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 .
This action is responsive to the claims filed 3/1/2024.
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/18/2024 has been considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
Reference character 304, first occurring at paragraph [0044] and there designating an input layer (see also paragraph [0045]). FIG. 3 designates the input with reference character 301 instead.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application; alternatively, applicant may amend the description in compliance with 37 CFR 1.121(b) so that the reference character used in the description for the input layer corresponds to the reference character shown in FIG. 3. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
301 (FIG. 3, designating the input, which paragraphs [0044] and [0045] designate as input layer 304);
303 (FIG. 3, designating the hidden layers collectively, which paragraph [0045] designates as hidden layers 310); and
408 (FIG. 4, designating the step of generating an output transaction framework, which paragraph [0055] describes without a reference character).
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
(a) [0022] recites "For examples, in some instances," which should read "For example, in some instances";
(b) [0025] recites "etc.).The provider computing system," which lacks a space before "The";
(c) [0028] recites "can utilize to obtained requested data," which should read "can utilize to obtain requested data";
(d) [0028] recites that "The one or more APIs process the requests by interacting with their respective databases or servers, and responds with structured data," in which "responds" should read "respond";
(e) [0036] recites "employs reinforcement learning, method of training a machine learning model where an agents interacts with data," which should read "employs reinforcement learning, which is a method of training a machine learning model where an agent interacts with data";
(f) [0045] recites "Each hidden layer has a respective number of nodes (312, 314 and 316)," whereas the same paragraph and FIG. 3 place nodes 316 in the output layer 308 rather than in a hidden layer;
(g) [0046] recites "As, such the softmax classifier," which should read "As such, the softmax classifier";
(h) [0050] recites "a period of time over which the proposing offers to pay back the loan," which should read "a period of time over which the proposing party offers to pay back the loan";
(i) [0052] recites "the historical transactions that led to a transaction may be labeled with an outcome of," which appears to be intended as "the historical transactions that led to a transaction closing may be labeled with an outcome of";
(j) [0053] recites "the machine leaning model," which should read "the machine learning model";
(k) paragraph [0059] recites "an earliest transaction stored in the internal data source 132," whereas the internal data source is designated by reference character 114 (paragraphs [0020] and [0024]) and reference character 132 designates the first external data source (paragraphs [0025] and [0026]);
(l) [0065] recites "the recommend options may be bolded," which should read "the recommended options may be bolded";
(m) [0065] recites "the recommended results 520, products 525, and services 530 has changed," in which "has changed" should read "have changed"; and
(n) [0067] recites "may rearranged the options," which should read "may rearrange the options"; and
(o) [0083] recites "explain the principals of the disclosure," which should read "explain the principles of the disclosure."
Appropriate correction is required.
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP 608.01(o). Correction of the following is required: claims 9 and 18 recite that the output transaction framework comprises "one or more output transaction framework terms," and claims 10 and 19 recite that the user interface comprises one or more selectable elements that allow the user "to change at least one of the one or more output transaction framework terms associated with the output transaction framework." The description does not use the term "output transaction framework terms" and does not describe a selectable element of a user interface through which the user changes a term of an output transaction framework. The description refers to initial transaction framework terms and final transaction framework terms of historical transactions (paragraphs [0049] through [0052]), and describes selectable elements for choosing data sources, search criteria, results, products, and services (paragraphs [0058] through [0065]), but it does not provide antecedent basis for the quoted claim terms.
Claim Objections
Claim 1 is objected to because of the following informalities:
In the final limitation, "the machine leaning model" should read "the machine learning model," consistent with the earlier recitation of "a machine learning model" in claim 1.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. - An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word "means" (or "step") in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word "means" (or "step") in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word "means" (or "step") are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word "means" (or "step") are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word "means," but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
(1) a transaction framework training system comprising hardware configured to: receive historical transaction framework data associated with a plurality of transaction frameworks corresponding to historical transactions performed by a provider, the historical transaction framework data comprising final transaction framework terms and outcome data for each historical transaction; generate a machine learning model configured to generate new transaction frameworks, the machine learning model generated using the historical transaction framework data, recited in claim 1. The term "system" is a generic placeholder, and the modifier "transaction framework training" identifies its task rather than implementing structure. The recitation that the system comprises "hardware" does not supply that structure, because the specification describes hardware only generically, as what a "circuit" may include, with examples ranging from transistors and logic gates to processors ([0077]-[0078]), rather than as any particular structure that performs the recited functions. The claimed function is to receive historical transaction framework data associated with a plurality of transaction frameworks corresponding to historical transactions performed by a provider, the historical transaction framework data comprising final transaction framework terms and outcome data for each historical transaction; generate a machine learning model configured to generate new transaction frameworks, the machine learning model generated using the historical transaction framework data. The corresponding structure is the transaction framework training system 121 of the AI system 200 (FIG. 1), implemented by one or more processors executing instructions stored in one or more memory devices ([0019], [0078]-[0079]), which receives the historical transaction framework data, including the final transaction framework terms and the outcome data, from the internal data source 114 or, by way of the provider computing system 102 and the application programming interfaces of the external data sources 130, from the external data sources 130 ([0024]-[0028], [0038]-[0039], [0049]-[0052], FIG. 4, step 402), and which generates the machine learning model 204 by applying the current state of the model to the training inputs 202 to produce predicted outputs 206, comparing the predicted outputs 206 with the actual outputs 210 at the comparator 208, and using the resulting error signal 212 to adjust the weights of the model, for example by backpropagation with a square error, root mean square error, or cross-entropy loss function, until the error is within a threshold or a threshold number of batches, epochs, or iterations is reached ([0040], [0042]-[0043], [0053], FIG. 2, FIG. 4, step 404). This limitation is interpreted to cover the foregoing structure and its disclosed procedure, and equivalents thereof.
(2) a transaction framework generation system comprising hardware configured to: receive input initial transaction framework data; and generate an output transaction framework by applying the input initial transaction framework data to the machine leaning model, recited in claim 1. The term "system" is a generic placeholder, and the modifier "transaction framework generation" identifies its task rather than implementing structure. The recitation that the system comprises "hardware" does not supply that structure, because the specification describes hardware only generically, as what a "circuit" may include, with examples ranging from transistors and logic gates to processors ([0077]-[0078]), rather than as any particular structure that performs the recited functions. The claimed function is to receive input initial transaction framework data; and generate an output transaction framework by applying the input initial transaction framework data to the machine leaning model. The corresponding structure is the transaction framework generation system 123 of the AI system 200 (FIG. 1), implemented by one or more processors executing instructions stored in one or more memory devices ([0019], [0078]-[0079]), which receives the input initial transaction framework data that a user enters through an interface of the client application 122 on the user device 104 ([0023], [0054], FIG. 4, step 406), and which generates the output transaction framework by applying that data to the trained machine learning model 204, the model pairing the input data with the initial or final transaction framework terms of the historical transaction framework data that share a prospective collateral, prospective timeline, anticipated purpose, or relevant industry, in some embodiments narrowing the paired historical data to transactions having a success outcome, and generating the output transaction framework from the paired historical data ([0043], [0055], FIG. 4, step 408). This limitation is interpreted to cover the foregoing structure and its disclosed procedure, and equivalents thereof.
(3) the transaction framework generation system is further configured to transmit the output transaction framework to the user via a user interface of the user device, recited in claim 9. The term "system" is a generic placeholder, and the modifier "transaction framework generation" identifies its task rather than implementing structure. The "hardware" that claim 1 recites for this system does not supply structure, for the reason stated in item (2). The claimed function is to transmit the output transaction framework to the user via a user interface of the user device. The corresponding structure is the transaction framework generation system 123, implemented by one or more processors executing instructions stored in one or more memory devices ([0019], [0078]-[0079]), which transmits the output transaction framework through the network interface circuit 110 and the network 106 to the user device 104 from which the input initial transaction framework data was received, for presentation in a user interface, such as the user interface 500 or the user interface 600, that the provider computing system 102 provides through the client application 122 ([0021], [0056]-[0057], [0068], FIGS. 5-6). This limitation is interpreted to cover the foregoing structure and its disclosed procedure, and equivalents thereof.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 U.S.C. 101
The following is a quotation of 35 U.S.C. 101:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1: This claim recites “A system comprising:”; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: This claim recites, inter alia:
receive historical transaction framework data associated with a plurality of transaction frameworks corresponding to historical transactions performed by a provider, the historical transaction framework data comprising final transaction framework terms and outcome data for each historical transaction: These limitations recite the mentally performable process, with the aid of pen and paper, of collecting and observing the terms on which a provider's past transactions were finally agreed, together with the outcome of each of those transactions. They also recite part of a commercial interaction: the final transaction framework terms are the terms agreed upon by the parties to each transaction, such as the negotiated collateral and repayment timeline of a loan ([0051]), and the outcome data records whether the transaction closed or was abandoned ([0052]).
receive input initial transaction framework data: These limitations recite the mentally performable process of observing the terms initially proposed for a prospective transaction, such as the collateral, timeline, purpose and industry that a party proposes ([0050], [0054]). Receiving a proposal for a transaction is likewise part of a commercial interaction between the parties to that transaction.
generate an output transaction framework by applying the input initial transaction framework data: These limitations recites generating a proposed framework for the prospective transaction, that is, the structure and terms on which that transaction would be arranged ([0014], [0068]-[0071]), from the terms initially proposed for it. The claim ties that output to the provider's past transactions through the order of its limitations: the output is generated with a model that was itself generated using the historical transaction framework data received in the first receiving limitation. A person structuring a transaction, such as a banker, can practically perform the same evaluation and judgment in the mind or with pen and paper, working directly from the records of past transactions observed in that first limitation, by comparing the proposed terms with the terms and outcomes of comparable past transactions and settling on terms for the new one. For claims 1-10, the Claim Interpretation section interprets this generating function under 35 U.S.C. 112(f) to cover the procedure described in [0055], in which the input terms are paired with the initial or final terms of past transactions that share a prospective collateral, prospective timeline, anticipated purpose or relevant industry, the paired transactions are, in some embodiments, narrowed to those having a successful outcome, and the output is generated from the paired transactions. That procedure is the same evaluation stated more particularly, namely matching a new proposal against comparable past deals, preferring the deals that closed, and drawing the new terms from them, and it is part of the abstract idea. Generating the output is also a commercial or legal interaction, namely structuring an agreement in the form of a contract between the parties to a transaction. The concept recited by these limitations, generating a framework for a new transaction from its initial terms based on the final terms and outcomes of past transactions, falls within the certain methods of organizing human activity grouping as a commercial or legal interaction, including agreements in the form of contracts and business relations (MPEP 2106.04(a)(2), subsection II.B), and within the mental processes grouping as observation, evaluation and judgment, the collection of information and its analysis being recited at a high level of generality such that they can practically be performed in the human mind (MPEP 2106.04(a)(2), subsection III, citing Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1353-54 (Fed. Cir. 2016)). It is comparable to combining information about a dealer's inventory with a customer's financial information to create a financing package, which was held to be the abstract idea of processing an application for financing in Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1054 (Fed. Cir. 2017), and to shopping for loan packages, which could be performed by humans without a computer in Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324 (Fed. Cir. 2016). The specification confirms that transaction frameworks are otherwise structured by people, who traditionally adhere to established models and to the frameworks already known to them ([0002], [0014]-[0015]). Because the limitations fall within two groupings, they are identified under both and are considered together as a single abstract idea for Prong Two and Step 2B (MPEP 2106.04, subsection II.B).
Thus, this claim recites a judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of this claim are as follows:
a transaction framework training system comprising hardware configured to;
a transaction framework generation system comprising hardware configured to: These additional elements each recite hardware configured framework to perform the recited functions, which, as interpreted under 35 U.S.C. 112(f), include the processors and memory devices that implement them. Thus, these additional elements recite generic computer components performing their ordinary function of executing stored instructions serving merely as tools to implement the underlying judicial exception. See MPEP 2106.05(f).
generate a machine learning model configured to generate new transaction frameworks, the machine learning model generated using the historical transaction framework data: These additional elements do not specify the type or structure of the machine learning model. As stated in the Claim Interpretation section, however, the training system that performs this function is interpreted under 35 U.S.C. 112(f) to generate the model by a particular procedure and its equivalents. The procedure is the ordinary supervised training loop by which a machine learning model learns from examples with known outputs. The specification describes it without any modification of that loop, naming backpropagation only as an example and the loss functions only as non-limiting examples ([0042]), and it states that the training sub-systems vary with the type of model used ([0040], [0043]). The procedure is applied to the provider's transaction records only so that the resulting model can carry out the evaluation identified under Prong One. The limitation, as interpreted, therefore uses a generic machine learning training technique as a tool to implement the abstract idea, which amounts to mere instructions to apply the exception, equivalent to adding the words 'apply it'. See MPEP 2106.05(f)). Additionally, these additional elements generally links the abstract idea to the technological environment of machine learning. See MPEP 2106.05(h). Iteratively training a model on selected data is incident to the very nature of machine learning and is not a technological improvement, Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025), and claims that do no more than apply generic machine learning to a new data environment, where no improvement to the machine learning model itself is disclosed, are not patent eligible, id. at 1212-14. The specification describes no improvement to how the model is trained or how it operates.
applying the input initial transaction framework data to the machine [[leaning]] learning (per the Claim Objections set forth above) model: These additional elements amounts to mere instructions to apply the exception using a generic computer and model, equivalent to adding the words 'apply it'. See MPEP 2106.05(f). Whatever particularity the pairing and narrowing procedure adds to the claim lies in the abstract idea itself, in the choice of which past transactions to compare and which to prefer. It improves the business result of that evaluation, not the way the model or the computer operates, and an improvement in the abstract idea itself is not an improvement in technology (MPEP 2106.05(a), subsection II). Using a generic computer to automate the structuring of a transaction that a person would otherwise perform is mere automation of a manual process, which is not an improvement in computer functionality (MPEP 2106.05(a), citing Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055 (Fed. Cir. 2017)).
The specification asserts that the system "represents a significant technical improvement" and that "the computer on which this AI system operates experiences marked improvement" through efficiency gains, reduced human error and the ability to adapt to changing market conditions ([0016]). Those statements are conclusory. The specification provides no technical detail from which a person of ordinary skill in the art would recognize an improvement in how the computer, its network or the machine learning model itself operates. The benefits the specification does describe are improvements in the business result: suggested frameworks drawn from more data sources and from frameworks unknown to the user, which are more creative and comprehensive and lead to more successful business transactions ([0015]). An improvement of that kind resides in the abstract idea itself, and claiming the improved speed or efficiency inherent in applying the abstract idea on a computer does not integrate the exception (MPEP 2106.05(f), citing Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367 (Fed. Cir. 2015)). Performing with existing machine learning technology a task previously undertaken by people, with greater speed and efficiency, does not make a claim eligible. Recentive, 134 F.4th at 1214. Nor does the claim include the components or steps of any technical improvement. Unlike the claims in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025) (precedential), which adjusted model parameters to optimize performance on a new machine learning task while protecting performance on a prior task, claim 1, even as interpreted, recites no change to how the model is trained or how it operates; the model is generated and used in the ordinary way for its ordinary purpose. The interpretation of claim 1 under 35 U.S.C. 112(f) has been considered in full, and it does not change this conclusion. In Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336-37 (Fed. Cir. 2016), the claimed means were construed under 35 U.S.C. 112(f) to require a particular algorithm that achieved an improvement in the computer database itself (MPEP 2106, subsection II), and in McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15 (Fed. Cir. 2016), the claimed rules improved an existing technological process. Here, the procedures that the interpretation brings into claim 1 are a textbook supervised training loop and a rule for selecting comparable past transactions and preferring the successful ones. The first is generic machine learning, and the second is part of the abstract idea and improves the deal-structuring result rather than any technology. Claim 1, as interpreted, therefore covers a particular way of evaluating past transactions, not a particular technological solution to a technological problem.
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include invoking computers or other machinery to apply the underlying judicial exception and adding the words equivalent to “apply it” with the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly mor than the abstract idea itself. See MPEP 2106.05.
Claim 2
Step 1: a machine, as in claim 1.
Step 2A Prong 1: The claim recites, inter alia:
wherein the historical transaction framework data further comprises initial transaction framework terms: These limitations further recite the abstract idea of claim 1 by adding, to the information observed about each past transaction, the terms initially proposed for it ([0050]). The added content narrows the information that is observed and evaluated in the abstract idea identified for claim 1; claim 2 recites no further act. The initially proposed terms of a past transaction are information a person can practically observe, and they are terms of the same commercial interaction identified for claim 1.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so this claim does not provide a practical application and is not considered to be significantly more. As such this claim is patent ineligible.
Claim 3
Step 1: a machine, as in claim 2.
Step 2A Prong 1: This claim recites, inter alia:
wherein the initial transaction framework terms further comprise at least one of a prospective collateral, a prospective timeline, an anticipated purpose, and a relevant industry: These limitations further recite the abstract idea by specifying the initially proposed terms, such as the collateral a borrower offers against a loan, the period over which the loan would be repaid, the anticipated use of the funds, and the industry to which the transaction relates ([0050]). These are terms of an agreement in the form of a contract, and observing them is a mental process.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so this claim does not provide a practical application and is not considered to be significantly more. As such this claim is patent ineligible.
Claim 4
Step 1: a machine, as in claim 1.
Step 2A Prong 1: This claim recites, inter alia:
wherein the historical transaction framework data further comprises one or more parties involved in the historical transactions:
These limitation further recite the abstract idea by adding to the observed information the parties to each past transaction, such as a proposing party and a prospective lender ([0049]). Noting who the parties to a transaction were is an observation, and the relationship between those parties is a business relation.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so this claim does not provide a practical application and is not considered to be significantly more. As such this claim is patent ineligible.
Claim 5
Step 1: a machine, as in claim 1.
Step 2A Prong 1: This claim recites the same abstract ideas as in claim 1 as the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of this claim are as follows:
wherein the historical transaction framework data is retrieved from at least one of an internal data source or an external data source: These limitations merely selects a particular data source or type of data to be manipulated and gathers the information that the abstract idea evaluates, which is insignificant extra-solution activity. See MPEP 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activities of selecting a particular data source or type of data to be manipulated and data gathering recited by “wherein the historical transaction framework data is retrieved from at least one of an internal data source or an external data source”. These insignificant extra-solution activities are well-understood, routine, conventional activity similar to storing and retrieving information in memory, Versata, 793 F.3d at 1334; OIP Techs., 788 F.3d at 1363, and receiving data over a network, buySAFE, 765 F.3d at 1355, see MPEP 2106.05(d)(II), and the specification describes the retrieval as ordinary requests to the application programming interfaces of the data sources, which respond with structured data in common formats ([0028]). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 6
Step 1: a machine, as in claim 1.
Step 2A Prong 1: This claim recites, inter alia:
wherein the outcome data for each historical transaction further comprises one of either a successful outcome or a failed outcome: These limitations further recite the abstract idea by specifying that the recorded outcome of each past transaction, which is among the information observed and evaluated in the abstract idea identified for claim 1, is either a successful outcome, such as a transaction that closed, or a failed outcome, such as a transaction that was abandoned ([0052]). The added content narrows the information that is observed and evaluated; claim 6 recites no act of classifying a transaction, and a recorded success or failure is information a person can practically observe and take into account.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so this claim does not provide a practical application and is not considered to be significantly more. As such this claim is patent ineligible.
Claim 7
Step 1: a machine, as in claim 1.
Step 2A Prong 1: This claim recites, inter alia:
wherein the final transaction framework terms further comprise at least one of a negotiated collateral, a negotiated timeline, a realized purpose, and a relevant industry: These limitations further recite the abstract idea by specifying the finally agreed terms, such as the collateral and repayment period agreed for a loan and the realized use of its proceeds ([0051]). These are terms of an agreement in the form of a contract, and observing them is a mental process.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so this claim does not provide a practical application and is not considered to be significantly more. As such this claim is patent ineligible.
Claim 8
Step 1: a machine, as in claim 1.
Step 2A Prong 1: This claim recites, inter alia:
wherein the input initial transaction framework data is received from a user: These limitations identifies the person proposing the transaction, which is part of the commercial interaction identified for claim 1.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of this claim are as follows:
via a user device: Since for claim 1, the Claim Interpretation section already includes receipt of that data through an interface of the client application 122 on the user device 104 in the corresponding structure of the generation system, and that structure was evaluated as an additional element for claim 1; claim 8 makes receipt from a user via a user device an express requirement of the claim. The user device is recited at a high level of generality, and the specification describes it as a desktop or laptop computer, a smartphone, a wearable device, or any other suitable computing device ([0029]). It merely represents generic computer machinery performing in its ordinary capacity. See MPEP 2106.05(f).
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include invoking computers or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly mor than the abstract idea itself. See MPEP 2106.05.
Claim 9
Step 1: a machine, as in claim 8.
Step 2A Prong 1: This claim recites, inter alia:
the output transaction framework further comprising one or more output transaction framework terms: These limitations further recite the abstract idea of a commercial interaction wherein the framework generated for the new transaction consists of proposed terms of that transaction, and proposing those terms is part of structuring an agreement.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of this claim are as follows:
wherein the transaction framework generation system is further configured to transmit the output transaction framework to the user via a user interface of the user device: As stated in the Claim Interpretation section, this function of the transaction framework generation system is interpreted under 35 U.S.C. 112(f) to cover the corresponding structure described in the specification, one or more processors executing instructions stored in memory that transmit the output through a network interface circuit and a network for presentation in a user interface ([0019], [0021], [0056]-[0057], [0078]-[0079]), and equivalents thereof. So interpreted, the system is generic computer machinery performing in its ordinary capacity (MPEP 2106.05(f)). Transmitting the result of the abstract idea to the user who requested it is insignificant post-solution activity, namely outputting the result, see MPEP 2106.05(g), and the user interface is recited at a high level of generality as a generic element of the user device, see MPEP 2106.05(f).
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 includes insignificant post-solution activity of outputting the result as recited by “wherein the transaction framework generation system is further configured to transmit the output transaction framework to the user”. This transmitting data to a user device, recited at this level of generality, is well-understood, routine, conventional activity. The courts have recognized sending messages over a network, OIP Techs., 788 F.3d at 1363, and a computer receiving and sending information over a network, buySAFE, 765 F.3d at 1355, see MPEP 2106.05(d)(II), and the specification describes the user interface as web pages generated and presented by user interface program logic and as provided through a client application on the user device ([0021], [0057]). The additional element further includes invoking computers or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly mor than the abstract idea itself. See MPEP 2106.05.
Claim 10
Step 1: a machine, as in claim 9.
Step 2A Prong 1: This claim recites, inter alia:
allow the user to change at least one of the one or more output transaction framework terms associated with the output transaction framework: These limitations further recites the abstract idea of a person reviewing a proposed transaction structure who can revise one of its terms mentally or with pen and paper, and revising the proposed terms of an agreement is part of the commercial interaction identified for claim 1.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of this claim are as follows:
wherein the user interface comprises one or more selectable elements configured to: These additional elements are recited at a high level of generality and merely provide a generic means for the user to enter a change; the claim specifies no particular arrangement, display or operation of the interface. Unlike the improved user interface of Core Wireless Licensing S.A.R.L. v. LG Electronics, Inc., 880 F.3d 1356, 1362-63 (Fed. Cir. 2018), which displayed a particular application summary in a particular manner, claim 10 uses a generic interface as a tool to receive the user's revision. That is mere instructions to apply the exception on a computer, see MPEP 2106.05(f).
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include invoking computers or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly mor than the abstract idea itself. See MPEP 2106.05.
Claims 11, 13-15 and 17-19
Step 1: These elements are directed to “A method comprising:”; therefore, these claims are directed to the statutory category of a process.
Step 2A Prong 1: These claims recite the same abstract ideas as in claims 1, 4-6 and 8-10, respectively, as the judicial exception.
Step 2A Prong 2: The judicial exception recited in these claims is not integrated into a practical application. The analysis at this step substantially mirrors that of claims 1, 4-6 and 8-10, respectively, subject to two differences in scope. First, the steps of claim 11 are performed by a transaction framework training system and a transaction framework generation system without any recitation of hardware. These named systems are no more than generic computing systems ([0019], [0023], [0040]) and merely serve as tools to implement the underlying judicial exception (MPEP 2106.05(f)). Second, as stated in the Claim Interpretation section, no limitation of claims 11-19 is interpreted under 35 U.S.C. 112(f), so the corresponding structure relied on in the analysis of claims 1 and 9 is not part of these claims. Their receiving steps require no particular data source or interface; their step of generating the machine learning model using the historical data covers any manner of generating a model from that data, with no particular training procedure; and their step of applying the input to the model covers any manner of running a model on the input, without the pairing and narrowing procedure of [0055]. For claims 14 and 17, the data source and the user device are additional elements that those claims introduce, rather than make express, and they are evaluated as for claims 5 and 8. The transmitting step of claim 18 is performed by the transaction framework generation system, a generic computing system, without the network interface structure identified for claim 9, and it is insignificant post-solution activity for the reasons given for claim 9 (MPEP 2106.05(g)).
Step 2B: The additional elements from Step 2A Prong 2 of these claims do not contain significantly more than the judicial exception. The analysis at this step substantially mirrors that of claims 1, 4-6 and 8-10, respectively.
Claim 12
Step 1: a process, as in claim 11.
Step 2A Prong 1: This claim recites, inter alia:
wherein the historical transaction framework data further comprises initial transaction framework terms, the initial transaction framework terms further comprising at least one of a prospective collateral, a prospective timeline, an anticipated purpose, and a relevant industry: Claim 12 recites in a single claim the subject matter that claims 2 and 3 recite together, and it does not mirror any single earlier claim. This limitation further recites the abstract idea of claim 11 by adding, to the information observed about each past transaction, the terms initially proposed for it, such as the collateral offered, the proposed repayment period, the anticipated use of funds, and the relevant industry ([0050]). These are terms of an agreement in the form of a contract, and observing them is a mental process. The added content narrows the information that is observed and evaluated in the abstract idea identified for claim 11; claim 12 recites no further act.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so this claim does not provide a practical application and is not considered to be significantly more. As such this claim is patent ineligible.
Claim 16
Step 1: a process, as in claim 11.
Step 2A Prong 1: This claim recites, inter alia:
wherein the final transaction framework terms further comprise a negotiated collateral, a negotiated timeline, a realized purpose, and a relevant industry: Unlike claim 7, claim 16 requires all four of these terms rather than at least one of them, so it does not mirror claim 7 and is analyzed separately. This limitation further recites the abstract idea of claim 11 by requiring that the observed final terms of each past transaction include the collateral and repayment period agreed by the parties, the realized use of the proceeds, and the relevant industry ([0051]). Requiring all four terms narrows the information observed, but that information remains the terms of an agreement in the form of a contract, which a person can practically observe and evaluate.
Thus, this claim further recites the judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so this claim does not provide a practical application and is not considered to be significantly more. As such this claim is patent ineligible.
Claim 20
Step 1: This claim recites “A non-transitory computer readable medium storing instructions”; therefore, it is directed to the status category of an article of manufacture.
Step 2A Prong 1: This claim recites the same abstract ideas as in claim 1 as the judicial exception.
Step 2A, Prong Two: The judicial excepti5on is not integrated into a practical application. The analysis at this step mirrors that of claim 1, subject to two differences in scope. First, the receiving and generating functions are performed by one or more processors executing instructions stored on a non-transitory computer readable medium, rather than by the training and generation systems of claim 1. The processors and the medium are generic computer equipment ([0078]-[0079]) that merely serve as tools to implement the underlying judicial exception, and placing the instructions on a non-transitory medium cannot integrate the exception (MPEP 2106.05(f)). Second, claim 20 is not interpreted under 35 U.S.C. 112(f), so the corresponding structure relied on in the analysis of claim 1, namely the data sources and interface through which the data is received, the supervised training procedure, and the pairing and narrowing procedure of [0055], is not part of the claim. Its receiving limitations require no particular source or interface, and its limitations of generating the model using the historical data and applying the input to the model cover any manner of doing so.
Step 2B: The additional elements from Step 2A Prong 2 of this claim does not contain significantly more than the judicial exception. The analysis at this step substantially mirrors that of claim 1.
Claim Rejections - 35 U.S.C. 102
The following is a quotation of the appropriate paragraph(s) of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office Action:
A person shall be entitled to a patent unless - (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-8, 11-15, 17, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bjonerud et al. (hereinafter Bjonerud), US 2019/0102835 A1.
Regarding independent claim 1, Bjonerud discloses a system comprising (Bjonerud: [0111], "a computer server system 100 in accordance with embodiments that can implement any of the disclosed components herein"; computer server system 100 (a system) implements the disclosed marketplace components): a transaction framework training system comprising hardware configured to (Bjonerud: [0136], "Artificial intelligence system 930 accepts the data from inputs 905, 910 and 915 to generate output 940 consisting of an automatic implementation of a corrective measure and validation"; artificial intelligence system 930 (a transaction framework training system) corrects and validates the decision making of the system; [0115], "The modules can include an operating system 115, data entry module 116 configured to provide data entry via a user interface, and all other functionality disclosed herein"; the disclosed functionality, including artificial intelligence system 930, is stored as software modules in memory 114 and executed by processor 122 (hardware)): receive historical transaction framework data associated with a plurality of transaction frameworks corresponding to historical transactions performed by a provider (Bjonerud: [0136], "Input 905 consists of all the input data in steps 1 through 7, including market data, borrower data, deal data and lender data"; artificial intelligence system 930 receives the deal data (historical transaction framework data) collected in the preceding steps; [0095], "Recent deal closing data from lenders: There are two ways to get data about recent deal closings. The first way is to get the Final Deal Terms as originated and closed on the system"; the deal data describes the deals (a plurality of transaction frameworks) of loans (historical transactions) originated and closed by lenders (a provider)), the historical transaction framework data comprising final transaction framework terms (Bjonerud: [0101], "These are the terms that the Borrower has decided to select out of all the bids they received for a prospective credit deal bid out on the platform"; the deal data (the historical transaction framework data) includes the Final Deal Terms (final transaction framework terms), the terms selected by the borrower for each credit deal) and outcome data for each historical transaction (Bjonerud: [0054], "The current invention aggregates not only loan data on closed loans but data on discarded loans that did not clear the market"; for each loan (each historical transaction) the system keeps loan data on closed loans and data on discarded loans (outcome data), which records whether the loan closed or did not clear the market; [0050], "The system evolves by comparing the system predictions with the outcomes of the deal bidding on the system as well as new loan and borrower data that enters onto the system"; the outcomes of the deal bidding of each credit deal bid out on the system are among the data from which the system evolves); generate a machine learning model configured to generate new transaction frameworks (Bjonerud: [0136], "Once artificial intelligence system 930 identifies a determined corrective action, it will then automatically adjust its rules and behaviors as a corrective action"; artificial intelligence system 930 adjusts the Rules and Behaviors for Handling Data (a machine learning model); [0100], "They will predict loan terms based on the similar deals of similar borrowers"; the Rules and Behaviors for Handling Data, modified automatically through the artificial intelligence, predict loan terms as projected deal terms (new transaction frameworks); [0044], "The claimed system and method utilize artificial intelligence that evolves through machine learning, as will be explained in more detail, from data collected and stored in secured databases"; the artificial intelligence through which the Rules and Behaviors for Handling Data are modified evolves through machine learning from the data collected and stored in the secured databases), the machine learning model generated using the historical transaction framework data (Bjonerud: [0109], "make modifications to the Rules and Behaviors that would have resulted in a more accurate projection or output and simultaneously maintain or improve the outcomes of Variance Threshold calculations from previously determined projections and Final Deal Terms"; the Rules and Behaviors for Handling Data (the machine learning model) are modified using the deal data (the historical transaction framework data) by comparing earlier projections with the Final Deal Terms; [0106], "A variance will be calculated compared to the value for each term as projected by the system based on the AI and Rules and Behaviors for Handling Data"; each term of the Final Deal Terms of a loan originated on the system is compared with the value that the Rules and Behaviors for Handling Data projected for that term, and the difference is calculated as a variance; [0107], "if the Calculated Variance crosses a high threshold, then the system will Automatically Implement Corrective Measures based on the Determined Corrective Action"; a correction is made when the variance crosses the high threshold, and no change is made while the variance stays at the low threshold; [0109], "altering the weightings of financial data, operating data, market data factors, and/or time that influence the projected deal terms"; one of the corrective actions alters the weightings that influence the projected deal terms; [0110], "Prior to the corrective action becoming active for live users, the system will re-calculate the variances for prior analysis for prior deals within a certain time frame"; each correction is checked against the variances of prior deals before it takes effect; this process, in which the projected terms are compared with the Final Deal Terms and the weightings that influence the projections are altered when the variance crosses the threshold and are checked against prior deals before use, performs the identical function in substantially the same way as the structure identified for this limitation in the Claim Interpretation section, which compares predicted outputs with actual outputs, adjusts the weights of the model with the resulting error each iteration or batch until the error is within a threshold, and validates the model before it is employed, and produces substantially the same result, weightings adjusted to reduce the difference between the projections and the actual terms; the remaining differences are insubstantial, because a correction made when the variance of a newly originated loan crosses the threshold is an error driven weight adjustment on a batch of one loan, and the other corrective actions that Bjonerud also permits, such as altering the rules for matching similar deals or the depth of history, are alternatives to the weight alteration rather than a replacement for it, so this process is an equivalent of that structure); and a transaction framework generation system comprising hardware configured to (Bjonerud: [0127], "Artificial intelligence system 530 accepts the data from inputs 505, 510, 515 and 520 to generate output 540 consisting artificial intelligence projected terms"; artificial intelligence system 530 (a transaction framework generation system) generates the projected terms and, like the other disclosed functionality, is executed by processor 122 (hardware)): receive input initial transaction framework data (Bjonerud: [0128], "a prospective borrower, who has already inputted a minimum requirement of information as discussed above, can input specific factors for a deal request, e.g., a loan amount, facility type, pricing, amortization, collateral, covenants, etc"; the prospective borrower enters into the system specific factors for a deal request (input initial transaction framework data) such as the requested loan amount, amortization and collateral; [0127], "The projected terms are derived from all the data in the system"; artificial intelligence system 530 receives the data in the system, which includes the specific factors for the deal request that the prospective borrower entered, to derive its projected terms; [0049], "The system can make decisions about the weighting of the factors listed above when determining which set of comparable deal terms should be used in predicting likely lenders and likely terms that a borrower could expect to receive"; the likely terms that the borrower could expect to receive for the deal request are predicted from comparable deal terms, so the deal request is received for that prediction); and generate an output transaction framework by applying the input initial transaction framework data to the machine [[leaning]] learning (interpreted per the Claim Objections set forth above) model (Bjonerud: [0097], "These projected terms are derived from the all the data in the system, e.g., market data, borrower data, lender data, historical data, etc., and the output predictions of the current Rules and Behaviors for Handling Data"; artificial intelligence system 530 generates the artificial intelligence projected terms (an output transaction framework) as output predictions of the current Rules and Behaviors for Handling Data (the machine learning model); [0132], "Input 605 consists of artificial intelligence projected terms and is equivalent to output 540. Input 610 consists of the actual terms received from all of the lenders offers. The actual terms are the terms that lenders have sent in as bids to the borrower during a bidding process of a credit deal"; the artificial intelligence projected terms (the output transaction framework) generated as output 540 are taken as input 605 together with input 610, the actual terms that the lenders sent in as bids to the borrower in the bidding process of a credit deal; [0133], "Artificial intelligence system 630 reviews each of the deal terms of the final deal terms received from a loan originated on the system where a variance is calculated compared to the value for each term as projected by the system"; each deal term of the final deal terms of the loan originated on the system is compared with the value projected by the system for that same term, so the artificial intelligence projected terms are projected for the terms of that same credit deal, the prospective credit deal that the borrower requested and bid out on the platform; [0049], "The system will be considering the similarities between financials, operations, and industries of the borrowers, as well as the similarities of the deal factors"; the projection is made for the specific factors for a deal request (the input initial transaction framework data) by selecting comparable deal terms of borrowers in similar industries and of deals whose deal factors are similar to those of the request; this process, in which the request is matched with historical deal terms that share its borrower industry and deal factors and the likely terms are predicted from those comparable deal terms, performs the identical function in substantially the same way as the structure identified for this limitation in the Claim Interpretation section, which pairs the input with historical terms sharing a collateral, timeline, purpose, or industry and generates the output from the paired historical data, and produces substantially the same result, an output set of terms derived from the most similar historical transactions, and is therefore an equivalent of that structure).
Regarding dependent claim 2, Bjonerud discloses the system of claim 1, wherein the historical transaction framework data further comprises initial transaction framework terms (Bjonerud: [0132], "Input 610 consists of the actual terms received from all of the lenders offers"; the input data collected for the artificial intelligence includes the actual terms of the lender offers; [0102], "These are the terms that lenders have sent in as bids to the borrower during a bidding process of a credit deal through the data chat room"; the deal data (the historical transaction framework data) includes the Actual Terms (initial transaction framework terms) that lenders first sent as bids before the borrower selected the Final Deal Terms).
Regarding dependent claim 3, Bjonerud discloses the system of claim 2, wherein the initial transaction framework terms further comprise at least one of a prospective collateral, a prospective timeline, an anticipated purpose, and a relevant industry (Bjonerud: [0195], "input the specific loan terms that they would like to send to a borrower in response to a loan request, including term, amortization, financial covenants, non-financial covenants, maturity dates, fees"; the bids of the lenders, the Actual Terms (the initial transaction framework terms), include the term and maturity dates (a prospective timeline) of the offered loan; [0156], "The AI will collect data on the original terms and on the modified terms that the lender may input after receiving feedback"; the original bid terms are collected as data; the listing requires only one member, and the offered term and maturity dates satisfy it).
Regarding dependent claim 4, Bjonerud discloses the system of claim 1, wherein the historical transaction framework data further comprises one or more parties involved in the historical transactions (Bjonerud: [0047], "The system can also catalog and match those borrowers with the loans they hold and the details of those loans such as pricing, collateral, covenants, loan amount, amortization, and terms"; the deal data (the historical transaction framework data) catalogs the borrowers (one or more parties) matched with the loans (the historical transactions) they hold).
Regarding dependent claim 5, Bjonerud discloses the system of claim 1, wherein the historical transaction framework data is retrieved from at least one of an internal data source or an external data source (Bjonerud: [0095], "The first way is to get the Final Deal Terms as originated and closed on the system. The second way is by looking at the existing deals entered into the system from borrowers on the platform"; the deal data (the historical transaction framework data) is obtained from the Final Deal Terms of deals originated and closed on the system and from the existing deals that borrowers entered into the system; [0045], "The present invention uses artificial intelligence to interpret data and evolve as new data is added. The data can be stored in secure databases and collected from a web platform that allows borrowers and lenders to input information, communicate, and transact"; the data collected from the web platform on which borrowers and lenders transact, including the deal data, is stored in the secure databases (an internal data source) of the system, from which the artificial intelligence takes the data it interprets as it evolves; the listing requires only one member).
Regarding dependent claim 6, Bjonerud discloses the system of claim 1, wherein the outcome data for each historical transaction further comprises one of either a successful outcome or a failed outcome (Bjonerud: [0054], "The current invention aggregates not only loan data on closed loans but data on discarded loans that did not clear the market"; for each loan (each historical transaction) the loan data on closed loans and data on discarded loans (the outcome data) records the loan either among the closed loans (a successful outcome) or among the discarded loans that did not clear the market (a failed outcome)).
Regarding dependent claim 7, Bjonerud discloses the system of claim 1, wherein the final transaction framework terms further comprise at least one of a negotiated collateral, a negotiated timeline, a realized purpose, and a relevant industry (Bjonerud: [0101], "These are the terms that will become the 'Borrower Deal Data' and assumes that the loan eventually closes with the same terms as proposed by the lender"; the Final Deal Terms (the final transaction framework terms) of a closed loan become Borrower Deal Data; [0066], "Borrower Deal Data: This is data related to a credit facility of the borrower and could include the following information: credit facility type, amortization, term, pricing, collateral, guaranty requirement, payment structure, covenants, origination date, etc"; the Borrower Deal Data includes the collateral (a negotiated collateral) and the term (a negotiated timeline) of the closed credit facility; the listing requires only one member).
Regarding dependent claim 8, Bjonerud discloses the system of claim 1, wherein the input initial transaction framework data is received from a user via a user device (Bjonerud: [0128], "a prospective borrower, who has already inputted a minimum requirement of information as discussed above, can input specific factors for a deal request"; the prospective borrower (a user) inputs the specific factors for a deal request (the input initial transaction framework data); [0045], "collected from a web platform that allows borrowers and lenders to input information, communicate, and transact"; the borrowers, including the prospective borrower (the user), input information on the web platform; [0113], "computer server system 100 includes processor 122 and other components communicating through internet/cloud 110, or any other communication medium, to a user device 124 such as a smartphone, tablet, etc"; the input reaches computer server system 100 from user device 124 (a user device); [0113], "In one embodiment, user device 124 implements a browser and communicates using Hypertext Markup Language ('HTML') to the remainder of system 100, which functions as a web server and provides web pages to user device 124 either directly or indirectly"; system 100 functions as a web server that provides web pages to the browser implemented on user device 124 (the user device), so the information that the prospective borrower (the user) inputs on the web platform, including the specific factors for the deal request (the input initial transaction framework data), is communicated from user device 124 to system 100).
Regarding independent claim 11, this is a method claim that is substantially the same as the system of claim 1. In addition, Bjonerud discloses a method comprising, with the receiving and generating steps performed by a transaction framework training system and by a transaction framework generation system (Bjonerud: [0119], "FIGS. 2-9 show exemplary embodiments of methods 200, 300, 400, 500, 600, 700, 800 and 900 for an autonomous marketplace system"; methods 200, 300, 400, 500, 600, 700, 800 and 900 (a method) are carried out as steps 1 through 8 of the autonomous marketplace system; [0136], "Artificial intelligence system 930 accepts the data from inputs 905, 910 and 915 to generate output 940 consisting of an automatic implementation of a corrective measure and validation"; artificial intelligence system 930 (a transaction framework training system) receives the deal data and adjusts the Rules and Behaviors for Handling Data; [0127], "Artificial intelligence system 530 accepts the data from inputs 505, 510, 515 and 520 to generate output 540 consisting artificial intelligence projected terms"; artificial intelligence system 530 (a transaction framework generation system) generates the projected terms).
Regarding dependent claim 12, the rejections of claims 2 and 3 are applied in the same manner to the corresponding method form recitation of claim 12.
Regarding dependent claims 13-15 and 17, the rejections of claims 4-6, and 8, respectively, are applied in the same manner to the corresponding method form recitations of claims 13-15, and 17.
Regarding independent claim 20, this is a non-transitory computer readable medium claim that is substantially the same as the system of claim 1. In addition, Bjonerud discloses a non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to (Bjonerud: [0116], "Memory 114, being non-transitory, may include a variety of computer-readable medium that may be accessed by processor 122"; memory 114 (a non-transitory computer readable medium) is accessed by processor 122 (one or more processors); [0115], "memory 114 may store software modules that provide functionality when executed by processor 122"; the software modules stored in memory 114, which include all the functionality disclosed in Bjonerud, are instructions that, when executed by processor 122, cause processor 122 to perform that functionality).
Claim Rejections - 35 U.S.C. 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office Action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 9-10 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Bjonerud, as applied in the rejections of claims 8 and 17 above, in view of Bouchard et al. (hereinafter Bouchard), US 2022/0051316 A1.
Regarding dependent claim 9, Bjonerud teaches the system of claim 8, the output transaction framework further comprising one or more output transaction framework terms (Bjonerud: [0132], "Input 605 consists of artificial intelligence projected terms and is equivalent to output 540"; the artificial intelligence projected terms (the output transaction framework) that artificial intelligence system 530 generates as output 540 are carried into step 5; [0133], "a variance is calculated compared to the value for each term as projected by the system"; the artificial intelligence projected terms comprise the value for each term as projected by the system (one or more output transaction framework terms)).
Bjonerud does not expressly teach wherein the transaction framework generation system is further configured to transmit the output transaction framework to the user via a user interface of the user device.
However, Bouchard teaches wherein the transaction framework generation system is further configured to transmit the output transaction framework to the user via a user interface of the user device (Bouchard: [0051], "method 200 can be performed by the FI system 102"; FI system 102 (the transaction framework generation system) performs method 200 for offering financing to a customer; [0058], "one or more financing options are generated and presented as a loan proposal to a user associated with the credit account. The loan proposal can be transmitted via a communications interface or any other suitable interface to a user device"; at step 214 the financing options generated for the customer are sent as a loan proposal (the output transaction framework) through a communications interface to the user device (the user device) of the user (the user) associated with the credit account; [0058], "The loan offer can be presented to the user on the user device via a GUI, such as GUI 176 of FIG. 1"; the loan proposal is displayed to that user on the GUI (a user interface) of the user device; [0033], "including the functionality for sending communications to and receiving transmissions from clients 170"; processor 106 of FI system 102 executes the sending of those communications to client 170; this sending of the generated loan proposal by the processor of the server-side system through its communications interface over the network to the device of the user for display in the graphical user interface of that device performs the identical function in substantially the same way to produce substantially the same result as the structure identified for this limitation in the Claim Interpretation section, and is therefore an equivalent of that structure).
Because Bjonerud and Bouchard are analogous art with both addressing the computer generation of loan terms in response to a borrower's request for financing, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the computer system of Bjonerud send the artificial intelligence projected terms that artificial intelligence system 530 generates for the prospective borrower's deal request to the web pages that computer server system 100 provides to user device 124 of that borrower, as Bouchard's FI system 102 transmits the loan proposal generated for a user through a communications interface to the user's device for display on its GUI, with a reasonable expectation of success, because computer server system 100 already sends data from processor 122 through communication device 120 over internet/cloud 110 and serves web pages to user device 124, so that the projected terms are delivered over that existing connection as further web page content, to teach wherein the transaction framework generation system is further configured to transmit the output transaction framework to the user via a user interface of the user device. This modification would have been motivated by the desire to present the loan terms generated for a request to the requesting user in real time or near real time following the request (Bouchard: [0058]).
Regarding dependent claim 10, Bjonerud, in view of Bouchard, teach the system of claim 9, wherein the user interface comprises one or more selectable elements configured to allow the user to change at least one of the one or more output transaction framework terms associated with the output transaction framework (Bouchard: [0058], "one or more financing options are generated and presented as a loan proposal to a user associated with the credit account"; the loan proposal (the output transaction framework) with its set of terms is presented on the user device of the user; [0060], "the user can adjust loan terms using a GUI which can include multiple sliders, menus, or other UI elements that allow the user to adjust one or more loan parameters"; the GUI (the user interface) includes multiple sliders, menus, or other UI elements (one or more selectable elements) with which the user adjusts the loan terms (the one or more output transaction framework terms) of the proposal, for example requesting a loan term of 3 months instead of the 6 months offered).
Regarding dependent claims 18 and 19, the rejections of claims 9 and 10 are applied in the same manner to the corresponding method-form recitations of claims 18 and 19.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Bjonerud, as applied in the rejection of claim 11 above, in view of Thun, US 2022/0067826 A1.
Regarding dependent claim 16, Bjonerud teaches the method of claim 11, wherein the final transaction framework terms further comprise a negotiated collateral, a negotiated timeline (Bjonerud: [0101], "These are the terms that will become the 'Borrower Deal Data' and assumes that the loan eventually closes with the same terms as proposed by the lender"; the Final Deal Terms (the final transaction framework terms) of a closed loan become Borrower Deal Data; [0066], "Borrower Deal Data: This is data related to a credit facility of the borrower and could include the following information: credit facility type, amortization, term, pricing, collateral, guaranty requirement, payment structure, covenants, origination date, etc"; the Borrower Deal Data includes the collateral (a negotiated collateral) and the term (a negotiated timeline) of the closed credit facility).
Bjonerud does not expressly teach a realized purpose, and a relevant industry.
However, Thun teaches a realized purpose (Thun: [0041], "Example 8 describes the input fields in csv-format with the credit rating information of corporate loan section for the data fields of the ESMA and the additional data fields"; the revised loan-level data template includes a corporate loan section describing each underlying corporate loan; [0060], "Origination Date,Maturity Date,Origination Channel,Purpose,Original Principal Balance,CRPL38 Currency,Current Principal Balance"; each corporate loan record reports, for a loan already originated and carrying both an original and a current principal balance, the Purpose (a realized purpose) of that loan alongside the origination date and maturity date with which it was made, so the purpose reported is the purpose of the loan as made), and a relevant industry (Thun: [0060], "Credit Impaired Obligor,Customer Type,NACE Industry Code"; the same corporate loan record carries the NACE Industry Code (a relevant industry) classifying the industry of the obligor of the loan).
Because Bjonerud and Thun are analogous art with both addressing the recording of loan-level data on originated commercial loans for credit analysis, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to record, in the method of Bjonerud, the Purpose and NACE Industry Code fields of Thun's corporate loan record as part of the Final Deal Terms kept as Borrower Deal Data for each closed credit facility, with a reasonable expectation of success, because Bjonerud's Borrower Deal Data is an open list of loan-level fields such as credit facility type, amortization, term, pricing, and collateral, Bjonerud already collects a loan purpose in the borrower's credit proposal and the industry type of each borrower, and its Rules and Behaviors for Handling Data already weigh the similarity of the industries of borrowers and of deal factors when selecting comparable deal terms, so that Thun's two fields are captured alongside the other terms of each closed deal without changing how the deal data is collected or used, to teach wherein the final transaction framework terms further comprise a negotiated collateral, a negotiated timeline, a realized purpose, and a relevant industry. This modification would have been motivated by the desire to increase transparency on the underlying loans and their performance by providing loan-by-loan information in a standard format (Thun: [0005]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
BAGHESTANI et al., US 2021/0357962 A1 (Nov. 18, 2021) (ABSTRACT Described herein is a system for generating financing structures. A learning engine may extract data sets associated with sellers of various products. The learning engine may be trained using the data sets. The learning engine may identify a subset of dimensions that cause a change in a determination of a final price for a given product. The learning engine may compute a value for each of the sellers with respect to each dimension. The learning engine may group the sellers into different clusters. The learning engine may generate using a model, including the subset of dimensions. The learning engine may receive a request to generate a financing structure for a specified product sold by a specified seller. The learning engine may generate financing structures for the specified product sold by the specified seller based on the generated model).
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