DETAILED ACTION
This office action is in response to communication filed on May 1, 2026.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on May 1, 2026 has been entered.
Response to Amendment
Amendments filed on May 1, 2026 have been entered.
Claims 1, 6, 8, 13, 15 and 19 have been amended.
Claims 7, 14 and 20 remain canceled.
Claim 21 has been added.
Claims 1-6, 8-13, 15-19 and 21 have been examined.
Response to Arguments
Applicant’s arguments, see Remarks (p. 8-9), filed on 05/01/2026, with respect to the advisory action comments regarding the amendments not satisfying the written description requirement under 35 U.S.C. 112 have been fully considered but are not persuasive.
Applicant argues (p. 8-9) that Amended claim 1 recites, in part, “controlling, using the degree of difference, at least one operational parameter of a drilling operation for forming a second wellbore, wherein controlling the at least one operational parameter comprises automatically adjusting, in response to detecting an anomaly based on the degree of difference: a rate of penetration, a rotation speed, or a mud flow rate of the drilling operation.” Applicant respectfully submits that support for this feature can be found in at least paragraph [0041] of the originally filed application. For example, paragraph [0041] of the originally filed application states, in part, that “[a]dditionally or alternatively, the symbolic dynamics service 214 can transmit the output 406 to a controller that can be used to automatically control the wellbore operation based on the output 406. In a particular example, the symbolic dynamics service 214 can transmit the output 406 to the controller, and the controller can automatically, such as without intervention from an operator of the wellbore operation, adjust (e.g., alter functional parameters, make no change, stop the wellbore operation, etc.) the wellbore operation based on the output 406.” Here, paragraph [0041] makes clear that output (e.g., based on the degree of difference) can be transmitted to the controller to cause the controller to alter functional parameters, among other things. Thus, at least paragraph [0041] of the originally filed application supports automatic control of parameters of a drilling operation based on output such as the degree of difference.
Additionally, paragraph [0015] discloses “drilling parameters.” One of ordinary skill in the relevant art at the time of filing the originally filed application would understand that some examples of drilling parameters can include rate of penetration, rotation speed, and a mud flow rate of the drilling operation. Thus in view of the one of ordinary skill in the relevant art, at least a combination of paragraphs [0015] and [0041] of the originally filed application provides written description support for the claimed feature of “controlling, using the degree of difference, at least one operational parameter of a drilling operation for forming a second wellbore, wherein controlling the at least one operational parameter comprises automatically adjusting, in response to detecting an anomaly based on the degree of difference: a rate of penetration, a rotation speed, or a mud flow rate of the drilling operation.”
These arguments are not persuasive.
The examiner submits that appropriate support for the claimed features was not found in the original disclosure and as explained in the MPEP:
“The Federal Circuit has pointed out that, under United States law, a description that merely renders a claimed invention obvious may not sufficiently describe the invention for the purposes of the written description requirement of 35 U.S.C. 112” (see MPEP 2163); and
“An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc)” (see MPEP 2163.03).
Applicant’s arguments, see Remarks (p. 9-11), filed on 05/01/2026, with respect to the rejection of claims 1-6, 8-13, 15-19 under 35 U.S.C. 101 have been fully considered. In view of the amendments to the claims addressing the issues raised in the previous office action, the rejection has been withdrawn (the examiner notes that although the amended features integrate the judicial exception into a practical application, proper support for the amended features in the original disclosure was not found, see Claim Rejections - 35 USC § 112 section).
Applicant argues (p. 10) that Amended claim 1 recites, in part, “controlling, using the degree of difference, at least one operational parameter of a drilling operation for forming a second wellbore, wherein controlling the at least one operational parameter comprises automatically adjusting, in response to detecting an anomaly based on the degree of difference, a rate of penetration, a rotation speed, or a mud flow rate of the drilling operation.” Here, amended claim 1 expressly recites control of a drilling operation for forming a second wellbore and is specific to the operational parameters adjusted and to what the adjustment is based upon (the degree of difference).
This argument is persuasive.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-6, 8-13, 15-19 and 21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “controlling, using the degree of difference, at least one operational parameter of a drilling operation for forming a second wellbore, wherein controlling the at least one operational parameter comprises automatically adjusting, in response to detecting an anomaly based on the degree of difference: a rate of penetration, a rotation speed, or a mud flow rate of the drilling operation” which is not disclosed in the original disclosure. Similar language is recited in independent claims 8 and 15, as well as in new dependent claim 21.
The original disclosure generally describes controlling a wellbore operation (see [0007], [0013], [0017], [0023]-[0024], [0034]-[0035], [0041]) and drilling operations in general (see [0015]-[0016]) without providing the specific parameters recited in the independent claims (a rate of penetration, a rotation speed, or a mud flow rate).
Examiner’s Note
Claims 1-6, 8-13, 15-19 and 21 were evaluated for patent eligibility under 35 U.S.C. 101 using the SUBJECT MATTER ELIGIBILITY TEST FOR PRODUCTS AND PROCESSES described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) to determine patent eligibility under 35 U.S.C. 101.
Regarding claim 1, the examiner submits that under Step 1 of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a machine/manufacture, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test:
the limitation “transforming the baseline data signal and the subsequent data signal into a first symbolic representation of the baseline data signal and a second symbolic representation of the subsequent data signal, respectively” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mathematical concepts to manipulate data (see specification at [0009]-[0011]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the limitation in the context of the claim mainly refers to applying mathematical concepts to transform data.
the limitation “determining, using symbolic dynamics to compare the first symbolic representation to the second symbolic representation, a degree of difference between the first symbolic representation and the second symbolic representation” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to manipulate data (see specification at [0012]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to compare data and obtain a result (i.e., a degree of difference).
Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, the additional elements recited in the claim:
“A system comprising: a processor; and a non-transitory computer-readable medium that includes instructions executable by the processor for causing the processor to perform operations” adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f));
“receiving a baseline data signal about a downhole tool and a subsequent data signal about the downhole tool, the downhole tool positionable in a wellbore associated with a wellbore operation” adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated, see specification at [0002], [0026], [0037]) (see MPEP 2106.05(g)) while generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)); and
“controlling, using the degree of difference, at least one operational parameter of a drilling operation for forming a second wellbore, wherein controlling the at least one operational parameter comprises automatically adjusting, in response to detecting an anomaly based on the degree of difference: a rate of penetration, a rotation speed, or a mud flow rate of the drilling operation” when considering the claim as a whole, integrates the judicial exception into a practical application by reflecting an improvement to other technology or technical field (e.g., controlling drilling operations based on symbolic dynamics analysis by adjusting in response to detecting an anomaly based on the degree of difference: a rate of penetration, a rotation speed, or a mud flow rate of the drilling operation) (see MPEP 2106.05(a)).
Therefore, these additional elements, when considered individually and in combination, integrate the judicial exception into a practical application. The claim, when considered as a whole, is eligible at Prong Two of the Revised Step 2A (see 2019 Revised Patent Subject Matter Eligibility Guidance – Revised Step 2A, see also MPEP 2106.04(d)).
Similarly, independent claims 8 and 15 are directed to patent eligible subject matter as explained above with regards to claim 1.
Regarding the dependent claims 2-6, 9-13, 16-19 and 21, they were found to be patent eligible under 35 U.S.C. 101 by incorporating the eligible subject matter of their corresponding independent claims.
Subject Matter Not Rejected Over Prior Art
Claims 1-6, 8-13, 15-19 and 21 are distinguished over the prior art of record for the following reasons:
Regarding claim 1.
Weber (US 20240118685 A1) discloses/teaches/suggests:
A system (Fig. 1; [0002]: an assistance apparatus for automatically identifying failure types of a technical system by monitoring time-series sensor data is presented (see [0003] regarding need to monitor operation in the oil and gas production field)) comprising:
a processor ([0053]: the assistance apparatus includes one or more processors); and
a non-transitory computer-readable medium (Fig. 1, item 14 – “data storage unit”) that includes instructions executable by the processor for causing the processor to perform operations ([0044], [0054]: data storage unit stores instructions to be executed by the one or more processors) comprising:
receiving a baseline data signal (Fig. 3, item M1; [0056], [0058]: first time series of sensor data measured by sensors over a period of time at the technical system is received) about a downhole tool (see [0003] regarding need to monitor operation in the oil and gas production field, which suggests the use of downhole tools) and a subsequent data signal (Fig. 3, item M3; [0063]: monitored time series of sensor data is obtained) about the downhole tool (see [0003] regarding need to monitor operation in the oil and gas production field, which suggests the use of downhole tools), the downhole tool positionable in a wellbore associated with a wellbore operation (see [0003] regarding need to monitor operation in the oil and gas production field, which suggests the use of downhole tools in a wellbore associated with a wellbore operation);
transforming the baseline data signal and the subsequent data signal into a first symbolic representation of the baseline data signal (Fig. 3, item M1; [0058]-[0059]: a symbolic representation is assigned to each temporal course determined from the first time series to provide failure patterns) and a second symbolic representation of the subsequent data signal (Fig. 3, item M3; [0063]-[0064]: a symbolic representation is assigned to each temporal course determined from the monitored time series), respectively;
determining a degree of difference between the first symbolic representation and the second symbolic representation (Fig. 3, item M4; [0066]: a similarity measure is determined based on a probability of the symbolic representation matching a failure pattern, with the ranking of the failure pattern being determined); and
controlling, using the degree of difference, at least one operational parameter of a drilling operation for forming a second wellbore, wherein controlling the at least one operational parameter comprises automatically adjusting, in response to detecting an anomaly based on the degree of difference: a rate of penetration, a rotation speed, or a mud flow rate of the drilling operation (Fig. 3, item M6; [0067]-[0069], [0073]: ranking of the failure pattern is output via a user interface (Fig. 1, item 13) while instructions to be applied to the technical system are also output (see [0003] regarding need to monitor operation in the oil and gas production field, which suggests controlling a drilling operation (see Applicant’s remarks at p. 9, par. 2 regarding one of ordinary skill in the art understanding that examples of drilling parameters include rate of penetration, rotation speed and a mud flow rate of the drilling operation))).
Khatkhate (A. Khatkhate, A. Ray, E. Keller, S. Gupta and S. C. Chin, “Symbolic time-series analysis for anomaly detection in mechanical systems,” in IEEE/ASME Transactions on Mechatronics, vol. 11, no. 4, pp. 439-447, Aug. 2006, doi: 10.1109/TMECH.2006.878544) discloses:
“This paper examines the efficacy of a novel method for anomaly detection in mechanical systems, which makes use of a hidden Markov model, derived from the time-series data of pertinent measurement(s). The core concept of the anomaly detection method is symbolic time-series analysis that is built upon the principles of Automata Theory, Information Theory, and Pattern Recognition. The performance of this method is compared with that of other existing pattern-recognition techniques from the perspective of early detection of small fatigue cracks in ductile alloy structures. The experimental apparatus, on which the anomaly detection method is tested, is a multi-degree-of-freedom mass-beam structure excited by oscillatory motion of two electromagnetic shakers. The evolution of fatigue crack damage at one or more failure sites are detected from symbolic time-series analysis of displacement sensor signals” (Abstract: anomaly detection in mechanical systems is performed using symbolic time-series analysis of sensor signals in order to determine fatigue crack damage in ductile alloy structures (see also ‘Introduction’ section)).
Friedlander (D. Friedlander, I. Chattopadhyay, A. Ray, S. Phoha and N. Jacobson, “Anomaly prediction in mechanical systems using symbolic dynamics,” Proceedings of the 2003 American Control Conference, 2003., Denver, CO, USA, 2003, pp. 4275-4280 vol.5, doi: 10.1109/ACC.2003.1240508) discloses:
“This paper presents anomaly prediction in complex mechanical systems at an early stage where anomaly is defined as an observable deviation from the nominal dynamical response. The anomaly prediction algorithm is built upon two-time-scale analysis of time series data and relies on a combination of Nonlinear Systems theory and Language theory. The algorithm has been validated for anomaly prediction on a rotorcraft gearbox testbed for two different types of anomalies” (Abstract: anomaly detection in complex mechanical system is performed using time series data analyzed using Nonlinear Systems theory and Language theory in order to determine anomalies on a rotorcraft gearbox (see also ‘Introduction’ section)).
Ray (Asok Ray, “Symbolic dynamic analysis of complex systems for anomaly detection,” Signal Processing, Volume 84, Issue 7, 2004, Pages 1115-1130, ISSN 0165-1684, https://doi.org/10.1016/j.sigpro.2004.03.011) discloses:
“This paper presents a novel concept of anomaly detection in complex dynamical systems using tools of Symbolic Dynamics, Finite State Automata, and Pattern Recognition, where time-series data of the observed variables on the fast time-scale are analyzed at slow time-scale epochs for early detection of (possible) anomalies. The concept of anomaly detection in dynamical systems is elucidated based on experimental data that have been generated from an active electronic circuit with a slowly varying dissipation parameter” (Abstract: anomaly detection in complex dynamical systems uses time series data and Symbolic Dynamics (see also ‘Introduction’ section)).
The closest prior art of record, taken individually or in combination, fail to teach or suggest (see italic text):
“determining, using symbolic dynamics to compare the first symbolic representation to the second symbolic representation, a degree of difference between the first symbolic representation and the second symbolic representation”
in combination with all other limitations within the claim, as claimed and defined by the applicant.
Regarding claim 8.
Weber (US 20240118685 A1) discloses/teaches/suggests:
A method (Fig. 3; [0002]: a method for automatically identifying failure types of a technical system by monitoring time-series sensor data is presented (see [0003] regarding need to monitor operation in the oil and gas production field)) comprising:
receiving, by a computing device (Fig. 1, item 10 – “assistance apparatus”), a baseline data signal (Fig. 3, item M1; [0056], [0058]: first time series of sensor data measured by sensors over a period of time at the technical system is received) about a downhole tool (see [0003] regarding need to monitor operation in the oil and gas production field, which suggests the use of downhole tools) and a subsequent data signal (Fig. 3, item M3; [0063]: monitored time series of sensor data is obtained) about the downhole tool (see [0003] regarding need to monitor operation in the oil and gas production field, which suggests the use of downhole tools), the downhole tool positionable in a wellbore associated with a wellbore operation (see [0003] regarding need to monitor operation in the oil and gas production field, which suggests the use of downhole tools in a wellbore associated with a wellbore operation);
transforming, by the computing device, the baseline data signal and the subsequent data signal into a first symbolic representation of the baseline data signal (Fig. 3, item M1; [0058]-[0059]: a symbolic representation is assigned to each temporal course determined from the first time series to provide failure patterns) and a second symbolic representation of the subsequent data signal (Fig. 3, item M3; [0063]-[0064]: a symbolic representation is assigned to each temporal course determined from the monitored time series), respectively;
determining, by the computing device, a degree of difference between the first symbolic representation and the second symbolic representation (Fig. 3, item M4; [0066]: a similarity measure is determined based on a probability of the symbolic representation matching a failure pattern, with the ranking of the failure pattern being determined); and
controlling, using the degree of difference, at least one operational parameter of a drilling operation for forming a second wellbore, wherein controlling the at least one operational parameter comprises automatically adjusting, in response to detecting an anomaly based on the degree of difference: a rate of penetration, a rotation speed, or a mud flow rate of the drilling operation (Fig. 3, item M6; [0067]-[0069], [0073]: ranking of the failure pattern is output via a user interface (Fig. 1, item 13) while instructions to be applied to the technical system are also output (see [0003] regarding need to monitor operation in the oil and gas production field, which suggests controlling a drilling operation (see Applicant’s remarks at p. 9, par. 2 regarding one of ordinary skill in the art understanding that examples of drilling parameters include rate of penetration, rotation speed and a mud flow rate of the drilling operation))).
Khatkhate (A. Khatkhate, A. Ray, E. Keller, S. Gupta and S. C. Chin, “Symbolic time-series analysis for anomaly detection in mechanical systems,” in IEEE/ASME Transactions on Mechatronics, vol. 11, no. 4, pp. 439-447, Aug. 2006, doi: 10.1109/TMECH.2006.878544) discloses:
“This paper examines the efficacy of a novel method for anomaly detection in mechanical systems, which makes use of a hidden Markov model, derived from the time-series data of pertinent measurement(s). The core concept of the anomaly detection method is symbolic time-series analysis that is built upon the principles of Automata Theory, Information Theory, and Pattern Recognition. The performance of this method is compared with that of other existing pattern-recognition techniques from the perspective of early detection of small fatigue cracks in ductile alloy structures. The experimental apparatus, on which the anomaly detection method is tested, is a multi-degree-of-freedom mass-beam structure excited by oscillatory motion of two electromagnetic shakers. The evolution of fatigue crack damage at one or more failure sites are detected from symbolic time-series analysis of displacement sensor signals” (Abstract: anomaly detection in mechanical systems is performed using symbolic time-series analysis of sensor signals in order to determine fatigue crack damage in ductile alloy structures (see also ‘Introduction’ section)).
Friedlander (D. Friedlander, I. Chattopadhyay, A. Ray, S. Phoha and N. Jacobson, “Anomaly prediction in mechanical systems using symbolic dynamics,” Proceedings of the 2003 American Control Conference, 2003., Denver, CO, USA, 2003, pp. 4275-4280 vol.5, doi: 10.1109/ACC.2003.1240508) discloses:
“This paper presents anomaly prediction in complex mechanical systems at an early stage where anomaly is defined as an observable deviation from the nominal dynamical response. The anomaly prediction algorithm is built upon two-time-scale analysis of time series data and relies on a combination of Nonlinear Systems theory and Language theory. The algorithm has been validated for anomaly prediction on a rotorcraft gearbox testbed for two different types of anomalies” (Abstract: anomaly detection in complex mechanical system is performed using time series data analyzed using Nonlinear Systems theory and Language theory in order to determine anomalies on a rotorcraft gearbox (see also ‘Introduction’ section)).
Ray (Asok Ray, “Symbolic dynamic analysis of complex systems for anomaly detection,” Signal Processing, Volume 84, Issue 7, 2004, Pages 1115-1130, ISSN 0165-1684, https://doi.org/10.1016/j.sigpro.2004.03.011) discloses:
“This paper presents a novel concept of anomaly detection in complex dynamical systems using tools of Symbolic Dynamics, Finite State Automata, and Pattern Recognition, where time-series data of the observed variables on the fast time-scale are analyzed at slow time-scale epochs for early detection of (possible) anomalies. The concept of anomaly detection in dynamical systems is elucidated based on experimental data that have been generated from an active electronic circuit with a slowly varying dissipation parameter” (Abstract: anomaly detection in complex dynamical systems uses time series data and Symbolic Dynamics (see also ‘Introduction’ section)).
The closest prior art of record, taken individually or in combination, fail to teach or suggest (see italic text):
“determining, by the computing device and using symbolic dynamics to compare the first symbolic representation to the second symbolic representation, a degree of difference between the first symbolic representation and the second symbolic representation”
in combination with all other limitations within the claim, as claimed and defined by the applicant.
Regarding claim 15.
Weber (US 20240118685 A1) discloses/teaches/suggests:
A non-transitory computer-readable medium (Fig. 1, item 14 – “data storage unit”) comprising instructions that are executable by a processing device ([0053]: an assistance apparatus (Fig. 1, item 10) includes one or more processors) for causing the processing device to perform operations ([0044], [0054]: data storage unit stores instructions to be executed by the one or more processors for automatically identifying failure types of a technical system by monitoring time-series sensor data is presented (see [0002]-[0003] regarding need to monitor operation in the oil and gas production field)) comprising:
receiving a baseline data signal (Fig. 3, item M1; [0056], [0058]: first time series of sensor data measured by sensors over a period of time at the technical system is received) about a downhole tool (see [0003] regarding need to monitor operation in the oil and gas production field, which suggests the use of downhole tools) and a subsequent data signal (Fig. 3, item M3; [0063]: monitored time series of sensor data is obtained) about the downhole tool (see [0003] regarding need to monitor operation in the oil and gas production field, which suggests the use of downhole tools), the downhole tool positionable in a wellbore associated with a wellbore operation (see [0003] regarding need to monitor operation in the oil and gas production field, which suggests the use of downhole tools in a wellbore associated with a wellbore operation);
transforming the baseline data signal and the subsequent data signal into a first symbolic representation of the baseline data signal (Fig. 3, item M1; [0058]-[0059]: a symbolic representation is assigned to each temporal course determined from the first time series to provide failure patterns) and a second symbolic representation of the subsequent data signal (Fig. 3, item M3; [0063]-[0064]: a symbolic representation is assigned to each temporal course determined from the monitored time series), respectively;
determining a degree of difference between the first symbolic representation and the second symbolic representation (Fig. 3, item M4; [0066]: a similarity measure is determined based on a probability of the symbolic representation matching a failure pattern, with the ranking of the failure pattern being determined); and
controlling, using the degree of difference, at least one operational parameter of a drilling operation for forming a second wellbore, wherein controlling the at least one operational parameter comprises automatically adjusting, in response to detecting an anomaly based on the degree of difference: a rate of penetration, a rotation speed, or a mud flow rate of the drilling operation (Fig. 3, item M6; [0067]-[0069], [0073]: ranking of the failure pattern is output via a user interface (Fig. 1, item 13) while instructions to be applied to the technical system are also output (see [0003] regarding need to monitor operation in the oil and gas production field, which suggests controlling a drilling operation (see Applicant’s remarks at p. 9, par. 2 regarding one of ordinary skill in the art understanding that examples of drilling parameters include rate of penetration, rotation speed and a mud flow rate of the drilling operation))).
Khatkhate (A. Khatkhate, A. Ray, E. Keller, S. Gupta and S. C. Chin, “Symbolic time-series analysis for anomaly detection in mechanical systems,” in IEEE/ASME Transactions on Mechatronics, vol. 11, no. 4, pp. 439-447, Aug. 2006, doi: 10.1109/TMECH.2006.878544) discloses:
“This paper examines the efficacy of a novel method for anomaly detection in mechanical systems, which makes use of a hidden Markov model, derived from the time-series data of pertinent measurement(s). The core concept of the anomaly detection method is symbolic time-series analysis that is built upon the principles of Automata Theory, Information Theory, and Pattern Recognition. The performance of this method is compared with that of other existing pattern-recognition techniques from the perspective of early detection of small fatigue cracks in ductile alloy structures. The experimental apparatus, on which the anomaly detection method is tested, is a multi-degree-of-freedom mass-beam structure excited by oscillatory motion of two electromagnetic shakers. The evolution of fatigue crack damage at one or more failure sites are detected from symbolic time-series analysis of displacement sensor signals” (Abstract: anomaly detection in mechanical systems is performed using symbolic time-series analysis of sensor signals in order to determine fatigue crack damage in ductile alloy structures (see also ‘Introduction’ section)).
Friedlander (D. Friedlander, I. Chattopadhyay, A. Ray, S. Phoha and N. Jacobson, “Anomaly prediction in mechanical systems using symbolic dynamics,” Proceedings of the 2003 American Control Conference, 2003., Denver, CO, USA, 2003, pp. 4275-4280 vol.5, doi: 10.1109/ACC.2003.1240508) discloses:
“This paper presents anomaly prediction in complex mechanical systems at an early stage where anomaly is defined as an observable deviation from the nominal dynamical response. The anomaly prediction algorithm is built upon two-time-scale analysis of time series data and relies on a combination of Nonlinear Systems theory and Language theory. The algorithm has been validated for anomaly prediction on a rotorcraft gearbox testbed for two different types of anomalies” (Abstract: anomaly detection in complex mechanical system is performed using time series data analyzed using Nonlinear Systems theory and Language theory in order to determine anomalies on a rotorcraft gearbox (see also ‘Introduction’ section)).
Ray (Asok Ray, “Symbolic dynamic analysis of complex systems for anomaly detection,” Signal Processing, Volume 84, Issue 7, 2004, Pages 1115-1130, ISSN 0165-1684, https://doi.org/10.1016/j.sigpro.2004.03.011) discloses:
“This paper presents a novel concept of anomaly detection in complex dynamical systems using tools of Symbolic Dynamics, Finite State Automata, and Pattern Recognition, where time-series data of the observed variables on the fast time-scale are analyzed at slow time-scale epochs for early detection of (possible) anomalies. The concept of anomaly detection in dynamical systems is elucidated based on experimental data that have been generated from an active electronic circuit with a slowly varying dissipation parameter” (Abstract: anomaly detection in complex dynamical systems uses time series data and Symbolic Dynamics (see also ‘Introduction’ section)).
The closest prior art of record, taken individually or in combination, fail to teach or suggest (see italic text):
“determining, using symbolic dynamics to compare the first symbolic representation to the second symbolic representation, a degree of difference between the first symbolic representation and the second symbolic representation”
in combination with all other limitations within the claim, as claimed and defined by the applicant.
Regarding claims 2-6, 9-13, 16-19 and 21.
They are also distinguished over the prior art of record due to their dependency.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Devendra Tolani, Symbolic Time Series Analysis (STSA) for Anomaly Detection, NIST, 2005
Reference discloses using symbolic dynamics analysis for anomaly detection while describing advantages and disadvantages of symbolic analysis.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINA CORDERO whose telephone number is (571)272-9969. The examiner can normally be reached 9:30 am - 6:00 pm.
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, ANDREW SCHECHTER can be reached at 571-272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LINA CORDERO/Primary Examiner, Art Unit 2857
Prosecution Insights