DETAILED ACTION
This is in reference communication received 05 June 2026. Claims 1 – 20 are pending for examination. 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 – 20 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.
Independent claim 1, representative of claims 13 and 18, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 1 recites invention directed to receiving a request via an application programming interface (API) to initiating an online session with a website associated with a payment instrument service is received, an online session is initiated based on user identifier and the session identifier; interaction data corresponding to interactions with different websites by a plurality of users having respective attributes is obtained and processed to cluster the plurality of users, wherein each cluster identifies a group of users having one or more common attributes based on the user interaction data; a cluster from the plurality of clusters is identified and a transaction matrix is generated associated with the users in the identified clusters, and using the transaction matrix to determine entities said users are associated with. Collaborative score is calculated and validated whether is collaborative score is above some threshold value. Based upon the validation and determination, a targeted response is presented to a first-user, wherein the targeted response is associated with an entity with whom first user had not transacted and were transacted with other users in the cluster.
These limitations describe marketing/sales/advertising activities. Collecting data about plurality of users and their activities corresponding to different websites and categorizing the user in the collected data into clusters based upon some categorizing attributes. When a request for and advertising content is received, cluster of users who meet the advertising criteria is determined, a first user associated with a first entity is identified based upon collaborative score and an offer is presented to the first user wherein the link in the offer includes a link to purchase an item from the second entity , as drafted, is a process that, under its broadest reasonable interpretation covers performance of organizing certain methods of human activity related to advertising, marketing or sales activities or behaviors but for the recitation of generic computer components. Accordingly, the claim recites an abstract idea.
The independent claims further recite the additional functional element of “using a machine learning model based on the user interaction data” to cluster users in one or more groups, and machine learning model is updated. Not only do this features fail to integrate the abstract idea into a practical application (see below), but it can also reasonably be seen as the conventional application of well-known machine learning concepts to cluster user in the obtained data in groups, amounts to mere instructions to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f).
Represented claims 13 and 18, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory (claim 13), and a non-transitory machine-readable medium comprising instructions that when executed by a processor of a machine cause the machine to perform the method addressed above (claim 18).
The processor, memory, and non-transitory machine-readable medium are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
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 of the abstract idea into a practical application, the additional element of the processor, memory, and non-transitory machine-readable medium amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
As for dependent claims 2 – 12, 14 – 17 and 19 – 20, these claims recite limitations that further define the same abstract idea of defining that sing the machine learning model will be used to perform K-means clustering; defining what attributes about user(s) will be referenced for generating groups of users; defining what data-values will be used for determining collaborative scores, defining types of users that be identified in the clusters; updating machine learning based upon additional monitored user interaction data; defining that cookies will be used to store identifying information of the associated user, and what data will be stored in the cookie, and defining that user interaction data will be queried from connectivity platform, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of organizing certain methods of human activity related to advertising, marketing or sales activities or behaviors but for the recitation of generic computer components. Accordingly, the claim recites an abstract idea.
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, 3 – 9, 11, 13 and 15 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Reedy et al. US Publication 2021/0390573 in view of Ans et al. US Publication 2024/0428214, Wiley et al. US Publication 2025/0156580 and Simon J. Blanchard et al. published article “When Referring Customers to Competitors Makes Business Sense [How to Do it]” hereinafter referred to as Blanchard.
Regarding claim 1 and representative claim 13 and 18, Reedy teaches recommendation system and method that utilizes machine learning models to recommend travel offer packages (i.e., products) relating to a travel experience of customers (Reedy, 0011], comprising:
one or more processors [Reedy, 0077]; and
memory storing thereon instructions that, when executed by the one or more processors, cause the system to perform operations [Reedy, 0077] comprising:
receiving an application programming interface (API) call to identify one or more offers presentable to a first user, wherein the API call is submitted during a request to access a website associated with a payment instrument service (Reedy, The one or more travel offer platforms may receive the request and may identify one or more offers associated with the travel experience based on the request. Reedy further teaches the recommendation platform may receive financial information from the one or more financial information source devices. In some implementations, the financial information may include information indicating one or more credit scores for the customers, timeliness of payments for one or more associated accounts of the customers, total deposits in one or more associated accounts of the customers, total expenses paid from the one or more associated accounts of the customers, transaction cards (e.g., credit cards, debit cards, loyalty cards, and/or the like) utilized by the customers, transaction accounts associated with the merchants, and/or the like.) [Reedy, 0039, 0015];
Reedy does not explicitly teach receiving the request to initiate an online session. However, Ans teaches system and method wherein a user is authenticated for access to transact with the kiosk. Next, a remoting feature is detected as being activated by the user at the kiosk. Then, a link is caused to be sent to a mobile device of the user in response to the remoting feature being activated. Finally, the user is permitted to continue transacting via the kiosk using the mobile device when the link is activated on the mobile device [Ans, 0007].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Reedy by adopting teachings of Ans to generate and use a secure transaction link to secure transaction for increasing cyber-security.
Reedy in view of Ans teaches system and method further comprising:
receiving a request via an application programming interface (API) to initiate an online session (as responded to above) [Ans, 0007], wherein the request includes at least a user identifier associated with a first user and a session identifier associated with the online session, and wherein the request is associated with a website associated with a payment instrument service (as responded to above) [Reedy, 0039, 0015];
initiating the online session based on the user identifier and the session identifier (Ans, Ans teaches system and method wherein a user is authenticated for access to transact with the kiosk. Next, a remoting feature is detected as being activated by the user at the kiosk. Then, a link is caused to be sent to a mobile device of the user in response to the remoting feature being activated. Finally, the user is permitted to continue transacting via the kiosk using the mobile device when the link is activated on the mobile device) [Ans, 0007];
obtaining user interaction data that corresponds to interactions with different websites by a plurality of users having respective attributes and identifies one or more transactions made by the plurality of users (Reedy, the recommendation platform may receive a plurality of sets of transaction data for transactions between a plurality of merchants and a plurality of customers, and may use a first machine learning model that has been trained to assign the plurality of customers to a plurality of clusters based on measures of similarity among the plurality of sets of transaction data.) [Reedy, 0011];
clustering the plurality of users into a plurality of clusters, wherein each cluster identifies a group of users having one or more common attributes, and wherein the clustering includes using a machine learning model and is based on the user interaction data (Reedy, using a first machine learning model that has been trained to assign the plurality of customers to a plurality of clusters based on measures of similarity among the plurality of sets of transaction data (block 420).) [Reedy, 0082, 0011];
identifying a cluster from the plurality of clusters, wherein the cluster includes the first user and a second user (Reedy, the recommendation platform may identify the one or more customers of the set of customers to provide with the one or more travel offer packages based on association of the one or more travel offer packages with other customers within a same cluster.) [Reedy, 0047];
determining, based on the transaction matrix, that the first user and the second user are associated with a first entity and that the second user is associated with a second entity; wherein the first user and the second user are associated with a first entity, and wherein the second user is associated with a second entity (Reedy, For example, the travel-related data items may indicate that the set of customers in the particular cluster enjoy fancy restaurants ( e.g., and thus may enjoy a city known for having nice restaurants). In another example, the travel-related data items may indicate that the set of customers in the particular cluster spent time at museums ( e.g., and thus may enjoy a city known for having museums). In some implementations, the travel-related data items may include other information, such as credit scores and/or payment histories of the set of customers in the particular cluster, social media posts about travel by the set of customers, and/or the like.) [Reedy, 0032, also see 0084]
Reedy in view And does not teach calculating collaborative score for combination of entities. However, Wylie teaches system and method for candidacy determination and request processing. Wylie teaches The consolidated external data 262 may be provided as input into one or more processes performed by the ETL system 258 …. to generate additional data types for consideration in the candidacy determinations. For example, the consolidated external data 262 may be input into one or more trained machine learning models retrieved from one or more of the first data storage systems 114 to generate one or more scores 268 [Wylie, 0074] to identify customers who meet most of the targeting criteria requirements.
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Reedy in view of Ans by adopting teachings of Wylie to improve identification of targeted offer to increase ROI.
Reedy in view of Ans and Wylie teaches system and method further comprising:
calculating a collaborative score for a combination of the first entity and the second entity (Wiley, one or more trained models and/or feature derivation rules associated with the offering entity may be applied by the first system to the consolidated external data to generate scores and/or derived features. The scores and/or derived features may be associated with the common identifier and aggregated with the consolidated external data for use in the candidacy determinations.) [Wiley, 0031];
validating that the collaborative score is above a threshold (Reedy, the recommendation platform may utilize collaborative filtering (e.g., customer-based collaborative filtering or item based collaborative filtering) to determine clusters of customers based on the transaction profiles, the demographic information, and/or the like to cluster customers having a level of similarity that satisfies a threshold.) [Reedy, 0021];
presenting, based on the validation, during the online session, and based on the determination, a response targeted at the first user via the API and including a link associated with the second entity (Reedy, process 400 may include providing, to one or more customer devices associated with one or more customers of the set of customers, one or more travel offer packages comprising at least one of the one or more offers relating to the travel experience (block 460).) [Reedy, 0086]; and
Reedy in view of Ans and Wiley does not explicitly teach presenting a targeted offer including link to purchase an item from the second entity (e.g., a concept similar to recommending competing merchants to the customer). However, Blanchard teaches Our study found that salespeople who offer specialist competitor referrals using our two-step process were more likely to drive their own focal product sales without losing non-focal product sales. These insights can empower sales staff to take greater control of the sales conversation, using referrals for strategic advantage. While the internet is awash with pricing information, many customers find it challenging to know if they are getting good deals or can trust their sellers. Providing specialist competitor referrals correctly can help create win-win deals for customers and merchants. Not only do they encourage customers to consummate transactions, but they can build the trust and perception of value needed for longer-term relationships. In a brutally competitive marketplace, that can make all the difference [Blanchard, page 4].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Reedy in view of Ans and Wiley by adopting teachings of Blanchard and offer competitor referrals to build the trust and perception of value needed for longer-term relationships.
Reedy in view of Ans, Wylie and Blanchard teaches system and method further comprising
presenting, based on the validation, during the online session, and based on the determination, a response targeted at the first user via the API [Reedy, 0086] and including a link associated with the second entity [Blanchard, page 4]; and
updating the machine learning model based on the response (Reedy, the recommendation platform may update a model and/or the plurality of clusters based on the one or more responses.) [Reedy, 0050].
Regarding claim 3 and representative claim 15, as combined and under the same rationale as above, Reedy in view of Ans, Wylie and Blanchard teaches system and method, wherein the one or more common attributes includes a common geographic location (Reedy, the transaction data may include transaction amounts of a plurality of transactions, identifications of merchants, identifications of transaction accounts (e.g., payment accounts, credit card accounts, bank accounts, and/or the like), dates and/or times, geographical locations, loyalty accounts (which may identify respective customers), and/or the like associated with one or more transactions of the plurality of transactions.) [Reedy, 0015].
Regarding claim 4 and representative claim 16, as combined and under the same rationale as above, Reedy in view of Ans, Wylie and Blanchard teaches system and method, wherein calculating the collaborative score comprises:
identifying first products that are associated with the first entity; identifying second products that are associated with the second entity; and deriving the collaborative score from an intersection of the first products and the second products (Wiley, one or more trained models and/or feature derivation rules associated with the offering entity may be applied by the first system to the consolidated external data to generate scores and/or derived features. The scores and/or derived features may be associated with the common identifier and aggregated with the consolidated external data for use in the candidacy determinations.) [Wiley, 0031].
Regarding claim 5 and representative claim 17, as combined and under the same rationale as above, Reedy in view of Ans, Wylie and Blanchard teaches system and method, wherein identifying first products includes identifying first transactions with the first entity and identifying second products includes identifying second transactions with the second entity (Reedy, the recommendation platform may receive a plurality of sets of transaction data for transactions between a plurality of merchants and a plurality of customers, and may use a first machine learning model that has been trained to assign the plurality of customers to a plurality of clusters based on measures of similarity among the plurality of sets of transaction data.) [Reedy, 0011].
Regarding claim 6, as combined and under the same rationale as above, Reedy in view of Ans, Wylie and Blanchard teaches system and method, wherein the cluster identifies a first transaction of an item by the first user at the first entity, a second transaction of the item by the second user at the first entity, and the third transaction of an additional the item by the second user at the second entity (Reedy, the recommendation platform may receive a plurality of sets of transaction data for transactions between a plurality of merchants and a plurality of customers, and may use a first machine learning model that has been trained to assign the plurality of customers to a plurality of clusters based on measures of similarity among the plurality of sets of transaction data.) [Reedy, 0011] , and wherein the link is associated with the additional item (Reedy, process 400 may include providing, to one or more customer devices associated with one or more customers of the set of customers, one or more travel offer packages comprising at least one of the one or more offers relating to the travel experience (block 460).) [Reedy, 0086].
Regarding claim 7, as combined and under the same rationale as above, Reedy in view of Ans, Wylie and Blanchard teaches system and method, wherein the first transaction, the second transaction, and the third tranaction correspond to respective categories and the cluster identifies a common category between transactions associated with users of a given cluster (Reedy, For example, the travel-related data items may indicate that the set of customers in the particular cluster enjoy fancy restaurants ( e.g., and thus may enjoy a city known for having nice restaurants). In another example, the travel-related data items may indicate that the set of customers in the particular cluster spent time at museums ( e.g., and thus may enjoy a city known for having museums). In some implementations, the travel-related data items may include other information, such as credit scores and/or payment histories of the set of customers in the particular cluster, social media posts about travel by the set of customers, and/or the like.) [Reedy, 0032].
Regarding claim 8, as combined and under the same rationale as above, Reedy in view of Ans, Wylie and Blanchard teaches system and method further comprising:
monitoring additional user interaction data generated by the first user; and updating the machine learning model according to the additional user interaction data (Reedy, the recommendation platform may retrain the model trained to identify the travel experience based on the one or more responses. In some implementations, the recommendation platform may update the plurality of clusters by removing, from the set of customers, one or more of the customers based on the one or more responses.) [Reedy, 0050].
Regarding claim 9 and representative claims 19 as combined and under the same rationale as above, Reedy in view of Ans, Wylie and Blanchard teaches system and method, wherein the request includes identifying information associated with the first user and wherein the user interaction data is obtained using the identifying information (Reedy, the transaction data may include transaction amounts of a plurality of transactions, identifications of merchants, identifications of transaction accounts (e.g., payment accounts, credit card accounts, bank accounts, and/or the like), dates and/or times, geographical locations, loyalty accounts (which may identify respective customers), and/or the like associated with one or more transactions of the plurality of transactions.) [Reedy, 0014]
Regarding claim 11, as combined and under the same rationale as above, Reedy in view of Ans, Wylie and Blanchard teaches system and method, wherein the user interaction data is obtained using the user identifier and the session identifier (Reedy, Reedy further teaches the recommendation platform may receive financial information from the one or more financial information source devices. In some implementations, the financial information may include information indicating one or more credit scores for the customers, timeliness of payments for one or more associated accounts of the customers, total deposits in one or more associated accounts of the customers, total expenses paid from the one or more associated accounts of the customers, transaction cards (e.g., credit cards, debit cards, loyalty cards, and/or the like) utilized by the customers, transaction accounts associated with the merchants, and/or the like.) [Reedy, 0015].
Claims 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Reedy et al. US Publication 2021/0390573 in view of Ans et al. US Publication 2024/0428214, Wiley et al. US Publication 2025/0156580 and Simon J. Blanchard et al. published article “When Referring Customers to Competitors Makes Business Sense [How to Do it]” hereinafter referred to as Blanchard and Rinkal Ji. Published article “introduction to K-means Clustering: hereinafter referred to as Rinkal.
Regarding claim 2 and representative claim 14, as combined and under the same rationale as above, Reedy in view of Ans, Wylie and Blanchard does not teach using K-means clustering using machine learning model. However, Rinkal teaches K-means clustering is an unsupervised machine learning approach that uses similarities to divide a dataset into K different clusters. The algorithm aims to minimize the within-cluster variance, ensuring that data points within the same cluster are as similar as possible while data points in different clusters are as dissimilar as possible [Rinkal, page 2].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Reedy in view of Ans, Wylie and Blanchard by adopting teachings of Rinkal and use similarities to divide a dataset into K different clusters.
Reedy in view of Ans, Wylie and Rinkal teaches system and method, wherein the clustering further comprises performing K-means clustering using the machine learning model (as responded to above) [Rinkal, page 2].
Claim 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Reedy et al. US Publication 2021/0390573 in view of Ans et al. US Publication 2024/0428214, Wiley et al. US Publication 2025/0156580 and Simon J. Blanchard et al. published article “When Referring Customers to Competitors Makes Business Sense [How to Do it]” hereinafter referred to as Blanchard and HitchHikers published article “Using Cookies to Identify Visitors” hereinafter referred to as Hitch-Hikers.
Regarding claim 10 and representative claim 20, Reedy in view of Ans, Wylie and Blanchard does not teach using cookies for identifying information. However, HitchHikers teaches Cookies are popular method for identifying visitors to your site. Cookies allow you to assign a unique ID to a user which can be used to identify a visitor each time they visit your search experience.
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Reedy in view of Ans, Wylie and Blanchard by adopting teachings of Hitch-Hikers and use cookies to assign unique ID to visitors to your website for tracking their preferences.
as combined and under the same rationale as above, Reedy in view of Ans, Wylie, Blanchard and Hitch-Hikers teaches system and method, wherein the identifying information is obtained from a cookie stored on a browser application implemented on a computing device associated with the first user (HitchHikers, Cookies are a popular method for identifying visitors that come to your site. Cookies allow you to assign a unique ID to a user which can be used to identify a visitor each time they visit your search experience.) [HitchHikers, page 1].
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Reedy et al. US Publication 2021/0390573 in view of Ans et al. US Publication 2024/0428214, Wiley et al. US Publication 2025/0156580 and Simon J. Blanchard et al. published article “When Referring Customers to Competitors Makes Business Sense [How to Do it]” hereinafter referred to as Blanchard and Malwarebytes published article “What are tracking cookies” hereinafter referred to as Malwarebytes.
Regarding claim 12, Reedy in view of Ans, Wylie and Blanchard does not teach obtaining user interaction data. However, Malwarebyte teaches Tracking cookies are types of computer cookies that marketers use to target you and retarget you with ads that may interest you based on your browsing habits. Some of the most common third-party tracking cookies originate from technology giants like Facebook or Google. These companies use their components across the web to send cookies that they can read to track you [Malwarebyte, page 2].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Reedy in view of Ans, Wylie and Blanchard by adopting teachings of Malwarebyte to use tracking cookies to track user behavior and deliver custom ads.
as combined and under the same rationale as above, Reedy in view of Ans, Wylie and Malwarybytes teaches system and method, wherein obtaining the user interaction data further comprises:
translating the identifying information into an external user identifier associated with the first user, wherein the external user identifier corresponds to a user data connectivity platform; 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 (Malwarebyte, Advertisers use these cookies to track your behavior and deliver custom ads. Let’s say you’re searching the Internet for winter tires for your vehicle. The tracking cookies will use this information to hit you with banners on social media pages offering you deals on winter tires or related products.) [Malwarebyte, page ].
Response to Arguments
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 because the amended claimed invention is not an abstract idea, is acknowledged and considered.
However, upon further review, the amended invention is deemed not eligible for patent under 35 USC 101 and have been responded to in Rejection under 35 USC 101 section.
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 because the amended claimed invention employs a new kind of file that enables a computer security system to do things it could not do before. The security profile approach allows access to be tailored for different users and ensures that threats are identified before a file reaches a user's computer. and ensures that threats are identified before a file reaches a user's computer. The fact that the security profile "identifies suspicious code" allows the system to accumulate and utilize, is acknowledged and considered.
However, upon further review, the amended invention is deemed not eligible for patent under 35 USC 101. As currently claimed, applicant has not positively claimed the limitations that identifies suspicious code, therefore, pending claimed invention is not eligible for patent under 35 USC 101.
Applicant's argument that pending claimed amended invention is eligible for patent because cited prior art does not teach the amended invention is acknowledged and considered.
However, while performing an updated search, an new prior art was identified that teaches added limitations. Therefore, applicant’s arguments are moot under new grounds of rejection.
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 Naresh Vig whose telephone number is (571)272-6810. The examiner can normally be reached Mon-Fri 06:30a - 04:00p.
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/NARESH VIG/Primary Examiner, Art Unit 3622
August 22, 2026
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