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 1-20 have been examined in this application.
The filling date of this application number recited above is 05-June-2024. Domestic Benefit/National Stage priority has been claimed for Provisional Application 63/471,010 in the Application Data Sheet, thus the examination will be undertaken in consideration of 05-June-2023, as the priority date, for applicable claims.
No information disclosure statement (IDS) has been filed to date.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11 and 14 (and claims 15-19 due to dependency) are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites “a local database” on lines 3-4 wherein the antecedent basis has been provided by the independent claim 1 on line 11, as disclosed “query a local database …”. It is unclear whether this limitation is reciting the same local database or a separate local database. Therefore, the claim is indefinite for failing to particularly point out and distinctly claim the subject matter, and clarification is required. For the purposes of compact prosecution, the claim limitation has been interpreted as “the local database”.
Claim 14 recites “the ML module” on lines 13-14 wherein:
there is insufficient antecedent basis for this limitation in this claim; and
the acronym “ML” has not been previously defined in this independent claim.
Therefore, the claim is indefinite for failing to particularly point out and distinctly claim the subject matter, and clarification is required. For the purposes of compact prosecution, in view of the mirrored independent claims 1 and 20, the claim limitation has been interpreted as “a machine learning (ML) module”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The Claims are directed to an abstract idea, Mental Process and/or Certain 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, 14, and 20, the claims recite “a method for an … predictive market place processing based on user-related data, comprising:
acquiring a user-related data from a user-entity [group] by a market place server (MPS) [group];
optimizing, by the MPS [group], the user-related data through an optimization [equation];
parsing, by the MPS [group], the optimized data to derive a plurality of key classifying features;
querying, by the MPS [group], a local database to retrieve local historical users'- related data based on the plurality of key classifying features;
generating, by the MPS [group], at least one classifier based on the plurality of key classifying features and the local historical users'-related data;
providing, by the MPS [group], the at least one at least one classifier to the [calculation unit] configured to generate a predictive model for producing at least one user recommendation parameter associated with qualifying the user for at least one business entity [group]; and
generating, by the MPS [group], at least one user qualification verdict based on the least one user recommendation parameter.”
The limitation of the claims recited above, considering the claims without the additional elements (e.g. system, non-transitory computer-readable medium, processor, memory, node, etc.), under its broadest reasonable interpretation (BRI), recites Mental Processes and/or Certain Methods of Organizing Human Activities. The method recited above is a process of acquiring data and performing predictive data analysis to provide user recommendations regarding market place data.
All these steps recited by the claims can be practically performed in the human mind, or by a human using a pen and paper. See 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 system to acquire, optimize, parse, query, generate, and provide data, performing a mental process on a generic computer system 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 claims recite a process of data analysis with recommendations related to financial data, transactions, and/or insurance, as disclosed by Specification:
[0041] “The data produced by AI- based user entity evaluation system may be used to match users and businesses (i.e., insurance providers) for pre-approve financing and may drastically reduce the timeline to find an insurance policy to buy and funding to close the transaction”; and
[0064] “The MPS node 102 may process the predictive outputs data received from the AI/ML module 107 to generate the qualification or approval verdict and/or risk assessment recommendations pertaining to the user and a particular insurance policy or a financial transaction”.
The invention also includes risk mitigation through fraud detection, as disclosed by Specification:
[0035] “Fraud Detection. Machine learning models may detect fraudulent activities by recognizing patterns and anomalies in real-time transactions, mitigating risks for both lenders and borrowers”.
The method of performing data analysis to provide recommendations associated with financial transaction and/or insurance policy, along with risk mitigation, is fundamental economic principles or practices and/or commercial or legal interactions, which are both under certain methods of organizing human activities.
Therefore, the claim recites an abstract idea, mental process and/or certain methods of organizing human activities.
This judicial exception is not integrated into practical application. In particular, the claims recite an additional element of “system”, “processor”, “module”, “node”, “network”, “memory”, “engine”, “database”, and “non-transitory computer-readable medium” to perform the method recited above by instructing the abstract idea to be performed “by” these generic computer components. 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. These elements are generic, off-the-shelf components available to the public, and does not require any specialized hardware or equipment to perform the claimed method, and are merely applied to perform its basic functionalities, such as: acquire data, optimize data, parse data, query data, generate data, and provide data, as disclosed by Specification:
[0069] “The MPS node 102 may be a computing device or a server computer, or the like, and may include a processor 204, which may be a semiconductor- based microprocessor, a central processing unit (CPU), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and/or another hardware device”
[0070] “Examples of the non-transitory computer readable medium 212 may include an electronic, magnetic, optical, or other physical storage device that contains or stores executable instructions. For example, the non-transitory computer readable medium 212 may be a Random-Access memory (RAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a hard disk, an optical disc, or other type of storage device”
[0094-0098] “FIG. 5 illustrates a block diagram of a system including computing device 500. The computing device 500 may comprise, but not be limited to the following: Mobile computing device, such as, but is not limited to, a laptop, a tablet, a smartphone, a drone, a wearable, an embedded device, a handheld device, an Arduino, an industrial device, or a remotely operable recording device; A supercomputer, an exa-scale supercomputer, a mainframe, or a quantum computer; A minicomputer, wherein the minicomputer computing device comprises, but is not limited to, an IBM AS500 / iSeries / System I, A DEC VAX / PDP, a HP3000, a Honeywell- Bull DPS, a Texas Instruments TI-990, or a Wang Laboratories VS Series; A microcomputer, wherein the microcomputer computing device comprises, but is not limited to, a server, wherein a server may be rack mounted, a workstation, an industrial device, a raspberry pi, a desktop, or an embedded device;”
Mere instructions to implement the abstract idea on a generic computer system, or merely using the generic computer system 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 acquire, optimize, parse, query, generate, or provide 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.
Additionally, the claims recite additional element “machine learning (ML) module”, and other elements related to the blockchain system such as a “node”. These additional elements are recited at a mere “apply it” level, wherein the ML module is merely applied as a black-box model to generate an output based on the provided input, and the various nodes are merely implemented to acquire data or provide data. As similarly discussed above, mere “apply it” is not indicative of integration into a practical application. Therefore, 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 claims lack 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. Merely using the generic computer system 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 instructions further cause the processor to derive a language metadata from user-related data and parse the user-related data based on the language metadata to derive the plurality of key classifying features.” The claim provides further steps regarding the data, which is still part of the abstract idea, and mere “apply it” is not indicative of integration into a practical application.
Claims 3 and 15 recite “wherein the instructions further cause the processor to retrieve remote historical users'-related data from at least one remote database based on the plurality of key classifying features, wherein the remote historical users'-related data is collected at locations associated with a plurality of business entities affiliated with financial and insurance institutions.” The claims provide further steps regarding the data, which is still part of the abstract idea, and the additional element of “remote database” is merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 4 and 16 recite “wherein the instructions further cause the processor to generate the at least one classifier based on the plurality of key classifying features and the local historical users'-related data combined with the remote historical users'-related data.” The claims provide further steps regarding the data, which is still part of the abstract idea, and the additional element of “remote database” is merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 5 and 17 recite “wherein the instructions further cause the processor to generate a user profile data based on the user's-related data and the plurality of key classifying features.” The claims provide further steps regarding the data, which is still part of the abstract idea, and mere “apply it” is not indicative of integration into a practical application.
Claims 6 and 18 recite “wherein the instructions further cause the processor to periodically monitor the user profile data to determine if at least one value of the user profile data deviates from a corresponding value of previous user profile data by a margin exceeding a pre-set threshold value.” The claims provide further steps regarding the data, which is still part of the abstract idea, and mere “apply it” is not indicative of integration into a practical application.
Claims 7 and 19 recite “wherein the instructions further cause the processor to, responsive to at least one value of the user profile data deviating from a corresponding value of the previous user profile data by the margin exceeding the pre-set threshold value, generate an updated at least one classifier based on user profile data and generate the at least one user qualification verdict based on an at least one user recommendation parameter produced by the predictive model in response to the updated at least one classifier.” The claims provide further steps regarding the data, which is still part of the abstract idea, and mere “apply it” is not indicative of integration into a practical application.
Claim 8 recites “wherein the instructions further cause the processor to record the at least one user recommendation parameter on a blockchain ledger along with the user profile data.” The claim provides further steps regarding the data, which is still part of the abstract idea, and the additional element of “blockchain lodger” is merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 9 recites “wherein the instructions further cause the processor to retrieve the at least one user recommendation parameter from the blockchain responsive to a consensus among the business node and the at least one market place server node.” The claim provides further steps regarding the data, which is still part of the abstract idea, and the additional element of “blockchain lodger” is merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 10 recites “wherein the instructions further cause the processor to execute a smart contract to record data reflecting user qualification and approval for the business entity associated with the at least one user recommendation parameter on the blockchain for future audits.” The claim provides further steps regarding the data, which is still part of the abstract idea, and the additional element of “smart contract” is merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 11 recites “wherein the instructions further cause the processor to generate a user-related risk assessment score based on user profile data comprising a credit history, user financial statements' data based on market conditions data derived from a local database.” The claim provides further steps regarding the data, which is still part of the abstract idea, and mere “apply it” is not indicative of integration into a practical application.
Claim 12 recites “wherein the instructions further cause the processor to detect fraudulent activities by recognizing user-related patterns and anomalies in real-time transactions based on the at least one user recommendation parameter associated with qualifying the user for the at least one business entity node.” The claim provides further steps regarding the data, which is still part of the abstract idea, and mere “apply it” is not indicative of integration into a practical application.
Claim 13 recites “wherein the instructions further cause the processor to collect user feedback data from social media and to generate a classifier based on features extracted from the user feedback data and provide an at least one classifier to the ML module to generate a predictive model for producing at least one recommendation parameter for the business entity node.” The claim provides further steps regarding the data, which is still part of the abstract idea, and mere “apply it” 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 2-13 and 15-19, 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 1-20 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, 5, 14-15, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over JIANG et al. (US 20220301020 A1) in view of Hayward et al. (US 20210256616 A1).
As per Claims 1, 14, and 20, JIANG discloses a method for an automated predictive market place processing based on user-related data (See Figures 1 and 2 disclosing the system and devices comprising hardware components), comprising:
acquiring a user-related data from a user-entity node by a market place server (MPS) node ([0055] “System 600 begins with data 602. For example data 602 may comprise historical data related to financing terms, deals, and collateral organized in a temporal fashion. At object 604, system 600 performs a data pull. For example, system 600 may pull data related to a specific time period and/or user account”);
optimizing, by the MPS node, the user-related data through an optimization engine ([0059] “For example, system 600 may use a financing terms optimization model that uses the same input as a cashout model. In system 600, the input data transformations may be controlled by the same feature definition file, which dictates the imputation logic and capping/flooring limits”);
parsing, by the MPS node, the optimized data to derive a plurality of key classifying features ([0059] “As object 606, system 600 performs a data preparation phase. At this phase, the system may perform normalization and/or standardization of data”);
…
generating, by the MPS node, at least one classifier based on the plurality of key classifying features and the local historical users'-related data ([0006] “The system may input the first feature input into a first machine learning model, wherein the first machine learning model is trained to determine additional user characteristics based on historical processing data and known user characteristics. The system may receive a first output from the first machine learning model, wherein the first output comprises the user characteristic and an additional user characteristic.”);
providing, by the MPS node, the at least one at least one classifier to the ML module configured to generate a predictive model for producing at least one user recommendation parameter associated with qualifying the user for at least one business entity node ([0061] “At object 608, system 600 generates simulated data that along with inputs from model 612 and model 610 (e.g., a cashout model) are scored at object 614” or see also [0006] “The system may generate a second feature input based on the first output. The system may input the second feature input into a second machine learning model, wherein the second machine learning model is trained to determine respective item characteristics, for given requests, for each of the plurality of available items, wherein each of the respective item characteristics is based on the first output and respective item rule sets for each of the plurality of available items”); and
generating, by the MPS node, at least one user qualification verdict based on the least one user recommendation parameter ([0061] “System 600 then proceeds to object 616 to optimize individual account level financing terms before proceeding to financing term model 618 … The target of the model 618 is the financing term (e.g., price or base buy-rate) returned by the optimization” wherein [0034] “In both cases, the end users may work with internal applications that generate real-time quotes or estimates for financing terms for a potential vehicle” or see also [0006] “The system may receive a second output from the second machine learning model. The system may generate for display, on a user interface, a first recommendation for a first item characteristic for a first item the plurality of available items based on the second output”).
Although JIANG teaches of utilizing historical data as training data for the first machine learning model, wherein the first machine learning model is used to generate a first output to generate a second output using a second machine learning model providing scores for real-time recommendations (e.g. quotes or estimates for financing terms for a potential vehicle displayed on a user interface), the prior art does not seem to explicitly disclose of querying a local database to retrieve local data. However, Hayward teaches:
querying, by the MPS node, a local database to retrieve local historical users'- related data based on the plurality of key classifying features ([0120] “The artificial neural network (or other artificial intelligence or machine learning algorithm, program, module, or model) may be trained to predict risk factors using historical claims data in addition to the telematics data, vehicle data, and/or claims data … Such a subset of claims may be identified by querying the electronic databases described above, or by any other suitable method” wherein the database may be a local database coupled to the system, as disclosed [0118] “A set of periodic telematics data, (e.g., a month's worth of telematics data) may be stored in association with a user's account in an electronic database coupled to client device 202 and/or server device 204”);
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize querying a local database to retrieve historical user data associated with key classifying features (e.g. claims data, telematics data, etc.) as in Hayward in the system executing the method of JIANG with the motivation of offering to [0018-0020] improve the automated system of providing accurate insurance claims utilizing machine learning methods as taught by Hayward over that of JIANG.
As per Claims 3 and 15, JIANG may not explicitly disclose, but Hayward discloses the system of claim 1, and the method of claim 14, wherein the instructions further cause the processor to retrieve remote historical users'-related data from at least one remote database based on the plurality of key classifying features, wherein the remote historical users'-related data is collected at locations associated with a plurality of business entities affiliated with financial and insurance institutions ([0101] “Client 202 may cause insurance risk related data to be stored in server 204 memory 252 and/or a remote insurance related database such as customer data 160” or see also [0107] “While the databases depicted in FIG. 2 are shown as being communicatively coupled to server 204, it should be understood that historical claim data 270, for example, may be located within separate remote servers or any other suitable computing devices communicatively coupled to server 204”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize remote database affiliate with insurance as in Hayward in the system executing the method of JIANG with the motivation of offering to [0018-0020] improve the automated system of providing accurate insurance claims utilizing machine learning methods as taught by Hayward over that of JIANG.
As per Claims 5 and 17, JIANG may not explicitly disclose, but Hayward discloses the system of claim 1, and the method of claim 14, wherein the instructions further cause the processor to generate a user profile data based on the user's-related data and the plurality of key classifying features ([0125] “If sufficient telematics data has been collected by input data collection application 216, then that data may be transmitted, along with an indication of the user's identity, to an electronic device (e.g., server device 204) where the trained neural network (or other artificial intelligence or machine learning algorithm, program, module, or model) may be operated using the transmitted data as parameters. The transmitted data may be combined with other data (e.g., customer data 272). The trained neural network (or other artificial intelligence or machine learning algorithm, program, module, or model) may output risk information (e.g., risk labels and/or a risk score) which may be used to construct a risk profile. The risk information/profile may then be received in the input data collection application 216, and may be displayed to a user”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize generating a risk profile of the user based on the data as in Hayward in the system executing the method of JIANG with the motivation of offering to [0018-0020] improve the automated system of providing accurate insurance claims utilizing machine learning methods as taught by Hayward over that of JIANG.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over JIANG, in view of Hayward, and in view of Kapoor (US 20140344145 A1).
As per Claim 2, JIANG may not explicitly disclose, but Kapoor discloses the system of claim 1, wherein the instructions further cause the processor to derive a language metadata from user-related data and parse the user-related data based on the language metadata to derive the plurality of key classifying features ([0064] “The Language Metadata 306 is configured for receiving, storing, retrieving, displaying and updating a plurality of translations of all user viewable application metadata for learning application 300 … In some embodiments, the language metadata is also used to determine purchase compatibility in the microlearning purchase management module 238 through learning application database 204 and to determine performance compatibility in the microlearning performance management module 240 through learning application database 204” or see also [0089] “The language preferences module 404 is configured for receiving, storing, retrieving and updating a plurality of language preference item choices displayed with corresponding language preference input areas to be chosen by funding user 122 … The preference items in language preferences module 404 are accessed by generator 408 to determine preferred or recommended learning users for funding by the funding user 122, and the generator 408 may correlate the preference items against the corresponding identity items of the learning users in user database 202 to determine the recommended learning users for display to funding user 122”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize language metadata and language preferences correlated to the user data to provide recommendations as in Kapoor in the system executing the method of JIANG with the motivation of offering to improve the automated system by providing information with the correct language of the user, which would improve the user experience and increase customer satisfaction, as taught by Kapoor over that of JIANG.
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over JIANG, in view of Hayward, and in view of Parundekar et al. (US 20150142789 A1).
As per Claims 4 and 16, JIANG may not explicitly disclose, but Parundekar discloses the system of claim 3, and the method of claim 15, wherein the instructions further cause the processor to generate the at least one classifier based on the plurality of key classifying features and the local historical users'-related data combined with the remote historical users'-related data ([0095] “In some embodiments, the device data aggregator 308 retrieves one or more of sensor data associated with the mobile computing device 135 from one or more first sensors 140, local data (e.g., local vehicle data) from the storage 245 and/or remote data (e.g., remote vehicle data) from the storage 145 in the data server 120 via the network 105. The device data aggregator 308 aggregates the retrieved data as the device data associated with the mobile computing device 135 … In additional embodiments, the device data aggregator 308 sends the device data to the travel mobility detector 314 and the score generator 402” wherein [0130] “In some embodiments, the score generator 402 receives device data associated with the mobile computing device 135 from the device data aggregator 308. The score generator 402 determines device bias data for the mobile computing device 135 based on the device data. The device bias data can be data describing one or more scoring preferences configured for the mobile computing device 135”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize aggregating data comprising local data and remote data to generate device bias data as in Parundekar in the system executing the method of JIANG with the motivation of offering to improve the system to provide more accurate and effective recommendations based on the user’s data as taught by Parundekar over that of JIANG.
Claims 6-7 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over JIANG, in view of Hayward, and in view of Fields et al. (US 9805601 B1).
As per Claims 6 and 18, JIANG may not explicitly disclose, but Fields discloses the system of claim 5, and the method of claim 17, wherein the instructions further cause the processor to periodically monitor the user profile data to determine if at least one value of the user profile data deviates from a corresponding value of previous user profile data by a margin exceeding a pre-set threshold value ([Col 43 Lines 17-31] “Determining an update to an insurance policy may include determining a change in one or more risk levels associated with operation of the vehicle 108 (or vehicle operation by one or more drivers). This may include comparing current vehicle-usage profiles with older vehicle-usage profiles containing data prior to the update … Some updates may include not changing any aspects of the insurance policy, such as when a change in risk levels associated with vehicle operation are below a threshold for updating the insurance policy”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize updating the insurance policy based on the determined risk level of the current usage profile being above a threshold as in Fields in the system executing the method of JIANG with the motivation of offering to provide more accurate and updated insurance policy based data analysis and [Col 56 Lines 55-60] help improve driving behavior as taught by Fields over that of JIANG.
As per Claims 7 and 19, JIANG may not explicitly disclose, but Fields discloses the system of claim 6, and the method of claim 18, wherein the instructions further cause the processor to, responsive to at least one value of the user profile data deviating from a corresponding value of the previous user profile data by the margin exceeding the pre-set threshold value, generate an updated at least one classifier based on user profile data and generate the at least one user qualification verdict based on an at least one user recommendation parameter produced by the predictive model in response to the updated at least one classifier ([Col 43 Lines 12-21] “At block 908, the external computing device 206 may determine an update or change to an insurance policy based upon the current vehicle-usage profile. The update may include a change to a premium, a coverage level, a coverage type, an exclusion, an insured driver, or other aspects of the policy, as discussed elsewhere herein. Determining an update to an insurance policy may include determining a change in one or more risk levels associated with operation of the vehicle 108 (or vehicle operation by one or more drivers)”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize updating the insurance policy based on the determined risk level of the current usage profile being above a threshold as in Fields in the system executing the method of JIANG with the motivation of offering to provide more accurate and updated insurance policy based data analysis and [Col 56 Lines 55-60] help improve driving behavior as taught by Fields over that of JIANG.
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over JIANG, in view of Hayward, in view of Fields, and in view of Padmanabhan (US 20210226774 A1).
As per Claim 8, JIANG may not explicitly disclose, but Padmanabhan discloses the system of claim 7, wherein the instructions further cause the processor to record the at least one user recommendation parameter on a blockchain ledger along with the user profile data ([0086] “For example, once metadata is defined and created via the blockchain metadata definition manager 196 and pushed onto the blockchain, any participating node 133 with access to the blockchain where that metadata definition resides can then create data records and store information onto the blockchain which adopts the defined metadata definition and thus complies with the newly created metadata definition”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the blockchain system to record data as in Padmanabhan in the system executing the method of JIANG with the motivation of offering to [0004-0012] improve, modify, and expand the “blockchain and related distributed ledger technologies by providing means for implementing user access controls in a metadata driven blockchain operating via Distributed Ledger Technology (DLT)” as taught by Padmanabhan over that of JIANG.
As per Claim 9, JIANG may not explicitly disclose, but Padmanabhan discloses the system of claim 8, wherein the instructions further cause the processor to retrieve the at least one user recommendation parameter from the blockchain responsive to a consensus among the business node and the at least one market place server node ([0087] “In one embodiment, the blockchain consensus manager 191 and blockchain metadata definition manager 196 work in conjunction to implement consensus on read functions as described further herein below with reference to FIGS. 10-12. A consensus on read is a specific type of consensus for controlling read access to data stored on the blockchain … Each node that approves of the read access responds with its portion of the shared secret that enables the requesting node to generate the key from the shared secrets to decrypt the data on the blockchain and access the data”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the consensus of nodes to read recorded data from the blockchain system as in Padmanabhan in the system executing the method of JIANG with the motivation of offering to [0004-0012] improve, modify, and expand the “blockchain and related distributed ledger technologies by providing means for implementing user access controls in a metadata driven blockchain operating via Distributed Ledger Technology (DLT)” as taught by Padmanabhan over that of JIANG.
As per Claim 10, JIANG may not explicitly disclose, but Padmanabhan discloses the system of claim 8, wherein the instructions further cause the processor to execute a smart contract to record data reflecting user qualification and approval for the business entity associated with the at least one user recommendation parameter on the blockchain for future audits ([0123] “all data is transparent and cryptographically verifiable and data and users are not owned by a single party, notwithstanding being hosted internal to the host organization, and the history 148 and state ledger 147 provide for an enhanced audit trail. The integration builder 153 permits the execution of smart contracts run on shared data as well as run against data which is owned by the network org 157 itself, such as metadata definitions which are accessible to all members but which nevertheless remain owned by the host organization”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the execution of smart contracts wherein the blockchain system comprises audit trails as in Padmanabhan in the system executing the method of JIANG with the motivation of offering to [0004-0012] improve, modify, and expand the “blockchain and related distributed ledger technologies by providing means for implementing user access controls in a metadata driven blockchain operating via Distributed Ledger Technology (DLT)” as taught by Padmanabhan over that of JIANG.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over JIANG, in view of Hayward, and in view of Bachann (US 20050010506 A1).
As per Claim 11, JIANG may not explicitly disclose, but Bachann discloses the system of claim 1, wherein the instructions further cause the processor to generate a user-related risk assessment score based on user profile data comprising a credit history, user financial statements' data based on market conditions data derived from a local database ([0007] “The common platform according to the invention allows for obtaining personal financial statements and commercial asset information and credit histories from potential borrowers, obtaining comprehensive property information and calculating historical operating performance (which takes into account, gross potential income, effective gross income, total operating expenses, NOI, debt service, net cash flow, property cash flow and DSC (NOI/debt service)), obtaining asset and credit information from third party credit bureaus, complete credit analysis including risk analysis and evaluation of the credit worthiness of a potential borrower” wherein Figures 3a-3c discloses the complete risk assessment).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize risk assessment based on user profile data comprising credit history and financial statement as in Bachann in the system executing the method of JIANG with the motivation of offering to [0008] “reduce delinquencies, improving underwriting consistency, increase loan volumes, improve risk-adjusted profitability and enhance overall customer satisfaction” as taught by Bachann over that of JIANG.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over JIANG, in view of Hayward, and in view of Einav (US 9047608 B1).
As per Claim 12, JIANG may not explicitly disclose, but Einav discloses the system of claim 1, wherein the instructions further cause the processor to detect fraudulent activities by recognizing user-related patterns and anomalies in real-time transactions based on the at least one user recommendation parameter associated with qualifying the user for the at least one business entity node ([Col 7 Lines 12-32] “In one embodiment, the fraud detection server includes a behavioral engine 217 that is coupled to the risk analyzer 200 and the data store 250 to perform a real-time risk assessment of transactions at a user-level as they occur. The behavioral engine 217 can monitor patterns of user behavior during on-line transactions and generate user behavior data 259 to reflect the patterns and a user-level analysis … Using information related to the behavioral patterns in the user behavior data 259, the behavioral engine 217 can construct a user pattern and identify anomalies in the behavior, which can suggest that a transaction is suspicious if it does not follow the typical behavioral pattern for a particular user”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize fraud detection monitoring user behavior patterns which identifies anomalies to perform real-time risk assessment on transactions as in Einav in the system executing the method of JIANG with the motivation of offering to [Col 1 Lines 7-53] improve risk assessments in fraud detection systems by reducing the number of false positives and increasing the accuracy for detection of real fraud transactions as taught by Einav over that of JIANG.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over JIANG, in view of Hayward, and in view of Sivaraman et al. (US 12033222 B1).
As per Claim 13, JIANG may not explicitly disclose, but Sivaraman discloses the system of claim 1, wherein the instructions further cause the processor to collect user feedback data from social media and to generate a classifier based on features extracted from the user feedback data and provide an at least one classifier to the ML module to generate a predictive model for producing at least one recommendation parameter for the business entity node ([Col 1 Lines 54-67 to Col 2 Lines 1-6] “cause the processor to compute a score associated with a user input posted in an electronic social network by a user that reflects a degree of agreement of other users in the electronic social network based on feedback from the other users, determine a recommendation for the user based on a user profile and the score, … In one embodiment, the recommendation comprises identification of a product or service and one or more other users that have commented regarding the product or service. The recommendation can be determined by way of machine learning, wherein machine learning is trained with training data based on score” or see also [Col 2 Lines 42-54] “conveying, by way of the social network, user input regarding a financial transaction, computing a score associated with a user based on feedback received regarding the user input reflecting a level of agreement of other users with the user input, and generating a recommendation of a product or service based on the score associated with the user … In one instance, machine learning can be employed to predict the product or service that is likely of interest”).
It would have been obvious to one of ordinary skill in