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 response to the amendment filed on 9 February 2026. Claims 1-6, 8-13 and 15-22 have been amended. Claims 7 and 14 have been cancelled. C Claims 1-6, 8-13 and 15-22 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on9 February 2026 has been entered.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,067,589. Although the claims at issue are not identical, they are not patentably distinct from each other because: though the wording are different, the limitations carried are either inherently implied or would have been obvious to one of ordinary skill in the art. 18/760, 949’s major different in language by the lacking of the steps of “receiving by a device data assocted with the transactions between a plurlity of merchant and a plurlity of customers”. One of ordinary skill in the art would have contemplated that data associated with a transactions must be received for generating data assocted with a plurality of merchant and the plurality of customers. 18/760, 949 recites “machine learing model” vs “the machine learning model associated with collaborative filtering model” the wordings are different, the limitation carried are either inherently implied or would have been obvious to one ordinary skill in the art. 18/760, 949 recites “ a theme” the wordings are different, the limitation carried are either inherently implied or would have been obvious to one ordinary skill in the art. 18/760, 949 recites “identifying by the device a subset of the one or more customers to provide one or more offers associated with the travel experience” vs “identifying by the device and based on financial information associated with one or more.. The omitted limitations however is deemed as irrelevant int inventive concept as it does not participate and/or linked to other steps in patentable manner. Further, it is widely known in the art that, in order to effectively preserve record for future reference identifying by the device and based on financial information is merely a routine work contemplatable by one of ordinary skill in the art.
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.
Step 1: The claims 1-6 are a method , claims 8- 13 are a device and claims 15-22 are media. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-6, 8-13 and 15-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A- Prong 1: the claims as a whole recite a method of organizing human interactions. The invention is a method generating data associated with transaction between a plurality a particular customer of a plurality of customer to a particular offer of a plurality of offers, assigning based on the predication the particular customer to a particular cluster, identifying the travel experience that has a threshold likelihood of being of interest to the particular customer associated with the particular cluster of the plurality of clusters, wherein the travel experience is identified based on information determined for a set of transaction data associated with the particular customer provide one or more offers associated with the travel experience and update on a response received for the one or more offer, which is a method of managing interactions between people. The mere nominal recitation of a generic device and generic a collaborative filtering model and machine learning model do not take the claim out of the methods of organizing human interactions grouping. Thus, the claim recites an abstract idea.
Step 2A-Prong 2 : The claim as a whole merely describes how to generally “apply” the concept of generating and updating information in a computer environment. The claimed computer components are recited at a high level of generality and are merely invoked as tools to perform an existing travel offer update process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Furthermore, the use of generic computing devices and standard ML algorithms to automate a business process generally falls under the category of an abstract idea, unless the claims recite a specific improvement to the underlying ML technology itself.
Step 2B: As noted previously, the claim as a whole merely describes how to generally “apply” the concept of updating medical records in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is ineligible.
Dependent claims 2-7, 9-14 and 16-20, these claims recite limitations that further define the same abstract idea noted in claims 1, 8, and 15. These claims do not contain any further additional elements per step 2A prong 2. Therefore, they are considered patent ineligible for the reason given above.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-6, 8-13 and 15-22 are rejected under 35 U.S.C. 103 as being unpatentable over Jayaraman et al. (US Pub., No., 2019/0370767 A1) in view of Edkin et al. (US Pub., 2019/0378050 A1)
With respect to claim 1, Jayaraman teaches a method, comprising:
assigning, by the device and based on the prediction, the particular second entity to a particular cluster of a plurality of clusters(paragraph [0028], discloses plurality of clusters is generated for the plurality of entities…, plurality of clusters may be generated using K-means clustering technique, paragraph [0043], discloses clustering model configured to generate the plurlity of clusters .., grouping the plurality entities based on similarities ),
wherein the first machine learning model has been trained to assign the plurality of second entities to the plurality of clusters based on measures of similarity among the data associated with the transactions(paragraph [0028], discloses intra-cluster similarities may be determined based on transactions pattern assocted with one or more entities and paragraph [0043], discloses clustering model configured to generate the plurlity of clusters .., grouping the plurality entities based on similarities);
identifying, by the device and based on transaction information associated with the one or more second entities, a subset of the one or more second entities to provide one or more resource proposals associated with the predicted transaction(paragraph [0032], discloses a real-time customized notification may be provided to promote each of other plurality of predicted entities to switch from the first payment mode to the second payment mode.., advertismtn offer, discount, coupon and so on [resource proposal]);
wherein the transaction information includes at least timeliness of a transaction(Fig. 4b, 403, paragraph [0030], discloses track number of transaction performed by the plurlity of entities via the second payment mode for a predefined duration of time .., and paragraph [0046], discloses the entry is monitored for a predefined duration of time );
providing, by the device and to the subset of the one or more second entities, the one or more resource proposals (paragraph [0049], discloses the customized notification may offer a discounting on product that the potential user most likely purchase with conditions that transactions for product need to be performed via the second payment mode …); and
updating, by the device and based on a message received in response to providing the one or more resource proposals, the second machine learning model(paragraph [0049], discloses promote each of the potential entities 214 to switch from the first payment mode to the second payment mode. In an embodiment, the notification provide module 206 may be configured to generate the notification 215 in real-time to provide to the potential entities. The notification data 215 may be provided to user devices of the potential entities 214. In an embodiment, the notification data 215 may be customized based on the transaction profile of corresponding potential entity).
Jayaraman teaches the above elements including generating, by a device, based on data associated with transactions between a plurality of first entities and a plurality of second entities, (paragraph [0040], discloses the transaction pattern 209 may be information with transactions assocted with each of the plurality of entities.., transaction made via credit account, debit accounts, prepaid account, bank account stored value account and so on), a prediction of a particular second entity, of the plurality of second entities, associated with a particular transaction, of a plurality of transactions (paragraph [0026], discloses such predictive model to predict the potential entities .., such predictive model may be machine learning model or deep learning model) generates the prediction based on collecting information from the plurality of the second entities(paragraph [0022], discloses prediction transaction pattern of entities may be tracked and cluster of entities with similar transaction pattern is generated and paragraph [0050], discloses predication of the plurality of potential entities may be performed at beginning ); identifying, by the device and a theme of predicted transaction that has a threshold likelihood of being of interest to one or more second entities, of the plurality of second entities, associated with the particular cluster (paragraph [0031], discloses plurality of clusters comprising the plurality of adapted entities are identified to be target cluster …, determined as potential entities to switch from the first payment mode to the second payment mode [interest of one or more second entities] and paragraph [0057], discloses computer the number of transactions with a predefined threshold value … , compare the number of transactions using the second payment mode ); and utilizing, by the device and for a subsequent prediction, (paragraph [0048], discloses remaining entities may be predicted to be the potential entities [subsequent predication]). Jayaraman failed to teach based on a first machine learning model associated with a collaborative filtering model, and wherein the collaborative filtering model ; prediction is based on using a second machine learning model, and the updated second machine learning model.
However, Edkin teaches based on a first machine learning model associated with a collaborative filtering model and wherein the collaborative filtering model (paragraph [0138], discloses using collaborative filtering technique the machine learning engine may identify that a node representing a transactions should be connected to the user’s employer..). prediction is based on using a second machine learning model, and the updated second machine learning model (paragraph [0008], discloses machine learning sub- including a plurality of machine learing sub-engine, the machine learning is programmed to perform steps including: training a machine learning model of a machine learning sub-engine of the machine learning engine using the transaction data and paragraph [0118], discloses the machine learing system predicts the classification attributes of the node). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for machine learning model or deep learning model of Jayaraman with a collaborative filtering technique, the machine learning engine and including machine learing sub-engine of Edkin in order to increase or decrease its complexity (see Edkin paragraph, [0066]) and feature construction techniques may be used to increase the accuracy and/or understanding of a feature vector (see Edkin paragraph, [0126]).
With respect to claim 2, Jayaraman in view of Edkin teaches elements of claim 1, furthermore, the method wherein identifying the subset of the one or more second entities is further based on additional information related to transactions associated with the one or more second entities(Fig. 4A, 403, and paragraph [0014] discloses identify plurality of adapted entities from plurality of entities [subset of the one or more second entities] that are adapted to user second payment mode based on spending behavior).
With respect to claim 3, Jayaraman in view of Edkin teaches elements of claim 1, furthermore, the method , wherein identifying the subset of the one or more second entities is further based on a customer an entity selection model trained using historical outcomes of providing the one or more resource proposals(paragraph [0049], discloses the notification may may be provided to user device of the potential entity .., notification customized based on the transaction profile of corresponding potential entity [historical outcome]).
With respect to claim 4, Jayaraman in view of Edkin teaches elements of claim 1, furthermore, the method wherein identifying the subset of the one or more second entities is further based on a likelihood of the one or more second entities accepting the one or more resource proposals(paragraph [0039], discloses received offers, such as coupons loaded into transaction account of the entity [accepting one or more resource proposal].., coupon redemption pattern).
With respect to claim 5, Jayaraman in view of Edkin teaches elements of claim 4, furthermore, the method wherein the likelihood of the one or more second entities accepting the one or more resource proposals is based on a threshold quantity of transactions related to the one or more resource proposals (paragraph [0039], discloses received offers, such as coupons loaded into transaction account of the entity …., coupon redemption pattern [threshold pattern]).
With respect to claim 6, Jayaraman in view of Edkin teaches elements of claim 4, furthermore, the method wherein the likelihood of the one or more second entities accepting the one or more resource proposals offers is based on: the subset of the one or more second entities having no more than a first threshold quantity of transactions related to a first entity merchant related to the one or more offers, or the subset of the one or more second entities having at least a second threshold quantity of transactions related to at least one of a product or a service related to the one or more resource proposals(paragraph [0039], discloses received offers, such as coupons loaded into transaction account of the entity …., coupon redemption pattern [threshold pattern]).
With respect to claim 8, Jayaraman teaches a device, comprising:
one or more memories and one or more processors, coupled to the one or more memories(Figure 1, 105, discloses one or more processors and 107 discloses memory and paragraph [0006], discloses the system includes a one or more processors and a memory ..) configured to:
assign, based on the prediction, the particular second entity to a particular cluster of a plurality of clusters(paragraph [0028], discloses plurality of clusters is generated for the plurality of entities…, plurality of clusters may be generated using K-means clustering technique, paragraph [0043], discloses clustering model configured to generate the plurlity of clusters .., grouping the plurality entities based on similarities ) ,
wherein the first machine learning model has been trained to assign the plurality of second entities to the plurality of clusters based on measures of similarity among the data associated with the transactions(paragraph [0028], discloses intra-cluster similarities may be determined based on transactions pattern assocted with one or more entities and paragraph [0043], discloses clustering model configured to generate the plurlity of clusters .., grouping the plurality entities based on similarities) ;
identify and based on transaction information associated with the one or more second entities, a subset of the one or more second entities to provide one or more resource proposals associated with the predicted transaction(paragraph [0032], discloses a real-time customized notification may be provided to promote each of other plurality of predicted entities to switch from the first payment mode to the second payment mode.., advertismtn offer, discount, coupon and so on [resource proposal]),
wherein the transaction information includes at least timeliness of a transaction(Fig. 4b, 403, paragraph [0030], discloses track number of transaction performed by the plurlity of entities via the second payment mode for a predefined duration of time .., and paragraph [0046], discloses the entry is monitored for a predefined duration of time );
provide, and to the subset of the one or more second entities, the one or more resource proposals (paragraph [0049], discloses the customized notification may offer a discounting on product that the potential user most likely purchase with conditions that transactions for product need to be performed via the second payment mode …); and
update, and based on a message received in response to providing the one or more resource proposals, the second machine learning model(paragraph [0049], discloses promote each of the potential entities 214 to switch from the first payment mode to the second payment mode. In an embodiment, the notification provide module 206 may be configured to generate the notification 215 in real-time to provide to the potential entities. The notification data 215 may be provided to user devices of the potential entities 214. In an embodiment, the notification data 215 may be customized based on the transaction profile of corresponding potential entity) .
Jayaraman teaches the above elements including generating, by a device, based on data associated with transactions between a plurality of first entities and a plurality of second entities, (paragraph [0040], discloses the transaction pattern 209 may be information with transactions assocted with each of the plurality of entities.., transaction made via credit account, debit accounts, prepaid account, bank account stored value account and so on), a prediction of a particular second entity, of the plurality of second entities, associated with a particular transaction, of a plurality of transactions (paragraph [0026], discloses such predictive model to predict the potential entities .., such predictive model may be machine learning model or deep learning model) generates the prediction based on collecting information from the plurality of the second entities(paragraph [0022], discloses prediction transaction pattern of entities may be tracked and cluster of entities with similar transaction patter is generated and paragraph [0050], discloses predication of the plurality of potential entities may be performed at beginning ); identify, and a theme of predicted transaction that has a threshold likelihood of being of interest to one or more second entities, of the plurality of second entities, associated with the particular cluster (paragraph [0031], discloses plurality of clusters comprising the plurlity of adapted entities are identified to be targe cluster …, determined as potential entities to switch from the first payment mode to the second payment mode [interest of one or more second entities] and paragraph [0057], discloses computer the number of transactions with a predefined threshold value … , compare the number of transactions using the second payment mode ); and utilize, and for a subsequent prediction, (paragraph [0048], discloses remaining entities may be predicted to be the potential entities [subsequent predication]). Jayaraman failed to teach based on a first machine learning model associated with a collaborative filtering model, and wherein the collaborative filtering model ; prediction is based on using a second machine learning model, and the updated second machine learning model.
However, Edkin teaches based on a first machine learning model associated with a collaborative filtering model and wherein the collaborative filtering model (paragraph [0138], discloses using collaborative filtering technique the machine learning engine may identify that a node representing a transactions should be connected to the user’s employer..). prediction is based on using a second machine learning model, and the updated second machine learning model (paragraph [0008], discloses machine learning sub- including a plurality of machine learing sub-engine, the machine learning is programmed to perform steps including: training a machine learning model of a machine learning sub-engine of the machine learning engine using the transaction data and paragraph [0118], discloses the machine learing system predicts the classification attributes of the node). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for machine learning model or deep learning model of Jayaraman with a collaborative filtering technique, the machine learning engine and including machine learing sub-engine of Edkin in order to increase or decrease its complexity (see Edkin paragraph, [0066]) and feature construction techniques may be used to increase the accuracy and/or understanding of a feature vector (see Edkin paragraph, [0126]).
With respect to claim 9, Jayaraman in view of Edkin teaches elements of claim 8, furthermore, the device wherein identify the subset of the one or more second entities is further based on additional information related to transactions associated with the one or more second entities(Fig. 4A, 403, and paragraph [0014] discloses identify plurlity of adapted entities from plurality of entities [subset of the one or more second entities] that are adapted to user second payment mode based on spending behavior).
With respect to claim 10, Jayaraman in view of Edkin teaches elements of claim 8, furthermore, the device , wherein identifying the subset of the one or more second entities is further based on a customer an entity selection model trained using historical outcomes of providing the one or more resource proposals(paragraph [0049], discloses the notification may may be provided to user device of the potential entity .., notification customized based on the transaction profile of corresponding potential entity [historical outcome]).
With respect to claim 11, Jayaraman in view of Edkin teaches elements of claim 8, furthermore, the device wherein identifying the subset of the one or more second entities is further based on a likelihood of the one or more second entities accepting the one or more resource proposals(paragraph [0039], discloses received offers, such as coupons loaded into transaction account of the entity [accepting one or more resource proposal].., coupon redemption pattern).
With respect to claim 12, Jayaraman in view of Edkin teaches elements of claim 11, furthermore, the device wherein the likelihood of the one or more second entities accepting the one or more resource proposals is based on a threshold quantity of transactions related to the one or more resource proposals (paragraph [0039], discloses received offers, such as coupons loaded into transaction account of the entity …., coupon redemption pattern [threshold pattern]).
With respect to claim 13, Jayaraman in view of Edkin teaches elements of claim 11, furthermore, the device wherein the likelihood of the one or more second entities accepting the one or more resource proposals offers is based on: the subset of the one or more second entities having no more than a first threshold quantity of transactions related to a first entity merchant related to the one or more offers, or the subset of the one or more second entities having at least a second threshold quantity of transactions related to at least one of a product or a service related to the one or more resource proposals. (paragraph [0039], discloses received offers, such as coupons loaded into transaction account of the entity …., coupon redemption pattern [threshold pattern]).
With respect to claim 15, Jayaraman teaches a non-transitory computer-readable medium storing a set of instructions , the set of instructions (paragraph [0007], discloses non-transitory computer readable medium including instructions stored ,…)comprising:
one or more memories and one or more processors, coupled to the one or more memories(Figure 1, 105, discloses one or more processors and 107 discloses memory and paragraph [0006], dislcies the system includes a one or more processors and a memory ..) configured to:
assign, based on the prediction, the particular second entity to a particular cluster of a plurality of clusters(paragraph [0028], discloses plurality of clusters is generated for the plurality of entities…, plurality of clusters may be generated using K-means clustering technique, paragraph [0043], discloses clustering model configured to generate the plurlity of clusters .., grouping the plurality entities based on similarities ) ,
wherein the first machine learning model has been trained to assign the plurality of second entities to the plurality of clusters based on measures of similarity among the data associated with the transactions(paragraph [0028], discloses intra-cluster similarities may be determined based on transactions pattern assocted with one or more entities and paragraph [0043], discloses clustering model configured to generate the plurlity of clusters .., grouping the plurality entities based on similarities) ;
identify and based on transaction information associated with the one or more second entities, a subset of the one or more second entities to provide one or more resource proposals associated with the predicted transaction(paragraph [0032], discloses a real-time customized notification may be provided to promote each of other plurality of predicted entities to switch from the first payment mode to the second payment mode.., advertismtn offer, discount, coupon and so on [resource proposal]),
wherein the transaction information includes at least timeliness of a transaction(Fig. 4b, 403, paragraph [0030], discloses track number of transaction performed by the plurlity of entities via the second payment mode for a predefined duration of time .., and paragraph [0046], discloses the entry is monitored for a predefined duration of time );
provide , by the device and to the subset of the one or more second entities, the one or more resource proposals (paragraph [0049], discloses the customized notification may offer a discounting on product that the potential user most likely purchase with conditions that transactions for product need to be performed via the second payment mode …); and
update, by the device and based on a message received in response to providing the one or more resource proposals, the second machine learning model(paragraph [0049], discloses promote each of the potential entities 214 to switch from the first payment mode to the second payment mode. In an embodiment, the notification provide module 206 may be configured to generate the notification 215 in real-time to provide to the potential entities. The notification data 215 may be provided to user devices of the potential entities 214. In an embodiment, the notification data 215 may be customized based on the transaction profile of corresponding potential entity) .
Jayaraman teaches the above elements including generating, by a device, based on data associated with transactions between a plurality of first entities and a plurality of second entities, (paragraph [0040], discloses the transaction pattern 209 may be information with transactions assocted with each of the plurality of entities.., transaction made via credit account, debit accounts, prepaid account, bank account stored value account and so on), a prediction of a particular second entity, of the plurality of second entities, associated with a particular transaction, of a plurality of transactions (paragraph [0026], discloses such predictive model to predict the potential entities .., such predictive model may be machine learning model or deep learning model), generates the prediction based on collecting information from the plurality of the second entities(paragraph [0022], discloses prediction transaction pattern of entities may be tracked and cluster of entities with similar transaction patter is generated and paragraph [0050], discloses predication of the plurality of potential entities may be performed at beginning ); identifying, by the device and a theme of predicted transaction that has a threshold likelihood of being of interest to one or more second entities, of the plurality of second entities, associated with the particular cluster (paragraph [0031], discloses plurality of clusters comprising the plurality of adapted entities are identified to be targe cluster …, determined as potential entities to switch from the first payment mode to the second payment mode [interest of one or more second entities] and paragraph [0057], discloses computer the number of transactions with a predefined threshold value … , compare the number of transactions using the second payment mode ); and utilizing, by the device and for a subsequent prediction, (paragraph [0048], discloses remaining entities may be predicted to be the potential entities [subsequent predication]). Jayaraman failed to teach based on a first machine learning model associated with a collaborative filtering model, and wherein the collaborative filtering model ; prediction is based on using a second machine learning model, and the updated second machine learning model.
However, Edkin teaches based on a first machine learning model associated with a collaborative filtering model and wherein the collaborative filtering model (paragraph [0138], discloses using collaborative filtering technique the machine learning engine may identify that a node representing a transactions should be connected to the user’s employer..). prediction is based on using a second machine learning model, and the updated second machine learning model (paragraph [0008], discloses machine learning sub- including a plurality of machine learing sub-engine, the machine learning is programmed to perform steps including: training a machine learning model of a machine learning sub-engine of the machine learning engine using the transaction data and paragraph [0118], discloses the machine learing system predicts the classification attributes of the node). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for machine learning model or deep learning model of Jayaraman with a collaborative filtering technique, the machine learning engine and including machine learing sub-engine of Edkin in order to increase or decrease its complexity (see Edkin paragraph, [0066]) and feature construction techniques may be used to increase the accuracy and/or understanding of a feature vector (see Edkin paragraph, [0126]).
With respect to claim 16, Jayaraman in view of Edkin teaches elements of claim 15, furthermore, the non-transitory computer readable medium wherein identifying the subset of the one or more second entities is further based on additional information related to transactions associated with the one or more second entities(Fig. 4A, 403, and paragraph [0014] discloses identify plurlity of adapted entities from plurality of entities [subset of the one or more second entities] that are adapted to user second payment mode based on spending behavior).
With respect to claim 17, Jayaraman in view of Edkin teaches elements of claim 15, furthermore, the non-transitory computer readable medium, wherein identifying the subset of the one or more second entities is further based on a customer an entity selection model trained using historical outcomes of providing the one or more resource proposals(paragraph [0049], discloses the notification may may be provided to user device of the potential entity .., notification customized based on the transaction profile of corresponding potential entity [historical outcome]).
With respect to claim 18, Jayaraman in view of Edkin teaches elements of claim 15, furthermore, the non-transitory computer readable medium wherein identifying the subset of the one or more second entities is further based on a likelihood of the one or more second entities accepting the one or more resource proposals(paragraph [0039], discloses received offers, such as coupons loaded into transaction account of the entity [accepting one or more resource proposal].., coupon redemption pattern).
With respect to claim 19, Jayaraman in view of Edkin teaches elements of claim 15, furthermore, the non-transitory computer readable medium wherein the likelihood of the one or more second entities accepting the one or more resource proposals is based on a threshold quantity of transactions related to the one or more resource proposals (paragraph [0039], discloses received offers, such as coupons loaded into transaction account of the entity …., coupon redemption pattern [threshold pattern]).
With respect to claim 20, Jayaraman in view of Edkin teaches elements of claim 18, furthermore, the non-transitory computer readable medium wherein the likelihood of the one or more second entities accepting the one or more resource proposals offers is based on: the subset of the one or more second entities having no more than a first threshold quantity of transactions related to a first entity merchant related to the one or more offers, or the subset of the one or more second entities having at least a second threshold quantity of transactions related to at least one of a product or a service related to the one or more resource proposals. (paragraph [0039], discloses received offers, such as coupons loaded into transaction account of the entity …., coupon redemption pattern [threshold pattern]).
With respect to claim 21, Jayaraman in view of Edkin teaches elements of claim 1, furthermore, the method further comprising: utilizing a K-nearest neighbor model for the first machine learning model to assign the particular second entity to the particular cluster based on a majority vote of neighbors of the particular second entity and the particular cluster being the most common among K-nearest neighbors(paragraphs [0028], [0043], and [0054] discloses K-means clustering technique implemented to generate the plurality of clusters).
With respect to claim 22, Jayaraman in view of Edkin teaches elements of claim 8, furthermore, the device wherein the one or more processors are further configured to: utilize a K-nearest neighbor model for the first machine learning model to assign the particular second entity to the particular cluster based on a majority vote of neighbors of the particular second entity and the particular cluster being the most common among K-nearest neighbors(paragraphs [0028], [0043], and [0054] discloses K-means clustering technique implemented to generate the plurlity of clusters).
The following prior arts are in the record:
Fleischman et al (US Patent No., 10,956,995 B1) discloses a user of a personal computing device may interact with a network-based travel service with respect to one or more travel items. The network-based travel service may monitor the users' interactions, determine user's travel interests, and provide relevant travel item provider devices information for generating user-specific offers.
Tamayo et al (US Pub., 2002/0083067 A1) discloses an enterprise-wide web data mining system, computer program product, and method of operation thereof, that uses Internet based data sources, and which operates in an automated and cost-effective manner. The enterprise web mining system comprises: a database coupled to a plurality
of data sources, the database operable to store data collected from the data sources; a data mining engine coupled to the web server and the database.
Quatse (US Pub., 2009/0177540 A1) discloses electronic systems for promotional offers are disclosed. An illustrative electronic system may include a computing device and a storage medium. The storage medium may contain one or more programming instructions that, when executed, cause the computing device to generate scores for customers from a customer database for distribution of limited quantities of promotional offers.
Jayaraman et al. (US Pub., No., 2019/0370767 A1) discloses embodiments of present disclosure relate to systems and methods for predicting potential entities to switch from first to second payment mode. Each entity of a plurality of entities is profiled using transaction pattern of corresponding entities. Clusters for the entities is generated based on profiling. Each cluster includes entities with similar transaction pattern.
Edkin et al. (US Pub., 2019/0378050 A1) discloses machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes.
Response to Arguments
Applicant's arguments of 35 U.S.C 101 rejections with respect to claims 1-6, 8-13 and 15-22 filed on 9 February 2026 have been fully considered but they are not persuasive.
Applicants arguments of (A) Step 2A -Prong 2 the claims integrate the alleged judicial exception into a practical application is not persuasive. Based on the USPTO's 2024 AI Subject Matter Eligibility Guidance and recent Federal Circuit case law, the claimed invention is directed to an abstract idea without significantly more and the rejections under 35 U.S.C. § 101 is maintained because the core issue is that it likely represents an abstract idea—specifically a "mental process" or a "method of organizing human activity"—without a clear technological improvement. Futher the claims are directed to an absurdist idea because:
Mathematical Concepts/Mental Processes: The steps of generating predictions via collaborative filtering and assigning entities to clusters are mathematical operations that can, in theory, be performed by a human with a pen and paper.
Methods of Organizing Human Activity: The ultimate goal—identifying "resource proposals" (offers) for "customers"—is a fundamental economic practice or business method, which courts regularly find ineligible.
Generic Application: Simply using machine learning (ML) as a tool to automate these business tasks is insufficient for eligibility if the ML itself is generic.
In order to overcome the 35 U.S.C 101 rejections Applicants should amend in a way that the claims provides a technical improvement, by focusing on Model Architecture Improvements: Rather than just "using" a model, focus on how the model is specifically optimized for this data. For example, if the collaborative filtering model is trained in a way that specifically reduces computational latency or improves accuracy over conventional methods, highlight that technical gain. The 35 U.S.C 101 rejection with respect to claim 1-6, 8-13 and 15-22 is maintained.
Applicant's arguments of 35 U.S.C 103 rejections with respect to claims 1-6, 8-13 and 15-22 filed on 15 August 2025 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/SABA DAGNEW/Primary Examiner, Art Unit 3682
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