DETAILED ACTION
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “explainable” in claim 13 is a relative term which renders the claim indefinite. The term “explainable” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term “explainable” renders the type of artificial intelligence (AI) techniques used indefinite.
The term “strategy” in claims 1, 5, 12, 13, 15, and 18 is a relative term which renders the claim indefinite. The term “strategy” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term “strategy” renders what is implemented based on the detected fault indefinite.
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 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter.
As per claim(s) 18-20, they are rejected because the applicant has provided evidence that the applicant intends the term "computer-readable medium" to include non-statutory matter. The applicant describes a computer-readable storage medium as including open ended language and thus it is reasonable to interpret it to include all possible mediums, including non-statutory mediums (Such computer programs may be stored in a computer readable medium, such as a computer readable storage medium or a computer readable signal medium. A computer readable storage medium may involve tangible mediums such as, but not limited to optical disks, magnetic disks, read-only memories, random access memories, solid-state devices, and drives, or any other types of tangible or non-transitory media suitable for storing electronic information. A computer readable signal medium may include mediums such as carrier waves, [0086]). The words "storage", "tangible", and/or "recording" are insufficient to convey only statutory embodiments to one of ordinary skill in the art absent an explicit and deliberate limiting definition or clear differentiation between storage media and transitory media in the disclosure. As such, the claim(s) is/are drawn to a form of energy. Energy is not one of the four categories of invention and therefore this/these claim(s) is/are not statutory. Energy is not a series of steps or acts and thus is not a process. Energy is not a physical article or object and as such is not a machine or manufacture. Energy is not a combination of substances and therefore not a composition of matter.
Since the specification describes "a computer readable medium" as comprising both transitory and non-transitory media, the claim encompasses both and is therefore non-statutory.
The examiner suggests amending the claim(s) to read as a "non-transitory computer-readable storage medium".
Claims 2, 8, 9, 14, 16, 17, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim(s) 2, 8, 9, 14, 16, 17, and 20 recite(s) the limitation(s) of “predicting multiple anomaly scores including at least two of a univariate anomaly score, a bivariate anomaly score, or a multivariate anomaly score”, and “calculating a predicted fault score indicating a likelihood of the sensor fault by generating an ensemble of anomaly scores based on the multiple anomaly scores” in claim 2,
“calculating a similarity score between the sensor data from the first sensor and sensor data from sensors in the plurality of related sensors” and “identifying the set of correlated sensors based on the similarity score between the sensor data from the first sensor and sensor data from the set of correlated sensors being above a threshold similarity score” in claim 8,
“calculating a macro-similarity score based on a full set of time-series sensors data from the first sensor during a first time period and a full set of time-series data from each sensor in the plurality of related sensors during the first time period” and “calculating a plurality of micro-similarity scores based on a plurality of subsets of the full set of time-series data from the first sensor during the first time period and a plurality of subsets of the full set of time-series data from each sensor in the plurality of related sensors during the first time period” in claim 9,
“wherein the sensor data from the one or more sensors in the set of correlated sensors is determined based on one or more of: a calculated similarity score between the sensor data from the first sensor and the sensor data from the sensors in the set of related sensors” in claim 14,
“calculating a similarity score between the sensor data from the first sensor and sensor data from sensors in the plurality of related sensors by one of: calculating a macro-similarity score based on a full set of time-series sensors data from the first sensor during a first time period and a full set of time-series data from each sensor in the plurality of related sensors during the first time period”, “calculating a plurality of micro-similarity scores based on a plurality of subsets of the full set of time-series data from the first sensor during the first time period and a plurality of subsets of the full set of time-series data from each sensor in the plurality of related sensors during the first time period”, and “identifying the set of correlated sensors based on the similarity score between the sensor data from the first sensor and sensor data from the set of correlated sensors being above a threshold similarity score” in claim 16,
“predicting multiple anomaly scores including at least two of a univariate anomaly score, a bivariate anomaly score, or a multivariate anomaly score” and “calculating a predicted fault score indicating a likelihood of the sensor fault by generating an ensemble of anomaly scores based on the multiple anomaly scores” in claim 17, and
“predicting multiple anomaly scores including at least two of a univariate anomaly score, a bivariate anomaly score, or a multivariate anomaly score” and “calculating a predicted fault score indicating a likelihood of the sensor fault by generating an ensemble of anomaly scores based on the multiple anomaly scores” in claim 20.
This/These limitation(s), as drafted, is(are) a process (processes) that, under its (their) broadest reasonable interpretation, cover(s) performance of the limitation(s) in the mind but for the recitation of generic computer components. That is, other than reciting “a memory” and “at least one processor” in claim 15 and “a computer-readable medium” in claim 18, nothing in the claim elements precludes the steps from practically being performed in the mind. The mere nominal recitation of generic processing components does not take the claim limitation(s) out of the mental processes grouping.
The examiner notes that “predicting multiple anomaly scores including at least two of a univariate anomaly score, a bivariate anomaly score, or a multivariate anomaly score” and “calculating a predicted fault score indicating a likelihood of the sensor fault by generating an ensemble of anomaly scores based on the multiple anomaly scores” each involve prediction which is based upon subjective factors, weights, and correspondences and the use of “anomaly scores”, each of which is also based upon a subjective thought process for the determination of the score and includes the concepts of evaluation, judgment, and opinion in claim 2,
“calculating a similarity score between the sensor data from the first sensor and sensor data from sensors in the plurality of related sensors” involves calculating a “score” which is based upon subjective factors, weights, and correspondences based on a thought process for the determination of the score and includes the concepts of evaluation, judgment, and opinion and “identifying the set of correlated sensors based on the similarity score between the sensor data from the first sensor and sensor data from the set of correlated sensors being above a threshold similarity score” involves identifying a set of sensors based upon subjectively determined “scores” and includes the concepts of observation, evaluation, judgment, and opinion in claim 8,
“calculating a macro-similarity score based on a full set of time-series sensors data from the first sensor during a first time period and a full set of time-series data from each sensor in the plurality of related sensors during the first time period” involves calculating a “score” which is based upon subjective factors, weights, and correspondences based on a thought process for the determination of the score and includes the concepts of evaluation, judgment, and opinion and “calculating a plurality of micro-similarity scores based on a plurality of subsets of the full set of time-series data from the first sensor during the first time period and a plurality of subsets of the full set of time-series data from each sensor in the plurality of related sensors during the first time period” involves calculating “scores” which are based upon subjective factors, weights, and correspondences based on a thought process for the determination of the scores and includes the concepts of evaluation, judgment, and opinion in claim 9,
“wherein the sensor data from the one or more sensors in the set of correlated sensors is determined based on one or more of: a calculated similarity score between the sensor data from the first sensor and the sensor data from the sensors in the set of related sensors” involves determining sensor data based on a subjectively calculated similarity score and includes the concepts of observation, evaluation, judgment, and opinion in claim 14,
“calculating a similarity score between the sensor data from the first sensor and sensor data from sensors in the plurality of related sensors by one of: calculating a macro-similarity score based on a full set of time-series sensors data from the first sensor during a first time period and a full set of time-series data from each sensor in the plurality of related sensors during the first time period” involves calculating a “score” which is based upon subjective factors, weights, and correspondences based on a thought process for the determination of the score and includes the concepts of evaluation, judgment, and opinion, “calculating a plurality of micro-similarity scores based on a plurality of subsets of the full set of time-series data from the first sensor during the first time period and a plurality of subsets of the full set of time-series data from each sensor in the plurality of related sensors during the first time period” involves calculating “scores” which are based upon subjective factors, weights, and correspondences based on a thought process for the determination of the scores and includes the concepts of evaluation, judgment, and opinion, and “identifying the set of correlated sensors based on the similarity score between the sensor data from the first sensor and sensor data from the set of correlated sensors being above a threshold similarity score” involves identifying a set of sensors based upon subjectively determined “scores” and includes the concepts of observation, evaluation, judgment, and opinion in claim 16,
“predicting multiple anomaly scores including at least two of a univariate anomaly score, a bivariate anomaly score, or a multivariate anomaly score” and “calculating a predicted fault score indicating a likelihood of the sensor fault by generating an ensemble of anomaly scores based on the multiple anomaly scores” each involve prediction which is based upon subjective factors, weights, and correspondences and the use of “anomaly scores”, each of which is also based upon a subjective thought process for the determination of the score and includes the concepts of evaluation, judgment, and opinion in claim 17, and
“predicting multiple anomaly scores including at least two of a univariate anomaly score, a bivariate anomaly score, or a multivariate anomaly score” and “calculating a predicted fault score indicating a likelihood of the sensor fault by generating an ensemble of anomaly scores based on the multiple anomaly scores” each involve prediction which is based upon subjective factors, weights, and correspondences and the use of “anomaly scores”, each of which is also based upon a subjective thought process for the determination of the score and includes the concepts of evaluation, judgment, and opinion in claim 20. Thus, the claim(s) recite(s) a mental process, concepts that may be performed in the human mind, in this case being observation, evaluation, judgment, and opinion.
This judicial exception is not integrated into a practical application because the additional elements are recited at a high level of generality, i.e., as generic processor performing a generic computer function. Generic processor limitations are no more than mere instructions to apply the exception using a generic computer component. The examiner notes that while “implementing a remediation strategy” in claim 1, 15, and 18 could potentially improve the functioning of a computer, it is not a particular solution to a specific problem (An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome, see MPEP 2106.05(a), The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it", see MPEP 2106.05(f)), but instead a generic solution to any and all possible problems. The examiner notes that “fault” and “remediation strategy” are a generic problem and solution and is equivalent to “apply it”, applying a generic “remediation” or solution to any and all “faults” or problems. 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. Therefore, the additional elements fail to improve the functionality of the computer itself.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology or effects a transformation or reduction of a particular article to a different state or thing. Their collective functions merely provide conventional computer implementation. Furthermore, the applicant’s own specification details the generic nature of the computing components, which also precludes them from presenting anything significantly more ([0074-0081], fig. 9).
Claim(s) 2, 8, 9, 14, 16, 17, and 20 is(are) therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3-8, 12-15, and 18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Khalaschi (U.S. Patent Application Publication No. 2016/0217627).
Regarding claim 1, Khalaschi discloses a method (a method for detecting and diagnosing sensor faults in an autonomous system (such as a robot, a flight simulator or an unmanned vehicle) that includes sensors and hardware components, according to which sensors are related to hardware components and correlations between data readings are recognized online and correlation between sensors is determined, [0021]) comprising:
receiving sensor data from a plurality of related sensors (sensors’ readings, [0021, 0049]));
identifying, for a first sensor in the plurality of related sensors, a set of correlated sensors in the plurality of related sensors (correlated sensor set, [0056, 0066]);
detecting a fault in the first sensor based on at least one of the sensor data received from the sensor, the sensor data received from the set of correlated sensors, and the sensor data received from other sensors ([0065], using the correlated sensor set to indicate a failure, [0066], [0078]); and
implementing a remediation strategy based on the detected fault of the first sensor (isolate the faulty component or sensor and thus provide diagnosis and addresses faults that their symptoms appear only over time, [0008], isolates the faulty component/sensor, [0019], determine a fault has occurred, a structural model is used for the diagnosis process, which indicates sensors' dependency on hardware components, and thus enables the isolation of the faulty sensor or component, [0038], Upon fault detection, the diagnosis procedure (described next) is invoked, [0077], [0082]).
Regarding claim 3, Khalaschi discloses wherein the plurality of related sensors are sensors monitoring a same system ([0021, 0087]).
Regarding claim 4, Khalaschi discloses wherein the plurality of related sensors comprises at least one of a physical sensor installed in a system or a virtual sensor derived from a set of physical sensors based on a physics-based models ([0021, 0087]).
Regarding claim 5, Khalaschi discloses wherein the first sensor is a first critical sensor, wherein a critical sensor is a sensor that captures critical data for monitoring a health of an underlying system and is used for at least one of identifying the remediation strategy, deriving business insights, or building solutions for problems relating to a set of downstream tasks ([0062, 0066]).
Regarding claim 6, Khalaschi discloses wherein the set of correlated sensors comprises a set of sensors with outputs correlated to an output of the first sensor ([0066]).
Regarding claim 7, Khalaschi discloses wherein the set of correlated sensors comprises multiple sensors in the plurality of related sensors ([0021, 0049]),
wherein an output of at least one sensor of the multiple sensors is not correlated to the output of the first sensor with a threshold correlation and identifying the set of correlated sensors is based on a function of the outputs of the multiple sensors being correlated to the first sensor with the threshold correlation ([0056]).
Regarding claim 8, Khalaschi discloses wherein identifying the set of correlated sensors comprises:
calculating a similarity score between the sensor data from the first sensor and sensor data from sensors in the plurality of related sensors ([0055]); and
identifying the set of correlated sensors based on the similarity score between the sensor data from the first sensor and sensor data from the set of correlated sensors being above a threshold similarity score ([0066]).
Regarding claim 12, Khalaschi discloses wherein the remediation strategy comprises using sensor data from one or more sensors in the set of correlated sensors to replace the sensor data from the first sensor ([0067]).
Regarding claim 13, Khalaschi discloses wherein the remediation strategy is based on a root cause analysis of the detected fault based on one or more explainable artificial intelligence (AI) techniques ([0116]).
Regarding claim 14, Khalaschi discloses wherein the sensor data from the one or more sensors in the set of correlated sensors is determined based on one or more of:
a calculated similarity score between the sensor data from the first sensor and the sensor data from the sensors in the set of related sensors (0055, 0066);
a geolocation of the first sensor and the sensors in the set of related sensors ([0045, 0062, 0066, 0067, 0069, 0087]); or
a time sequence of the first sensor and the sensors in the set of related sensors ([0051, 0052, 0064, 0071, 0083]).
Regarding claim(s) 15 and 18, claim(s) 15 and 18 recite substantially similar limitations to claim(s) 1 and is(are) therefore rejected using the same art and rationale set forth above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rohrkemper (U.S. Patent Application Publication No. 2023/0153680) discloses detecting faults based on correlated sensor data ([0023, 0025-0027]).
Vichare (U.S. Patent Application Publication No. 2016/0041948) discloses predicting anomalies based on sensor data correlations and implementing remediation plans ([0014, 0024]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA P LOTTICH whose telephone number is (571)270-3738. The examiner can normally be reached Mon - Fri, 9:00am - 5:30pm.
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, Bryce Bonzo can be reached at 5712723655. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSHUA P LOTTICH/ Primary Examiner, Art Unit 2113
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