DETAILED ACTION
The following is a first office action upon examination of application number 18/792023. Claims 1-20 are pending in the application and have been examined on the merits discussed below.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/1/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date.
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed applications, Application Nos. 15/926183 and 17/657797, fail to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Prior-filed applications do not provide any disclosure/description of 1) providing, by the one or more processors, the plurality of aggregated transaction data to a machine-learning model trained to identify relevancy patterns within the plurality of aggregated transaction data and a plurality of geographical data associated with the sub-merchant, and to output a relevancy prediction based on the relevancy patterns; and 2) determining, by the one or more processors, that the relevancy prediction exceeds a predetermined relevancy threshold (in independent claims 1, 10, and 18). Accordingly, claims 1-20 are not entitled to the benefit of the prior applications.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1) Claims 1-9 are directed to a method; thus these claims are directed to a process, which is one of the statutory categories of invention. Claims 10-17 are directed to a system comprising one or more processors; thus the system comprises a device or set of devices, and therefore, is directed to a machine which is a statutory category of invention. Claims 18-20 are directed to a non-transitory computer-readable medium, which is a manufacture, and this a statutory category of invention.
(Step 2A) The claims recite an abstract idea instructing how to identify relevant data and display analytics data, which is described by claim limitations reciting:
receiving, … a plurality of transaction data … associated with a sub-merchant;
extracting … a plurality of transaction articles from the plurality of transaction data;
determining … an associated weight for each transaction article of the plurality of transaction articles;
storing … in a transaction database … each transaction article and the associated weight as a plurality of aggregated transaction data;
providing … the plurality of aggregated transaction data to … model trained to identify relevancy patterns within the plurality of aggregated transaction data and a plurality of geographical data associated with the sub-merchant, and to output a relevancy prediction based on the relevancy patterns;
determining … that the relevancy prediction exceeds a predetermined relevancy threshold;
in response to the determining, generating … the one or more display regions of the … dashboard, the one or more display regions including a plurality of sub-merchant analytics data; and
transmitting … the one or more display regions of the electronic dashboard.
The identified limitations in the claims describing identifying relevant data and displaying analytics data (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers fundamental economic practices or, alternatively, the “Mental Processes” grouping of abstract ideas since the identified limitations can be performed by a human, mentally or with pen and paper. Dependent claims 7, 8, and 16, recite limitations that further narrow the abstract idea (i.e., identifying relevant data and displaying analytics data); therefore, these claims are also found to recite an abstract idea.
This judicial exception is not integrated into a practical application because additional elements such as the one or more processors; one or more point of sale (POS) terminals; acquirer processor computing system; and computing device associated with the electronic dashboard in claim 1, the memory storing instructions and a machine-learning model; one or more processors operatively connected to the memory and configured to execute the instructions to perform operations; one or more processors; acquirer processor computing system; and computing device associated with the electronic dashboard in claim 10, the non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause an acquirer processor computing system to perform a method; one or more processors; one or more point of sale (POS) terminals; acquirer processor computing system; and computing device associated with the electronic dashboard in claim 18, do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a processor/computer. Additional elements in claims 4 and 13 reciting that certain steps are performed in real-time only add computer implantation of abstract steps and do provide an improvement to the computer. The court in FairWarning found that accelerating a process of analyzing audit log data does not show an improvement to the computer when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016).
Additional elements reciting a machine-learning model trained… do not improve the computer or technology; further, these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Similarly, additional elements 6 and15 related to a second machine-learning model trained do not provide an improvement and only generally link the abstract idea to a technological environment.
Additional elements such as receiving, by one or more processors, a plurality of transaction data from one or more point of sale (POS) terminals…; storing, by the one or more processors and in a transaction database in electronic communication with the acquirer processor computing system…; and transmitting, by the one or more processors, the one or more display regions of the electronic dashboard to a computing device associated with the electronic dashboard do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only add insignificant extra-solution activities (data gathering, storage and transmission). Similarly, additional elements in claims 2, 3, 5, 9, 11, 12, 14, 17, 19, and 20 reciting the electronic dashboard; and receiving, by the one or more processors, blocks of data from the acquirer processor computing system… do not provide an improvement and only add insignificant extra-solution activities. Accordingly, these additional element do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the hardware additional elements amount to no more than mere instructions to apply the exception using a generic computer component (See Spec. [0028]). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additional elements in claims 4 and 13 reciting that certain steps are performed in real-time only add computer implantation of abstract steps and do provide an improvement to the computer. Additional elements reciting a machine-learning model trained… do not improve the computer or technology; further, these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Additional elements 6 and15 related to a second machine-learning model trained do not provide an improvement and only generally link the abstract idea to a technological environment. Additional elements such as receiving, by one or more processors, a plurality of transaction data from one or more point of sale (POS) terminals…; storing, by the one or more processors and in a transaction database in electronic communication with the acquirer processor computing system…; and transmitting, by the one or more processors, the one or more display regions of the electronic dashboard to a computing device associated with the electronic dashboard do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only add insignificant extra-solution activities (data gathering, storage and transmission). Additional elements in claims 2, 3, 5, 9, 11, 12, 14, 17, 19, and 20 reciting the electronic dashboard; and receiving, by the one or more processors, blocks of data from the acquirer processor computing system… do not provide an improvement and only add insignificant extra-solution activities (data display and gathering). With respect to data gathering and transmission limitations, the courts have recognized the use of computers to receive and transmit data as well-understood, routine, and conventional, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). With respect to data storage limitations, the courts have recognized storing and retrieving information in memory as well-understood, routine, and conventional, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. With respect to data display limitations, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)). In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 4-10, and 13-18 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2024/0095742 (Chen).
As per claim 1, Chen teaches: a computer-implemented method for generating one or more display regions of an electronic dashboard using an acquirer processor computing system, the method comprising: receiving, by one or more processors, a plurality of transaction data from one or more point of sale (POS) terminals associated with a sub-merchant; ([0027] Payment processor component 300 includes a transaction authorization component 305 and a transaction processing component 308. The transaction processing component 308 takes as input information about a potential transaction from a merchant 302A-302N. It then decides whether or not to request validation for the transaction from the credit card issuer 304 and/or bank 306)
extracting, by the one or more processors, a plurality of transaction articles from the plurality of transaction data; ([0046] …transaction data (or both) may be transformed and/or enriched prior to use as either retraining data or input to the model at runtime. [0112] extracting one or more features from the historical transaction data; and
determining, by the one or more processors, an associated weight for each transaction article of the plurality of transaction articles; storing, by the one or more processors and in a transaction database in electronic communication with the acquirer processor computing system, each transaction article and the associated weight as a plurality of aggregated transaction data; ([0046] …either historical transaction data or potential transaction data (or both) may be transformed and/or enriched prior to use as either retraining data or input to the model at runtime. This may include, for example, normalizing addresses to known addresses, augmenting a raw IP address with a geographical location, and adding indicators that the transaction was performed via a VPN or from a known bot IP address to the transaction data. [0020] … the database 126 includes storage devices that store information accessed and generated by the processing system 106. [0073])
providing, by the one or more processors, the plurality of aggregated transaction data to a machine-learning model trained to identify relevancy patterns within the plurality of aggregated transaction data and a plurality of geographical data associated with the sub-merchant, and to output a relevancy prediction based on the relevancy patterns; ([0046] Furthermore, in an example embodiment, either historical transaction data or potential transaction data (or both) may be transformed and/or enriched prior to use as either retraining data or input to the model at runtime. This may include, for example, normalizing addresses to known addresses, augmenting a raw IP address with a geographical location, and adding indicators that the transaction was performed via a VPN or from a known bot IP address to the transaction data. [0035] Features used by the first machine learning algorithm 312 (as well as by the trained decline model 310) may include, but are not limited to, time features (day of week, hour of day, timezone, etc.), customer data (email address, billing address, time since created, etc.), client data (Internet Protocol address, request headers, browser, operating system, session identification, etc.), card metadata (bank identification number (BIN), bank, country, prepaid, debit or credit, etc.), payment data (amount, currency, shipping address, etc.), and historical counters across many dimensions (card, email address, customer, merchant, IP address, etc.). [0003] Machine learning may be used to train fraud prevention models that output a score; output a score (relevancy prediction). [0064] … The decline model may have thus been separately trained to output the decline score as a prediction of a likelihood that a particular transaction would be fraudulent).
determining, by the one or more processors, that the relevancy prediction exceeds a predetermined relevancy threshold; ([0079] In an example embodiment, the output of the decline model 510 is a score indicating a likelihood that the potential transaction is fraudulent. A transaction blocking component 516 then compares the score to a fraud tolerance threshold for the appropriate merchant (i.e., the merchant corresponding to the transaction being considered). If the score exceeds the threshold, then the transaction is blocked. If the score does not exceed the threshold, then the transaction is not blocked)
in response to the determining, generating, by the one or more processors, the one or more display regions of the electronic dashboard, the one or more display regions including a plurality of sub-merchant analytics data; and transmitting, by the one or more processors, the one or more display regions of the electronic dashboard to a computing device associated with the electronic dashboard ([0079] … If the score exceeds the threshold, then the transaction is blocked. If the score does not exceed the threshold, then the transaction is not blocked [0066] When a potential transaction is blocked, the transaction may be prevented from processing. Additionally, a graphical user interface of one or more users may be notified so that they display an indication that the transaction has been blocked… users may be, for example, a user associated with a merchant who is receiving the transaction. [0026])
As per claim 4, Chen teaches: wherein the one or more display regions including the plurality of sub-merchant analytics data is generated in real-time ([0018] …The client application 104 also provides a number of interfaces described herein, which can present an output in accordance with the methods described herein to a user of the client device 108. [0066] When a potential transaction is blocked, the transaction may be prevented from processing. Additionally, a graphical user interface of one or more users may be notified so that they display an indication that the transaction has been blocked).
As per claim 5, Chen teaches: updating, by the one or more processors, the one or more display regions of the electronic dashboard based on one or more inputs of a user associated with the computing device ([0019] …in response to receiving the input from the user, communicates information back to the client device 108 via the network 110 to be presented to the user).
As per claim 6, Chen teaches: providing, by the one or more processors, a plurality of stored historical aggregated transaction data to a second machine-learning model trained to identify historical relevancy patterns within the plurality of stored historical aggregated transaction data and a plurality of stored historical geographical data associated with the sub-merchant, and to output a historical relevancy prediction based on the historical relevancy patterns ([0046] Furthermore, in an example embodiment, either historical transaction data or potential transaction data (or both) may be transformed and/or enriched prior to use as either retraining data or input to the model at runtime. This may include, for example, normalizing addresses to known addresses, augmenting a raw IP address with a geographical location, and adding indicators that the transaction was performed via a VPN or from a known bot IP address to the transaction data. [0035] Features used by the first machine learning algorithm 312 (as well as by the trained decline model 310) may include, but are not limited to, time features (day of week, hour of day, timezone, etc.), customer data (email address, billing address, time since created, etc.), client data (Internet Protocol address, request headers, browser, operating system, session identification, etc.), card metadata (bank identification number (BIN), bank, country, prepaid, debit or credit, etc.), payment data (amount, currency, shipping address, etc.), and historical counters across many dimensions (card, email address, customer, merchant, IP address, etc.). [0003] Machine learning may be used to train fraud prevention models that output a score. [0064] … The decline model may have thus been separately trained to output the decline score as a prediction of a likelihood that a particular transaction would be fraudulent).
As per claim 7, Chen teaches: wherein the plurality of transaction data includes one or more of personally identifiable data, payment vehicle data, customer data, geographic location data, product data, service data, merchant data, and sub-merchant data ([0046] Furthermore, in an example embodiment, either historical transaction data or potential transaction data (or both) may be transformed and/or enriched prior to use as either retraining data or input to the model at runtime. This may include, for example, normalizing addresses to known addresses).
As per claim 8, Chen teaches: wherein the plurality of sub-merchant analytics data includes a unique sub-merchant identifier and a unique payment facilitator identifier ([0051] …a payment identifier (such as a credit card number) and a merchant identifier).
As per claim 9, Chen teaches: wherein the one or more display regions of the electronic dashboard further includes one or more of a demographics report, a notification report, and a financial report associated with the sub-merchant ([0066] When a potential transaction is blocked, the transaction may be prevented from processing. Additionally, a graphical user interface of one or more users may be notified so that they display an indication that the transaction has been blocked).
As per claim 10, this claim recites limitations substantially similar to those addressed by the rejection of claim 1, above; therefore, the same rejection applies.
As per claim 13, this claim recites limitations substantially similar to those addressed by the rejection of claim 4, above; therefore, the same rejection applies.
As per claim 14, this claim recites limitations substantially similar to those addressed by the rejection of claim 5, above; therefore, the same rejection applies.
As per claim 15, this claim recites limitations substantially similar to those addressed by the rejection of claim 6, above; therefore, the same rejection applies.
As per claim 16, this claim recites limitations substantially similar to those addressed by the rejection of claim 7, above; therefore, the same rejection applies.
As per claim 17, this claim recites limitations substantially similar to those addressed by the rejection of claim 9, above; therefore, the same rejection applies.
As per claim 18, this claim recites limitations substantially similar to those addressed by the rejection of claim 1, above; therefore, the same rejection applies.
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.
Claim(s) 2, 3, 11, 12, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0095742 (Chen); in view of US 2009/0248524 (Defoy).
As per claim 2, although not explicitly taught by Chen, Defoy teaches: determining, by the one or more processors, that a first percentage of display regions of the electronic dashboard is occupied by existing content; and determining, by the one or more processors, one or more display regions associated with a second percentage of display regions of the electronic dashboard that is unoccupied by content ([0050] …If the algorithm detects a resolution of 1680.times.1050, it will recognize that a larger amount of software space remains unused by the software and will therefore serve the user a larger ad (e.g. 160.times.600 wide skyscraper) and/or additional ads. The algorithm has also been conceived to ensure that a user's screen is not over saturated with ads; that is to say it will serve smaller ads to a user who has a lesser amount of unused space on his/her screen. It has also been conceived to maximize advertising revenues for the software publisher).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Chen with the aforementioned teachings of Defoy with the motivation of ensuring screen space is not oversaturated with content (Defoy [0050]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Defoy to the system of Chen would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the detection of unused space.
As per claim 3, although not explicitly taught by Chen, Defoy teaches: receiving, by the one or more processors, blocks of data from the acquirer processor computing system that indicate a current display status for each of the first percentage of display regions and for each of the one or more display regions associated with the second percentage of display regions ([0050] …If the algorithm detects a resolution of 1680.times.1050, it will recognize that a larger amount of software space remains unused by the software and will therefore serve the user a larger ad (e.g. 160.times.600 wide skyscraper) and/or additional ads. The algorithm has also been conceived to ensure that a user's screen is not over saturated with ads; that is to say it will serve smaller ads to a user who has a lesser amount of unused space on his/her screen. It has also been conceived to maximize advertising revenues for the software publisher).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Chen with the aforementioned teachings of Defoy with the motivation of ensuring screen space is not oversaturated with content (Defoy [0050]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Defoy to the system of Chen would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the detection of unused space.
As per claims 11-12, these claims recite limitations substantially similar to those addressed by the rejection of claims 2-3, respectively; therefore, the same rejection applies.
As per claims 19-20, these claims recite limitations substantially similar to those addressed by the rejection of claims 2-3, respectively; therefore, the same rejection applies.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2024/0193207 (Seyed) – discloses a machine learning model that calculates a measure of relevancy and evaluates items against a relevancy threshold ([0007][0086]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN TORRICO-LOPEZ whose telephone number is (571)272-3247. The examiner can normally be reached M-F 10AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571)272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALAN TORRICO-LOPEZ/ Primary Examiner, Art Unit 3625