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 .
Response to Arguments
Applicant's arguments filed February 13th, 2025 have been fully considered but they are not persuasive.
Regarding 35 U.S.C. § 101, the independent claims 1, 9, and 17 remain directed to an abstract idea—namely, the collection, analysis, and classification of behavioral data to determine whether to file a Suspicious Activity Report (SAR). As set forth in the previous Office Action, these claims recite limitations such as receiving behavioral data, determining a behavior trace, classifying behavior into predefined categories, analyzing behavioral traces to infer intended goals, and using these results to decide whether to file an SAR. These limitations fall within judicial exceptions, as they involve mental processes that could be performed by a human, data gathering techniques, and categorization of data, which are considered abstract ideas under MPEP 2106.04(a)(2)(III).
Applicant argues that the amendments to the claims integrate the abstract idea into a practical application by reciting the determination of whether to file an SAR and the provision of a rationale to assist law enforcement in preventing criminal activity. Applicant analogizes the claims to Example 47, Claim 3 of the July 2024 Subject Matter Eligibility Guidance, arguing that the claims implement proactive measures akin to a security system improvement. However, this analogy is unpersuasive. Unlike Example 47, which improves a computer’s functionality by modifying how a security system operates in response to detected anomalies, the present claims merely automate a decision-making process for human investigators. The claims do not improve how a computer functions, nor do they introduce a specific technological solution to a problem. Instead, they rely on generic computing elements such as a processor and memory to execute an abstract classification scheme, which is insufficient to render them patent-eligible under MPEP 2106.05(f). Furthermore, the step of filing an SAR is an administrative decision that does not alter how data is processed or analyzed in a non-abstract manner. Because the claims do not integrate the abstract idea into a practical application, the analysis proceeds to Step 2B.
Under Step 2B, the claims fail to provide significantly more than the abstract idea itself. The reliance on generic computing elements does not amount to an inventive concept because the claimed method follows well-known machine learning and fraud detection principles commonly applied in financial monitoring systems. The applicant has not demonstrated that the recited steps introduce an unexpected result or an unconventional approach beyond automating mental processes and reporting outputs to law enforcement. Accordingly, the claims remain ineligible under 35 U.S.C. § 101.
Regarding 35 U.S.C. § 103, claims 1-20 remain unpatentable as they are obvious over Reddy in view of Juban. The applicant argues that Reddy does not disclose certain limitations, particularly (1) "actions performed by a person in response to each behavioral state" and (2) "determining at least two potential intended goals of the person (Remarks, pg. 4)." However, these arguments do not overcome the rejection. Reddy (¶0069) discloses identifying behavioral patterns indicative of criminal intent, which inherently includes analyzing user behavior in response to different stimuli. The applicant’s attempt to distinguish "behavioral states" from "actions performed in response" is unpersuasive (Remarks, pg. 5), as fraud detection inherently relies on tracking how individuals act in response to financial triggers. Furthermore, Reddy (¶0073) teaches classifying individuals based on suspicious behavioral intent, which includes the inference of intended financial and criminal goals. When combined with Juban (col. 19:33-47), which discloses categorization of behavior across multiple domains (including financial, professional, and transactional activities), it would have been obvious for a person of ordinary skill in the art (POSITA) to apply these known classification techniques to distinguish between fraudulent intent and legitimate financial behavior. The applicant has not provided evidence that these methods yield an unexpected or novel result, and thus, the rejection under 35 U.S.C. § 103 remains appropriate.
Furthermore, the applicant asserts that Reddy does not disclose historical behavior trace data and that its "scenarios" are not equivalent to historical information (Remarks, pg. 6). However, this argument is also unpersuasive, as Juban explicitly teaches the use of historical transaction patterns for classification, which a POSITA would naturally apply to Reddy’s fraud detection methods. The use of historical data in fraud detection is a routine and predictable extension of known classification models, making the claimed method obvious.
Accordingly, the rejections under 35 U.S.C. § 101 and 35 U.S.C. § 103 are maintained, as the applicant’s arguments fail to demonstrate either eligibility or non-obviousness.
Claim Rejections - 35 USC § 112
Claims 7 and 16 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 7 and 16 recites the limitation "the money laundering crime, the fraud, and the cyber-crime.” There is insufficient antecedent basis for this limitation in the claim, as the parent claims 1 and 9 introduce "a money laundering crime, a fraud, and a cyber-crime.”
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claims are directed to abstract ideas without a practical application nor significantly more to overcome the judicial exception.
Regarding claim 1 and analogous claims 9 and 17:
Step 1: is the claim directed to one of the four statutory categories?
Yes, the limitation is directed to a method.
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation: “determining, by the at least one processor, at least one behavior trace based on the received information;” and “and using a result of the determining, a result of the classifying, and a result of the analyzing to determine whether to file a Suspicious Activity Report (SAR) with respect to the person, and to provide a rationale for the SAR,” is directed to a mental process of judgment under MPEP 2106.04(a)(2)(III), and “and classifying, by the at least one processor, each of the determined at least one behavior trace into a respective category from among a predetermined plurality of behavioral categories, ”analyzing each of the determined at least one behavior trace to determine at least two potential intended goals of the person;” is directed to a mental process of evaluation under MPEP 2106.04(a)(2)(III).
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitations: “receiving, by the at least one processor, information that relates to a behavior of a person;” is directed to mere data gathering under MPEP 2106.05(g); further, the limitations: “wherein the at least one behavior trace comprises a sequence of behavioral states and actions performed by the person in response to each respective behavioral state” and “and wherein the at least two potential intended goals include: at least one from among committing a money laundering crime, committing a fraud, and committing a cyber-crime;” and “and at least one from among owning a house, owning a product, working for a company, creating a company, and making payments to a utility company” are directed to field of use under MPEP 2106.05(h).
Examiner notes that the limitation in analogous claim 9: “a processor;a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor is configured to:” is directed to mere instructions to apply an exception under MPEP 2106.05(f).
Examiner notes that the limitation in analogous claim 18: “ the storage medium comprising executable code which, when executed by a processor, causes the processor to:” is directed to mere instructions to apply an exception under MPEP 2106.05(f).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitations: “receiving, by the at least one processor, information that relates to a behavior of a person;” is directed to well-understood, routine, and conventional activity under MPEP 2106.05(d) of “receiving or transmitting data over a network;” further, the limitation: “wherein the at least one behavior trace comprises a sequence of behavioral states and actions performed by the person in response to each respective behavioral state” and “and wherein the at least two potential intended goals include: at least one from among committing a money laundering crime, committing a fraud, and committing a cyber-crime;” and “and at least one from among owning a house, owning a product, working for a company, creating a company, and making payments to a utility company” is directed to field of use under MPEP 2106.05(h).
Examiner notes that the limitation in analogous claim 9: “a processor; a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor is configured to:” is directed to mere instructions to apply an exception under MPEP 2106.05(f).
Examiner notes that the limitation in analogous claim 18: “ the storage medium comprising executable code which, when executed by a processor, causes the processor to:” is directed to mere instructions to apply an exception under MPEP 2106.05(f).
Regarding claim 2 and analogous claims 10 and 18:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes, the limitation is dependent on claim 1.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “wherein the information that relates to the behavior of the person includes information that relates to at least one financial transaction executed by the person” is directed to field of use under MPEP 2106.05(h).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “wherein the information that relates to the behavior of the person includes information that relates to at least one financial transaction executed by the person” is directed to field of use under MPEP 2106.05(h).
Regarding claim 3 and analogous claims 11 and 19:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation: “wherein the determining of the at least one behavior trace comprises applying a relational instance-based learning algorithm to the received information” is directed to a mental process of evaluation under MPEP 2106.04(a)(2)(C)(III).
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “and obtaining information that indicates the at least one behavior trace as an output of the relational instance-based learning algorithm” is directed to mere data gathering under MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “and obtaining information that indicates the at least one behavior trace as an output of the relational instance-based learning algorithm” is directed to well-understood, routine, and conventional activity of “receiving or transmitting data over network” under MPEP 2106.05(d).
Regarding claim 4 and analogous claim 12:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation: “wherein the classifying includes using at least one machine learning algorithm to compare the determined at least one behavior trace with historical behavior trace data to determine the respective category” is directed to a mental process of judgment under MPEP 2106.04(a)(2)(III).
Regarding claim 5 and analogous claim 13:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes, the limitation is dependent on claim 1.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “wherein the predetermined plurality of behavioral categories includes a first category that corresponds to behaviors that indicate an intention to commit a crime and a second category that corresponds to behaviors that indicate standard non-criminal activity” is directed to field of use under MPEP 2106.05(h).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “wherein the predetermined plurality of behavioral categories includes a first category that corresponds to behaviors that indicate an intention to commit a crime and a second category that corresponds to behaviors that indicate standard non-criminal activity” is directed to field of use under MPEP 2106.05(h).
Regarding claim 6 and analogous claims 14 and 20:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation: “further comprising analyzing each of the determined at least one behavior trace to determine a potential intended goal of the person” is directed to a mental process of judgment under MPEP 2106.04(a)(2)(III).
Regarding claim 7 and analogous claim 15:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
No. The limitation: “wherein the analyzing includes determining whether the determined at least one behavior trace indicates an increased probability of behavior that includes a financial crime” is directed to a mental process of judgment under MPEP 2106.04(a)(2)(III).
Regarding claim 8 and analogous claim 16:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes, the limitation is dependent on claim 1.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “wherein the financial crime includes at least one from among the moneylaundering crime, the fraud, and the cyber-crime” is directed to field of use under MPEP 2106.05(h).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “wherein the financial crime includes at least one from among the money-laundering crime, the fraud, and the cyber-crime” is directed to field of use under MPEP 2106.05(h).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 7-13, 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over US Pre-Grant Patent 2019/0259033 (Reddy et al; Reddy) in view of US Patent 11,263,703 (Juban et al; Juban).
Regarding claim 1 and analogous claims 9 and 17:
Reddy teaches:
1. A method for detecting an anomaly in human behavior, the method being implemented by at least one processor, the method comprising:
(Reddy, ¶0049)
“According to another embodiment, there is provided a computer implemented method of detecting anomalies or suspicious activities in multi-dimensional financial activity streams (FACTS) comprised of multi-dimensional data points [i.e. A method for detecting an anomaly in human behavior, the method being implemented by at least one processor, the method comprising:]”
2. receiving, by the at least one processor, information that relates to a behavior of a person;
(Reddy, ¶0053)
“In FIG. 1, data sources 211, 212, 213, and 214 are computer accessible components that provide and/or stores data from banks or other institutions involved in transactions or that are sources of data that may be relevant in the classification of transactions as suspicious or representing illegal or otherwise bad behavior.”
3. determining, by the at least one processor, at least one behavior trace based on the received information;
(Reddy, ¶0069)
“In response to the data inputs, feature discovery processor 1104 performs feature learning to find common features and distinct features of the individual that are indicative of unique behavior patterns that are signatures of bad intent.”
4. analyzing each of the determined at least one behavior trace to determine at least two potential intended goals of the person;
(Reddy, ¶0099)
“…the scenario “Suspicious Customer Attributes” identifies customers with attributes that are marked as red flags. The attributes to be identified as red flags are dependent on the policies set by the financial institution, a state government and the federal government. In one embodiment, the KYC (Know Your Customer) screening process gathers information on customers during the account opening phase. It is during this time that the red flags are identified. Examples of customer attributes that are considered as red flags: (i) Politically Exposed Person, (ii) Foreign Financial Official, (iii) Is on a Watchlist, (iv) Is on a Blacklist, (v) Has a Non Physical Address, (vi) Is a Non-Resident, (vii) Has a Suspicious Activity Report filed, (viii) Has a Criminal Record, (ix) Has a Recalcitrant Account, (x) Has a Blacklisted Account, (xi) Has an Income to Expense Mismatch, (xii) Has a Risky Occupation, and (xiii) Has a Risky Business.”
5. wherein the at least one behavior trace comprises a sequence of behavioral states and actions performed by the person in response to each respective behavioral state.
(Reddy, ¶0069)
“That is, the feature discovery process performed by processor 1104 identifies features of the individual's behavior that indicates a certain likelihood that they will act with criminal intent and/or bad faith.”
6. and wherein the at least two potential intended goals include: at least one from among committing a money laundering crime, committing a fraud, and committing a cyber-crime;
(Reddy, ¶0071)
“Scenarios help the end user to define various types of behavior one would use to detect suspicious activity. In one embodiment, scenarios are user-defined behaviors/typologies that evaluate and examine a customer's profile, transactions, account history, and other underlying customer attributes to generate alerts based off of the thresholds set, to indicate suspicious or money laundering activities [i.e. and wherein the at least two potential intended goals include: at least one from among committing a money laundering crime,].”
7. and at least one from among owning a house, owning a product, working for a company, creating a company, and making payments to a utility company.
(Reddy, ¶0071)
“In one embodiment, scenarios are user-defined behaviors/typologies that evaluate and examine a customer's profile, transactions, account history, and other underlying customer attributes to generate alerts based off of the thresholds set, to indicate suspicious or money laundering activities.”
Examiner notes that under BRI, having an account in a particular bank is itself a product that the consumer has invested in. See attached NPL: Bank Products.
Reddy does not teach:
1. and classifying, by the at least one processor, each of the determined at least one behavior trace into a respective category from among a predetermined plurality of behavioral categories,
2. and using a result of the determining, a result of the classifying, and a result of the analyzing to determine whether to file a Suspicious Activity Report (SAR) with respect to the person, and to provide a rationale for the SAR,
Juban teaches:
1. and classifying, by the at least one processor, each of the determined at least one behavior trace into a respective category from among a predetermined plurality of behavioral categories,
(Juban, col. 19: 33-47)
“The feature set may be broken down by feature class [i.e. and classifying, by the at least one processor, each of the determined at least one behavior trace into a respective category], such as party attributes (e.g., attributes or characteristics of the client including both internal and externally available data), party behaviors (e.g., behavior of parties as demonstrated through transactions, wires, or other actions that leave a digital trace), anomalies (e.g., abnormal transaction patterns relative to stated business; abnormal patterns relative to historical benchmark; abnormal patterns relative to stated income), associations (e.g., proximity to known money launderers; similar transaction patterns to known money launderers; associations with high-risk businesses or countries), and segmentation (e.g., segmentation based on country, transaction behavior, business sector, legal entity type, shared accounts, high-frequency relationships) [i.e. from among a predetermined plurality of behavioral categories,].”
2. and using a result of the determining, a result of the classifying, and a result of the analyzing to determine whether to file a Suspicious Activity Report (SAR) with respect to the person, and to provide a rationale for the SAR,
(Juban, col. 18: 11-15)
“As shown in FIG. 14C, the machine learning model may analyze raw data (including transaction data, account holder data, watch lists, and public domain data) across disparate data sources, and unify or aggregate such data into a unified, federated data lake…[i.e. and using a result of the determining, a result of the classifying, and a result of the analyzing].”
(Juban, col. 18: 31-36)
“Sophisticated visualizations of client transactions and associations can be provided by the machine learning model. Further, effective SAR identification can be performed with minimal false positives. The investigators may prepare reports using the results and/or the visualizations of the machine learning model [i.e. to determine whether to file a Suspicious Activity Report (SAR) with respect to the person, and to provide a rationale for the SAR,].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Reddy with Juban. The motivation is to include the well-known, well-understood scheme of multi-label classification, which “is a classification task labeling each sample with m labels from n-classes possible classes, where m can be 0 to n_classes inclusive.” See attached NPL: “1.12. Multiclass and multioutput algorithms,” Scikit Learn.
Regarding claim 2 and analogous claims 10 and 18:
The combination of Reddy and Juban teach the method of claim 1.
Reddy teaches:
1. wherein the information that relates to the behavior of the person includes information that relates to at least one financial transaction executed by the person.
(Reddy, ¶0028)
“A banking enterprise digital genome engine and method for using the same to enable banking enterprises to create the digital gene expression of the banking customers, accounts and their financial activity streams (FACTS).”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Reddy with Juban. The motivation is the same as claim 1.
Regarding claim 3 and analogous claims 11 and 19:
The combination of Reddy and Juban teach the method of claim 1.
Reddy teaches:
1. wherein the determining of the at least one behavior trace comprises applying a relational instance-based learning algorithm to the received information
(Reddy, ¶0029)
Thus, unlike the prior art, in one embodiment, the detection of financial activity streams (“FACTS”) as “suspicious” or “unsuspicious” is done by the application of a financial genome combined with deep neural network algorithms that convert FACTS into a set of signals representing most relevant “threat vector” measured at regular intervals for each newly arrived data point in the embedded space.
2. and obtaining information that indicates the at least one behavior trace as an output of the relational instance-based learning algorithm.
(Reddy, ¶0030)
“Clustering of these features in the similarity measures characterizes different behavioral patterns, such that all the normal activities are inside “safe” clusters and all anomalies are outside the safe clusters.”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Reddy with Juban. The motivation is the same as claim 1.
Regarding claim 4 and analogous claim 12:
The combination of Reddy and Juban teach the method of claim 1.
1. wherein the classifying includes using at least one machine learning algorithm to compare the determined at least one behavior trace with historical behavior trace data to determine the respective category.
(Reddy, ¶0071)
“Scenarios help the end user to define various types of behavior one would use to detect suspicious activity. In one embodiment, scenarios are user-defined behaviors/typologies that evaluate and examine a customer's profile, transactions, account history, and other underlying customer attributes to generate alerts based off of the thresholds set, to indicate suspicious or money laundering activities [i.e. to compare the determined at least one behavior trace with historical behavior trace data to determine the respective category.]. In one embodiment, processor 1104 executes an application that helps configure scenarios, update or manipulate thresholds for scenarios and manage the computations for these scenarios…These features are fed as input data to the machine learning models [i.e. wherein the classifying includes using at least one machine learning algorithm].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Reddy with Juban. The motivation is the same as claim 1.
Regarding claim 5 and analogous claim 13:
The combination of Reddy and Juban teach the method of claim 1.
Reddy teaches:
1. wherein the predetermined plurality of behavioral categories includes a first category that corresponds to behaviors that indicate an intention to commit a crime and a second category that corresponds to behaviors that indicate standard non-criminal activity.
(Reddy, ¶0030)
“Clustering of these features in the similarity measures characterizes different behavioral patterns, such that all the normal activities are inside “safe” clusters and all anomalies are outside the safe clusters.”
Examiner notes that contextually, the safe clusters refer to non-criminal activity and the anomalies refer to criminal activity in the accounts.
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Reddy with Juban. The motivation is the same as claim 1.
Regarding claim 7 and analogous claim 15:
The combination of Reddy and Juban teach the method of claim 1.
1. wherein the analyzing includes determining whether the determined at least one behavior trace indicates an increased probability of behavior that includes a financial crime.
(Reddy, ¶0075)
“Once the fingerprint for a customer has been generated, the fingerprint is used to determine the probability of whether each new transaction being conducted by the customer is a bad act (e.g., whether the customer is acting with bad intent).”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Reddy with Juban. The motivation is the same as claim 1.
Regarding claim 8 and analogous claim 16:
The combination of Reddy and Juban teach the method of claim 1.
1. wherein the financial crime includes at least one from among the moneylaundering crime, the fraud, and the cyber-crime.
(Reddy, ¶0073)
“In one embodiment, the anomaly is automatically identified as a potentially fraudulent activity or suspicious activity and provides enhanced detection of suspicious activity or fraudulent behavior.”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Reddy with Juban. The motivation is the same as claim 1.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 PAUL JUSTIN BREENE whose telephone number is (571)272-6320. 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, Michael J Huntley can be reached on 303-297-4307. 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.
/P.J.B./ Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129