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 .
DETAILED ACTION
1. This office action is in response to an amendment received on 3/31/26.
2. Claims 1, 3, 6-11, 13-20 are amended.
3. Claims 1, 3-11, 13-20 are pending.
RESPONSE TO ARGUMENTS
Applicant argues#1
i. PRONG One: Claims 1, 3-11, and 13-20 Do Not Recite an Abstract Idea Applicant respectfully submits that claims 1, 3-11, and 13-20 are patent eligible under Step 2A, Prong One, because the claims do not recite an abstract idea. On page 16, the Office Action alleges that the recited limitations fall within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas. Applicant respectfully disagrees.
Applicant respectfully submits that the claims do not recite "Certain Methods of Organizing Human Activity." According to the MPEP § 2106.04(a)(II), the enumerated grouping of "Certain Methods of Organizing Human Activity" is used to describe:
Fundamental economic principles or practices (including hedging, insurance, mitigating risk); Commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); Managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules of instructions).
MPEP § 2106.04(a)(2)(II) goes on to state that "this grouping is limited to activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior and relationships or interactions between people and is not to be expanded beyond these enumerated sub-groupings." Applicant respectfully submits that the claims do not recite elements that fall under these enumerated categories.
Examiner Response
Examiner respectfully disagrees.
The claims have been properly classified as a commercial interaction (where the user is interacting with a computer (the computer device of claim 1), steps for predicting a users future spending behavior.
See MPEP 21016.04(a)(2) which states:
Second, this grouping is limited to activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior and relationships or interactions between people, and is not to be expanded beyond these enumerated sub-groupings except in rare circumstances as explained in MPEP § 2106.04(a)(3). Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping.
The rejection is maintained.
Applicant argues#2
To begin, the claims involve "managing payment variability by devising a schedule of payments to account for predicted spending patterns of users of a transaction account issuer that occur periodically over time." (Applicant's Specification [0011]). For example, amended claim 1, recites as follows:
1. A system, comprising: a computing device comprising a processor and a memory; and machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least: retrieve training data associated with a user, the training data comprising historical transaction data and transaction account balance data; train an eligibility data model to identify spend structure to predict a level of volatility of spending by the user for a future period of time based at least in part on the training data; determine, using the eligibility data model, a predicted level of volatility of spending for the user for a period of time; train a balance prediction data model to identify spend behavior to predict a future spending behavior of the user based at least in part on the training data; determine, using the balance prediction data model, a predicted future spend behavior for the user during the period of time; generate a payment schedule for the user for the period of time based at least in part on the predicted future spend behavior of the user and the predicted level of volatility of spending for the user; and for each interval of the period of time, issue a payment statement to the user based on the payment schedule.
Applicant respectfully submits that the claim does not recite concepts that fall under these enumerated categories. In the August 4, 2025, Memo entitled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" (hereinafter 101 Memo"), distinguishing claims that recite a judicial exception from claims that merely involve a judicial exception is discussed on page 3 as follows:
Examiners should be careful to distinguish claims that merely recite an exception (which require further analysis) from claims that merely involve an exception (which are eligible and do not require further eligibility analysis).
Examiner Response
Examiner respectfully disagrees.
The limitations (retrieve training data associated with a user, the training data comprising historical transaction data and transaction account balance data; to identify spend structure to predict a level of volatility of spending by the user for a future period of time based at least in part on the training data; determine, a predicted level of volatility of spending for the user for a period of time; to identify spend behavior to predict a future spending behavior of the user based at least in part on the training data; determine, a predicted future spend behavior for the user during the period of time; generate a payment schedule for the user for the period of time based at least in part on the predicted future spend behavior of the user and the predicted level of volatility of spending for the user; and for each interval of the period of time, issue a payment statement to the user based on the payment schedule) is part of the identified abstract idea.
The rejection is maintained.
Applicant argues#3
Consider for example, the published USPTO examples 39, which illustrates claim limitations that merely involve an abstract idea, and 47, which shows limitations that recite an abstract idea. The claim limitation "training the neural network in a first stage using the first training set" of example 39 does not recite a judicial exception. Even though "training the neural network" involves a broad array of techniques and/or activities that may involve or rely upon mathematical concepts, the limitation does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols. Contrast this with the limitation "training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm" of claim 2 of example 47. This limitation requires specific mathematical calculations by referring to the mathematical calculations by name, i.e., a backpropagation algorithm and a gradient descent algorithm, and therefore recites a judicial exception, namely an abstract idea.
Applicant respectfully submits that at most claim 1 merely involves a fundamental business practice or commercial interaction and does not recite "activity that falls within the enumerated sub-groupings off fundamental economic principles or practices, commercial or legal interactions." (MPEP § 2106.04(a)(2)(II)).
For example, amended claim 1 recites elements such as "train an eligibility data model to identify spend structure to predict a level of volatility of spending by the user for a future period of time based at least in part on the training data" and "train a balance
prediction data model to identify spend behavior to predict a future spending behavior of the user based at least in part on the training data." These operations are neither commercial interactions, legal interactions, nor agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. Instead, amended claim 1 provides for an "eligibility model 330 [that] can include a digitally constructed model that predicts a variability level of a user's spending behavior during a defined period of time" and a "balance prediction model [that] can include a digitally constructed model that predicts a monthly payment schedule that will cover a user's actual spending during a defined person of time." (Applicant's Specification [0036]). For at least these reasons, Applicant respectfully submits that amended claim 1 fails to recite an abstract idea that falls within the "Certain Methods of Organizing Human Activity" grouping. For the same reasons, to the extent applicable, Applicant submits that claims 9 and 18 fail to recite a "Certain Method of Organizing Human Activity." In addition, Applicant submits that dependent claims 3-8, 10, 11, 13-18, 19, and 20 do not recite a "Certain Method of Organizing Human Activity" as they depend from claims 1, 9, and 18, respectively.
Examiner Response
Examiner respectfully disagrees.
With respect to hypothetical example 39, on page 9 of the Subject Matter Eligibility Examples (Subject Matter Eligibility Examples: Abstract ideas), The analysis states in step 2A, prong 1, is there a judicial exception recited? The analysis states, the claim does not recite any of the judicial exceptions enumerated in the 2019 PEG.
In the instant application, the claims are reciting the identified abstract idea (a commercial interaction), therefore Example 39 does not apply.
As far as example 47 is concerned, claim 3 was determined to be eligible claim, pages 12 of July 24 USPTO SME document states:
The claimed invention reflects this improvement in the technical field of network intrusion detection. Steps (d)-(f) provide for improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets. Specifically, the claim reflects the improvement in step (d), dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f). These steps reflect the improvement 12 described in the background. Thus, the claim as a whole integrates the judicial exception into a practical application such that the claim is not directed to the judicial exception. The additional elements in steps (d)-(f), when considered in combination, integrate the abstract idea into a practical application because the claim improves the functioning of a computer or technical field. See MPEP 2106.04(d)(1) and 2106.05(a). The claimed invention reflects this improvement in the technical field of network intrusion detection. Thus, the claim as a whole integrates the judicial exception into a practical application (Step 2A, Prong Two: YES), such that the claim is not directed to the judicial exception. (Step 2A: NO). The claim is eligible.
This is unlike the claims of the instant invention where the eligibility model is recited at a high level of generalty, operating in its ordinary capacity and is being used as a tool to implement the steps of the identified abstract idea.
The rejection is maintained.
Applicant argues#4
ii. PRONG TWO: Claims 1, 3-11, and 13-20 Recite Additional Elements that
Integrate Any Alleged Judicial Exception into a Practical Application
Applicant respectfully submits that even if the claims did recite a judicial exception or an abstract idea, an assertion with which Applicant strongly disagrees, claims 1, 3-11, and 13-20 would still be patent eligible under Step 2A, Prong Two, because claims 1, 3- 11, and 13-20 recite a technical solution to a technical problem. In the discussion of whether a claim is directed to patent-ineligible subject matter under Step 2A, MPEP §
2106.04(d)(1) states that "[a] claim reciting a judicial exception is not directed to the judicial exception if it also recites additional elements demonstrating that the claim as a whole integrates the exception into a practical application. One way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technology or technical field." (Emphasis added). MPEP § 2106.05(a) guides this determination stating (emphasis added):
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.
Applicant respectfully submits that the claim as a whole integrates any alleged judicial exception into a practical application by providing a technical solution to a technological process sufficiently described by Applicant's specification. For instance, Applicant has claimed various solutions that solve technical problems. For example, amended claim 1 recites: 1. A system, comprising: a computing device comprising a processor and a memory; and machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least: retrieve training data associated with a user, the training data comprising historical transaction data and transaction account balance data; train an eligibility data model to identify spend structure to predict a level of volatility of spending by the user for a future period of time based at least in part on the training data; determine, using the eligibility data model, a predicted level of volatility of spending for the user for a period of time; train a balance prediction data model to identify spend behavior to predict a future spending behavior of the user based at least in part on the training data; determine, using the balance prediction data model, a predicted future spend behavior for the user during the period of time; generate a payment schedule for the user for the period of time based at least in part on the predicted future spend behavior of the user and the predicted level of volatility of spending for the user; and for each interval of the period of time, issue a payment statement to the user based on the payment schedule.
(Emphasis added). Applicant's specification details that the above identified features of amended claim 1 recite a technical solution to an improvement in "one or more data models.. [that] can account for and offset spikes and variability in a predicted spending behavior of the user." (Applicant's Specification [0011]). For example, discussion of the technical problem is set forth in at least the following paragraphs of Applicant's Specification (emphasis added):
[0001] It is common for persons to incur expenses during the same monthly periods from year to year, such as around work or school events and/or holidays. For example, a person or family can plan for a trip during Thanksgiving or can purchase new clothes in advance of starting a new school year. Accordingly, such periodic events can result in a level of spending variability (or lack of consistency) in the spending pattern or behavior of the user and a level of payment variability on their monthly billing/account statements to financial institutions. Thus, there is a need for planning tools to allow for such spending patterns to be anticipated and for proactive payment plans to be implemented.
[0011] Disclosed are various approaches for managing payment variability by devising a schedule of payments to account for predicted spending patterns of users of a transaction account issuer that occur periodically over time. By evaluating the historical spend patterns of a user, structures or patterns in the historical spending behavior of the user can be identified and used to predict the spending behavior of the user during a set period (e.g., a 12 month period). Accordingly, based on the predicted spending behavior, a payment plan can be developed, using one or more data models, at the beginning of the period and can account for and offset spikes and variability in a predicted spending behavior of the user, such that monthly payments within the payment plan are balanced and are known in advance by the user. In this way, the user will not have large swings in the amount they owe from month to month. Accordingly, systems and methods of the present disclosure can provide a regular structure to the payment plan of a user, which allows for the user to have better control over planning and making payments on a month to month basis.
[0017] FIG. 2 gives an overview of an exemplary embodiment of a predictive spending and payment management system 200 of the present disclosure. The predictive spending and payment management system 200 is designed to model the spending behavior of a user based on at least historical spending and payment patterns (e.g., transaction data, balance data, etc.) of the user and identify if the user is eligible to participate in a payment plan. To understand the user's spending behavior, other user data can also be input in the model, such as, but not limited to, user demographic data (e.g., age, gender, residential location, etc.), user linked accounts, user relationships, balance, demographic data of the user, etc. The predictive spending and payment management system can then predict the future spending patterns of the user for a subsequent period (e.g., 12 month period, 24 month period, etc.) and establish a payment plan that is offered to the user to account for the predicted spending pattern of the user.
[0062] Depending upon the past balances and transactions, the eligibility data model is executed to identify if a user fits into a predictable payment plan. Accordingly, the method 800 includes predicting (820), using the eligibility data model, the level of volatility of spending for a user based on at least the historical transaction data and the transaction account balance data of the user during a previous year. For example, the eligibility model 330 can analyze a user spend structure and profile preferences to identify if the user has a predictable spend structure. In various embodiments, the spending behavior of the user can be categorized as fitting or falling into a certain level of spending volatility, (e.g., such as categorizing the user's spend structure into a low, medium, or high level of volatility, where a high volatility corresponds to low predictability and vice versa). Thus, in various embodiments, users that fit in the low category can be presented with an offer to enroll in a scheduled payment plan for the upcoming year (or other set period of time) by the TAI computing environment 210 (e.g., account eligibility API 240 deployed by the TAI computing environment 210).
[0063] The method 800 further includes training (830) a balance prediction data model that predicts future spending behavior of the user based on at least historical transaction data. Accordingly, with the training data, the balance prediction data model engine 326 can create and prepare a balance prediction data model 340 for the prospective user of the scheduled payment plan/program. In various embodiments, such data models can be generated with user trends over time and variations in spend behavior by using transaction data, balances, demographics (e.g., user- location, gender, age, etc.).
[0064] Depending upon the past balances and transactions, the balance prediction data model 340 is executed to identify if a user fits into a predictable payment plan. Accordingly, the method 800 includes predicting (840), using the balance prediction data model, a future spend behavior for the user during an upcoming period of time, wherein the upcoming period of time comprises a plurality of months. In various embodiments, the balance prediction model 340 can predict a user's spending patterns or behaviors over a specific period of time (e.g., an annual period) and the resulting transaction account balance based on historical spends.
Applicant respectfully submits that the model training process that is used in claim 1 to "train an eligibility data model to identify spend structure to predict a level of volatility of spending by the user for a future period of time based at least in part on the training data," and "train a balance prediction data model to identify spend behavior to predict a future spending behavior of the user based at least in part on the training data," is a technical solution to the technical problem of predictive analysis as described in the quoted sections of the specification above. For example, the Specification describes the automation of specific predictive tasks (e.g., "train[ing] an eligibility data model to identify spend structure to predict a level of volatility of spending" and "train[ing] a balance prediction data model to identify spend behavior to predict a future spend behavior")through the incorporation of particular claimed training rules to "determine, using the eligibility data model, a predicted level of volatility of spending" and "determine, using the balance prediction data model, a predicted future spend behavior."
Therefore, Applicant respectfully submits that the combination of the claims in view of the specification provide not just the abstract idea of a customized payment plan but integrates any alleged abstract idea into a practical application by providing a specific set of training rules/steps to generate a plan. Accordingly, claims 1, 3-11, and 13-20 satisfy the standard for subject-matter eligibility. Because Applicant is claiming a technical solution to a technical problem, claims 1, 3-11, and 13-20 are not abstract under Step 2A, Prong Two. Accordingly, Applicant respectfully requests that the rejections of claims 1, 3-11, and 13-20 under 35 U.S.C. § 101 be withdrawn.
Examiner Response
Examiner respectfully disagrees.
Applicant argued the claims present a technical improvement. Examiner does not find this argument persuasive. Applicant’s claims do not improve technology; the underlying technology remains unaffected by the claims. Applicant is addressing a business problem (predicting a user’s future spending behavior) with a business solution. Applicant is merely using existing technology (for its intended purpose) to implement the business solution. Any improvements lie in the abstract idea itself, not in underlying technology.
Spec para 37 is reproduced below:
[0037] In some embodiments, the eligibility model 330 and/or the balance prediction model 340 divides a population or data points into different groups to produce a collection of data points based on similarity and dissimilarity features between such data points. Data points in the same groups are more like other data points in the same group and dissimilar to the data points in other groups. In some implementations the eligibility model 330 and/or the balance prediction model 340 can utilize a k-means technique or other suitable clustering technique
In the spec paras that applicant refers to, along with para 37, discloses the additional elements (the eligibility model & balance prediction data model) are recited a high level of generality, operating in their ordinary capacity, being used as a tool to implement the steps of the identified abstract idea.
Therefore there are no additional elements in the claim that are indicative of integration into a practical application.
The rejection is maintained.
Applicant argues#5
B. STEP 2B: Claims 1, 3-11, and 13-20 Amount to Significantly More than Any
Alleged Judicial Exception
Even if the pending claims were directed to an abstract idea - an assertion or contention with which Applicant strongly disagrees - they still amount to significantly more than an abstract idea because the claims contain an inventive concept. The Office Action (p. 17) alleges that "[t]he claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception." Applicant respectfully disagrees.
Applicant respectfully submits that claims 1, 3-11, and 13-20 amount to significantly more than the alleged abstract idea. MPEP § 2106.05(I) describes how to determine whether claims amount to significantly more than any judicial exception:
Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself. Because this approach considers all claim elements, the Supreme Court has noted that 'it is consistent with the general rule that patent claims 'must be considered as a whole." Consideration of the elements in combination is particularly important, because even if an additional element does not amount to significantly more on its own, it can
still amount to significantly more when considered in combination with the other elements of the claim.(Internal citations omitted). The elements of the claims must be "considered in combination because the inventive concept inquiry requires more than recognizing that each claim element, by itself, was known in the art." MPEP § 2106.05(I)(B). When viewed as a combination it is clear that the claims contain an inventive concept of an "eligibility data model" and a "balance prediction data model" to generate a payment schedule as described in amended claim 1.
Examiner Response
Examiner respectfully disagrees.
This argument has been addressed above with respect to Applicant argues#4 above.
There are no additional elements that amount to significantly more than the identified abstract idea, see the section101 rejection below.
The rejection is maintained.
Applicant argues#6
Additionally, on page 17 the Office Action states that "[m]ere instructions to implement an abstract idea, on or with the use of a generic computer components, even without any computer components, cannot provide an inventive concept - rendering the claim patent ineligible." The "Apply It" consideration is addressed in the 101 Memo which states on page 4 that "Examiners are cautioned not to oversimplify claim limitations and expand the application of the 'apply it' consideration." For example, the 101 Memo (p.4) provides that: When evaluating [the 'apply it' consideration and the improvements consideration], examiners may consider the following: 1. Whether the claim recites only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished, or the claim covers a particular solution to a problem or a particular way to achieve a desired outcome. 2. Whether the claim invokes computers or other machinery merely as a tool to perform an existing process, or whether the claim purports to improve computer capabilities or to improve an existing technology.
Applicant respectfully submits that amended claim 1 provides an improvement to a technical field. A particular solution to a problem by at least elements such as "train an eligibility data model to identify spend structure to predict a level of volatility of spending by the user for a future period of time based at least in part on the training data" and "determine, using the eligibility data model, a predicted level of volatility of spending for the user for a period of time." Further, the instant claims are not merely limiting any alleged abstract idea to a computer environment by simply performing the idea via a computer, but rather is an innovation in computing technology, namely, amended claim 1 describes a specific process of "identify[ing] spend structure to predict a level of volatility of spending" and "identify[ing] spend behavior to predict a future spending behavior" to "generate a payment schedule." Therefore, Applicant respectfully submits that amended claim 1 amounts to significantly more than an abstract idea because the claims contain an inventive concept.
For at least these reasons, Applicant respectfully submits that, while claims 1, 3- 11, and 13-20 are not directed to an abstract idea as previously discussed, they would alternatively amount to significantly more than an abstract idea. Therefore, Applicant respectfully requests that the rejection of claims 1, 3-11, and 13-20 be withdrawn.
Examiner Response
Examiner respectfully disagrees.
This argument has been addressed above with respect to Applicant argues#4 above.
The rejection is maintained.
Claim Rejections- 35 U.S.C § 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.
1. Claims 1, 3-11, 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 9, 18 are directed to a system, method and computer readable medium which are statutory categories of invention. (Step 1: YES).
Representative claim 1 recites the limitations of:
A system, comprising: a computing device comprising a processor and a memory; and machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least:
retrieve training data associated with a user, the training data comprising historical transaction data and transaction account balance data;
train an eligibility data model to identify structure to predict a level of volatility of spending by the user for a future period of time based at least in part on the training data;
determine, using the eligibility data model, a predicted level of volatility of spending for the user based for a period time;
train a balance prediction data model to identify spend behavior to predict a future spending behavior of the user based at least in part on the training data;
determine, using the balance prediction data model, a predicted future spend behavior for the user during the period of time;
generate a payment schedule for the user for the period of time based at least in part on the predicted future spend behavior of the user and the predicted level of volatility of spending for the user;
for each interval of the period of time, issue a payment statement to the user based on the payment schedule.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity.
The claim recites elements that are in bold above, which covers performance of the limitation as a commercial interaction, steps for predicting a user’s future spending behavior (e.g., retrieve training data associated with a user, the training data comprising historical transaction data and transaction account balance data; to identify structure to predict a level of volatility of spending by the user for a future period of time based at least in part on the training data; determine, a predicted level of volatility of spending for the user based for a period time; to identify spend behavior to predict a future spending behavior of the user based at least in part on the training data; determine, a predicted future spend behavior for the user during the period of time; generate a payment schedule for the user for the period of time based at least in part on the predicted future spend behavior of the user and the predicted level of volatility of spending for the user; for each interval of the period of time, issue a payment statement to the user based on the payment schedule)
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a Commercial Interaction, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Claims 9, 18 are abstract for similar reasons.
(Step 2A-Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) 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 (MPEP 2106.05.f), (2) Adding insignificant extra solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Claims 1, 9, 18 includes the following additional elements:
-A computing device comprising a processor and a memory
-An eligibility data model
- A balance prediction model
-the training of the eligibility data model
-the training of the balance prediction model
-A computer readable medium
The computing device, eligibility data model, balance prediction model, computer readable medium, the training of the eligibility data model, the training of the balance prediction model are recited at a high level of generality and are being used in their ordinary capacity and are being used as a tool for implementing the steps of the identified abstract idea, see MPEP 2106.05(f), where applying a computer or using a computer as a tool to perform the abstract idea is not indicative of a practical application.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea
Therefore claims 1, 9, 18 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited in the claim beyond the judicial exception.
Mere instructions to implement an abstract idea, on or with the use of generic computer components, or even without any computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claims 1, 9, 18 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 3-8, 10-11, 13-17, 19-20 further define the abstract idea that is present in their respective independent claims 1, 9, 18 and thus correspond to Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above.
Therefore, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims (3-8, 10-11, 13-17, 19-20) are directed to an abstract idea. Thus, the claims 1, 3-11, 13-20 are not patent-eligible.
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 MOHAMMAD Z SHAIKH whose telephone number is (571)270-3444. The examiner can normally be reached M-T, 9-600; Fri, 8-11, 3-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BENNETT SIGMOND can be reached at 303-297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MOHAMMAD Z SHAIKH/Primary Examiner, Art Unit 3694 6/8/2026
Prosecution Insights