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 .
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 27, 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 6 and 14 are objected to because of the following informalities: The period following “user queries” should be a semicolon.
Claim 8 is also objected to for dependency on claim 6.
Appropriate correction is required.
Specification
The disclosure is objected to because of the following informalities:
[0054]: "determine whether to store the data in sanitization blockchain 120" should read "determine whether to store the data in sanitized blockchain 120"
Appropriate correction is required.
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.
Claim 8 is 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. Claim 8 recites the limitation "the transaction data related to vehicle transactions.” There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the examiner will assume that claim 8 reads “The computer-implemented method of claim 7,” given that claim 7 provides antecedent basis for this limitation.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”).
Claim 1
Step 1: The claim is directed to a computer-implemented method, and is therefore directed to the statutory category of processes.
Step 2A Prong 1: The claim recites:
“…designate the first data as outlier data relative to the set of data in the sanitized blockchain”; This limitation could encompass mentally determining that the first data is an outlier relative to the set of data in the sanitized blockchain.
“…identify a pattern of anomalous data”; This limitation could encompasses mentally identifying a pattern of anomalous data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “applying the first machine learning algorithm to a first data to… [perform the judicial exception]” and “applying a second machine learning algorithm to data stored in the anomaly blockchain to… [perform the judicial exception].” However, these limitations amount to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)). The claim also further recites “training a first machine learning algorithm using a set of data published on a sanitized blockchain.” However, this limitation amounts to merely linking the use of the judicial exception to the technological environment of model training (MPEP § 2106.05(h)). The claim also further recites “publishing the first data to an anomaly blockchain” and “publishing the anomalous data from the anomaly blockchain to the sanitized blockchain.” However, these limitations amount to the insignificant extra-solution activity of mere data gathering and outputting (MPEP § 2106.05(g)).
Step 2B: The claim does not contain significantly more than the judicial exception. The publishing limitations, in addition to being insignificant extra-solution activity, also recite the well-understood, routine, and conventional activity of receiving and transmitting data over a network (MPEP § 2106.05(d)(II) 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)). Otherwise, the analysis at this step mirrors that of step 2A, prong 2. As an ordered whole, the claim is directed to the abstract idea of designating data as outlier data and identifying a pattern of anomalous data. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“…determine whether the second data conforms to the set of data in the sanitized blockchain”; This limitation could encompass mentally determining whether the second data conforms to the set of data in the sanitized blockchain.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “applying the first machine learning algorithm to the second data to… [perform the judicial exception],” however, this limitation amounts to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)). The claim also further recites “receiving a second data” and “publishing the second data to the sanitized blockchain to generate an updated set of data,” however, these limitations amount to the insignificant extra-solution activity of mere data gathering and outputting (MPEP § 2106.05(g)). The claim also further recites “retraining the first machine learning algorithm using the updated set of data,” however this limitation amounts to merely linking the use of the judicial exception to the technological environment of model training (MPEP § 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving and publishing data limitations, in addition to being insignificant extra-solution activity, also recite the well-understood, routine, and conventional activity of receiving and transmitting data over a network (MPEP § 2106.05(d)(II) 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)). Otherwise, the analysis at this step mirrors that of step 2A, prong 2.
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exception as claim 2.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the retraining occurs using an unsupervised learning technique.” However, this merely further limits the retraining limitation of claim 2, and still amounts to merely linking the use of the judicial exception to the technological environment of model training (MPEP § 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2.
Claim 4
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“applying a clustering technique to the first data and the set of data in the sanitized blockchain”; This limitation encompasses mentally clustering the first data and the set of data in the sanitized blockchain using a clustering technique.
“designating the first data as outlier data by identifying the first data as being outside of a cluster determined from the set of data in the sanitized blockchain”; This limitation encompasses mentally identifying the first data as being outside of a cluster determined from the set of data in the sanitized blockchain to designate the first data as outlier data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein applying the first machine learning algorithm further comprises… [performing the judicial exception].” However, this limitation amounts to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2.
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“determining the data stored in the anomaly blockchain exceeds a threshold amount”; This limitation encompasses mentally determining the data stored in the anomaly blockchain exceeds a threshold amount.
“when the data stored in the anomaly blockchain exceeds the threshold amount, applying a clustering technique to the data stored in the anomaly blockchain”; This limitation encompasses mentally clustering data stored in the anomaly blockchain using a clustering technique, when the data stored in the anomaly blockchain exceeds the threshold amount.
“determining the data stored in the anomaly blockchain forms a grouped pattern”; This limitation encompasses mentally determining the data stored in the anomaly blockchain forms a grouped pattern.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein applying the second machine learning algorithm further comprises… [performing the judicial exception].” However, this limitation amounts to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2.
Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
“…provide a predictive analysis in response to user queries”; This limitation encompasses mentally generating a predictive analysis in response to user queries.
“…a prediction generated…based on the parameter value and the set of data on the sanitized blockchain”; This limitation encompasses mentally generating a prediction based on the parameter value and the set of data on the sanitized blockchain.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “training a third machine learning algorithm using the set of data in the sanitized blockchain to… [perform the judicial exception].” However, this limitation amounts to merely linking the use of the judicial exception to the technological environment of model training (MPEP § 2106.05(h)). The claim also further recites “receiving, from a user device, a query including a parameter value corresponding to a field in the set of data published on the sanitized blockchain” and “returning, to the user device, a prediction…” However, these limitations amount to the insignificant extra solution activity of mere data gathering and outputting (MPEP § 2106.05(g)). The claim also further recites that the prediction is generated “by the third machine learning algorithm.” However, this limitation amounts to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving and returning limitations, in addition to being insignificant extra solution activity, are also directed to the well-understood, routine, and conventional activity of receiving and transmitting data over a network (MPEP § 2106.05(d)(II) 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)). Otherwise, the analysis at this step mirrors that of step 2A, prong 2.
Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the set of data published on the sanitized blockchain includes transaction data related to vehicle transactions,” however this limitation merely further limits the data used to train a first machine learning algorithm, which still amounts to merely linking the use of the judicial exception to the technological environment of model training (MPEP § 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2.
Claim 8
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the transaction data related to vehicle transactions further comprises at least one of the following: make and model of a vehicle, year of the vehicle, mileage of the vehicle, a geographical location corresponding to a sale, and price of the sale,” however this limitation merely further limits the data used to train a first machine learning algorithm, which still amounts to merely linking the use of the judicial exception to the technological environment of model training (MPEP § 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2.
Claims 9-14
Step 1: The claims recite a system, and are therefore directed to the statutory category of machines.
Step 2A Prong 1: The claims recite the same judicial exceptions as claims 1-6, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis at this step corresponds to that of claims 1-6, respectively, except insofar as claims 9-14 recite a system rather than a method. The claims further recite “A system for scrubbing anomalies from an expanding dataset, comprising: a memory; and at least one processor coupled to the memory.” However, this limitation amounts to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)).
Step 2B: The claims do not contain significantly more than the judicial exception. The analysis at this step mirrors that of claims 1-6, respectively, except insofar as claims 9-14 recite a system rather than a method, and the system limitation amounts to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)) as stated above.
Claim 15
Step 1: The claim recites a non-transitory computer-readable device, and therefore is directed to the statutory category of articles of manufacture.
Step 2A Prong 1: The claim recites:
“…determine whether the first data should be designated as outlier data relative to a set of data used to train the first machine learning algorithm, wherein the set of data is published on a sanitized blockchain”; This limitation encompasses mentally determining whether the first data should be designated as outlier data relative to a set of data used to train the machine learning algorithm and published on a sanitized blockchain.
“…identify a pattern of anomalous data”; This limitation encompasses mentally identifying a pattern of anomalous data.
“…provide a predictive analysis in response to user queries”; This limitation encompasses mentally generating a predictive analysis in response to user queries.
“…a prediction generated… based on the parameter value and the set of data on the sanitized blockchain”; This limitation encompasses mentally generating a prediction based on the parameter value and the set of data on the sanitized blockchain.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “A non-transitory computer-readable device having instructions stored thereon,” “applying a first machine learning algorithm to a first data to… [perform the judicial exception],” “applying a second machine learning algorithm to data stored in the anomaly blockchain to… [perform the judicial exception],” and that the prediction is generated “by the third machine learning algorithm.” However, these limitations amount to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)). The claim also further recites “publishing the first data to an anomaly blockchain,” “publishing the anomalous data from the anomaly blockchain to the sanitized blockchain,” “receiving, from a user device, a query including a parameter value corresponding to a field in the set of data on the sanitized blockchain,” and “returning, to the user device, a prediction…” However, these limitations amount to the insignificant extra solution activity of mere data gathering and outputting (MPEP § 2106.05(g)). The claim also further recites “training a third machine learning algorithm using the set of data in the sanitized blockchain to… [perform the judicial exception].” However, this limitation amounts to merely linking the use of the judicial exception to the technological environment of model training (MPEP § 2106.05(h)).
Step 2B: The claims do not contain significantly more than the judicial exception. The publishing, receiving, and returning limitations, in addition to reciting insignificant extra solution activity, are also directed to the well-understood, routine, and conventional activity of receiving and transmitting data over a network (MPEP § 2106.05(d)(II) 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)). Otherwise, the analysis at this step mirrors that of step 2A, prong 2.
Claim 16
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites:
“…determine whether the second data conforms to the set of data published on the sanitized blockchain”; This limitation could encompass mentally determining whether the second data conforms to the set of data on the sanitized blockchain.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “applying the first machine learning algorithm to the second data to… [perform the judicial exception],” however, this limitation amounts to mere instructions to apply a judicial exception on a generic computer programmed with a generic class of computer algorithms (MPEP § 2106.05(f)). The claim also further recites “receiving a second data” and “publishing the second data to the sanitized blockchain to generate an updated set of data,” however, these limitations amount to the insignificant extra-solution activity of mere data gathering and outputting (MPEP § 2106.05(g)). The claim also further recites “retraining the first machine learning algorithm and the third machine learning algorithm using the updated set of data,” however this limitation amounts to merely linking the use of the judicial exception to the technological environment of model training (MPEP § 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The receiving and publishing data limitations, in addition to reciting insignificant extra-solution activity, are also directed to the well-understood, routine, and conventional activity of receiving and transmitting data over a network (MPEP § 2106.05(d)(II) 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)). Otherwise, the analysis at this step mirrors that of step 2A, prong 2.
Claims 17-19
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claims recite the same judicial exceptions as claims 3-5, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis at this step corresponds to that of claims 3-5, respectively.
Step 2B: The claims do not contain significantly more than the judicial exception. The analysis at this step corresponds to that of claims 3-5, respectively.
Claim 20
Step 1: An article of manufacture, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as claim 15.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the set of data includes data related to vehicle transactions,” however this limitation merely further limits the data used to train a first machine learning algorithm, which still amounts to merely linking the use of the judicial exception to the technological environment of model training (MPEP § 2106.05(h)).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A prong 2.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4, 9, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Sayadi et al. (NPL: “Anomaly Detection Model Over Blockchain Electronic Transactions”) (“Sayadi”) in view of Dods et al. (US 10873456) (“Dods”) and Signorini et al. (EP 3285248) (“Signorini”).
Regarding claim 1, Sayadi discloses “A computer-implemented method for scrubbing anomalies from an expanding dataset, comprising:
training a first machine learning algorithm using a set of data published on a sanitized blockchain (Sayadi, IV.A: “We use Bitcoin transaction data obtained by a data source on the Bitcoin blockchain…We use this data as a set of normal behavior data for our anomaly detection model” and Sayadi, IV.C: “We move to the training stage. Only normal transactions data are in our hands. This explains our choice for an unsupervised machine learning algorithm OCC (One Class Classification) [11] where only positive samples are available. We used OCSVM (One Class Support Vector Machines) [8] in our work. Because it has shown better performance in several application domains and especially in anomaly detection”; the examiner is interpreting “sanitized blockchain” to be a blockchain containing data that is previously deemed to be conforming, as supported by paragraph [0014] of the instant application specification, therefore the “Bitcoin blockchain” is a sanitized blockchain because it contains only normal transaction data);
applying the first machine learning algorithm to a first data to designate the first data as outlier data relative to the set of data in the sanitized blockchain (Sayadi, III: “In the first step, we apply a behavioral analysis in which we use the One-Class SVM algorithm to detect outliers” and Sayadi, IV.D: “In the first stage, we detected 15 anomalies using OCSVM algorithm”; the examiner notes that the first anomaly detected corresponds to “a first data”, and the first data is designated as outlier data “relative to the set of data in the sanitized blockchain” because the One-Class SVM was trained on the set of data in the sanitized blockchain) …
applying a second machine learning algorithm to… [outlier data] to identify a pattern of anomalous data…” (Sayadi, III: “In Step 2, we apply K-means clustering algorithm to gather similar attacks in order to specify their types” and Sayadi, III.B: “For our proposition then, we will use only the negative output points of the first stage One Class SVM n(x1, x2,…,xn) for all f (x) < 0 as input data for the second stage K-means algorithm in order to regroup these outliers in S clusters; the examiner notes that types of attacks corresponds to patterns of anomalous data).
Sayadi does not appear to explicitly disclose “publishing the first data to an anomaly blockchain,” that the second machine learning algorithm is applied to “data stored in the anomaly blockchain,” or “publishing the anomalous data from the anomaly blockchain to the sanitized blockchain.”
However, Dods discloses “publishing…data to an anomaly blockchain” and “data stored in the anomaly blockchain.” (Dods, col 5, lines 43-49: “The technology disclosed describes system and method implementations of data origin authentication and machine data integrity using deep learning-based approaches to identify and isolate anomalies and to identify and trigger appropriate remedial actions including pushing block level representations of at least some anomaly information into a blockchain network as described in a smart contract” and Dods, col 20, lines 48-50: “DApp 136 is used to store the anomaly reports in the tamper-proof blockchain network 106”; the examiner notes that blockchain network 106 corresponds to “an anomaly blockchain” because it is a blockchain that stores anomaly information).
Dods and the instant application both relate to machine learning and blockchain and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Sayadi with Dods to include the step of “publishing the first data to an anomaly blockchain” and to have the second machine learning algorithm disclosed by Sayadi be applied to the data stored in the anomaly blockchain disclosed by Dods, and one would have been motivated to do so for the purpose of allowing for exceptions and anomalies to be reported by multiple actors in a trusted block chain centric network, and to trigger appropriate remedial actions to anomalies (see Dods, col 5, lines 16-17 and 32-37).
Neither Sayadi nor Dods appears to explicitly disclose “publishing the anomalous data from the anomaly blockchain to the sanitized blockchain.”
However, Signorini discloses “publishing… anomalous data from… [one] blockchain to… [another] blockchain” (Signorini, [0007]: “More particularly, the invention provides for a method of detecting a security threat within a network of connected devices that share a ledger of transactions between them under the form of exchanged blockchain messages, comprising the steps of: building an enhanced blockchain by adding forked chains discarded at a device, to a standard blockchain; inspecting added forked chains in the enhanced blockchain; detecting an anomaly based on patterns in the added forked chains in the enhanced blockchain; identifying the security threat by reviewing all transactions of the ledger in the forked chain in which an anomaly has been detected, and in the standard blockchain leading up to the network attack entry point; and including the enhanced blockchain in the exchanged messages”; the examiner notes that the enhanced blockchain containing a detected anomaly (exchanged message) is received by another device in the network to add to a standard blockchain).
Signorini and the instant application both relate to anomaly detection using a blockchain and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Sayadi and Dods with Signorini to include “publishing the anomalous data from the anomaly blockchain to the sanitized blockchain,” and one would have been motivated to do so for the purpose of improving the security of the blockchain network by providing the ability to detect malicious/strange behaviors or attacks which are not yet known/distributed on a global scale (see Signorini, [0024]).
Regarding claim 4, the rejection of claim 1 is incorporated. Sayadi as modified by Dods and Signorini further discloses “wherein applying the first machine learning algorithm further comprises:
applying a clustering technique to the first data and the set of data in the sanitized blockchain; and designating the first data as outlier data by identifying the first data as being outside of a cluster determined from the set of data in the sanitized blockchain (Sayadi, IV.D: “In the first stage, we detected 15 anomalies using OCSVM algorithm” and Sayadi, III.A: In a space, One-Class SVM makes it possible to separate all the points of data of the origin by maximizing the distance of this hyperplane at the origin. Thus, obtaining as a result a binary function that captures the regions of the input space and the probability density of the data in these regions. According to the training data, this function returns +1 in a normal region and -1 elsewhere. The decision function g(x) for one-class SVMs is defined as follows… Depending on the sign of decision function, normal and outlying points are defined. The magnitude of the decision function is proportional to the distance to the decision boundary. One-class SVM f(x) simply outputs a binary label : normal when positive, outlying otherwise; the examiner notes that the normal region corresponds to a cluster determined from the set of data in the sanitized blockchain (training data) and the first data is determined to be an outlier when it is outside the normal region).
Regarding claim 9, Sayadi discloses “A system for scrubbing anomalies from an expanding dataset, comprising: a memory; and at least one processor coupled to the memory and configured to: [perform the method]” (Sayadi, IV.D: “In order to mount our experiment, we develop our proper program with Python using Spyder programming API with the Framework Anaconda on the first stage and Orange3 API on the stage 2”).
Claim 9 is a system claim corresponding to method claim 1, and the remainder of the rejection follows the same rationale as that of claim 1 above.
Regarding claim 12, the rejection of claim 9 is incorporated. Claim 12 is a system claim corresponding to method claim 4, and the rejection of the claim follows the same rationale as that of claim 4 above.
Claims 2, 3, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Sayadi in view of Dods and Signorini, and further in view of Pi et al. (WO 2019036095) (“Pi”).
Regarding claim 2, the rejection of claim 1 is incorporated. Sayadi as modified by Dods and Signorini further discloses
“receiving a second data (Sayadi, V: “We used 548 data for test and 16 data containing anomalies”; the examiner notes that the second data in the test set corresponds to “a second data”);
applying the first machine learning algorithm to the second data to determine whether the second data conforms to the set of data in the sanitized blockchain…” (Sayadi, IV.C: “After data normalization, we have split our dataset on training data for the training level of our model and test data
in order to detect the outliers over anomaly transactions. A score will be given to the OCSVM anomaly detection model for each type of outputting a decision, (1) if normal data or (−1) if abnormal data”; the examiner notes that the trained One-Class SVM algorithm (trained on the set of data in the sanitized blockchain) is applied to the second test data to determine if it is an outlier)
Sayadi, Dods, and Signorini do not appear to explicitly disclose “publishing the second data to the sanitized blockchain to generate an updated set of data; and retraining the first machine learning algorithm using the updated set of data.”
However, Pi discloses
“publishing… data to… [a] blockchain to generate an updated set of data (Pi, [39]: “In the example of FIG. 5, the anomaly detection computer maintains a Blockchain ledger 270 that records transactions involving power generation or use. These transactions may be between producers, between consumers, or between producers and consumers. As new blocks (i.e., transactions) are recorded in the ledger, the data used for anomaly detection can be updated accordingly”); and
retraining…[a] machine learning algorithm using the updated set of data” (Pi, [46]: “At step 625, if the probability of an anomaly is above the threshold value, an alert message is generated for one or more system operators… In some embodiments, one or more feedback messages from the system operators are received in response to the alert message. The CNN may then be retrained based on this feedback. For example, if the system operator indicates that a particular event is not anomalous, the CNN may label the data accordingly and use it in its training set”).
Pi and the instant application both relate to anomaly detection using machine learning and blockchain and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to have modified the combination of Sayadi, Dods, and Signorini with the teachings of Pi to include “publishing the second data to the sanitized blockchain to generate an updated set of data; and retraining the first machine learning algorithm using the updated set of data,” and one would have been motivated to do so for the purpose of improving the accuracy of the anomaly detection machine learning algorithm (see Pi, [26]).
Regarding claim 3, the rejection of claim 2 is incorporated. Sayadi as modified by Dods and Signori further discloses “wherein… training occurs using an unsupervised learning technique” (Sayadi, IV.C: “This explains our choice for an unsupervised machine learning algorithm OCC (One Class Classification) [11] where only positive samples are available”), but does not appear to explicitly disclose “retraining.”
However, Pi discloses “retraining” (Pi, [46]: “At step 625, if the probability of an anomaly is above the threshold value, an alert message is generated for one or more system operators… In some embodiments, one or more feedback messages from the system operators are received in response to the alert message. The CNN may then be retrained based on this feedback. For example, if the system operator indicates that a particular event is not anomalous, the CNN may label the data accordingly and use it in its training set”).
Pi and the instant application both relate to anomaly detection using machine learning and blockchain and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to have modified the combination of Sayadi, Dods, and Signorini with the teachings of Pi to include “wherein the retraining occurs using an unsupervised learning technique,” and one would have been motivated to do so for the purpose of improving the accuracy of the anomaly detection machine learning algorithm (see Pi, [26]).
Regarding claim 10, the rejection of claim 9 is incorporated. Claim 10 is a system claim corresponding to method claim 2 and the rejection of the claim follows the same rationale as that of claim 2 above.
Regarding claim 11, the rejection of claim 10 is incorporated. Claim 11 is a system claim corresponding to method claim 3 and the rejection of the claim follows the same rationale as that of claim 2 above.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Saydi in view of Dods and Signorini, and further in view of Bhave et al. (US 20210191935) (“Bhave”).
Regarding claim 5, the rejection of claim 1 is incorporated. Sayadi as modified by Dods and Signorini further discloses “…applying a clustering technique to the data stored in the anomaly blockchain; and determining the data stored in the anomaly blockchain forms a grouped pattern” (Sayadi, III: “In Step 2, we apply K-means clustering algorithm to gather similar attacks in order to specify their types” and Sayadi, III.B: “For our proposition then, we will use only the negative output points of the first stage One Class SVM n(x1, x2,…,xn) for all f (x) < 0 as input data for the second stage K-means algorithm in order to regroup these outliers in S clusters).
Sayadi, Dods, and Signorini do not appear to explicitly disclose the further limitations of the claim. However, Bhave discloses “determining the data stored in the anomaly… [memory] exceeds a threshold amount” and “when the data stored in the anomaly… [memory] exceeds the threshold amount… [set a flag]” (Bhave, [0023]: “The inference engine then compares the actual data point to the expected data point and draws an inference if the actual data point is an anomaly. If it is, the inference engine sends the data point along with its time of collection to the thumbnail model for storage in an anomaly memory” and [0025]: “If the metadata reports begin to build up over time, it is time to generate a new thumbnail. A comparator or software process in the thumbnail generator (or elsewhere) compares the number of anomalies to a threshold and sets a flag, typically in the ingest layer, when that threshold is exceeded”).
Bhave and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Sayadi, Dods, and Signorini with the teachings of Bhave to include “determining the data stored in the anomaly blockchain exceeds a threshold amount, and when the data stored in the anomaly blockchain exceeds the threshold amount, applying a clustering technique to the data stored in the anomaly blockchain; and determining the data stored in the anomaly blockchain forms a grouped pattern,” and one would have been motivated to do so for the purpose of ensuring that the anomaly storage does not become overfull.
Regarding claim 13, the rejection of claim 9 is incorporated. Claim 13 is a system claim corresponding to method claim 5 and the rejection of the claim follows the same rationale as that of claim 5 above.
Claims 6, 14, 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sayadi in view of Dods and Signorini, and further in view of Ross et al. (US 20190179940) (“Ross”).
Regarding claim 6, the rejection of claim 1 is incorporated. Sayadi as modified by Dods and Signorini discloses the set of data on the sanitized blockchain, but does not appear to explicitly disclose the further limitations of the claim.
However, Ross discloses “training a… machine learning algorithm using… [a] set of data… to provide a predictive analysis in response to user queries (Ross, [0008]: “Training instances for a machine learning model can then be generated based on the variation parameters and their corresponding values, and the machine learning model trained utilizing the training instances… Accordingly, the machine learning model can be trained to enable processing, using the trained machine learning model, of a provided year to predict a quantity of doctors in China based on the provided year” and [0009]: “After the machine learning model is trained, machine learning model output that is based on the trained machine learning model can be provided in response to the search query);
receiving, from a user device, a query including a parameter value corresponding to a field in the set of data…(Ross, [0006]: “As a working example of some implementations, assume a user interacts with a client device to submit a query of “How many doctors will there be in China in 2050?” to a search engine” and [0007]: “Continuing with the working example, the query can be parsed to determine one or more entities referenced in the search query, and at least one particular parameter, of the one or more entities, that is sought by the search query... One or more structured databases can then be queried based on the at least one parameter and the one or more entities… If it is determined that no entry is available, or there is no known value defined for the entry, variations of the parameter can be generated, and the structured database(s) queried based on the variations and the one or more entities to determine variation values for the variations”; the examiner notes that the parameter value parsed from the user query must correspond to a field in the set of data, given that it is able to be used to query the database); and
returning, to the user device, a prediction generated by the… machine learning algorithm based on the parameter value and the set of data…” (Ross, [0009]: “After the machine learning model is trained, machine learning model output that is based on the trained machine learning model can be provided in response to the search query. For example, the parameter from the query (e.g., “2050” in the working example) can be processed using the trained machine learning model to generate a prediction (e.g., a “quantity of doctors in China” in the working example), and the prediction provided as machine learning model output”).
Ross and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Sayadi, Dods, and Signorini with the teachings of Ross to include the machine learning algorithm disclosed by Ross as a third machine learning algorithm, and the steps of “training a third machine learning algorithm using the set of data in the sanitized blockchain to provide a predictive analysis in response to user queries; receiving, from a user device, a query including a parameter value corresponding to a field in the set of data published on the sanitized blockchain; and returning, to the user device, a prediction generated by the third machine learning algorithm based on the parameter value and the set of data on the sanitized blockchain” and one would have been motivated to do so for the purpose of enabling a user to satisfy his/her informational needs via the machine learning model output, without requiring the user to submit additional varied queries, reducing the use of computational resources (see Ross, [0019]).
Regarding claim 14, the rejection of claim 9 is incorporated. Claim 14 is a system claim corresponding to method claim 6 and the rejection of the claim follows the same rationale as that of claim 6 above.
Regarding claim 15, Sayadi discloses “A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device (Sayadi, IV.D: “In order to mount our experiment, we develop our proper program with Python using Spyder programming API with the Framework Anaconda on the first stage and Orange3 API on the stage 2”), cause the at least one computing device to perform operations comprising:
applying a first machine learning algorithm to a first data to determine whether the first data should be designated as outlier data relative to a set of data used to train the first machine learning algorithm, wherein the set of data is published on a sanitized blockchain (Sayadi, IV.A: “We use Bitcoin transaction data obtained by a data source on the Bitcoin blockchain…We use this data as a set of normal behavior data for our anomaly detection model” and Sayadi, IV.C: “We move to the training stage. Only normal transactions data are in our hands. This explains our choice for an unsupervised machine learning algorithm OCC (One Class Classification) [11] where only positive samples are available. We used OCSVM (One Class Support Vector Machines) [8] in our work. Because it has shown better performance in several application domains and especially in anomaly detection. After data normalization, we have split our dataset on training data for the training level of our model and test data in order to detect the outliers over anomaly transactions. A score will be given to the OCSVM anomaly detection model for each type of outputting a decision, (1) if normal data or (−1) if abnormal data”; the examiner notes that the Bitcoin blockchain corresponds to a “sanitized blockchain” because it contains only normal transaction data without anomalies, and it is used to train the OCSVM algorithm that is applied to detect outliers);
applying a second machine learning algorithm to… [outlier data] to identify a pattern of anomalous data…” (Sayadi, III: “In Step 2, we apply K-means clustering algorithm to gather similar attacks in order to specify their types” and Sayadi, III.B: “For our proposition then, we will use only the negative output points of the first stage One Class SVM n(x1, x2,…,xn) for all f (x) < 0 as input data for the second stage K-means algorithm in order to regroup these outliers in S clusters; the examiner notes that similar attacks corresponds to “a pattern of anomalous data”).
Sayadi does not appear to explicitly disclose the further limitations of the claim.
However, Dods discloses “publishing…data to an anomaly blockchain” and “data stored in the anomaly blockchain.” (Dods, col 5, lines 43-49: “The technology disclosed describes system and method implementations of data origin authentication and machine data integrity using deep learning-based approaches to identify and isolate anomalies and to identify and trigger appropriate remedial actions including pushing block level representations of at least some anomaly information into a blockchain network as described in a smart contract” and Dods, col 20, lines 48-50: “DApp 136 is used to store the anomaly reports in the tamper-proof blockchain network 106”; the examiner notes that blockchain network 106 corresponds to “an anomaly blockchain” because it is a blockchain that stores anomaly information).
Dods and the instant application both relate to machine learning and blockchain and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified Sayadi with Dods to include the step of “publishing the first data to an anomaly blockchain” and to have the second machine learning algorithm disclosed by Sayadi be applied to the data stored in the anomaly blockchain disclosed by Dods, and one would have been motivated to do so for the purpose of allowing for exceptions and anomalies to be reported by multiple actors in a trusted block chain centric network, and to trigger appropriate remedial actions to anomalies (see Dods, col 5, lines 16-17 and 32-37).
Neither Sayadi nor Dods appears to explicitly disclose the further limitations of the claim.
However, Signorini discloses “publishing… anomalous data from… [one] blockchain to… [another] blockchain” (Signorini, [0007]: “More particularly, the invention provides for a method of detecting a security threat within a network of connected devices that share a ledger of transactions between them under the form of exchanged blockchain messages, comprising the steps of: building an enhanced blockchain by adding forked chains discarded at a device, to a standard blockchain; inspecting added forked chains in the enhanced blockchain; detecting an anomaly based on patterns in the added forked chains in the enhanced blockchain; identifying the security threat by reviewing all transactions of the ledger in the forked chain in which an anomaly has been detected, and in the standard blockchain leading up to the network attack entry point; and including the enhanced blockchain in the exchanged messages”; the examiner notes that the enhanced blockchain containing a detected anomaly (exchanged message) is received by another device in the network to add to a standard blockchain).
Signorini and the instant application both relate to anomaly detection using a blockchain and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Sayadi and Dods with Signorini to include “publishing the anomalous data from the anomaly blockchain to the sanitized blockchain,” and one would have been motivated to do so for the purpose of improving the security of the blockchain network by providing the ability to detect malicious/strange behaviors or attacks which are not yet known/distributed on a global scale (see Signorini, [0024]).
Sayadi, Dods, and Signorini do not appear to explicitly disclose the further limitations of the claim.
However, Ross discloses “training a… machine learning algorithm using… [a] set of data… to provide a predictive analysis in response to user queries (Ross, [0008]: “Training instances for a machine learning model can then be generated based on the variation parameters and their corresponding values, and the machine learning model trained utilizing the training instances… Accordingly, the machine learning model can be trained to enable processing, using the trained machine learning model, of a provided year to predict a quantity of doctors in China based on the provided year” and [0009]: “After the machine learning model is trained, machine learning model output that is based on the trained machine learning model can be provided in response to the search query);
receiving, from a user device, a query including a parameter value corresponding to a field in the set of data…(Ross, [0006]: “As a working example of some implementations, assume a user interacts with a client device to submit a query of “How many doctors will there be in China in 2050?” to a search engine” and [0007]: “Continuing with the working example, the query can be parsed to determine one or more entities referenced in the search query, and at least one particular parameter, of the one or more entities, that is sought by the search query... One or more structured databases can then be queried based on the at least one parameter and the one or more entities… If it is determined that no entry is available, or there is no known value defined for the entry, variations of the parameter can be generated, and the structured database(s) queried based on the variations and the one or more entities to determine variation values for the variations”; the examiner notes that the parameter value parsed from the user query must correspond to a field in the set of data, given that it is able to be used to query the database); and
returning, to the user device, a prediction generated by the… machine learning algorithm based on the parameter value and the set of data…” (Ross, [0009]: “After the machine learning model is trained, machine learning model output that is based on the trained machine learning model can be provided in response to the search query. For example, the parameter from the query (e.g., “2050” in the working example) can be processed using the trained machine learning model to generate a prediction (e.g., a “quantity of doctors in China” in the working example), and the prediction provided as machine learning model output”).
Ross and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Sayadi, Dods, and Signorini with Ross to include the machine learning algorithm disclosed by Ross as a third machine learning algorithm, and the steps of “training a third machine learning algorithm using the set of data in the sanitized blockchain to provide a predictive analysis in response to user queries; receiving, from a user device, a query including a parameter value corresponding to a field in the set of data published on the sanitized blockchain; and returning, to the user device, a prediction generated by the third machine learning algorithm based on the parameter value and the set of data on the sanitized blockchain” and one would have been motivated to do so for the purpose of enabling a user to satisfy his/her informational needs via the machine learning model output, without requiring the user to submit additional varied queries, reducing the use of computational resources (see Ross, [0019]).
Regarding claim 18, the rejection of claim 15 is incorporated. Claim 18 is a non-transitory computer-readable device claim corresponding to method claim 4, and the rejection of the claim 18 follows the same rationale as the rejection of claim 4 above.
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Sayadi in view of Dods and Signorini, and further in view of Nagla et al. (US 20180018723) (“Nagla”).
Regarding claim 7, the rejection of claim 1 is incorporated. Saydi as modified by Dods and Signorini does not appear to explicitly disclose the further limitations of the claim.
However, Nagla discloses “wherein…[a] set of data published on… [a] blockchain includes transaction data related to vehicle transactions” (Nagla, [0097]: “Vehicle records are maintained using blocks organized in blockchains stored in blockchain storage 304 of entities 102, 104, 106, 108, 110, and 112” and [0101]: “A block of the vehicle record may store various data elements for the vehicle and transaction information, for example, the nature of the transaction, parties to the transaction, document sections, contractual clauses, version information, and/or electronic representatives or derivatives of the same”).
Nagla and the instant application both relate to managing vehicle transaction data on a blockchain and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Sayadi, Dods, and Signorini so that the set of data published on the sanitized blockchain includes transaction data related to vehicle transactions, as disclosed by Nagla, and one would have been motivated to do so for the purpose of ascertaining the integrity and veracity of vehicle records and to ensure they are not susceptible to tampering, unauthorized changes, and inadvertent changes (see Nagla, [0004]).
Regarding claim 8, the rejection of claim 7 is incorporated. Sayadi as modified by Dods, Signorini, and Nagla further discloses “wherein the transaction data related to vehicle transactions further comprises at least one of the following: make and model of a vehicle, year of the vehicle, mileage of the vehicle, a geographical location corresponding to a sale, and price of the sale” (Nagla, [0101]: “A block of the vehicle record may store various data elements for the vehicle and transaction information, for example, the nature of the transaction, parties to the transaction, document sections, contractual clauses, version information, and/or electronic representatives or derivatives of the same. The blocks are stored in blockchain storage 304. Vehicle data can include the make of the vehicle, the model of the vehicle, mileage, and other vehicle data, for example. The transaction data can include ownership information, collision or accident information, repair information, insurance information, financing information and so on”).
Nagla and the instant application both relate to managing vehicle transaction data on a blockchain and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of Sayadi, Signorini, and Dods with the teachings of Nagla so that the transaction data related to vehicle transactions further comprises at least one of the following: make and model of a vehicle, year of the vehicle, mileage of the vehicle, a geographical location corresponding to a sale, and price of the sale, and one would have been motivated to do so for the purpose of ascertaining the integrity and veracity of vehicle records and to ensure they are not susceptible to tampering, unauthorized changes, and inadvertent changes (see Nagla, [0004]).
Claims 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Sayadi in view of Dods, Signorini, Ross, and Pi.
Regarding claim 16, the rejection of claim 15 is incorporated. Sayadi as modified by Dods, Signorini, and Ross further discloses
“receiving a second data (Sayadi, V: “We used 548 data for test and 16 data containing anomalies”; the examiner notes that the second data in the test set corresponds to “a second data”);
applying the first machine learning algorithm to the second data to determine whether the second data conforms to the set of data in the sanitized blockchain…” (Sayadi, IV.C: “After data normalization, we have split our dataset on training data for the training level of our model and test data in order to detect the outliers over anomaly transactions. A score will be given to the OCSVM anomaly detection model for each type of outputting a decision, (1) if normal data or (−1) if abnormal data”; the examiner notes that the trained One-Class SVM algorithm (trained on the set of data in the sanitized blockchain) is applied to the second test data to determine if it is an outlier).
Sayadi, Dods, Signorini, and Ross do not appear to explicitly disclose “publishing the second data to the sanitized blockchain to generate an updated set of data; and retraining the first machine learning algorithm and the third machine learning algorithm using the updated set of data.”
However, Pi discloses
“publishing… data to… [a] blockchain to generate an updated set of data (Pi, [39]: “In the example of FIG. 5, the anomaly detection computer maintains a Blockchain ledger 270 that records transactions involving power generation or use. These transactions may be between producers, between consumers, or between producers and consumers. As new blocks (i.e., transactions) are recorded in the ledger, the data used for anomaly detection can be updated accordingly”); and
retraining…[a] machine learning algorithm using the updated set of data” (Pi, [46]: “At step 625, if the probability of an anomaly is above the threshold value, an alert message is generated for one or more system operators… In some embodiments, one or more feedback messages from the system operators are received in response to the alert message. The CNN may then be retrained based on this feedback. For example, if the system operator indicates that a particular event is not anomalous, the CNN may label the data accordingly and use it in its training set”).
Pi and the instant application both relate to anomaly detection using machine learning and blockchain and are analogous. It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to have modified the combination of Sayadi, Dods, Signorini, and Ross with the teachings of Pi to include “publishing the second data to the sanitized blockchain to generate an updated set of data; and retraining the first machine learning algorithm and the third machine learning algorithm using the updated set of data,” and one would have been motivated to do so for the purpose of improving the accuracy of the machine learning algorithms (see Pi, [26]).
Regarding claim 17, the rejection of claim 16 is incorporated. Claim 17 is a non-transitory computer-readable device claim corresponding to method claim 3, and the rejection of claim 17 follows the same rationale as the rejection of claim 3 above.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Sayadi in view of Dods, Signorini, and Ross, and further in view of Bhave.
Regarding claim 19, the rejection of claim 15 is incorporated. Claim 19 is a non-transitory computer-readable device claim corresponding to method claim 5, and the rejection of claim 19 follows the same rationale as the rejection of claim 5 above.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Sayadi in view of Dods, Signorini, and Ross, and further in view of Nagla.
Regarding claim 20, the rejection of claim 15 is incorporated. Claim 20 is a non-transitory computer-readable device claim corresponding to method claim 7, and the rejection of claim 20 follows the same rationale as the rejection of claim 7 above.
Double Patenting
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 timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-4, 7, and 9-12 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 7, and 8-11 of U.S. Patent No. 11615272 in view of Sayadi and Dods.
Regarding claims 1-4, 7, and 9-12, reference claims 1-4, 7, and 8-11 respectively anticipate all the limitations of the instant claims, except insofar as they recite “first blockchain” and “second blockchain” rather than “sanitized blockchain” and “anomaly blockchain,” and “anomalous” rather than “outlier data”.
However, Sayadi discloses a “sanitized blockchain” (Sayadi, IV.A: “We use Bitcoin transaction data obtained by a data source on the Bitcoin blockchain…We use this data as a set of normal behavior data for our anomaly detection model) and “outlier data” (Sayadi, III: “In the first step, we apply a behavioral analysis in which we use the One-Class SVM algorithm to detect outliers”).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the reference application with the teachings of Sayadi so that the “first blockchain” is a “sanitized blockchain,” and the data determined to be “anomalous” is designated as “outlier data,” and one would have been motivated to do so for the purpose of improving the accuracy of the blockchain anomaly detection model (see Sayadi, VI).
Sayadi does not appear to explicitly disclose an “anomaly blockchain.” However, Dods discloses an “anomaly blockchain” (Dods, col 20, lines 48-50: “DApp 136 is used to store the anomaly reports in the tamper-proof blockchain network 106”).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to have modified the combination of the reference application and Sayadi with the teachings of Dods so that the “second blockchain” is an “anomaly blockchain,” and one would have been motivated to do so for the purpose of allowing for exceptions and anomalies to be reported by multiple actors in a trusted block chain centric network, and to trigger appropriate remedial actions to anomalies (see Dods, col 5, lines 16-17 and 32-37).
The claims of the instant application and the claims of the reference patent are compared in the table below.
Instant Application
U.S. Patent No. 11615272
1. A computer-implemented method for scrubbing anomalies from an expanding dataset, comprising:
training a first machine learning algorithm using a set of data published on a sanitized blockchain;
applying the first machine learning algorithm to a first data to designate the first data as outlier data relative to the set of data in the sanitized blockchain;
publishing the first data to an anomaly blockchain;
applying a second machine learning algorithm to data stored in the anomaly blockchain to identify a pattern of anomalous data; and
publishing the anomalous data from the anomaly blockchain to the sanitized blockchain.
1. A computer-implemented method for scrubbing anomalies from an expanding dataset, comprising:
receiving data;
applying a first machine learning algorithm to the data to determine whether the data is anomalous relative to a set of data used to train the first machine learning algorithm, wherein the set of data is published on a first blockchain, and wherein the first machine learning algorithm is supervised or semi-supervised; when the data is determined to be anomalous relative to the set of data,
publishing the data to a second blockchain different from the first blockchain;
monitoring data of the second blockchain;
applying a second machine learning algorithm to the data of the second blockchain to identify a pattern of anomalous data; and
in response to identifying the pattern, publishing the anomalous data of the second blockchain to the first blockchain.
2. The computer-implemented method of claim 1, further comprising:
receiving a second data;
applying the first machine learning algorithm to the second data
to determine whether the second data conforms to the set of data in the sanitized blockchain;
publishing the second data to the sanitized blockchain to generate an updated set of data; and
retraining the first machine learning algorithm using the updated set of data.
2. The computer-implemented method of claim 1, further comprising:
receiving second data;
applying the first machine learning algorithm to the second data;
identifying the second data as conforming with the set of data used to train the first machine learning algorithm;
publishing the second data to the first blockchain to generate an updated set of data; and
retraining the first machine learning algorithm using the updated set of data.
3. The computer-implemented method of claim 2, wherein the retraining occurs using an unsupervised learning technique.
3. The computer-implemented method of claim 2, wherein the retraining occurs using an unsupervised learning technique.
4. The computer-implemented method of claim 1,
wherein applying the first machine learning algorithm further comprises: applying a clustering technique to the first data and the set of data in the sanitized blockchain; and designating the first data as outlier data by identifying the first data as being outside of a cluster determined from the set of data in the sanitized blockchain.
4. The computer-implemented method of claim 1, wherein the first machine learning algorithm is semi-supervised and
wherein applying the first machine learning algorithm further comprises: applying a clustering technique to the data and the set of data; and determining the data to be anomalous by identifying the data as being outside of a cluster determined from the set of data.
7. The computer-implemented method of claim 1, wherein the set of data published on the sanitized blockchain includes transaction data related to vehicle transactions.
7. The computer-implemented method of claim 6, wherein the set of data includes transaction data related to vehicle transactions.
9. A system for scrubbing anomalies from an expanding dataset, comprising: a memory; and at least one processor coupled to the memory and configured to:
train a first machine learning algorithm using a set of data published on a sanitized blockchain;
apply the first machine learning algorithm to a first transaction data to designate the first transaction data as outlier data relative to the set of data in the sanitized blockchain;
publish the first transaction data to an anomaly blockchain;
apply a second machine learning algorithm to data stored in the anomaly blockchain to identify a pattern of anomalous data; and
publish the anomalous data from the anomaly blockchain to the sanitized blockchain.
8. A system for scrubbing anomalies from an expanding dataset, comprising: a memory; and at least one processor coupled to the memory and configured to:
publish a set of data to a first blockchain;
apply a first machine learning algorithm to transaction data to determine whether the transaction data is anomalous relative to the set of data used to train the first machine learning algorithm, wherein the first machine learning algorithm is supervised or semi-supervised; when the transaction data is determined to be anomalous relative to the set of data,
publish the transaction data to a second blockchain different from the first blockchain;
monitor data of the second blockchain;
apply a second machine learning algorithm to the data of the second blockchain to identify a pattern of anomalous data; and in response to identifying the pattern,
publish the anomalous data of the second blockchain to the first blockchain.
10. The system of claim 9, wherein the at least one processor is further configured to:
receive a second transaction data;
apply the first machine learning algorithm to the second transaction data to determine whether the second transaction data conforms to the set of data in the sanitized blockchain;
publish the second transaction data to the sanitized blockchain to generate an updated set of data; and retrain the first machine learning algorithm using the updated set of data.
9. The system of claim 8, wherein the at least one processor is further configured to:
receive second transaction data;
apply the first machine learning algorithm to the second transaction data;
identify the second transaction data as conforming with the set of data used to train the first machine learning algorithm;
publish the second transaction data to the first blockchain to generate an updated set of data; and retrain the first machine learning algorithm using the updated set of data.
11. The system of claim 10, wherein the retraining occurs using an unsupervised learning technique.
10. The system of claim 9, wherein the retraining occurs using an unsupervised learning technique.
12. The system of claim 9, wherein to apply the first machine learning algorithm, the at least one processor is further configured to:
apply a clustering technique to the first transaction data and the set of data in the sanitized blockchain; and designate the first transaction data as outlier data by identifying the first transaction data as being outside of a cluster determined from the set of data published in the sanitized blockchain.
11. The system of claim 8, wherein the first machine learning algorithm is semi-supervised and wherein to apply the first machine learning algorithm, the at least one processor is further configured to:
apply a clustering technique to the transaction data and the set of data; and determine the transaction data to be anomalous by identifying the transaction data as being outside of a cluster determined from the set of data.
Conclusion
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/G.A.D./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125