DETAILED ACTION
Status of the Application
Claims 1-20 have been examined in this application. This communication is the first action on the merits. The Information Disclosure Statement (IDS) filed on June 18, 2025 has been acknowledged.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 8, & 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 6, & 15 of U.S. Patent No. 11,687,954. Although the claims at issue are not identical, they are not patentably distinct from each other because:
As per claims 1, 8, and 15, these claims of the present application contain nearly identical limitations to claim 6 of ‘954 except claim 6 of ‘954 includes additional narrowing limitations not present in the present claims. Here, claim 1, and similarly claims 8 and 15, of the present application recites:
perform natural language processing to extract a set of features from unstructured data, wherein the set of features is associated with information associated with an online location;
iteratively train, based on the set of features, a machine learning model to generate a trained machine learning model;
determine that a score associated with an output of the trained machine learning model satisfies a threshold by applying the trained machine learning model to the information and input into the trained machine learning model a similarity value determined between features of the set of features, wherein the score indicates a likelihood that the online location is associated with a brand;
determine, based on the score, the brand associated with the online location;
add information associated with the online location to a graph comprising links indicating that the online location and at least one physical location are associated with a same brand; and
generate, based on information associated with the graph, one or more credentials that are configured to enable completing one or more transactions at at least one of the online location or the at least one physical location,
yet, these limitations are substantially repeated in claims 6 of ‘954 (as indicated by the underlined portions of the claim reproduced below) with added further narrowing limiting elements (i.e., the portions of the claim below that are not underlined) that teach all the elements of claims 1, 8, and 15 of the present claims by reciting:
receiving, by a device and from one or more data sources, information indicating a plurality of data sets, wherein the plurality of data sets indicate information associated with respective physical locations or online locations;
identifying, by the device, a data set, from the plurality of data sets, that indicates information associated with an online location, wherein the information includes at least one of an entity name, an address, a phone number, a uniform resource locator, an entity identifier, or metadata;
parsing, by the device, the data set to identify information for a set of features, wherein parsing the information comprises:
identifying the set of features by obtaining the set of features from structured data by performing natural language processing to extract the set of features from unstructured data;
iteratively training a machine training model, comprising at least one of a neural network or a support vector, to generate a trained machine learning model, wherein the trained machine learning model is trained using the set of features;
determining, by the device and based on applying the trained machine learning model to the information for the set of features, that a score associated with an output of the trained machine learning model satisfies a threshold, wherein the score indicates a likelihood that the online location is associated with a brand;
determining, based on the score, the brand associated with the online location;
pairing, by the device, the online location with the brand in the database such that the online location is linked with a first physical location of the brand in the database; and
adding information associated with the online location to a graph associated with the brand, wherein the graph provides link information associated with at least one online location and at least one physical location related to the brand, and wherein the graph comprises links indicating that the at least one online location and at least one physical location are associated with the same brand;
receiving a request to generate one or more temporary credentials associated with the brand;
identifying, based on the graph, the at least one online location and the at least one physical location associated with the brand; and
generating based on identifying the at least one online location and the at least one physical location associated with the brand, the one or more temporary credentials, wherein the one or more temporary credentials are enabled to complete one or more transactions at the at least one online location and the at least one physical location.
While, in the generating step, claims 1, 8, & 15 of the present claims generates the credentials based on “information associated with the links” whereas claim 6 of ‘954 respectively generates the credentials based on “identifying the at least one online location and the at least one physical location associated with the brand,” in claim 6 of ‘954 “identifying the at least one online location and the at least one physical location associated with the brand” is identified based on the graph that provides the link information, and thus, this difference is an obvious variance. Further, although claim 8 of the present claims is directed to “[a] device, comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to” and claim 15 of the present claims is directed to “non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to,” claims 8 and 15 of the present invention perform substantially the same steps of the method of claim 6 of ‘954, and claims 1 and 15 of ‘954 disclose a system and a computer readable medium, performing steps, that comprise all the hardware components the device and computer readable medium of the claims 8 and 15 of the present invention, and thus, claims 8 and 15 of the present invention, respectively, are obvious over claim 6 of ‘954 in view of claim 1 of ‘954 and obvious over claim 6 of ‘954 in view of claim 15 of ‘954.
The differences between claim 1, 6, & 15 of ‘954 and 1, 8, & 15 of the present invention amount to no more than an obvious rearrangement of parts disclosed in a single reference yielding expected results. Accordingly, claims 1, 6, & 15 of ‘954 teach all the elements of claims 1, 8, & 15 of the present application, and thus, claims 1, 8, & 15 of the present application are at least obvious in view of claims 1, 6, & 15 of '954.
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 an abstract idea without significantly more. The claims (claim 1, and similarly claims 2-20) recite “extract a set of features from unstructured data, wherein the set of features is associated with information associated with an online location; … determine that a score associated with an output of … satisfies a threshold by applying … to the information and input into … a similarity value determined between features of the set of features, wherein the score indicates a likelihood that the online location is associated with a brand; determine, based on the score, the brand associated with the online location; add information associated with the online location to a graph comprising links indicating that the online location and at least one physical location are associated with a same brand; and generate, based on information associated with the graph, one or more credentials that are configured to enable completing one or more transactions at at least one of the online location or the at least one physical location.” Claims 1-20, in view of the claim limitations, recite the abstract idea of extracting information including features regarding an online location for completing a transaction associated with a brand, determining a score based on the features satisfies a threshold, determining a brand associated with the online location based on the score, add the locations to a graph if the locations are associated with the entity, the graph including link information indicating an online location is associated with a physical location associated with the same brand, and providing credentials to complete transactions at the locations based on the links.
As a whole, each of the limitations above manage sales and marketing activities of merchant and brand entities by identifying if a set for locations are locations of the merchant and/or brand entities, linking the locations that are associated with the merchant and/or brand entities, and providing credentials for completing transactions at merchant locations, and further, pursuant to the broadest reasonable interpretation this features manage the personal human behavior, relationships, and transactions of the merchant and brand business entities; therefore, the claims recite a certain method of organizing human activity. Furthermore, as a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation extracting information including features regarding an online location for completing a transaction associated with a brand, determining a score based on the features satisfies a threshold, determining a brand associated with the online location based on the score, add the locations to a graph if the locations are associated with the entity, the graph including link information indicating an online location is associated with a physical location associated with the same brand, and providing credentials to complete transactions at the locations based on the links could all be reasonably interpreted as a human mentally observing information regarding the entities, a human using judgement to identify and parse the information and determine features of the entities, a human mentally evaluating the features to determine a score regarding whether locations are associated with the entities based on the score satisfying a threshold, a human adding the links and link information to a graph either mentally or with a pen and paper, and a human providing credentials for completing a purchase manually and/or with a pen and paper, and thus, the claims recite a mental process. Further, with respect to the dependent claims, aside from the additional elements beyond the recited abstract idea addressed below under the second prong of Step 2A and 2B, the limitations of dependent claims 2-7, 9-14, & 16-20 recite similar further abstract limitations to those discussed above that narrow the abstract idea recited in the independent claims because, aside from the computer components and systems performing the claimed functions the limitations of claims recite mental processes that can be practically performed mentally by observing, evaluating, and judging information mentally and/or with a pen and paper and recite a certain method of organizing human activity that manages business interactions and the sales and marketing activity. Accordingly, the claims recite a certain method of organizing human activity and mental processes, and thus, the claims recite an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of “[a] device, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to: perform natural language processing,” “iteratively train, based on the set of features, a machine learning model to generate a trained machine learning model,” and “the trained machine learning model” in claims 1, “machine learning model comprises at least one of a neural network or a support vector machine” in claims 2, 9, & 16, “a virtual identifier, a virtual card number, or a temporary transaction card” in claims 4, 11, & 18, “[a] method, comprising: performing, by a device, natural language processing,” “iteratively training, by the device and based on the set of features, a machine learning model to generate a trained machine learning model,” “by the device,” and “the trained machine learning mode” in claim 8, “[a] non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to: perform natural language processing,” “iteratively train, based on the set of features, a machine learning model,” and “the trained machine learning model” in claim 15; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. In addition, these features merely generally link the abstract idea to a technical field/environment, namely a generic computing environment. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-7, 9-14, & 16-20 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s specification at [0071] (discussing the embodiments of the invention are implemented by a device including a bus, a processor, a memory, a storage component) and Margolin (US 20210201186 A1) at [0077] (discussing the invention is implemented using one or more general purpose computer system). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, electronic record keeping, storing and retrieving information in memory, and presenting offers, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-7, 9-14, & 16-20 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claims as a whole amount to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Examiner notes, unlike the parent Application, in the independent claims, the information extracted by the natural language processing are not used to iteratively train the machine learning model comprising one of a neural network or a support vector machine, the credentials are not temporary credentials, and the credentials are not actually used to complete a transaction, as recited in the present invention. Therefore, unlike the parent Application, as recited, these features merely generally link the abstract idea to a technological environment/field of use.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Margolin (US 20210201186 A1), hereinafter Margolin, in view of Bhargava (US 20190340595 A1), hereinafter Bhargava.
Regarding claim 1, Margolin discloses a device, comprising ([0012], [0024], [0035], [0083]-[0084]):
one or more memories; and one or more processors, coupled to the one or more memories, configured to ([0012], [0024], [0035], [0083]-[0084]):
perform … to extract a set of features from unstructured data, wherein the set of features is associated with information associated with an online location ([0047]-[0048], in phase one, the module 200 discussed above may access an electronic database that contains data about these entities 310-312, 320-321, and 330-344 and retrieve their respective geographical location data corresponding to their actual physical addresses, wherein the entities 330-344 are entities whose offline presence is still unknown at this point (i.e. identify candidate locations), [0036]-[0037], the program 210 of the module 200 may determine that a first subset of the merchants each have a physical presence (e.g., known to operate offline), a second subset of the merchants each have no physical presence (e.g., known to not operate offline), and a third subset of the merchants each have an unknown physical presence (e.g., uncertain whether the merchant operates offline or not) (i.e. identify candidate locations), and in phase two, the program 210 of the module 200 identifies which of the merchants in the third subset (e.g., the merchants that have unknown offline locations) are within some proximity (e.g., within a predefined distance) of any of the merchants in the first subset (e.g., the merchants that have known offline locations), and these merchants are then grouped into the first subset, meaning that they are deemed as having a known offline presence, the program 210 of the module 200 also identifies which of the merchants in the third subset are not within the proximity of any of the merchants in the second subset (e.g., the merchants that are known to have no offline locations), these merchants are then grouped into the second subset, meaning that they are deemed as having no-offline presence, and these merchants that are grouped into the first subset and second subset are then removed from the third subset of the merchants);
iteratively train, based on the set of features, a machine learning model to generate a trained machine learning model ([0018]-[0019], in the third phase, the machine learning model is trained, and in the fourth phase, the second and third phase may be repeated a number of times until the offline presence status has been verified for all the merchants, or the confidence threshold condition in the third phase, [0038], in phase three of the process, the machine learning model 220 of the module 200 trains a machine learning model based on the economic traits of the merchants in the first subset and the second subset, and the machine learning model is trained using the non-geolocation attributes and geolocation data of the merchants, [0058]-[0059], a machine learning model is trained in phase three of the process using the attributes data obtained in phase one of the process, e.g., the economic trait data of the labeled entities, and the data for these entities may be used as the training data for the machine learning model to predict which of the remaining unlabeled entitles 330, 333-334, 337-339, 341 and 343-344 whose offline presence is unknown that have an offline presence);
determine that a score associated with an output of the trained machine learning model satisfies a threshold by applying the trained machine learning model to the information and input into the trained machine learning model a similarity value determined between features of the set of features, wherein the score indicates a likelihood that the online location is associated with a brand ([0013]-[0114], [0018], [0035], [0047], [0059]-[0061], for a merchant having an online presence, the machine learning model predicts a first probability for the entity having an offline presence, after being trained, the machine learning model predicts a first probability for the entity having an offline presence (i.e. the geographic location retrieved in phase two is determined to be associated with the entity) and set a first confidence threshold for the first probability, e.g., in the example in fig. 2-4, the module 200 accesses an electronic database that contains data about these entities 330-344 and respective geographical location data in graph 300 representing their actual physical address, including X-axis and Y-axis representing the longitude and latitude, the machine learning model predicts that the entity 333 has a first probability of 0.93 (93%) of having an offline presence (i.e. the geographic location of 333 is determined to be associated with the entity), and if the first probability exceeds the first confidence threshold, the entity is now labeled as having an offline presence (i.e. determine the score output from the ML indicating the likelihood of that candidate locations are a same entity/the offline location and the online presences are of the same merchant/entity), e.g., in the example in fig. 2-4, the module 200 the machine learning model predicts that the entity 333 has a first probability of 0.93 (93%) of having an offline presence, and since the first probability value of 0.93 is greater than the value of 0.9 for the first confidence threshold, the entity 333 in fig. 4 is now labeled as having an offline presence (i.e. determining the geographic location as an offline location is associated with the same merchant/entity as the online presence of the merchant/entity, [0014], [0098], wherein each of the merchants/entities have an online presence, wherein some may only have an online presence);
determine, based on the score, the brand associated with the online location ([0013]-[0114], [0018], [0035], [0047], [0059]-[0061], for a merchant having an online presence, the machine learning model predicts a first probability for the entity having an offline presence, after being trained, the machine learning model predicts a first probability for the entity having an offline presence (i.e. the geographic location retrieved in phase two is determined to be associated with the entity) and set a first confidence threshold for the first probability, if the first probability exceeds the first confidence threshold, the entity is now labeled as having an offline presence (i.e. determine the score output from the ML indicating the likelihood of that candidate locations are a same entity/merchant/brand the offline location and the online presences are of the same merchant/entity/brand), e.g., in the example in fig. 2-4, the module 200 the machine learning model predicts that the entity 333 has a first probability of 0.93 (93%) of having an offline presence, and since the first probability value of 0.93 is greater than the value of 0.9 for the first confidence threshold, the entity 333 in fig. 4 is now labeled as having an offline presence (i.e. determining the geographic location as an offline location is associated with the same merchant/entity as the online presence of the merchant/entity), [0014], [0098], wherein the merchants/entities have an online and offline presence, and some may only have an online presence and no offline presence);
add information associated with the online location to a graph comprising links indicating that the online location and at least one physical location are associated with a same brand ([0018], [0047], [0060]-[0061], figs. 2-4 provide graphs that visually illustrate a simplified example of the machine learning process, wherein, in graph 300, 310-312, 320-321, and 330-344 entities are represented as a dot or point in the graph, the module 200 accesses the database that contains data about these entities 310-312, 320-321, and 330-344, and if the first probability for an entity in the graph in fig. 2-4 to be the offline presence of the entity with an online exceeds the first confidence threshold, the entity in the graph in fig. 2-4 is now labeled as having an offline presence (i.e. adding to the graph, wherein labeling the entity that is represented in the graph provides link information associated with an online location and a physical location of the entity and indicates that the at least one online location and the at least one physical location are associated with the same entity), e.g., in the example in fig. 2-4, the module 200 accesses an electronic database that contains data about these entities 330-344 and respective geographical location data in graph 300 representing their actual physical address presented in the graphical representation of figs. 2-4, including X-axis and Y-axis representing the longitude and latitude, the machine learning model predicts that the entity 333 on the graph has a first probability of 0.93 (93%) of having an offline presence, and since the first probability value of 0.93 is greater than the value of 0.9 for the first confidence threshold, the entity 333 in the graph depicted in fig. 4 is now labeled as having an offline presence (i.e. adding to the graph, wherein labeling the entity that is represented in the graph provides link information associated with an online location and a physical location of the entity and indicates that the at least one online location and the at least one physical location are associated with the same entity), [0063], the entity 333 has been added as an entity with a known offline presence (i.e. the geographic location of 333 is added to the graph)); and
generate, based on information associated with the graph ([0078], [0080], the output value produced by the machine learning may predict a probability of a specific merchant having an offline presence, or the specific merchant having no offline presence (e.g., the merchant operates only online), [0116], the machine learning models accurately predicts which merchants have offline locations, which can be used by transaction platforms and/or payment providers to prevent fraud or provide improved client services) ….
While Margolin discloses all of the above, including perform … to extract a set of features from unstructured data, wherein the set of features is associated with information associated with an online location; … and
generate, based on information associated with the graph (as above), and generally discusses one or more credentials that are configured to enable completing one or more transactions at at least one of the online location or the at least one physical location ([0031], merchant server 140 also may include a checkout application 155 which may be configured to facilitate the purchase by user 105 of goods or services online or at a physical POS or store front, wherein checkout application 155 may receive and process a payment confirmation from server 170, as well as transmit transaction information to the payment provider and receive information from the payment provider (e.g., a transaction ID), and checkout application 155 may be configured to receive payment via a plurality of payment methods including cash, credit cards, debit cards, checks, money orders, or the like), Margolin does not expressly disclose the remaining elements of the following limitations, which however, are taught by further teachings in Bhargava.
Bhargava teaches perform natural language processing to extract a set of features from unstructured data, wherein the set of features is associated with information associated with an online location ([0039], [0061], at step 206, the conversational agent uses natural language processing and artificial intelligence logic to extract information, such as a type of a product to be purchased, a brand name and brand model associated with the product to be purchased, a location associated with the intended purchase and/or one or more payment cards that the user has at the user's disposal and which can be used for executing the payment transaction, [0072], the intended purchase at a merchant location is, e.g., at the facility 105 or using the merchant Website); … and
generate, based on information …, one or more credentials that are configured to enable completing one or more transactions at at least one of the online location or the at least one physical location ([0127]-[0131], the method 800 causes, by the server, provisioning at least one payment option offered by issuers of payment cards to the user during the interaction with the conversational agent, and processing of a payment transaction subsequent to execution of the intended purchase by the user, including, when the user executes the intended purchase, e.g., at a POS terminal or as an online transaction, the server system authenticates the user from the information associated with the payment transaction, and if the transaction amount matches the estimate of the transaction value of the selected payment option, the server system processes the payment transaction by the issuer of the payment card (i.e. the payment card used by the user for the purchase) as per the payment option selected by the user, [0113], the payment server 140 can authentication an identity of the user using a one-time password (OTP), [0095]-[0096], information for the payment options associated with the respective payment cards for the intended purchase, include ‘PLAN VALID TIMEFRAME’ for the payment option, [0077], [0104]-[0105], fig. 3, in an interaction 350 with the conversation agent (chatbot), as depicted in fig. 3, the chatbot indicates the payment options are only valid for purchases made in the next 20 days, [0032], [0034], the “payment account” used throughout the description refers to a financial account used to fund the financial transaction/“payment transaction” and may be a virtual or temporary payment account, and the “payment card” used throughout the description refers to a physical or virtual card linked with a payment account to fund the financial transaction to a merchant).
Margolin and Bhargava are analogous fields of invention because both address the problem of obtaining and providing relevant information regarding merchants based on locations and managing payment transactions at the merchants. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Margolin the ability to perform natural language processing to extract information associated with a set of features from unstructured data associated with an online location and generate, based on information, credentials for completing transactions at the online location and the physical location, as taught by Bhargava, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of performing natural language processing to extract information associated with a set of features from unstructured data associated with an online location and generating, based on information associated with the graph, temporary credentials for completing transactions at the online location and the physical location, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Margolin with the aforementioned teachings of Bhargava in order to produce the added benefit of assisting users in selecting suitable payment options from among available to them, assisting the users with their queries, and facilitating processing of the payment transaction using the selected payment option. [0006].
Regarding claim 2, the combined teachings of Margolin and Bhargava teach the device of claim 1 (as above). Further, Margolin discloses wherein the machine learning model comprises at least one of a neural network or a support vector machine ([0078], the machine learning process discussed above may be an artificial neural network, [0082], although the above discussions pertain to an artificial neural network as an example of machine learning, it is understood that other types of machine learning methods may also be suitable to implement the various aspects of the present disclosure including support vector machines (SVMs), Bayesian network is an acyclic probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG)).
Regarding claim 3, the combined teachings of Margolin and Bhargava teach the device of claim 1 (as above). Further, while Margolin discloses all of the above and discusses payment credentials ([0031]), Margolin does not expressly disclose the remaining elements of the following limitations, which however, are taught by further teachings in Bhargava.
Bhargava teaches wherein the one or more credentials are associated with a temporary credential ([0113], the payment server 140 can authentication an identity of the user using a one-time password (OTP), [0032], [0034], the “payment account” used throughout the description refers to a financial account used to fund the financial transaction/“payment transaction” and may be a virtual or temporary payment account, and the “payment card” used throughout the description refers to a physical or virtual card linked with a payment account to fund the financial transaction to a merchant).
Margolin and Bhargava are analogous fields of invention because both address the problem of obtaining and providing relevant information regarding merchants based on locations and managing payment transactions at the merchants. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to have modified Margolin with the aforementioned teachings of Bhargava in order to produce the added benefit of assisting users in selecting suitable payment options from among available to them, assisting the users with their queries, and facilitating processing of the payment transaction using the selected payment option. [0006].
Regarding claim 4, the combined teachings of Margolin and Bhargava teach the device of claim 1 (as above). Further, while Margolin discloses all of the above and discusses payment credentials ([0031]), Margolin does not expressly disclose the remaining elements of the following limitations, which however, are taught by further teachings in Bhargava.
Bhargava teaches wherein the one or more credentials are associated with at least one of: a virtual identifier, a virtual card number, or a temporary transaction card ([0113], the payment server 140 can authentication an identity of the user using a one-time password (OTP), [0032], [0034], the “payment account” used throughout the description refers to a financial account used to fund the financial transaction/“payment transaction” and may be a virtual or temporary payment account, and the “payment card” used throughout the description refers to a physical or virtual card linked with a payment account to fund the financial transaction to a merchant).
Margolin and Bhargava are analogous fields of invention because both address the problem of obtaining and providing relevant information regarding merchants based on locations and managing payment transactions at the merchants. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to have modified Margolin with the aforementioned teachings of Bhargava in order to produce the added benefit of assisting users in selecting suitable payment options from among available to them, assisting the users with their queries, and facilitating processing of the payment transaction using the selected payment option. [0006].
Regarding claim 5, the combined teachings of Margolin and Bhargava teach the device of claim 1 (as above). Further, Margolin discloses wherein the one or more processors are further configured to: determine, based on information associated with a physical location, the brand ([0014]-[0015], [0018], [0047], [0052]-[0053], [0060]-[0061], [0098], the machine learning model predicts a first probability for the entity with an online presence representing a particular merchant having an offline presence based on data retrieved from the database in phase one, including the geolocation, non-geolocation, and economic trait data, such as categories of business, operating hours, and if the first probability exceeds the first confidence threshold, the entity is now labeled as having an offline presence, e.g., in the example in fig. 2-4, the machine learning model predicts that the entity 333 has a first probability of 0.93 (93%) of having an offline presence, and since the first probability value of 0.93 is greater than the value of 0.9 for the first confidence threshold, the entity 333 in fig. 4 is now labeled as having an offline presence (i.e. labeling the online presence with the geographic location of the offline presence of the particular merchant/brand), [0063], the entity 333 has been added as an entity with a known offline presence (i.e. pairing the online presence with the geographic location of the offline presence of the particular merchant/brand), [0013]-[0014], wherein the database contains various information of merchants, such as average number/dollar of transactions per year/month/day, geographical location, online locations (e.g., the URL or website), offline locations (e.g., a physical brick-and-mortar store location), or other attributes of particular merchants with an online presence (i.e. the merchant, the merchant URL/website and merchant store location is a physical location of the brand labeled with the online location)).
Regarding claim 6, the combined teachings of Margolin and Bhargava teach the device of claim 1 (as above). Further, Margolin discloses wherein the graph includes a plurality of nodes associated with information related to at least one of the online location or the at least one physical location ([0018], [0047], [0060]-[0061], figs. 2-4 provide graphs that visually illustrate a simplified example of the machine learning process, wherein, in graph 300, 310-312, 320-321, and 330-344 entities are represented as a dot or point in the graph, the module 200 accesses the database that contains data about these entities 310-312, 320-321, and 330-344, and if the first probability for an entity in the graph in fig. 2-4 to be the offline presence at the geographic location of the entity with an online presence exceeds the first confidence threshold, the entity in the graph in fig. 2-4 is now labeled as having an offline presence (i.e. labeling the entity in the graph as being an offline presence of an entity in the graph with an online presence – connecting via links the node with other nodes representing the same entity), including X-axis and Y-axis representing the longitude and latitude, the machine learning model predicts that the entity 333 on the graph has a first probability of 0.93 (93%) of having an offline presence, and since the first probability value of 0.93 is greater than the value of 0.9 for the first confidence threshold, the entity 333 in the graph depicted in fig. 4 is now labeled as having an offline presence (i.e. labeling the entity in the graph as being an offline presence of an entity with an online presence – connecting via links the node with other nodes representing the same entity)).
Regarding claim 7, the combined teachings of Margolin and Bhargava teach the device of claim 6 (as above). Further, Margolin discloses wherein at least one of the links of the graph connect nodes of the plurality of nodes ([0018], [0047], [0060]-[0061], figs. 2-4 provide graphs that visually illustrate a simplified example of the machine learning process, wherein, in graph 300, 310-312, 320-321, and 330-344 entities are represented as a dot or point in the graph, the module 200 accesses the database that contains data about these entities 310-312, 320-321, and 330-344, and if the first probability for an entity in the graph in fig. 2-4 to be the offline presence at the geographic location of the entity with an online presence exceeds the first confidence threshold, the entity with the online in the graph in fig. 2-4 is now labeled as having an offline presence (i.e. labeling the entity in the graph as being an offline presence of an entity with an online presence – links of the nodes that connect nodes in the graph representing the offline presence with the online presence of the merchant), including X-axis and Y-axis representing the longitude and latitude, the machine learning model predicts that the entity 333 on the graph has a first probability of 0.93 (93%) of having an offline presence, and since the first probability value of 0.93 is greater than the value of 0.9 for the first confidence threshold, the entity 333 in the graph depicted in fig. 4 is now labeled as having an offline presence (i.e. labeling the entity in the graph as being an offline presence of an entity with an online presence – connecting via links the node with other nodes representing the same entity)).
Regarding claims 8-14, these claims are substantially similar to claims 1-7, respectively, and are, therefore, rejected on the same basis as claims 1-7. While claims 8-14 are directed toward a method, Margolin discloses a method as claimed. [0012], [0024], [0035], [0083]-[0084].
Regarding claims 15-20, these claims are substantially similar to claims 1-6, respectively, and are, therefore, rejected on the same basis as claims 1-6. While claims 15-20 are directed toward a non-transitory computer-readable medium storing a set of instructions executed by processors of a device to cause the device to perform operations, Margolin discloses a computer-readable medium as claimed. [0012], [0024], [0035], [0083]-[0084].
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Lu, et al. (US 20210173825 A1) disclosing systems and methods that identify pairs of the entities as duplicate entities based on scores generated by a machine learning model and determine a canonical entity in each of the duplicate entities by merging fields in the duplicate entities, Abstract;
Alonso, et al. (US 20190155961 A1) disclosing systems and methods that generate a knowledge graph that links names associated with a first subject matter category (C1) (such as brands) with names associated with a second subject matter category (C2) (such as products), wherein the link between any C1 node and any C2 node represents a relationship between a C1 name and a C2 name, e.g., the C1 node may correspond to a group of names that includes MICROSOFT, MSFT, etc., all associated with the company Microsoft Corporation, and C2 node may correspond to a group of names that includes WINDOWS, OUTLOOK, etc., all associated with products produced by Microsoft Corporation, the graph creation system 104 can also establish links between names within a single category, e.g., query-to-site (Q-to-S) graph the Q-to-S graph formation component 804 extracts information from the query-click log identifying web pages that users clicked on in direct response to submitting a query including the brand name “MSFT,” and one such page may correspond to an official website sponsored by Microsoft Corporation. Abstract, [0032], [0034], [0039], [0118].
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CHARLES GUILIANO
Primary Examiner
Art Unit 3623
/CHARLES GUILIANO/ Primary Examiner, Art Unit 3623
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