DETAILED ACTION
This is a non-final office action in response to claims on 10/14/2024. Claims 1-20 are pending. The effective filling date is 07/23/2024.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/19/2025 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1-Claims 1-19 are directed to a system ,which is patent eligible subject matter. Claim 20 is directed to a non-transitory computer readable storage medium, which is an article of manufacture, a patent eligible subject matter. Claims 1-20 pass Step 1.
Step 2A, Prong 1-The independent claim 1, and similarly claim 19 and 20, recite:
A system configured to provide data pertaining to accounting data, comprising a processing circuitry, the processing circuitry (additional elements analyzed in Step 2A, prong 2) configured to perform the following method:
a. obtain a data item indicative of an invoice associated with a business entity (obtaining data is collecting information, and is a mental process under MPEP 2106.04(a)(2)(III)(A) 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));
b. predict at least one time of payment associated with the data item, based on at least on a payment-due time associated with the data item, the prediction utilizing at least one machine learning model trained to perform the prediction based at least on times of payment of invoices associated with at least one business entity, and on payment-due times associated with the invoices (making a prediction using data, is analyzing data, which is grouped as a mental process under MPEP 2106.04(a)(2)(III)(A) 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); additionally, a prediction model, that uses a calculation can also be grouped as a mathematical relationship under MPEP 2106.04(a)(2)(I)(A) 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).) ; and
c. provide the predicted at least one time of payment (providing the results of a calculation, which is the results of analysis of data, is grouped as a mental process under MPEP 2106.04(a)(2)(III)(A) 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)).
Step 2A, Prong 2- The additional elements of independent claims 1, and similarly claim 19 and 20, include a processing circuitry, machine learning model, and a non-transitory computer readable storage medium. This judicial exception is not integrated into a practical application because the claims invoke computer as tools to perform an existing process, when the circuitry and storage medium is used to perform the process or obtaining information, analyzing information and displaying the results of the information’s, the circuitry is being used as the tool to perform. Under MPEP 2106.05(f)(2) when the claim invoke the computer as a tool to execute the abstract idea it for not integrate the judicial exception into a practical application (TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.).
A machine learning model is the name assigned to the algorithmic way to make a prediction, but when a model is described with generality, it falls under the judicial exception of merely adding the words “apply it” to the judicial exception under MPEP 2106.05(f)(3).
Step 2B-The independent claim 1, and similarly claim 19 and 20, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the elements are described generally as tools to perform the abstract idea under MPEP 2106.05(f).
Dependent Claims
Claims 2-18, further describe the source of information, and additional analysis steps by the machine learning model. The additional limitations remain mental processes, as they describe what information is being transferred and analyzed, and do not provide details that would be more than the idea of exchanging information. There are not additional elements that would integrate the abstract idea into a practical application or provide significantly more under MPEP 2106.05(f) as previously described.
Claim Objections
Claim 8 is objected to because of the following informalities: the claim ends in a semicolon, it needs to end in a period. Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5, 9-14, 16-18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2024/0354786 A1 Kunwar et al. (hereinafter Kunwar).
Regarding claim 1, Kunwar teaches a system configured to provide data pertaining to accounting data, comprising a processing circuitry (Kunwar [0033] computer system includes circuitry), the processing circuitry configured to perform the following method:
a. obtain a data item indicative of an invoice associated with a business entity (Kunwar [0010] data receiver module configured to receive invoice data; [0039] external database include invoices);
b. predict at least one time of payment associated with the data item, based on at least on a payment-due time associated with the data item, the prediction utilizing at least one machine learning model trained to perform the prediction based at least on times of payment of invoices associated with at least one business entity, and on payment-due times associated with the invoices (Kunwar [0015] a date prediction module that uses financial transactions to predict payment, the prediction is a machine learning (ML) model; [0016] estimate a date); and
c. provide the predicted at least one time of payment (Kunwar [0017] plurality of modules includes a data output module configured to output the payment pattern).
Regarding claim 2, Kunwar teaches the system of claim 1, the method further comprising:
d. perform the steps (a) to (c) in respect of at least one additional data item, the at least one additional first record constituting the data item (Kunwar [0041] performing a granularity application on the data to output a new clusters based on the granularity; Fig. 8).
Regarding claim 3, Kunwar teaches the system of claim 1, wherein the at least one machine learning model is trained to perform the prediction based at least on correspondence, of the invoices associated with the at least the business entity, to other data items indicative of payment transactions associated with at least the business entity, which include at least times of transaction payment of the other data items (Kunwar [0013] the granularity calculations are used to create clusters, and use correspondence and pattern generation modules to predict payments; Fig.8).
Regarding claim 5, Kunwar teaches the system of claim 3, wherein the other data items indicative of payment transactions comprise enriched data items, wherein the performing of the enrichment utilizes at least other one machine learning model trained to identify correspondence, of the other data items, to the invoices associated with at least the business entity, the enrichment thereby determining at least one of: the business entity; and a financial classification category associated with the other data items (Kunwar [0013] the granularity calculations are used to create clusters, and use correspondence and pattern generation modules to predict payments; Fig.8; [0071-0072] the buckets are created by grouping, and one grouping can be category or company code, ie business entity).
Regarding claim 9, Kunwar teaches the system of claim 1, wherein the predicting, of the at least one time of payment associated with the data item, comprises weighting the prediction based on a relative recency of corresponding invoices of the invoices (Kunwar [0093] the prediction is determined based on weighting based on prior decision trees with prior invoices).
Regarding claim 10, Kunwar teaches the system of claim 1, wherein the predicting of the at least one time of payment associated with the data item, comprises weighting the prediction based on an invoice amount of the corresponding invoices of the invoices (Kunwar [0093] the prediction is determined based on weighting based on prior decision trees with prior invoices).
Regarding claim 11, Kunwar teaches the system of claim 1, wherein the predicting of the at least one time of payment associated with the data item, is based at least on an invoice amount of the data item (Kunwar [0010] the invoice data can include invoice amount, which is the input to ML to make a prediction).
Regarding claim 12, Kunwar teaches the system of claim 1, wherein the method further comprising:
e. predict at least one payment amount parameter associated with the data item, utilizing the at last one machine learning model (Kunwar [0055] pattern selection module is able to calculate the sum total amount); and
f. provide the predicted payment amount parameter (Kunwar [0055] the prediction is shown in Table 3).
Regarding claim 13, Kunwar teaches the system of claim 1, wherein the at least one machine learning model is trained to perform the prediction based at least on business entity-specific times of payment of invoices associated with the business entity (Kunwar [0041] the grouping may be based on the company code or business unit level, meaning the grouping can be based on a single business entity).
Regarding claim 14, Kunwar teaches the system of claim 1, wherein the at least one machine learning model is trained to perform the prediction based at least on times of payment of invoices associated with a plurality of business entities (Kunwar [0041] the grouping may also be based on amount, where a plurality of business (payer) that have a high invoice amount are compared and used to create a prediction).
Regarding claim 16, Kunwar teaches the system of claim 1, wherein the system further configured to: g. predict at least one total payment amount in at least one time period, wherein the at least one total payment amount is based on predictions of payment amounts in the time period for a plurality of data items associated with a payment-receiving business entity (Kunwar [0054] date-shift-pattern shows the period where invoices were cleared).
Regarding claim 17, Kunwar teaches the system of claim 16, wherein the method further comprising: h. displaying, on a user device, at least the total payment amount (Kunwar [0114] the machine learning computing system displays the transaction payment patterns).
Regarding claim 18, Kunwar teaches the system of claim 1, wherein the system further configured to re-train the at least one machine learning model, based on an error in the prediction (Kunwar [0065] retraining machine learning model based on changes in payment plans; [0097-0099] errors are used to make changes in payment plans; [0112-0113]).
Regarding claim 20, Kunwar teaches a non-transitory computer readable storage medium tangibly embodying a program of instructions that, when executed by a processing circuitry of a system (Kunwar [0033] computer system includes circuitry), cause the processing circuitry to perform a method of system configured to provide data pertaining to accounting data, the method comprising:
a. obtain a data item indicative of an invoice associated with a business entity (Kunwar [0010] data receiver module configured to receive invoice data; [0039] external database include invoices);
b. predict at least one time of payment associated with the data item, based on at least on a payment-due time associated with the data item, the prediction utilizing at least one machine learning model trained to perform the prediction based at least on times of payment of invoices associated with at least one business entity, and on payment-due times associated with the invoices (Kunwar [0015] a date prediction module that uses financial transactions to predict payment, the prediction is a machine learning (ML) model; [0016] estimate a date); and
c. provide the predicted at least one time of payment (Kunwar [0017] plurality of modules includes a data output module configured to output the payment pattern).
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.
Claims 4, 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Kunwar in view of US 2025/0217385 A1 Kunwar et al. (hereinafter Kunwar'385).
Regarding claim 4, Kunwar fails to explicitly disclose the system of claim 3. Kunwar fails to explicitly disclose wherein the invoices are obtained from at least one source, wherein the other data items are based on information obtained from at least one other source, distinct from the at least one source. Kunwar’385 is in the field of machine learning transaction identification (Kunwar’385 Abstract, machine learning transaction planning) and teaches wherein the invoices are obtained from at least one source, wherein the other data items are based on information obtained from at least one other source, distinct from the at least one source (Kunwar’385 [0012] different transaction datasets includes ERP platforms, purchases, sales, payments, investments). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the analysis of invoices of Kunwar with multiple types of data sets as taught by Kunwar’385. The motivation for doing so would be to use machine learning to identify important data points from a variety of different documents (Kunwar’385 [0009] identification of specific transaction elements of documents).
Regarding claim 6, Kunwar teaches the system of claim 4. Kunwar fails to explicitly disclose wherein the at least one source is a system associated with one is one of a general ledger and an enterprise resource planning (ERP) system. Kunwar’385 teaches wherein the at least one source is a system associated with one is one of a general ledger and an enterprise resource planning (ERP) system (Kunwar’385 [0012] different transaction datasets includes ERP platforms, purchases, sales, payments, investments). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the analysis of invoices of Kunwar with multiple types of data sets as taught by Kunwar’385. The motivation for doing so would be to use machine learning to identify important data points from a variety of different documents (Kunwar’385 [0009] identification of specific transaction elements of documents).
Regarding claim 7, Kunwar teaches the system of claim 4. Kunwar fails to explicitly disclose wherein the at least one other source is a system associated with one of: a bank, an investment company, a payment service provider (PSP). Kunwar’385 teaches wherein the at least one other source is a system associated with one of: a bank, an investment company, a payment service provider (PSP) (Kunwar’385 [0012] different transaction datasets includes ERP platforms, purchases, sales, payments, investments). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the analysis of invoices of Kunwar with multiple types of data sets as taught by Kunwar’385. The motivation for doing so would be to use machine learning to identify important data points from a variety of different documents (Kunwar’385 [0009] identification of specific transaction elements of documents).
Allowable Subject Matter
Claims objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 8: specifically identifying predicted delays relative to the specific due date not taught in prior art.
8. The system of claim 1, wherein the at least one machine learning model is configured to identify at least one of the following: i. delays in the times of payment of the invoices, relative to the payment-due times associated with the invoices; ii. dates within a month of the times of payment of the invoices.
Claim 15: the prior art fails to teach, either alone or in combination a machine learning model to perform all seven functions.
15. The system of claim 1, wherein the at least one machine learning model comprises a plurality of machine learning models that perform the following functions: i. predict a payment amount parameter associated with the data item; ii. predict a delay associated the at least one time of payment, relative to the payment-due times associated with the data item; iii. predict at least one date within a month of the at least one time of payment; iv. determine aging weights utilized to weight the prediction based on a relative recency of corresponding invoices of the invoices; v. predict the delay associated the at least one time of payment, based at least on an invoice amount of the data item; vi. predict the delay associated the at least one time of payment, based at least on at least one external factor, the at least one external factor not derived from the invoices; and vii. predict the at least one payment amount parameter, based at least on the at least one external factor.
Claim 19: The combination of enrichment of records and determination of risk metrics to compare is not found in prior art.
19. A system to provide data pertaining to accounting data, comprising a processing circuitry, the processing circuitry configured to perform the following method: a. obtain, from at least one first source, a first record indicative of an actual financial transaction, paid via the first source; b. perform enrichment on the first record, thereby determining at least one of: a counterparty associated with the first record, the counterparty being indicative of the business; and a financial classification category associated with the first record, wherein the performing of the enrichment utilizes at least one other machine learning model trained to identify correspondence, of first records indicative of actual financial transactions associated with a corresponding business entity, to second data, wherein the second data comprise second records indicative of accounting information associated with the corresponding business entity; c. derive an enriched first record, based on the enrichment; d. identify at least one potentially matching second record, having a potential match with the enriched first record; e. repeat said step (d) with respect of a plurality of first records and a plurality of second records, thereby deriving a plurality of enriched first records and a plurality of corresponding potentially matching second records; f. obtain a data item indicative of an invoice associated with a receiving business entity and a payor business entity of a plurality of payor business entities; g. identify a sub-set of the plurality of enriched first records, where the subset is/are indicative of a payment to a receiving business entity; h. identify a plurality of payor business entities associated with the sub-set of the plurality of enriched first records; i. determine a risk metric, the risk metric being indicative of a concentration of income, to be paid the receiving business entity, in a sub-set of payor business entities, the risk metric being calculated by the following formula: (Sum_1 + …. + Sum_i + … + Sum_N) ^ 2 / ((Sum_1) ^ 2 + …. + Sum_i ^ 2 + … + Sum_N) ^ 2), wherein: N = a number of the plurality of payor business entities; Sum_1 = Sum of payment amounts associated with first enriched first records, of the sub-set of the plurality of enriched first records, that are associated with payment by payor business entity 1; Sum_i = Sum of payment amounts associated with i-th enriched first records, of the sub-set of the plurality, that are associated with payment by payor business entity i, wherein = 1 to N; and Sum_N = Sum of payment amounts associated with N-th enriched first records, of the sub-set of the plurality, that are associated with payment by payor business entity N, wherein: (Sum_1 + …. + Sum_i + … + Sum_N) = Sum of payment amounts associated with the sub-set of the plurality.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2025/0156939 A1 Wang et al. teaches predictive management of account balances (Abstract).
Conclusion
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/JESSICA E SULLIVAN/ Examiner, Art Unit 3627
/FAHD A OBEID/ Supervisory Patent Examiner, Art Unit 3627