following papers have been received and placed of record in the file:
Information Disclosure Statement(s) as received on 3/4/25 are considered by the Examiner.
Allowable Subject Matter
Claim 5 is objected to as being dependent upon a rejected base claim, but would be allowable if the Objections to the Abstract/Specification, Claim Objections and 35 USC § 101 Claim Rejections listed in the paragraph(s) below are corrected and the claim is rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Information Disclosure Statement
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Specification
The abstract of the disclosure is objected to because of the following informalities:
Typically abbreviations/acronyms are used after an expansion is provided to the abbreviations/acronyms. However, in the Abstract, “ML” is used before it is expanded. It is suggested to use expansions before using their abbreviations/acronyms. Appropriate correction is required.
The disclosure is objected to because of the following informalities:
Typically abbreviations/acronyms are used after an expansion is provided to the abbreviations/acronyms. However, in the Specification, “ML” is used before it is expanded. It is suggested to use expansions before using their abbreviations/acronyms. Appropriate correction is required.
Claim Objections
Claims 1-16 and 18 are objected to because of the following informalities:
The limitation “…the IoT data…” in claim 1, line 3, should be “…the labelled IoT data…” (emphasis added) in order to resolve the lack of antecedent basis in the limitations. Appropriate correction is required. Similar corrections are required in claim 6, line 2; claim 14, line 4 and claim 18, line 4.
The limitation “…the labelled IoT devices data…” in claim 1, line 7, should be “…the labelled IoT data…” (emphasis added) in order to resolve the lack of antecedent basis in the limitations. Appropriate correction is required. Similar corrections are required in claim 14, line 8 and claim 18, line 8.
Typically abbreviations/acronyms are used after an expansion is provided to the abbreviations/acronyms. However, in claims 1, 6-9, 12, 14 and 18, the abbreviation/acronym “ML” is used before it is expanded. It is suggested to use expansions before using their abbreviations/acronyms. Appropriate correction is required.
The limitation “…the IoT data of a plurality of IoT devices…” in claim 2, lines 1-2, should be “…[[the]] IoT data of [[a]] the plurality of IoT devices…” (emphasis added) in order to resolve the lack of antecedent basis in the limitations. Appropriate correction is required.
The limitation “…a type of IoT data…” in claim 2, line 3, should be “…a type of the IoT data…” (emphasis added) in order to resolve the lack of antecedent basis in the limitations. Appropriate correction is required.
The limitation “…analyzing characteristics…” in claim 3, line 1, should be “…analyzing the characteristics…” (emphasis added) in order to resolve the lack of antecedent basis in the limitations. Appropriate correction is required.
The limitation “…wherein the resulting correlation coefficients…” in claim 5, line 1, should be “…wherein [[the]] resulting correlation coefficients…” (emphasis added) in order to resolve the lack of antecedent basis in the limitations. Appropriate correction is required.
The limitation “…live IoT data…” in claim 9, line 2, should be “…the live IoT data…” (emphasis added) in order to resolve the lack of antecedent basis in the limitations. Appropriate correction is required. Similar corrections are required in claim 10, line 1 and claim 12, line 2.
The term “if” in claims 10 and 13 should be removed and/or replaced where appropriate because any positive or negative condition following the term “if” is interpreted as a condition that may never happened. The claim limitations should be positively recited. Appropriate correction is required.
The limitation “…a data inconsistency…” in claim 13, line 1, should be “…[[a]] the data inconsistency…” (emphasis added) in order to resolve the lack of antecedent basis in the limitations. Appropriate correction is required.
All dependent claims are objected to as having the same deficiencies as the claims they depend from.
Note: For examination purposes, the claims will be interpreted based on the claim language suggested by the Examiner.
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 1-2, 6-10, 12, 14-16 and 18 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.
Claims 1, 14 and 18, at least in part, recite the steps of: obtaining labelled IoT data, the IoT data being collected from a plurality of IoT devices by a monitoring system; analyzing characteristics of the labelled IoT data and identifying features of IoT data inconsistency; training, using the labelled IoT devices data and the features of IoT data inconsistency, a ML model to predict the IoT data inconsistency; and generating, using the labelled IoT data and the features of IoT data inconsistency, a set of inconsistency rules to be applied to live IoT data predicted as inconsistent by the ML model.
The limitations recited in claims 1, 14 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 “processing circuits,” nothing in the claim element precludes the steps from practically being performed in the mind.
This judicial exception is not integrated into a practical application. In particular, the claim limitations are not indicative of integration into a practical application. Taking the claim elements separately, the additional elements of performing the steps with processing circuits - merely implements the abstract idea on a computer environment. Considered in combination, the steps of Applicant’s method add nothing that is not already present when the steps are considered separately.
The remaining claim limitations recited in the dependent claims merely narrow the abstract idea and do not recite further additional technical elements. Thus, claims 1-2, 6-10, 12, 14-16 and 18 are directed to an abstract idea.
Regarding the independent claims, the technical elements of performing the steps with processing circuits merely implement the abstract idea on a computer environment. Additionally, the dependent claims do not recite further technical elements.
When considering the elements and combinations of elements, the claim(s) as a whole, do not amount to significantly more than the abstract idea itself. This is because the claims do not amount to an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment; the claims merely amounts to the application or instructions to apply the abstract idea on a computer; or the claims amounts to nothing more than requiring a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry.
The analysis above applies to all statutory categories of invention. Accordingly, claims 1-2, 6-10, 12, 14-16 and 18 are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same rationale.
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 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 of this title, 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 1-4, 6, 9-15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over “Wu” (US PGPUB 2023/0075005) in view of “Srinivas et al.” (US PGPUB 2021/0392150) (Hereinafter Srinivas).
With respect to claim 1, Wu teaches a computer implemented method for predicting Internet of Things (IoT) data inconsistency (Abstract), comprising:
obtaining labelled IoT data, the IoT data being collected from a plurality of IoT devices (Figs. 1-2, [0037], [0045]) by a monitoring system (collecting sensor data from a plurality of sensors; Fig. 4, [0070]-[0071], [0077]);
analyzing characteristics of the labelled IoT data and identifying features of IoT data inconsistency (generating features related to anomaly detection/prediction by performing correlational analysis between different sensor signals and/or different sensors; Fig. 4, [0073]-[0075]);
training, using the labelled IoT devices data and the features of IoT data inconsistency, a ML model to predict the IoT data inconsistency (training a model based on a combination of historical data associated with the asset and the features related to anomaly detection/prediction; Fig. 6, [0080]-[0082]).
Wu does not teach generating, using the labelled IoT data and the features of IoT data inconsistency, a set of inconsistency rules to be applied to live IoT data predicted as inconsistent by the ML model.
However, Srinivas teaches generating, using the labelled IoT data and the features of IoT data inconsistency, a set of inconsistency rules to be applied to live IoT data predicted as inconsistent by the ML model (identifying a particular IoT device exhibiting anomalous behavior and generating rules to remedy the identified anomalous behavior; [0073], [0107]).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate generating inconsistency rules to Wu because Wu discloses recognizing data inconsistency ([0080]) and Srinivas suggests generating inconsistency rules ([0073]).
One of ordinary skill in the art would be motivated to utilize the teachings of Srinivas in the Wu system in order to provide more effective data inconsistency detection and remediation.
With respect to claim 2, Wu as modified teaches the method of claim 1. Wu further teaches wherein the labelled IoT data comprises the IoT data of a plurality of IoT devices and a plurality of features for each of the IoT data including at least one of: a type of IoT data, a type of IoT device, a location of acquisition of the IoT data, a time of acquisition of the IoT data, a destination for the IoT data, a sampling rate of the IoT data, a latency-sensitiveness of the IoT data, a network condition when the IoT data was acquired, and an indication whether the IoT data is consistent or not (sensor data is collected from a plurality of sensors associated with an asset. The sensor data includes data (e.g., values, profiles, parameters) that characterizes an aspect of the asset and is of interest or related to various anomalies that should be detected and predicted; Figs. 1-2, [0037], [0045], [0071]-[0072]).
With respect to claim 3, Wu as modified teaches the method of claim 1. Wu further teaches wherein analyzing characteristics of the labelled IoT data comprises measuring a degree of correlation between features of the labelled IoT data (the sensor data is comprised of a plurality of sensor signals each corresponding to a sensor associated with the asset, and the one or more features are generated based at least on performing correlational analysis between different sensor signals and/or different sensors. In one or more embodiments, performing correlational analysis between different sensor signals and/or different sensors comprises determining a Pearson correlation coefficient, a Spearman correlation coefficient, and/or the like between each pair of sensor signals of the sensor data and generating a correlation matrix based at least on the determined correlation coefficients. It may be appreciated that due to the pairwise nature of the correlational analysis, the correlation matrix is an upper-diagonal square matrix. Using the correlation matrix, one or more portions of the sensor data (e.g., one or more sensor signals) that may be interrelated, interdependent, and/or correlated are identified, and the one or more features are specifically generated based at least on the identified portions of the sensor data; [0073]).
With respect to claim 4, Wu as modified teaches the method of claim 3. Wu further teaches wherein the degree of correlation is measured using Spearman's correlation coefficients between the features of the labelled IoT data (the sensor data is comprised of a plurality of sensor signals each corresponding to a sensor associated with the asset, and the one or more features are generated based at least on performing correlational analysis between different sensor signals and/or different sensors. In one or more embodiments, performing correlational analysis between different sensor signals and/or different sensors comprises determining a Pearson correlation coefficient, a Spearman correlation coefficient, and/or the like between each pair of sensor signals of the sensor data and generating a correlation matrix based at least on the determined correlation coefficients. It may be appreciated that due to the pairwise nature of the correlational analysis, the correlation matrix is an upper-diagonal square matrix. Using the correlation matrix, one or more portions of the sensor data (e.g., one or more sensor signals) that may be interrelated, interdependent, and/or correlated are identified, and the one or more features are specifically generated based at least on the identified portions of the sensor data; [0073]).
With respect to claim 6, Wu as modified teaches the method of claim 1. Srinivas further teaches wherein the set of inconsistency rules is further used for refining the IoT data collected from the plurality of IoT devices for continuous training of the ML model (providing feedback data to a learning engine that cause the learning engine to modify its machine-trained parameters (e.g., weight values) to produce a different set of discriminating features for IoT devices in the future. Adjusting the set of features used to define one or more classification rules based on the different set of discriminating features produced by the learning engine for the IoT devices; [0064], [0086]).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate refining a model using inconsistency rules to Wu because Wu discloses training a model ([0080]) and Srinivas suggests refining a model using inconsistency rules ([0064]).
One of ordinary skill in the art would be motivated to utilize the teachings of Srinivas in the Wu system in order to improve model efficiency and accuracy.
With respect to claim 9, Wu as modified teaches the method of claim 1. Wu further teaches further comprising performing data inconsistency prediction on live IoT data, using the ML model (model is trained to understand, regenerate, predict, and/or the like normal and non-anomalous sensor data collected from the plurality of sensors associated with an asset and/or feature data; [0081], [0083]).
With respect to claim 10, Wu as modified teaches the method of claim 9. Srinivas further teaches further comprising, if a data inconsistency of live IoT data is predicted, generating refinement actions using the set of inconsistency rules (identifying a particular IoT device is exhibiting anomalous behavior and generating rules to remedy the identified anomalous behavior; [0073], [0087], [0107]).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate generating refinement actions using inconsistency rules to Wu because Wu discloses recognizing data inconsistency ([0080]) and Srinivas suggests generating refinement actions using inconsistency rules ([0073]).
One of ordinary skill in the art would be motivated to utilize the teachings of Srinivas in the Wu system in order to provide more effective data inconsistency detection and remediation.
With respect to claim 11, Wu as modified teaches the method of claim 10. Srinivas further teaches further comprising using the refinement actions to guide live data collection to avoid future inconsistencies (identifying a particular IoT device is exhibiting anomalous behavior and generating rules to remedy the identified anomalous behavior; [0073], [0087], [0107]).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate using refinement actions to Wu because Wu discloses recognizing data inconsistency ([0080]) and Srinivas suggests using refinement actions ([0073]).
One of ordinary skill in the art would be motivated to utilize the teachings of Srinivas in the Wu system in order to provide more effective data inconsistency detection and remediation.
With respect to claim 12, Wu as modified teaches the method of claim 1. Wu further teaches further comprising, performing a data inconsistency verification on live IoT data, using the ML model and information in the labeled IoT data (model is trained to understand, regenerate, predict, and/or the like normal and non-anomalous sensor data collected from the plurality of sensors associated with an asset and/or feature data; [0081]-[0083]).
With respect to claim 13, Wu as modified teaches the method of claim 12. Srinivas further teaches further comprising, if a data inconsistency is verified, generating a resolution action using the set of inconsistency rules, to fix the IoT data inconsistency (identifying a particular IoT device is exhibiting anomalous behavior and generating rules to remedy the identified anomalous behavior; [0073], [0087], [0107]).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate using refinement actions to Wu because Wu discloses recognizing data inconsistency ([0080]) and Srinivas suggests using refinement actions ([0073]).
One of ordinary skill in the art would be motivated to utilize the teachings of Srinivas in the Wu system in order to provide more effective data inconsistency detection and remediation.
The limitations of claim 14 are rejected in the analysis of claim 1 above and this claim is rejected on that basis. Furthermore, Wu discloses an apparatus comprising processing circuits and a memory (Fig. 3, [0066]) as recited in claim 14.
With respect to claim 15, Wu as modified teaches the apparatus of claim 14. Wu further teaches wherein the apparatus is a data inconsistency resolution system (Fig. 3, [0066]).
The limitations of claim 18 are rejected in the analysis of claim 1 above and this claim is rejected on that basis. Furthermore, Wu discloses a non-transitory computer readable media (Fig. 3, [0068]) as recited in claim 18.
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Srinivas, and further in view of “Guzik et al.” (US PGPUB 2022/0172087) (Hereinafter Guzik).
With respect to claim 7, Wu as modified teaches the method of claim 1. Wu does not teach wherein training the ML model comprises training a plurality of ML models to address different types of performance in terms of accuracy, precision, recall and F1-score.
However, Guzik teaches wherein training the ML model comprises training a plurality of ML models to address different types of performance in terms of accuracy, precision, recall and F1-score (receiving a first data model and a second data model. The first data model includes a first set of attributes, a first set of predictions, a first margin of error , and the underlying data set. The second data model includes a second set of attributes; [0014], [0029], [0031], [0033]).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate training multiple models to Wu because Wu discloses training a model ([0080]) and Guzik suggests training multiple models ([0029]).
One of ordinary skill in the art would be motivated to utilize the teachings of Guzik in the Wu system in order to rapidly and efficiently optimize model performance.
With respect to claim 8, Wu as modified teaches the method of claim 7. Guzik further teaches wherein the plurality of ML models is selected among Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) (implementing the data source correlation techniques for machine learning models and convolutional neural network models; [0042]).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate training multiple models to Wu because Wu discloses training a model ([0080]) and Guzik suggests training multiple models ([0042]).
One of ordinary skill in the art would be motivated to utilize the teachings of Guzik in the Wu system in order to rapidly and efficiently optimize model performance.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Srinivas, and further in view of “Cheng et al.” (US 11,038,910) (Hereinafter Cheng).
With respect to claim 16, Wu as modified teaches the apparatus of claim 14. Wu does not teach wherein the apparatus is an IoT gateway.
However, Cheng teaches wherein the apparatus is an IoT gateway (a machine learning (ML) model of an IoT gateway receives operating data from an IoT device and makes a determination as to whether or not the operating data indicate a normal or anomalous operating behavior of the IoT device; col. 3, lines 14-23).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate an IoT gateway to Wu because Wu discloses an IoT platform/network ([0037]) and Cheng suggests using an IoT gateway (col. 3, lines 14-23).
One of ordinary skill in the art would be motivated to utilize the teachings of Cheng in the Wu system in order to rapidly and efficiently detect anomalous operating behavior of IoT devices.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Maturana et al. US 2022/0103591. Discloses detecting anomalies in network communication.
Yadav et al. US 2016/0359695. Discloses collecting and analyzing data for anomaly detection.
Bhattacharyya et al. US 2020/0090070. Discloses analyzing IoT data in real-time and predicting future events.
Pallath et al. US 2017/0102978. Discloses detecting anomalies in an IoT network.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Johnny B Aguiar whose telephone number is (571)272-3563. The examiner can normally be reached on Monday to Friday 7:30 am - 5:30 pm EST.
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, Joon Hwang can be reached on (571) 272-4036. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHNNY B AGUIAR/
Primary Examiner, Art Unit 2447
July 27, 2026
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