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 .
Election/Restrictions
Applicant’s election without traverse of Invention I, claims 1-18 in the reply filed on May 26, 2026 is acknowledged.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 – Statutory Categories
As indicated in the preamble of the claim, the examiner finds the claim is directed to a process, machine, manufacture, or composition of matter.(Claims 1-9 are processes (typically methods) and Claims 10-18 are machines). Accordingly, step 1 is satisfied.
Step 2A – Prong 1: was there a Judicial Exception Recited
Claim 10 (and similarly Claim 1) recites the following abstract concepts that are found to include “abstract idea.” Any additional elements will be analyzed under Step 2A-Prong 2 and Step 2B:
A system comprising:
a server comprising one or more processors and one or more memories; and
a server application executing on the one or more processors of the server, configured for performing operations comprising:
generating, by a transaction model of a general model, a target transaction vector for a target transaction record (See MPEP 2106.04(a)(2)(I) Mathematical Formulas or Equations - organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014)., MPEP 2106.04(a)(2(III) Mental Processes - a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and See PEG Example 47, Claim 2, “Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion.” “the broadest reasonable interpretation of discretizing in step (b) also encompasses mathematical concepts (e.g., rounding data values) that can be performed mentally. Step (c) requires specific mathematical calculations (a backpropagation algorithm and a gradient descent algorithm) to perform the training of the ANN and therefore encompasses mathematical concepts”);
generating, by the general model, a plurality of account vectors for a plurality of accounts (See MPEP 2106.04(a)(2)(I) Mathematical Formulas or Equations - organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014)., MPEP 2106.04(a)(2(III) Mental Processes - a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and See PEG Example 47, Claim 2, “Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion.” “the broadest reasonable interpretation of discretizing in step (b) also encompasses mathematical concepts (e.g., rounding data values) that can be performed mentally. Step (c) requires specific mathematical calculations (a backpropagation algorithm and a gradient descent algorithm) to perform the training of the ANN and therefore encompasses mathematical concepts”);
generating a match score between the plurality of account vectors and the target transaction vector (See MPEP 2106.04(a)(2)(I) Mathematical Formulas or Equations - organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014)., MPEP 2106.04(a)(2(III) Mental Processes - a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and See PEG Example 47, Claim 2, “Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion.” “the broadest reasonable interpretation of discretizing in step (b) also encompasses mathematical concepts (e.g., rounding data values) that can be performed mentally. Step (c) requires specific mathematical calculations (a backpropagation algorithm and a gradient descent algorithm) to perform the training of the ANN and therefore encompasses mathematical concepts”);
selecting, by the general model, a first account identifier of an account in the plurality of accounts using the match score (See MPEP 2106.04(a)(2)(I) Mathematical Formulas or Equations - organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014)., MPEP 2106.04(a)(2(III) Mental Processes - a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and See PEG Example 47, Claim 2, “Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion.” “the broadest reasonable interpretation of discretizing in step (b) also encompasses mathematical concepts (e.g., rounding data values) that can be performed mentally. Step (c) requires specific mathematical calculations (a backpropagation algorithm and a gradient descent algorithm) to perform the training of the ANN and therefore encompasses mathematical concepts”);
generating, by the transaction model, a plurality of historical transaction vectors for a plurality of historical transaction records (See MPEP 2106.04(a)(2)(I) Mathematical Formulas or Equations - organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014)., MPEP 2106.04(a)(2(III) Mental Processes - a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and See PEG Example 47, Claim 2, “Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion.” “the broadest reasonable interpretation of discretizing in step (b) also encompasses mathematical concepts (e.g., rounding data values) that can be performed mentally. Step (c) requires specific mathematical calculations (a backpropagation algorithm and a gradient descent algorithm) to perform the training of the ANN and therefore encompasses mathematical concepts”);
generating a comparison score between the plurality of historical transaction vectors and the target transaction vector (See MPEP 2106.04(a)(2)(I) Mathematical Formulas or Equations - organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014)., MPEP 2106.04(a)(2(III) Mental Processes - a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and See PEG Example 47, Claim 2, “Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion.” “the broadest reasonable interpretation of discretizing in step (b) also encompasses mathematical concepts (e.g., rounding data values) that can be performed mentally. Step (c) requires specific mathematical calculations (a backpropagation algorithm and a gradient descent algorithm) to perform the training of the ANN and therefore encompasses mathematical concepts”);
selecting a second account identifier of a historical transaction record in the plurality of historical transaction records according to the comparison score (See MPEP 2106.04(a)(2)(I) Mathematical Formulas or Equations - organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014)., MPEP 2106.04(a)(2(III) Mental Processes - a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and See PEG Example 47, Claim 2, “Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion.” “the broadest reasonable interpretation of discretizing in step (b) also encompasses mathematical concepts (e.g., rounding data values) that can be performed mentally. Step (c) requires specific mathematical calculations (a backpropagation algorithm and a gradient descent algorithm) to perform the training of the ANN and therefore encompasses mathematical concepts”);
selecting one of the first account identifier and the second account identifier as a selected account identifier for the target transaction record (See MPEP MPEP 2106.04(a)(2(III) Mental Processes - a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and See PEG Example 47, Claim 2, “Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion.”); and
storing the target transaction record with the selected account identifier ((See MPEP 2106.04(a)(2(III) Mental Processes - a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), and See PEG Example 47, Claim 2, “Under its broadest reasonable interpretation when read in light of the specification, the “analyzing” encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion.”).
Claim 10 (and similarly Claim 1) is directed to a series of steps for selecting account identifiers for transactions, which are mental processes being calculated using mathematical equations. The mere nominal recitation of a server, one or more processors, one or more memories, a server application, transaction model, and general model, does not take the claim out of the mathematical equations and mental processes. Thus, Claim 10 (and similarly Claim 1) recites an abstract idea.
Step 2A – Prong 2: Can the Judicial Exception Recited be integrated into a practical application
Limitations that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
The identified abstract idea of exemplary Claim 10 (and similarly Claim 1) is not integrated into a practical application. The additional elements are: a server, one or more processors, one or more memories, a server application, transaction model, and general model that implements the underlying abstract idea. These additional elements are broadly recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. Claim 10 (and similarly Claim 1) is directed to an abstract idea.
Step 2B – Significantly More Analysis
Claim 10 (and similarly Claim 1) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and in combination, steps a), b), c), etc., do not add significantly more to the exception because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Claim 10 (and similarly Claim 1) is ineligible.
Claim 11 (and similarly Claim 2) recites the abstract idea of mathematical concepts and mental processes. See MPEP 2106.04(a)(2)(I) and 2106.04(a)(2)(III).
Claim 12 (and similarly Claim 3) recites the abstract idea of mathematical concepts and mental processes. See MPEP 2106.04(a)(2)(I) and 2106.04(a)(2)(III).
Claim 13 (and similarly Claim 4) recites the abstract idea of mathematical concepts and mental processes. See MPEP 2106.04(a)(2)(I) and 2106.04(a)(2)(III).
Claim 14(and similarly Claim 5) recites the abstract idea of mathematical concepts and mental processes. See MPEP 2106.04(a)(2)(I) and 2106.04(a)(2)(III).
Claim 15 (and similarly Claim 6) recites the abstract idea of mathematical concepts and mental processes. See MPEP 2106.04(a)(2)(I) and 2106.04(a)(2)(III).
Claim 16 (and similarly Claim 7) recites the abstract idea of mathematical concepts and mental processes. See MPEP 2106.04(a)(2)(I) and 2106.04(a)(2)(III).
Claim 17 (and similarly Claim 8) recites the abstract idea of mathematical concepts and mental processes. See MPEP 2106.04(a)(2)(I) and 2106.04(a)(2)(III).
Claim 18 (and similarly Claim 9) recites the abstract idea of mathematical concepts and mental processes. See MPEP 2106.04(a)(2)(I) and 2106.04(a)(2)(III).
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.
Claim(s) 1-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pat Pub 2021/0173916 “Ortiz”, in view of US Pat Pub 2016/0055427 “Adjaoute”, further in view of US Pat Pub 2021/0027300 “Chetia”.
As per Claims 1 and 10, Ortiz discloses a method and system comprising:
generating, by a transaction model of a general model, a target transaction vector for a target transaction record (Ortiz: [0047] The features can be represented as a floating point latent vector extracted from the raw image data, or a floating point vector generated from an encoder neural network can be adapted to learn a compression of the raw image data into the floating point latent vector defined by the feature set and [0115], train one or more machine learning models using non-specific models as baselines, and then tune them using the recorded sections);
generating, by the general model, a plurality of account vectors for a plurality of accounts (Ortiz: [0047] The features can be represented as a floating point latent vector extracted from the raw image data, or a floating point vector generated from an encoder neural network can be adapted to learn a compression of the raw image data into the floating point latent vector defined by the feature set and [0115], train one or more machine learning models using non-specific models as baselines, and then tune them using the recorded sections);
Ortiz fails to disclose a method and system comprising:
generating a match score between the plurality of account vectors and the target transaction vector;
selecting, by the general model, a first account identifier of an account in the plurality of accounts using the match score;
generating, by the transaction model, a plurality of historical transaction vectors for a plurality of historical transaction records;
generating a comparison score between the plurality of historical transaction vectors and the target transaction vector;
selecting a second account identifier of a historical transaction record in the plurality of historical transaction records according to the comparison score;
selecting one of the first account identifier and the second account identifier as a selected account identifier for the target transaction record; and
storing the target transaction record with the selected account identifier.
Adjaoute teaches a method and system comprising:
selecting, by the general model, a first account identifier of an account in the plurality of accounts using the match score (Adjaoute: [0228]: In every case, embodiments of the present invention include adaptive learning that combines three learning techniques to evolve the artificial intelligence classifiers. First is the automatic creation of profiles, or smart-agents, from historical data, e.g., long-term profiling'. [0229]: 'Each profile for each smart-agent comprises knowledge extracted field-by-field, such as merchant category code (MCC));
generating, by the transaction model, a plurality of historical transaction vectors for a plurality of historical transaction records (Adjaoute: [0228]: In every case, embodiments of the present invention include adaptive learning that combines three learning techniques to evolve the artificial intelligence classifiers. First is the automatic creation of profiles, or smart-agents, from historical data, e.g., long-term profiling'. [0229]: 'Each profile for each smart-agent comprises knowledge extracted field-by-field, such as merchant category code (MCC));
generating a comparison score between the plurality of historical transaction vectors and the target transaction vector (Adjaoute: [0228]: In every case, embodiments of the present invention include adaptive learning that combines three learning techniques to evolve the artificial intelligence classifiers. First is the automatic creation of profiles, or smart-agents, from historical data, e.g., long-term profiling'. [0229]: 'Each profile for each smart-agent comprises knowledge extracted field-by-field, such as merchant category code (MCC));
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ortiz to include presenting account identifier for the record as taught by Adjaoute, when using machine learning to select an account identifier for a transaction record as taught by Ortiz with the motivation of providing advanced training of predictive models (Adjaoute: [0008]).
Ortiz and Adjaoute fail to disclose a method and system comprising:
generating a match score between the plurality of account vectors and the target transaction vector;
selecting a second account identifier of a historical transaction record in the plurality of historical transaction records according to the comparison score;
selecting one of the first account identifier and the second account identifier as a selected account identifier for the target transaction record; and
storing the target transaction record with the selected account identifier.
Chetia teaches a method and system comprising:
generating a match score between the plurality of account vectors and the target transaction vector (Chetia: [0084], transaction service provider system 104 may determine the average of a first payment transaction vector associated with a first payment transaction of the plurality of payment transaction vectors and a second payment transaction vector associated with a second payment transaction of the plurality of payment transaction vectors.);
selecting a second account identifier of a historical transaction record in the plurality of historical transaction records according to the comparison score (Chetia: [0087], receiving instant transaction data associated with an instant payment transaction (e.g., a transaction received at a point in time after the plurality of transactions associated with the transaction data were processed and/or received by the transaction service provider system 104). In such an example, transaction service provider system 104 may determine the account embedding vector associated with the account based on determining that the instant payment transaction satisfies a risk assessment threshold. In such an example, transaction service provider system 104 may compare a value of one or more parameters associated with one or more payment transactions from among the plurality of payment transactions associated with the payment transaction data to a value of a parameter of the instant payment transaction associated with instant payment transaction data. Transaction service provider system 104 may then determine, based on the comparison, that the instant payment transaction satisfies the risk assessment threshold. Where transaction service provider system 104 determines that the instant payment transaction satisfies the risk assessment threshold, transaction service provider system 104 may determine the account embedding vector associated with the account.);
selecting one of the first account identifier and the second account identifier as a selected account identifier for the target transaction record (Chetia: [0087], receiving instant transaction data associated with an instant payment transaction (e.g., a transaction received at a point in time after the plurality of transactions associated with the transaction data were processed and/or received by the transaction service provider system 104). In such an example, transaction service provider system 104 may determine the account embedding vector associated with the account based on determining that the instant payment transaction satisfies a risk assessment threshold. In such an example, transaction service provider system 104 may compare a value of one or more parameters associated with one or more payment transactions from among the plurality of payment transactions associated with the payment transaction data to a value of a parameter of the instant payment transaction associated with instant payment transaction data. Transaction service provider system 104 may then determine, based on the comparison, that the instant payment transaction satisfies the risk assessment threshold. Where transaction service provider system 104 determines that the instant payment transaction satisfies the risk assessment threshold, transaction service provider system 104 may determine the account embedding vector associated with the account.); and
storing the target transaction record with the selected account identifier (Chetia: [0094] transaction service provider system 104 may store the predicted transaction aggregate vectors associated with the predicted payment transactions in a database. In another example, transaction service provider system 104 may store the predicted transaction aggregate vectors associated with the predicted payment transactions in a database).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ortiz and Adjaute to include generating transaction vectors to match to a baseline identifier of a matched account as taught by Chetia, when using machine learning to select an account identifier for a transaction record as taught by Ortiz and Adjaute with the motivation to analyze a relationship between the performance of a unit based on data associated with the unit and one or more known features of the unit (Chetia: [0003]).
As per Claims 2 and 11, Ortiz discloses a method and system, further comprising:
training a comparison model to generate the comparison score from a pair of transaction records using the transaction model from the general model (Ortiz: [0019], [0115]).
As per Claims 3 and 12, Ortiz fails to disclose but Adjaoute teaches a method and system, further comprising:
selecting one of the general model, from a plurality of machine learning models, and a custom model, from the plurality of machine learning models, as a baseline model using information from an entity profile (Adjaoute: [0008] and [0090]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ortiz to include selecting the general model and a custom model as a baseline model using information from a profile as taught by Adjaoute, when using machine learning to select an account identifier for a transaction record as taught by Ortiz with the motivation of providing advanced training of predictive models (Adjaoute: [0008]).
As per Claims 4 and 13, Ortiz discloses a method and system, further comprising:
generating a name embedding vector from the target transaction record using the name embedding model of the general model (Ortiz: [0115], [0147], [0277]); and
generating the target transaction vector from the name embedding vector and the target transaction record using the transaction model of the general model (Ortiz: [0115], [0147], [0277]).
As per Claims 5 and 14, Ortiz discloses a method and system, further comprising:
in response to satisfaction of a threshold by an entity profile, training a custom model, used as a baseline model and linked to the entity profile, to generate account identifiers from transaction records using a name embedding model and an adapter model (Ortiz: [0115]).
As per Claims 6 and 15, Ortiz discloses a method and system, wherein the comparison score is generated by a Siamese network model (Ortiz: [0350]).
As per Claims 7 and 16, Ortiz discloses a method and system, wherein generating the comparison score between the target transaction vector and a historical transaction vector of the plurality of historical transaction vectors comprises:
processing the target transaction vector and the historical transaction vector through a set of neural network layers to generate the comparison score (Ortiz: [0277]-[0278]).
As per Claims 8 and 17, Ortiz discloses a method and system, wherein the comparison score is generated by an in-session model configured to operate during an entity session and executes on a plurality of few-shot examples (Ortiz: [0277]-[0278]).
As per Claims 9 and 18, Ortiz fails to disclose but Adjaoute teaches a method and system, wherein the comparison score is used to select a set of historical transaction records in the plurality of historical transaction records, wherein the set forms a neighborhood of historical transaction records for the target transaction record, and wherein selecting the first account identifier and the second account identifier is based on the neighborhood (Adjaoute: [0228]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify Ortiz to include presenting account identifier for the record as taught by Adjaoute, when using machine learning to select an account identifier for a transaction record as taught by Ortiz with the motivation of providing advanced training of predictive models (Adjaoute: [0008]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REVA R MOORE whose telephone number is (571)270-7942. The examiner can normally be reached M-Th: 9:00-6:00.
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/REVA R MOORE/ Examiner, Art Unit 3627
/FAHD A OBEID/ Supervisory Patent Examiner, Art Unit 3627
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