DETAILED ACTION
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 .
Status of Claims
This action is in reply to the application filed on 05/22/2024.
Claims 1-20 are currently pending and have been examined.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In the instant case, claims 1, 14 and 23 are directed to a method, system, and non-transitory computer-readable recording medium.
For the purposes of this analysis, representative claim 1 is addressed. Abstract ideas are in bold below, and represents updating customer data via transaction data which represents commercial and legal interaction, which is “Certain methods of organizing human activity” in prong one of step 2A (MPEP 2106.04(a)).
receiving, by a computer program executed by an electronic device, a plurality of transactions conducted with a financial instrument issued to a customer;
extracting, by the computer program, metadata from one of the plurality of transactions;
retrieving, by the computer program, a plurality of stored mutable characteristics for the customer;
determining, by the computer program, that the metadata indicates a possible change in one of the plurality of stored mutable characteristics;
requesting, by the computer program, confirmation that the one stored mutable characteristic has changed; and
updating, by the computer program, the one stored mutable characteristic in response to receiving confirmation that the one stored mutable characteristic has changed.
The additional elements of claim 1 such as “receiving, by a computer program executed by an electronic device, a plurality of transactions conducted with a financial instrument issued to a customer”, “extracting, by the computer program, metadata from one of the plurality of transactions”, and “retrieving, by the computer program, a plurality of stored mutable characteristics for the customer; requesting, by the computer program, confirmation that the one stored mutable characteristic has changed” represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular field of use. Furthermore, “extracting, by the computer program, metadata from one of the plurality of transactions” lacks detail on how “extracting … metadata from one of the plurality of transactions” is accomplished, therefore it amounts to no more than “apply it” (MPEP 2106.05(f)(1)). Additionally, “receiving, by a computer program executed by an electronic device, a plurality of transactions conducted with a financial instrument issued to a customer” represent the use of computer functions to perform the abstract idea. (MPEP 2106.05(f)(2)). Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to (i.e., automate) the acts of banknote suspension and reinstatement.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration into a practical application, the additional elements amount to no more than mere instructions to apply the abstract idea of using generic computer components. The claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea of updating customer data via transaction data
Hence, claims 1, and 12 are not patent eligible.
Dependent claims 2-11 and 13-20 recited additional details which only further narrow the abstract idea and do not add any additional features, alone or in combination, that would provide a practical application or provide significantly more.
Claim 5 and 16 recites the additional elements of “the confidence level is determined using a trained machine learning engine.” does no more than use a computer as a tool to perform an abstract idea and do no more than generally link the abstract idea to a particular field of use. Therefore, as it is no more than apply it does not improve the functioning of a computer, or improve other technology or technical field.
Claim 11 and 20 recites the additional elements of “receiving, by the computer program, geolocations for customer access to a computer application executed by a mobile electronic device associated with the customer” does no more than use a computer as a tool to perform an abstract idea and do no more than generally link the abstract idea to a particular field of use. Therefore, as it is no more than apply it does not improve the functioning of a computer, or improve other technology or technical field.
The claims as a whole do not amount to significantly more than the abstract idea itself. This is because the claims do not affect an improvement to another technology or technical field, the claims do not amount to an improvement to the functioning of a computer system itself, and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
Accordingly, there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 either status.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, 11-17 and 20are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fenichel et al. (US 2023/0222613 A1)
Regarding claims 1 and 12
A method, comprising: receiving, by a computer program executed by an electronic device, a plurality of transactions conducted with a financial instrument issued to a customer; (See at least Fenichel [0106] In step 535, after the browser extension has detected that a page has a customer address and identified an address element, the extension may scrape the element for the address provided by the user. Using JavaScript or a similar scraping tool, the extension can capture the value of the inputted address. In some embodiments, this may require scraping multiple elements to obtain all of the pieces of a customer address.
extracting, by the computer program, metadata from one of the plurality of transactions; (See at least Fenichel [0107] Depending on the stage of the user workflow, the browser extension may scrape different fields within third-party website 612. In aspects such as shown in step 534A, browser extension 630 scrapes the address from online form 613 (without interrupting user interactions with the web page continues (e.g., steps 523-524)), such as the shipping address 628 or billing address 629
retrieving, by the computer program, a plurality of stored mutable characteristics for the customer; (See at least Fenichel [0047] Browser extension 230 sends the user location data 246 to a location database 240 on internal server 280. In some embodiments, the internal server 280 and location database 240 may be operated by the same party which operates browser extension 230. Internal server 280 and location database 240 may also be operated by a third-party hosting service, a software-as-a-service (SaaS) company, or a similar entity working on behalf of the party making the change of address determination.
determining, by the computer program, that the metadata indicates a possible change in one of the plurality of stored mutable characteristics; (See at least Fenichel [0110] and [0111]: [0110] In step 550, the machine learning model analyzes the location data records for the user and generates a prediction as to whether the user has changed their address. [0111] If in step 550 the machine learning model generates a prediction that the user has likely changed their address, in step 560, a change of address process may be initiated for the user based on the prediction from step 550. As previously discussed with FIG. 2 in step 260, the change of address process may include prompting the user upon their next login to confirm the current address, flagging the user for internal review by a human analyst, prompting the user to update the address in a third-party registry, and more.
requesting, by the computer program, confirmation that the one stored mutable characteristic has changed; and (See at least Fenichel [0054] A change of address process initiated in step 260 may include several different steps or processes. In some implementations, the change of address process may include prompting the user upon their next login to confirm their current address. In other implementations, the change of address process may include flagging the user for internal review by a human analyst. In further implementations, the change of address process may include prompting the user to update the address in a third-party registry, such as the USPS National Change of Address Registry. These embodiments should be considered as examples of potential workflows, and unlisted workflows for the same purpose are also covered by this disclosure.
updating, by the computer program, the one stored mutable characteristic in response to receiving confirmation that the one stored mutable characteristic has changed. (See at least Fenichel [0053] Based on the prediction indicating a user is likely to have changed addresses, a change of address process may be initiated for the user in step 260.
Regarding claims 2 and 13
wherein the stored mutable characteristics comprise a home address, a phone number, a marital status, a surname, a family status, or an employment status. (See at least Fenichel [0037] Aspects discussed herein may relate to methods and techniques for obtaining user location data from web activity by the user and analyzing that location data in a machine-learning model to determine if the user has changed addresses. Based on records associated with the user, the machine learning model analyzes the location data makes a prediction whether a user has changed their address. If the machine learning model predicts that a user has changed their address, a change of address process for the user is initiated.
Regarding claims 3 and 14
wherein the step of determining, by the computer program, that the metadata indicates a possible change in one of the plurality of stored mutable characteristics comprises: identifying, by the computer program, a mutable characteristic in the metadata that differs from the one stored mutable characteristic. (See at least Fenichel [0059] and [0060]: [0059] In step 340, the internal database generates location data records, where each location data record comprises location data 246 and access time 247. In some embodiments, each location data record may also include additional data 248 that would be helpful for the change of address prediction. A similar database of location data records may be used in FIGS. 9—10. The database's functionality in those workflows will be discussed with respect to those figures later herein. [0060] Next, in step 350, the machine learning model analyzes the location data records for the user and generates a prediction as to whether the user has changed their address.
Regarding claims 4 and 15
determining, by the computer program, a confidence level in the possible change in the one stored mutable characteristic. (See at least Fenichel [0061] If in step 350 the machine learning model generates a prediction that the user has likely changed their address, in step 360, a change of address process may be initiated for the user based on the prediction from step 350. As previously discussed with FIG. 2 in step 260, the change of address process may include prompting the user upon their next login to confirm the current address, flagging the user for internal review by a human analyst, prompting the user to update the address in a third-party registry, and more.
Regarding claims 5 and 16
wherein the confidence level is determined using a trained machine learning engine. (See at least Fenichel [0061] If in step 350 the machine learning model generates a prediction that the user has likely changed their address, in step 360, a change of address process may be initiated for the user based on the prediction from step 350. As previously discussed with FIG. 2 in step 260, the change of address process may include prompting the user upon their next login to confirm the current address, flagging the user for internal review by a human analyst, prompting the user to update the address in a third-party registry, and more.
Regarding claims 6 and 17
wherein at least one of the plurality of transactions is a card present transaction. (See at least Fenichel [0129] In step 950, the machine learning model evaluates both sets of location data records and generates a prediction as to whether the user has changed addresses. This model would be trained similarly to the model described in FIG. 8, with the addition of training data including in-person transactions as well as online transactions.
Regarding claims 11 and 20
receiving, by the computer program, geolocations for customer access to a computer application executed by a mobile electronic device associated with the customer; (See at least Fenichel [0071] In some embodiments, step 419 may be skipped even if step 418 is executed. In these embodiments, third-party website 212 may include a geolocation API request that is executed by browser 211, but does not return a user geolocation. For example, a user may block geolocation API requests from acquiring the user's geolocation by: denying permission when the API requests the user's location; using an adblocker that prevents geolocation APIs from executing; using a browser that blocks geolocation API requests from website that do not use HTTPS protocols; and more.
wherein the computer program further determines the possible change in the one stored mutable characteristic based on the geolocations. (See at least Fenichel [0079] An IP address lookup API, which identifies a geolocational position or region based on an IP address and the public router information included within the IP address, may be used to convert the IP address of internet-enabled 210 to location data 246. This IP address lookup may be performed by browser extension 230 to refine the IP address prior to step 439, or may be performed as part of steps 440 or 450. In some embodiments, machine-learning model 250 from FIG. 2 may also be trained with location information in the form of IP addresses, and the model may analyze the location data in an IP address format without converting the IP address into a geolocational position or region.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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 either status.
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 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Fenichel et al. (US 2023/0222613 A1) in view of Soldahl (US 11,847,655 B1)
Regarding claims 7 and 18
Fenichel does not specifically teach: wherein a threshold number of transactions having changes in the one stored mutable characteristic before requesting confirmation from the customer.
However, Soldahl teaches at least at (Col 14 lines 41-48) Examples of transaction rules are so numerous as to defy a complete list; however, below are some non-limiting examples. For example, transaction destination new to source, large transaction amount, transaction frequency satisfies a threshold, transaction within a determined threshold beneath a reporting limit, total amount of transaction for a party in the last 7 days exceeds a first threshold and the number of transactions exceeds a second threshold, etc.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the Predicting customer change of address based on web browsing activity and transactions of Fenichel in view of with the Multi-vector suspicious activity identifier as taught by Soldahl in order to a transaction rule includes a condition that if met, based on the transaction data 232, triggers the action engine 344 to take a specified action(s). (Soldahl (Col 14 lines 38-40))
Claims 8-10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Fenichel et al. (US 2023/0222613 A1) in view of Chen et al. (US 11,151,468 B1)
Regarding claims 8 and 19
Fenichel does not specifically teach: The method of claim 1, wherein the transactions that are associated with good or service are associated with a change in the one stored mutable characteristic.
However Chen teaches at least at (Col 25 lines 26-41) In some embodiments, the abnormality detection techniques described can be used to identify changes in life-stage such as moving, graduation, marriage, having babies, etc. When there is a change in the life-stage, the locations of the transactions, stores visited, and timing of the purchase can be different from the user's normal patterns. Therefore, identifying a reduction of the similarity between the current transactions and the historical transactions may be an indication of such events. The identification of such a change may cause selection and transmission of specific content to the user. The specific content may be selected based on the new transactions. For example, if the change detected indicates a new home, the content may be related to home ownership (e.g., home improvement stores, insurance, home service providers, appliances, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the Predicting customer change of address based on web browsing activity and transactions of Fenichel in view of with the Behavior analysis using distributed representation of event data as taught by Chen in order to behavior detection features may be used to provide content or other information to users based on detected behaviors. (Chen (Col 25 lines 23-25)))
Regarding claims 9
Fenichel does not specifically teach: wherein the good or service is associated with a move to a new area.
However Chen teaches at least at (Col 25 lines 26-41) In some embodiments, the abnormality detection techniques described can be used to identify changes in life-stage such as moving, graduation, marriage, having babies, etc. When there is a change in the life-stage, the locations of the transactions, stores visited, and timing of the purchase can be different from the user's normal patterns. Therefore, identifying a reduction of the similarity between the current transactions and the historical transactions may be an indication of such events. The identification of such a change may cause selection and transmission of specific content to the user. The specific content may be selected based on the new transactions. For example, if the change detected indicates a new home, the content may be related to home ownership (e.g., home improvement stores, insurance, home service providers, appliances, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the Predicting customer change of address based on web browsing activity and transactions of Fenichel in view of with the Behavior analysis using distributed representation of event data as taught by Chen in order to behavior detection features may be used to provide content or other information to users based on detected behaviors. (Chen (Col 25 lines 23-25))
Regarding claim 10
Fenichel does not specifically teach: wherein the good or service is associated with a change in family status.
However Chen teaches at least at (Col 8 lines 43-51) Abnormal behavior of credit card transaction usage, may indicate that the credit card is being used by someone who is not an authorized user, thus it can point to fraudulent usage of the cards. In addition, for marketing type of applications, detection of the abnormal behavior can indicate that there is either a short-term behavior change such as travel, vacationing, or long-term life-stage change such as marriage, graduation, family with new born, etc. that causes the shift of behavior.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the Predicting customer change of address based on web browsing activity and transactions of Fenichel in view of with the Behavior analysis using distributed representation of event data as taught by Chen in order to behavior detection features may be used to provide content or other information to users based on detected behaviors. (Chen (Col 25 lines 23-25))
Prior Art of Record Not Currently Relied Upon
Allen (US 2021/0166322 A1) Teaches: Dynamic reconfigurable insurance product.
Duncan (US 2020/0160384 A1) Teaches: Event system leveraging users mobility behaviors.
Hyder (US 2008/0243531 A1) Teaches: Method for predictive targeting on online advertising using life profiling.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY MARK JAMES whose telephone number is (571)272-5155. The examiner can normally be reached M-F 8:30am - 5:00pm EST.
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/GREGORY M JAMES/Examiner, Art Unit 3692
/DAVID P SHARVIN/Primary Examiner, Art Unit 3692