DETAILED ACTION
This is a non-final, first office action on the merits. Claims 2-21 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant is claiming Foreign Priority to Foreign Applications IN201941022292 filed on 06/05/2019.
Status of Claims
Preliminary Applicant’s amendment date 03/13/2024, added new claims 2-21; canceled claim 1.
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 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 2-21 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claims 2, 15, and 20 recite an abstract idea. Claims 2, 15, and 20 include “accessing historical data associated with a plurality of historical events, wherein an outcome of each historical event of the plurality of historical events is known; generating, at least in part by performing a process on the historical data, one or more rules for predicting the outcome for each historical event of the plurality of historical events; editing, the one or more rules; and causing the edited one or more rules to be presented ……accessing a model, wherein the model is constructed based on a plurality of historical transactions with known classification labels; training the model; generating, at least in part based on the training of the model, one or more rules usable to classify the plurality of historical transactions; editing the one or more rules based on one or more specified criteria; and causing at least a subset of the edited one or more rules to be presented……accessing a model that includes a plurality of decision trees, wherein the model is constructed based on a plurality of historical transactions with known classification labels and trained at least in part based on traversals of a plurality of potential paths of the plurality of decision trees; obtaining, at least in part based on the model, a rule usable to perform a classification task; revising an appearance of the generated rule, wherein the revised appearance of the generated rule meets one or more specified criteria for human understanding or legibility; and presenting, the generated rule with the revised appearance”.
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the elements describe a process for identifying and suggesting data classification rules. As a result, claims 2, 15, and 20 recite an abstract idea under Step 2A Prong One.
Claims 3-14, 16-19, and 21 further describe the process for identifying and suggesting data classification rules. As a result, claims 3-14, 16-19, and 21 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 2, 15, and 20.
With respect to Step 2A Prong Two of the framework, claims 2, 15, and 20 do not include additional elements that integrate the abstract idea into a practical application. Claims 2, 15, and 20 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 2, 15, and 20 include a machine learning process, automatically without human intervention, a user interface, a non-transitory memory, one or more hardware processors, a machine learning model, a non-transitory machine-readable medium. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 2, 15, and 20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 3-5, 8, and 14 do not include any additional elements beyond those recited with respect to claims 2, 15, and 20. As a result, claims 3-5, 8, and 14 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 2, 15, and 20.
Claims 6-7, 9-13, 16-19, and 21 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 6-7, 9-13, 16-19, and 21 include automatically, a user interface, and a machine learning model. When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 6-7, 9-13, 16-19, and 21 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claims 2, 15, and 20 do not include additional elements amounting to significantly more than the abstract idea. As noted above, claims 2, 15, and 20 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 2, 15, and 20 include a machine learning process, automatically without human intervention, a user interface, a non-transitory memory, one or more hardware processors, a machine learning model, a non-transitory machine-readable medium. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, independent claims 2, 15, and 20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 3-5, 8, and 14 do not include any additional elements beyond those recited with respect to claims 2, 15, and 20. As a result, claims 3-5, 8, and 14 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 2, 15, and 20.
Claims 6-7, 9-13, 16-19, and 21 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 6-7, 9-13, 16-19, and 21 include automatically, a user interface, and a machine learning model. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 6-7, 9-13, 16-19, and 21 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 2-21 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-5, 7, 9-10, 13-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Meron et al. (US Pub No. 2018/0046939) (hereinafter Meron et al.) in view of Agrawal et al. (US Pub No. 2018/0350006) (hereinafter Agrawal et al.).
Regarding claim 2, Meron discloses a method, comprising:
accessing historical data associated with a plurality of historical events, wherein an outcome of each historical event of the plurality of historical events is known (see Meron, para [0051], wherein selecting data relating to one or more past transactions from a database, e.g., the transaction database 152; and para [0065], wherein randomly select data relating to a predefined number of past transactions, for example, 2,000 past transactions that are known to involve the use of a stolen credit card);
generating, at least in part by performing a machine learning process on the historical data, one or more rules for predicting the outcome for each historical event of the plurality of historical events (see Meron, para [0022], wherein the machine learning system generates machine learning rules based on the evaluation and adds the rules to a rule engine; para [0018], wherein a machine learning system randomly samples matured (e.g., past) transactions, from the past transaction data set, and their associated features (e.g., the IP address of the user device initiating a transaction, the geo-location of the user device, a transaction amount, and the type of a currency used; and para [0047], wherein after applying the test data set to a decision tree, based on comparing the prediction results produced by the decision tree and the known nature of the transactions, the decision tree construction module 156 may determine an accuracy level for each node in the decision tree);
automatically editing, the one or more rules (see Meron, paras [0002] & [0045], wherein automated machine learning feature processing and transaction classification….the feature identification module 154 may apply multiple different machine learning algorithms or modify the input (e.g., the initial set of features) to the one or more machine learning algorithms); and
causing the automatically edited one or more rules to be presented (see Meron, paras [0002] & [0045], wherein automated machine learning feature processing and transaction classification….the feature identification module 154 may apply multiple different machine learning algorithms or modify the input (e.g., the initial set of features) to the one or more machine learning algorithms).
Meron et al. fails to explicitly disclose editing, without human intervention, the one or more rules; and causing the automatically edited one or more rules to be presented via a user interface.
Analogous art Agrawal discloses automatically editing, without human intervention, the one or more rules (see Agrawal, paras [0063]-[0064], wherein automatic machine or computer-implemented tagging of records without human intervention. Data tagging or labeling is defined by adding data tags to data based on attributes of the data…….create the scoring algorithms. Data training server 220, which generates score rules defined by the scoring model using training data, includes one or more feature values for entity classification); and
Analogous art Agrawal discloses causing the automatically edited one or more rules to be presented via a user interface (see Agrawal, paras [0063]-[0064], wherein automatic machine or computer-implemented tagging of records without human intervention. Data tagging or labeling is defined by adding data tags to data based on attributes of the data…….create the scoring algorithms. Data training server 220, which generates score rules defined by the scoring model using training data, includes one or more feature values for entity classification; and para [0051], wherein receive user input through the at least one graphical user interface).
Meron directed to a system for automated machine learning feature processing and transaction classification. Agrawal directed to determining performance of a decision tree. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Meron, regarding the Automated Machine Learning Feature Processing, to have included automatically editing, without human intervention, the one or more rules; and causing the automatically edited one or more rules to be presented via a user interface because both inventions teach optimize automated software algorithms. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 3, Meron discloses the method of claim 2, wherein:
the plurality of historical events comprise a plurality of transactions (see Meron, para [0018], wherein a machine learning system randomly samples matured (e.g., past) transactions); and
the outcome of each historical event comprises an indication of whether there was a presence of fraud (see Meron, para [0018], wherein the transaction matures into ( e.g., is determined to be) a fraud/benign transaction and is then included in a past transaction data set (that can be used to identify an initial set of features).
Regarding claim 4, Meron discloses the method of claim 2, further comprising constructing a plurality of decision trees based on the historical data, wherein the one or more rules are generated at least in part by traversing a plurality of paths of the plurality of decision trees (see Meron, para [0073], wherein the method 300 includes a decision tree construction process. The method 300, for example, may include constructing (308) a decision tree based on the second set of feature es; para [0018], wherein the transaction matures into ( e.g., is determined to be) a fraud/benign transaction and is then included in a past transaction data set (that can be used to identify an initial set of features; and para [0084], wherein generating the total number of transaction categorization rules based on traversing from a root node of the decision tree to each end node in the one or more end nodes).
Regarding claim 5, Meron discloses the method of claim 2, wherein the generating comprises:
generating a first plurality of rules at least in part by performing a machine learning process on the historical data (see Meron, para [0013], wherein constructing machine learning rules based on a refined number of features and applying the rules to categorize (or classify) future transactions on a real time basis);
evaluating an efficacy of each rule of the first plurality of rules in predicting the outcome of at least some of the plurality of historical events (see Meron, para [0014], wherein a test data set may be applied to the decision tree to determine which one or more tree paths produce more accurate (e.g., reliable) results. Tree paths (or the nodes included therein) that can produce results above a predefined accuracy ( e.g., 90%) are selected to construct additional machine learning rules. The additional rules can be used to categorize a transaction within a reduced response time (e.g., 1 minute)); and
selecting a subset of the first plurality of rules that meet a predetermined efficacy threshold as the one or more rules generated (see Meron, para [0092], wherein
selecting a subset of the first number of transaction categorization rules; para [0048], wherein the rule generation module 158 generates one or more transaction categorization rules based on nodes having an accuracy level equal to or greater than a predefined accuracy level (e.g., 95%)).
Regarding claim 7, Meron discloses the method of claim 2, wherein the automatically editing comprises simplifying a content of the one or more rules (see Meron, para [0053], wherein reduce the number of machine learning rules to be generated).
Regarding claim 9, Meron discloses the method of claim 2, wherein the automatically editing comprises editing the one or more rules for legibility (see Meron, paras [0002] & [0045], wherein automated machine learning feature processing and transaction classification….the feature identification module 154 may apply multiple different machine learning algorithms or modify the input (e.g., the initial set of features) to the one or more machine learning algorithms; and para [0026], wherein produce more user-friendly rule descriptions, e.g., descriptions that are more informative and easily understood by a human user).
Regarding claim 10, Meron discloses the method of claim 2, wherein the automatically editing is performed through a plurality of editing iterations (see Meron, para [0045], wherein modify the input (e.g., the initial set of features) to the one
or more machine learning algorithms; and para [0054], wherein several machine learning algorithms may be applied and several iterations of a same algorithm may be applied).
Regarding claim 13, Meron discloses the method of claim 2, wherein the automatically edited one or more rules, as set forth above with claim 2.
Meron et al. fails to explicitly disclose presented as suggestions, to a human user, for inclusion in a rule-based event classification platform.
Analogous art Agrawal discloses presented as suggestions, to a human user, for inclusion in a rule-based event classification platform (see Agrawal, para [0095], wherein receives input including, for example, one or more noncompliant scored cases for constant surveillance to help identify misuse and abuse updates and to provide those updates into the rules in the dynamic scoring system. The compliance processor also provides an intervention algorithm to automatically monitor specified card programs and provide suggestions for updates to move the program closer or back into compliance. In an aspect of the invention, the interface 550 may be a web-based, flexible application for commercial payment programs for maximization of savings and benefits by operating according to a company's policies; para [0046], wherein waiting for human intervention to update the rules gradually; and para [0048], wherein misuse" and "abuse" refer to the characterization or classification of a transaction based on predictions using attributes of the associated data to determine the nature of a transaction).
One of ordinary skill in the art would have recognized that applying the known technique of Agrawal, would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2.
Regarding claim 14, Meron discloses the method of claim 2, further comprising: predicting, based at least in part on the one or more rules, an outcome of one or more events different from the plurality of historical events (see Meron, para [0047], wherein after applying the test data set to a decision tree, based on comparing the prediction results produced by the decision tree and the known nature of the transactions, the decision tree construction module 156 may determine an accuracy level for each node in the decision tree; Meron, para [0053], wherein reduce the number of machine learning rules to be generated; and para [0057], wherein predefined number transactions (e.g., 100 transactions, such as, a fraudulent purchase transaction involving a tablet and a smartphone using a credit card from a payment account that has not been used at all in the past ten months; and a legitimate purchase transaction involving a smartphone using a credit line applied for mid-transaction).
Regarding claim 15, Meron discloses a system, comprising:
a non-transitory memory (see Meron, para [0093]); and
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations (see Meron, para [0093]) comprising:
accessing a machine learning model, wherein the machine learning model is constructed based on a plurality of historical transactions with known classification labels (see Meron, para [0013], wherein constructing machine learning rules based on a refined number of features and applying the rules to categorize (or classify) future transactions on a real time basis; para [0047], wherein after applying the test data set to a decision tree, based on comparing the prediction results produced by the decision tree and the known nature of the transactions, the decision tree construction module 156 may determine an accuracy level for each node in the decision tree; para [0039], wherein account takeover or ATO transaction maturation may be about two months, with about 20% of the ATO transactions identified within a week. A mature transaction is a labeled (e.g., as a fraud/benign transaction) and comprises a tagged data set for the machine learning purpose);
training the machine learning model (see Meron, para [0020], wherein the machine learning system learns a decision tree based on the subset of features);
generating, at least in part based on the training of the machine learning model, one or more rules usable to classify the plurality of historical transactions (see Meron, para [0002], wherein automated machine learning feature processing and transaction classification; para [0020], wherein the machine learning system learns a decision tree based on the subset of features; para [0022], wherein the machine learning system generates machine learning rules based on the evaluation and adds the rules to a rule engine; and para [0083], wherein transaction categorization rules generated using a decision tree can be used to classify a pending transaction, on a substantially real time basis, as a fraudulent transaction or a benign transaction);
automatically editing the one or more rules (see Meron, paras [0002] & [0045], wherein automated machine learning feature processing and transaction classification….the feature identification module 154 may apply multiple different machine learning algorithms or modify the input (e.g., the initial set of features) to the one or more machine learning algorithms).
Meron et al. fails to explicitly disclose based on one or more specified criteria; and causing at least a subset of the automatically edited one or more rules to be presented via a user interface.
Analogous art Agrawal discloses automatically editing the one or more rules based on one or more specified criteria (see Agrawal, paras [0063]-[0064], wherein automatic machine or computer-implemented tagging of records without human intervention. Data tagging or labeling is defined by adding data tags to data based on attributes of the data…….create the scoring algorithms. Data training server 220, which generates score rules defined by the scoring model using training data, includes one or more feature values for entity classification; and para [0094], wherein the score influencing rules may refer to stored logic for comparing a transaction against criteria set in one or more standard rules, set of rules, or customizable rules to identify potential out-of-policy spend); and
Analogous art Agrawal discloses causing at least a subset of the automatically edited one or more rules to be presented via a user interface (see Agrawal, paras [0063]-[0064], wherein automatic machine or computer-implemented tagging of records without human intervention. Data tagging or labeling is defined by adding data tags to data based on attributes of the data…….create the scoring algorithms. Data training server 220, which generates score rules defined by the scoring model using training data, includes one or more feature values for entity classification; para [0015], wherein one graphical user interface comprising at least a subset of the plurality of settled transactions).
Meron directed to a system for automated machine learning feature processing and transaction classification. Agrawal directed to determining performance of a decision tree. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Meron, regarding the Automated Machine Learning Feature Processing, to have included automatically editing the one or more rules based on one or more specified criteria; and causing at least a subset of the automatically edited one or more rules to be presented via a user interface because both inventions teach optimize automated software algorithms. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 16, Meron discloses the system of claim 15, wherein:
the machine learning model comprises a plurality of trees (see Meron, para [0020], wherein the machine learning system learns a decision tree based on the subset of features); and
the training of the machine learning model comprises traversing a plurality of paths of the plurality of trees (see Meron, para [0073], wherein the method 300 includes a decision tree construction process. The method 300, for example, may include constructing (308) a decision tree based on the second set of feature es; para [0018], wherein the transaction matures into (e.g., is determined to be) a fraud/benign transaction and is then included in a past transaction data set (that can be used to identify an initial set of features; and para [0084], wherein generating the total number of transaction categorization rules based on traversing from a root node of the decision tree to each end node in the one or more end nodes).
Regarding claim 17, Meron discloses the system of claim 15, wherein the operations further comprise evaluating a performance of the automatically edited one or more rules (see Meron, paras [0002] & [0045], wherein automated machine learning feature processing and transaction classification….the feature identification module 154 may apply multiple different machine learning algorithms or modify the input (e.g., the initial set of features) to the one or more machine learning algorithms).
Meron et al. fails to explicitly disclose wherein the subset of the automatically edited one or more rules to be presented are rules that meet a specified performance threshold.
Analogous art Agrawal discloses wherein the subset of the automatically edited one or more rules to be presented are rules that meet a specified performance threshold (see Agrawal, para [0015], wherein one graphical user interface comprising at least a subset of the plurality of settled transactions; paras [0063]-[0064], wherein automatic machine or computer-implemented tagging of records without human intervention. Data tagging or labeling is defined by adding data tags to data based on attributes of the data…….create the scoring algorithms. Data training server 220, which generates score rules defined by the scoring model using training data, includes one or more feature values for entity classification; and para [0094], wherein the score influencing rules may refer to stored logic for comparing a transaction against criteria set in one or more standard rules, set of rules, or customizable rules to identify potential out-of-policy spend; and para [0116], wherein a threshold value is met in one or a combination of a record's attributes, a field in the record may be labeled as an outlier, for further characterizing the record. If something is scored high using performance tagging, an administrator review and score the performance tag as incorrect to make the score lower).
One of ordinary skill in the art would have recognized that applying the known technique of Agrawal would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2.
Regarding claim 19, Meron discloses the system of claim 15, wherein the automatically edited one or more rules are presented as suggestions, to a human user, for inclusion in a classification platform, as set forth above with claim 13, and wherein the operations further comprise:
Meron et al. fails to explicitly disclose selecting, based on input received via the user interface, at least a first rule of the automatically edited one or more rules to include in the classification platform; and classifying, based at least in part on the first rule, one or more transactions with unknown classification labels.
Analogous art Agrawal discloses selecting, based on input received via the user interface, at least a first rule of the automatically edited one or more rules to include in the classification platform (see Agrawal, para [0093], wherein the compliance management processor 542 includes a dashboard that is used to provide metrics, e.g., a macro view of certain performance factors. Compliance management processor 542 also includes displays for the selection and updating of records during auditing. For example, an audit of non-compliant transactions can be sorted by at least one or more of consumer demographic details, merchant details, or supplier details……identify questionable transactions to be processed through the compliance management processor 542. Sampling statistics may refer to a sampling of results to define conditions for handling a case. The score influencing rules may refer to stored logic for comparing a transaction against criteria set in one or more standard rules, set of rules, or customizable rules to identify potential out-of-policy spend; paras [0117]-[0118], wherein automatically modifies the scoring model. In a non-limiting embodiment, the system makes use of the known and available misuse and abuse data to learn using unsupervised machine learning algorithms to find new patterns and generate more accurate reason codes. The scores and codes become more accurate when the self-adapting feedback is used to make new determinations by identifying categories of good and bad cases with case dispositive data and influencing scoring with new rules…..These supervised learning labels and rules may define or refer to policies for using the system); and
Analogous art Agrawal discloses classifying, based at least in part on the first rule, one or more transactions with unknown classification labels (see Agrawal, para [0059], wherein generates predictions on new raw data for which the target is not known. For example, to train a model to predict if a commercial card transaction is a misuse or abuse, training data is used that contains transactions for which the target is known (e.g., a label that indicates whether a commercial card transaction is abused or not abused). Training of a model is accomplished by using this data, resulting in a model that attempts to predict whether new data will be abuse/misuse or not; and paras [0068] & [0102], wherein the reason code may indicate that the transaction was classified as "fraudulent" due to the transaction amount being larger than a specified threshold and the address not being verified or (i.e., unknown)).
One of ordinary skill in the art would have recognized that applying the known technique of Agrawal, would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Meron et al. (US Pub No. 2018/0046939) (hereinafter Meron et al.), in view of Agrawal et al. (US Pub No. 2018/0350006) (hereinafter Agrawal et al.), and further He et al. (US Pub No. 2015/0006491) (hereinafter He et al.).
Regarding claim 6, Meron discloses the method of claim 5, further comprising:
evaluating the efficacy of each of the automatically edited one or more rules (see Meron, para [0047], wherein after applying the test data set to a decision tree, based on comparing the prediction results produced by the decision tree and the known nature of the transactions, the decision tree construction module 156 may determine an accuracy level for each node in the decision tree; paras [0002] & [0045], wherein automated machine learning feature processing and transaction classification….the feature identification module 154 may apply multiple different machine learning algorithms or modify the input (e.g., the initial set of features) to the one or more machine learning algorithms).
Meron et al. and Agrawal et al. combined fail to explicitly disclose re-evaluating; and causing, based on the re-evaluating, only rules that meet the predetermined efficacy threshold to be automatically presented via the user interface.
Analogous art Agrawal discloses re-evaluating the efficacy of each of the automatically edited one or more rules; and causing, based on the re-evaluating, only rules that meet the predetermined efficacy threshold to be automatically presented via the user interface (see Agrawal, para [0064], wherein if the average confidence score for a batch of resulting best records 730 falls below a particular threshold value, then the computing system may generate a flag indicating that the existing best record creation rules 720 may need to be reviewed and edited to increase their efficacy. Similarly, the computing system may monitor the number of resulting best records with confidence scores that fall below a particular threshold value as the best record creation rules 720 are applied. If, over some period of time or as some number of matched and harmonized records are processed, the number of resulting best records with confidence scores below a threshold value exceeds a predetermined number, then the computer system may also indicate that the best record creation rules 720 need to be reevaluated and possibly edited to increase the efficacy; and para [0061], wherein the strategy composer/tester 121 includes a graphical user interface (GUI), receiving the best record creation strategy).
Meron directed to a system for automated machine learning feature processing and transaction classification. He directed to editing the violating data field until it complies with the corresponding validation rule. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Meron, regarding the Automated Machine Learning Feature Processing, to have included re-evaluating the efficacy of each of the automatically edited one or more rules; and causing, based on the re-evaluating, only rules that meet the predetermined efficacy threshold to be automatically presented via the user interface because both inventions teach improving data quality assessment. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Meron et al. (US Pub No. 2018/0046939) (hereinafter Meron et al.), in view of Agrawal et al. (US Pub No. 2018/0350006) (hereinafter Agrawal et al.), and further Hetherington et al. (US Pub No. 2020/0302318) (hereinafter Hetherington et al.).
Regarding claim 8, Meron discloses the method of claim 7, wherein the simplifying the content, as set forth above with claim 7.
Meron et al. and Agrawal et al. combined fail to explicitly disclose comprises simplifying a syntax of the one or more rules.
Analogous art Hetherington discloses simplifying a syntax of the one or more rules (see Hetherington, para [0083], wherein the syntax of the rules…..generates an optimized ruleset that has human readable text).
Meron directed to a system for automated machine learning feature processing and transaction classification. He directed to editing the violating data field until it complies with the corresponding validation rule. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Meron, regarding the Automated Machine Learning Feature Processing, to have included simplifying a syntax of the one or more rules because both inventions teach improving data quality assessment. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 18, Meron discloses the system of claim 15, wherein the automatically editing, as set forth above with claim 15.
Meron et al. and Agrawal et al. combined fail to explicitly disclose performed at least in part by simplifying a syntax of the one or more rules or reducing a length of the one or more rules.
Analogous art Hetherington discloses simplifying a syntax of the one or more rules (see Hetherington, para [0083], wherein the syntax of the rules…..generates an optimized ruleset that has human readable text).
One of ordinary skill in the art would have recognized that applying the known technique of Hetherington would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 8.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Meron et al. (US Pub No. 2018/0046939) (hereinafter Meron et al.), in view of Agrawal et al. (US Pub No. 2018/0350006) (hereinafter Agrawal et al.), and further Nair et al. (US Pat No. 10,410,140) (hereinafter Nair et al.).
Regarding claim 11, Meron discloses the method of claim 2, wherein the automatically editing, as set forth above with claim 2.
Meron et al. and Agrawal et al. combined fail to explicitly disclose replacing a numeric value for a categorical feature of the historical data with a conditional value.
Analogous art Nair discloses replacing a numeric value for a categorical feature of the historical data with a conditional value (see Nair, column 3, lines 51-57, wherein the value of the categorical variable C of the prediction input vector can be replaced with its corresponding weight value or numeric variable value to generate a substitute prediction input vector. For example, if the value of the categorical variable C in the prediction input vector is c2, it can be replaced with the value of w2 to generate a substitute vector. A prediction result can be computed using the substitute prediction input vector and the machine learning model…).
Meron directed to a system for automated machine learning feature processing and transaction classification. Nair directed to induction system for text categorization. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Meron, regarding the Automated Machine Learning Feature Processing, to have included replacing a numeric value for a categorical feature of the historical data with a conditional value because both inventions teach improving performance. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Meron et al. (US Pub No. 2018/0046939) (hereinafter Meron et al.), in view of Agrawal et al. (US Pub No. 2018/0350006) (hereinafter Agrawal et al.), and further King et al. (US Pub No. 2011/0078585) (hereinafter King et al.).
Regarding claim 12, Meron discloses the method of claim 2.
Meron et al. and Agrawal et al. combined fail to explicitly disclose causing an unedited version of the one or more rules to be presented via the user interface.
Analogous art King discloses causing an unedited version of the one or more rules to be presented via the user interface (see King, para [0540], wherein Unedited Version 1 of a document 701 document contains ordinary text at location 702; and para [0520], wherein may be retrieved or generated at the time of use, may be based on information or rules in an associated database 544; and para [0289], wherein Graphical User Interface components provided by the OS).
Meron directed to a system for automated machine learning feature processing and transaction classification. King directed to document processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Meron, regarding the Automated Machine Learning Feature Processing, to have included causing an unedited version of the one or more rules to be presented via the user interface because both inventions teach improving performance. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Meron et al. (US Pub No. 2018/0046939) (hereinafter Meron et al.) in view of Stiansen et al. (US Pat No. 8,726,379) (hereinafter Stiansen et al.).
Regarding claim 20, Meron discloses the a non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:
accessing a machine learning model that includes a plurality of decision trees, wherein the machine learning model is constructed based on a plurality of historical transactions with known classification labels and trained at least in part based on traversals of a plurality of potential paths of the plurality of decision trees (see Meron, para [0013], wherein constructing machine learning rules based on a refined number of features and applying the rules to categorize (or classify) future transactions on a real time basis; para [0047], wherein transactions of a particular nature, e.g., known fraudulent transactions where a stolen user identity is used to apply for a purchase credit line. The test data set may be smaller than the past transaction data set used to select the "best" features. After applying the test data set to a decision tree, based on comparing the prediction results produced by the decision tree and the known nature of the transactions, the decision tree construction module 156 may determine an accuracy level for each node in the decision tree; para [0020], wherein the machine learning system learns a decision tree based on the subset of features; para [0037], wherein generates one or more transaction categorization rules and categorizes (e.g., classifies) a future, pending, or past transaction based on the one or more transaction categorization rules on a substantially real time basis (e.g., while the transaction is still taking place or within a predefined time frame after the transaction has completed; paras [0083]-[0084], wherein transaction categorization rules generated using a decision tree can be used to classify a pending transaction, on a substantially real time basis, as a fraudulent transaction or a benign transaction…….generating the total number of transaction categorization rules based on traversing from a root node of the decision tree to each end node in the one or more end nodes; and para [0014], wherein a test data set may be applied to the decision tree to determine which one or more tree paths produce more accurate (e.g., reliable) results. Tree paths (or the nodes included therein) that can produce results above a predefined accuracy ( e.g., 90%) are selected to construct additional machine learning rules. The additional rules can be used to categorize a transaction within a reduced response time (e.g., 1 minute));
obtaining, at least in part based on the machine learning model, a machine-generated rule usable to perform a classification task (see Meron, para [0040], wherein data relating to the mature transactions are stored in the transaction database 152, from which a predefined number of machine-based transactions selected at step 302 can be randomly selected; paras [0002] & [0022], wherein automated machine learning feature processing and transaction classification; and paras [0037] & [0083], wherein generates one or more transaction categorization rules and categorizes (e.g., classifies) a future, pending, or past transaction based on the one or more transaction categorization rules on a substantially real time basis (e.g., while the transaction is still taking place or within a predefined time frame after the transaction has completed).
Meron et al. fails to explicitly disclose automatically revising an appearance of the machine-generated rule, wherein the automatically revised appearance of the machine-generated rule meets one or more specified criteria for human understanding or legibility; and presenting, via a user interface, the machine-generated rule with the automatically revised appearance.
Analogous art Stiansen discloses automatically revising an appearance of the machine-generated rule, wherein the automatically revised appearance of the machine-generated rule meets one or more specified criteria for human understanding or legibility (see Stiansen, column 25, lines 9-21, wherein a customer may conditionally allow a proposed transaction from a suspicious IP Address, giving the appearance to the IP Address user that the transaction has been accepted, but the customer may then validate the purchase before finalizing the order and releasing products or services……transaction involving a credit or debit card, the credit card company or bank may place a hold on the transaction and contact the cardholder of record to validate the transaction before releasing the funds. In an embodiment where the client is not able to validate the transaction, this fact itself may also be collected (10) and used by the system to assess the risk associated with the IP Address at issue; column 21, lines 9-13, wherein the rating process (18) uses an algorithm, or set of algorithms, to determine the categories of risk activity for an IP Address, the likelihood of a category of risk activity for an IP Address, and/or the severity of threat presented for a category of risk activity for an IP Address; column 24, lines 19-22, wherein database (28) is synchronized (26) with the staging database (22) and thus contains IP Addresses determined to be malicious according to the criteria selected by the client (24); and column 1, lines 36-39, wherein "IP Address" is a thirty-two bit binary number, commonly represented visually in "dotted-decimal" format for improved human-readability, such as: 150.50.10.34.); and
Analogous art Stiansen discloses presenting, via a user interface, the machine-generated rule with the automatically revised appearance (see Stiansen, column 9, lines 4-5, wherein the operational context is a graphical user interface, "real time"; column 21, lines 9-13, wherein the rating process (18) uses an algorithm, or set of algorithms, to determine the categories of risk activity for an IP Address, the likelihood of a category of risk activity for an IP Address, and/or the severity of threat presented for a category of risk activity for an IP Address; column 19, lines 23-29, wherein a collection agent (10) identifies a legitimate and/or valid source and/or destination IP Address for a bogon IP Address and/or Martian packet…..).
Meron directed to a system for automated machine learning feature processing and transaction classification. Stiansen directed to detecting, classifying, and rating security threats. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Meron, regarding the Automated Machine Learning Feature Processing, to have included automatically revising an appearance of the machine-generated rule, wherein the automatically revised appearance of the machine-generated rule meets one or more specified criteria for human understanding or legibility; and presenting, via a user interface, the machine-generated rule with the automatically revised appearance because both inventions teach optimize the intelligence-gathering functions. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Meron et al. (US Pub No. 2018/0046939) (hereinafter Meron et al.) in view of Stiansen et al. (US Pat No. 8,726,379) (hereinafter Stiansen et al.), and further Nomi et al. (US Pub No. 2020/0257927) (hereinafter Nomi et al.).
Regarding claim 21, Meron discloses the non-transitory machine-readable medium of claim 20, wherein the automatically revising, as set forth above with claim 20.
Meron et al. and Stiansen et al. combined fail to explicitly disclose comprises revising a syntax of the machine-generated rule or shortening a length of the machine-generated rule.
Analogous art Nomi discloses revising a syntax of the machine-generated rule (see Nomi, para [0229], wherein the user interface for simulation 61 is provided with a comment display region 61e for automatically generating a sentence explaining a summary of a result such as to what extent an objective variable has changed by changing which feature value, and displaying the sentence together with a graph. The sentence is generated by the control unit 10…..).
Meron directed to a system for automated machine learning feature processing and transaction classification. Nomi directed to simulating a change of an objective variable due to a change of a value of a feature value. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Meron, regarding the Automated Machine Learning Feature Processing, to have included revising a syntax of the machine-generated rule or shortening a length of the machine-generated rule because both inventions teach optimize the intelligence-gathering functions. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. (US Pub No. 2007/0027674; US Pub No. 2005/0267850; US Pub No. 2020/0394659; US Pub No. 2013/0282627; US Pub No. 2018/0365696; US Pub No. 2018/0247220; US Pub No. 2013/0198119; US Pub No. 2016/0036844; US Pub No. 2020/0190585; DE Johnson, FJ Oles, T Zhang, T Goetz (A decision-tree-based symbolic rule induction system for text categorization) IBM Systems Journal, 2002•ieeexplore.ieee.org; and Z Chen, LD Van Khoa, EN Teoh, A Nazir (Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review) - … and Information Systems, 2018 - Springer.
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/HAFIZ A KASSIM/Primary Examiner, Art Unit 3623 06/08/2026
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