DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Applicant elects to prosecute the claims of Group II, (claims 12-20) with traverse, and the claims of Group I (claims 1-11) are withdrawn from consideration.
3. Claims 12-20 are pending and presented for examination.
Claim Rejections - 35 USC § 101
4. 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.
5. Claims 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The representative claim 12 recites:
A computerized system, comprising:
one or more processors; and computer storage memory having computer-executable instructions stored thereon which, when executed by the one or more processors, implement a method comprising:
accessing, from a data store in computer memory, first data associated with a plurality of heat transfer fluid samples, each heat transfer fluid sample of the plurality of heat transfer fluid samples being associated with a first plurality of parameters;
converting the first data into a first set of feature vectors and mapping the first set of feature vectors in a vector space, each feature vector of the first set of feature vectors representing a corresponding heat transfer fluid sample and the first plurality of parameters;
receiving second data associated with a heat transfer fluid;
converting the second data into a second feature vector and mapping the second feature vector in the vector space;
computing, via one or more machine learning models, a distance, in the vector space, between at least one of the first set of feature vectors and the second feature vector; based at least in part on the computing,
estimating at least one of: a fluid life or condition score for the heat transfer fluid; and based at least in part on the computing,
generating a user interface element that indicates the at least one of the fluid life or condition score.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category (process).
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and/or mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because the additional limitations in the claim are only: one or more processors; and computer storage memory having computer-executable instructions stored thereon which, when executed by the one or more processors, implement a method comprising:… a data store in computer memory, receiving second data associated with a heat transfer fluid;
…one or more machine learning models,….generating a user interface element that indicates the at least one of the fluid life or condition score.” The limitation “receiving second data associated with a heat transfer fluid: is recited at a high level of generality (i.e., gathering or collecting data) such that it amounts no more than mere instructions to apply the exception using a generic means of collecting system.
The limitations “one or more processors; and computer storage memory having computer-executable instructions stored thereon which, when executed by the one or more processors, implement a method comprising:… a data store in computer memory” are recited at a high level of generality (i.e., as a computer structures performing a generic computer function of processing and storing information) such that they amount no more than mere instructions to apply the exception using a generic computer components.
Further, the limitation “generating a user interface element that indicates the at least one of the fluid life or condition score” is recited at a high level of generality (i.e., as a computer structures performing a generic computer function of displaying information) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Furthermore, claim 12 recites the additional element(s) of using generic AI/ML technology, i.e. one or more machine learning models, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the one or more machine learning models merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of one or more machine learning models to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
Claim 12 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted above, the additional elements are recited at a high level of generality (i.e., as gathering data, storing and processing data using a generic computer components (processor and memory)). Further, the additional elements are conventional in the art, as evidenced by the art of record (see, Zhao et al. US 2014/0151560 (hereinafter, Zhao), ([0083], Fig. 5A) [0083]), and Potyrailo et al. US 2017/0138922 (hereinafter, Potyrailo), ([0131], Figs. 74-76). Therefore, claim 12 is directed to an abstract idea without significantly more.
The claim is not patent eligible.
Dependent claims 13-15, add further details of the identified abstract idea. The claims are not patent eligible.
Dependent claim 16, recites additional element of “based at least in part on the at least one of the fluid life or condition score exceeding a threshold, generating a recommendation signal that indicates instructions to service the heat transfer fluid.” However, this limitation is recited at a high level of generality (i.e., as a computer structures performing a generic computer function of displaying information) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Further, the additional element is conventional in the art, as evidenced by the art of record (see, Zhao, ([0083], Fig. 5A), and Potyrailo, ([0131], Figs. 74-76). Therefore, claim 16 is directed to an abstract idea without significantly more. The claim is not patent eligible.
Dependent claims 17-20, the claims are rejected with the same rationale as in claim 16 as explained above.
Claim Objection
6. Claim 20 is objected to because of the following informalities: Claim 20 recites “…implement a method further…” should read “ …implement [[a]] the method further…. Appropriate correction is required.
Claim Rejections - 35 USC § 103
7. In the event the determination of the status of the application as subject to AlA 35 U.S.C. 102 and 103 (or as subject to pre-AlA 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.
8. Claims 12-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wakiru et al. “A data mining approach for lubricant-based fault diagnosis” July 2020 (hereinafter, Wakiru), in view of Wang et al. “Trajectory Similarity Based Prediction for Remaining Useful Life Estimation”, 2010 (hereinafter, Wang), in further view of Potyrailo et al. US 2017/0138922 (hereinafter, Potyrailo).
9. Regarding claim 12, Wakiru discloses a computerized system, comprising:
one or more processors; and computer storage memory having computer-executable instructions stored thereon which, when executed by the one or more processors, implement a method comprising:
accessing, from a data store in computer memory, first data associated with a plurality of heat transfer fluid samples, each [fluid sample] of the plurality of fluid samples being associated with a first plurality of parameters (page 272, section 3.2 Data collection and pre-processing: utilizes empirical used oil analysis data that covered the period from 2011 to 2016 with 21 lubricant test parameters analysed against recommended standards (see Table 5). The plant employs an off-line sampling strategy, where engine oil samples are drawn monthly and taken for analysis in an independent laboratory….[Further]: page 274: the data represented 1,103 sample observations);
converting the first data into a first set of feature and mapping the first set of feature, each feature of the first set of feature representing a corresponding fluid sample and the first plurality of parameters (page 280, section 4.2.3: Selection of variables and feature reduction. During the selection of the critical variables to be used in the predictive model,…The sixteen variables selected included viscosity at 40oC, flash point, total base number, magnesium, calcium, zinc, silicon, sodium, water, carbon, iron, chromium, lead, copper, nickel and vanadium.… [Further], pages 269, 274: KNN is a non-parametric algorithm that generates predictions for a sample by finding the k nearest samples and assigning the most represented class among them using a distance function… While reviewing KNN, the number of neighbours to check for each sample k ranged {1,3,5}, while the distance used to search for the neighbours varied between Euclidian and Manhattan. (p=1 or 2 respectively));
receiving second data associated with a fluid; converting the second data into a second feature vector and mapping the second feature in the space (page 274, section 3.5 Data scoring and splitting: the sample observation are split into training and testing data…[Further] page 269: KNN generates predictions for a sample by finding the k nearest samples and assigning the most represented class among them using a distance function);
computing, via one or more machine learning models, a distance, between at least one of the first set of feature and the second feature; based at least in part on the computing (pages 269, 274, section 3.5: KNN (i.e., machine learning model) is a non-parametric algorithm that generates predictions for a sample by finding the k nearest samples and assigning the most represented class among them using a distance function… While reviewing KNN, the number of neighbours to check for each sample k ranged {1,3,5}, while the distance used to search for the neighbours varied between Euclidian and Manhattan. (p=1 or 2 respectively));
estimating at least one of: a fluid life or condition score for the fluid; and based at least in part on the computing, generating [an] element that indicates the at least one of the fluid life or condition score (page 274 section 3.5 Data scoring and splitting: the data were scored by analysts evaluating each of the selected parameters against a limit or threshold set by the Original Equipment Manufacturers (OEM) and moderated by the power plant. The data represented 1,103 sample observations where 197 (17.9%) were scored as 1 (PASS) and the rest as 0 (FAIL). The observed rate was attributed to the fact that score 0 included samples that were at the borderline and exhibited cautionary advice to the maintenance team…[Further], pages 280 and 285: It is proposed that the future classification of sample results should embed a three-tier level, “PASS”, “CAUTION” and “FAIL” ).
Wakiru does not disclose:
heat transfer fluid sample, converting the first data into a first set of feature vector and mapping of feature vectors in a vector space, and generating a user interface element that indicates the at least one of the fluid life or condition score.
However, Wang discloses:
converting the first data into a first set of feature vector and mapping of feature vectors in a vector space (pages 15 and 39, section 3.2: multi-dimensional feature space… Let X= (x1, x2, ….Xn)T be an N-dimensional feature vector extracted from the raw data collected….[Further], page 74, section 4.5.3: PCA applies the following transformation to all the samples ˜y from both training and test instances….page 92: The samples from training and test instances are then transformed to the PC domain…page 48: compute the minimal weighted Euclidean distance. See also page 28: RUL (Remaining useful life) estimation using oil-based condition monitoring data set)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wakiru to use converting the first data into a first set of feature vector and mapping of feature vectors in a vector space as taught by Wang. One would have been motivated to do so in order to apply the converting and mapping of feature vectors methodology of a remaining useful life system as known in the art and as taught by Wang in an estimating a fluid life system such as that of Wakiru, thereby providing a known benefit of a quantitative remaining useful life of fluid for maintenance planning (Wang, page 28).
Wakiru in view of Wang does not disclose:
heat transfer fluid sample, and generating a user interface element that indicates the at least one of the fluid life or condition score.
However, Potyrailo discloses:
heat transfer fluid sample ([0005]), and generating a user interface element that indicates the at least one of the fluid life or condition score ([0272], [0275]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wakiru in view of Wang to use heat transfer fluid sample, and generating a user interface element that indicates the at least one of the fluid life or condition score as taught by Potyrailo. The motivation for doing so would have been in order to analyze heat transfer fluid sample and providing an alert to schedule maintenance (Potyrailo, [0005], [0272]).
10. Regarding claim 13, Wakiru in view of Wang in view of Potyrailo disclose system of claim 12, as disclosed above.
Wakiru further discloses wherein the first plurality of parameters include at least two parameter values selected from a group of parameter values consisting of: a high boilers value, a base number value, a low boilers value, an opacity value, a color value, a conductivity value, an absorbance value a moisture content value, pH value, an acid number value, an insoluble solids value, a bromine index value, a viscosity value, a carbon residue value, a specific gravity value, a flash point value, a non-evaporable content value, a contamination value, an autoignition temperature value, an operating temperature value, an on-stream time value, and a mixture value (page 280:
variables selected included viscosity at 40oC, flash point, total base number).
11. Regarding claim 14, Wakiru in view of Wang in view of Potyrailo disclose system of claim 12, as disclosed above.
Wakiru further discloses wherein the second data includes a second plurality of parameters, and wherein the distance is based at least in part on values of the second plurality of parameters indicated in the second feature relative to other values of the first plurality of parameters indicated in another feature of the first set of feature (page 280, section 4.2.3: during the selection of the critical variables to be used in the predictive model,…The sixteen variables selected included viscosity at 40oC, flash point, total base number, magnesium, calcium, zinc, silicon, sodium, water, carbon, iron, chromium, lead, copper, nickel and vanadium.… [Further], pages 269, 274: KNN is a non-parametric algorithm that generates predictions for a sample by finding the k nearest samples and assigning the most represented class among them using a distance function… While reviewing KNN, the number of neighbours to check for each sample k ranged {1,3,5}, while the distance used to search for the neighbours varied between Euclidian and Manhattan. (p=1 or 2 respectively)).
Wakiru in view of Potyrailo does not disclose:
plurality of parameters indicated in feature vectors.
However, Wang discloses:
plurality of parameters indicated in feature vectors (pages 15 and 39, section 3.2: multi-dimensional feature space… Let X= (x1, x2, ….Xn)T be an N-dimensional feature vector extracted from the raw data collected….[Further], page 74, section 4.5.3: PCA applies the following transformation to all the samples ˜y from both training and test instances….page 92: The samples from training and test instances are then transformed to the PC domain).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wakiru to use plurality of parameters indicated in feature vectors as taught by Wang. One would have been motivated to do so in order to apply the feature vectors methodology of a remaining useful life system as known in the art and as taught by Wang in an estimating a fluid life system such as that of Wakiru, thereby providing a known benefit of a quantitative remaining useful life of fluid for maintenance planning (Wang, page 28).
12. Regarding claim 15, Wakiru in view of Wang in view of Potyrailo disclose system of claim 12, as disclosed above.
Wakiru further discloses wherein the estimating is further based on training the machine learning model using the plurality of heat fluid transfer samples (page 269: KNN is a non-parametric algorithm that generates predictions for a sample by finding the k nearest samples and assigning the most represented class among them using a distance function… [Further], page 274: Random split validation on training and testing data… The scored data for the selected variables were used to build different binary classification models…. While reviewing KNN, the number of neighbours to check for each sample k ranged {1,3,5}, while the distance used to search for the neighbours varied between Euclidian and Manhattan. (p=1 or 2 respectively)). Also, Wang discloses TSBP model are retrained using all 150 training instances and then validated (see, page 101).
13. Regarding claim 16, Wakiru in view of Wang in view of Potyrailo disclose system of claim 12, wherein the computer storage memory having computer-executable instructions stored thereon which, when executed by the one or more processors, as disclosed above.
Wakiru further discloses based at least in part on the at least one of the fluid life or condition score exceeding a threshold, generating a recommendation signal that indicates instructions to service the fluid (page 274, section 3.5, page 284).
Wakiru in view of Wang does not disclose:
based at least in part on the at least one of the fluid life or condition score, generating a recommendation signal that indicates instructions to service the heat transfer fluid.
However, Potyrailo discloses:
based at least in part on the at least one of the fluid life or condition score, generating a recommendation signal that indicates instructions to service the heat transfer fluid ([0005], [0272], [0275]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wakiru in view of Wang to use based at least in part on the at least one of the fluid life or condition score, generating a recommendation signal that indicates instructions to service the heat transfer fluid as taught by Potyrailo. The motivation for doing so would have been in order to analyze heat transfer fluid sample and providing an alert to schedule maintenance (Potyrailo, [0005], [0272]).
14. Regarding claim 18, the claim is rejected with the same rationale as in claim 16.
15. Regarding claim 17, Wakiru in view of Wang in view of Potyrailo disclose system of claim 12, as disclosed above.
Wakiru further discloses wherein the fluid life is indicative of at least one of: an estimate of life remaining for the heat transfer fluid or an estimate indicating a point in time at which the heat transfer fluid will expire (pages 268 and 284: RUL (remaining useful life). Also, Wang and Potyrailo disclose RUL (Wang, page 4, and Potyrailo [0275]).
16. Regarding claim 19, Wakiru in view of Wang in view of Potyrailo disclose system of claim 12, wherein the computer storage memory having computer-executable instructions stored thereon which, when executed by the one or more processors, as disclosed above.
Wakiru in view of Wang further disclose generating historical trend data of at least one parameter associated with the fluid (Wakiru, page 280 , and Wang, page 4).
Wakiru in view of Wang does not disclose:
generating historical trend data of at least one parameter associated with the heat transfer fluid; and in response to the generating of the historical trend data, causing display of the historical trend data and an identifier that indicates the at least one parameter.
However, Potyrailo discloses:
generating historical trend data of at least one parameter associated with the heat transfer fluid; and in response to the generating of the historical trend data, causing display of the historical trend data and an identifier that indicates the at least one parameter, ([0263], [0268], Figs. 74-76).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wakiru in view of Wang to use implement a method further comprising: generating historical trend data of at least one parameter associated with the heat transfer fluid; and in response to the generating of the historical trend data, causing display of the historical trend data and an identifier that indicates the at least one parameter as taught by Potyrailo. The motivation for doing so would have been in order to show the relationship between the remaining life of the heat transfer fluid and the degradation value (Potyrailo, [0263]).
17. Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Wakiru, in view of Wang, in view of Potyrailo, in further view of Zhao et al. 2014/0151560 (hereinafter, Zhao).
18. Regarding claim 20, Wakiru in view of Wang in view of Potyrailo disclose system of claim 19, wherein the computer storage memory having computer-executable instructions stored thereon which, when executed by the one or more processors, as disclosed above.
Wakiru further disclose receiving of the second data… and a graphical user interface (GUI) to ease interpretability and offer first-hand key insights for prompt intervention can be advanced (pages 274 and 286). Further, Wang discloses various I/O interfaces may allow communication with user interface devices, such as a display 1726, keyboard 1728, electronic mouse 1730, and printer 1732, that may be used for viewing and inputting configuration information ([0131]).
Wakiru in view of Wang in view of Potyrailo does not disclose:
prompting a user to obtain a sample kit to cause extraction of at least a portion of the heat transfer fluid in its operating system, and wherein the receiving of the second data occurs based on the prompting.
However, Zhao discloses:
“The "Run Route" screen provides a list of all samples to be done for this route ….the user follows the route displayed to him, loads the unit's sample cell with oil from an asset at each inspection point, initiates the automatic computerized analysis methods, and notes any reported alai indications” ([0007], [0020], [0041], [0093]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wakiru in view of Wang in view of Potyrailo to use prompting a user to obtain a sample kit to cause extraction of at least a portion of the heat transfer fluid in its operating system, and wherein the receiving of the second data occurs based on the prompting as taught by Zhao. The motivation for doing so would have been in order to assist the user with choosing for the proper analysis of different oils (Zhao, [0007]).
Conclusion
19. Examiner has cited particular columns and line numbers, and/or paragraphs, and/or pages in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
20. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EYOB HAGOS whose telephone number is (571)272-3508. The examiner can normally be reached on 8:30-5:30PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Shelby Turner can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Eyob Hagos/
Primary Examiner, Art Unit 2857
Prosecution Insights