Status of Claims
1. This is a Non-final office action in response to communication received on March 07, 2025. Claims 1-20 are pending and examined herein.
Double Patenting
2. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper time wise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b). The Applicant can resolve this rejection as follows:
– Amend the claim(s)
– Cancel the claim(s)
– File a proper terminal disclaimer
However,
– Declarations under 37 CFR 1.131 are NOT sufficient to overcome this rejection
This is an obviousness type double patenting rejection (ODP). Only one patent can be issued per invention. Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-4 of U.S. Patent No. 12,260,299. Although the conflicting claims are not identical, they are not patentably distinct from each other because the difference is the following:
Limitations in instant claim: A system for using machine learning to accurately predict outcomes of large-scale cable fire tests, comprising: a test chamber in which the large-scale cable test is administered on a set of large-scale cables; a cone calorimeter configured to administer (i) a small-scale cable fire test on a set of small-scale cables, and (ii) the small-scale cable fire test on an additional small-scale cable; a processor; a memory; and a non-transitory computer-readable memory interfaced with the processor and the memory, and storing instructions thereon that, when executed by the processor, cause the processor to obtain a first set of results of the large-scale cable fire test administered on the set of large-scale cables; obtain a second set of results of the small-scale cable fire test administered on the set of small-scale cables; clean the first set of results and the second set of results to remove incomplete data, conflicting data, and erroneous data, resulting in a set of cleaned data; input the set of cleaned data into each machine learning model of a plurality of machine learning models to determine a machine learning model of the plurality of machine learning models that is most accurate in assessing how a given large-scale version of a given small-scale cable would perform on the large-scale cable fire test; obtain an additional set of results of the small-scale cable fire test administered on the additional small-scale cable; input the additional set of results into the most accurate machine learning model; and after inputting the additional set of results into the most accurate machine learning model, output a result from the most accurate machine learning model..
Limitations in the Patent: A system for using machine learning to accurately predict outcomes of large-scale cable fire tests, comprising:
a test chamber in which the large-scale cable test is administered on a set of large-scale cables in accordance with NFPA 262 cable testing requirements;
a cone calorimeter configured to administer (i) a small-scale cable fire test on a set of small-scale cables, and (ii) the small-scale cable fire test on an additional small-scale cable;
a processor;
a memory; and
a non-transitory computer-readable memory interfaced with the processor and the memory, and storing instructions thereon that, when executed by the processor, cause the processor to:
obtain a first set of results of the large-scale cable fire test administered on the set of large-scale cables, the first set of results comprising, for each of the set of large-scale cables, a max flame spread distance, a peak optical density, and an average optical density,
obtain a second set of results of the small-scale cable fire test administered on the set of small-scale cables, the second set of results comprising, for each of the set of small-scale cables, a diameter, a peak heat release rate, a total heat release, a heat of combustion, a total smoke metric, and an ignition time,
clean the first set of results and the second set of results to remove incomplete data, conflicting data, and erroneous data, resulting in a set of cleaned data,
segment the set of cleaned data between a training dataset and a validation dataset, wherein the training dataset at least partially overlaps with the validation set,
train a plurality of machine learning models using the training dataset, wherein the plurality of machine learning models are of different types,
input the validation dataset into each machine learning model of the plurality of machine learning models to determine a machine learning model of the plurality of machine learning models that is most accurate in assessing how a given large-scale version of a given small-scale cable would perform on the large-scale cable fire test,
obtain an additional set of results of the small-scale cable fire test on the additional small-scale cable, the additional set of results comprising, for the additional small-scale cable, an additional diameter, an additional peak heat release rate, an additional total heat release, an additional heat of combustion, an additional total smoke metric, and an additional ignition time,
input the additional set of results into the most accurate machine learning model, and
after inputting the additional set of results into the most accurate machine learning model, output a result from the most accurate machine learning model, the result (i) comprising at least one of: an average optical density output, a peak optical density output, or a max flame spread distance output, and (ii) predicting an outcome of a large-scale version of the additional small-scale cable tested according to the large-scale cable fire test.
However, it would be obvious to person having ordinary skill in the art that the claim limitations of the instant application are fully encompassed by the claims in the patent. Some of the claim limitations of independent claim in the parent are simply being claimed as dependent claims, for instance note claims 12 and 13 and some of the recitation has been omitted, for instance note “in accordance with NFPA 262 cable testing requirements.” These changes would be obvious to a PHOSITA as the instant claims are attempting to cover slightly broader scope and extend the patent coverage. Further, the claimed elements would continue to function in substantially similar manner. Only one invention is granted per invention.
Claim Rejections - 35 USC § 101
3. 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 1-20 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. Next using the 2019 Revised Patent Subject Matter Eligibility Guidances (hereinafter 2019 PEG) the rejection as follows has been applied.
Under step 1, per MPEP 2106.03, claims 1-8 are a method; claims 9-16 are a system; and claims 17-20 are a non-transitory CRM. Thus, each claim 1-20, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Under Step 2A Prong One, per MPEP 2106.04, prong one asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."
Next, per 2019 PEG, to determine whether a claim recites an abstract idea in Prong One, examiners are now to: (I) Identify the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea; and (II) determine whether the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 PEG. If the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I, analysis should proceed to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
(I) An abstract idea as recited per abstract recitation of claims 1-20 [i.e. recitation with the exception of additional elements as noted and analyzed under step 2A prong two and step 2B inquiries below, i.e. under step 2A prong one the Examiner considered claim recitation other than the additional elements (which once again are expressly noted below) to be the abstract recitation] (II) is that of collecting and evaluating testing data via models, trained using results of large-scale cable fire test and small-scale cable fire test, such that outcome of a large-scale product test can be predicted which is mental processes as it observes/collects data, evaluates data (or analyzes data via model), judges and formulates opinion (or predicts an outcome) and mathematical concepts (as the analysis is dependent on evaluation of data via models).
The phrase "Mental processes" applies to concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Further, see MPEP 2106.04(a)(2) III. A-D.
The phrase "Mathematical concepts" applies to mathematical relationships, mathematical formulas or equations, mathematical calculations. Further, see MPEP 2106.04(a)(2) I. A-C.
Therefore, the identified limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of 2019 PEG, thus analysis now proceeds to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
Under Step 2A Prong Two, per MPEP 2106.04, prong two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B (where it may still be eligible if it amounts to an ‘‘inventive concept’’).
Next, per 2019 PEG, Prong Two represents a change from prior guidance. The analysis under Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon. Examiners evaluate integration into a practical application by: (I) Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (II) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit.
Accordingly, the examiner will evaluate whether the claims recite one or more additional element(s) that integrate the exception into a practical application of that exception by considering them both individually and as a whole.
The claim elements in addition to the abstract idea, i.e. additional elements, per claims 1-20 are test chamber, small-scale and large-scale cables, a cone calorimeter, a processor, and machine learning models (per claim 1); a user interface (per claim 8); and a non-transitory computer-readable memory interfaced with the processor and the memory, and storing instructions thereon that, when executed by the processor (additionally per claims 9 and 17). These additional elements would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), as generic components utilized to produce cable fire test results and evaluating them through models to predict outcome of future long cable test. The additional elements are simply utilized as generic tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)). The additional elements are generic as they are described at a high level of generality, see at least as-filed Figs. 1A, 1B, and their associated disclosure. Further, the claims appear to be implementing testing and makes use of machine learning to predict outcome of a future large scale cable test for commercial reasons such as cost savings, see at least as-filed spec. paras. [0003]-[0005] and [0009]. The processor executing the "apply it" instruction makes use of one or more results, for instance note (i) a set of large-scale cables tested according to the large-scale cable fire test, and (ii) small-scale cable fire test data as administered by a cone calorimeter to generate a machine learning model which is described at a high level of generality, see at least as-filed spec. paras. [0016] and [0031]-[0032]. Regarding (i) large-scale cables test data tested by a Steiner Tunnel or chamber and (ii) cone calorimeter administered small-scale test based data, they are considered as insignificant extra solution activity such as pre-solution e.g. data gathering. Thus regarding (i) large-scale test data it appears the processor executing "apply it" instructions is further connected to one or more additional computing devices merely sending/receiving data over a network, note receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) - for instance to obtain large scale test data, see at least as-filed spec. paras. [0013]; [0015]; and [0047]; and regarding (ii) cone calorimeter administered small-scale test based data is also considered insignificant extra solution activity (see MPEP 2106.05(g)). Thus, the process is similar to collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group) - certain result here is a predicted outcome of large scale test based on evaluation of small-scale test results and past large-scale test results via a trained, cleaned, segmented, creating machine learning models, determining most accurate model through validation dataset, and using the accurate model to analyze additional results to output. The abstract idea is intended to be merely carried out in a technical or virtual environment such as collecting data via a network and analyzing data via a generic processor based on machine learning model (which is a field of use utilized to evaluate and predict an outcome) (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception as explained above.
Thus, the abstract idea of collecting and evaluating testing data via a model, trained using results of large-scale cable fire test and small-scale cable fire test, such that outcome of a large-scale product test can be predicted (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B, per MPEP 2106.05, as it applies to claims 1-20 the Examiner will evaluate whether the foregoing additional elements analyzed under prong two, when considered both individually and as a whole provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). The abstract idea of collecting and evaluating testing data via a model, trained using results of large-scale cable fire test and small-scale cable fire test, such that outcome of a large-scale product test can be predicted - has not been applied in an eligible manner. The claim elements in addition to the abstract idea are simply being utilized as generic tools to execute "apply it" instructions as they are described at a high level of generality. Additionally, the abstract idea is intended to be merely carried out in a technical environment, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (Id. or note step 2A prong two).
Regarding, insignificant solution activity such as data gathering or post solution activity such as displaying on interface, the Examiner relies on court cases and publications that demonstrate that such a way to gather data and display information is indeed well-understood, routine, or conventional in the industry or art, at least note as follows:
(i) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) [similarly here at least outcome of large scale cable fire tests is received over a network];
(ii) (a) CN 110082391 A note "flame combustion performance test method of igniting furniture assembly". material property testing method-stage conventional horizontal and vertical combustion method, the oxygen index method, NBS smoke box method, thermal analysis and cone calorimeter method, the present stage commonly used is a cone calorimeter (CONE) method, according to GB/T 16172-2007 " heat release rate testing method of building material", and (b) Cone Calorimeter, The National Institute of Standards and Technology (NIST), June 5, 2014, https://www.nist.gov/laboratories/tools-instruments/cone-calorimeter [similarly here at least data or results of set of small-scale cables tested according to the small-scale cable fire test administered by the cone calorimeter is utilized as input to generate a machine learning model described at a high level of generality]; and
(iii) Affinity v DirecTV - "The court rejected the argument that the computer components recited in the claims constituted an “inventive concept.” It held that the claims added “only generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’” and that “recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” Id. at 1324-25 (citations omitted). The court noted that nothing in the asserted claims purported to improve the functioning of the computer itself or “effect an improvement in any other technology or technical field.” Mortgage Grader, 811 F.3d at 1325 (quoting Alice, 134 S. Ct. at 2359)." [similarly here information is displayed on an interface, for instance see claims 8, and 16].
Next, in view of compact prosecution only further analysis per the Berkheimer Memo dated April 19, 2018 is being conducted as the following additional elements would be readily apparent as generic to a person having ordinary skill in the art (hereinafter PHOSITA), in other words analysis is similar to Berkheimer claim 1 and not claims 4-7 where there was "a genuine issue of material fact in light of the specification," nevertheless the Examiner finds the additional elements when considered both individually and as a combination to be well-understood, routine or conventional and expressly supports in writing as follows:
(1) Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims." - [similarly here an algorithm or machine learning model is utilized to perform mathematical or statistical analysis of the product test data to predict outcome];
(2) (a) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d, and (b) Determining an estimated outcome and setting a price, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 [similarly here product test data is utilized to train machine learning algorithm or model to estimate or predict an outcome]; and
(3) The Examiner provides citation to one or more publications as noting the well-understood, routine, conventional nature of machine learning as follows:
i) Chandramouli, Patent: US 8,442,683 note para. [0005]-[0007] and [0029]-[0033]; (ii) Lee, Pub. No.: US 2002/0107926 note para. [0020]; (iii) Kwok, Pub. No.: US 2002/0150295 note para. [0015]; (iv) Teller, Pub. No.: US 2004/0133081 [0236]-[0238]; (v) Agrawal and Srikant Patent No.: US 6546389 note "As recognized herein, the primary task of data mining is the development of models about aggregated data. Accordingly, the present invention understands that it is possible to develop accurate models without access to precise information in individual data records."; (vi) Deshpande et al., Pub. No.: US 2015/0134413 [0046] Using the target and input features, in step F3 of FIG. 1, a plurality of forecasting models are built for a product or a product category, a location, and a time window. A plurality of forecasting models can be built using existing machine learning based methods and/or time-series forecasting methods, and using the standard training-testing-validation methods. In an exemplary embodiment, only the highest quality models with high quality (high accuracy, precision, recall, etc.) are retained.; [0078] The processing system forecasting engine 202 can also include a forecasting model building engine 224 and a forecast calculation engine 226. In the model building stage, target and input features based on a customer or a customer segment's past data are used to train, test, and validate different types of forecasting models using machine learning and/or time series forecasting based approaches. Individual models are retained depending on the performance. The output of plurality of these retained models can then be fused into a single model 228. The fusion can be based on a rule-based approach or by assigning weights to individual model and combining those using ranking or combination techniques." (vii) Wei et al., Pub. No.: US 2015/0235260 [0080] Then, analysis module 532 may determine one or more predefined model(s) 546 based on event data 538 and the one or more targeting criteria. For example, analysis module 532 may use training and testing subsets of this information to generate one or more machine-learning models. The one or more predefined model(s) 546 may allow estimates of the number of future events to be determined for terms 544 in the one or more targeting criteria 542.; (viii) Beatty, Pub. No.: US 2012/0166267 see [0177] note "the prediction of conversion rate is performed by a machine-learning system that is trained using historical purchase data available to the ad system. The training set contains instances of purchase/no purchase decisions and many data points about the (user, context, offer). For example, the training examples might contain the following data points about the offer that was made to a user: price of offer, % discount of offer, popularity of merchant, time of day, gender of user, income of user, interests of user, websites visited by user, categories of websites visited by user, search queries by user, category of business, number of friends that had purchased the offer, "closeness" of friends that had purchased the offer, physical distance between the user's home and the business, physical distance between the user's workplace and the business, the "cluster id" of the user (generated by a clustering algorithm that placed, and users into clusters based on similar attributes of preferences)."; (ix) 2018/0001184 [0509] "health related characteristics associated with members known to have a particular health condition, may be used to “train” the neural network. There are several types of neural network models. The selection of which model or combination to use may be implementation dependent, and implementation accuracy may vary based on model used, data analyzed, and desired objective of the model"; (x) 2019/0147371 "For example, the validation platform may utilize one or more pre-processing techniques to pre-process the data, such as data cleansing techniques, data reduction techniques, data transformation techniques, and/or the like. In some implementations, the validation platform may select the one or more pre-processing techniques based on a variety of factors, such as a type associated with the data (e.g., video data, image data, text data, and/or the like), whether a source of the data provides voluminous data that needs to be cleaned and/or reduced in size, whether the data is provided in a format that requires conversion to a particular format that may be utilized by the validation platform, and/or the like."; and (xi) 2020/0115066 "machine learning is applied to machine learning (“automated machine learning”). The entire process can be automated indeed, including using multiple models and comparing results. In most cases, humans are involved in machine learning (“Human in the loop”). Data engineers or developers are responsible for maintaining the data pipeline: ingesting data, cleaning data, feature engineering, and model discovery (roles of a teacher, a curator of training data, and an analyst of results). In “deep learning” for example, data scientists seek for the right neural network architectures and parameters."
Therefore the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Reason(s) for Withdrawal of Claim Rejections Under 35 U.S.C § 103
4. The reasons for non-applicability of the prior art in the instant application is substantially similar to the ones as set forth in the parent in the Non-Final Rejection of record July 18, 2024.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and all the references on PTO-892 Notice of Reference Cited should be duly noted by the Applicant as they can be subsequently used during prosecution.
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/DIPEN M PATEL/Primary Examiner, Art Unit 3621
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