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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 10-18-2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 8-14 and 17-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hecht et al. (U.S. Patent No. 12,664,531).
With regard to claim 1, Hecht teaches a system to analyze content ([col. 51, lines 15-30] the system may include a learning architecture (e.g., a machine learning architecture, an artificial intelligence architecture, and the like) structured to analyze historical data), the system comprising:
a server ([col. 20, lines 49-54] the publication circuit 342 can be coupled to a web server; [col. 28, lines 20-34] computing devices may be server devices) configured to:
acquire, one or more uniform resource locators (URLs) from a computing device ([col. 15, lines 30-35] The web service may be identifiable using a unique network address, such as an IP address, a uniform resource locator (URL); [col. 22, lines 11-25] source system URL), wherein each URL is associated, individually, with a unique website ([col. 22, 56-66] the customer may use a user interface of an online banking website), wherein a unique website is associated with one or more webpages ([col. 22, 56-66] the customer may use a user interface of an online banking website to revoke access by the biller exchange computing system 330 to one or more biller websites);
render, on a user interface of the computing device, a minimum processing charge to analyze each URL ([col. 22, 56-66] the customer may use a user interface of an online banking website to revoke access by the biller exchange computing system 330 to one or more biller websites. In some embodiments, the customer uses a user interface provided by the biller exchange computing system 330);
receive, an analysis confirmation input, corresponding to each URL from the computing device (Fig. 12B; Fig. 12D; Fig. 13C; [col. 32, lines 66 – col. 43, line 5] As shown in FIG. 12C, the exchange bill pay application 1110 can be structured to redirect the user's browser to a URL associated with the login page for the selected biller. In some embodiments, the exchange bill pay application 1110 displays a sign-on page 1240 that allows the user to log into the selected biller's system);
access, each URL based on the received analysis confirmation input for each URL (Fig. 12B; Fig. 12D; Fig. 13C), to generate a data corpus of each associated URL ([col. 51, lines 1-30] when limited resources are available, the consumer may specify that a vehicle loan bill should be paid before a retailer account bill, that only a minimum balance should be paid on a particular bill (e.g., a retailer account bill, a credit card bill), and the like. In some embodiments, the system may include a learning architecture (e.g., a machine learning architecture, an artificial intelligence architecture, and the like) structured to analyze historical data, automatically generate fund inflow projections, and fund outflow projections and use the projections in accordance with the consumer-defined prioritization schema);
analyze, the generated data corpus of each URL by utilizing a machine learning model, to compute a billable amount for each URL ([col. 51, lines 1-30] when limited resources are available, the consumer may specify that a vehicle loan bill should be paid before a retailer account bill, that only a minimum balance should be paid on a particular bill (e.g., a retailer account bill, a credit card bill), and the like. In some embodiments, the system may include a learning architecture (e.g., a machine learning architecture, an artificial intelligence architecture, and the like) structured to analyze historical data, automatically generate fund inflow projections, and fund outflow projections and use the projections in accordance with the consumer-defined prioritization schema);
render, the computed billable amount for each URL, at the computing device (Fig. 12B; Fig. 12D; Fig. 13C);
receive, an analysis input corresponding to each URL, from the computing device (Fig. 12B; Fig. 12D; Fig. 13C; [col. 32, lines 66 – col. 43, line 5] As shown in FIG. 12C, the exchange bill pay application 1110 can be structured to redirect the user's browser to a URL associated with the login page for the selected biller. In some embodiments, the exchange bill pay application 1110 displays a sign-on page 1240 that allows the user to log into the selected biller's system);
execute, analysis of the data corpus of each URL, based on the received analysis input to generate an analysis outcome ([col. 51, lines 1-30] the system may include a learning architecture (e.g., a machine learning architecture, an artificial intelligence architecture, and the like) structured to analyze historical data, automatically generate fund inflow projections, and fund outflow projections and use the projections in accordance with the consumer-defined prioritization schema. In some embodiments, the system may generate a proposed prioritization schema for the consumer based on utilizing a machine learning algorithm (e.g., a neural network, convolutional neural network, recurrent neural network, linear regression model, and sparse vector machine). For example, the system can input the proposed prioritization schema into the machine learning model and receive a prioritization of fund inflow projections and fund outflow projections); and
render, the generated analysis outcome of each URL at the computing device (Fig. 12B; Fig. 12D; Fig. 13C; [col. 32, lines 66 – col. 43, line 5] As shown in FIG. 12C, the exchange bill pay application 1110 can be structured to redirect the user's browser to a URL associated with the login page for the selected biller. In some embodiments, the exchange bill pay application 1110 displays a sign-on page 1240 that allows the user to log into the selected biller's system).
With regard to claim 2, the limitations are addressed above and Hecht teaches wherein the server extracts data from each hyperlink embedded in each webpage associated with the website ([col. 15, lines 30-35] The web service may be identifiable using a unique network address, such as an IP address, a uniform resource locator (URL); [col. 22, lines 11-25] the customer may use a user interface of an online banking website), wherein each webpage is displayed upon access of the URL (Fig. 12B; Fig. 12D; Fig. 13C; [col. 22, 56-66] the customer may use a user interface of an online banking website to revoke access by the biller exchange computing system 330 to one or more biller websites).
With regard to claim 3, the limitations are addressed above and Hecht teaches wherein the analysis input comprises at least one, selected from:
a selection input to analyze a specific section of the webpage or a list of sections needs to be omitted for analysis;
an analysis parameter;
a priority order ([col. 50, lines 43-55] example embodiments of real-time bill pay enhancements shown at least in FIGS. 16-33 encompass functionality directed to consumer workflow and controls, user interfaces (e.g., graphical user interfaces (GUIs)), forecasting, and prioritization. The system (e.g., biller exchange computing system 210, biller exchange computing system 330, and/or biller exchange computing system 430) enables the consumer to more readily identify the bills that need to be paid and determine when the bills should be paid); and
an acceptance or a rejection of analysis ([col. 54, lines 45-67] At 1809c, the bill payment infrastructure can return a confirmation. At 1810, the bill payment infrastructure can notify the biller if the RFP is accepted or rejected).
With regard to claim 4, the limitations are addressed above and Hecht teaches wherein the analysis parameter comprises a content-specific customization input to customize an analysis criterion ([col. 22, lines 11-30] the OAuth token is extended (customized) to include further information, such as a customer identifier, source system URL, a biller's product identifier or other account information, target system (biller or biller processor computing system) URL, payment information (e.g., source account information, a monthly payment amount, an auto-pay amount, a pre-set additional monthly principal payment for installment loans, etc.), custom security policy information required by the biller (e.g., customer challenge questions and answers, customer PIN code, etc.)).
With regard to claim 5, the limitations are addressed above and Hecht teaches wherein the server transmits a notification to the computing device ([col. 22, lines 65-67] generate an electronic notification for transmission to the biller), based on a completion status of analysis of the data corpus ([col. 51, lines 1-30] when limited resources are available, the consumer may specify that a vehicle loan bill should be paid before a retailer account bill, that only a minimum balance should be paid on a particular bill (e.g., a retailer account bill, a credit card bill), and the like. In some embodiments, the system may include a learning architecture (e.g., a machine learning architecture, an artificial intelligence architecture, and the like) structured to analyze historical data, automatically generate fund inflow projections, and fund outflow projections and use the projections in accordance with the consumer-defined prioritization schema).
With regard to claim 8, the limitations are addressed above and Hecht teaches wherein the server enables a collaborative workflow integration ([col. 10, lines 32-40] FIG. 2A describes at a high level a centralized biller exchange computing system 210 that enables communication between multiple financial institutions and billers. The infrastructure of FIG. 2A is shown from the perspective of the biller exchange computing system 210, which provides the API features that connects multiple billers and financial institutions; [col. 50,lines 43-55] embodiments of real-time bill pay enhancements shown at least in FIGS. 16-33 encompass functionality directed to consumer workflow and controls), wherein said collaborative workflow integration allows multiple users to work with role-based access controls ([col. 10, lines 32-40] FIG. 2A describes at a high level a centralized biller exchange computing system 210 that enables communication between multiple financial institutions and billers. The infrastructure of FIG. 2A is shown from the perspective of the biller exchange computing system 210, which provides the API features that connects multiple billers and financial institutions).
With regard to claim 9, the limitations are addressed above and Hecht teaches wherein the server depicts at the computing device ([col. 22, lines 65-67] generate an electronic notification for transmission to the biller), an option for the continuous or scheduled analysis of the website and provides real-time alerts ([abstract] transmitting, by the processing circuit to the second processing circuit via a real-time payment (RTP) network; [col. 6, lines 1-5] systems and methods relating to real-time bill pay enhancements via a bill pay platform allowing enhanced billing between billers and customers; [col. 11, lines 4-13] The biller exchange computing system 210 enables real-time executions, including for example, customer-biller enrollment, biller information inquiry, payment transactions, and delivery of bills), if the content is suspected of being artificial intelligence (AI) generated ([col. 51, lines 15-30] the system may include a learning architecture (e.g., a machine learning architecture, an artificial intelligence architecture, and the like) structured to analyze historical data, automatically generate fund inflow projections, and fund outflow projections and use the projections in accordance with the consumer-defined prioritization schema. In some embodiments, the system may generate a proposed prioritization schema for the consumer based on utilizing a machine learning algorithm).
With regard to claim 10, the method claim corresponds to the system claim 1, respectively, and therefore is rejected with the same rationale.
With regard to claim 11, the method claim corresponds to the system claim 2, respectively, and therefore is rejected with the same rationale.
With regard to claim 12, the method claim corresponds to the system claim 3, respectively, and therefore is rejected with the same rationale.
With regard to claim 13, the method claim corresponds to the system claim 4, respectively, and therefore is rejected with the same rationale.
With regard to claim 14, the method claim corresponds to the system claim 5, respectively, and therefore is rejected with the same rationale.
With regard to claim 17, the method claim corresponds to the system claim 8, respectively, and therefore is rejected with the same rationale.
With regard to claim 18, the method claim corresponds to the system claim 9, respectively, and therefore is rejected with the same rationale.
With regard to claim 19, the product claim corresponds to the system claim 1, respectively, and therefore is rejected with the same rationale.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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 6-7 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Hecht et al. (U.S. Patent No. 12,664,531) in view of Keren et al. (U.S. 2021/0248624).
With regard to claim 6, the limitations are addressed above and Hecht teaches wherein the data corpus comprises textual data ([col. 15, lines 10-24] a text file), multimedia data ([col. 15, lines 10-24] a web service message in a suitable web service message format (e.g., representational state transfer (REST)), document files ([col. 15, lines 10-24] a SQL data set, a protocol buffer message stream, an instantiated class implemented in a suitable object-oriented programming language (e.g., Java, Ruby, C#, etc.), an XML file, a text file, an Excel file), scripts ([col. 15, lines 10-24] a SQL data set, a protocol buffer message stream, an instantiated class implemented in a suitable object-oriented programming language (e.g., Java, Ruby, C#, etc.), an XML file), forms ([col. 15, lines 10-24] The distributed API is used by computing systems to exchange data and make function calls in a structured format…a web service message in a suitable web service message format (e.g., representational state transfer (REST)), dynamic content ([col. 15, lines 50-65] The processing resource and/or memory resource may be dynamically allocated as needed to perform the functionality described herein), structured data ([col. 15, lines 5-10] The distributed API is used by computing systems to exchange data and make function calls in a structured format), user-generated content ([col. 39, lines 45-61] the bill pay billers added by the user may include any of on-us or off-us billers subscribed to the exchange. For example, the systems and methods described relative to FIG. 11-13C can enable a user to access mortgage loan bills, credit card bills, retail credit bills, utility bills, etc., and these bills may be generated by a number of billers who may or may not be affiliated with a financial institution at which the user holds the source account for paying the bills), navigation elements ([col. 16, lines 21-38] a browser plug-in with navigable controls), site maps ([col. 23, lines 55-67] the data store of each respective entity may include a mapping data structure (such as a table) that correlates a reference to a specific system), cookies and tracking scripts ([col. 64, lines 45-53] processor instructions and related data (e.g., database components, object code components, script components)), and accessibility features ([col. 63, lines 54-67] the one or more processors may execute instructions stored in the memory or may execute instructions otherwise accessible to the one or more processors). However, Hecht does not specifically teach:
- metadata
- Robots.txt instructions
- search engine optimization (SEO) elements
Keren teaches a system for collecting content of websites and domain-name data such as websites [abstract]. Keren also teaches the data corpus comprises metadata ([0444] analysis of the content of a web-page or a website, may similarly apply to analysis by the system of the content (and related meta-data) of a page or a group of pages; [0456] tags or meta-data in the HTML code; [0460] site metadata; [0473] meta-data of various domains and websites and web-pages…the system analyzes the content and meta-data and determines the RPID score of such online venue), Robots.txt instructions ([0100] Scanner module 101 may be an automatic and robotic tool; [0110] scanning DNS servers, “robot” modules that scan online data), and search engine optimization (SEO) elements ([0046] generating a cost effectiveness score for Search Engine Optimization (SEO) operations performed for a website of the brand owner…obtaining a user indication of monetary investment in SEO performed between the first time point and the second time point; (d) generating the cost effectiveness score by taking into account, at least, the change between the first ranking and the second ranking, and said monetary investment in SEO; [0051] Search Engine Optimization (SEO) data of said web-site; [0152] (e) Localization capability, including local SEO and/or local translations. For example, performing different SEO operations dedicated for the local language (for example, multilingual capability to edit titles, tags, etc.)). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to have modified the system of performing exchanges based on an exchange model utilizing data elements taught by Hecht, with the cost effectiveness score for Search Engine Optimization (SEO) operations performed for a website of the brand owner taught by Keren, to have achieved a system and method that supports biller enrollment and payments engine architectures.
With regard to claim 7, the limitations are addressed above and Hecht teaches wherein the server implements a predictive content impact modeling ([col. 7, lines 50-63] This data may be used to generate programmatically codified counterparty relationships and/or to enable the generation of bills by making predictions based on historical data (e.g., payment amount predictions, due date predictions, etc.); [col. 26, lines 21-30] the payment transaction information 518 is exposed, via the API, for mining historical trends, predicting future payments, etc.), wherein said predictive content impact modelling utilizes a machine learning technique to predict success of content based on historical data ([col. 51, lines 15-30] the system may include a learning architecture (e.g., a machine learning architecture, an artificial intelligence architecture, and the like) structured to analyze historical data), engagement metrics ([col. 52, lines 27-58] the system may be structured to identify other relevant consumer events and prompt the consumer to engage in biller discovery and matchmaking). However, Hecht does not specifically teach:
- SEO performance
Keren teaches a system for collecting content of websites and domain-name data such as websites [abstract]. Keren also teaches the data corpus comprises a search engine optimization (SEO) performance ([0046] generating a cost effectiveness score for Search Engine Optimization (SEO) operations performed for a website of the brand owner…obtaining a user indication of monetary investment in SEO performed between the first time point and the second time point; (d) generating the cost effectiveness score by taking into account, at least, the change between the first ranking and the second ranking, and said monetary investment in SEO; [0051] Search Engine Optimization (SEO) data of said web-site; [0152] (e) Localization capability, including local SEO and/or local translations. For example, performing different SEO operations dedicated for the local language (for example, multilingual capability to edit titles, tags, etc.)). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to have modified the system of performing exchanges based on an exchange model utilizing data elements taught by Hecht, with the cost effectiveness score for Search Engine Optimization (SEO) operations performed for a website of the brand owner taught by Keren, to have achieved a system and method that supports biller enrollment and payments engine architectures.
With regard to claim 15, the method claim corresponds to the system claim 6, respectively, and therefore is rejected with the same rationale.
With regard to claim 16, the method claim corresponds to the system claim 7, respectively, and therefore is rejected with the same rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREA C. LEGGETT whose telephone number is (571)270-7700. The examiner can normally be reached M-F 9am-5pm.
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/ANDREA C LEGGETT/Primary Examiner, Art Unit 2171
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