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 the Application
Claims 1-30 have been examined in this application.
The filling date of this application number recited above is 09-December-2024. Domestic Benefit/National Stage priority has been claimed for Provisional Application 63/616,029 in the Application Data Sheet, thus the examination will be undertaken in consideration of 29-December-2023, as the priority date, for applicable claims.
No information disclosure statement (IDS) has been filed to date.
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 1-30 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. The Claims are directed to an abstract idea, Mental Processes and/or Methods of Organizing Human Activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
As per Claims 1 and 28, the claims recite “a … method for … processing of a products application, comprising:
receiving the products application, the products application including application data associated with an applicant;
obtaining a plurality of website addresses for electronic resources related to behavior associated with the applicant;
aggregating content data corresponding to the behavior into the application data;
preprocessing the application data into preprocessed data by normalizing the application data for input into a [model];
inputting the preprocessed data into the [model], the [model] including:
a training dataset including data associated with a historical application, an underwriting decision associated with the historical application, financial gain data associated with the historical application, and user-provided feedback associated with the historical application;
a feature selection [entity] configured to:
receive a plurality of candidate features associated with the behavior;
analyze the plurality of candidate features to determine a feature weight indicating the predictive value of each feature of the plurality of candidate features with respect to a target outcome; and
select, based on the feature weight, a subset of features;
a hyperparameter tuning [entity] configured to:
… generate a set of hyperparameters;
define a search space of candidate hyperparameters and associated ranges based on one or more model performance requirements and the training dataset; and
iteratively adjust the hyperparameters using a tuning strategy; and
a prediction model configured to compute an expected application outcome score based on the training dataset, the feature selection [entity], and the hyperparameter tuning [entity];
generate an underwriting decision based on the expected application outcome score, the underwriting decision being one chosen from the set of rejected, conditional, and approved;
if the underwriting decision is approved, provide the products application and application data for display on a [paper] associated with a user; and
receive a selection associated with the products application from the [paper] associated with the user.”
The limitation of the claim recited above, considering the claim without the additional elements (e.g. system, database, processor, device, module, etc.), under its broadest reasonable interpretation (BRI), recites Mental Processes. The method recited above is a process of receiving and obtaining data, inputting data into a model (e.g. equation), analyzing data, selecting data, determining data, adjusting data, generating data, and displaying data. These steps can be performed in the human mind, or by a human using a pen and paper, which is a mental process, as disclosed by MPEP 2106.04(III)(A):
“In contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:
• a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
• claims to "comparing BRCA sequences and determining the existence of alterations," where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 763, 113 USPQ2d 1241, 1246 (Fed. Cir. 2014);
• a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011); and
• a claim to identifying head shape and applying hair designs, which is a process that can be practically performed in the human mind, In re Brown, 645 Fed. App'x 1014, 1016-17 (Fed. Cir. 2016) (non-precedential).”
Although the claim may recite using a computer for the claimed process, performing a mental process on a generic computer still recite a mental process. See MPEP 2106.04(III)(C):
“Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").”
Additionally, the claim, under BRI, recites Certain Methods of Organizing Human Activities. The claim limitations recite the steps of data processing in the field of investment (e.g. loan applications, crowdsource investment, etc.), with the goal of mitigating risk as disclosed by Specification:
[0002] “A system that provides a wide range of investment opportunities can benefit all investors using the system by easing risks and offering opportunities to achieve personal investment goals”
[0052] “Some disclosed embodiments involve financial gain … For lenders, financial gain might come from approving low-risk loans with competitive interest rates, while effectively minimizing losses from high-risk applicants. For investors, financial gain may arise from diversified investment opportunities within automated platforms that use machine learning to improve portfolio returns. By accurately evaluating an applicant's financial reliability profiles and tailoring investment choices, machine learning-enabled systems can provide users with better financial outcomes”
which may be fundamental economic principles or practices and/or commercial or legal interactions, under certain methods of organizing human activities. Therefore, the claims recite an abstract idea.
This judicial exception is not integrated into practical application. In particular, the claims recite an additional element of “system”, “database”, “processor”, “machine learning model”, “module”, and “device” to perform the method recited above by instructing the abstract idea to be performed “by” these generic computer components. These additional elements are generic, off-the-shelf components available to the public, and does not require any specialized hardware or equipment to “automatically” perform the claimed functions of receiving data, obtaining data, providing data, generating data, adjusting data, transmitting data, or displaying data, as disclosed by Specification:
[0022] “The non-transitory computer readable medium refers to any type of physical non-transitory computer readable medium on which information or data readable by a processor can be stored as described and exemplified elsewhere herein”
[0024] “Some disclosed embodiments involve at least one processor. A processor refers to any physical device or group of devices having electric circuitry that performs a logic operation on an input or inputs, as described and exemplified elsewhere herein”
[0088] “Some disclosed embodiments involve a device associated with the user. The device may include smartphones, tablets, laptops, desktops, or wearable technology”
These general computer components are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer system. For example, mere instructions to display information on a GUI does not improve computer functionality, see MPEP 2106.05(a)(I) example that the courts have indicated may not be sufficient to show an improvement in computer-functionality: “Arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019)”. The machine learning model is recited at a mere “apply-it” level as a black-box application to provide data in response to the input data. Mere instructions to implement the abstract idea on a computer, or merely using the computer as a tool to perform the abstract idea (e.g. mere “apply it”) is not indicative of integration into a practical application; see MPEP 2106.05(f). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, compare, transmit, or display data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activities) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, the additional element of using a computer based system is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer system. The claim lacks sufficient technical details to provide how these limitations may provide technological steps or technical details on how it is particularly implemented on a computer to improve its system or any of its underlying hardware or components (e.g. how it is performed on the computer, how it could improve the computer itself, how it could manipulate the computer to function in a specific way other than its generic functionality, and/or how it could improve any of the underlying technology), but merely applies the generic computer system to perform its generic functionalities. Mere instructions to implement the abstract idea on a computer, or merely using the computer as a tool to perform the abstract idea (e.g. mere “apply it”) is not indicative of an inventive concept (aka “significantly more”). In view of the Specification cited above, the judicial exception is not applied with or used by a particular machine. As held in Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 199 (1978) and Bancorp Services v. Sun Life, 687 F.3d 1266, 1276, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012), “the routine use of a computer to perform calculations cannot turn an otherwise ineligible mathematical formula or law of nature into patentable subject matter.” The claims are not patent eligible.
Regarding dependent claims, they are still directed to an abstract idea without significantly more.
Claim 2 recites “wherein the processor is further configured to execute the instructions to: process a payment associated with the selection through an investment card and an investment account; collect a pre-calculated payback associated with the selection; and return the pre-calculated payback to an account associated with the user.” The claim provides details regarding the transaction, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 3 recites “wherein the feature selection module is further configured to dynamically modify the candidate features based on the user-provided feedback.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 4 recites “wherein the dynamic modification occurs after the selection.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 5 recites “wherein the target outcome is maximizing a satisfaction score associated with the user, the satisfaction score being calculated based on the selection.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 6 recites “wherein the user is a plurality of users.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 7 recites “wherein the analysis of candidate features includes one or more of a permutation importance, SHAP (SHapley Additive exPlanations), and LIME (Local Interpretable Model-agnostic Explanations).” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 8 recites “wherein the content data is collected from the plurality of website addresses by a web scraping module configured to: extract raw content data from the plurality of website addresses by parsing the content of web pages associated with the plurality of website addresses, the raw content data including text, images, or metadata; and format the raw content data into the content data, such that the content data is suitable for processing by the processor.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 9 recites “wherein the content data is collected from the plurality of website addresses by an Application Programming Interface (API) configured to: access raw content data from an external source by connecting to the API, the raw content data including text, numerical values, or media content; and format the raw content data into the content data, such that the content data is suitable for processing by the processor.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 10 recites “wherein the application data includes an image; and the preprocessing includes resizing, normalizing, and standardizing image dimensions associated with the image and pixel values associated with the image to ensure consistency; applying filtering techniques to enhance quality of the image; and converting the image into a format compatible with the feature selection module.” The claim provides details regarding the data analysis including an image, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 11 recites “wherein the application data includes a video; and the preprocessing includes segmenting the video into a plurality of frames at predefined intervals to extract representative visual content while reducing data volume; resizing, normalizing, and standardizing the plurality of frames to ensure consistency; applying enhancement techniques to the plurality of frames; and converting the plurality of frames into a format compatible with the feature selection module.” The claim provides details regarding the data analysis including a video, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 12 recites “wherein the application data includes text; and the preprocessing includes removing one or more unwanted characters, including special symbols, emojis, and hyperlinks, for standardization; performing stop-word removal; stemming or lemmatization to reduce text complexity; and converting the text into a format compatible with the feature selection module.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 13 recites “wherein the application data includes text data; and the machine learning model is further configured to perform natural language processing to understand the meaning of the text data.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 14 recites “wherein the application data includes multilingual application data; and the machine learning model is further configured to process the multilingual application data.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 15 recites “wherein adjusting the hyperparameters is based on the underwriting decision and a type of financial product associated with the selection.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 16 recites “wherein the plurality of website addresses for electronic resources includes at least one chosen from the set of social media platforms, forums, and other user-generated content platforms that contain publicly available content including posts, comments, images, and videos.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 17 recites “wherein the plurality of website addresses for electronic resources includes at least one privately owned external database, wherein data collection associated with the at least one privately owned external database is in accordance with an applicable data usage agreement.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 18 recites “wherein the electronic resources include one or more chosen from the set of: criminal history, education history, driving records, civil court records, and professional licenses.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 19 recites “wherein the hyperparameter tuning module is further configured to increase a user satisfaction score associated with the expected application outcome score.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 20 recites “wherein the prediction model includes one or more of a decision tree model, a support vector machine, and a deep learning model.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 21 recites “wherein the application data includes a desired interest rate.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 22 recites “wherein the investment card is associated with a spending limit.” The claim provides details regarding the transaction card, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 23 recites “wherein the return recurs for a predetermined period of time.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 24 recites “wherein returning the pre-calculated payback includes distributing a proportional amount based on a funding request associated with the products application.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 25 recites “wherein the return occurs automatically when the products application results in a capital gain.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 26 recites “wherein the user is an employee associated with an institution; and the investment account is created from an employee payroll associated with the institution.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 27 recites “wherein the processing of the payment uses blockchain technology.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements (e.g. blockchain) are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 29 recites “wherein the method further comprises suggesting an timing for the selection based on a predicted financial gain associated with the products application.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 30 recites “wherein the machine learning model predicts a likelihood of selection associated with the products application, the likelihood of selection being based on interest data associated with the user and product application selection history data associated with the user.” The claim provides details regarding the data analysis, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
These additional steps of each claims fail to remedy the deficiencies of their parent claim above because they are merely further limiting the rules used to conduct the previously recited abstract idea, and are therefore rejected for at least the same rationale as applied to their parent claim above.
Claims 2-27 and 28-30, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are sufficient to integrate into a practical application and do not amount to significantly more than the judicial exception. Similarly to the independent claim, each claim recites using a generic computer system to perform the abstract idea as mentioned above. Mere “apply it” is not “significantly more”. Therefore, prong 2 and step 2B analysis are similar to above and these claims are not eligible.
Therefore, Claims 1-30 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-6, 9, 13, 19-20, and 28-30 are rejected under 35 U.S.C. 103 as being unpatentable over Porter et al. (US 11611653 B1), in view of Taifi et al. (US 20210224862 A1), and in view of Hepp et al. (US 11989776 B1).
As per Claims 1 and 28, Porter discloses a system for automating processing of a products application, comprising: a database storing a set of instructions; and a processor (See Figure 1 disclosing the system) configured to execute the set of instructions to:
receive the products application, the products application including application data associated with an applicant ([Col 1 Lines 52-57] “In a non-limiting example, a user may use a computer-based tool associated with an entity to submit an electronic request (e.g., submit inputs and receive recommendations for one or more investment products, such as mutual funds). For example, the user may submit the electronic request to purchase mutual funds”);
obtain a plurality of [datasets] for electronic resources related to behavior associated with the applicant (See Figure 3 – step 302, as disclosed [Col 17 Lines 1-9] “In a first step 302, the system server may receive datasets from data sources. The system server may retrieve the datasets from the data sources. The data sources may include, but not limited to, multiple application servers and data generation devices, which are configured to generate multiple data records associated with each dataset. The data sources may include, but not limited to, multiple data repositories. Each data repository may store the data records associated with each dataset in an electronic format”);
aggregate content data corresponding to the behavior into the application data (See Figure 3 – step 306, as disclosed [Col 17 Lines 30-38] “The processing operation may result in normalizing and aggregating the data records in the pre-processed datasets.”);
preprocess the application data into preprocessed data by normalizing the application data for input into a machine learning model (See Figure 3 – steps 304 and 306, as disclosed [Col 17 Lines 19-38] “In a next step 304, the system server may retrieve the datasets from the system database … The system server may pre-process the datasets to standardize the datasets into a common data format … In a next step 306, the system server may retrieve the pre-processed datasets from the system database … The system server may process the pre-processed datasets. The processing operation may result in normalizing and aggregating the data records in the pre-processed datasets”);
input the preprocessed data into the machine learning model (See Figure 3- step 310 “Execute AI model using user records as inputs”), the machine learning model including:
a training dataset including data associated with a historical application, an underwriting decision associated with the historical application, financial gain data associated with the historical application, and user-provided feedback associated with the historical application (See Figure 3 – step 308, as disclosed [Col 17 Lines 39-53] “In a next step 308, the system server may generate an AI model, using logistic regression and gradient boosting tree. The system server may train the AI model using the normalized and aggregated datasets. The system server may continuously re-train the AI model using any new supply of normalized and aggregated datasets. The AI model may include a neural network. The neural network may include an interconnected group of artificial neurons where each neuron may represent a redemption-related attribute. Non-limiting examples of the redemption-related attributes may include, but not limited to, a past financial history of the user operating the user computer, assets information of the user, current market value of the assets of the user, a number of transactions per month by the user, an amount of transactions of the user, and the like”);
a feature selection module configured to:
receive a plurality of candidate features associated with the behavior (See Figure 3 – step 310, as disclosed [Col 17 Lines 66-67 to Col 18 Lines 1-5] “In a next step 310, the system server may execute the AI model using the information associated with each user as an input to calculate the priority score for each user. The information may include, but not limited to, at least one transaction made by the user involving at least one investment fund, investment funds associated with the user, and user characteristics.”);
analyze the plurality of candidate features to determine a feature weight indicating the predictive value of each feature of the plurality of candidate features with respect to a target outcome (See Figure 3 – step 312, as disclosed [Col 18 Lines 17-22] “In a next step 312, the system server may receive the priority score associated with each user from the AI model. The priority score may be indicative of a performance of each user relative to other users. The priority score may be a measure of a predictive nature of trading decisions of each user”); and
select, based on the feature weight, a subset of features ([Col 18 Lines 22-26] “The system server may use the priority score of each user to produce a ranked list of users. The system server may use information in the user profile record associated with each user along with the priority score of each user to produce the ranked list of users.”);
a hyperparameter tuning module configured to: automatically generate a set of hyperparameters; define a search space of candidate hyperparameters and associated ranges based on one or more model performance requirements and the training dataset; and iteratively adjust the hyperparameters using a tuning strategy ([Col 9 Lines 1-12] “In operation, when the system server 106 may identify the new data records in the data sources 104, the system server 106 may process and use the new data records to re-train the AI model. As a result, the AI model may use a back-propagation method to reconfigure the mathematical functions (e.g., weight and bias factors), and revise itself to account for the new data records. Accordingly, the AI model is never complete, and may be iteratively trained each time the new data records are identified. The system server 106 may use a variety of methods to train and re-train the AI model, such as, evolutionary algorithm methods, simulated annealing methods, expectation-maximization methods, and non-parametric methods” or see also [Col 3 Lines 38-49] “The system server may use the data to train the AI model. The system server may continuously retrain and update the AI model with new data. The training of the AI model may include randomized experiments using the data. The system server may perform one or more randomized experiments to measure an effect of a specific intervention on a redemption rate of the users. The system server may generate a training dataset for each randomized experiment, such that, a sufficiently large number of the users are selected according to a pre-defined sampling strategy, and then randomly assigned to either a treatment group or a control group”); and
a prediction model configured to compute an expected application outcome score based on the training dataset, the feature selection module, and the hyperparameter tuning module ([Col 17 Lines 58-63] “The execution of the AI model may generate a priority score for the user. The priority score may correspond to a probability of the user redeeming their investment funds. Using the priority score, the system server may be able to predict a redemption rate for each investment fund owned by the user”);
generate an underwriting decision based on the expected application outcome score, the underwriting decision being [ranked] (See Figure 3 – step 314, as disclosed [Col 18 Lines 28-33] “In a next step 314, the system server may establish communication with the user computer of each user based on the priority score and the rank of the user. Initially, the system server may select a communication channel from multiple communication channels for each user based on the priority score and the rank of the user”);
if the underwriting decision is [highest rank], provide the products application and application data for display on a device associated with a user ([Col 18 Lines 36-42] “The system server may evaluate all users based on the priority score and the rank of each user. The system server may determine a first subset of users with highest priority scores to be most likely to redeem on their investment funds. Based on the highest priority score, the system server may select an online video communication channel for the first subset of users”); and
receive a selection associated with the products application from the device associated with the user ([Col 12 Lines 23-26] “The system server 106 may present the investment plan on the graphical user interface of the user computer 102. Using the user computer 102, the user may select the investment plan”).
Although Porter teaches obtaining plurality of dataset associated with the user from plurality of external sources, wherein the dataset may be in electronic format, Porter does not seem to explicitly disclose obtaining a plurality of website addresses for electronic resources related to behavior associated with the applicant. However, Taifi discloses:
obtain a plurality of website addresses for electronic resources related to behavior associated with the applicant ([0173] “Targetable user identifiers may be identified by any suitable means. In some embodiments, targetable users are identified based on, for example, a user browsing history. In some embodiments, a targetable user filter 316 of the system 300 monitors browsing history of all users and stores information about the browsing history in the targetable user data store 320. The stored information may include any suitable information such as a user identifier for the user or his web browser, information about web pages visited such as URL information, browser cookie information, etc.”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize identifying targetable user based on user browsing history, which includes visited URL information, as in Taifi in the system executing the method of Porter with the motivation of offering to [0200] improve audience targeting which would provide better user experience and reduce data consumption for machine learning models as taught by Taifi over that of Porter.
Although Porter teaches ranking the users based on the priority score, providing different communications based on the evaluated rank of each user, and receiving a selection of presented investment plans, Porter does not seem to explicitly disclose generating the decision being one chosen from the set of rejected, conditional, and approved. However, Hepp discloses:
generate an underwriting decision based on the expected application outcome score, the underwriting decision being one chosen from the set of rejected, conditional, and approved (Claim 1 “generating a decision to approve the request or reject the request using on a machine learning (ML) model trained based on the capacity of the user to repay the digital item, the historical information, and the attribute”);
if the underwriting decision is approved, provide the products application and application data for display on a device associated with a user (Claim 1 “generating a document for the user for processing the digital item based on the decision to approve the request, and further: transmitting the document to the client device”); and
receive a selection associated with the products application from the device associated with the user (Claim 1 “transmitting the document to the client device, wherein the user is provided access to the document via the platform to be executed and a lender is provided access to the document via the platform for review and/or distribution”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the machine learning model trained to generate a decision to approve or reject the request as in Hepp in the system executing the method of Porter with the motivation of offering to improve efficiency and precision of machine learning models to increase benefits of the users as taught by Hepp over that of Porter.
As per Claim 3, Porter may not explicitly disclose, but Hepp discloses the system of claim 1, wherein the feature selection module is further configured to dynamically modify the candidate features based on the user-provided feedback ([Col 7 Lines 38-53] “In some embodiments, the exception management and rules can change over time based on secondary market investor feedback on accepted/rejected assets. Accordingly, the secondary market investor feedback is passed back from 270 into API 118 and stored in data lake 120A. A rejection decision and reason for rejection would be parsed out and stored as historical data in data lake 120A, which influences future business rules. Secondary market investor feedback includes information on loan eligibility, borrower credit, income, employment and asset evaluation, and property evaluation and warning messages. This information may be captured in the data lake and analyzed in machine learning models to discover and/or remediate risk before loan submitting to the secondary market investors for their assessment and purchase decision”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize user feedback to modify data analyzed for machine learning models as in Hepp in the system executing the method of Porter with the motivation of offering to provide [Col 7 Lines 54-55] “more straight-through processing and higher acceptance of loans” as taught by Hepp over that of Porter.
As per Claim 4, Porter may not explicitly disclose, but Hepp disclose the system of claim 3, wherein the dynamic modification occurs after the selection ([Col 18 Lines 13-23] “In some cases where applicable the virtual assistant will also make required corrections as it develops as a “smart assistant” based on the amount of loans processed through the platform. The virtual assistant is also in a continuous listening mode and will pop-up (by default) as the user makes corrections to the data presented on the GUI. The business rules 814 can be determined by predetermined validation rules (ex. MISMO, lender business rules) or can be created through learned information using historical data, secondary market investor feedback, or from ML algorithms on successful cases”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize virtual assistant providing dynamic modifications during user interactions as in Hepp in the system executing the method of Porter with the motivation of offering to improve efficiency and precision of machine learning models to increase benefits of the users as taught by Hepp over that of Porter.
As per Claim 5, Porter disclose the system of claim 1, wherein the target outcome is maximizing a satisfaction score associated with the user, the satisfaction score being calculated based on the selection ([Col 18 Lines 16-22] “In a next step 312, the system server may receive the priority score associated with each user from the AI model. The priority score may be indicative of a performance of each user relative to other users. The priority score may be a measure of a predictive nature of trading decisions of each user”).
As per Claim 6, Porter disclose the system of claim 1, wherein the user is a plurality of users ([Col 12 Lines 32-34] “A webserver 114 may host an investment company website accessible to users on their user computers 102, where content presented via various webpages may be controlled”).
As per Claim 9, Porter may not explicitly disclose, but Hepp discloses the system of claim 1, wherein the content data is collected from the plurality of website addresses by an Application Programming Interface (API) configured to: access raw content data from an external source by connecting to the API, the raw content data including text, numerical values, or media content; and format the raw content data into the content data, such that the content data is suitable for processing by the processor ([Col 6 Lines 39-60] “Fx platform 100 includes an application programming interface (API) 118 to access input data (e.g., credit reports, income, assets, and the like) from original sources or through data validation intermediaries to obtain validated financial information needed to evaluate the credit and capacity of borrower 103 to repay the mortgage loan … Original source documents containing income, employment or asset information will have document recognition and data extraction capability within the Fx platform 100 to index the documents and extract data from documents and deliver structured data to be used in underwriting the mortgage loan. API 118 also validates the borrower information against any fraud, error, discrepancy, or any other data misalignment, via an authentication and fraud service component. In some embodiments, Fx platform 100 receives and validates loan application data from borrower 103 (e.g., employment, income, assets, and the like), through API 118”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize API accessing and formatting raw data for processing as in Hepp in the system executing the method of Porter with the motivation of offering to improve efficiency and precision of machine learning models to increase benefits of the users as taught by Hepp over that of Porter.
As per Claim 13, Porter may not explicitly disclose, but Hepp discloses the system of claim 1, wherein the application data includes text data; and the machine learning model is further configured to perform natural language processing to understand the meaning of the text data ([Col 18 Lines 33-35] “ML algorithms 860A may include a Natural language generation/procession (NLG) technology using a 3-layered neural network (Layer 1-Relu, Layer 2-Relu, Layer 3-Sigmoid)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize machine learning algorithms including natural language generation/procession technology as in Hepp in the system executing the method of Porter with the motivation of offering to improve efficiency and precision of machine learning models to increase benefits of the users as taught by Hepp over that of Porter.
As per Claim 19, Porter discloses the system of claim 1, wherein the hyperparameter tuning module is further configured to increase a user satisfaction score associated with the expected application outcome score ([Col 8 Lines 60-67 to Col 9 Lines 1-6] “As the system server 106 encounters new data records, the system server 106 may re-train the AI model to learn new redemption related user behavior and attributes acquired from the new data records. The AI model may then reconfigure itself to adapt to the new redemption related user behavior and attributes, which may lead to a more accurate identification of the potential redemption indicators and activities associated with the user. In operation, when the system server 106 may identify the new data records in the data sources 104, the system server 106 may process and use the new data records to re-train the AI model. As a result, the AI model may use a back-propagation method to reconfigure the mathematical functions (e.g., weight and bias factors), and revise itself to account for the new data records”).
As per Claim 20, Porter may not explicitly disclose, but Hepp disclose the system of claim 1, wherein the prediction model includes one or more of a decision tree model, a support vector machine, and a deep learning model ([Col 8 Lines 19-37] “A mortgage analytical engine 112 may include a machine learning or artificial intelligence algorithm (e.g., a neural network and the like) processing that trains and runs algorithms to provide predictive analysis and operational performance improvement insights to Fx platform 100 … Examples of neural network algorithms that may be used may include convolutional neural networks (CNNs), deep learning neural networks, genetic adversarial networks (GANs), supervised neural networks, unsupervised neural networks, and the like”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize machine learning algorithms including deep learning neural network as in Hepp in the system executing the method of Porter with the motivation of offering to improve efficiency and precision of machine learning models to increase benefits of the users as taught by Hepp over that of Porter.
As per Claim 29, Porter disclose the method of claim 28, wherein the method further comprises suggesting an timing for the selection based on a predicted financial gain associated with the products application ([Col 14 Lines 16-22] “Upon execution of the AI model, the AI model may identify a likelihood that a user will redeem (e.g., repayment of any money market fixed-income security at or before the asset's maturity date) an asset (e.g., investment fund) within a time period at a higher amount than a previous time period. The identification of the likelihood may be associated with a triggering circumstance”).
As per Claim 30, Porter disclose the method of claim 28, wherein the machine learning model predicts a likelihood of selection associated with the products application, the likelihood of selection being based on interest data associated with the user and product application selection history data associated with the user ([Col 18 Lines 36-39] “The system server may evaluate all users based on the priority score and the rank of each user. The system server may determine a first subset of users with highest priority scores to be most likely to redeem on their investment funds”).
Claims 2, 22, 24, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, and in view of Bent, II et al. (US 10672000 B1).
As per Claim 2, Porter may not explicitly disclose, but Bent discloses the system of claim 1, wherein the processor is further configured to execute the instructions to:
process a payment associated with the selection through an investment card and an investment account ([Col 21 Lines 17-29] “Still referring to FIG. 4F, and referring additionally to FIG. 4H, selection of repay loan button 480 on the loan portion of GUI 400 may cause navigation to a loan repayment portion of GUI 400. As shown, the loan repayment portion of GUI 400 can allow a user to take actions with respect to one or more outstanding loans against one or more core accounts. A repayment amount field 484 may be provided for a user to enter a monetary amount to be repaid with respect to a given loan, up to the full amount of the loan. A payment source menu 486, e.g., a drop down menu, may be provided for a user to select a payment method for a loan repayment, e.g., a bank account or credit card”);
collect a pre-calculated payback associated with the selection ([Col 23 Lines 37-46] “In embodiments, to facilitate the process of payback, the electronic interface generator may be configured to provide a comparable GUI listing the different core investments in each account of the beneficiary, and the total amount that is to be paid back. Accordingly, in embodiments, the GUI may include a field adjacent to or otherwise associated with one or more of the respective investments in one or more of the accounts of the beneficiary for entering a monetary amount or a percentage of the payback amount to go to/purchase the respective investment”); and
return the pre-calculated payback to an account associated with the user ([Col 23 Lines 46-50] “Thus, in embodiments these adjacent or associated fields would allow the user to specify for each account the amount or the percentage that the user wants to go back to/purchase the particular investment in the account”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize loan repayment GUI specifying each account and the amount for investment and payback as in Bent in the system executing the method of Porter with the motivation of offering to provide [Col 5 Lines 4-6] “new and improved system for processing a loan fund account associated with a retirement plan, such as a defined benefits plan and/or a defined contribution plan” as taught by Bent over that of Porter.
As per Claim 22, Porter may not explicitly disclose, but Bent discloses the system of claim 2, wherein the investment card is associated with a spending limit ([Col 21 Lines 26-28] “A payment source menu 486, e.g., a drop down menu, may be provided for a user to select a payment method for a loan repayment, e.g., a bank account or credit card” wherein a credit card is associated with a spending limit).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize credit card to process payment as in Bent in the system executing the method of Porter with the motivation of offering to provide [Col 5 Lines 4-6] “new and improved system for processing a loan fund account associated with a retirement plan, such as a defined benefits plan and/or a defined contribution plan” as taught by Bent over that of Porter.
As per Claim 24, Porter may not explicitly disclose, but Bent discloses the system of claim 2, wherein returning the pre-calculated payback includes distributing a proportional amount based on a funding request associated with the products application ([Col 23 Lines 41-49] “Accordingly, in embodiments, the GUI may include a field adjacent to or otherwise associated with one or more of the respective investments in one or more of the accounts of the beneficiary for entering a monetary amount or a percentage of the payback amount to go to/purchase the respective investment. Thus, in embodiments these adjacent or associated fields would allow the user to specify for each account the amount or the percentage that the user wants to go back to/purchase the particular investment in the account”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize specifying payback distribution for each account as in Bent in the system executing the method of Porter with the motivation of offering to provide [Col 5 Lines 4-6] “new and improved system for processing a loan fund account associated with a retirement plan, such as a defined benefits plan and/or a defined contribution plan” as taught by Bent over that of Porter.
As per Claim 27, Porter may not explicitly disclose, but Hepp discloses the system of claim 2, wherein the processing of the payment uses blockchain technology ([Col 5 Lines 40-52] “Servers 130 may include any device having an appropriate processor, memory, and communications capability for hosting a financial exchange application, a digital database, and a mortgage analytical engine … In some embodiments, servers 130 may include a network portal for a lender to offer mortgage services to one or more borrowers … In some embodiments, at least one of servers 130 may be part of a blockchain network, and have public and private encryption keys to access public ledgers”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize blockchain network as in Hepp in the system executing the method of Porter with the motivation of offering to improve security for mortgage services and transactions as taught by Hepp over that of Porter.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, and in view of Ramesh et al. (US 20210304204 A1).
As per Claim 7, Porter may not explicitly disclose, but Ramesh discloses the system of claim 1, wherein the analysis of candidate features includes one or more of a permutation importance, SHAP (SHapley Additive exPlanations), and LIME (Local Interpretable Model-agnostic Explanations) ([0034] “For example, narrative generator 134 may utilize, as input, an output graph from a machine learning prediction explainer and/or a neural network prediction explainer (e.g., local interpretable model-agnostic explanations (LIME) or SHapley Additive exPlanations (SHAP)). The output graph may include a feature importance of each feature in the flagged transactions (or unflagged transactions, as necessary for agent review), where the feature importance includes data indicating how important the feature was in classifying the transaction (e.g., flagging the transaction as prohibited or not)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize LIME or SHAP for machine learning prediction explainer as in Ramesh in the system executing the method of Porter with the motivation of offering to [0044-0045] provide improvement of performance in data classification and improve the performance measure of the machine learning model as taught by Ramesh over that of Porter.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, and in view of Johnson et al. (US 20210272040 A1).
As per Claim 8, Porter may not explicitly disclose, but Johnson discloses the system of claim 1, wherein the content data is collected from the plurality of website addresses by a web scraping module configured to:
extract raw content data from the plurality of website addresses by parsing the content of web pages associated with the plurality of website addresses, the raw content data including text, images, or metadata ([0207] “Process 1500 is shown to include extracting text data from various text sources. In particular, process 1500 is shown to include extracting text data from news sources, analyst notes, PMW notes, and other text (e.g., Twitter, Facebook, earnings calls, etc.). Sources of textual information may be highly configurable and customizable depending on a type of source desired, what sources are available, etc. In effect, process 1500 can include obtaining all forms of input text related to an organization. Text data extraction can be include extracting the text using various applications such as a Web scraper, manual entry by users, database retrieval, and/or other suitable forms of text extraction”); and
format the raw content data into the content data, such that the content data is suitable for processing by the processor ([0207] “Before the text data is utilized, process 1500 may include cleaning the data. Cleaning the data can remove unnecessary, corrupted, and/or inaccurate information from the text data. For example, cleaning the data may include deleting any extracted text in a foreign language if the foreign language is not supported for analysis. As another example, cleaning the data may include removing any text associated with advertisements that obtained in Web scrapping”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize text extraction from multiple sources (e.g. multiple web addresses) as in Johnson in the system executing the method of Porter with the motivation of offering to [0207] improve text extraction to provide more accurate data as taught by Johnson over that of Porter.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, and in view of SNOEK et al. (US 20170031934 A1).
As per Claim 10, Porter may not explicitly disclose, but SNOEK discloses the system of claim 1, wherein the application data includes an image; and the preprocessing includes resizing, normalizing, and standardizing image dimensions associated with the image and pixel values associated with the image to ensure consistency; applying filtering techniques to enhance quality of the image; and converting the image into a format compatible with the feature selection module ([0060] “The AI application may include a preprocess module 504 that may be configured (using for example, the JAVA programming language) to convert the format of an image 506 and then crop and/or resize the image 508 … The SceneDetect Backend Engine 512 may be configured to further preprocess 514 the image by scaling 516 and cropping 518. For example, the image may be scaled and cropped so that the resulting image is 224 pixels by 224 pixels. These dimensions may map to the input dimensions of a neural network. The neural network may be configured by a deep neural network block 520 to cause various processing blocks of the SOC 100 to further process the image pixels with a deep neural network. The results of the deep neural network may then be thresholded 522 and passed through an exponential smoothing block 524 in the classify application 510”. See also [0054] “As shown in FIG. 3B, the exemplary deep convolutional network 350 includes multiple convolution blocks (e.g., C1 and C2). Each of the convolution blocks may be configured with a convolution layer, a normalization layer (LNorm), and a pooling layer. The convolution layers may include one or more convolutional filters, which may be applied to the input data to generate a feature map” and [0081] “In one aspect of the present disclosure, each device may compute a standardized feature vector for each type of media file or for each modality of each media file”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize media file preprocessing as in SNOEK in the system executing the method of Porter with the motivation of offering to provide [0032] “an effective and efficient way to improve the quality and expressiveness of media labels may be achieved that rests on the intuition that if different media items are similar in content and their label, the label is more likely to be correct” as taught by SNOEK over that of Porter.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, and in view of Huang et al. (US 20250191369 A1).
As per Claim 11, Porter may not explicitly disclose, but Huang discloses the system of claim 1, wherein the application data includes a video; and the preprocessing includes segmenting the video into a plurality of frames at predefined intervals to extract representative visual content while reducing data volume; resizing, normalizing, and standardizing the plurality of frames to ensure consistency; applying enhancement techniques to the plurality of frames; and converting the plurality of frames into a format compatible with the feature selection module ([0188] “In at least one embodiment, video data can be provided from client device 502 for enhancement in provider environment 506” wherein [0108] “At 303 through 305, the process 300 involves pre-processing, via an image pre-processing pathway, the video component of the model input. Specifically, at 303, the process segments the video into a plurality of equal sized chunks and provides a frame for each of the segments. Providing a frame for each respective segment can include, in at least one embodiment, averaging and/or sampling multiple frames of the original video that correspond to the respective segment” and see also [0204] “In at least one embodiment, OpenVINO comprises one or more software tools and/or modules for model optimization, also referred to as a model optimizer … In at least one embodiment, a model optimizer performs various neural network operations, such as modifying inputs to a model (e.g., resizing inputs to a model), modifying a size of inputs of a model (e.g., modifying a batch size of a model), modifying a model structure (e.g., modifying layers of a model), normalization, standardization, quantization (e.g., converting weights of a model from a first representation, such as floating point, to a second representation, such as integer), and/or variations thereof”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize video data preprocessing as in Huang in the system executing the method of Porter with the motivation of offering to provide [0059] improved core capabilities, improve downstream video tasks, and double the baseline performance for temporal metrics while simultaneously providing superior explanations as taught by Huang over that of Porter.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, and in view of YUAN et al. (CN 110738047 A).
As per Claim 12 , Porter may not explicitly disclose, but YUAN discloses the system of claim 1, wherein the application data includes text; and the preprocessing includes removing one or more unwanted characters, including special symbols, emojis, and hyperlinks, for standardization; performing stop-word removal; stemming or lemmatization to reduce text complexity; and converting the text into a format compatible with the feature selection module ([Pages 7 to 8] “In an alternative embodiment, in the step (2), pre-processing the text to obtain the corresponding vocabulary specifically comprises: filtering the noise data, a noise in the text data in the Weibo text often contains some meaningless even influence the experimental analysis, it is necessary to remove, removing the noise data in the text comprises removing insignificant symbols, emoji expression and http link, removing the non-Weibo text data with webpage label format … For removing the noise text after word segmentation to obtain the corresponding vocabulary … after the word segmentation to obtain the corresponding vocabulary, wherein the filtering out stop words, vocabulary data word will be included in some common, to express semantic interest no words help, such as all punctuation, and "of", "the", etc., these stop words, words not large for subsequent analysis significance, so it needs the filtered words vocabulary in off, to ensure the analysis efficiency”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize text data preprocessing as in YUAN in the system executing the method of Porter with the motivation of offering to [Page 8] to ensure and improve analysis efficiency and accuracy for subsequent mining analysis as taught by YUAN over that of Porter.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, and in view of Sethumadhavan et al. (US 20180075192 A1).
As per Claim 14, Porter may not explicitly disclose, but Sethumadhavan discloses the system of claim 1, wherein the application data includes multilingual application data; and the machine learning model is further configured to process the multilingual application data ([0032] “For example, in some embodiments the disclosed system is a flexible, highly-scalable big-data enterprise system that understands concepts, and associations and relationships between the concepts from unstructured text using machine learning and nlp (natural language processing) techniques. The system is completely language independent and domain independent as it extracts the concepts and relationships directly from its input text. Thus, the code generation using belief networks can be constructed and utilized across multilingual documents”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize machine learning system processing multilingual documents as in Sethumadhavan in the system executing the method of Porter with the motivation of offering to [0009] improve automation and efficiency of machine learning models to enable rapid, accurate and automated processing of data as taught by Sethumadhavan over that of Porter.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, and in view of Garcia et al. (US 20240029181 A1).
As per Claim 15, Porter may not explicitly disclose, but Garcia discloses the system of claim 1, wherein adjusting the hyperparameters is based on the underwriting decision and a type of financial product associated with the selection ([0047] “Thus, the features with importance below the threshold can be dropped. Hyperparameters for each asset type model can be independently tuned using various hyper-parameter optimization algorithms” wherein [0082] “Referring to FIG. 4, FIG. 4 is a model 400 for hyperparameter tuning. Hyperparameters may improve the performance of the binary classifiers that infer asset types and influence the speed and quality of the learning process. As mentioned above, the hyperparameters for each binary classifier may be tuned using hyper-parameter optimization algorithms such as the Tree of Parzen Estimators algorithm, random search, grid search, and various Bayesian optimization algorithms”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize hyperparameter tuning as in Garcia in the system executing the method of Porter with the motivation of offering to [0077] “efficiently generate an effective training model” as taught by Garcia over that of Porter.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, and in view of Brinkman et al. (US 20200365188 A1).
As per Claim 16, Porter may not explicitly disclose, but Brinkman discloses the system of claim 1, wherein the plurality of website addresses for electronic resources includes at least one chosen from the set of social media platforms, forums, and other user-generated content platforms that contain publicly available content including posts, comments, images, and videos ([0071] “Additionally, or alternatively, machine learning can be used to extract some metadata. When analyzing text and other information extracted from a website, social media, comments, a chat stream, and so forth, machine learning can be used to summarize the information” and [0072] “Additionally, or alternatively, other information can be extracted when analyzing text and other information extracted from a website, social media, comments, a chat stream, and so forth. For example, machine learning or other analysis techniques can be used to extract information such as a user sentiment associated with a particular statement, the overall sentiment of a chat stream, or group of comments, and so forth”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize extracting information from websites such as social media with machine learning techniques as in Brinkman in the system executing the method of Porter with the motivation of offering to [0001-0004] improve automation in collecting data as taught by Brinkman over that of Porter.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, and in view of Crabtree et al. (US 20210019674 A1).
As per Claim 17, Porter may not explicitly disclose, but Crabtree discloses the system of claim 1, wherein the plurality of website addresses for electronic resources includes at least one privately owned external database, wherein data collection associated with the at least one privately owned external database is in accordance with an applicable data usage agreement ([0052] “Once the query has been analyzed semantically, the system then pulls information from a variety of available sources using data-extraction and web-scraping techniques. This includes all private, public, and proprietary sources accessible by the system. A comprehension engine, using the same semantic computing techniques, observes and infers primary, secondary, and tertiary relationships and attributes of the entity in question … In some embodiments, the system may use web ontology language (OWL) and financial industry business ontology (FIBO) standards” or see also [0125] “Once the semantic search engine 2502 has determined the intended search 2511 parameters, being an individual or corporate entity in this case, a function call is made to an extraction engine 2010 that begins to coalesce data from all available private, public, and proprietary data sources 3130 using web-scrapers and data-extraction tools”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize web-scraping techniques applied to private web sources as in Crabtree in the system executing the method of Porter with the motivation of offering to [0003-0005] improve data collection techniques to reduce risks in involved parties as taught by Crabtree over that of Porter.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, and in view of Sarceda et al. (US 20220329545 A1).
As per Claim 18, Porter may not explicitly disclose, but Sarceda discloses the system of claim 1, wherein the electronic resources include one or more chosen from the set of: criminal history, education history, driving records, civil court records, and professional licenses ([0035] “NLP module 130 may be configured to analyze and process documents uploaded by an end user via the website … NLP module 130 may be configured to extract information that may be used by customization module 128 for generating the profile of the user. For example, NLP module 130 may extract information directed to: employment history, education history, job skills, keywords, and the like”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize extracting user information, such as education history, as in Sarceda in the system executing the method of Porter with the motivation of offering to provide more accurate user profile by extracting relevant user information as taught by Sarceda over that of Porter.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, and in view of Advani et al. (US 20070265960 A1).
As per Claim 21 , Porter may not explicitly disclose, but Advani discloses the system of claim 1, wherein the application data includes a desired interest rate ([0080] “FIG. 17A shows a graphical user interface by which a user is provided with a basic mortgage calculator 17095 for a person-to-person reverse mortgage, in accordance with an embodiment of the invention … In addition, the user may input a desired interest rate 17097”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize user input data including desired interest rate as in Advani in the system executing the method of Porter with the motivation of offering to improve user experience by providing flexible user input options as taught by Advani over that of Porter.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, in view of Bent, and in view of Volpert et al. (US 20100153296 A1).
As per Claim 23, Porter may not explicitly disclose, but Volpert discloses the system of claim 2, wherein the return recurs for a predetermined period of time ([0021] “Still another aspect of the present invention comprises an embodiment of a method of administering a unit investment trust comprising a scheduled liquidation date … The targeted payment schedule specifies payments to be made by the unit investment trust for each unit until the scheduled liquidation date of the unit investment trust and provides for payments to be made for each unit with a recurring predetermined time interval between payments”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize targeted payment schedule with recurring predetermined time intervals as in Volpert in the system executing the method of Porter with the motivation of offering to [0018] improve payment certainty of the targeted payment schedule as taught by Volpert over that of Porter.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, in view of Bent, and in view of Turner (US 20060167779 A1).
As per Claim 25, Porter may not explicitly disclose, but Turner discloses the system of claim 2, wherein the return occurs automatically when the products application results in a capital gain ([0003] “The second aspect involves a mechanism that allows for all or a portion of the invested capital to be returned to the investor once a predetermined capital gain criterion is met”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize capital gain rule to provide automatic return as in Turner in the system executing the method of Porter with the motivation of offering to [0095] minimize losses while improving user experience for investors without constant intervention as taught by Turner over that of Porter.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Porter, in view of Taifi, in view of Hepp, in view of Bent, and in view of ZHANG et al. (US 20170213288 A1).
As per Claim 26, Porter may not explicitly disclose, but ZHANG discloses the system of claim 2, wherein the user is an employee associated with an institution; and the investment account is created from an employee payroll associated with the institution ([0032] “In some embodiments, the investment system may be configured to operate with an institutional investor (i.e., a company) to generate personalized investment allocations and/or personalized investment structures for company employees. For instance, such employee personalized investment allocations and/or personalized investment structures may be configured to provide exposure to alternative assets, conditions, or the like than those associated with the company. In some embodiments, the employee's personalized investment structure may include an investment fund (i.e., ETF) recommended for employees or included in an employee retirement savings plan (i.e., 401k, 403b, or the like)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the investment system using investment accounts such as employee retirement savings plan (e.g. 401k) as in ZHANG in the system executing the method of Porter with the motivation of offering to improve investment guidance without user interventions to specific investment accounts as taught by ZHANG over that of Porter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
SIDDIQUE et al. (US 20160210602 A1) discloses image enhancement [0173] including standardized sizing system and normalization [0146], and resizing [0156].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY H JUNG whose telephone number is (571)270-5018. The examiner can normally be reached Mon - Fri 9:30 - 5:30.
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/HENRY H JUNG/ Examiner, Art Unit 3695
/CHRISTINE M Tran/ Supervisory Patent Examiner, Art Unit 3695
