DETAILED ACTION
Acknowledgements
This action is in response to Applicant’s filing on Nov. 12, 2025, and is made Non-Final. This action is being examined by James H. Miller, who is in the eastern time zone (EST), and who can be reached by email at James.Miller1@uspto.gov or by telephone at (469) 295-9082.
Interviews
Examiner interviews are available by telephone or, preferably, by video conferencing using the USPTO’s web-based collaboration platform. Applicants are strongly encouraged to schedule via the USPTO Automated Interview Request (AIR) portal at http://www.uspto.gov/interviewpractice. Interviews conducted solely for the purpose of “sounding out” the examiner, including by local counsel acting only as a conduit for another practitioner, are not permitted under MPEP § 713.03. The Office is strictly enforcing established interview practice, and applicants should ensure that every interview request is directed toward advancing prosecution on the merits in compliance with MPEP §§ 713 and 713.03.
For after-final Interview requests, supervisory approval is required before an interview may be granted. Each AIR should specifically explain how the After-Final Interview request will advance prosecution—for example, by identifying targeted arguments responsive to the rejection of record, alleged defects in the examiner’s analysis, proposed claim amendments, or another concrete basis for discussion. See MPEP § 713. If the AIR form’s character limits prevent inclusion of all pertinent details, Applicants may send a contemporaneous email to the examiner at James.Miller1@uspto.gov.
The examiner is generally available Monday through Friday, 10:00 a.m. to 4:00 p.m. EST.
For any GRANTED Interview Request, Applicant can expect an email within 24 hours confirming an interview slot from the dates/times proposed and providing collaboration tool access instructions. For any DENIED Interview Request, the record will include a communication explaining the reason for the denial.
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 .
Continued Examination Under 37 CFR 1.114
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 Nov. 12, 2025, has been entered.
Claim Status
The status of claims is as follows:
Claims 21–40 remain pending and examined with Claims 21, 28, and 35 in independent form.
Claims 21, 28, and 35 are presently amended.
No Claims are presently cancelled or added.
Response to Amendment
Applicant's Amendment has been reviewed against Applicant’s Specification filed Nov. 27, 2023, [“Applicant’s Specification”] and accepted for examination.
Response to Arguments
35 U.S.C. § 101 Argument
Applicant argues this is a "close call" citing the August 2025 USPTO Reminders Memo stating examiners should only reject when "more likely than not (>50%)" claim is ineligible. Applicant’s Reply at 14–6.
Examiner respectfully disagrees.
Examiner acknowledges the guidance in the Reminders Memo regarding the “more likely than not” standard. However, this is not a close call. As detailed in the responses below and the Final Office Action, the claims are directed to abstract ideas under multiple groupings (fundamental economic practice, method of organizing human activity, mathematical concepts), and fail to integrate those abstract ideas into a practical application or provide significantly more than the abstract ideas themselves. The claims fall squarely within well-established precedent including Alice (generic computer implementation of abstract idea), Bancorp Services (speed/efficiency limitations insufficient), and the recent Recentive Analytics decision (applying established ML methods to new data environment insufficient).
Applicant argues the Reminders Memo states mental processes must be "practically performable in human mind." The amended claims recite K-means clustering, ML training, and real-time processing that humans cannot perform mentally. Applicant’s Reply at 15–6. Applicant further argues the amended claims recite "automatically select...without input by a user" the second account based on ML output, which cannot be performed by human mind within time constraints.
Examiner respectfully disagrees.
While K-means clustering and ML training may not be purely mental, they can be performed with pen and paper. See, Leskovec, Jure, Anand Rajaraman, and Jeffrey David Ullman. "Mining of Massive Datasets." (2019) (“NPL Leskovec”) is prior art and additional evidence of a human’s ability to perform K-means clustering and train an ML model without the aid of a computer. NPL Leskovec, p. 268 (Example 7.8) (cited herein on PTO-892). Additionally, the underlying concept—selecting which account to use based on balances, costs, and preferences—is a mental process. The mathematical algorithms are themselves abstract ideas. The claims also recite fundamental economic practices and methods of organizing human activity, independent of the mental process category.
Alice expressly held that automation of conventional processes using generic computers is insufficient for eligibility. The fact that selection is automatic rather than manual does not transform the abstract idea. The time constraint is a field-of-use limitation. Under Recentive Analytics, efficiency gains (faster selection) without improving underlying ML technology are insufficient.
Applicant argues the amended claims are analogous to Example 39 (neural network training). By training ML model using K-means clustered historical data from users who achieved specified goals, claims improve the ML model itself, not just apply it to new data. Applicant’s Reply at 16–7.
Examiner respectfully disagrees.
Example 39 involved improvement to neural network architecture/training methodology itself. Here, the claims use conventional K-means clustering and standard training on historical data—no improvement to ML technology disclosed. The claims merely select “what training data to use” (users who achieved goals), not “how to train differently”. Under Recentive Analytics, applying established ML methods to new data (financial account selection) without improving ML models themselves is ineligible. The specification confirms "any suitable" ML technique may be used (¶ 50), indicating no specific ML innovation. Example 39 is distinguished.
Applicant argues the amended claims are analogous to Example 47 (network intrusion detection with real-time remedial action). The claims detect account changes and take real-time remedial action (automatically select second account) within processing time limit, integrating abstract idea into practical application. Applicant’s Reply at 17–8.
Examiner respectfully disagrees.
Example 47 involved cybersecurity—detecting network intrusions and dropping packets/blocking traffic by addressing a technical problem in computer networks. Here, the claimed system addresses a business problem (optimizing account selection for user goals), not a technical problem. The real-time constraint is merely a field-of-use limitation defining when the abstract idea is applied, not transforming its nature. Alice held that implementing abstract ideas on generic computers at generic speeds is insufficient. The "remedial action" is the business decision itself (selecting an account), not a technical solution. Example 47 is distinguished.
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 21–40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Analysis
Step 1: Claims 21–40 are directed to a statutory category. Claims 21–27 recite a “system” and are therefore, directed to the statutory category of a “machine.” Claims 28–34 recite a “method” and are therefore, directed to the statutory category of a “process.” Claims 35–40 recite a “non-transitory computer-readable storage medium” and are therefore, directed to the statutory category of an "article of manufacture.”
Representative Claim
Claim 21 is representative [“Rep. Claim 21”] of the subject matter under examination and recites, in part, emphasis added by Examiner to identify limitations with normal font indicating the abstract idea exception, bold limitations indicating additional elements. Each limitation is identified by a letter for later use as a shorthand notation in referencing/describing each limitation. Portions of the claim use italics to identify intended use limitations1 and underline, as needed, in further describing the abstract idea exception:
[A] 21. A system comprising: at least one processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
[B] train a machine learning model by:
[C] identifying historical account selections for system user accounts that achieved a specified goal associated with the system user accounts;
[D] segmenting the system user accounts into groups based on a K-means clustering of transaction histories of the system user accounts;
[E] inferring, based on processing the historical account selections, financial account selections for a cluster set of the system user accounts from the K-means clustering; and
[F] providing, to the machine learning model, training data that is representative of financial account selections of the cluster set of the system user accounts that achieved the specified goal associated with the system user accounts;
[G] receive, from a third-party system, a request to approve a transaction of a requested value, the transaction associated with a system user account, the system user account associated with a plurality of financial accounts;
[H] determine whether the plurality of financial accounts have funds greater than the requested value;
[I] select, using the machine learning model and within a time limit corresponding to a real time processing of the transaction, a first financial account of the plurality of financial accounts to provide the requested value of the transaction based on a plurality of inputs to the machine learning model in response to a determination indicating that the plurality of financial accounts have greater than the requested value, wherein the plurality of inputs includes a user-specified result;
[J] detect, within the time limit corresponding to the real time processing of the transaction, a change to the first financial account of the plurality of financial accounts;
[K] based on detecting the change to the first financial account, output, by the machine learning model and within the time limit corresponding to the real time processing of the transaction, an account identifier indicating a second financial account of the plurality of financial accounts to provide the requested value of the transaction based on a second plurality of inputs to the machine learning model comprising account balances of the plurality of financial accounts, cost information for the transaction, and the user-specified result; and
[L] automatically select, within the time limit corresponding to the real time processing of the transaction, the second financial account for completion of the transaction in accordance with the account identifier output by the machine learning model.
Claims are directed to an abstract idea exception.
Step 2A, Prong One: Rep. Claim 21 recites "automatically select, within the time limit corresponding to the real time processing of the transaction, the second financial account for completion of the transaction in accordance with the account identifier output,” in Limitation L, which recites commercial or legal interactions under the organizing human activity exception because “completion of the transaction” recites “sales activities or behaviors, and business relations” between two people. MPEP § 2106.04(a)(2)(II)(B). Limitations B–K are the required steps and data inputs required to “complet[e] [ ] the transaction” and therefore, recite the same exception. Id.
Alternatively, Limitations B–L, recites a mathematical relationship under the mathematical concepts exception because "a process that employs mathematical algorithms [“identifying,” “segmenting,” and “inferring” using “K-means clustering”] to manipulate existing information to generate additional information [“select a first/second financial account”] is an abstract idea.” Digitech Image Techs. LLC v. Elecs. For Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014); MPEP § 2106.04(a)(2) (citing Digitech (“The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula.”)
Alternatively, Limitations B–L, as drafted, recite the abstract idea exception of mental processes that under the broadest reasonable interpretation, cover performance in the human mind or with pen and paper, but for the recitation of the generic computer components indicated in bold. MPEP § 2106.04(a)(2)(III).
Claims recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:
• 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);
. . .
• a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011).
MPEP § 2106.04(a)(2)(III)(A). For example, but for the generic computer components claim language, here, Limitations B–L, recite collecting information (Limitations B, F, G) and analyzing it (Limitations C, D, E, H, I, J, K, L) where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind. For example, Limitations C, D, and E are mental processes that are practically performed in the human mind or with pen and paper because it requires mere “observation, evaluation, judgment, and/or opinion” to “identify[ ] account selections for system user accounts that achieved a specified result” (Limitation C); “segment[ ] the system user account into groups based on a K-means clustering” (Limitation D); and inferring financial account selections …from the K-means clustering” (Limitation E). Leskovec, Jure, Anand Rajaraman, and Jeffrey David Ullman. "Mining of Massive Datasets." (2019) (“NPL Leskovec”) is prior art and additional evidence of a human’s ability to identify and segment inputs using K-means clustering without the aid of a computer. NPL Leskovec, p. 268 (Example 7.8) (cited herein on PTO-892). Once segmented, Limitation E is mental processes that are practically performed in the human mind or with pen and paper because it requires mere “observation, evaluation, judgment, and/or opinion” in any known way, which encompasses mental processes. Limitation H is also a mental process that is practically performed in the human mind or with pen and paper because collecting and comparing known information (i.e., “the plurality of financial accounts have funds greater than the requested value”) are steps that can be practically performed in the human mind under Classen. Limitation I is also a mental process that is practically performed in the human mind or with pen and paper because it also requires mere “observation, evaluation, judgment, and opinion” to “select … a first financial account … in response to the determination [in Limitation H].” Limitations J and K are mental processes that are practically performed in the human mind or with pen and paper because it requires mere “observation, evaluation, judgment, and/or opinion” to “detect a change to the first financial account,” (Limitation J) and “select[ing] … a second financial account … based on account balances … and user-specified result” (Limitation K) in nearly any way for the “detecting” and “selecting.” Last, Limitation L is a mental process that is practically performed in the human mind or with pen and paper because it requires mere “observation, evaluation, judgment, and/or opinion” to “transfer the requested value from the second financial account,” (Limitation L) in any known way including written methods, like transferring a negotiable instrument (e.g., check).
If a claim limitation under BRI, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract idea exception. MPEP § 2106.04(a)(2)(III). Accordingly, the pending claims recite the combination of these abstract idea exceptions.
Step 2A, Prong Two: Rep. Claim 21 does not contain additional elements that integrate the abstract idea exception into a practical application because the additional elements are mere instructions to apply the abstract idea exception. MPEP § 2106.05(f). The additional elements are limited to the computer components and indicated in bold, supra. The additional elements are: A system comprising: at least one processor; at least one non-transitory computer-readable storage medium storing instructions; a machine learning model; and a K-means clustering and a third-party system.
Regarding the system comprising: at least one processor; at least one non-transitory computer-readable storage medium storing instructions; a machine learning model; and a K-means clustering and a third-party system, Applicant’s Specification does not otherwise describe them or describes them using exemplary language as a general-purpose computer, as a part of a general-purpose computer, or as any known and exemplary (generic) computer component known in the prior art. Thus, Applicant takes the position that such hardware/software is so well known to those of ordinary skill in the art that no explanation is needed under 35 U.S.C. § 112(a). Lindemann Maschinenfabrik GMBH v. Am. Hoist & Derrick Co., 730 F.2d 1452, 1463 (Fed. Cir. 1984) (citing In re Meyers, 410 F.2d 420, 424 (CCPA 1969) (“[T]he specification need not disclose what is well known in the art”). E.g., Spec. ¶ 11 (“the transaction approval system may automatically change or override any pre-existing relationship between a particular customer account and the customer's method of payment (e.g., a debit card of the customer that is linked to a specific customer account) in order to satisfy a goal or preference of the customer or the system (on behalf of the customer)”); ¶ 13 (“the transaction approval system can then select the appropriate account (or accounts) of the customer to fund a payment request that is advantageous to the customer and satisfies any goal or preference of the customer”); ¶ 17 (“Payment device 112 may be any of, for example, a payment card having a magnetic strip that is swiped in a magnetic reader of a payment reader, a payment device having a Europay/MasterCard/Visa (EMV) chip that is inserted into a corresponding EMV slot of a payment reader (e.g., a credit or debit card, a gift card, a proxy card, etc.), and near field communication (NFC) enabled devices such as a smart phone or EMV card that is tapped at a payment reader and that transmits payment information over a secure wireless connection.”); ¶ 17 (“The term "customer" (also referred to herein as a "user" of a transaction processing system) may be any individual or entity that may purchase or intend to purchase a product or service from a merchant 120.”); ¶ 17 (“A customer 110 may also have one or more customer devices 114 (which in some embodiments, may also act in whole or in part as payment device 112), which may be, for instance, a mobile phone such as an iPhone or Android device, an iPad or tablet device, a laptop or touchscreen device, a PC or stationary computing device, or any other practical device that can communicate via a communication network. In an embodiment, customer device 114 may be any device capable of receiving information over one or more communications networks.”); 17 (“the user device 114 presents information to a user via a display on or connected to the device 114, and takes input from the customer in relation thereto (for example, through interactive graphics or hyperlinks) via a touchscreen, mouse, keyboard, stylus, or any other appropriate input device.”); ¶ 17 (“customer device 114 may be capable of receiving and displaying notification data via a dedicated application (app) or website, email, instant messaging, voice, short message service (SMS), voicemail, or any other appropriate type of communication.”); ¶ 18 (“Environment 100 may also include one or more merchants 120. The term "merchant" may be understood to encompass any business or other entity that sells, leases, or otherwise provides goods or services to customer 110”); ¶¶ 19, 20 (“Card issuer system 140 (also referred to herein as an "issuing bank") may include any number of computing servers”); ¶ 22 (“communication network 160 may be any suitable communication network”); ¶ 22 (“when the network 160 is the Internet, any of the components of environment 100 may use the transmission control protocol/Internet protocol (TCP/IP) for communication.”); ¶ 22 (“a merchant bank 130 may communicate with the card issuer system 140 via … any similar card networks”); ¶ 23 (“any number of merchants, customers, or devices may be used in any number of configurations. … any device associated with a financial account may be used in any transaction involving the deduction of funds from that financial account.”); ¶ 26 (“memory 210 may refer to any suitable storage medium”); ¶ 26 (“embodiments described herein are not limited to any particular arrangement”); ¶ 27 (“account selection logic 224 … may variously represent one or more algorithms, computational models, decision making rules or instructions, or the like implemented as software code or computer-executable instructions (i.e., routines, programs, objects, components, data structures, etc.) … any configuration of tile depicted logical components may be used, whether implemented by hardware, software, firmware, or any combination thereof.”);¶ 31 (“transaction approval system 150 may be implemented in whole or in part as a machine learning system (e.g., neural network software) for achieving the functionalities described herein” … “account selection logic 224 … may be implemented at least in part as one or more machine learning algorithms”); ¶ 32 (account selection logic 224 … may be variously implemented in software, hardware, firmware or any combination thereof … [and] can be stored and transported on any non-transitory computer- readable medium,” which can be “any device or system that can contain or store a computer program”); ¶ 33 (“The logics of the transaction approval system 150 depicted in FIG. 2 may be executed by one or more conventional processing elements 250, such as a central processing unit (CPU), digital signal processor, other specialized processor or combination of processors, or other circuitry that communicates to and drives the other elements within the transaction approval system 150 via a local interface 260, which can include at least one bus.”) ¶ 33 (“the processor 250 may comprise an artificial neural network or other type of configuration for performing machine learning functions based on instructions stored in memory 210”); ¶ 50 (describing machine learning model is generic terms as receiving particular data, manipulating data “using a K-means clustering algorithm or another alternate thereto,” and outputting “one or more account identifiers and/or information identifying one or more types of account”); ¶ 53 (“amount allocated to each selected account … dependent [on] factors such … [which] may be done … alternatively to the use of the machine learning models.”). The generic processor, here, appears to perform calculations (functions) that are programmed by software. Spec. ¶ 33. This is a computer doing what it is designed to do—performing directions it is given to follow.
Limitation A describes the processor and non-transitory computer-readable storage medium storing instructions communicating in some unspecified way to perform the steps of the claimed invention, which represents the abstract idea exception itself. Performing the steps of the abstract idea exception itself simply adds a general-purpose computer after the fact to an abstract idea exception, MPEP § 2106.05(f)(2), or generically recites an effect of the judicial exception. MPEP § 2106.05(f)(3).
Therefore, the claim as a whole, looking at the additional elements individually and in combination, are no more than mere instructions to apply the exception using generic computer components and is not a practical application. MPEP § 2106.05(f). The additional elements do not integrate the abstract idea exception into a practical application because they do not impose any meaningful limits on the abstract idea exception. Accordingly, Rep. Claim 21 is directed to an abstract idea.
Rep. Claim 21 is not substantially different than Independent Claims 28 and 35 and includes all the limitations of Rep. Claim 21. Independent Claims 28 and 35 contain no additional elements. Therefore, Independent Claims 28 and 35 are also directed to the same abstract idea.
The claims do not provide an inventive concept.
Step 2B: Rep. Claim 21 fails Step 2B because the claim as whole, looking at the additional elements individually and in combination, are not sufficient to amount to significantly more than the recited judicial exception. As discussed with respect to Step 2A, Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer and/or generic computer components. MPEP § 2106.05(f). The same analysis applies here in Step 2B. Mere instructions to apply an exception using a generic computer and/or generic computer components cannot provide an inventive concept. MPEP § 2106.05(I).
The additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the identified judicial exception.
The pending claims in their combination of additional elements is not inventive. First, the claims are directed to an abstract idea. Second, each additional element represents a currently available generic computer technology, used in the way in which it is commonly used (individually generic). Last, Applicant’s Specification discloses that the combination of additional elements is not inventive. Spec., ¶ 27 (“any configuration of the depicted logical components may be used”); ¶¶ 56, 57 (steps/functions may be performed in any order); ¶¶ 11, 13, 17, 18, 19, 20, 22, 23, 26, 27, 31, 32, 33, 50, 53 (known and generic (exemplary) computer equipment as explained and cited supra.)
Thus, Examiner finds the additional elements of Rep. Claim 21 are elements that have been recognized as well-understood, routine, and conventional (“WURC”) activity in the particular field of this invention based on Applicant’s own disclosure2. Spec. ¶¶ 11, 13, 17, 18, 19, 20, 22, 23, 26, 27, 31, 32, 33, 50, 53; MPEP § 2106.05(d). Specifically, Applicant’s Specification discloses the recited additional elements (i.e., a system comprising: at least one processor; at least one non-transitory computer-readable storage medium storing instructions; a machine learning model; a K-means clustering, and a third-party system), are generic computer components. Further, the machine learning model and K-means clustering were well-understood at the time of filing to persons of ordinary skill and merely operate on the generic components. Spec. ¶ 2 (“The disclosure generally relates to the field of machine learning, and more particularly to applying machine learning algorithms” for the intended stated purpose; NPL Leskovec, p. 268 (Example 7.8) (cited on PTO-892). The Examiner also finds the functions of receiving, storing, transmitting, and processing (e.g., performing mathematical operations on) data, described in Limitations A–L are all normal functions of a generic computer.
There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements in combination adds nothing that is not already present when looking at the elements individually. Their collective functions merely provide conventional computer implementation of the abstract idea at a high level of generality. Thus, Rep. Claim 21 does not provide an inventive concept.
Rep. Claim 21 is not substantially different than Independent Claims 28 and 35 and includes all the limitations of Rep. Claim 21. Independent Claims 28 and 35 contain no additional elements. Therefore, Independent Claims 28 and 35 also do not recite an inventive concept.
Dependent Claims Not Significantly More
The dependent claims have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. § 101. Dependent claims are dependent on Independent Claims and include all the limitations of the Independent Claims. Therefore, all dependent claims recite the same Abstract Idea. Dependent claims do not contain additional elements that integrate the abstract idea exception into a practical application or recite an inventive concept because the additional elements: (1) are mere instructions to apply the abstract idea exception; and/or (2) further limit the abstract idea exception of the Independent Claims. The abstract idea itself cannot provide the inventive concept or practical application. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claims 22, 23, 24, 25, 26, 29, 30, 31, 32, 33, 36, 37, 38, and 39 all recite “wherein” clauses or limitations that further limit the abstract idea of the Independent Claims and contain no additional elements. An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claims 27, 34, and 40 all recite “wherein” clauses that further limits the abstract idea of the Independent Claims but contains the additional elements of: a graphical user interface of a client device (Claims 27, 34, 40). For the same reasoning as explained in Step 2A, Prong Two, and Step 2B, supra, these additional elements do not provide a practical application or inventive concept because it is amounts to mere instructions to apply the exception with a computer. MPEP § 2106.05(f). Applicant Specification teaches the client device may be any generic computer device known in the prior art. Spec. ¶ 17 (“customer devices 114 may be any device capable of receiving information over one or more communications networks”); ¶ 19 (Card issuing system 1140 … may include any number of [generic] computer server”). Likewise, Applicant’s Specification teaches the graphical user interface (software) is generic and known in the prior art because it is undescribed software that merely operates on the generic client device. Spec. ¶ 17 (“Customer 110 can use their device 114 to access, view, and/or take action in response to delivered content. In an embodiment, the user device 114 presents information to a user via a display on or connected to the device 114 and takes input from the customer in relation thereto (for example, through interactive graphics or hyperlinks) via a touchscreen, mouse, keyboard, stylus, or any other appropriate input device.”). An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Conclusion
Claims 21–40 are therefore drawn to ineligible subject matter as they are directed to an abstract idea without significantly more. The analysis above applies to all statutory categories of invention. As such, the presentment of Rep. Claim 21 otherwise styled as another statutory category is subject to the same analysis.
Examiner Statement of Prior Art—No Prior Art Rejections
Based on the prior art search results, the prior art of record fails to anticipate or render obvious the claimed subject matter of the instant application. While some individual features of Claims 21–40 may be shown in the prior art of record—no known reference, alone or in combination, would provide the invention of Claims 21–40.
The prior art most closely resembling the applicant’s claimed invention are:
Lunceford et al., U.S. Pat. Pub. No. 2021/0201316, is pertinent because it trains a machine learning account selection algorithm using transaction and financial account information. Lunceford, ¶ 41.
Walters et al., U.S. Pat. Pub. No. 2021/0042723, is pertinent because it trains a machine learning account selection algorithm using behavior of the customer. Walters, ¶¶ 27,48.
Bonfigli et al., U.S. Pat. Pub. No. 2021/0027357, is pertinent because it trains a machine learning account selection algorithm based on labeled data. Bonfigli, ¶¶ 93, 94.
NPL: Anonymous. IP.com Number: IPCOM000257024D, is pertinent because it discloses training a machine learning account selection algorithm based on a user’s pattern history. Anonymous, p. 003.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES H MILLER whose telephone number is (469)295-9082. The examiner can normally be reached M-F: 10- 4 PM (EST).
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 M 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.
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/JAMES H MILLER/Primary Examiner, Art Unit 3694
1 Statements of intended use fail to limit the scope of the claim under BRI. MPEP § 2103(I)(C).
2 See Changes in Examination Procedure Pertaining to Subject Matter Eligibility, Recent Subject Matter Eligibility Decision (Berkheimer v. HP, Inc.), 3-4, https://www.uspto.gov/sites/default/files/documents/memo-berkheimer-20180419.PDF (April, 18, 2018) (That additional elements are well-understood, routine, or conventional may be supported by various forms of evidence, including "[a] citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s).").