DETAILED ACTION
This communication is in response to application no. 19/055141 filed 17 February 2025.
Claims 1-21 are currently pending and have been examined.
Claims 1-21 are rejected as shown in this 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 .
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 1-21 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.
Step 1
Claims 1-7 recite a method which is considered a process. Claims 8-14 recite a system comprising one or more processors and memory which is considered a machine or manufacture. Claims 15-21 recite a non-transitory computer-readable storage medium which is considered a machine or manufacture. Thus, claims 1-21 all fall into a statutory category.
Step 2A-Prong One
(Claims 1, 8, and 15) The “identifying a set of available offers, wherein the set of available offers corresponds to different entities associated with the payment instrument service” step, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “computer-implemented” language in claims 1, the “cause the system to” language in claim 8, or the “cause the computer system to” language in claim 15, the claim encompasses a user manually using given information to identify a set of offers. These claims fall into the mental processes grouping of abstract ideas. These claims recite an abstract idea.
(Claims 2, 9, and 16) The “identifying a set of rules corresponding to the set of available offers, wherein the set of rules defines different requirements for rendering of different offers from the set of available offers” step, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “computer-implemented” language in claims 2, the “cause the system to” language in claim 9, or the “cause the computer system to” language in claim 16, the claim encompasses a user manually using given information to identify a set of rules. These claims fall into the mental processes grouping of abstract ideas. These claims recite an abstract idea.
(Claims 5, 12, and 19) The “determining that the identifying information does not include a user identifier” step, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “computer-implemented” language in claims 5, the “cause the system to” language in claim 12, or the “cause the computer system to” language in claim 19, the claim encompasses a user manually making a determination that the information does not include an identifier. These claims fall into the mental processes grouping of abstract ideas. These claims recite an abstract idea.
(Claims 7, 14, and 21) The “translating the identifying information into an external user identifier associated with the user, wherein the external user identifier corresponds to a user data connectivity platform that obtains the user interaction data from different external sources” step, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “computer-implemented” language in claims 7, the “cause the system to” language in claim 14, or the “cause the computer system to” language in claim 21, the claim encompasses a user manually translating information into an identifier. These claims fall into the mental processes grouping of abstract ideas. These claims recite an abstract idea.
(Claims 1-21) These claims recite the concept of providing targeted offers to a user based on historical information known about the user and other users (see, for example, “receiving an application programming interface (API) call to identify one or more offers for rendering through an interface implemented through a landing page associated with a payment instrument service, wherein the API call includes identifying information associated with a user and with an ongoing online session, and wherein the API call is submitted during a request to access the landing page; obtaining user interaction data corresponding to interactions with different external online assets by the user, wherein the user interaction data is obtained using the identifying information; identifying a set of available offers, wherein the set of available offers corresponds to different entities associated with the payment instrument service; processing the user interaction data and the set of available offers through a machine learning algorithm to automatically select a set of offers from the set of available offers, wherein the machine learning algorithm is trained using a dataset of historical user interaction data and corresponding offers presented to different users through the interface; transmitting executable instructions that, as a result of being executed by a computing device of the user, cause the computing device to render the set of offers through the interface; monitoring in real-time and through the interface user interaction with the set of offers; and updating the machine learning algorithm according to the user interaction with the set of offers” in claim 1). This concept falls into the certain methods of organizing human activity grouping of abstract ideas including commercial interactions, which includes advertising, marketing, or sales activities or behaviors. Thus, these claims recite an abstract idea.
Step 2A-Prong Two
This judicial exception is not integrated into a practical application. The claims recite the additional element of a computer (found in claims 1-7), a system comprising one or more processors and memory storing thereon instructions (found in claims 8-14), or a non-transitory computer-readable storage medium storing hereon executable instructions (found in claims 15-21) and includes no more than mere instructions to apply the exception using a generic computer component. The computer, system, or medium does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed previously with respect to Step 2A-Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(f). The claims do not provide an inventive concept (significantly more than the abstract idea). The claims are ineligible.
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 of this title, 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 1-4, 6-11, 13-18, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0051282 (“Morin”) in view of US 2017/0039601 (“Gopal”).
Regarding Claims 1, 8, and 15, Morin teaches a computer-implemented method (See “A method for generating recommended offers, the method performed by one or more processors” in claim 1.), a system, comprising: one or more processors; and memory storing thereon instructions (See “system comprising: one or more processors; and at least one memory coupled to the one or more processors and storing instructions” in claim 9.), and a non-transitory, computer-readable storage medium storing thereon executable instructions (See “non-transitory computer-readable medium storing instructions” in claim 17.); the method comprising:
obtaining user interaction data corresponding to interactions with different external online assets by the user (See “Referring to FIG. 1, FIG. 2, and FIG. 5 together, in one embodiment, current user's data 205, including, but not limited to, current user's demographic data 210, current user's accounts data 212, current user's clickstream data 214, current user's transaction data 216, and current user's credit history data 218 for the current user is obtained from any one or more of the sources of current user's data, and/or user data, discussed herein, and/or as known in the art” in ¶ 0167.);
identifying a set of available offers, wherein the set of available offers corresponds to different entities associated with the payment instrument service (See “current offer data representing one or more current offers associated with one or more products is obtained, in one embodiment, from one or more product and/or service providers and/or a current offer database” in ¶ 0067, “the current offer data includes one or more financial service and product offers that are “quick action” offers related to financial services and products, such as, but not limited to, credit cards and/or lines of credit, or any other financial service or product offers” in ¶ 0068, and “the current offer data 225 includes data such as, but not limited to: the names of providers, such as financial institutions, associated with the offers; categories of the providers, such as financial institutions, associated with the offers; terms of the offers; rewards or loyalty programs associated with the offers; and approval criteria and/or acceptance rates associated with the offers” in ¶ 0141.);
processing the user interaction data and the set of available offers through a machine learning algorithm to automatically select a set of offers from the set of available offers, wherein the machine learning algorithm is trained using a dataset of historical user interaction data and corresponding offers presented to different users through the interface (See “the historical offer performance model training data 155 includes, but is not limited to, one or more of: data indicating whether a given historical offer made to a user was clicked on, or otherwise interacted with, by the user” in ¶ 0116, “the historical offers represented in the historical offer model training data 125 of the user model training data are correlated to the respective users represented in the model training data 105 to whom the historical offers were made. As noted above, in one embodiment, the historical offer model training data 125 is analyzed and processed to identify historical offer attributes and generate historical offer attribute model training data 153 associated with the historical offers represented in the historical offer model training data 125. Consequently, in various embodiments, the historical offer model training data 125 includes historical offer attribute model training data 153 associated with two or more historical offers and users, and, in various embodiments, millions of offers, tens of millions of offers, hundreds of millions of offers, or more, and users/consumers” in ¶ 0117, ¶ 0118, “FIG. 5 is a hybrid functional and flow diagram 500 of the online runtime execution of a method and system for using machine learning techniques to identify and recommend relevant offers, in accordance with one embodiment” in ¶ 0166, “at 549, portions of the current offer data 225 representing each current offer represented in current offer data 225 having an associated predicted current user's interest level, as indicated by the predicted current user's interest level data 273 correlated to that offer, that is greater than, or equal to, the threshold predicted current user's interest level represented by the threshold predicted current user's interest level data 273 are collected and used to generate offer recommendation data 281” in ¶ 0176, and Fig. 5 showing the input of “CURRENT OFFER DATA” which correlates to the claimed “set of available offers” and the selection of “OFFER RECOMMENDATION DATA” which correlates to the claimed “set of offers.”);
transmitting executable instructions that, as a result of being executed by a computing device of the user, cause the computing device to render the set of offers through the interface (See “FIG. 4 shows an illustrative example of offer recommendation data provided via a data management system user interface display 403 displayed on a user computing system display screen 401 of a user computing system 400, which, in this specific illustrative example, is a smart phone” in ¶ 0154 and Fig. 4, and item 629 “PROVIDE THE OFFER RECOMMENDATION DATA TO THE CURRENT USER OPERATION” in Fig. 6.);
monitoring in real-time and through the interface user interaction with the set of offers; and updating the machine learning algorithm according to the user interaction with the set of offers (See “the offer receiver's response or interaction with the quick action offers are not only conducted within a relatively short time of the offer being made, but the current user's acceptance of the offer, and the offer provider's acceptance of the current user's acceptance of the offer, i.e., the performance of the offer, can be monitored and determined in a relatively short timeframe. This, in turn, allows for relative real-time re-training and automatic adjustment of the one or more offer/attribute matching models, and disclosed methods and systems for using machine learning techniques to identify and recommend relevant offers, to changes in the real world operating environment” in ¶ 0077, “providing the one or more recommended offers to the current user; monitoring interactions of the current user with the one or more recommended offers; updating performance data associated with the one or more recommended offers based on the monitored interactions; and modifying the one or more user interest prediction algorithms based on the performance data” in claim 2, and items 631 “MONITOR THE CURRENT USER'S INTERACTION WITH THE ONE OR MORE CURRENT OFFERS OF THE OFFER RECOMMENDATION DATA OPERATION” and 633 “USE DATA REPRESENTING THE CURRENT USER'S INTERACTION WITH THE ONE OR MORE CURRENT OFFERS TO MODIFY THE ONE OR MORE USER INTEREST PREDICTION ALGORITHMS OPERATION” in Fig. 6.).
Morin does not expressly teach receiving an application programming interface (API) call to identify one or more offers for rendering through an interface implemented through a landing page associated with a payment instrument service, wherein the API call includes identifying information associated with a user and with an ongoing online session, and wherein the API call is submitted during a request to access the landing page; wherein the user interaction data is obtained using the identifying information.
However, Gopal teaches receiving an application programming interface (API) call to identify one or more offers for rendering through an interface implemented through a landing page associated with a payment instrument service, wherein the API call includes identifying information associated with a user and with an ongoing online session, and wherein the API call is submitted during a request to access the landing page; wherein the user interaction data is obtained using the identifying information (See “The channel integration server 130 also identifies one or more ad networks 130 (such as Google™ Adsense™) and uses an API of that selected ad network 130 to pass the anonymous session identifier and consumer identifying information obtained from the financial transaction session identifier” in ¶ 0047, “the channel integration server 130 uses existing APIs of the ad networks 120 to access available profiles for a consumer” in ¶ 0048, and “The identity information is passed by the financial service cloud 140, using an API, to the channel integration server 130. The channel integration server 130 passes the identity information, using multiple APIs, to the ad networks 120; and the ad networks' APIs return multiple profiles back to the channel integration server 130 (the processing of which was presented above). The financial service cloud 140 may also use an API for a financial cloud (financial institution) 150 to acquire a financial profile for the consumer known and recorded by the financial institution. This profile is combined with the normalized profile (embodiment discussed above) acquired from the channel integration server 130. The financial service cloud 140 may determine based on inspection of the consumer's normalized profile and the consumer's financial profile one or more specific advertisements that are targeted to the consume” in ¶ 0054.).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Morin and Gopal to utilize the offer selection method of Morin in the API environment of Gopal. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding Claims 2, 9, and 16, Morin further teaches identifying a set of rules corresponding to the set of available offers, wherein the set of rules defines different requirements for rendering of different offers from the set of available offers (See “the current offer attribute data 253 includes, but is not limited to, data indicating: the names of providers, such as financial institutions, associated with the current offers, categories of the providers, such as financial institutions, associated with the current offers, terms of the offers, rewards or loyalty programs associated with the current offers, and approval criteria and/or acceptance rates associated with the current offers. In various embodiments, the current offer attribute data 253 includes any other offer attribute data indicating the nature, requirements, operation, or structure of the current offers as discussed herein” in ¶ 0144, “the current offer attribute data 253 associated with each current offer in the current offer data 225 is correlated to the respective current offer” in ¶ 0145, and “current user's attribute data 243 and current offer attribute data 253 are provided as input data to the rule generation module 160 and the one or more user interest prediction algorithms (not shown) of the offer/attribute matching model 161” in ¶ 0146.); and processing the set of rules with the user interaction data and the set of available offers through the machine learning algorithm to identify the set of offers (See “the current user's attribute data 243 and the current offer attribute data 253 are processed by the one or more user interest prediction algorithms of the offer/attribute matching model 161 to generate current user's interest prediction data 271 indicating the predicted interest of the current user for each of the current offers in the current offer data 225” in ¶ 0149 and “a threshold predicted current user's interest level as indicated by a threshold value represented by current user's interest prediction data 271 is defined and threshold predicted current user's interest level data 272 is generated and provided to offer recommendation module 280” in ¶ 0150.).
Regarding Claims 3, 10, and 17, Morin does not expressly teach the identifying information is obtained from a cookie stored on a browser application implemented on the computing device.
However, Gopal teaches the identifying information is obtained from a cookie stored on a browser application implemented on the computing device (See “The web applications 110B may, in some instances, include cookies, stored local to a processing device of the consumer” in ¶ 0043 and “a consumer's cookie data stored in browsers on their processing devices have profile information that the ad networks 120 have access to when specific web applications or mobile applications are accessed by the consumer” in ¶ 0045.).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Morin and Gopal to utilize a cookie. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding Claims 4, 11, and 18, Morin does not expressly teach the identifying information includes a user identifier associated with the user and a session identifier corresponding to the ongoing online session, and wherein the user interaction data is obtained using the user identifier and the session identifier.
However, Gopal teaches the identifying information includes a user identifier associated with the user and a session identifier corresponding to the ongoing online session, and wherein the user interaction data is obtained using the user identifier and the session identifier (See “The channel integration server 130 also identifies one or more ad networks 130 (such as Google™ Adsense™) and uses an API of that selected ad network 130 to pass the anonymous session identifier and consumer identifying information obtained from the financial transaction session identifier” in ¶ 0047, “the channel integration server 130 uses existing APIs of the ad networks 120 to access available profiles for a consumer” in ¶ 0048, and “The identity information is passed by the financial service cloud 140, using an API, to the channel integration server 130. The channel integration server 130 passes the identity information, using multiple APIs, to the ad networks 120; and the ad networks' APIs return multiple profiles back to the channel integration server 130 (the processing of which was presented above). The financial service cloud 140 may also use an API for a financial cloud (financial institution) 150 to acquire a financial profile for the consumer known and recorded by the financial institution. This profile is combined with the normalized profile (embodiment discussed above) acquired from the channel integration server 130. The financial service cloud 140 may determine based on inspection of the consumer's normalized profile and the consumer's financial profile one or more specific advertisements that are targeted to the consume” in ¶ 0054.).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Morin and Gopal to capture user and session identifiers for use. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding Claims 6, 13, and 20, Morin further teaches the set of offers is categorized according to a set of offer parameters, and wherein the set of offers is rendered through the interface according to the set of offer parameters (See “the portion of current offer data representing one or more current offers having a threshold current user's interest level as indicated in the user interest prediction data for the current offers is transformed into recommended offer data that includes the portions of the current offer data representing the one or more current offers having a threshold current user's interest level. In one embodiment, the recommended offer data is then provided to the user” in ¶ 0071, “a threshold predicted current user's interest level as indicated by a threshold value represented by current user's interest prediction data 271 is defined and threshold predicted current user's interest level data 272 is generated and provided to offer recommendation module” in ¶ 0150, “the offer recommendation module 280 is executed by using one or more processors and one or more algorithms, such as data comparison algorithms, to compare the current user's interest prediction data 271 for each current offer of current offer data 225 to the threshold predicted current user's interest level data 272. In one embodiment, offer recommendation module 280 transforms the portion of the current offer data 225 representing current offers having a current user's interest level, as indicated by the associated current user's interest prediction data 271, at least as great as the threshold predicted current user's interest level indicated by the threshold predicted current user's interest level data 272 into offer recommendation data 281 that includes the portion of the current offer data 225 representing one or more current offers having a threshold current user's interest level” in ¶ 0151, and “As also seen in FIG. 4, offer recommendation data 281 provided via the data management system user interface display 403 and credit card offer 411 includes a listing of parameters/features 425 indicating matched current user's/offer attribute pairs and/or key factors and considerations used to process, recommend, and rank credit card offer 411” in ¶ 0158.).
Regarding Claims 7, 14, and 21, Morin does not expressly teach obtaining the user interaction data further comprises: translating the identifying information into an external user identifier associated with the user, wherein the external user identifier corresponds to a user data connectivity platform that obtains the user interaction data from different external sources; and transmitting a query to obtain the user interaction data, wherein the query includes the external user identifier, and wherein when the query is received by the user data connectivity platform, the user data connectivity platform provides the user interaction data.
However, Gopal teaches obtaining the user interaction data further comprises: translating the identifying information into an external user identifier associated with the user, wherein the external user identifier corresponds to a user data connectivity platform that obtains the user interaction data from different external sources; and transmitting a query to obtain the user interaction data, wherein the query includes the external user identifier, and wherein when the query is received by the user data connectivity platform, the user data connectivity platform provides the user interaction data (See “the architecture 100 permits a consumer transaction at an ATM 110G to experience the same directed marketing that the consumer experiences on other communication channels 110 while at the ATM 110G. This is achieved by obtaining or assigning a communication session identifier for a financial transaction that a consumer is performing at the ATM 110G and generating another anonymous session identifier that links the communication session identifier of the financial transaction. The financial service cloud 150 pushes the anonymous session identifier up to the channel integration server 130. The channel integration server 130 identifies and selects one or more ad networks 120 and passes the anonymous session identifier. This creates a direct link between a selected ad network 120 and the ongoing financial communication session occurring with the consumer on the ATM 110G. The select ad network 120 has access to one or more profiles of the consumer over tracked communication channels 110. So, now the selected ad network 120 can display targeted advertisements to the consumer on the ATM 110G that are in the same format that the selected ad network would present if the consumer was accessing one of the channels 110. The interface having the targeted advertisement (from the selected ad network 120) is overlaid on top of the ATM 110G interface for the financial transaction by the financial service cloud 140” in ¶ 0035, ¶ 0046, ¶ 0047, and ¶ 0073.).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Morin and Gopal to utilize an external identifier such as the anonymous session identifier as described in Gopal. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Morin in view of Gopal, and further in view of US 2011/0184813 (“Barnes”).
Regarding Claims 5, 12, and 19, Morin does not expressly teach determining that the identifying information does not include a user identifier; and generating a cookie that encodes a new user identifier associated with the user, wherein the cookie is used to track ongoing user interactions with the interface and the set of offers.
However, Gopal teaches wherein the cookie is used to track ongoing user interactions with the interface and the set of offers (See “The web applications 110B may, in some instances, include cookies, stored local to a processing device of the consumer” in ¶ 0043 and “a consumer's cookie data stored in browsers on their processing devices have profile information that the ad networks 120 have access to when specific web applications or mobile applications are accessed by the consumer” in ¶ 0045.).
Further, Barnes teaches determining that the identifying information does not include a user identifier; and generating a cookie that encodes a new user identifier associated with the user (See “At block 2006, the processing circuit determines that a request for a web page is received during a session in which the registered user is not logged in to the web site and therefore receives and stores an anonymous user ID for the user, such as temporary ID, cookie, or other identifier” in ¶ 0133.).
It would have been obvious to one having ordinary skill in the art at the time of filing to combine the teachings of Morin, Gopal, and Barnes to generate a cookie as described in Barnes and utilized as described in Gopal. The claimed invention is merely a combination of old elements, in the combination each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 11,157,954 (“Belanger”): Belanger discloses an offer recommendation API which receives requests along with consumer identifying information. Offers are provided embedded within web pages or other containers.
US 2024/0070703 (“Guild”): Guild discloses a machine learning model for selecting a particular offer from a stream of offers.
US 2023/0030686 (“Mukherjee”): Mukherjee discloses a server that manages product presentations for offers from an offer API.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEREDITH A LONG whose telephone number is (571)272-3196. The examiner can normally be reached Mon - Fri 9:30 - 6.
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/MEREDITH A LONG/Primary Examiner, Art Unit 3622
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