DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 27 January 2026 has been entered.
The following is a non-final office action in response to the application filed 27 January 2026.
Applicant’s amendments to claims 1, 11, and 20 cancellation of Claims 7 and 17 have been received and are acknowledged. Claims 2-3, 6, 8, 12-13, 16, 18, were previously cancelled.
Claims 1, 4-5, 9-11, 14-15, 19, and 20-26 have been examined and are pending.
Response to Arguments
Applicant's arguments filed 27 January 2026 have been fully considered but they are not persuasive.
With regard to the rejections under 35 USC 101, Applicant argues: (1) Applicant argues that the previous Office Action “ fails to consider any technical problem addressed by the instant application, how a solution to the problem is accomplished, or that the claim is directed to a particular solution to a problem/particular way to achieve a desired outcome…” (Applicant’s response, 14) Referencing the Specification [2, 25, 121] , Applicant asserts that the recited features of Claim 1 solve the described technical problems and integrate the alleged abstract idea into a practical application and “achieves a particular desired outcome… improving user experiences with enterprises…”. (Applicant’s response, 14 -15) (3) Applicant further argues the claimed features “go beyond routine or conventional components configured in a known manner, … recite elements which amount to significantly more than any alleged judicial exception…relating to non-generic computer operations (e.g., triggering a lock on the entry to the defined window based on an update to a data entry), a machine learning model trained to generate simulations of a plurality of orders for the dataset based at least on at least some of the historical transactions, and the utilization of various circuits within an API gateway circuit. These are not routine or conventional components, when viewed individually or as an ordered combination, …recite a specific combination of features which achieve a technical effect and amount to significantly more than any alleged judicial exception….” (Applicant’s response,15)
Examiner respectfully disagrees. As noted in the rejection previously and below the recited claims do not amount to a “practical application” or “significantly more” than the abstract idea. The ‘transformation” of data to other data is at most an improvement to the abstract idea (i.e. “optimized schedule” as previously argued; the applying of a “the invoice dataset” to a machine learning model to “generate” user interface with a calendar view and a heat map). The recited “triggered lock’ is a data input indicating a fixed data point. As noted in the rejection below no improvement to computer technology is recited; rather the claims are directed to “apply it” using generic computing elements recited at a high level of generality (See MPEP 2106.05 (f)) As noted in the rejection below the computing elements recited are claimed at a high level of generality (See Specification, [35], general purpose single – or multi-chip processor, [125] general purpose processor…[163] any suitable computing system or device … network.. [183-184] general purpose processors, [125] general purpose computing devices… ) As such Applicant’s arguments are not persuasive (Applicant’s arguments 1, 2 and 3).
The rejections under 35 USC 102 and 35 USC 103 were previously withdrawn in the Final Office Action of 10/27/2025.
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, 4-5, 9-11, 14-15, 19, and 20-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. ____ (2014).
The claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In the instant case, the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
(1) In the instant case, the claims are directed towards a method, non-transitory computer readable medium, and the system of organizing payment schedules using a graphical user interfaces generated based on user inputs. In the instant case, Claims 1, 4-5, 9-10 are directed to a process. Claims 11,14-15, 19 and 26 are directed to a system. Claims 20-25 are directed to a non-transitory computer readable medium.
(2a) Prong 1: Organizing payment schedules is categorized in/akin to the abstract idea subject matter grouping of: methods of organizing human activity [organizing human activity (commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)]. As such, the claims include an abstract idea.
The specific limitations of the invention are (a) identified to encompass the abstract idea include:
Claims 1, 11, and 20
(Currently Amended) A method comprising:
establishing, …, a connection… the connection associated with an entity having one or more first accounts with the first … and a second account with the application of the second …;
receiving, …, …, a dataset corresponding to the second account, the dataset comprising a plurality of data entries corresponding to respective invoices, each data entry of the plurality of data entries comprising an amount, due date, and one or more trade terms associated with payment of the invoice;
retrieving, …, from one or more data stores of the first…, account data corresponding to the one or more first accounts with the first…, the account data including accounts receivable information relating to historical transactions with the one or more first accounts;
applying, …, the dataset and the account data as first inputs to a machine learning model trained to generate simulations of a plurality of orders for the dataset, based at least on at least some of the historical transactions, each simulation associated with a respective score
selecting, …, a first order of the plurality of orders for the dataset, based on a score of the first order being a maximum of the respective scores for the plurality of orders;
generating, …, a first user…including a calendar view showing each data entry of the dataset over a time period ordered according to the first order, the calendar view including a heat map representing color coded windows over the time period representative of a simulated balance over the time period;
receiving, …, an update to move an entry of the plurality of entries to a defined window within the time period, the update triggering a lock on the entry to the defined window;
applying, … the dataset and the account data, including an indication of the lock to the defined window, as second inputs to the machine learning model trained to generate updated simulations of the plurality of orders for the dataset based on the lock of the entry to the defined window, each updated simulation associated with a respective updated score;
selecting, …, a second order of the plurality of orders for the dataset, based on an updated score of the second order being a maximum of the respective updated scores for the plurality of orders;and
generating…, a second user interface including the calendar view showing each data entry of the dataset over the time period ordered according to the second order, the calendar view including an updated heatmap,
…, …, an indication of a first interaction with the second user interface comprising a drag-and-drop of an … corresponding to at least one data entry of the plurality of data entries from a first date box to a second date box;
responsive to the first interaction with the second user interface, …, by the one or more processors, a time period within the calendar view according to a date associated with the second date box; and
responsive to a second interaction with the second user interface to initiate payment according to the second order, initiating, …, one or more payments for the invoice at the date corresponding to the second date box according to the second order.
11. (Currently Amended) A first … comprising:
…:
establish a connection … the connection associated with an entity having one or more first accounts with the first computing system and a second account with the application of the second computing system;
receive, …, a dataset corresponding to the second account, the dataset comprising a plurality of data entries corresponding to respective invoices, each data entry of the plurality of data entries comprising an amount, due date, and one or more trade terms associated with payment of the invoice;
retrieve, …, account data corresponding to the one or more first accounts with the first …, the account data including accounts receivable information relating to historical transactions with the one or more first accounts;
apply the dataset and the account data as inputs to a machine learning model trained to generate simulations of a plurality of orders for the dataset, based at least on at least some of the historical transactions, each simulation associated with a respective score
select a first order of the plurality of orders for the dataset, based on a score of the first order being a maximum of the respective scores for the plurality of orders;
generate a first …including a calendar view showing each data entry of the dataset over a time period ordered according to the first order, the calendar view including a heat map representing color coded windows over the time period representative of a simulated balance over the time period;
…, … an update to move an entry of the plurality of entries to a defined window within the time period, the update triggering a lock on the entry to the defined window;
apply the dataset and the account data, including an indication of the lock to the defined window, as second inputs to the machine learning model trained to generate updated simulations of the plurality of orders for the dataset based on the lock of the entry to the defined window, each updated simulation associated with a respective updated score;
select a second order of the plurality of orders for the dataset, based on an updated score of the second order being a maximum of the respective updated scores for the plurality of orders;and
generate a second… including the calendar view showing each data entry of the dataset over the time period ordered according to the second order, the calendar view including an updated heatmap,
… an indication of a first interaction with the second user interface comprising a drag-and-drop of an… corresponding to at least one data entry of the plurality of data entries from a first date box to a second date box;
responsive to the first interaction with the second user interface, … a time period within the calendar view according to a date associated with the second date box; and
responsive to a second interaction with the second user interface to initiate payment according to the second order, initiate, via the first … one or more payments for the invoice at the date corresponding to the second date box according to the second order.
20. (Currently Amended) A … to:
establish a connection …, the connection associated with an entity having one or more first accounts with the first … and a second account with the application of the second …;
…, …, a dataset corresponding to the second account, the dataset comprising a plurality of data entries corresponding to respective invoices, each data entry of the plurality of data entries comprising an amount, due date, and one or more trade terms associated with payment of the invoice;
… …, account data corresponding to the one or more first accounts with the first computing system, the account data including accounts receivable information relating to historical transactions with the one or more first accounts;
apply the dataset and the account data as inputs to a machine learning model trained to generate simulations of a plurality of orders for the dataset, based at least on at least some of the historical transactions, each simulation associated with a respective score
select a first order of the plurality of orders for the dataset, based on a score of the first order being a maximum of the respective scores for the plurality of orders;
generate a first …including a calendar view showing each data entry of the dataset over a time period ordered according to the first order, the calendar view including a heat map representing color coded windows over the time period representative of a simulated balance over the time period optimized order for the dataset;
…, from … an update to move an entry of the plurality of entries to a defined window within the time period, the update triggering a lock on the entry to the defined window;
apply the dataset and the account data, including an indication of the lock to the defined window, as second inputs to the machine learning model trained to generate updated simulations of the plurality of orders for the dataset based on the lock of the entry to the defined window, each updated simulation associated with a respective updated score;
select a second order of the plurality of orders for the dataset, based on an updated score of the second order being a maximum of the respective updated scores for the plurality of orders;and
generate a second …including the calendar view showing each data entry of the dataset over the time period ordered according to the second order, the calendar view including an updated heatmap,
… an indication of a first interaction with the second user interface comprising a drag-and-drop of …. corresponding to at least one data entry of the plurality of data entries from a first date box to a second date box;
responsive to the first interaction with the second user interface, … a time period within the calendar view according to a date associated with the second date box; and
responsive to a second interaction with the second user interface to initiate payment according to the second order, initiate, …, one or more payments for the invoice at the date corresponding to the second date box according to the second order..
As stated above, this abstract idea falls into the (b) subject matter grouping of: methods of organizing human activity .
Prong 2: When considered individually and in combination, the instant claims are do not integrate the exception into a practical application because the steps of establishing…; applying…; selecting… generating…; applying…. selecting…generating…intiaiting….do not apply, rely on, or use the judicial exception in a manner that that imposes a meaningful limitation on the judicial exception (i.e. the abstract idea).
The instant recited claims including additional elements (i.e. receiving…retrieving...receiving…receiving… updating..) do not improve the functioning of the computer or improve another technology or technical field nor do they recite meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The limitations merely recite: “apply it” (or an equivalent) or merely include instructions to implement an abstract idea on a computer or merely uses a computer a as tool to perform an abstract idea or generally link the use of the judicial exception to a particular technological environment or field of use (See MPEP 2106.05 (f) and (g))
(2b) In the instant case, the claims are directed towards a method, non-transitory computer readable medium, and the system of organizing payment schedules using a graphical user interfaces generated based on user inputs. In the instant case, Claims 1, 4-5, 9-10 are directed to a process. Claims 11,14-15, 19 and 26 are directed to a system. Claims 20-25 are directed to a non-transitory computer readable medium.
Additionally, the claims (independent and dependent) do not include additional elements that individually or in combination are sufficient to amount to significantly more than the judicial exception of abstract idea (i.e. provide an inventive concept). As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of: ( (establishing..)… connection, computing system, application, processors ) merely uses a computer a as tool to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception or merely uses generic computing elements to perform well known, routine, and conventional functions. (See MPEP 2106.05 (d) and (f) ) (Specification, [35], general purpose single – or multi-chip processor, [125] general purpose processor…[163] any suitable computing system or device … network.. [183-184] general purpose processors, [125] general purpose computing devices… )
The dependent claims have also been examined and do not correct the deficiencies of the independent claims.
It is noted that claim (2-10, 12-19) introduces the additional elements of: receiving… (Claims 4, 14 and 21) various …wherein…clauses further defining elements such user interface (Claims 5, 15 and 22; 10,15 and 26 ); update… (Claims 23; 9, 19 and 24). These elements are not a practical application of the judicial exception because these limitations merely recite: “apply it” (or an equivalent) or merely include instructions to implement an abstract idea on a computer or merely uses a computer a as tool to perform an abstract idea or merely uses generic computing elements to perform well known, routine, and conventional functions or generally link the use of the judicial exception to a particular technological environment or field of use (See MPEP 2106.05 (d) and (f)) Further these limitations taken alone or in combination with the abstract do not amount to significantly more than the abstract idea alone because these elements merely use a computer a as tool to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception or merely uses generic computing elements to perform well known, routine, and conventional functions. (See MPEP 2106.05 (d) and (f) ) (Specification, [35], general purpose single – or multi-chip processor, [125] general purpose processor…[163] any suitable computing system or device … network.. [183-184] general purpose processors, [125] general purpose computing devices… )
Therefore, Claims 1, 4-5, 9-11, 14-15, 19, and 20-26 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Prior Art
The closest prior art of record US 10572727 B1, Sachtleben et al. (Sachtleben) generally discloses a method and system of migrating information from first account to a second account including a calendar view feature. The non-patent literature of Taylor, K. “ Viz Variety Show: When to use heatmap calendars” 8 March 2017. ( https://www.tableau.com/blog/viz-variety-show-heatmaps-66330). (Taylor) generally teaches the use of and creation of calendar heat maps to convey information. The prior art of US 10282780 B1, Ley et al. (Ley) further discloses a method and system of scheduling and tracking account activity including a ‘drag and drop,’ feature.
Even though the prior art of record discloses the general concepts cited above, the prior art of record fails to teach applying a machine learning techniques to an optimized scheduling calendar including heatmap data and a locked input feature. The specific claim language that the prior art of record fails to teach is:
applying, by the one or more processors, the dataset and the account data as inputs to a machine learning model trained to generate simulations of a plurality of orders for the dataset based at least on at least some of the historical transactions, each simulation associated with a respective score
selecting , by the one or more processors, a first order of a plurality of orders for the dataset, based on a score of the first order being a maximum of the respective scores for the plurality of orders
generating, by the one or more processors, a first user interface including a calendar view showing each data entry of the dataset over a time period ordered according to the first order, the calendar view including a heat map representing color coded windows over the time period representative of a simulated balance over the time period
receiving, by the one or more processors, from a computing device, an update to move an entry of the plurality of entries to a defined window within the time period, the update triggering a lock on the entry to the defined window
applying, by the one or more processors, the dataset and the account data, including an indication of the lock to the defined window, as second inputs to the machine learning model trained to generate updated simulations of the plurality of orders for the dataset based on the lock of the entry to the defined window, each updated simulation associated with a respective updated score;
selecting, by the one or more processors, a second order of the plurality of orders for the dataset, based on an updated score of the second order being a maximum of the respective updated scores for the plurality of orders;
generating, by the one or more processors, a second user interface including the calendar view showing each data entry of the dataset over the time period ordered according to the second order, the calendar view including an updated heatmap….
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 7,979,329 B2, System and method for generating optimal bill/payment schedule
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHA PUTTAIA H whose telephone number is (571)270-1352. The examiner can normally be reached on Monday- Friday 8:00am - 5:00 pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abhishek Vyas, can be reached on (571) 270-1836. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASHA PUTTAIA H/Primary Examiner, Art Unit 3691
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