DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a Final Office Action in response to communications received June 02, 2026. Claim 2 has been canceled. Claims 1-2, 9, 11-12 and 17 have been amended. No new claims have been added. Therefore, claims 1-20 are pending and addressed below.
Priority
Application No. 18817332 filed 08/28/2024 is a Continuation in Part of 18389126 , filed 11/13/2023.
Applicant Name/Assignee: iBusiness Funding LLC
Inventor(s): Levy, Justin
Response to Arguments/Amendments
Drawings
Applicant’s amendments to the specification in response to the objection set forth in the previous Office Action for failing to comply with 37 CFR 1.84(p)(4) is sufficient to overcome the objection to the drawings. The examiner withdraws the objection to the drawing.
Claim Rejections - 35 USC § 101
Applicant's arguments filed June 02, 2026 have been fully considered but they are not persuasive.
In the remarks applicant argues that the independent claims 1, 11 and 17 recite a specific authorization architecture controlling whether a lending verdict data structure effectuating transfer of digital assets via read/write access to the user’s account responsive to a verified approval indication. The indication associated with lender entity node where the LS (lender server) node and lender node are peer nodes. The claim limitations as a whole integrate the alleged lending abstract concept into a particular authorization mechanism which governs whether and how the asset transfer is authorized and effectuated rather the reciting a financial outcome. Applicant’s argument is not persuasive. The LS node is merely applied as a computer environment to perform the abstract idea of “acquire…data”, “analyze …data”, “search…database”, “generate …feature vector based on …plurality of features and …historical …data”, “execute …model, the execution comprising providing information related to…lending verdict, the data structure being executable …to effectuate a …transfer of …assets to an account”, “render …transfer of assets to account…via read/write provided through execution” which as a combination is not directed toward a technical process to improve any underlying technology or functionality, to provide a technical solution to a problem rooted or caused by technology itself, does not transform an article to a different state or thing. The limitations merely generally link the use of the abstract idea to a particular technological environment. The LS node and ML model merely provides a field of use to perform the abstract idea. The limitations are high level lacking any technical details reciting high level functions with an expected result for performing the identified abstract idea. The claimed LS node the application of generic technology for use in implementing the abstract idea. Such application of server node technology as applied to execute models is known application in the art (see US Pub No. 2022/0215492 A1 by McKenna IV – para 0105; US Patent No. 10,698,766 B2 by Zhao et al.-Abstract; Col 2 lines 25-35, Col 11 lines 50-67; US Pub No. 2019/0138934 A1 by Prakash et al- para 0095, para 0122, para 0125). The rejection is maintained.
In the remarks applicant argues that under step 2A prong 1, the alleged abstract idea does not consider the amended limitations which goes beyond reciting business decision implemented on a generic processor. The claim limitations execute lending verdict data structure on a verified approval indication associated with lender entity node which are peer nodes for asset transfer when approval state is present. The system authorizes and transfers assets. Applicant’s argument is not persuasive. The claim limitations as a whole are not directed toward server node technology or any other technological process. Rather the limitations as a whole are directed toward the transfer of assets based on the results of an analysis. Such processes are directed toward the abstract idea of methods of organizing human activity. The rejection is maintained.
In the remarks applicant argues that under step 2A prong 2, the amended claims integrate the alleged abstract idea into a practical application. Applicant argues the amendments which apply LS server node, goes beyond generic computer elements “processor”, “network”, “artificial intelligence model” and “predictive model” that output the verdict. The verified approval indication associated with at least one lender node together with LS node operating as a peer node going beyond what lending outcome is desired, releasing and execution of the transfer by a verified approval state which in integral condition for authorization and execution of asset transfer rather than an insignificant extra solution activity.
Applicant argues the specification discloses that the lending verdict upon approval indication in the verdict data structure where the assets are accessed via read/write access via instructions. The specification discloses lender entities connected to lending server node to execute a transaction to release the loan approval verdict where the lender entity nodes may serve as peer nodes participate in authorization rendering data structure executable responsive to a verified approval indication associated with lender entity node operating as a peer node. Applicant argues the amended claims support a practical application imposing meaningful limits on any alleged abstract idea by requiring approval indication associated with lender entity node operating as a peer node with the LS node to effectuate the transfer. This process goes beyond data gathering, analysis and output. The particular authorization architecture controls whether the transfer instruction changing the authorization mechanism the asset transfer. The claims therefore satisfy MPEP 2106.04(II)(2), by applying the alleged abstract idea in a technological environment in a manner improving security and authorization of asset transfer. Applicant’s argument is not persuasive. The LS (lending server) node is merely provided as a computer environment for performing the abstract idea of asset transfers based on analysis results. The specification discloses how the technology is applied for use in performing the abstract idea and does not focus on any process directed toward the technology itself for improvement or any other indications of patent eligibility. The rejection is maintained.
In the remarks applicant argues that the previous Office actions analysis is not applicable to the amended claim limitations. The amended limitations require the result of the AI/ML lending analysis to an authorization architecture that controls whether the lending verdict is executable. The claim limitations recite how the transfer is gated and authorized to effectuate transfer via read/write access to the user’s account responsive to a verified approval indication associated with at least one lender entity node operating as a peer node with the LS node. Applicant argues the amended claims are analogous to Enfish, DDR Holdings which provide patent eligibility as the limitation are rooted in computer and network technology. Applicant’s arguments are not persuasive. The limitations as claimed are not rooted in computer and network technology, instead merely apply technology to perform a financial transaction. Unlike Enfish which improved upon the capacity of database functionality or DDR Holdings which provided a solution to a problem rooted/caused by technology itself, the current limitations merely confine the financial activity to a particular technical environment for implementation. The rejection is maintained.
Applicant argues the under step 2B, the claimed subject matter provides significantly more than the alleged abstract idea. The ordered combination of the limitations is not merely generic processor executing data manipulation. The claimed sequence ties the AI/ML lending parameters and lending verdict to an authorization gate that conditions that the data structure on an approval indication associated with a lender entity node operating as a peer node providing an inventive concept because it uses a particular authorization mechanism to control whether the transfer instruction is executable rather the appending conventional computer implementation. Applicant’s argument is not persuasive. As discussed above and as evidence in argument 1, Peer Server node technical environments applied to receive and input vector data into a ML model that is executed to output a expected outcome for use in a business practice is not significantly more than applying known technology to implement as abstract idea. The courts have held that applying technology to implement an abstract idea is not sufficient and does not provide significantly more that the alleged abstract idea. The limitations are high level with expected outcomes directed toward the abstract idea and not technology itself. The rejection is maintained.
In the remarks applicant argues that based on arguments above and the dependency of the independent claims, dependent claims 2-10, 12-16 and 18-20 are also patent eligible. The examiner respectfully disagrees. See response above, the rejection is maintained.
Claim Rejections - 35 USC § 103
Applicant's arguments are moot in light of the new ground of rejection that was necessitated by Applicant's amendments. Based on an updated search of the art, a new reference was used in the rejection below
Claim Interpretation
With respect to the claim language “transfer of digital assets to the electronic account is effectuated via read/write access”, in computer technology “read/write” means for a computing device to retrieve or store data into a record/storage. Accordingly, the examiner is interpreting the “transfer of digital asset to the electronic account” to be the recording of payments/transfer of assets into a payment record in electronic computing environments.
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 instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
In reference to Claims 1-10:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a system, as in independent Claim 1 and the dependent claims. Such systems fall under the statutory category of "machine." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The claimed invention is directed to an abstract idea without significantly more. System claim 1 recites an operational process (1) acquire user data (2) analyze data (3) determine features (4) search local user database…causing …retrieval of [data], (5) generate …feature vector based on …features and …data (6) execute …model…comprising providing…vector as input to…model …predictive model is generated…producing lending parameter (7) output …data structure comprising information related to …lending verdict, …data structure …to effectuate …transfer of …assets…(8) render data structure executable…to a verified approval indication associated with the entity node, the transfer of digital assets to the account …”
The claimed limitations which under its broadest reasonable interpretation, covers performance of transaction process. The specification describes applying digital assets for loan applications as part of leverage for loan applications (para 0003). The claimed systems is applied to collect user data related to loan applications, effectuate mechanisms to remit, deny or curate loan application results for users to secure assets (para 0005) The specification discloses applying the LS node for applying the abstract t idea as hosting the AL/ML model to receive data,(para 0046, para 0050, para 0053, para 0057, para 0059, para 0063, para 0065), generate feature vector, ingest feature vector, analyze loan risk parameters (para 0051, para 0058), monitor and detect parameter deviation to provide updated parameter (para 0065) and process the predictive outputs data received from the AI/ML model to generate a lending verdict (para 0064) Accordingly, when considered as a whole, in light of the specification, the claimed subject matter is directed toward receiving and analyzing financial data generate feature vector data as input to a model in order to generate lending parameters and outputting data structures comprising lending verdicts to effectuate transfer of assets. Such concepts can be found in the abstract category of commercial interactions and transactions. These concepts are enumerated in Section I of the 2019 revised patent subject matter eligibility guidance published in the federal register (84 FR 50) on January 7, 2019) is directed toward abstract category of methods of organizing human activity.
STEP 2A Prong 2: The identified judicial exception is not integrated into a practical application because the claims fail to provide indications of patent eligible subject matter that integrate the alleged abstract idea into a practical application. The additional elements recited in the claim beyond the abstract idea include a system comprising “lending server node and …lender entity node…having peer nodes” “a processor of the LS node”, “a network”, “artificial intelligence model” and “a predictive model”
The claimed LS node processor applied to perform the operations of “acquire…data” over a network, “search over the network” and based on query “retrieve data”.
The predictive model applied to output data structure comprising lending verdict.
which According to MPEP 2106.05(d) II (see also MPEP 2106.05(g)) is insignificant extra solution activity. The courts have recognized the following computer functions are claimed in a merely generic manner (e.g., at a high level of generality) where technology is merely applied to perform the abstract idea or as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014)
Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93
The claim limitations (“acquiring” [receiving], “retrieving”, “output” [transmitting] ) are recited at a high level of generality without details of technical implementation and thus are insignificant extra solution activity.
The additional element “LS node processor” is applied to “analyze” data to determine plurality of features, “search” a local database based on query, “generate at least one feature vector”, “execute …model” comprising “providing…feature vector as input to the …model ...” and “render data structure executable such that transfer of asset to account effectuated via read/write access which are processes not directed toward technology or any other indications of patent eligible subject matter but rather to apply technology to analyze user data for lending analysis and to vectorize data which is mere data manipulation that is inputted into a model for analysis in order to generate lending parameters and to execute asset transfer where the transfer is written/read to the account.
The additional element “artificial intelligence model” applied to “generate…at least one lending parameter” which is a process that is not directed toward technology but rather a commercial activity.
The operations performed by the “processor” and “model” are recited at a high-level of generality such that it amounts to no more than applying the exception using generic computer components for the purpose of analyzing financial transaction data to mitigate fraud and perform a deposit transaction. The claim limitations when considered individually fail to provide any indications of patent eligible subject matter, according to MPEP guidance (see MPEP 2106.05 (a)-(c), (e )-(h).
(i) an improvement to the functioning of a computer;
(ii) an improvement to another technology or technical field;
(iii) an application of the abstract idea with, or by use of, a
particular machine;
(iv) a transformation or reduction of a particular article to a
different state or thing; or
(v) other meaningful limitations beyond generally linking the
use of the abstract idea to a particular technological environment.
When the claims are taken as an ordered combination or as a whole, the combination of limitations, the combination of limitations (1) “acquire”, (2) “analyze” data and (3) “determine based on analysis features” and (4) “search database based on query for retrieval of user related data that corresponds to determined features of limitations (1)-(3) which is directed toward collecting, analyzing and manipulating user data for a financial activity. The limitations (5) generate a vector feature from limitations 1-4 combined with limitation (6) executing a model comprising the vector of limitation (5) for input to the model where the model generates a predictive model that produces a lending parameter – which as a combination is mot directed toward technology but rather applying a model to analyze manipulated inputted data in order to generate a lending parameter which is a for a financial activity. The combination of limitations 1-6 and (7)-(8) “output…data structure comprising information related to lending verdict…” and “render data responsive to approval indication the transfer of assets in the electronic account”– which when considered as a combinations is directed toward collecting, analyzing user data in order to generate lending parameters and output lending verdict where an approval includes transfer of assets to an account which is not an indication of patent eligibility under step 2A, but rather a commercial interaction.
MPEP guidance (see MPEP 2106.05 (a)-(c), (e )-(h). The claim limitations as a whole, as an ordered combination and the combination of steps not integrate the judicial exception into a practical application as the claim process fails to impose meaningful limits upon the abstract idea. This is because the claimed subject matter fails to provide additional elements or combination or elements that go beyond applying technology as a tool to perform the identified abstract idea. The functions recited by the mobile device in the claims recite the concept of a financial activity.
The specification discloses applying the LS node for applying the abstract t idea as hosting the AL/ML model to receive data,(para 0046, para 0050, para 0053, para 0057, para 0059, para 0063, para 0065), generate feature vector, ingest feature vector, analyze loan risk parameters (para 0051, para 0058). The specification discloses the system nodes as connected to the network and blockchain for use in performing the abstract idea (para 0066). The specification lacks any technical details as to the technical process for the “read/write” access via executed instructions. The claim limitations and specification lacks technical disclosure on what the technical problem was and how the claimed limitations provide a technical solution to a technical problem rather than a solution to a problem found in the abstract idea. Taking the claim elements separately, or as a combination, the operation performed by the mobile device processor and communication unit at each step of the process is purely in terms of results desired and devoid of implementation of details. Technology is not integral to the process as the claimed subject matter is so high level that any generic programming could be applied and the functions could be performed by any known means. Furthermore, the claimed functions do not provide an operation that could be considered as sufficient to provide a technological implementation or application of/or improvement to this concept (i.e. integrated into a practical application).
The integration of elements do not improve upon technology or improve upon computer functionality or capability in how computers carry out one of their basic functions. The integration of elements do not provide a process that allows computers to perform functions that previously could not be performed. The integration of elements do not provide a process which applies a relationship to apply a new way of using an application. The limitations do not recite a specific use machine or the transformation of an article to a different state or thing. The limitations do not provide other meaningful limits beyond generally linking the use of the abstract idea to a particular technological environment. The resource claimed performing the steps is merely a “field of use” application of technology. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments apply what generic computer functionality in the related arts. The steps are still a combination made to perform a financial activity and does not provide any of the determined indications of patent eligibility set forth in the 2019 USPTO 101 guidance. The additional steps only add to those abstract ideas using generic functions, and the claims do not show improved ways of, for example, an particular technical function for performing the abstract idea that imposes meaningful limits upon the abstract idea. Moreover, Examiner was not able to identify any specific technological processes that goes beyond merely confining the abstract idea in a particular technological environment, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim provides no technical details regarding how the operations performed by the “resource”. Instead, similar to the claims at issue in Intellectual Ventures I LLC v. Capital One Financial Corp., 850 F.3d 1332 (Fed. Cir. 2017), “the claim language . . . provides only a result-oriented solution with insufficient detail for how a computer accomplishes it. Our law demands more.” Intellectual Ventures, 850 F.3d at 1342 (citing Elec. Power Grp. LLC v. Alstom, S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016)). The claim is directed to an abstract idea
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements recited in the claim beyond the abstract idea include a system comprising “lending server node and …lender entity node…having peer nodes” “a processor of the LS node”, “a network”, “artificial intelligence model” and “a predictive model.”
Taking the claim elements separately, the function performed by the computer elements at each step of the process is purely conventional. Using computer components (processor, network, artificial intelligence model and predictive model) to perform the operations “acquire”, “analyze”, “determine”, “search”, “retrieval”, “generate”, “execute…model…providing …vector as input”, “producing …lending vector”, “output…data structure comprising information…” ----are some of the most basic functions of a computer.
The claimed operations of the artificial intelligence model and predictive model amounts to no more than mere instructions to “apply” the abstract idea. According to Alice, limitations that are “applied” (or an equivalent) with the abstract idea as mere instructions to implement the abstract idea on a computer or requiring no more than a generic compute to perform generic computer functions that are well understood activities known to the industry, are not enough to qualify as “significantly more”. As a result, none of the hardware recited by the system claims offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via computers.... The claim limitations do not recite that any of the “devices” perform more than a high level generic function .... None of the limitations recite technological implementation details for any of these steps, but instead recite only results desired to be achieved by any and all possible means. .. . Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
When the claims are taken as a whole, as an ordered combination, the combination of steps does not add “significantly more” by virtue of considering the steps as a whole, as an ordered combination. All of these computer functions are generic, routine, conventional computer activities that are performed only for their conventional uses. See Elec. Power Grp. v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016). Also see In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1316 (Fed. Cir. 2011) Absent a possible narrower construction of the terms “acquire”, “analyze”, “determine”, “search”, “retrieval”, “generate”, “execute…model…providing …vector as input”, “producing …lending vector”, “output…data structure comprising information…”, “render data structure …responsive …, whether considered individually or as a sequence combination ... are functions can be achieved by any general purpose computer without special programming. None of these activities are used in some unconventional manner nor do any produce some unexpected result. In short, each step does no more than require a generic computer to perform generic computer functions.
As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. Invest Pic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018). Considered as an ordered combination, the computer components of Applicant’s claimed functions add nothing that is not already present when the steps are considered separately. The sequence of data reception-analysis modification-output is equally generic and conventional. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (“acquire”, “analyze”, “determine”, “search”, “retrieval”, “generate”, “execute…model…providing …vector as input”, “producing …lending vector”, “output…data structure comprising information…” recited as an abstraction), Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission), Two-Way Media Ltd. v. Comcast Cable Communications, LLC, 874 F.3d 1329, 1339 (Fed. Cir. 2017) (sequence of processing, routing, controlling, and monitoring). The ordering of the steps is therefore ordinary and conventional. The analysis concludes that the claims do not provide an inventive concept because the additional elements recited in the claims do not provide significantly more than the recited judicial exception.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
[0007] In accordance with one or more embodiments, a system is provided that includes one
or more processors and/or computing devices configured to provide functionality in accordance
with such embodiments. In accordance with one or more embodiments, functionality is embodied
in steps of a method performed by at least one computing device. In accordance with one or more
embodiments, program code (or program logic) executed by a processor(s) of a computing device
to implement functionality in accordance with one or more such embodiments is embodied in, by
and/or on a non-transitory computer-readable medium
[0028] Certain embodiments and principles will be discussed in more detail with reference to
the figures. According to some embodiments, the present disclosure provides systems and methods for a DI-based framework that can perform automated loan processing/approval based on users 'related data. As discussed herein, a user should be understood to be a user or entity, and for
purposes of this disclosure will be referenced as a "user" without limiting the scope, as understood
by those of ordinary skill in the art. As discussed below, the disclosed DI framework can
implement any type of known or to be known artificial intelligence and/or machine learning
(Al/ML) algorithms, techniques, models, and the like.
[0074] In some embodiments, the electronic documents (e.g., digital assets) can be securely
stored in a database, which as discussed herein, can be any type of known or to be known
centralized or decentralized storage. For example, the storage can be a public blockchain, private
blockchain, look-up table (LUT), memory, memory stack, distributed ledger and/or any other type
of secure data repository.
[0076] At block 304, the processor 204 may parse the user data to derive a plurality of features.
According to some embodiments, processor 204 can analyze the user data by parsing the data, and extracting, deriving or otherwise identifying the plurality of features.
[0077] In some embodiments, as discussed above, such analysis can be performed via process
204 implementing any type of known or to be known computational analysis technique, algorithm,
mechanism or technology to analyze the user data.
[0078] In some embodiments, processor 204 may execute and/or include a specific trained
artificial intelligence / machine learning model (Al/ML), a particular machine learning model
architecture, a particular machine learning model type (e.g., convolutional neural network (CNN),
recurrent neural network (RNN), autoencoder, support vector machine (SVM), and the like), or
any other suitable definition of a machine learning model or any suitable combination thereof.
[0115] Consistent with an embodiment of the disclosure, the aforementioned CPU 520, the
bus 530, the memory unit 550, a PSU 550, and the plurality of 1/0 units 560 may be implemented
in a computing device, such as computing device 500. Any suitable combination of hardware,
software, or firmware may be used to implement the aforementioned units. For example, the CPU
520, the bus 530, and the memory unit 550 may be implemented with computing device 500 or
any of other computing devices 500, in combination with computing device 500. The
aforementioned system, device, and components are examples and other systems, devices, and
components may comprise the aforementioned CPU 520, the bus 530, the memory unit 550,
consistent with embodiments of the disclosure.
[0116] At least one computing device 500 may be embodied as any of the computing elements
illustrated in all of the attached figures, including the LS node 102 (FIG. 2). A computing device
500 does not need to be electronic, nor even have a CPU 520, nor bus 530, nor memory unit 550.
The definition of the computing device 500 to a person having ordinary skill in the art is "A device
that computes, especially a programmable [usually] electronic machine that performs high-speed
mathematical or logical operations or that assembles, stores, correlates, or otherwise processes
information." Any device which processes information qualifies as a computing device 500,
especially if the processing is purposeful.
With respect to the server node as applied for performing the financial analysis and transaction, the specification discloses:
[0046] Referring to FIG. lA, the example network 100 includes the lending server (LS) node
102 connected to a cloud server node(s) 105 over a network. The LS node 102 is configured to
host an AI/ML module 107. The LS node 102 may receive user data from a user 111. The LS
node 102 may receive a call data related to communication between the user 111 and responding
entity that may be implemented as chat bot (not shown).
[0050] The LS node 102 may query a local users' database for the historical local users' data
103 associated with the current user 111 data. The LS node 102 may acquire relevant remote
users' data 106 from a remote database residing on a cloud server 105. The remote users' data
106 may be collected from other lending facilities. The remote users' data 106 may be collected
from the users of the same (or similar) condition, age, language, etc. as the local users' who are
associated with the current user-related data of the user 111 based on submitted documents 112.
[0051] The LS node 102 may generate a feature vector or classifier data based on the userrelated
data, user 111 call data and the collected users' data (i.e., pre-stored local data 103 and
remote data 106). The LS node 102 may ingest the feature vector data into an AI/ML module 107.
The AI/ML module 107 may generate a predictive model(s) 108 based on the feature vector data
to predict lending parameters for automatically generating a lending verdict and/or lending
recommendations to be provided to the lender entities 113 (e.g., loan officers, underwriters, other
practitioners, etc.). The lending parameters and/or loan risk assessment parameters may be further analyzed by the LS node 102 prior to generation of the loan verdict. In some embodiments, the lending parameters may be used for adjustment of the loan terms. Once the loan verdict is
determined, an alert/notification may be sent to the lending entity 113 for a final approval.
[0053] Referring to FIG. lB, the example network 100' includes the lending server (LS) node
102 connected to a cloud server node(s) 105 over a network. The LS node 102 is configured to
host an AI/ML module 107. The LS node 102 may receive user data from a user 111. The LS
node 102 may receive a call data related to communication between the user 111 and responding
entity that may be implemented as a chat bot (not shown).
[0057] The LS node 102 may query a local users' database for the historical local users' data
103 associated with the current user 111 data. The LS node 102 may acquire relevant remote
users' data 106 from a remote database residing on a cloud server 105. The remote users' data
106 may be collected from other lending facilities. The remote users' data 106 may be collected
from the users of the same (or similar) condition, age, language, etc. as the local users' who are
associated with the current user-related data of the user 111 based on submitted documents 112.
[0058] The LS node 102 may generate a feature vector or classifier data based on the user related
data, user 111 call data and the collected users' data (i.e., pre-stored local data 103 and remote data 106). The LS node 102 may ingest the feature vector data into an AI/ML module 107.
The AI/ML module 107 may generate a predictive model(s) 108 based on the feature vector data
to predict lending parameters for automatically generating a lending verdict and/or lending
recommendations to be provided to the lender entities 113 (e.g., loan officers, underwriters, other
practitioners, etc.). The lending parameters and/or loan risk assessment parameters may be further analyzed by the LS node 102 prior to generation of the loan verdict. In some embodiments, the lending parameters may be used for adjustment of the loan terms. Once the loan verdict is
determined, an alert/notification may be sent to the lender entity nodes 113 for a final approval.
[0059] In some embodiments, the LS node 102 may receive the predicted lending parameters
from a permissioned blockchain 110 ledger 109 based on a consensus from the lender entity nodes 113 confirming, for example, loan approval/disapproval verdict, payment plan, schedule and other loan conditions. Additionally, confidential historical user-related information and previous users' - related lending parameters may also be acquired from the permissioned blockchain 110. The newly acquired user-related data with corresponding predicted loan verdict and lending
recommendation parameters data may be also recorded on the ledger 109 of the blockchain 110 so it can be used as training data for the predictive model(s) 108. In this implementation the LS node 102, the cloud server 105, the lender entity nodes 113 and user entities(s) 101 may serve as
blockchain 110 peer nodes. In some embodiments, local users' data 103 and remote users' data
106 may be duplicated on the blockchain ledger 109 for higher security of storage.
[0063] The LS node 102 is configured to host an AI/ML module 107. As discussed above
with respect to FIGs. lA-B, the LS node 102 may receive the user data provided by the user
entities(s) 101 (FIG. lA) and pre-stored users' data retrieved from local and remote databases. As
discussed above, the pre-stored users' data may be retrieved from the ledger 109 of the blockchain 110.
[0064] The AI/ML module 107 may host, compile, generate and train a predictive model(s)
108 based on the received user-related data 202 and the users' -related data provided by the LS
node 102. As discussed above, the AI/ML module 107 may provide predictive outputs data in the
form of lending parameters for automatic generation of landing verdict and/or landing
recommendations for the lender entities 113 (see FIG. lB). The LS node 102 may process the
predictive outputs data received from the AI/ML module 107 to generate the lending verdict and/or
lending risk assessment recommendation pertaining to a particular user engagement.
With respect to the read/write operations, the specification discloses:
[0087] As discussed herein, such verdict can be compiled as a set of executable instructions, that an upon approval indication in the verdict data structure, can be sent to the lender such that an electronic account housing the requested digital assets can be securely accessed via the read/write access provided via execution of the executable instructions. Thus, the requested funds, for example, can be automatically and securely (e.g., according to a known or to be known encryption, for example) accessed and sent to the electronic account of the user.
[0107] An exemplary storage medium may be coupled to the processor such that the processor
may read information from, and write information to, the storage medium. In the alternative, the
storage medium may be integral to the processor. The processor and the storage medium may
reside in an application specific integrated circuit ("ASIC"). In the alternative embodiment, the
processor and the storage medium may reside as discrete components. For example, FIG. 5
illustrates an example computing device (e.g., a server node) 500, which may represent or be
integrated in any of the above-described components, etc.
[0151] - Non-volatile memory which can retain stored information even after power is
removed, for example, but not limited to, Read-Only Memory (ROM) 553, Programmable ROM
(PROM) 555, Erasable PROM (EPROM) 555, Electrically Erasable PROM (EEPROM) 556 (e.g.,
flash memory and Electrically Alterable PROM [EAPROM]), Mask ROM (MROM), One Time
Programmable (OTP) ROM I Write Once Read Many (WORM), Ferroelectric RAM (FeRAM),
Parallel Random-Access Machine (PRAM), Split-Transfer Torque RAM (STT-RAM), Silicon Oxime Nitride Oxide Silicon (SONOS), Resistive RAM (RRAM), Nano RAM (NRAM), 3D
XPoint, Domain-Wall Memory (DWM), and millipede memory
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 2-10 these dependent claim have also been reviewed with the same analysis as independent claim 1. Dependent claim 2 is directed toward performing the operations “receive …data”, “derive a language metadata” and “parse call data based on language metadata to derive a plurality of key features” which is directed toward data manipulation, organization and categorization- directed toward data organization and not the technology itself. For data, mere “manipulation” of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting /n re Warmerdam, 33 F.3d 1354, 1355, 1360 (Fed. Cir. 1994). (see MPEP 2106.05(a) I). Dependent claim 3 is directed toward “retrieve …data”-insignificant extra solution activity. Dependent claim 4 is directed toward “generate…feature vector” -mere data manipulation and not the technology itself - CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting /n re Warmerdam, 33 F.3d 1354, 1355, 1360 (Fed. Cir. 1994). Dependent claim 5 is directed toward “generate …user profile data…”- directed toward data organization and not the technology itself (see MPEP 2106.05(a) I. Dependent claim 6 is directed toward “periodically monitor…user profile data… [for a condition]” -insignificant extra solution activity. Dependent claim 7 is directed toward “responsive to …value of …user data deviating from the value of the previous user profile data…exceeding the pre-set threshold value”, “generate an updated feature vector…” and “generate the lending verdict…”- directed toward based on data value parameter generating updated vectors for analysis to generate a lending verdict -which is analyzing financial data to generate financial parameter a business practice. Dependent claim 8 is directed toward record lending parameter on …ledger- insignificant extra solution activity. Dependent claim 9 is directed toward “retrieve …parameter from the blockchain responsive to …consensus among LS node and …lender entity node”- insignificant extra solution activity. Dependent claim 10 is directed toward “execute smart contract to record data…” -insignificant extra solution activity.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 2-10 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claims 11-16:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a method, as in independent Claim 11 and the dependent claims. Such methods fall under the statutory category of "process." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The steps of Method claim 11 corresponds to operations of system claim 1. Therefore, claim 11 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2: The steps of Method claim 11 corresponds to operations of system claim 1. The additional elements recited in the claim beyond the abstract idea include a “lending server node”, “artificial intelligence model” and “predictive model” where the device performs the steps corresponding to the system processor of claim 1 and the “artificial intelligence model” and “predictive model” perform the steps corresponding to the operations performed by the “artificial intelligence model” and “predictive model” of claim 1. Therefore, claim 11 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include a “lending server node”, “artificial intelligence model” and “predictive model” –is purely functional and generic. Nearly every computer lending server node for implementing a method is capable of performing the basic computer functions -of “acquiring…data”, “analyzing…data”, “searching….database”, “search causing …retrieval…data”, “generating…feature vector”, “executing…model …providing vector feature as input to model”, “generate predictive model”, the “predictive model” output…data structure…lending verdict”, “rendering …verified approval …such that transfer of digital asses to …electronic account effectuated via read/write execution” - As a result, none of the hardware or models recited by the method claims offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via computers.
The steps of Method claim 11 steps corresponds to system functions claim 1. Therefore, claim 11 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
[0028] Certain embodiments and principles will be discussed in more detail with reference to
the figures. According to some embodiments, the present disclosure provides systems and methods for a DI-based framework that can perform automated loan processing/approval based on users 'related data. As discussed herein, a user should be understood to be a user or entity, and for
purposes of this disclosure will be referenced as a "user" without limiting the scope, as understood
by those of ordinary skill in the art. As discussed below, the disclosed DI framework can
implement any type of known or to be known artificial intelligence and/or machine learning
(Al/ML) algorithms, techniques, models, and the like.
[0074] In some embodiments, the electronic documents (e.g., digital assets) can be securely
stored in a database, which as discussed herein, can be any type of known or to be known
centralized or decentralized storage. For example, the storage can be a public blockchain, private
blockchain, look-up table (LUT), memory, memory stack, distributed ledger and/or any other type
of secure data repository.
[0076] At block 304, the processor 204 may parse the user data to derive a plurality of features.
According to some embodiments, processor 204 can analyze the user data by parsing the data, and extracting, deriving or otherwise identifying the plurality of features.
[0077] In some embodiments, as discussed above, such analysis can be performed via process
204 implementing any type of known or to be known computational analysis technique, algorithm,
mechanism or technology to analyze the user data.
[0078] In some embodiments, processor 204 may execute and/or include a specific trained
artificial intelligence / machine learning model (Al/ML), a particular machine learning model
architecture, a particular machine learning model type (e.g., convolutional neural network (CNN),
recurrent neural network (RNN), autoencoder, support vector machine (SVM), and the like), or
any other suitable definition of a machine learning model or any suitable combination thereof.
[0114] Embodiments of the present disclosure may comprise a computing device having a
central processing unit (CPU) 520, a bus 530, a memory unit 550, a power supply unit (PSU) 550,
and one or more Input/ Output (1/0) units. The CPU 520 coupled to the memory unit 550 and the
plurality of 1/0 units 560 via the bus 530, all of which are powered by the PSU 550. It should be
understood that, in some embodiments, each disclosed unit may actually be a plurality of such
units for the purposes of redundancy, high availability, and/or performance. The combination of
the presently disclosed units is configured to perform the stages of any method disclosed herein.
[0115] Consistent with an embodiment of the disclosure, the aforementioned CPU 520, the
bus 530, the memory unit 550, a PSU 550, and the plurality of 1/0 units 560 may be implemented
in a computing device, such as computing device 500. Any suitable combination of hardware,
software, or firmware may be used to implement the aforementioned units. For example, the CPU
520, the bus 530, and the memory unit 550 may be implemented with computing device 500 or
any of other computing devices 500, in combination with computing device 500. The
aforementioned system, device, and components are examples and other systems, devices, and
components may comprise the aforementioned CPU 520, the bus 530, the memory unit 550,
consistent with embodiments of the disclosure.
With respect to the server node as applied for performing the financial analysis and transaction, the specification discloses:
[0046] Referring to FIG. lA, the example network 100 includes the lending server (LS) node
102 connected to a cloud server node(s) 105 over a network. The LS node 102 is configured to
host an AI/ML module 107. The LS node 102 may receive user data from a user 111. The LS
node 102 may receive a call data related to communication between the user 111 and responding
entity that may be implemented as chat bot (not shown).
[0050] The LS node 102 may query a local users' database for the historical local users' data
103 associated with the current user 111 data. The LS node 102 may acquire relevant remote
users' data 106 from a remote database residing on a cloud server 105. The remote users' data
106 may be collected from other lending facilities. The remote users' data 106 may be collected
from the users of the same (or similar) condition, age, language, etc. as the local users' who are
associated with the current user-related data of the user 111 based on submitted documents 112.
[0051] The LS node 102 may generate a feature vector or classifier data based on the userrelated
data, user 111 call data and the collected users' data (i.e., pre-stored local data 103 and
remote data 106). The LS node 102 may ingest the feature vector data into an AI/ML module 107.
The AI/ML module 107 may generate a predictive model(s) 108 based on the feature vector data
to predict lending parameters for automatically generating a lending verdict and/or lending
recommendations to be provided to the lender entities 113 (e.g., loan officers, underwriters, other
practitioners, etc.). The lending parameters and/or loan risk assessment parameters may be further analyzed by the LS node 102 prior to generation of the loan verdict. In some embodiments, the lending parameters may be used for adjustment of the loan terms. Once the loan verdict is
determined, an alert/notification may be sent to the lending entity 113 for a final approval.
[0053] Referring to FIG. lB, the example network 100' includes the lending server (LS) node
102 connected to a cloud server node(s) 105 over a network. The LS node 102 is configured to
host an AI/ML module 107. The LS node 102 may receive user data from a user 111. The LS
node 102 may receive a call data related to communication between the user 111 and responding
entity that may be implemented as a chat bot (not shown).
[0057] The LS node 102 may query a local users' database for the historical local users' data
103 associated with the current user 111 data. The LS node 102 may acquire relevant remote
users' data 106 from a remote database residing on a cloud server 105. The remote users' data
106 may be collected from other lending facilities. The remote users' data 106 may be collected
from the users of the same (or similar) condition, age, language, etc. as the local users' who are
associated with the current user-related data of the user 111 based on submitted documents 112.
[0058] The LS node 102 may generate a feature vector or classifier data based on the user related
data, user 111 call data and the collected users' data (i.e., pre-stored local data 103 and remote data 106). The LS node 102 may ingest the feature vector data into an AI/ML module 107.
The AI/ML module 107 may generate a predictive model(s) 108 based on the feature vector data
to predict lending parameters for automatically generating a lending verdict and/or lending
recommendations to be provided to the lender entities 113 (e.g., loan officers, underwriters, other
practitioners, etc.). The lending parameters and/or loan risk assessment parameters may be further analyzed by the LS node 102 prior to generation of the loan verdict. In some embodiments, the lending parameters may be used for adjustment of the loan terms. Once the loan verdict is
determined, an alert/notification may be sent to the lender entity nodes 113 for a final approval.
[0059] In some embodiments, the LS node 102 may receive the predicted lending parameters
from a permissioned blockchain 110 ledger 109 based on a consensus from the lender entity nodes 113 confirming, for example, loan approval/disapproval verdict, payment plan, schedule and other loan conditions. Additionally, confidential historical user-related information and previous users' - related lending parameters may also be acquired from the permissioned blockchain 110. The newly acquired user-related data with corresponding predicted loan verdict and lending
recommendation parameters data may be also recorded on the ledger 109 of the blockchain 110 so it can be used as training data for the predictive model(s) 108. In this implementation the LS node 102, the cloud server 105, the lender entity nodes 113 and user entities(s) 101 may serve as
blockchain 110 peer nodes. In some embodiments, local users' data 103 and remote users' data
106 may be duplicated on the blockchain ledger 109 for higher security of storage.
[0063] The LS node 102 is configured to host an AI/ML module 107. As discussed above
with respect to FIGs. lA-B, the LS node 102 may receive the user data provided by the user
entities(s) 101 (FIG. lA) and pre-stored users' data retrieved from local and remote databases. As
discussed above, the pre-stored users' data may be retrieved from the ledger 109 of the blockchain 110.
[0064] The AI/ML module 107 may host, compile, generate and train a predictive model(s)
108 based on the received user-related data 202 and the users' -related data provided by the LS
node 102. As discussed above, the AI/ML module 107 may provide predictive outputs data in the
form of lending parameters for automatic generation of landing verdict and/or landing
recommendations for the lender entities 113 (see FIG. lB). The LS node 102 may process the
predictive outputs data received from the AI/ML module 107 to generate the lending verdict and/or
lending risk assessment recommendation pertaining to a particular user engagement.
With respect to the read/write operations, the specification discloses:
[0087] As discussed herein, such verdict can be compiled as a set of executable instructions, that an upon approval indication in the verdict data structure, can be sent to the lender such that an electronic account housing the requested digital assets can be securely accessed via the read/write access provided via execution of the executable instructions. Thus, the requested funds, for example, can be automatically and securely (e.g., according to a known or to be known encryption, for example) accessed and sent to the electronic account of the user.
[0107] An exemplary storage medium may be coupled to the processor such that the processor
may read information from, and write information to, the storage medium. In the alternative, the
storage medium may be integral to the processor. The processor and the storage medium may
reside in an application specific integrated circuit ("ASIC"). In the alternative embodiment, the
processor and the storage medium may reside as discrete components. For example, FIG. 5
illustrates an example computing device (e.g., a server node) 500, which may represent or be
integrated in any of the above-described components, etc.
[0151] - Non-volatile memory which can retain stored information even after power is
removed, for example, but not limited to, Read-Only Memory (ROM) 553, Programmable ROM
(PROM) 555, Erasable PROM (EPROM) 555, Electrically Erasable PROM (EEPROM) 556 (e.g.,
flash memory and Electrically Alterable PROM [EAPROM]), Mask ROM (MROM), One Time
Programmable (OTP) ROM I Write Once Read Many (WORM), Ferroelectric RAM (FeRAM),
Parallel Random-Access Machine (PRAM), Split-Transfer Torque RAM (STT-RAM), Silicon Oxime Nitride Oxide Silicon (SONOS), Resistive RAM (RRAM), Nano RAM (NRAM), 3D
XPoint, Domain-Wall Memory (DWM), and millipede memory
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 12-16 these dependent claim have also been reviewed with the same analysis as independent claim 11. The steps of Dependent claim 12 corresponds to the operations of dependent claim 2. Therefore, dependent claim 12 has been analyzed and rejected as previously discussed with respect to claim 2. The steps of Dependent claim 13 corresponds to the operations of dependent claim 3. Therefore, dependent claim 13 has been analyzed and rejected as previously discussed with respect to claim 3. The steps of Dependent claim 14 corresponds to the operations of dependent claim 4. Therefore, dependent claim 14 has been analyzed and rejected as previously discussed with respect to claim 4. Dependent claim 15 is directed toward generating a user profile data based on data sets to determine if value of user profile data deviates from a value of previous user profile data by margin exceeding pre-set threshold value- analyzing and organizing user transaction data to determine value deviations according to threshold margins applied for a business practice. The steps of Dependent claim 16 corresponds to the operations of dependent claim 7. Therefore, dependent claim 16 has been analyzed and rejected as previously discussed with respect to claim 7.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 2-10 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claims 17-20:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a non-transitory computer-readable medium, as in independent Claim 17 and the dependent claims. Such mediums fall under the statutory category of "manufacture." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The instructions of medium claim 17 corresponds to operations of system claim 1. Therefore, claim 17 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2: The instructions of medium claim 17 corresponds to operations of system claim 1. The additional elements recited in the claim beyond the abstract idea include a “non-transitory computer-readable medium encoded with computer-executable instructions …executed by a lending server node” performs the instruction corresponding to the system processor of claim 1 and the “artificial intelligence model” and “predictive model” perform the instructions corresponding to the operations performed by the “artificial intelligence model” and “predictive model” of claim 1. Therefore, claim 17 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include a “non-transitory computer-readable medium encoded with computer-executable instructions …executed by a processor of a device”, “artificial intelligence model” and “predictive model” –is purely functional and generic. Nearly every computer device for implementing a method is capable of performing the basic computer functions -of “acquiring…data”, “analyzing…data”, “searching….database”, “search causing …retrieval…data”, “generating…feature vector”, “executing…model…providing vector feature as input to model”, “generate predictive model”, the “predictive model” output…data structure…lending verdict” - As a result, none of the hardware executable instructions or models recited by the method claims offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via computers. Even though the claim is directed to a manufacture, the claim is not "truly drawn to a specific" computer readable medium, but rather is directed toward the method of performing the identified abstract idea. Furthermore, the "incidental use" of a computer does not allow the claim to meet the Alice 2A or 2B requirements, by providing “significantly more” than the identified abstract idea.
The instructions of medium claim 17 steps correspond to system functions claim 1. Therefore, claim 17 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
[0067] The LS node 102 may also include a non-transitory computer readable medium 212
that may have stored thereon machine-readable instructions executable by the processor 204.
Examples of the machine-readable instructions are shown as 214-222 and are further discussed
below. 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.
[0106]… The above embodiments of the present disclosure may be implemented in hardware, in computer-readable instructions executed by a processor, in firmware, or in a combination of the above. The computer computer readable instructions may be embodied on a computer-readable medium, such as a storage medium. For example, the computer computer-readable instructions may reside in random access memory ("RAM"), flash memory, read-only memory ("ROM"), erasable programmable read-only
memory ("EPROM"), electrically erasable programmable read-only memory ("EEPROM"),
registers, hard disk, a removable disk, a compact disk read-only memory ("CD-ROM"), or any
other form of storage medium known in the art.
With respect to the server node as applied for performing the financial analysis and transaction, the specification discloses:
[0046] Referring to FIG. lA, the example network 100 includes the lending server (LS) node
102 connected to a cloud server node(s) 105 over a network. The LS node 102 is configured to
host an AI/ML module 107. The LS node 102 may receive user data from a user 111. The LS
node 102 may receive a call data related to communication between the user 111 and responding
entity that may be implemented as chat bot (not shown).
[0050] The LS node 102 may query a local users' database for the historical local users' data
103 associated with the current user 111 data. The LS node 102 may acquire relevant remote
users' data 106 from a remote database residing on a cloud server 105. The remote users' data
106 may be collected from other lending facilities. The remote users' data 106 may be collected
from the users of the same (or similar) condition, age, language, etc. as the local users' who are
associated with the current user-related data of the user 111 based on submitted documents 112.
[0051] The LS node 102 may generate a feature vector or classifier data based on the userrelated
data, user 111 call data and the collected users' data (i.e., pre-stored local data 103 and
remote data 106). The LS node 102 may ingest the feature vector data into an AI/ML module 107.
The AI/ML module 107 may generate a predictive model(s) 108 based on the feature vector data
to predict lending parameters for automatically generating a lending verdict and/or lending
recommendations to be provided to the lender entities 113 (e.g., loan officers, underwriters, other
practitioners, etc.). The lending parameters and/or loan risk assessment parameters may be further analyzed by the LS node 102 prior to generation of the loan verdict. In some embodiments, the lending parameters may be used for adjustment of the loan terms. Once the loan verdict is
determined, an alert/notification may be sent to the lending entity 113 for a final approval.
[0053] Referring to FIG. lB, the example network 100' includes the lending server (LS) node
102 connected to a cloud server node(s) 105 over a network. The LS node 102 is configured to
host an AI/ML module 107. The LS node 102 may receive user data from a user 111. The LS
node 102 may receive a call data related to communication between the user 111 and responding
entity that may be implemented as a chat bot (not shown).
[0057] The LS node 102 may query a local users' database for the historical local users' data
103 associated with the current user 111 data. The LS node 102 may acquire relevant remote
users' data 106 from a remote database residing on a cloud server 105. The remote users' data
106 may be collected from other lending facilities. The remote users' data 106 may be collected
from the users of the same (or similar) condition, age, language, etc. as the local users' who are
associated with the current user-related data of the user 111 based on submitted documents 112.
[0058] The LS node 102 may generate a feature vector or classifier data based on the user related
data, user 111 call data and the collected users' data (i.e., pre-stored local data 103 and remote data 106). The LS node 102 may ingest the feature vector data into an AI/ML module 107.
The AI/ML module 107 may generate a predictive model(s) 108 based on the feature vector data
to predict lending parameters for automatically generating a lending verdict and/or lending
recommendations to be provided to the lender entities 113 (e.g., loan officers, underwriters, other
practitioners, etc.). The lending parameters and/or loan risk assessment parameters may be further analyzed by the LS node 102 prior to generation of the loan verdict. In some embodiments, the lending parameters may be used for adjustment of the loan terms. Once the loan verdict is
determined, an alert/notification may be sent to the lender entity nodes 113 for a final approval.
[0059] In some embodiments, the LS node 102 may receive the predicted lending parameters
from a permissioned blockchain 110 ledger 109 based on a consensus from the lender entity nodes 113 confirming, for example, loan approval/disapproval verdict, payment plan, schedule and other loan conditions. Additionally, confidential historical user-related information and previous users' - related lending parameters may also be acquired from the permissioned blockchain 110. The newly acquired user-related data with corresponding predicted loan verdict and lending
recommendation parameters data may be also recorded on the ledger 109 of the blockchain 110 so it can be used as training data for the predictive model(s) 108. In this implementation the LS node 102, the cloud server 105, the lender entity nodes 113 and user entities(s) 101 may serve as
blockchain 110 peer nodes. In some embodiments, local users' data 103 and remote users' data
106 may be duplicated on the blockchain ledger 109 for higher security of storage.
[0063] The LS node 102 is configured to host an AI/ML module 107. As discussed above
with respect to FIGs. lA-B, the LS node 102 may receive the user data provided by the user
entities(s) 101 (FIG. lA) and pre-stored users' data retrieved from local and remote databases. As
discussed above, the pre-stored users' data may be retrieved from the ledger 109 of the blockchain 110.
[0064] The AI/ML module 107 may host, compile, generate and train a predictive model(s)
108 based on the received user-related data 202 and the users' -related data provided by the LS
node 102. As discussed above, the AI/ML module 107 may provide predictive outputs data in the
form of lending parameters for automatic generation of landing verdict and/or landing
recommendations for the lender entities 113 (see FIG. lB). The LS node 102 may process the
predictive outputs data received from the AI/ML module 107 to generate the lending verdict and/or
lending risk assessment recommendation pertaining to a particular user engagement.
With respect to the read/write operations, the specification discloses:
[0087] As discussed herein, such verdict can be compiled as a set of executable instructions, that an upon approval indication in the verdict data structure, can be sent to the lender such that an electronic account housing the requested digital assets can be securely accessed via the read/write access provided via execution of the executable instructions. Thus, the requested funds, for example, can be automatically and securely (e.g., according to a known or to be known encryption, for example) accessed and sent to the electronic account of the user.
[0107] An exemplary storage medium may be coupled to the processor such that the processor
may read information from, and write information to, the storage medium. In the alternative, the
storage medium may be integral to the processor. The processor and the storage medium may
reside in an application specific integrated circuit ("ASIC"). In the alternative embodiment, the
processor and the storage medium may reside as discrete components. For example, FIG. 5
illustrates an example computing device (e.g., a server node) 500, which may represent or be
integrated in any of the above-described components, etc.
[0151] - Non-volatile memory which can retain stored information even after power is
removed, for example, but not limited to, Read-Only Memory (ROM) 553, Programmable ROM
(PROM) 555, Erasable PROM (EPROM) 555, Electrically Erasable PROM (EEPROM) 556 (e.g.,
flash memory and Electrically Alterable PROM [EAPROM]), Mask ROM (MROM), One Time
Programmable (OTP) ROM I Write Once Read Many (WORM), Ferroelectric RAM (FeRAM),
Parallel Random-Access Machine (PRAM), Split-Transfer Torque RAM (STT-RAM), Silicon Oxime Nitride Oxide Silicon (SONOS), Resistive RAM (RRAM), Nano RAM (NRAM), 3D
XPoint, Domain-Wall Memory (DWM), and millipede memory
The instructions of Dependent claim 18 corresponds to the operations of dependent claim 8. Therefore, dependent claim 18 has been analyzed and rejected as previously discussed with respect to claim 8. The instructions of Dependent claim 19 corresponds to the operations of dependent claim 9. Therefore, dependent claim 19 has been analyzed and rejected as previously discussed with respect to claim 9. The instructions of Dependent claim 20 corresponds to the operations of dependent claim 10. Therefore, dependent claim 20 has been analyzed and rejected as previously discussed with respect to claim 10.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claim(s) 1, 11 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2020/0302335 A1 by Golding (Golding) and further in view of WO 2022/133210 A2 by Cella et al (Cella)
In reference to Claim 1:
Golding teaches:
(Currently Amended) A system ((Golding) in at least para 0006) comprising:
a lending server (LS) node ((Golding) in at least Fig. 2, FIG. 14B; para 0167 wherein the prior art teaches the process may be implemented applying a server, para 0172-0173 wherein the prior art teaches system be part of a network which includes multiple nodes each node corresponding to the computing system and may be implemented in distributed system connected to other nodes) and at least one lender entity node, the LS node and the at least one lender entity node being peer nodes ((Golding) in at least para 0173-0176 wherein the prior art teaches the node correspond to a blade in a server that is connected to other nodes in data center and network nodes provide services to receive request and transmit responses to the client device capable of performing all or portion of invention, para 0180-0182) ; and
a processor of the LS node ((Golding) in at least FIG. 14A-B; para 0006, para 0009, para 0167, para 0172-0176) configured to:
acquire, over a network, user data from an entity ((Golding) in at least para 0153, para 0176);
analyze the user data by performing a computational analysis on the user data, and determine, based on the computational analysis, a plurality of features ((Golding) in at least para 0078, para 0080, para 0096-0097, para 0121-0122, para 0128, para 0181);
search, over the network, a local users database based on a query comprising the plurality of features, the search causing electronic retrieval of local historical users-related data that corresponds to the plurality of features ((Golding) in at least para 0027 wherein the prior art teaches information about users include loan history of the user, para 0141-0143 wherein the prior art teaches the database includes list of records and transaction data, para 0182, para 0184-0185);
generate at least one feature vector based on the plurality of features and the local historical users-related data ((Golding) in at least para 0005-0008, para 0026-0027, para 0033, para 0044, para 0046, para 0049, para 0122-0125, para 0141-0143 wherein the prior art teaches the database includes list of records and transaction data);
execute an artificial intelligence / machine learning (AI/ML) model, the execution comprising providing the at least one feature vector as input to the AI/ML model, such that a predictive model is generated, the predictive model producing at least one lending parameter ((Golding) in at least Abstract; para 0005-0006, para 0008, para 0026-0027, para 0033, para 0044-0045, para 0046, para 0049, para 0052, para 0056, para 0063, para 0072, para 0094, para 0122-0124, para 0128-0130, para 0146, para 0149, para 0165, para 0172-0173); and
output, based on execution of the AI/ML model via the predictive model, a data structure comprising information related to a user-related lending verdict, the data structure being executable so as to effectuate a secure transfer of digital assets to an electronic account of the user ((Golding) in at least para 0028-0029, para 0031, para 0036, para 0053-0054, para 0056, para 0060, para 0072, para 0090, para 0146) and
Golding does not explicitly teach:
render the data structure executable responsive to a verified approval indication associated with the at least one entity node, such that the secure transfer of digital assets to the electronic account is effectuated via read/write access provided through execution of the data structure..
Cella teaches:
render the data structure executable responsive to a verified approval indication associated with the at least one entity node, such that the secure transfer of digital assets to the electronic account is effectuated via read/write access provided through execution of the data structure. ((Cella) in at least para 0001823 ).
Both Golding and Cella are directed toward users seeking credit and or assets from others implemented in blockchain node technology. Cella teaches the motivation that one of ordinary skill in the art in a loan process that loan related actions upon completion need to be recorded without limitation (see para 0283) . It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify borrowing and loan analysis process of Golding to include recording the recording of transfer of an asset as taught by Cella since Cella teaches the motivation that one of ordinary skill in the art in a loan process that loan related actions upon completion need to be recorded without limitation (see para 0283)
In reference to Claim 11:
The combination of Golding and Cella discloses the limitations of independent claim 11.
The steps of method claim 11 correspond to the operations of system claim 1. The additional limitations recited in claim 1 that go beyond the limitations of claim 1 include a “device” ((Golding) in at least para 0025, para 0030) to perform the operation that correspond to claim 1. Therefore, claim 11 has been analyzed and rejected as previously discussed with respect to claim 1.
In reference to Claim 17:
The combination of Golding and Cella discloses the limitations of independent claim 17.
The instructions of medium claim 17 executed by a processor correspond to the operations of system claim 1. The additional limitations recited in claim 17 that go beyond the limitations of claim 1 include a “non-transitory computer readable medium comprising instructions executable by a processor, ((Golding) in at least abstract; para 0008, para 0171) to perform the operation that correspond to claim 1. Therefore, claim 17 has been analyzed and rejected as previously discussed with respect to claim 1.
Claim(s) 2-8 and 10 of claim 1 above; Claim(s) 12-16 of claim 11 above; Claim(s) 18 and 20 of claim 17 above is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2020/0302335 A1 by Golding (Golding) in view of WO 2022/133210 A2 by Cella et al (Cella) as applied to claim 1 above, and further in view of US Patent No. 11,860,852 B1 by Harvey (Harvey) and US Pub No. 2020/0311808 A1 by Srivastava et al. (Srivastava).
In reference to Claim 2:
The combination of Golding and Cella discloses the limitations of independent claim 1. Golding further discloses the limitations of dependent claim 2
(Currently Amended) The system of claim 1 (see rejection of claim 1 above), wherein the processor is further configured to:
Golding does not explicitly teach:
receive user call data from a chat bot associated with the at least one lender entity node, the call data comprising data generated during user’s communication with the chat bot;
derive a language metadata from the call data; and
parse the call data based on the language metadata to derive a plurality of key features.
Harvey teaches:
receive user call data from a chat bot associated with the at least on lender entity …, the call data comprising data generated during user’s communication with the chat bot ((Harvey) in at least Abstract; Col 2 lines 5-11, Col 2 lines 24-33, Col 4 lines 20-24, lines 47-67, Col 6 lines 29-39, Col 8 lines 5-13);
derive a language metadata from the call data ((Harvey) in at least Col 4 lines 47-67, Col 6 lines 39-51, Col 8 lines 5-18, 31-50); and
parse the call data based on the language metadata to derive a plurality of key features. ((Harvey) in at least abstract; Col 2 lines 11-15, lines 31-37, Col 6 lines 53- Col 7 lines 1-15, lines 27-37, Col 12 lines 10-18)
Both Golding and Harvey are directed toward collecting data for loan 38. applications. Harvey teaches the motivation of applying chatbot for collecting data as well as parsing call data to derive key features in order to verify the veracity of statements. It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify Ref. Golding to include chatbot for collecting data of in Ref. Harvey since Harvey teaches the motivation of applying chatbot for collecting data as well as parsing call data to derive key features in order to verify the veracity of statements.
Srivastava teaches:
associated with the at least one lender entity node receive borrower data ((Srivastava) in at least para 0027-0028, para 0038-0039, para 0042-0043, para 0045, para 0084)
Both Golding and Srivastava are directed toward collecting data for loan applications that applies blockchain node technology to represent features of a loan process. Srivastava teaches the motivation of blockchain nodes being established for entities such as lenders in order to model lender approvals. It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify Ref. Golding to include lender node feature in Ref. Srivastava since Srivastava teaches the motivation of blockchain nodes being established for entities such as lenders in order to model lender approvals.
In reference to Claim 3:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of dependent claim 2. Golding further discloses the limitations of dependent claim 3
(Original) The system of claim 2 (see rejection of claim 2 above), wherein the processor is further configured to
Golding does not explicitly teach:
retrieve remote historical users’-related data from at least one remote users’ database based on the local historical users’-related data, wherein the remote historical users’-related data is collected at locations associated with a plurality of lender entities affiliated with financial institutions.
Harvey teaches:
retrieve remote historical users’-related data from at least one remote users’ database based on the …historical users’-related data, wherein the remote historical users’-related data is collected at locations associated with a plurality of lender entities affiliated with financial institutions. ((Harvey) in at least Col 2 lines 5-10, lines 30-35, Col 4 lines 10-17, Col 5 lines 60-67, Col 6 lines 11-13 wherein the prior art teaches records may be retrieved from a plurality of different sources such as a database or another computing device associated with a third party; Col 10 lines 66-Col 11 lines 1-10)
Both Golding and Harvey are directed toward analyzing financial data with respect to loan applications. Harvey teaches the motivation of analyzing historical user financial statements in order to identify indicators of inaccurate/false or true values or aspects in the historical statements. It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify Ref. Golding to analyze historical data from a plurality of different sources as taught by Harvey since Harvey teaches the motivation of analyzing historical user financial statements in order to identify indicators of inaccurate/false or true values or aspects in the historical statements
Cella teaches:
retrieve remote historical users’-related data from at least one remote users’ database based on the local historical users’-related data, wherein the remote historical users’-related data is collected at locations associated with a plurality of lender entities affiliated with financial institutions. ((Cella) in at least para 000223-000224, para 000284-000285, para 000287-000291, para 000293, para 000314, para 000316, para 000321, para 000323)
Both Golding and Cella are directed toward collecting data related to loans and transactions. Cella teaches the motivation of collecting historical and locational data according to regulation requirements and in order to meet financial conditions for the loan. It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify Ref. Golding to include historical financial data of Cella since It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify Ref. Golding to include lender historical data of Cella
In reference to Claim 4:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of dependent claim 3. Golding further discloses the limitations of dependent claim 4.
(Original) The system of claim 3 (see rejection of claim 3 above),
wherein the processor is further configured to generate the at least one feature vector based on the plurality of features and the local historical users’-related data combined with the remote historical users’-related data and the plurality of key features. ((Golding) in at least para 0005-0008, para 0026-0027, para 0033, para 0044, para 0046, para 0049, para 0070, para 0122-0125, para 0027 wherein the prior art teaches information about users include loan history of the user, para 0141-0143 wherein the prior art teaches the database includes list of records and transaction data, para 0184, para 0186).
Srivastave teaches:
…the plurality of features and the local historical borrowers'-related data combined with the remote historical borrowers'-related data and the plurality of key features ((Srivastave) in at least para 0044-0045, para 0058, para 0072, para 0075, para 0077-0078),
Both Golding and Srivastave are directed toward collecting loan application data.
Srivastave teaches the motivation of different historical borrower related data in order to determine risk for lenders in a loan application. It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify Ref. Golding to include the borrowers historical related data of Srivastave since Srivastave teaches the motivation of different historical borrower related data in order to determine risk for lenders in a loan application.
In reference to Claim 5:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of dependent claim 4. Golding further discloses the limitations of dependent claim 5.
(Original) The system of claim 4 (see rejection of claim 4 above), wherein the processor is further configured to
generate a user profile data based on the user data and the plurality of key features. ((Golding) in at least para 0070)
In reference to Claim 6:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of dependent claim 5. Golding further discloses the limitations of dependent claim 6.
(Original) The system of claim 5 (see rejection of claim 5 above), wherein the processor is further configured to
periodically monitor the user profile data to determine if at least one value of the user profile data deviates from a value of previous user profile data by a margin exceeding a pre-set threshold value.
Cella teaches:
periodically monitor the user profile data to determine if at least one value of the user profile data deviates from a value of previous user profile data by a margin exceeding a pre-set threshold value.((Cella) in at least para 000227, para 000232-000233, para 000239, para 000262, para 000314, para 000320, para 000915, para 0001287)
Both Golding and Cella are directed toward collecting data for determining lending solutions. Cella teaches the motivation of monitoring borrower profile related of data in order to provide underwriting data for use in determining a loan decision. It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify Ref. Golding credit analysis to include the profile monitoring of Cella since Cella teaches the motivation of monitoring borrower profile related of data in order to provide underwriting data for use in determining a loan decision
In reference to Claim 7:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of dependent claim 6. Golding further discloses the limitations of dependent claim 7
(Original) The system of claim 6 (see rejection of claim 6 above),
wherein the processor is further configured to, responsive to the at least one value of the user profile data deviating from the value of the previous user profile data by the margin exceeding the pre-set threshold value, generate an updated feature vector based on current user profile data and generate the lending verdict based on the at least one lending parameter produced by the predictive model in response to the updated feature vector.
Cella teaches:
wherein the processor is further configured to, responsive to the at least one value of the user profile data deviating from the value of the previous user profile data by the margin exceeding the pre-set threshold value .((Cella) in at least para 000227, para 000232-000233, para 000239, para 000262, para 000314, para 000320), generate an updated feature vector based on current user profile data and generate the lending verdict based on the at least one lending parameter produced by the predictive model in response to the updated feature vector. ((Cella) in at least para 0001308-0001309, para 0001853, para 0001880- 0001881, para 0001883-0001884, para 0001950-0001951).
Both Golding and Cella are directed toward collecting data for determining risk for loan applications where a lending verdict and parameter is determined and outputted. Cella teaches the motivation of monitoring borrower characteristic data for use in underwriting decision where the inputted data is vectorized and inputted into learning algorithm for lending analysis. It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify Ref. Golding loan analysis to include the monitoring user characteristic data of Cella since Cella teaches the motivation of monitoring borrower characteristic data for use in underwriting decision where the inputted data is vectorized and inputted into learning algorithm for lending analysis
In reference to Claim 8:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of dependent claim 7. Golding further discloses the limitations of dependent claim 8
(Original) The system of claim 7 (see rejection of claim 7 above),
wherein the processor is further configured to record the at least one lending parameter on a blockchain ledger along with the user profile data. ((Golding) in at least abstract; para 0005, para 0007, para 0140-0143)
In reference to Claim 10:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of dependent claim 8. Golding further discloses the limitations of dependent claim 10
(Original) The system of claim 8 (see rejection of claim 8 above), wherein the processor is further configured to
Golding does not explicitly teach:
execute a smart contract to record data reflecting a loan approved for the user associated with the lending verdict and the at least one lender entity node on the blockchain for future audits.
Scrivastava teaches:
execute a smart contract to record data reflecting a loan approved for the user associated with the lending verdict and the at least one lender entity node on the blockchain for future audits. ((Srivastava) in at least para 0034, para 0047, para 0054, para 0057, para 0074).
Both Golding and Srivastava are directed toward loan processing activities. 48. Srivastava teaches the motivation of smart contract between buyer and seller for verifying the information of the customer and to help prevent the same customer from obtaining a product from the buyer across two or more sellers. It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify Ref. Golding loan process to include a smart contract as taught by Srivastava since Srivastava teaches the motivation of smart contract between buyer and seller for verifying the information of the customer and to help prevent the same customer from obtaining a product from the buyer across two or more sellers.
In reference to Claim 12:
The combination of Golding and Cella discloses the limitations of independent claim 11. Golding further discloses the limitations of dependent claim 12
The steps of method claim 12 corresponds to operations of system claim 2. Therefore, claim 12 has been analyzed and rejected as previously discussed with respect to claim 2
In reference to Claim 13:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of dependent claim 12. Golding further discloses the limitations of dependent claim 13
The steps of method claim 13 corresponds to operations of system claim 3. Therefore, claim 13 has been analyzed and rejected as previously discussed with respect to claim 3
In reference to Claim 14:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of dependent claim 13. Golding further discloses the limitations of dependent claim 14
The steps of method claim 14 corresponds to operations of system claim 4. Therefore, claim 14 has been analyzed and rejected as previously discussed with respect to claim 4
In reference to Claim 15:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of dependent claim 14. Golding further discloses the limitations of dependent claim 15
The steps of method claim 15 corresponds to operations of system claim 6. Therefore, claim 15 has been analyzed and rejected as previously discussed with respect to claim 6
In reference to Claim 16:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of dependent claim 15. Golding further discloses the limitations of dependent claim 16
The steps of method claim 16 corresponds to operations of system claim 7. Therefore, claim 16 has been analyzed and rejected as previously discussed with respect to claim 7
In reference to Claim 18:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of independent claim 17. Golding further discloses the limitations of dependent claim 18
The instructions of medium claim 18 corresponds to operations of system claim 8. Therefore, claim 18 has been analyzed and rejected as previously discussed with respect to claim 8
In reference to Claim 20:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of dependent claim 18. Golding further discloses the limitations of dependent claim 20
The instructions of medium claim 10 corresponds to operations of system claim 10. Therefore, claim 20 has been analyzed and rejected as previously discussed with respect to claim 10
Claim(s) 9 of claim 1 above, Claim(s) 19 of claim 18 above is/are rejected under 35 U.S.C. 103 as being unpatentable over 52. US Pub No. 2020/0302335 A1 by Golding (Golding) in view of US Patent No. 11,860,852 B1 by Harvey (Harvey) and US Pub No. 2020/0311808 A1 by Srivastava et al. (Srivastava) in view of in view of WO 2022/133210 A2 by Cella et al (Cella), and further in view of US Pub No. 2021/0226774 A1 by Padmanabhan (Padmanabhan)
In reference to claim 9:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of dependent claim 8. Golding further discloses the limitations of dependent claim 9
(Currently Amended) The system of claim 8 (see rejection of claim 8 above),
Golding does not explicitly teach:
retrieve the at least one lending parameter from the blockchain responsive to a consensus among the LS node and the at least one lender entity node.
Padmanabhan teaches:
retrieve the at least one lending parameter from the blockchain responsive to a consensus among the LS node and the at least one lender entity node. ((Padmanabhan) in at least para 0087, para 0089, para 0093, para 0127, para 0197, para 0243-0244, para 0417, para 0445, para 0475)
Both Golding and Padmanabhan are directed toward applying blockchain 53. technology to record data. Padmanabhan teaches the motivation of applying consensus mechanism in order to verify transactions and maintain security. It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify the blockchain function details of Ref. Golding to include consensus with respect to blockchain of Padmanabhan since Padmanabhan teaches the motivation of applying consensus mechanism in order to verify transactions and maintain security.
In reference to Claim 18:
The combination of Golding, Cella, Harvey and Scrivastava discloses the limitations of dependent claim 18. Golding further discloses the limitations of dependent claim 19
The instructions of medium claim 19 corresponds to operations of system claim 9. Therefore, claim 19 has been analyzed and rejected as previously discussed with respect to claim 9
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US Patent No. 8,504,456 B2 by Griffin et al.; US Pub. No. 2022/0122062 A1 by Mayblum et al.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARY M GREGG whose telephone number is (571)270-5050. The examiner can normally be reached M-F 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine Behncke can be reached at 571-272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARY M GREGG/Examiner, Art Unit 3695
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695
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