DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, 18/398,224, was filed on Dec. 28, 2023, and does not claim foreign priority or domestic benefit to any other application.
The effective filing date is after the AIA date of March 16, 2013, and so the application is being examined under the “first inventor to file” provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under ether status.
Status of the Application
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/07/2026 has been entered.
This Non-Final Office Action is in response to Applicant’s communication of 01/07/2026.
Claims 1-3, 5-11, 13-17, and 19-20 are pending, of which claims 1, 9, and 17 are independent.
Claims 4, 12, and 18 were previously cancelled. Independent claims 1, 9, and 17, and dependent claims 8, 13, 16, and 19 are currently amended.
All pending claims have been examined on the merits.
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 1-3, 5-11, 13-17, and 19-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to an abstract idea, without “significantly more”.
In regards to Step 1 of the Alice/Mayo analysis, independent claim 1 is a method claim, claim 9 is an apparatus claim, and claim 17 is an article of manufacture claim or product by process claim (“non-transitory computer readable medium”).
For the sake of compact prosecution, we continue with the Alice/Mayo “abstract idea” analysis.
The abstract idea elements recited in independent claim 1 are shown in italic font. The “additional elements” and “extra solution steps” are shown in italic and underlined font:
1. (Currently Amended) A machine-learning based (ML-based) computing method for determining one or more unique identifiers associated with one or more first users based on one or more information associated with one or more financial transactions, the ML-based computing method comprising:
receiving, by one or more hardware processors, one or more inputs from one or more electronic devices of one or more second users, wherein the one or more inputs comprise first information related to at least one of one or more magnetic ink character recognition (MICR) numbers, one or more international bank account numbers (IBAN), and one or more identities associated with the one or more first users:
extracting, by the one or more hardware processors, one or more data comprising second information related to one or more identifiers associated with the one or more first users, from one or more databases based on the one or more inputs received from the one or more electronic devices of the one or more second users, wherein the one or more identifiers associated with the one or more first users comprise at least one of one or more first identifiers, one or more second identifiers and one or more third identifiers, associated with the one or more first users;
dynamically mapping, by the one or more hardware processors, at least one of: the one or more first identifiers, the one or more second identifiers and the one or more third identifiers, associated with the one or more first users, on the first information related to at least one of the one or more magnetic ink character recognition (MICR) numbers, the one or more international bank account numbers (IBAN), and the one or more identities associated with the one or more first users, by a machine learning model, comprising:
mapping, by the one or more hardware processors, the one or more identifiers associated with the one or more first users by adapting the one or more second users when the one or more first identifiers associated with the one or more first users comprise nil identifiers;
mapping, by the one or more hardware processors, a single identifier associated with the one or more first users on the first information, and the one or more identities associated with the one or more first users, to determine one or more unique identifiers associated with the one or more first users; and
mapping, by the one or more hardware processors, the two or more identifiers associated with the one or more first users on the first information and the one or more identities associated with the one or more first users, by the machine learning model,
wherein the machine learning model comprise a K-nearest neighbors (KNN) model, configured with one or more hyperparameters; and
wherein the one or more hyperparameters comprises a K-value hyperparameter and a distance metric hyperparameter;
determining, by the one or more hardware processors, the one or more unique identifiers associated with the one or more first users based on the mapping of at least one of: the one or more first identifiers, the one or more second identifiers and the one or more third identifiers, associated with the one or more first users, on the first information related to at least one of: the one or more magnetic ink character recognition (MICR) numbers, the one or more international bank account numbers (IBAN), and the one or more identities associated with the one or more first users: and
providing, by the one or more hardware processors, an output of the determined one or more unique identifiers associated with the one or more first users, to the one or more second users on a user interface associated with the one or more electronic devices; and
updating, by the one or more hardware processors, one or more database records associated with the one or more financial transactions by linking the determined one or more unique identifiers to the one or more identities associated with the one or more first users to facilitate reconciliation of the one or more financial transactions.
More specifically, claims 1-3, 5-11, 13-17, and 19-20 recite an abstract idea: “Certain Methods of Organizing Human Activity", specifically “Commercial or Legal Interactions (Including Agreements in the form of Contracts; Legal Obligations; Advertising, Marketing, or Sales Activities or Behaviors; Business Relations)”, as discussed in MPEP §2106(a)(2) Parts (I) and (II), and in the 2019 Revised Patent Subject Matter Eligibility Guidance.
The “Commercial or Legal Interactions” elements include (emphasis added):
“A machine-learning based (ML-based) computing method for determining one or more unique identifiers associated with one or more first users based on one or more information associated with one or more financial transactions”.
“mapping … at least one of: the one or more first identifiers, the one or more second identifiers and the one or more third identifiers, associated with the one or more first users, on the first information related to at least one of the one or more magnetic ink character recognition (MICR) numbers, the one or more international bank account numbers (IBAN), and the one or more identities associated with the one or more first users, by a machine learning model”.
“determining … the one or more unique identifiers associated with the one or more first users based on mapping of at least one of: the one or more first identifiers, the one or more second identifiers and the one or more third identifiers, associated with the one or more first users, on the first information related to at least one of: the one or more magnetic ink character recognition (MICR) numbers, the one or more international bank account numbers (IBAN), and the one or more identities associated with the one or more first users”.
Moreover, claims 1-3, 5-11, 13-17, and 19-20 recite “Mathematical Concepts", specifically “Mathematical Relationships”, “Mathematical Formulas or Equations”, and “Mathematical Calculations”, as discussed in MPEP §2106.04(a)(2) Part (IV), and in the 2019 Revised Patent Subject Matter Eligibility Guidance.
The mathematic elements include (emphasis added):
“A machine-learning based (ML-based) computing method for determining one or more unique identifiers associated with one or more first users based on one or more information associated with one or more financial transactions”.
dynamically mapping … at least one of: the one or more first identifiers, the one or more second identifiers and the one or more third identifiers, associated with the one or more first users, on the first information related to at least one of the one or more magnetic ink character recognition (MICR) numbers, the one or more international bank account numbers (IBAN), and the one or more identities associated with the one or more first users, by a machine learning model, comprising:
mapping … the one or more identifiers associated with the one or more first users by adapting the one or more second users when the one or more first identifiers associated with the one or more first users comprise nil identifiers;
mapping … a single identifier associated with the one or more first users on the first information, and the one or more identities associated with the one or more first users, to determine one or more unique identifiers associated with the one or more first users; and
mapping … the two or more identifiers associated with the one or more first users on the first information and the one or more identities associated with the one or more first users, by the machine learning model,
wherein the machine learning model comprise a K-nearest neighbors (KNN) model, configured with one or more hyperparameters; and
wherein the one or more hyperparameters comprises a K-value hyperparameter and a distance metric hyperparameter;
“determining … the one or more unique identifiers associated with the one or more first users based on mapping of at least one of: the one or more first identifiers, the one or more second identifiers and the one or more third identifiers, associated with the one or more first users, on the first information related to at least one of: the one or more magnetic ink character recognition (MICR) numbers, the one or more international bank account numbers (IBAN), and the one or more identities associated with the one or more first users”
The “additional elements” include: “one or more hardware processors”, “one or more databases”, and “one or more electronic devices”.
The “additional extra-solution elements” include:
“receiving, by one or more hardware processors, one or more inputs from one or more electronic devices of one or more second users”,
“extracting, by the one or more hardware processors, one or more data comprising second information related to one or more identifiers associated with the one or more first users, from one or more databases”,
“providing, by the one or more hardware processors, an output of the determined one or more unique identifiers associated with the one or more first users … on a user interface associated with the one or more electronic devices”, and
“updating, by the one or more hardware processors, one or more database records associated with the one or more financial transactions”.
This abstract idea is not integrated into a practical application, because:
The claim is directed to an abstract idea with additional generic computer elements. The generically recited computer elements (“one or more hardware processors”, “one or more databases”, and “one or more electronic devices”) do not add a meaningful limitation to the abstract idea, because they amount to simply implementing the abstract idea on a computer. The claim amounts to adding the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
The extra-solution activities (“receiving … one or more inputs from one or more electronic devices of one or more second users”, “extracting … one or more data comprising second information related to one or more identifiers associated with the one or more first users, from one or more databases”, “providing … an output of the determined one or more unique identifiers associated with the one or more first users … on a user interface associated with the one or more electronic devices”, and “updating … one or more database records associated with the one or more financial transactions”) do not add a meaningful limitation to the method, as they are insignificant extra-solution activity;
The combination of the abstract idea with the additional elements (generically recited computer elements), and/or with the extra-solution activities, does not integrate the abstract idea into a practical application.
The claim merely generally links the use of the abstract idea to a particular technological environment or field of use (e.g. electric grid data is not claiming the actual electric grid)- see MPEP 2106.05(h) ].
The claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea, because:
When considering the elements "alone and in combination" (“one or more hardware processors”, “one or more databases”, and “one or more electronic devices”), they do not add significantly more (also known as an "inventive concept") to the exception, because they amount to simply implementing the abstract idea on a computer. Instead, they merely add the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea.
In regards to the extra solution activities (“receiving … one or more inputs from one or more electronic devices of one or more second users”, “extracting … one or more data comprising second information related to one or more identifiers associated with the one or more first users, from one or more databases”, “providing … an output of the determined one or more unique identifiers associated with the one or more first users … on a user interface associated with the one or more electronic devices”, and “updating … one or more database records associated with the one or more financial transactions”), these are recognized as such by the court decisions listed in MPEP § 2106.05(d).
More specifically, in regards to the “extracting” step, see the court cases Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) (storing and retrieving information in memory); and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (storing and retrieving information in memory).
More specifically, in regards to the “receiving” step, see the court cases OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) and (presenting offers and gathering statistics), OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Moreover, in regards to the “displaying” or “providing … an output … on a user interface associated with the one or more electronic devices” step, see Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 120 U.S.P.Q.2d 1844 (Fed. Cir. 2016) (Holding that the claimed menu graphic user interface is an abstract idea under 35 USC §101, because claimant "[did] not claim a particular way of programming or designing the software to create menus that have these features, but instead merely claims the resulting systems").
Moreover, in regards to the “electronic recordkeeping” or “updating … one or more database records associated with the one or more financial transactions” step, see the court cases Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log).
The Examiner holds that the independent claims “use a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data)” or “simply add a general purpose computer or computer components after the fact to an abstract idea”.
Independent claims 9 and 17 are rejected on the same grounds as independent claim 1. Independent claim 17 is also rejected on the grounds that it recites a computer-readable medium, which is merely another generic computer component.
All dependent claims are also rejected, because they merely further define the abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 9, 10, and 17 are rejected under 35 U.S.C. §102 (a)(2) as being anticipated by US-2024/0338935-A1 to Pedone et al. (“Pedone”. Filed on Apr. 6, 2023).
In regards to claim 1,
1. A machine-learning based (ML-based) computing method for determining one or more unique identifiers associated with one or more first users based on one or more information associated with one or more financial transactions, the ML-based computing method comprising:
receiving, by one or more hardware processors, one or more inputs from one or more electronic devices of one or more second users, wherein the one or more inputs comprise first information related to at least one off one or more magnetic ink character recognition (MICR) numbers, one or more international bank account numbers (IBAN), and one or more identities associated with the one or more first users:
(See Pedone, para. [0124]: “In some embodiments, the content recognition analysis can rely in part on MICR techniques. The MICR techniques generally require a dedicated magnetic reader device that is integrated with, or in signal communication with, the user computing device or provider terminal computing device. Portions of a transfer instrument can include characters printed or generated with magnetic ink or toner that are detected by the magnetic reader device to identify characters.”)
extracting, by the one or more hardware processors, one or more data comprising second information related to one or more identifiers associated with the one or more first users, from one or more databases based on the one or more inputs received from the one or more electronic devices of the one or more second users,
(See Pedone, para. [0007]: “In one embodiment, a system for electronic transfer instrument security and error detection includes a computer with at least one processor and a memory device that stores data and executable code. The executable code causes the processor to transmit system configuration data to a network computer that compares the system configuration data to stored system configuration data and returns end user data. The system configuration data is unique to the computer being operated by user and can be used to verify the computer and user identity. The system returns end user data, such as a user identification or user product identification (e.g., account numbers).”)
wherein the one or more identifiers associated with the one or more first users comprise at least one of one or more first identifiers, one or more second identifiers and one or more third identifiers, associated with the one or more first users;
(See Pedone, para. [0007]: “In one embodiment, a system for electronic transfer instrument security and error detection includes a computer with at least one processor and a memory device that stores data and executable code. The executable code causes the processor to transmit system configuration data to a network computer that compares the system configuration data to stored system configuration data and returns end user data. The system configuration data is unique to the computer being operated by user and can be used to verify the computer and user identity. The system returns end user data, such as a user identification or user product identification (e.g., account numbers).”)
dynamically mapping, by the one or more hardware processors, at least one of: the one or more first identifiers, the one or more second identifiers and the one or more third identifiers, associated with the one or more first users, on the first information related to at least one of the one or more magnetic ink character recognition (MICR) numbers, the one or more international bank account numbers (IBAN), and the one or more identities associated with the one or more first users, by a machine learning model, comprising:
(See Pedone, para. [0124]: “In some embodiments, the content recognition analysis can rely in part on MICR techniques. The MICR techniques generally require a dedicated magnetic reader device that is integrated with, or in signal communication with, the user computing device or provider terminal computing device. Portions of a transfer instrument can include characters printed or generated with magnetic ink or toner that are detected by the magnetic reader device to identify characters.”)
(See Pedone, para. [0166]: “The provider external server processes communication data requests sent to, and received from, the user computing device or from third party applications and computing devices. The external server routes communications requesting sensitive data through the internal server for secure communication. The internal server in turn communicates with other back end components of the provider system, such as databases and servers that store sensitive user data (e.g., account numbers, addresses, resource availability data or account balances, etc.).”)
mapping, by the one or more hardware processors, the one or more identifiers associated with the one or more first users by adapting the one or more second users when the one or more first identifiers associated with the one or more first users comprise nil identifiers;
(See Pedone, para. [0236]: “The Secure Threshold can represent a calculated likelihood that a transfer instrument is, or is not, fraudulent or contains errors. For instance, the Secure Threshold can be set to “99%,” which represents a 99% probability that the transfer does not include any transfer tags. In that case, a transaction is sent to the Deposit Platform only when the Secure Agent determines a Secure Score of 99% indicating at least a 99% probability that the underlying transaction is not fraudulent or erroneous. Those of skill in the art will appreciate that the foregoing example is not intended to be limiting, and the Secure Score need not always represent a percentage between zero and one-hundred. In other embodiment, the Secure Score can be a scaled or normalized value falling within zero to one, or some other numeric range.”)
The Examiner interprets that a “zero score” is equivalent to a “nil identifier”.
mapping, by the one or more hardware processors, a single identifier associated with the one or more first users on the first information, and the one or more identities associated with the one or more first users, to determine one or more unique identifiers associated with the one or more first users; and
(See Pedone, para. [0058]: “Supervised learning software systems are trained using content data that is well-labeled or “tagged.” During training, the supervised software systems learn the best mapping function between a known data input and expected known output (i.e., labeled or tagged content data). Supervised natural language processing software then uses the best approximating mapping learned during training to analyze unforeseen input data (never seen before) to accurately predict the corresponding output. Supervised learning software systems often require extensive and iterative optimization cycles to adjust the input-output mapping until they converge to an expected and well-accepted level of performance, such as an acceptable threshold error rate between a calculated probability and a desired threshold probability.”)
mapping, by the one or more hardware processors, the two or more identifiers associated with the one or more first users on the first information and the one or more identities associated with the one or more first users, by the machine learning model,
wherein the machine learning model comprises a K-nearest neighbors (KNN) model, configured with one or more hyperparameters; and
wherein the one or more hyperparameters comprises a K-value hyperparameter and a distance metric hyperparameter;
(See Pedone, para. [0056]: “A machine learning program may be configured to implement stored processing, such as decision tree learning, association rule learning, artificial neural networks, recurrent artificial neural networks, long short term memory networks, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, genetic algorithms, k-nearest neighbor (“KNN”), and the like. Additionally or alternatively, the machine learning algorithm may include one or more regression algorithms configured to output a numerical value in response to a given input. Further, the machine learning may include one or more pattern recognition algorithms—e.g., a module, subroutine or the like capable of translating text or string characters and/or a speech recognition module or subroutine. The machine learning modules may include a machine learning acceleration logic (e.g., a fixed function matrix multiplication logic) that implements the stored processes or optimizes the machine learning logic training and interface.”)
determining, by the one or more hardware processors, the one or more unique identifiers associated with the one or more first users based on mapping of at least one of: the one or more first identifiers, the one or more second identifiers and the one or more third identifiers, associated with the one or more first users, on the first information related to at least one of: the one or more magnetic ink character recognition (MICR) numbers, the one or more international bank account numbers (IBAN), and the one or more identities associated with the one or more first users:
(See Pedone, para. [0124]: “In some embodiments, the content recognition analysis can rely in part on MICR techniques. The MICR techniques generally require a dedicated magnetic reader device that is integrated with, or in signal communication with, the user computing device or provider terminal computing device. Portions of a transfer instrument can include characters printed or generated with magnetic ink or toner that are detected by the magnetic reader device to identify characters.”)
providing, by the one or more hardware processors, an output of the determined one or more unique identifiers associated with the one or more first users, to the one or more second users on a user interface associated with the one or more electronic devices; and
(See Pedone, para. [0040]: “The integrated software applications also typically provide a graphical user interface (“GUI”) on the user computing device display screen 140 that allows the user 110 to utilize and interact with the user computing device. Example GUI display screens are depicted in the attached figures. The GUI display screens may include features for displaying information and accepting inputs from users, such as text boxes, data fields, hyperlinks, pull down menus, check boxes, radio buttons, and the like. One of ordinary skill in the art will appreciate that the exemplary functions and user-interface display screens shown in the attached figures are not intended to be limiting, and an integrated software application may include other display screens and functions.”)
updating, by the one or more hardware processors, one or more database records associated with the one or more financial transactions by linking the determined one or more unique identifiers to the one or more identities associated with the one or more first users to facilitate reconciliation of the one or more financial transactions.
(See Pedone, para. [0052]: “External systems 270 and 272 represent any number and variety of data sources, users, consumers, customers, enterprises, and groups of any size. In at least one example, the external systems 270 and 272 represent remote terminal utilized by the enterprise system 200 in serving users 110. In another example, the external systems 270 and 272 represent electronic systems for processing payment transactions.)”)
In regards to claim 2,
2. The machine-learning based (ML-based) computing method of claim 1, wherein:
the one or more first users comprise at least one of: one or more customers, one or more organizations, one or more corporations, one or more parent companies, one or more subsidiaries, one of more joint ventures, one or more partnerships, one or more governmental bodies, one or more associations, and one or more legal entities; and
(See Pedone, para. [0052]: “External systems 270 and 272 represent any number and variety of data sources, users, consumers, customers, enterprises, and groups of any size. In at least one example, the external systems 270 and 272 represent remote terminal utilized by the enterprise system 200 in serving users 110. In another example, the external systems 270 and 272 represent electronic systems for processing payment transactions. The system may also utilize software applications that function using external resources 270 and 272 available through a third-party provider, such as a Software as a Service (“SasS”), Platform as a Service (“PaaS”), or Infrastructure as a Service (“IaaS”) provider running on a third-party cloud service computing device. For instance, a cloud computing device may function as a resource provider by providing remote data storage capabilities or running software applications utilized by remote devices.”)
Also, the Examiner holds that this feature is directed to intended use.
the one or more second users comprises at least one of one or more data analysts, one or more business analysts, one or more cash analysts, one or more financial analysts, one or more collection analysts, one or more debt collectors, and one or more professionals associated with cash and collection management.
(See Pedone, para. [0227]: “An example system for securing and validating electronic transfer instruments and detecting errors is shown in FIG. 13 . The system includes a Secure Agent software engine that processes electronic transfer instruments, transfer activity data, system configuration data, and end user data to detect potential errors or instances of fraud collectively referred to herein as “transfer tags.” The Secure Agent utilizes artificial intelligence and machine learning technology to determine a Secure Score that represents the likelihood an electronic transfer instrument is fraudulent or contains errors such that the underlying transfer should not be processed and “posted” by the Deposit Platform. The Deposit Platform receives posting data from the Transfer Instrument Process Engine and completes the transfers by posting the transfers to a product account.”)
Also, the Examiner holds that this feature is directed to intended use.
In regards to claim 9, it is rejected on the same grounds as claim 1.
In regards to claim 10, it is rejected on the same grounds as claim 2.
In regards to claim 17, it is rejected on the same grounds as claim 1.
Response to Amendments
Re: Claim Rejections - 35 USC § 101
The 35 U.S.C. §101 rejection of all pending claims has been amended, as necessitated by Applicant’s amendments to the claims.
Re: Claim Rejections - 35 USC § 102
The 35 U.S.C. §§102(a)(1) and (a)(2) rejection of independent claims 1, 9, and 17 and dependent claims 2 and 10 as being anticipated by US-2019/0213822-A1 to Jain et al. (“Jain”. Filed on Jan. 6, 2018. Published on Jul. 11, 2019) was previously withdrawn.
A new 35 U.S.C. §102 (a)(2) rejection has been applied.
Conclusion
Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
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.
Any inquiry concerning this communication or earlier communications should be directed to Examiner Ayal Sharon, whose telephone number is (571) 272-5614, and fax number is (571) 273-1794. The Examiner can normally be reached from Monday to Friday between 9 AM and 6 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SPE Christine Behncke can be reached at (571) 272-8103 or at christine.behncke@uspto.gov. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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Sincerely,
/Ayal I. Sharon/
Examiner, Art Unit 3695
March 14, 2026