DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the communications filed on 07/27/2026.
Claims 1-2, 6, 10, and 12-13, have been amended and is hereby entered.
Claims 1-24 are currently pending and have been examined.
This action is made Non-FINAL.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for future claim amendments to avoid U.S.C 112(a) issues that can arise. The Examiner thanks the Applicant in advance.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/27/2026 has been entered.
Claim Objection
Claim 6 is objected to because of the following informalities:
Claim 6: lines 6-7 recites the limitation “the comparison meets or exceeds the one or mroe predetermined threshold parameters.” The word “more” is misspelled. For compact examination purposes, Examiner interpreted the instance recited in Claim 6: lines 6-7, as “the comparison meets or exceeds the one or more predetermined threshold parameters.” Appropriate correction is required.
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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of identifying, processing, and communicating a user-specific product offering for which the user is pre-approved without significantly more.
Claim 1 is directed to a system which is one of the statutory categories of invention (Step 1: YES).
Claim 1 is directed to a system comprising: a data intelligence platform comprising one or more servers, one or more processors and a memory storing computer-readable instructions that, when executed by the one or more processors, cause the data intelligence platform to: receive, from among one or more independent data sources, data and information associated with a user, the data and information comprising activity data associated with one or more user accounts, household data associated with the user, and collateral data associated with a collateral asset, the activity data, the household data and the collateral data comprising disparate data types; pre-process the received data and information associated with the user; provide, as input to a first machine learning (ML) modeling algorithm, characteristics data from among the pre-processed data and information, the characteristics data relating to the collateral asset, wherein the first ML modeling algorithm is trained, using a first training data set, to select a collateral valuation model from among multiple available collateral valuation models, the first training data set comprising historic performance data that has been pre- processed, the historic performance data defining an extent to which each of the multiple available collateral valuation models has been accurate or inaccurate in determining a respective value profile based on the characteristics data; execute the first ML modeling algorithm, from among a plurality of ML modeling algorithms, to generate as output a selected collateral valuation model from among the multiple available collateral valuation models; execute the selected collateral valuation model to generate a collateral profile associated with the collateral asset; evaluate a current performance of the selected collateral valuation model; generate a second training data set that includes the historic performance data and current performance data of the selected collateral valuation model; retrain the first ML modeling algorithm using the second training data set; execute a second ML modeling algorithm from among the plurality of ML modeling algorithms to generate an activity profile associated with the user, the second ML modeling algorithm receiving the activity data as input; execute a third ML modeling algorithm from among the plurality of ML modeling algorithms to generate a household profile associated with the user, the third ML modeling algorithm receiving the household data as input, wherein the plurality of ML modeling algorithms are executed concurrently and cooperatively, and output generated by at least one of the plurality of ML modeling algorithms is provided as input to at least one other of the plurality of ML modeling algorithms combine data from among the collateral profile, the activity profile and the household profile to create a digital asset that integrates the disparate data types into a single electronic bundle, and convert, via a data converter, the digital asset into a format that is compatible with one or more downstream processing systems; apply advanced analytics to the digital asset, in connection with one or more rules and policies, to identify a user-specific product offering for which the user is pre-approved; communicate the user-specific product offering to a user device associated with the user via one or more communication channels; monitor at least one among the one or more independent data sources for a change to the data and information, and responsive to a detected change, automatically re-execute at least one of the plurality of ML modeling algorithms to update at least one of the collateral profile, the activity profile and the household profile and to update the digital asset generate a single interactive graphical user interface (GUI) for display on the user device, the interactive GUI having a configured arrangement comprising a user input region, a notification indication region, a display region that simultaneously displays the user-specific product offering, terms of the user-specific product offering, and account data associated with the one or more user accounts, and a communication interaction region configured to initiate a live communication session and to share content within the interactive GUI; and automatically and dynamically update, in real-time and without user input, at least the display region of the interactive GUI to reflect a change resulting from the re-execution of the at least one of the plurality of ML modeling algorithms. These series of steps describe the abstract idea of identifying, processing, and communicating a user-specific product offering for which the user is pre-approved (with the exception of the italicized and bolded terms above), which is mitigating risk of using incomplete, inaccurate and/or outdated data while processing user-specific product offerings; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the identifying and processing of a user-specific product offering for which the user is pre-approved, including a home equity lending product having a particular set of characteristics (line or credit limit, interest rate, etc.) for which the pre-approved user is determined to qualify, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The computer device limitations, e.g., a data intelligence platform, one or more servers, one or more processors, memory, one or more independent data sources, first machine learning (ML) modeling algorithm, ML modeling algorithm, plurality of ML modeling algorithms, second ML modeling algorithm, third ML modeling algorithm, digital asset, single electronic bundle, data converter, one or more downstream processing systems, user device, collateral valuation model, multiple available collateral valuation models, selected collateral valuation model, one or more communication channels, and single interactive graphical user interface (GUI) do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 1 is directed to an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of a data intelligence platform, one or more servers, one or more processors, memory, one or more independent data sources, first machine learning (ML) modeling algorithm, ML modeling algorithm, plurality of ML modeling algorithms, second ML modeling algorithm, third ML modeling algorithm, digital asset, single electronic bundle, data converter, one or more downstream processing systems, user device, collateral valuation model, multiple available collateral valuation models, selected collateral valuation model, one or more communication channels, and single interactive graphical user interface (GUI), are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 1 is directed to an abstract idea (Step 2A-Prong 2: NO).
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element of o a data intelligence platform, one or more servers, one or more processors, memory, one or more independent data sources, first machine learning (ML) modeling algorithm, ML modeling algorithm, plurality of ML modeling algorithms, second ML modeling algorithm, third ML modeling algorithm, digital asset, single electronic bundle, data converter, one or more downstream processing systems, user device, collateral valuation model, multiple available collateral valuation models, selected collateral valuation model, one or more communication channels, and single interactive graphical user interface (GUI) are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 1 is not patent eligible.
Dependent claims 2-24 are directed to a system, which recites steps that describe the abstract idea of identifying, processing, and communicating a user-specific product offering for which the user is pre-approved. Specifically, dependent claims 2, 14-15, and 17-18 are directed to a system, performing a series of steps that describe the abstract idea of identifying, processing, and communicating a user-specific product offering for which the user is pre-approved (with the exception of the italicized and bolded terms above), which is mitigating risk of using incomplete, inaccurate and/or outdated data while processing user-specific product offerings; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the identifying and processing of a user-specific product offering for which the user is pre-approved, including a home equity lending product having a particular set of characteristics (line or credit limit, interest rate, etc.) for which the pre-approved user is determined to qualify, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Thus, claims 2-24 are directed to an abstract idea. The additional elements of a data intelligence platform, one or more servers, one or more processors, memory, one or more independent data sources, first machine learning (ML) modeling algorithm, ML modeling algorithm, plurality of ML modeling algorithms, second ML modeling algorithm, third ML modeling algorithm, digital asset, single electronic bundle, data converter, one or more downstream processing systems, user device, collateral valuation model, multiple available collateral valuation models, selected collateral valuation model, one or more communication channels, single interactive graphical user interface (GUI), combination of one or more third-party systems, one or more user devices, one or more storage devices, one or more components of the data intelligence platform, one or more ML modeling algorithms, storage device, web portal, and application program interface (API) are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements, a data intelligence platform, one or more servers, one or more processors, memory, one or more independent data sources, first machine learning (ML) modeling algorithm, ML modeling algorithm, plurality of ML modeling algorithms, second ML modeling algorithm, third ML modeling algorithm, digital asset, single electronic bundle, data converter, one or more downstream processing systems, user device, collateral valuation model, multiple available collateral valuation models, selected collateral valuation model, one or more communication channels, single interactive graphical user interface (GUI), combination of one or more third-party systems, one or more user devices, one or more storage devices, one or more components of the data intelligence platform, one or more ML modeling algorithms, storage device, web portal, and application program interface (API), do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Dependent claims 2-24 have further defined the abstract idea that is present in their respective independent claim 1; and thus correspond to Certain Methods of Organizing Human Activity and hence are abstract in nature for the reason presented above. The dependent claims 2-24 do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, claims 2-24 are directed to an abstract idea.
Thus, claims 1-24 are not patent-eligible.
Response to Arguments
With respect to the 35 U.S.C. 112(b) rejection of claims 1-24, the rejection is withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 07/27/2026.
Applicant's arguments filed on 07/27/2026 have been fully considered, but are not persuasive due to the following reasons:
With respect to the rejection of claims 1-20 under 35 U.S.C. 101, Applicant arguments are moot in view of the grounds of rejections presented above in this office action. The arguments are addressed to the extent they apply to the amended claims.
Applicant argues that the “the amendments submitted herein address both pillars by more clearly centering the claim on two concrete, unconventional technological improvements a specific GUI arrangement and a concurrent/cooperative, nested ML architecture rather than on a financial outcome the system produces, as alleged…. The amended claims now recite a single interactive GUI having a "configured arrangement" comprising four distinct, simultaneously-presented regions: (i) a user input region, (ii) a notification indication region, (iii) a display region that simultaneously displays the user- specific product offering, its terms, and account data, and (iv) a communication interaction region configured to initiate a live communication session and to share content within the interactive GUI. The claim further requires that at least the display region is "automatically and dynamically update[d], in real-time and without user input," to reflect changes resulting from ML re-execution. This is not generic "displaying data on a screen." To the contrary, it is a specific, claimed interface structure analogous to the "summary window" held eligible in Core Wireless Licensing S.A.R.L. v. LG Electronics, Inc., 880 F.3d 1356 (Fed. Cir. 2018) that solves a concrete display problem: how to present a user with a consolidated, real-time view of an offering, its terms, account data, and live communication access in a single screen…. Under Step 2A, Prong Two, this specific GUTI arrangement is an improvement to the functioning of a computer or other technology (MPEP § 2106.05(a)), because it recites a particular interface structure that changes how information is presented to and interacted with by the user -not merely instructions to display data on a generic screen….. Under Step 2A, Prong Two, this concurrent/cooperative, nested architecture is an improvement to the functioning of a computer or other technology (MPEP § 2106.05(a)), because it recites a specific computational structure not a generic invocation of ML -that solves the identified technical problem of integrating disparate data types from independent sources. Under Step 2B, the combination of concurrent execution, nested output-as-input relationships, disparate-data integration into a single electronic bundle, and format conversion via a data converter is not well-understood, routine, or conventional….. It is also noted that the amended claims align with the eligible claims in USPTO Examples 37 (Relocation of Icons on GUI eligible because the claim recited a specific interface improvement), 47 (Training a Neural Network eligible claim 3 recited a specific technical architecture for anomaly prediction that improved computer functionality), and 48 (Speech Separation eligible claim 3 recited a specific ML architecture that improved technology). Like those examples, the amended claim recites specific technical structures (the GUI arrangement and the concurrent/nested ML pipeline) that improve how the computer system operates, rather than merely applying an abstract idea with generic components.”
Examiner respectfully disagrees.
Under Step 2A: Prong 1, Examiner respectfully notes that claim 1, as amended, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of identifying, processing, and communicating a user-specific product offering for which the user is pre-approved; without significantly more. The series of steps recited in claim1, as amended, describe the abstract idea of identifying, processing, and communicating a user-specific product offering for which the user is pre-approved, which correspond to Certain Methods of Organizing Human Activity: fundamental economic principles or practices (including hedging, insurance, mitigating risk) and/or commercial or legal interactions. Furthermore, the system limitations, e.g., a data intelligence platform, one or more servers, one or more processors, memory, one or more independent data sources, first machine learning (ML) modeling algorithm, ML modeling algorithm, plurality of ML modeling algorithms, second ML modeling algorithm, third ML modeling algorithm, digital asset, single electronic bundle, data converter, one or more downstream processing systems, user device, collateral valuation model, multiple available collateral valuation models, selected collateral valuation model, one or more communication channels, and single interactive graphical user interface (GUI) do not necessarily restrict the claim from reciting an abstract idea.
Moreover, Examiner respectfully notes that the claims are first analyzed in the absence of technology to determine if it recites an abstract idea. The additional limitations of technology are then considered to determine if it restricts the claim from reciting an abstract idea. In this case, and as discussed in the July 2024 Guidance Update on Patent Subject Matter Eligibility, it is determined that the additional limitations of technology do not necessarily restrict the claim from reciting an abstract idea. Furthermore, Examiner respectfully notes that the recited features in the limitations: receive, from among one or more independent data sources, data and information associated with a user, the data and information comprising activity data associated with one or more user accounts, household data associated with the user, and collateral data associated with a collateral asset, the activity data, the household data and the collateral data comprising disparate data types; pre-process the received data and information associated with the user; provide, as input to a first machine learning (ML) modeling algorithm, characteristics data from among the pre-processed data and information, the characteristics data relating to the collateral asset, wherein the first ML modeling algorithm is trained, using a first training data set, to select a collateral valuation model from among multiple available collateral valuation models, the first training data set comprising historic performance data that has been pre- processed, the historic performance data defining an extent to which each of the multiple available collateral valuation models has been accurate or inaccurate in determining a respective value profile based on the characteristics data; execute the first ML modeling algorithm, from among a plurality of ML modeling algorithms, to generate as output a selected collateral valuation model from among the multiple available collateral valuation models; execute the selected collateral valuation model to generate a collateral profile associated with the collateral asset; evaluate a current performance of the selected collateral valuation model; generate a second training data set that includes the historic performance data and current performance data of the selected collateral valuation model; retrain the first ML modeling algorithm using the second training data set; execute a second ML modeling algorithm from among the plurality of ML modeling algorithms to generate an activity profile associated with the user, the second ML modeling algorithm receiving the activity data as input; execute a third ML modeling algorithm from among the plurality of ML modeling algorithms to generate a household profile associated with the user, the third ML modeling algorithm receiving the household data as input, wherein the plurality of ML modeling algorithms are executed concurrently and cooperatively, and output generated by at least one of the plurality of ML modeling algorithms is provided as input to at least one other of the plurality of ML modeling algorithms combine data from among the collateral profile, the activity profile and the household profile to create a digital asset that integrates the disparate data types into a single electronic bundle, and convert, via a data converter, the digital asset into a format that is compatible with one or more downstream processing systems; apply advanced analytics to the digital asset, in connection with one or more rules and policies, to identify a user-specific product offering for which the user is pre-approved; communicate the user-specific product offering to a user device associated with the user via one or more communication channels; monitor at least one among the one or more independent data sources for a change to the data and information, and responsive to a detected change, automatically re-execute at least one of the plurality of ML modeling algorithms to update at least one of the collateral profile, the activity profile and the household profile and to update the digital asset generate a single interactive graphical user interface (GUI) for display on the user device, the interactive GUI having a configured arrangement comprising a user input region, a notification indication region, a display region that simultaneously displays the user-specific product offering, terms of the user-specific product offering, and account data associated with the one or more user accounts, and a communication interaction region configured to initiate a live communication session and to share content within the interactive GUI; and automatically and dynamically update, in real-time and without user input, at least the display region of the interactive GUI to reflect a change resulting from the re-execution of the at least one of the plurality of ML modeling algorithms” are simply making use of a computer and the computer limitations do not necessarily restrict the claim from reciting an abstract idea as discussed above under Step 2A-Prong 1 of the 35 U.S.C. 101 rejection.
Hence, Examiner has also considered each and every arguments under Step 2A-Prong 1 and concludes that these arguments are not persuasive. For example, under Step 2A-Prong 1, Examiner considers each and every limitation to determine if the claim recites an abstract idea. In this case, it is determined that the claim recites an abstract idea and the additional limitations of a computer device does not necessarily restrict the claim from reciting an abstract idea. The recited steps, as amended, are abstract in nature as there are no technical/technology improvements as a result of these steps. Thus, the claim recites an abstract idea. Whether the claim integrates the abstract idea into a practical application by providing technical/technology improvements are considered under Step 2A-Prong 2.
With respect to Applicant’s Step 2A: Prong II arguments, Examiner respectfully disagrees. Under Step 2A: Prong II, Examiner respectfully notes that there is no improved technology in simply receiving, selecting, inputting, preprocessing, providing, using, defining, determining, generating, combining, creating, processing, applying, identifying, communicating, evaluating, monitoring, updating, displaying, and outputting data (i.e., user data and information, user account information, household data, collateral asset data, profile data, asset data, historical data, and etc.). The disclosed invention cannot be equated to improvement to technological practices or computers. There is no technical improvement at all. Instead, Applicant recites “receive, from among one or more independent data sources, data and information associated with a user, the data and information comprising activity data associated with one or more user accounts, household data associated with the user, and collateral data associated with a collateral asset, the activity data, the household data and the collateral data comprising disparate data types; pre-process the received data and information associated with the user; provide, as input to a first machine learning (ML) modeling algorithm, characteristics data from among the pre-processed data and information, the characteristics data relating to the collateral asset, wherein the first ML modeling algorithm is trained, using a first training data set, to select a collateral valuation model from among multiple available collateral valuation models, the first training data set comprising historic performance data that has been pre- processed, the historic performance data defining an extent to which each of the multiple available collateral valuation models has been accurate or inaccurate in determining a respective value profile based on the characteristics data; execute the first ML modeling algorithm, from among a plurality of ML modeling algorithms, to generate as output a selected collateral valuation model from among the multiple available collateral valuation models; execute the selected collateral valuation model to generate a collateral profile associated with the collateral asset; evaluate a current performance of the selected collateral valuation model; generate a second training data set that includes the historic performance data and current performance data of the selected collateral valuation model; retrain the first ML modeling algorithm using the second training data set; execute a second ML modeling algorithm from among the plurality of ML modeling algorithms to generate an activity profile associated with the user, the second ML modeling algorithm receiving the activity data as input; execute a third ML modeling algorithm from among the plurality of ML modeling algorithms to generate a household profile associated with the user, the third ML modeling algorithm receiving the household data as input, wherein the plurality of ML modeling algorithms are executed concurrently and cooperatively, and output generated by at least one of the plurality of ML modeling algorithms is provided as input to at least one other of the plurality of ML modeling algorithms combine data from among the collateral profile, the activity profile and the household profile to create a digital asset that integrates the disparate data types into a single electronic bundle, and convert, via a data converter, the digital asset into a format that is compatible with one or more downstream processing systems; apply advanced analytics to the digital asset, in connection with one or more rules and policies, to identify a user-specific product offering for which the user is pre-approved; communicate the user-specific product offering to a user device associated with the user via one or more communication channels; monitor at least one among the one or more independent data sources for a change to the data and information, and responsive to a detected change, automatically re-execute at least one of the plurality of ML modeling algorithms to update at least one of the collateral profile, the activity profile and the household profile and to update the digital asset generate a single interactive graphical user interface (GUI) for display on the user device, the interactive GUI having a configured arrangement comprising a user input region, a notification indication region, a display region that simultaneously displays the user-specific product offering, terms of the user-specific product offering, and account data associated with the one or more user accounts, and a communication interaction region configured to initiate a live communication session and to share content within the interactive GUI; and automatically and dynamically update, in real-time and without user input, at least the display region of the interactive GUI to reflect a change resulting from the re-execution of the at least one of the plurality of ML modeling algorithms.” The recited features in the limitations do not result in computer functionality or technical improvement. Examiner respectfully notes that Applicant is using a computer to input, process, and output data. The recited features in the limitations does not disclose a technical solution to technical problem, but simply a business solution. Specifically, the recited steps, as amended, are merely managing/processing data (MPEP 2106.05(d)(II)) and does not result in computer functionality or technical improvement. Thus, Applicant has simply provided a business method practice of processing data (asset and profile-related data), and no technical solution or improvement has been disclosed.
Furthermore, there is no technology/technical improvement as a result of implementing the abstract idea. The recited limitations in the pending claims simply amount to the abstract idea of identifying, processing, and communicating a user-specific product offering for which the user is pre-approved. There is no computer functionality improvement or technology improvement. The claim does not provide a technical solution to a technical problem. If there is an improvement, it is to the abstract idea and not to technology. Furthermore, Examiner respectfully notes that the machine learning models operating "concurrently/cooperatively" with no technical explanation of how that is happening is not an improvement to machine learning. Similarly, Examiner respectfully notes that the graphical user interface (GUI) is a GUI with various pieces of real-time data, but there is no actual improvements to GUI technology. Additionally, Examiner notes that it is important to keep in mind that an improvement in the judicial exception itself (e.g., recited fundamental economic principle or practice and/or commercial interaction) is not an improvement in technology (See, MPEP 2106.05(a)(II)). Thus, the claim does not integrate the abstract idea into a practical application; and these arguments are not persuasive.
Unlike the eligible claims discussed in the 2019 and 2024 updated USPTO's Subject Matter Eligibility Examples [Example 39, Examples 47, Example 48, and Example 49], claim 1, as amended, recites steps at a high level of generality. In addition, all uses of the recited judicial exceptions require such gathering, processing, and outputting of data; thus, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary steps of data gathering, processing, and output. The claim simply makes use of a computer as a tool to apply the abstract idea without transforming the abstract idea into a patent eligible subject matter. Furthermore, the ‘electronic ( interactive) / automatic’ features simply amounts to mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017). Thus, the automation feature is not sufficient to show an improvement in computer-functionality or technology/technical improvements (see MPEP 2106.05(a)(1)). Thus, these arguments are not persuasive
Additionally, these steps, as amended, are recited as being performed by a data intelligence platform, one or more servers, one or more processors, memory, one or more independent data sources, first machine learning (ML) modeling algorithm, ML modeling algorithm, plurality of ML modeling algorithms, second ML modeling algorithm, third ML modeling algorithm, digital asset, single electronic bundle, data converter, one or more downstream processing systems, user device, collateral valuation model, multiple available collateral valuation models, selected collateral valuation model, one or more communication channels, and single interactive graphical user interface (GUI. The a data intelligence platform, one or more servers, one or more processors, memory, one or more independent data sources, first machine learning (ML) modeling algorithm, ML modeling algorithm, plurality of ML modeling algorithms, second ML modeling algorithm, third ML modeling algorithm, digital asset, single electronic bundle, data converter, one or more downstream processing systems, user device, collateral valuation model, multiple available collateral valuation models, selected collateral valuation model, one or more communication channels, and single interactive graphical user interface (GUI are recited at a high level of generality, and are used as a tool to perform the generic computer function of receiving, processing, and outputting data. See MPEP 2106.05(f). Similar to 2024 Guidance Update on Patent Subject Matter Eligibility: Example 47 (claim 2), amended claim 1 recites a data intelligence platform, one or more servers, one or more processors, memory, one or more independent data sources, first machine learning (ML) modeling algorithm, ML modeling algorithm, plurality of ML modeling algorithms, second ML modeling algorithm, third ML modeling algorithm, digital asset, single electronic bundle, data converter, one or more downstream processing systems, user device, collateral valuation model, multiple available collateral valuation models, selected collateral valuation model, one or more communication channels, and single interactive graphical user interface (GUI, which are simply used to perform an abstract idea, as discussed above in Step 2A, Prong 1, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Specifically, the recitation of “a data intelligence platform, one or more servers, one or more processors, memory, one or more independent data sources, first machine learning (ML) modeling algorithm, ML modeling algorithm, plurality of ML modeling algorithms, second ML modeling algorithm, third ML modeling algorithm, digital asset, single electronic bundle, data converter, one or more downstream processing systems, user device, collateral valuation model, multiple available collateral valuation models, selected collateral valuation model, one or more communication channels, and single interactive graphical user interface (GUI” in the limitations merely indicates a field of use or technological environment in which the judicial exception is performed. The claims, as amended, merely confines the use of the abstract idea to a particular technological environment; and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. Hence, Claim 1, as amended, do not integrate the abstract idea into a practical application. Thus, these arguments are not persuasive.
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 101 rejection of claims 1-24.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is the following:
Jain (U.S. Patent Application Pub. No. US 2019/0080399 A1) “Automated collateral risk and business performance assessment system”
Ross (U.S. Patent No. US 11,062,378 B1) “Next product purchase and lapse predicting tool”
Liu (U.S. Patent Application Pub. No. US 2021/0118074 A1) “Digital Real Estate Transaction Processing Platform”
Jennings (U.S. Patent No. US 11,270,375 B1) “Next product purchase and lapse predicting tool”
Shen (U.S. Patent Application Pub. No. US 2022/0103589 A1) “Predicting data tampering using augmented machine learning models”
Koren (U.S. Patent Application Pub. No. US 2022/0327504 A1) “Systems and method for automatic transaction routing and execution”
Maiman (U.S. Patent Application Pub. No. US 2022/0335447 A1) “Systems and methods for object preference prediction”
Sells (U.S. Patent Application Pub. No. US 2023/0043702 A1) “Multi-modal routing engine and processing architecture for currency orchestration of transactions”
Zheng (U.S. Patent Application Pub. No. US 2023/0254455 A1) “P Camera Platform Incorporating Schedule and Stature”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H MUSTAFA whose telephone number is (571)270-7978. The examiner can normally be reached M-F 8:00 - 5:00.
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/MOHAMMED H MUSTAFA/Examiner, Art Unit 3693
/CHO YIU KWONG/Primary Examiner, Art Unit 3693
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