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 .
Summary
This Final Office Action in response to the communication received on January 23, 2026.
Claims 6 and 14 have been amended.
Claims 1-5 have been withdrawn
Claims 10-11 and 18-19 have been cancelled.
Claims 1-9, 12-17, and 20-24 are pending.
The effective filing date of the claimed invention is June 26, 2020.
Response to Amendment
Amendments to Claims 6 and 14 are acknowledged. In response to the PTAB Decision dated May 28, 2025, the 35 USC 103 rejection of Claims 6-9, 12-17, and 20-24 is overcome, and the 35 USC 101 rejection is introduced.
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 6-9, 12-17, and 20-24 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 6-9, 12-13, and 23-24 are processes and Claims 14-17 and 20-22 are machines). Accordingly, step 1 is satisfied.
Step 2A – Prong 1: was there a Judicial Exception Recited
Claim 6 (and similarly Claims 14) 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 method for training a machine learning model, comprising:
receiving transaction categorization data comprising a plurality of transaction records of a plurality of users categorized into a plurality of accounts of the plurality of users (See MPEP 2106.04(a)(2)(III) mental processes, See Guidance, 84 Fed. Reg. at 52; see also Elec Power Grp., LLC v. Alston S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016), characterizing collecting information, analyzing information by seps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas);
determining a set of unlabeled user transaction records associated with a user (See MPEP 2106.04(a)(2)(III) mental processes, See Guidance, 84 Fed. Reg. at 52; see also Elec Power Grp., LLC v. Alston S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016), characterizing collecting information, analyzing information by seps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas);
determining popularities of vendors in the set of unlabeled user transaction records associated with the user based on occurrences of the vendors in the plurality of transaction records of the plurality of users (See MPEP 2106.04(a)(2)(III) mental processes, See Guidance, 84 Fed. Reg. at 52; see also Elec Power Grp., LLC v. Alston S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016), characterizing collecting information, analyzing information by seps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas);
determining categorization consistencies of the vendors in the transaction categorization data based on, for each respective vendor of the vendors, how frequently a respective subset of the transaction categorization data comprising all respective transaction records of the plurality of transaction records that are associated with the respective vendor indicates that one or more users of the plurality of users categorized multiple transaction records in the respective subset of the transaction categorization data into a same account of the plurality of accounts (See MPEP 2106.04(a)(2)(III) mental processes, See Guidance, 84 Fed. Reg. at 52; see also Elec Power Grp., LLC v. Alston S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016), characterizing collecting information, analyzing information by seps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas);
determining confidence estimates for the categorization consistencies of the vendors based on numbers of transaction records used to determine the categorization consistencies of the vendors (See MPEP 2106.04(a)(2)(III) mental processes, See Guidance, 84 Fed. Reg. at 52; see also Elec Power Grp., LLC v. Alston S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016), characterizing collecting information, analyzing information by seps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas);
selecting a first transaction record of the set of unlabeled user transaction records to display to the user for categorization based on a transaction prioritization scheme that prioritizes transactions according to relevance for model training, wherein the transaction prioritization scheme is based on the popularities of the vendors, the categorization consistencies of the vendors, and the confidence estimates for the categorization consistencies of the vendors (See MPEP 2106.04(a)(2)(III) mental processes, See Guidance, 84 Fed. Reg. at 52; see also Elec Power Grp., LLC v. Alston S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016), characterizing collecting information, analyzing information by seps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas);
providing one or more inputs to a machine learning model based on the first transaction record (See MPEP 2106.04(a)(2)(III) mental processes, See Guidance, 84 Fed. Reg. at 52; see also Elec Power Grp., LLC v. Alston S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016), characterizing collecting information, analyzing information by seps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas);
determining a recommended account for the first transaction record based on one or more outputs received from the machine learning model in response to the one or more inputs (See MPEP 2106.04(a)(2)(III) mental processes, See Guidance, 84 Fed. Reg. at 52; see also Elec Power Grp., LLC v. Alston S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016), characterizing collecting information, analyzing information by seps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas);
displaying the first transaction record and the recommended account (See MPEP 2106.04(a)(2)(III) mental processes, See Guidance, 84 Fed. Reg. at 52; see also Elec Power Grp., LLC v. Alston S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016), characterizing collecting information, analyzing information by seps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas);
receiving, in response to the displaying, a categorization of the first transaction record into a given account of a set of accounts of the user (See MPEP 2106.04(a)(2)(III) mental processes, See Guidance, 84 Fed. Reg. at 52; see also Elec Power Grp., LLC v. Alston S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016), characterizing collecting information, analyzing information by seps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas);
generating training data for the machine learning model based on the categorization of the first transaction record into the given account (See MPEP 2106.04(a)(2)(III) mental processes, See Guidance, 84 Fed. Reg. at 52; see also Elec Power Grp., LLC v. Alston S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016), characterizing collecting information, analyzing information by seps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas); and
training the machine learning model using the generated training data, wherein the trained machine learning model is used to automatically categorize subsequent transactions (See MPEP 2106.04(a)(2)(III) mental processes, See Guidance, 84 Fed. Reg. at 52; see also Elec Power Grp., LLC v. Alston S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016), characterizing collecting information, analyzing information by seps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas, See also PEG Example 47, Claim 2, the processing of continuous training data may be practically performed in the human mind using observation, evaluation, judgment, and opinion, The limitations “(a) receiving, at a computer, continuous training data” and “(f) outputting the anomaly data from the trained ANN” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.).
Claim 6 (and similarly Claims 14) recites a way of categorizing transaction data by vendor type to create filtered categorized data constituted by collecting information, analyzing and presenting the results, e.g., observation, evaluation, judgment, opinion, which characterize mental processes. The mere nominal recitation of a machine learning model, a display, processors, and memory does not take the claim out of the method of organizing human interactions. Thus, Claim 6 (and similarly Claims 14) 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 6 (and similarly Claims 14) is not integrated into a practical application. The additional elements are: a machine learning model, a display, processors, and memory 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 6 (and similarly Claims 14) is directed to an abstract idea.
Step 2B – Significantly More Analysis
Claim 6 (and similarly Claims 14) 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) receive transaction categorization data, b) determine a set of unlabeled user transaction records, c) determine popularities of vendors, e) determine confidence estimates, f) select a first transaction record to display to the user for categorization, g) provide inputs to the machine learning model, h) display the first transaction record and the recommended account, i) receive a categorization, j) generate training data for the machine learning model, and k) train the machine learning model using the generated training data, 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 6 (and similarly Claims 14) is ineligible.
Claim 7 (and similarly Claim 15) recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 8 (and similarly Claim 16) recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 9 (and similarly Claim 17) recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 12 (and similarly Claim 20) recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 13 recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 21 (and similarly Claim 23) recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Claim 22 (and similarly Claim 24) recites the abstract idea of mathematical concepts. See MPEP 2106.04(a)(2)(I).
Prior Art
The prior arts of record fail to teach the overall combination of Claims 6-9, 12-17, and 20-24. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Exemplary claim 1 recites the following:
A method for training a machine learning model, comprising:
receiving transaction categorization data comprising a plurality of transaction records of a plurality of users categorized into a plurality of accounts of the plurality of users;
determining a set of unlabeled user transaction records associated with a user;
determining popularities of vendors in the set of unlabeled user transaction records associated with the user based on occurrences of the vendors in the plurality of transaction records of the plurality of users;
determining categorization consistencies of the vendors in the transaction categorization data based on, for each respective vendor of the vendors, how frequently a respective subset of the transaction categorization data comprising all respective transaction records of the plurality of transaction records that are associated with the respective vendor indicates that one or more users of the plurality of users categorized multiple transaction records in the respective subset of the transaction categorization data into a same account of the plurality of accounts;
determining confidence estimates for the categorization consistencies of the vendors based on numbers of transaction records used to determine the categorization consistencies of the vendors;
selecting a first transaction record of the set of unlabeled user transaction records to display to the user for categorization based on a transaction prioritization scheme that prioritizes transactions according to relevance for model training, wherein the transaction prioritization scheme is based on the popularities of the vendors, the categorization consistencies of the vendors, and the confidence estimates for the categorization consistencies of the vendors;
providing one or more inputs to a machine learning model based on the first transaction record;
determining a recommended account for the first transaction record based on one or more outputs received from the machine learning model in response to the one or more inputs;
displaying the first transaction record and the recommended account;
receiving, in response to the displaying, a categorization of the first transaction record into a given account of a set of accounts of the user;
generating training data for the machine learning model based on the categorization of the first transaction record into the given account; and
training the machine learning model using the generated training data, wherein the trained machine learning model is used to automatically categorize subsequent transactions. (Emphasis added to highlight features that distinguish over the prior art).
US Pat Pub 2019/0012733 “Gorman” discloses reconciling a transaction against data in a database to identify the transaction parameters based on text descriptors provided for the transaction. Gorman fails to disclose selecting a first transaction record of the set of unlabeled user transaction records to display to the user for categorization based on a transaction prioritization scheme that prioritizes transactions according to relevance for model training, wherein the transaction prioritization scheme is based on the popularities of the vendors, the categorization consistencies of the vendors, and the confidence estimates for the categorization consistencies of the vendors.
US Pat Pub 2019/0318031 “Slim” teaches techniques for displaying reduced data sets based on pre-classification of a larger data set. Slim fails to teach selecting a first transaction record of the set of unlabeled user transaction records to display to the user for categorization based on a transaction prioritization scheme that prioritizes transactions according to relevance for model training, wherein the transaction prioritization scheme is based on the popularities of the vendors, the categorization consistencies of the vendors, and the confidence estimates for the categorization consistencies of the vendors.
US Pat Pub 2019/0205993 “Rodriguez” teaches the centralization processing of transaction and payment data to categorize transaction data across different accounts and systems. Rodriguez fails to teach selecting a first transaction record of the set of unlabeled user transaction records to display to the user for categorization based on a transaction prioritization scheme that prioritizes transactions according to relevance for model training, wherein the transaction prioritization scheme is based on the popularities of the vendors, the categorization consistencies of the vendors, and the confidence estimates for the categorization consistencies of the vendors.
US Pat Pub 2009/0222364 “McGlynn” teaches attribute-based transaction categorization that utilizes transaction designation attributes other than or in addition to a payee name to provide reduced user effort and improved accuracy in the categorization of transactions. McGlynn fails to teach selecting a first transaction record of the set of unlabeled user transaction records to display to the user for categorization based on a transaction prioritization scheme that prioritizes transactions according to relevance for model training, wherein the transaction prioritization scheme is based on the popularities of the vendors, the categorization consistencies of the vendors, and the confidence estimates for the categorization consistencies of the vendors.
Response to Arguments
Applicant's arguments filed January 23, 2026 have been fully considered but they are not persuasive.
35 USC 101
Applicant argues that the claimed method and systems provide a technical solution to problems arising in the field of machine learning model training by selecting training data that has inferential value for the training process and filtering redundant and less-relevant data from the training process. As a result a model may be effectively trained using a much smaller set of training data in a much shorter and more efficient training processes. Applicant further cites similarities to PEG Example 39. While Example 39 does deal with training a neural network, it is in the context of electronic facial detection. A more relevant PEG Example for this application is Example 47, Claim 2. Claim 2 recites limitation b) discretizing the continuous training data to generate input data, and c) training the ANN based on the input data and a selected training algorithm to generate a trained artificial neural network. The analysis of Example 47, Claim 2 is found to be similarly relevant to the instant claims.
Under Step 2A, Prong One, Step (b) of Example 47, Claim 2 recites discretizing continuous training data to generate input data by processes including rounding, binning, or clustering continuous data, which may be practically performed in the human mind using 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.
Under Step 2A, Prong Two, limitations b) and c) are recited as being performed by a computer. Independent claim 6 of this application does not specifically state that the actions are being performed by a computer or processor. Claim 14 does recite that the steps are being performed by a processor. For the sake of argument, we will assume that both independent claims are being performed by a computing device. Under this rationale the claims are found that the computer is recited at a high level of generality. The computer is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f), and used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Under Step 2B, the recitations of steps of receiving and filtering unlabeled user transaction records to be selected for providing as inputs to a machine learning model and training a machine learning model are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a computer to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Additionally, the purported improvements of “prioritizing” transaction data are not technical in nature, and are therefore improvements to the abstract idea.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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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