DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the communications filed on 01/21/2026.
Claims 1, 2, and 13 have been amended and are hereby entered.
Claims 9 and 18 has been canceled.
Claims 1-8, 10-17, and 19-20 are currently pending and have been examined.
This action is made Final.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for future claim amendments to avoid U.S.C 112(a) issues that can arise. The Examiner thanks the Applicant in advance.
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 1-8, 10-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of determining a borrowing limit based on a borrowing base without significantly more.
Claim 1 is directed to a method, which is one of the statutory categories of invention; and Claim 13 is directed to a system, which is one of the statutory categories of invention. (Step 1: YES).
Claim 1 is directed to a method for end-to-end automation of borrowing base calculations, which recites a series of steps, e.g., receiving, by a borrowing base computer program, documents for collateral, assets, and liabilities from a borrower electronic device for a borrower; extracting, by the borrowing base computer program, information for the collateral, assets, and liabilities from the documents; calculating, by the borrowing base computer program and from the information, a borrowing base for the borrower; forecasting, by the borrowing base computer program, a future borrowing base based on historical documents for the borrower; sending, by the borrowing base computer program, the borrowing base and the future borrowing base to borrower electronic device; receiving, by the borrowing base computer program, acknowledgement from the borrower electronic device; setting, by the borrowing base computer program, a borrowing limit based on the borrowing base; receiving, by the borrowing base computer program, updated documents for the borrower; extracting, by the borrowing base computer program, updated information from the updated documents; calculating, by the borrowing base computer program and from the updated information, an updated borrowing base for the borrower; and setting, by the borrowing base computer program, an updated borrowing limit based on the borrowing base. These series of steps describe the abstract idea of determining a borrowing limit based on a borrowing base (with the exception of the italicized and bolded terms above), which is the processing a borrower’s collateral, assets, and liabilities information to determine and update the borrower’s borrowing limit based on a borrowing base; therefore, corresponding to a commercial interaction. Hence, a commercial interaction is a Certain Methods of Organizing Human Activity. Also, the abstract idea is the evaluation of information of collateral, assets, and liabilities from a borrower’s documents to process and update a borrowing limit based on a borrowing base, which is a concept performed in the human mind. Therefore, a concept performed in the human mind is a Mental Process. Furthermore, the determining of a borrowing limit based on a borrowing base involves the calculating of the borrowing base of a borrower; therefore, corresponding to mathematical calculation, and/or formula or equations, and/or relationship. Hence, a mathematical calculation, and/or formula or equations, and/or relationship is a Mathematical Concept. The system limitations, e.g., a borrowing base computer program, and borrower electronic device do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 1 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of a borrowing base computer program, and borrower electronic device are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 1 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a borrowing base computer program, and borrower electronic device are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 1 is not patent eligible.
Dependent claims 2-8 and 10-12 are directed to a method, which recites a series of steps that describe the abstract idea of determining a borrowing limit based on a borrowing base. Furthermore, dependent claims 5 and 8 are directed to a method, which recites a series of steps: “further comprising: identifying, by the borrowing base computer program, a type of each of the documents using a machine learning model, wherein the documents are unstructured documents; and wherein the documents are received from an accounting system associated with the borrower.” The series of steps of claims 2-8 and 10-12 describe the abstract idea of determining a borrowing limit based on a borrowing base (with the exception of the italicized and bolded terms above), which is the processing a borrower’s collateral, assets, and liabilities information to determine and update the borrower’s borrowing limit based on a borrowing base; therefore, corresponding to a commercial interaction. Hence, a commercial interaction is a Certain Methods of Organizing Human Activity. Also, the abstract idea is the evaluation of information of collateral, assets, and liabilities from a borrower’s documents to process and update a borrowing limit based on a borrowing base, which is a concept performed in the human mind. Therefore, a concept performed in the human mind is a Mental Process. Furthermore, the determining of a borrowing limit based on a borrowing base involves the calculating of the borrowing base of a borrower; therefore, corresponding to mathematical calculation, and/or formula or equations, and/or relationship. Hence, a mathematical calculation, and/or formula or equations, and/or relationship is a Mathematical Concept. Thus, claims 2-8 and 10-12 recite an abstract idea. The additional elements of a borrowing base computer program, and borrower electronic device, machine learning model, and accounting system are no more than simply applying the abstract idea using generic computer elements. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: a borrowing base computer program, and borrower electronic device, machine learning model, and accounting system, do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Claim 13 is directed to a system, comprising: a borrower electronic device for a borrower; a document source for documents for collateral, assets, and liabilities from the borrower electronic device, wherein the documents comprise accounts receivable aging reports, accounts payable aging reports, aging reconnaissance reports, equipment valuation, inventory valuation, and/or real property valuation information; a lender electronic device that is configured to receive the documents from the document source, to extract information for the collateral, assets, and liabilities from the documents, to calculate, from the information, a borrowing base for the borrower, to forecast a future borrowing base based on historical documents for the borrower, to send the borrowing base and the future borrowing base to the borrower electronic device, to receive acknowledgement from the borrower electronic device, to receive updated documents for the borrower from the document source, to extract updated information from the documents, and to calculate, from the updated information, an updated borrowing base for the borrower; and a loan servicing system that is configured to set a borrowing limit for the borrower based on the borrowing base, and to set an updated borrowing limit for the borrower based on the updated borrowing base. These series of steps describe the abstract idea of determining a borrowing limit based on a borrowing base (with the exception of the italicized and bolded terms above), which is the processing a borrower’s collateral, assets, and liabilities information to determine and update the borrower’s borrowing limit based on a borrowing base; therefore, corresponding to a commercial interaction. Hence, a commercial interaction is a Certain Methods of Organizing Human Activity. Also, the abstract idea is the evaluation of information of collateral, assets, and liabilities from a borrower’s documents to process and update a borrowing limit based on a borrowing base, which is a concept performed in the human mind. Therefore, a concept performed in the human mind is a Mental Process. Furthermore, the determining of a borrowing limit based on a borrowing base involves the calculating of the borrowing base of a borrower; therefore, corresponding to mathematical calculation, and/or formula or equations, and/or relationship. Hence, a mathematical calculation, and/or formula or equations, and/or relationship is a Mathematical Concept. The system limitations, e.g., a borrower electronic device, document source, lender electronic device, and loan servicing system, do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 13 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of a borrower electronic device, document source, lender electronic device, and loan servicing system, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 13 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 13 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a borrower electronic device, document source, lender electronic device, and loan servicing system are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 13 is not patent eligible.
Dependent claims 14-17 and 19-20 are directed to a system, which perform the steps that describe the abstract idea of determining a borrowing limit based on a borrowing base. Furthermore, dependent claims 16 and 17 are directed to a system, which perform the steps: “wherein the lender electronic device is further configured to identify a type of each of the documents using a machine learning model, wherein the documents are unstructured documents, wherein the machine learning model is trained with a plurality of document formats to predict a format of each of the documents; and wherein the document source comprises an accounting system associated with the borrower.” The series of steps of claims 14-17 and 19-20 describe the abstract idea of determining a borrowing limit based on a borrowing base (with the exception of the italicized and bolded terms above), which is the processing a borrower’s collateral, assets, and liabilities information to determine and update the borrower’s borrowing limit based on a borrowing base; therefore, corresponding to a commercial interaction. Hence, a commercial interaction is a Certain Methods of Organizing Human Activity. Also, the abstract idea is the evaluation of information of collateral, assets, and liabilities from a borrower’s documents to process and update a borrowing limit based on a borrowing base, which is a concept performed in the human mind. Therefore, a concept performed in the human mind is a Mental Process. Furthermore, the determining of a borrowing limit based on a borrowing base involves the calculating of the borrowing base of a borrower; therefore, corresponding to mathematical calculation, and/or formula or equations, and/or relationship. Hence, a mathematical calculation, and/or formula or equations, and/or relationship is a Mathematical Concept. Thus, claims 14-17 and 19-20 recite an abstract idea. The additional elements of a borrower electronic device, document source, lender electronic device, loan servicing system, machine learning model, and accounting system are no more than simply applying the abstract idea using generic computer elements. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: a borrower electronic device, document source, lender electronic device, loan servicing system, machine learning model, and accounting system, do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Dependent claims 2-8, 10-12, 14-17, and 19-20 have further defined the abstract idea that is present in their respective independent claims: Claims 1 and 13; and thus correspond to Certain Methods of Organizing Human Activity, and/or Mental Processes, and/or Mathematical Concepts and are abstract in nature for the reason presented above. The dependent claims 2-8, 10-12, 14-17, and 19-20 do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, claims 2-8, 10-12, 14-17, and 19-20 are directed to an abstract idea without significantly more.
Thus, claims 1-8, 10-17, and 19-20 are not patent-eligible.
Claim Rejections - 35 USC § 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 7-8, 10-15, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Walter (U.S. Patent Application Publication No. US 2014/0058925 A1; hereinafter “Walter”), in view of Diriye (U.S. Patent Publication No. US 2020/0118131 A1; hereinafter “Diriye”).
Regarding Claim 1:
Walter teaches:
A method for end-to-end automation of borrowing base calculations, comprising: (Walter, Systems and methods automate and manage electronic exchange in the asset-based lending industry, particularly between borrowers and lenders (See, Abstract));
receiving, by a borrowing base computer program, documents for collateral, assets, and liabilities from a borrower electronic device for a borrower; (Walter, the processing of the various assets (e.g., invoices or accounts receivable, fixed assets, inventory) are brought together starting at 948. In particular, at 948 the import worker application 401 d sums the eligible accounts receivable value, the eligible fixed assets value, and/or the eligible inventory value. At 950, the import worker application 401 d stores the sum as an eligible borrowing base value. Such reflects the eligible amount of all assets which may serve as collateral for a loan. (See, Para. 60, 61, 152-156, 185; Abstract; Fig. 1, 4));
extracting, by the borrowing base computer program, information for the collateral, assets, and liabilities from the documents; (Walter, an asset data optimizer may include a system that extracts raw asset data from borrower asset management systems and normalizes the extracted raw data into a standard form, optimized to asset-based borrowing base determination (See, Para. 15, 24-29; Abstract));
calculating, by the borrowing base computer program and from the information, a borrowing base for the borrower; (Walter, an integrated calculator may include a system that calculates intermediate and total eligible amounts on a customer-by-customer, invoice-by-invoice basis, from standardized, asset-based-lending-optimized data. The calculate eligible totals method 1200 may, for example, be used in calculating eligible customer accounts receivable 910, calculating eligible totals of fixed assets 928 and/or eligible totals of inventory 940 of the calculate transaction method 900 (FIG. 9). In particular, the calculate eligible totals method 1200 applies various rules, typically specified by the lender, to the asset values to determine whether the asset values that are eligible for establishing an eligible borrowing base for the lender to evaluate (See, Para. 9-18, 154-155, 169; Fig. 3-4, 7, 9, 12-13; Abstract));
forecasting, by the borrowing base computer program, a future borrowing base based on historical documents for the borrower; (Walter, the processing of the various assets (e.g., invoices or accounts receivable, fixed assets, inventory) are brought together starting at 948. In particular, at 948 the import worker application 401 d sums the eligible accounts receivable value, the eligible fixed assets value, and/or the eligible inventory value. At 950, the import worker application 401 d stores the sum as an eligible borrowing base value. Such reflects the eligible amount of all assets which may serve as collateral for a loan. At 952, the import worker application 401 d sums the eligible borrowing base value with other loan amounts 954 and current borrowing amounts 956. In particular, the other loan amounts 954 and current borrowing 956 are subtracted from the eligible borrowing base. The other loan amounts 954 and/or current borrowing amounts 956 may be supplied by the borrower and/or by the lender. At 958, the import worker application 401 d stores the sum as a credit line available amount (See, Para. 13, 15-19, 154-155; Fig. 4, 9));
sending, by the borrowing base computer program, the borrowing base and the future borrowing base to the borrower electronic device (Walter, A calculation viewer may include a system that displays intermediate and total eligible amounts of an asset-based borrowing base on a per-customer or per-asset basis, which amounts include intermediate totals of all eligibility and exclusions, and which may highlight or emphasize adjustments and per-customer exceptions (See, Para. 15-23, 77, 102, 144; Abstract));
receiving, by the borrowing base computer program, acknowledgement from the borrower electronic device; (Walter, FIG. 5 shows a method 500 of enqueueing a transaction for use in operation of an asset-based lending management system …. At 502, the Web server 401 c (FIG. 4) begins the enqueueing transaction method 500, for example in response to extraction and normalization of asset-related data associated with a particular borrower. At 504, the Web server 401 c receives and validates the normalized asset-related data received from the import application or extraction program 401 b (FIG. 4). Validation may include validating that all discrete pieces of data are present and/or that various values are within expected limits or ranges. (acknowledgement) (See, Para. 110-116; Abstract; Fig. 4-5));
setting, by the borrowing base computer program, a borrowing limit based on the borrowing base; (Walter, an asset data optimizer may include a system that extracts raw asset data from borrower asset management systems and normalizes the extracted raw data into a standard form, optimized to asset-based borrowing base determination. (See, Para.15-16; Abstract) At 952, the import worker application 401 d sums the eligible borrowing base value with other loan amounts 954 and current borrowing amounts 956. In particular, the other loan amounts 954 and current borrowing 956 are subtracted from the eligible borrowing base. The other loan amounts 954 and/or current borrowing amounts 956 may be supplied by the borrower and/or by the lender. At 958, the import worker application 401 d stores the sum as a credit line available amount (See, Para.154-155));
receiving, by the borrowing base computer program, updated documents for the borrower; (Walter, In most implementations, the most up-to-date or current asset-related information (e.g., accounts receivable, inventory, fixed assets) will be stored by the server computers 110 or processor-based devices 112 of the respective borrower 104, or stored on computers or databases of an agent of the borrower 104 such as an accounting firm and/or a firm providing ERP services. The asset-based lending management system 102 may retrieve, import or extract the asset-related information from the server computers 110, processor-based devices 112, or the nontransitory computer- or processor-readable storage media 120 of the respective borrowers 104 or their agents. The asset-based information may be retrieved periodically (e.g., daily, weekly, monthly, quarterly) or on demand. (See, Para. 66; Fig. 1); At 716, the import worker application 401 d updates stored customer information in a nontransitory processor-readable storage medium. Such may include adding new variations on entity names identified via the process of unifying customer entities. Such customer information may be stored to any of a variety of data structures, for instance records, tables, linked lists, or relational databases. The stored customer information may be used to expedite future review of asset-related information; (See, Para.128-136; Fig. 4, 7));
extracting, by the borrowing base computer program, updated information from the updated documents; (Walter, In most implementations, the most up-to-date or current asset-related information (e.g., accounts receivable, inventory, fixed assets) will be stored by the server computers 110 or processor-based devices 112 of the respective borrower 104, or stored on computers or databases of an agent of the borrower 104 such as an accounting firm and/or a firm providing ERP services. The asset-based lending management system 102 may retrieve, import or extract the asset-related information from the server computers 110, processor-based devices 112, or the nontransitory computer- or processor-readable storage media 120 of the respective borrowers 104 or their agents. The asset-based information may be retrieved periodically (e.g., daily, weekly, monthly, quarterly) or on demand. (See, Para. 66; Fig. 1); At 716, the import worker application 401 d updates stored customer information in a nontransitory processor-readable storage medium. Such may include adding new variations on entity names identified via the process of unifying customer entities. Such customer information may be stored to any of a variety of data structures, for instance records, tables, linked lists, or relational databases. The stored customer information may be used to expedite future review of asset-related information; (See, Para.128-136; Fig. 4, 7));
calculating, by the borrowing base computer program and from the updated information, an updated borrowing base for the borrower; and (Walter, an integrated calculator may include a system that calculates intermediate and total eligible amounts on a customer-by-customer, invoice-by-invoice basis, from standardized, asset-based-lending-optimized data. The calculate eligible totals method 1200 may, for example, be used in calculating eligible customer accounts receivable 910, calculating eligible totals of fixed assets 928 and/or eligible totals of inventory 940 of the calculate transaction method 900 (FIG. 9). In particular, the calculate eligible totals method 1200 applies various rules, typically specified by the lender, to the asset values to determine whether the asset values that are eligible for establishing an eligible borrowing base for the lender to evaluate (See, Para. 9-18, 154-155, 169; Fig. 3-4, 7, 9, 12-13; Abstract));
setting, by the borrowing base computer program, an updated borrowing limit based on the borrowing base. (Walter, a system …. calculates an appropriate amount for accounts receivable to be counted as an eligible asset towards a borrowing base based at least in part on accounts payable to the business entity. (See, Para. 9, 16-19; 21-23; Abstract); At 952, the import worker application 401 d sums the eligible borrowing base value with other loan amounts 954 and current borrowing amounts 956. In particular, the other loan amounts 954 and current borrowing 956 are subtracted from the eligible borrowing base. The other loan amounts 954 and/or current borrowing amounts 956 may be supplied by the borrower and/or by the lender. At 958, the import worker application 401 d stores the sum as a credit line available amount….. At 964, the import worker application 401 d takes the least of all inputs greater than and equal to zero. The import worker application 401 d may employ a maximum loan amount 966. The maximum loan amount 966 may be supplied by the borrower and/or by the lender, and may be stored in nontransitory processor-readable storage media. The import worker application 401 d stores the resulting value as a new credit line available amount 968. (See, Para. 155-157; Fig. 4, 9)).
Walter does not specifically teach receiving, by the borrowing base computer program, [acknowledgement] from borrower electronic device.
However, Diriye further teaches the following limitation:
receiving, by the borrowing base computer program, [acknowledgement] from borrower electronic device. (Diriye, the system receives a request from a borrowing party (e.g., through an interface provided on a device associated with the borrowing party). For example, the request may be a request to secure a loan or a line of credit in exchange for providing an asset, such as a digital asset or non-digital asset, as collateral. The request may be accompanied by the borrowing party's acceptance of one or more terms associated with a specific loan, as advertised by a lending party. ……. The system then sends the request to the borrowing party for acceptance and receives a confirmation from the borrowing party. (See, Para.92; Fig. 5)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Walter with the features of Diriye’s system because “provides a system that includes a blockchain network, which includes first and second blockchain nodes. The first blockchain node is configured to receive a request to transfer an asset, and generate a blockchain transaction including a smart contract identifier and one or more parameters. The second blockchain node is configured to obtain one or more rules from a smart contract that corresponds to the smart contract identifier and compare the one or more parameters to the one or more rules to obtain a risk level. In response to the risk level is greater than a threshold, the second blockchain node is configured to not execute the blockchain transaction. In response to the risk level is not greater than the threshold, the second blockchain node is configured to execute the transaction. The request includes the smart contract identifier and the one or more parameters. The asset includes one of a trade item or a service to be performed.” “The system may trigger one or more of the above mentioned amelioration actions. The GUI can be configured to send alert, trigger and notify the relevant user of the transaction or contract, including taking actions or providing feedback to a user on the user computing device. In further embodiments, a system may display requirements, regulations, or constraints on a GUI or user computing device. A GUI may display a notification of a high-risk transaction or contract using the multi-dimensional risk array. The user is notified and can accept or take ameliorating actions.” (Diriye, Para. 5 and 49).
Regarding Claims 2 and 14:
Walter teaches:
further comprising: normalizing, by the borrowing base computer program, the documents into a normalized format; and (Walter, an asset data optimizer may include a system that extracts raw asset data from borrower asset management systems and normalizes the extracted raw data into a standard form, optimized to asset-based borrowing base determination. (See, Para. 15, 21, 23, 96, 104-105, 124-136; Abstract; Fig. 3, 7));
normalizing, by the borrowing base computer program, the updated documents into the normalized format. (Walter, an asset data optimizer may include a system that extracts raw asset data from borrower asset management systems and normalizes the extracted raw data into a standard form, optimized to asset-based borrowing base determination.(See, Para. 15, 21, 23, 96, 104-105; Abstract; Fig. 3, 7);The normalize attachment data method 700 terminates at 738 until called again. Alternatively, the normalize attachment data method 700 may run concurrently with other methods or processes, for example, as one of multiple threads on a multi-threaded processor system. (See, Para.124-137)).
Regarding Claims 3 and 15:
Walter teaches:
wherein the borrowing base computer program calculates the borrowing base by subtracting a value of ineligibles from a value of the collateral and/or assets for the borrower. (Walter, At 1206, the import worker application 401 d subtracts exclusions from the starting value. The exclusions may be specified by the lender, and may be stored in nontransitory processor-readable storage media. Exclusions may take a variety of forms. For example, exclusions may include the types of assets (e.g., accounts receivable, fixed assets, inventory) that a lender will consider when deciding on making a loan or advance or in evaluating compliance with loan conditions or terms. (See, Para. 21-23, 155, 172, 185; Fig. 4 (element:401 d), 9 (elements:952-962), 12 (element:1206), 13 (element:1306))).
Regarding Claim 4:
Walter teaches:
wherein the documents comprise accounts receivable aging reports, accounts payable aging reports, aging reconnaissance reports, equipment valuation, inventory valuation, and/or real property valuation information. (Walter, the processing of the various assets (e.g., invoices or accounts receivable, fixed assets, inventory) are brought together starting at 948. In particular, at 948 the import worker application 401 d sums the eligible accounts receivable value, the eligible fixed assets value, and/or the eligible inventory value. At 950, the import worker application 401 d stores the sum as an eligible borrowing base value. Such reflects the eligible amount of all assets which may serve as collateral for a loan. (See, Para. 60, 61, 152-156, 185; Abstract; Fig. 1, 4)).
Regarding Claim 7:
Walter teaches:
wherein the extracted information is stored in a standard document format. (Walter, an asset data optimizer may include a system that extracts raw asset data from borrower asset management systems and normalizes the extracted raw data into a standard form, optimized to asset-based borrowing base determination. (See, Para. 15, 21, 23, 96, 104-105, 124-136; Abstract; Fig. 3, 7)).
Regarding Claims 8 and 17:
Walter teaches:
wherein the documents are received from an accounting system associated with the borrower. (Walter, receiving parameters input by the trusted third party evaluation system from at least a first lender computer system controlled by a first lender, the parameters input specifying a number of parameters imposed by at least the first lender; receiving respective borrower accounting information including data and metadata for each of a number of borrowers by the evaluation system from each of a number of accounting modules executed on a number of source computer systems which are distinct from the first lender computer system, the data and metadata representative of a number of accounts maintained by each borrower, the data and metadata representative of a plurality of invoices; and for each of a plurality of borrower accounts maintained by the first lender and logically associated with a respective one of the borrowers (See, Para. 20, 22, 67, 70-72; Fig. 2; Abstract)).
Regarding Claims 9 and 18:
(Cancelled).
Regarding Claims 10 and 19:
Walter teaches:
further comprising: identifying, by the borrowing base computer program, a trend in the borrowing base. (Walter, Broadly, a contra account automator may include a system that matches customers and vendors to identify a single business entity which is both a customer and a vendor of a borrower, and which calculates an appropriate amount for accounts receivable to be counted as an eligible asset towards a borrowing base based at least in part on accounts payable to the business entity. Advantageously, the system may treat two or more business which are closely related as a single business entity for the purposes of contra accounts. For example, a customer A and a vender B may each be different subsidiaries of the same corporation, hence treated as contra accounts. Also for example, a customer C's brother owns a vendor D, and thus are treated as contra accounts to each other……A calculation viewer may include a system that displays intermediate and total eligible amounts of an asset-based borrowing base on a per-customer or per-asset basis, which amounts include intermediate totals of all eligibility and exclusions, and which may highlight or emphasize adjustments and per-customer exceptions. (See, Para. 16-29)).
Regarding Claim 11:
Walter teaches:
wherein the updated documents are received periodically. (Walter, In most implementations, the most up-to-date or current asset-related information (e.g., accounts receivable, inventory, fixed assets) will be stored by the server computers 110 or processor-based devices 112 of the respective borrower 104, or stored on computers or databases of an agent of the borrower 104 such as an accounting firm and/or a firm providing ERP services. The asset-based lending management system 102 may retrieve, import or extract the asset-related information from the server computers 110, processor-based devices 112, or the nontransitory computer- or processor-readable storage media 120 of the respective borrowers 104 or their agents. The asset-based information may be retrieved periodically (e.g., daily, weekly, monthly, quarterly) or on demand. (See, Para. 66; Fig. 1); At 716, the import worker application 401 d updates stored customer information in a nontransitory processor-readable storage medium. Such may include adding new variations on entity names identified via the process of unifying customer entities. Such customer information may be stored to any of a variety of data structures, for instance records, tables, linked lists, or relational databases. The stored customer information may be used to expedite future review of asset-related information; (See, Para.128-136; Fig. 4, 7)).
Regarding Claims 12 and 20:
Walter teaches:
wherein the borrowing limit is updated when the updated borrowing base and the borrowing base differ by more than a threshold amount. (Walter, Determining an eligible total accounts receivable value for the respective borrower account may include multiplying an accounts receivable amount by a defined advance rate for the first lender. Determining an eligible total accounts receivable value for the respective borrower account may include limiting the eligible total to a maximum accounts receivable amount specified by the first lender. Determining an eligible total accounts receivable value for the respective borrower account may include multiplying an accounts receivable amount by a defined advance rate for the first lender. Determining an eligible total accounts receivable value for the respective borrower account may include limiting the eligible total to a maximum accounts receivable amount specified by the first lender. (See, Para. 12, 20-23, 147-153)).
Regarding Claim 13:
Walter teaches:
A system, comprising: a borrower electronic device for a borrower; (Walter, Systems and methods automate and manage electronic exchange in the asset-based lending industry, particularly between borrowers and lenders (See, Abstract); The accounting package or module and/or an ERP system or module may be resident on borrower computing system(s) or on a system controlled by a third party such as an accountant or other agent of the borrower. (See, Para. 84, 94);
a document source for documents for collateral, assets, and liabilities from the borrower electronic device, wherein the documents comprise accounts receivable aging reports, accounts payable aging reports, aging reconnaissance reports, equipment valuation, inventory valuation, and/or real property valuation information; (Walter, the processing of the various assets (e.g., invoices or accounts receivable, fixed assets, inventory) are brought together starting at 948. In particular, at 948 the import worker application 401 d sums the eligible accounts receivable value, the eligible fixed assets value, and/or the eligible inventory value. At 950, the import worker application 401 d stores the sum as an eligible borrowing base value. Such reflects the eligible amount of all assets which may serve as collateral for a loan. (See, Para. 60, 61, 152-156, 185; Abstract; Fig. 1, 4));
a lender electronic device that is configured to receive the documents from the document source, (Walter, method of operation of a trusted third party evaluation system that includes at least one processor, at least one nontransitory processor-readable medium, and at least one communications port may be summarized as including receiving parameters input by the trusted third party evaluation system from at least a first lender computer system controlled by a first lender, the parameters input specifying a number of parameters imposed by at least the first lender; receiving respective borrower accounting information including data and metadata for each of a number of borrowers by the evaluation system from each of a number of accounting modules executed on a number of source computer systems which are distinct from the first lender computer system (See, Para. 20-21; Abstract));
to extract information for the collateral, assets, and liabilities from the documents, (Walter, an asset data optimizer may include a system that extracts raw asset data from borrower asset management systems and normalizes the extracted raw data into a standard form, optimized to asset-based borrowing base determination (See, Para. 15, 24-29; Abstract));
to calculate, from the information, a borrowing base for the borrower, (Walter, an integrated calculator may include a system that calculates intermediate and total eligible amounts on a customer-by-customer, invoice-by-invoice basis, from standardized, asset-based-lending-optimized data. The calculate eligible totals method 1200 may, for example, be used in calculating eligible customer accounts receivable 910, calculating eligible totals of fixed assets 928 and/or eligible totals of inventory 940 of the calculate transaction method 900 (FIG. 9). In particular, the calculate eligible totals method 1200 applies various rules, typically specified by the lender, to the asset values to determine whether the asset values that are eligible for establishing an eligible borrowing base for the lender to evaluate (See, Para. 9-18, 154-155, 169; Fig. 3-4, 7, 9, 12-13; Abstract));
to forecast a future borrowing base based on historical documents for the borrower, (Walter, the processing of the various assets (e.g., invoices or accounts receivable, fixed assets, inventory) are brought together starting at 948. In particular, at 948 the import worker application 401 d sums the eligible accounts receivable value, the eligible fixed assets value, and/or the eligible inventory value. At 950, the import worker application 401 d stores the sum as an eligible borrowing base value. Such reflects the eligible amount of all assets which may serve as collateral for a loan. At 952, the import worker application 401 d sums the eligible borrowing base value with other loan amounts 954 and current borrowing amounts 956. In particular, the other loan amounts 954 and current borrowing 956 are subtracted from the eligible borrowing base. The other loan amounts 954 and/or current borrowing amounts 956 may be supplied by the borrower and/or by the lender. At 958, the import worker application 401 d stores the sum as a credit line available amount (See, Para. 13-19, 154-155; Fig. 4, 9));
to send the borrowing base and the future borrowing base to the borrower electronic device, (Walter, A calculation viewer may include a system that displays intermediate and total eligible amounts of an asset-based borrowing base on a per-customer or per-asset basis, which amounts include intermediate totals of all eligibility and exclusions, and which may highlight or emphasize adjustments and per-customer exceptions (See, Para. 15-23, 77, 102, 144; Abstract); In particular, at 948 the import worker application 401 d sums the eligible accounts receivable value, the eligible fixed assets value, and/or the eligible inventory value….. At 958, the import worker application 401 d stores the sum as a credit line available amount (See, Para. 13, 15-19, 154-155; Fig. 4, 9));
to receive [acknowledgement] from the borrower electronic device, (Walter, FIG. 5 shows a method 500 of enqueueing a transaction for use in operation of an asset-based lending management system …. At 502, the Web server 401 c (FIG. 4) begins the enqueueing transaction method 500, for example in response to extraction and normalization of asset-related data associated with a particular borrower. At 504, the Web server 401 c receives and validates the normalized asset-related data received from the import application or extraction program 401 b (FIG. 4). Validation may include validating that all discrete pieces of data are present and/or that various values are within expected limits or ranges. (acknowledgement) (See, Para. 110-116; Abstract; Fig. 4-5));
to receive updated documents for the borrower from the document source, (Walter, In most implementations, the most up-to-date or current asset-related information (e.g., accounts receivable, inventory, fixed assets) will be stored by the server computers 110 or processor-based devices 112 of the respective borrower 104, or stored on computers or databases of an agent of the borrower 104 such as an accounting firm and/or a firm providing ERP services. The asset-based lending management system 102 may retrieve, import or extract the asset-related information from the server computers 110, processor-based devices 112, or the nontransitory computer- or processor-readable storage media 120 of the respective borrowers 104 or their agents. The asset-based information may be retrieved periodically (e.g., daily, weekly, monthly, quarterly) or on demand. (See, Para. 66; Fig. 1); At 716, the import worker application 401 d updates stored customer information in a nontransitory processor-readable storage medium. Such may include adding new variations on entity names identified via the process of unifying customer entities. Such customer information may be stored to any of a variety of data structures, for instance records, tables, linked lists, or relational databases. The stored customer information may be used to expedite future review of asset-related information; (See, Para.128-136; Fig. 4, 7));
to extract updated information from the documents, and (Walter, In most implementations, the most up-to-date or current asset-related information (e.g., accounts receivable, inventory, fixed assets) will be stored by the server computers 110 or processor-based devices 112 of the respective borrower 104, or stored on computers or databases of an agent of the borrower 104 such as an accounting firm and/or a firm providing ERP services. The asset-based lending management system 102 may retrieve, import or extract the asset-related information from the server computers 110, processor-based devices 112, or the nontransitory computer- or processor-readable storage media 120 of the respective borrowers 104 or their agents. The asset-based information may be retrieved periodically (e.g., daily, weekly, monthly, quarterly) or on demand. (See, Para. 66; Fig. 1); At 716, the import worker application 401 d updates stored customer information in a nontransitory processor-readable storage medium. Such may include adding new variations on entity names identified via the process of unifying customer entities. Such customer information may be stored to any of a variety of data structures, for instance records, tables, linked lists, or relational databases. The stored customer information may be used to expedite future review of asset-related information; (See, Para.128-136; Fig. 4, 7));
to calculate, from the updated information, an updated borrowing base for the borrower; and (Walter, an integrated calculator may include a system that calculates intermediate and total eligible amounts on a customer-by-customer, invoice-by-invoice basis, from standardized, asset-based-lending-optimized data. The calculate eligible totals method 1200 may, for example, be used in calculating eligible customer accounts receivable 910, calculating eligible totals of fixed assets 928 and/or eligible totals of inventory 940 of the calculate transaction method 900 (FIG. 9). In particular, the calculate eligible totals method 1200 applies various rules, typically specified by the lender, to the asset values to determine whether the asset values that are eligible for establishing an eligible borrowing base for the lender to evaluate (See, Para. 9-18, 154-155, 169; Fig. 3-4, 7, 9, 12-13; Abstract));
a loan servicing system that is configured to set a borrowing limit for the borrower based on the borrowing base, and (Walter, an asset data optimizer may include a system that extracts raw asset data from borrower asset management systems and normalizes the extracted raw data into a standard form, optimized to asset-based borrowing base determination. (See, Para.15-16; Abstract) At 952, the import worker application 401 d sums the eligible borrowing base value with other loan amounts 954 and current borrowing amounts 956. In particular, the other loan amounts 954 and current borrowing 956 are subtracted from the eligible borrowing base. The other loan amounts 954 and/or current borrowing amounts 956 may be supplied by the borrower and/or by the lender. At 958, the import worker application 401 d stores the sum as a credit line available amount (See, Para.154-155));
to set an updated borrowing limit for the borrower based on the updated borrowing base. (Walter, a system …. calculates an appropriate amount for accounts receivable to be counted as an eligible asset towards a borrowing base based at least in part on accounts payable to the business entity. (See, Para. 9, 16-19; 21-23; Abstract); At 952, the import worker application 401 d sums the eligible borrowing base value with other loan amounts 954 and current borrowing amounts 956. In particular, the other loan amounts 954 and current borrowing 956 are subtracted from the eligible borrowing base. The other loan amounts 954 and/or current borrowing amounts 956 may be supplied by the borrower and/or by the lender. At 958, the import worker application 401 d stores the sum as a credit line available amount….. At 964, the import worker application 401 d takes the least of all inputs greater than and equal to zero. The import worker application 401 d may employ a maximum loan amount 966. The maximum loan amount 966 may be supplied by the borrower and/or by the lender, and may be stored in nontransitory processor-readable storage media. The import worker application 401 d stores the resulting value as a new credit line available amount 968. (See, Para. 155-157; Fig. 4, 9)).
Walter does not specifically teach receive [acknowledgement] from the borrower electronic device.
However, Diriye further teaches the following limitation:
to receive [acknowledgement] from the borrower electronic device. (Diriye, the system receives a request from a borrowing party (e.g., through an interface provided on a device associated with the borrowing party). For example, the request may be a request to secure a loan or a line of credit in exchange for providing an asset, such as a digital asset or non-digital asset, as collateral. The request may be accompanied by the borrowing party's acceptance of one or more terms associated with a specific loan, as advertised by a lending party. ……. The system then sends the request to the borrowing party for acceptance and receives a confirmation from the borrowing party. (See, Para.92; Fig. 5)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Walter with the features of Diriye’s system because “provides a system that includes a blockchain network, which includes first and second blockchain nodes. The first blockchain node is configured to receive a request to transfer an asset, and generate a blockchain transaction including a smart contract identifier and one or more parameters. The second blockchain node is configured to obtain one or more rules from a smart contract that corresponds to the smart contract identifier and compare the one or more parameters to the one or more rules to obtain a risk level. In response to the risk level is greater than a threshold, the second blockchain node is configured to not execute the blockchain transaction. In response to the risk level is not greater than the threshold, the second blockchain node is configured to execute the transaction. The request includes the smart contract identifier and the one or more parameters. The asset includes one of a trade item or a service to be performed.” “The system may trigger one or more of the above mentioned amelioration actions. The GUI can be configured to send alert, trigger and notify the relevant user of the transaction or contract, including taking actions or providing feedback to a user on the user computing device. In further embodiments, a system may display requirements, regulations, or constraints on a GUI or user computing device. A GUI may display a notification of a high-risk transaction or contract using the multi-dimensional risk array. The user is notified and can accept or take ameliorating actions.” (Diriye, Para. 5 and 49).
Claims 5-6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Walter (U.S. Patent Application Publication No. US 2014/0058925 A1; hereinafter “Walter”), in view of Diriye (U.S. Patent Publication No. US 2020/0118131 A1; hereinafter “Diriye”), and further in view of Ghosh (U.S. Patent Publication No. US 2023/0317215 A1; hereinafter “Ghosh).
Regarding Claim 5:
Walter teaches:
further comprising: identifying, by the borrowing base computer program, a type of each of the documents [using a machine learning model], wherein the documents are unstructured documents. (Walter, The retrieval may be directly, or may be via application programming interface (API) calls. Such may require that the asset-based lending management computer system(s) 202 have sufficient right, permission, privilege or authority for such an action The system memory 269 may also include other communications programs, for example, a Web client or browser that permits the borrower associated computer systems 206 and particularly the lender associated computer systems 208 to access and exchange data with sources such as Web sites of the Internet, corporate intranets, extranets, or other networks. Such may require that the lender associated computer systems 208 have sufficient right, permission, privilege or authority for accessing a given borrower's asset-related information via the asset-based lending management computer system(s) 202. The browser may, for example, be markup language based, such as Hypertext Markup Language (HTML), Extensible Markup Language (XML) or Wireless Markup Language (WML), and may operate with markup languages that use syntactically delimited characters added to the data of a document to represent the structure of the document. (See, Para. 64-65, 77-80, 89-90; Abstract)).
Walter and Diriye do not specifically teach identifying, by the borrowing base computer program, a type of each of the documents [using a machine learning model], wherein the documents are unstructured documents.
However, Ghosh further teaches the following limitation:
identifying, by the borrowing base computer program, a type of each of the documents [using a machine learning model], wherein the documents are unstructured documents. (Ghosh, The document format analysis unit 210 may be configured to use the one or more machine learning models and/or rules-based models when analyzing documents to identify relevant sections of structured or unstructured documents. …..The document information datastore 230 may include information mapping a particular machine learning or rules-based model that may be used to analyze a particular type of document. The models may be created using the model development and training unit 215 to create new model and/or to update existing models to handle new types of documents to be analyzed. ….. The document format analysis unit 210 may be configured to identify the type of document using metadata associated with the document, by analyzing the contents of the document, by analyzing a file type extension of a filename of the document, and/or by providing the document as an input to a machine learning model configured to receive a document as an input and to output of a prediction of the type of the document; The data analysis unit 220 may generate an assessment of the probability of business success based on resources, patents, expertise of the organization and/or individuals in the organization, partnerships with other organizations and/or individuals, financial status of organization (See, Para. 29 and 39)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Walter and Diriye with the features of Ghosh’s system because “techniques for automating the acquisition and assessment of data for designing clinical studies, comparing outcomes of other historic studies and their market performance and/or for conducting assessments of the technical and business risks involved with such studies are described. These techniques provide a technical solution to the problem of accurately acquiring, assessing, comparing, and analyzing the large volumes of data associated with such projects in a timely manner. The techniques herein utilized may be used to develop machine-learning and/or rules-based models that may rapidly identify and analyze large volumes of data to automatically generate context-based recommendations for designing a clinical study and/or for conducting assessments of the risks associated with a study. These techniques may provide significant cost saving, time savings, and labor savings compared with the current manual and labor-intensive techniques.” (Ghosh, Para. 21).
Regarding Claim 6:
Walter teaches:
wherein [the machine learning model is trained with a plurality of document formats] to predict a format of each of the documents. (Walter, Web client or browser that permits the borrower associated computer systems 206 and particularly the lender associated computer systems 208 to access and exchange data with sources such as Web sites of the Internet, corporate intranets, extranets, or other networks. Such may require that the lender associated computer systems 208 have sufficient right, permission, privilege or authority for accessing a given borrower's asset-related information via the asset-based lending management computer system(s) 202. The browser may, for example, be markup language based, such as Hypertext Markup Language (HTML), Extensible Markup Language (XML) or Wireless Markup Language (WML), and may operate with markup languages that use syntactically delimited characters added to the data of a document to represent the structure of the document. (See, Para. 64-65, 77-80, 89-90; Abstract)).
Walter and Diriye do not specifically teach that [the machine learning model is trained with a plurality of document formats] to predict a format of each of the documents.
However, Ghosh further teaches the following limitation:
wherein [the machine learning model is trained with a plurality of document formats] to predict a format of each of the documents. (Ghosh, The document format analysis unit 210 may be configured to use the one or more machine learning models and/or rules-based models when analyzing documents to identify relevant sections of structured or unstructured documents. …..The document information datastore 230 may include information mapping a particular machine learning or rules-based model that may be used to analyze a particular type of document. The models may be created using the model development and training unit 215 to create new model and/or to update existing models to handle new types of documents to be analyzed. ….. The document format analysis unit 210 may be configured to identify the type of document using metadata associated with the document, by analyzing the contents of the document, by analyzing a file type extension of a filename of the document, and/or by providing the document as an input to a machine learning model configured to receive a document as an input and to output of a prediction of the type of the document; The data analysis unit 220 may generate an assessment of the probability of business success based on resources, patents, expertise of the organization and/or individuals in the organization, partnerships with other organizations and/or individuals, financial status of organization (See, Para. 29 and 39)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Walter and Diriye with the features of Ghosh’s system because “techniques for automating the acquisition and assessment of data for designing clinical studies, comparing outcomes of other historic studies and their market performance and/or for conducting assessments of the technical and business risks involved with such studies are described. These techniques provide a technical solution to the problem of accurately acquiring, assessing, comparing, and analyzing the large volumes of data associated with such projects in a timely manner. The techniques herein utilized may be used to develop machine-learning and/or rules-based models that may rapidly identify and analyze large volumes of data to automatically generate context-based recommendations for designing a clinical study and/or for conducting assessments of the risks associated with a study. These techniques may provide significant cost saving, time savings, and labor savings compared with the current manual and labor-intensive techniques.” (Ghosh, Para. 21).
Regarding Claim 16:
Walter teaches:
wherein the lender electronic device is further configured to identify a type of each of the documents using [a machine learning model], wherein the documents are unstructured documents, wherein [the machine learning model is trained with a plurality of document formats] to predict a format of each of the documents. (Walter, The retrieval may be directly, or may be via application programming interface (API) calls. Such may require that the asset-based lending management computer system(s) 202 have sufficient right, permission, privilege or authority for such an action The system memory 269 may also include other communications programs, for example, a Web client or browser that permits the borrower associated computer systems 206 and particularly the lender associated computer systems 208 to access and exchange data with sources such as Web sites of the Internet, corporate intranets, extranets, or other networks. Such may require that the lender associated computer systems 208 have sufficient right, permission, privilege or authority for accessing a given borrower's asset-related information via the asset-based lending management computer system(s) 202. The browser may, for example, be markup language based, such as Hypertext Markup Language (HTML), Extensible Markup Language (XML) or Wireless Markup Language (WML), and may operate with markup languages that use syntactically delimited characters added to the data of a document to represent the structure of the document. (See, Para. 64-65, 77-80, 89-90; Abstract));
Walter and Diriye do not specifically teach wherein the lender electronic device is further configured to identify a type of each of the documents using [a machine learning model], wherein the documents are unstructured documents, wherein [the machine learning model is trained with a plurality of document formats] to predict a format of each of the documents.
However, Ghosh further teaches the following limitation:
wherein the lender electronic device is further configured to identify a type of each of the documents using [a machine learning model], wherein the documents are unstructured documents, wherein [the machine learning model is trained with a plurality of document formats] to predict a format of each of the documents. (Ghosh, The document format analysis unit 210 may be configured to use the one or more machine learning models and/or rules-based models when analyzing documents to identify relevant sections of structured or unstructured documents. …..The document information datastore 230 may include information mapping a particular machine learning or rules-based model that may be used to analyze a particular type of document. The models may be created using the model development and training unit 215 to create new model and/or to update existing models to handle new types of documents to be analyzed. ….. The document format analysis unit 210 may be configured to identify the type of document using metadata associated with the document, by analyzing the contents of the document, by analyzing a file type extension of a filename of the document, and/or by providing the document as an input to a machine learning model configured to receive a document as an input and to output of a prediction of the type of the document; The data analysis unit 220 may generate an assessment of the probability of business success based on resources, patents, expertise of the organization and/or individuals in the organization, partnerships with other organizations and/or individuals, financial status of organization (See, Para. 29 and 39)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Walter and Diriye with the features of Ghosh’s system because “techniques for automating the acquisition and assessment of data for designing clinical studies, comparing outcomes of other historic studies and their market performance and/or for conducting assessments of the technical and business risks involved with such studies are described. These techniques provide a technical solution to the problem of accurately acquiring, assessing, comparing, and analyzing the large volumes of data associated with such projects in a timely manner. The techniques herein utilized may be used to develop machine-learning and/or rules-based models that may rapidly identify and analyze large volumes of data to automatically generate context-based recommendations for designing a clinical study and/or for conducting assessments of the risks associated with a study. These techniques may provide significant cost saving, time savings, and labor savings compared with the current manual and labor-intensive techniques.” (Ghosh, Para. 21).
Response to Arguments
With respect to the claim objections of claims 1, 2, and 13 made in the Non-Final Office Action mailed on 10/21/2025, the objections have been withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 01/21/2026.
Applicant's arguments filed on 01/21/2026 have been fully considered, but are not persuasive due to the following reasons:
With respect to the rejection of claims 1-20 under 35 U.S.C. 101, Applicant arguments are moot in view of the grounds of rejections presented above in this office action. The arguments are addressed to the extent they apply to the amended claims.
Applicant argues that “Claims 1-20 stand rejected under 35 U.S.C. § 101 as allegedly directed to a judicial exception without significantly more. Specifically, the Office Action contends that the claims are directed to a judicial exception, do not integrated the alleged judicial exception into a practical application, and do not recite "significantly more" than the alleged judicial exception. See Office Action, pages 3-11. Applicant respectfully disagrees.”
Examiner respectfully disagrees.
Under Step 2A: Prong I, Examiner respectfully notes that the claims as amended, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of determining a borrowing limit based on a borrowing base, without significantly more. The series of steps recited in the claims, as amended, describe the abstract idea, which is the processing a borrower’s collateral, assets, and liabilities information to determine and update the borrower’s borrowing limit based on a borrowing base; therefore, corresponding to a commercial interaction. Hence, a commercial interaction is a Certain Methods of Organizing Human Activity. Also, the abstract idea is the evaluation of information of collateral, assets, and liabilities from a borrower’s documents to process and update a borrowing limit based on a borrowing base, which is a concept performed in the human mind. Therefore, a concept performed in the human mind is a Mental Process. Furthermore, the determining of a borrowing limit based on a borrowing base involves the calculating of the borrowing base of a borrower; therefore, corresponding to mathematical calculation, and/or formula or equations, and/or relationship. Hence, a mathematical calculation, and/or formula or equations, and/or relationship is a Mathematical Concept. Furthermore, the system limitations of Independent Claim 1, for example, e.g., a borrowing base computer program, and borrower electronic device, do not necessarily restrict the claim from reciting an abstract idea. Furthermore, Examiner respectfully notes that the claims are first analyzed in the absence of technology to determine if it recites an abstract idea. The additional limitations of technology are then considered to determine if it restricts the claim from reciting an abstract idea. In this case, and as discussed in the 2019 and 2024 updated Guidance on Patent Subject Matter Eligibility, it is determined that the additional limitations of technology do not necessarily restrict the claim from reciting an abstract idea. Furthermore, Examiner respectfully notes that the recited features in the limitations of Claim 1: “receiving, by a borrowing base computer program, documents for collateral, assets, and liabilities from a borrower electronic device for a borrower; extracting, by the borrowing base computer program, information for the collateral, assets, and liabilities from the documents; calculating, by the borrowing base computer program and from the information, a borrowing base for the borrower; forecasting, by the borrowing base computer program, a future borrowing base based on historical documents for the borrower; sending, by the borrowing base computer program, the borrowing base and the future borrowing base to borrower electronic device; receiving, by the borrowing base computer program, acknowledgement from the borrower electronic device; setting, by the borrowing base computer program, a borrowing limit based on the borrowing base; receiving, by the borrowing base computer program, updated documents for the borrower; extracting, by the borrowing base computer program, updated information from the updated documents; calculating, by the borrowing base computer program and from the updated information, an updated borrowing base for the borrower; and setting, by the borrowing base computer program, an updated borrowing limit based on the borrowing base” are simply making use of a computer and the computer limitations do not necessarily restrict the claim from reciting an abstract idea as discussed above under Step 2A-Prong 1 of the 35 U.S.C. 101 rejection. Hence, Examiner has also considered each and every arguments under Step 2A-Prong 1 and concludes that these arguments are not persuasive. For example, under Step 2A-Prong 1, Examiner considers each and every limitation to determine if the claim recites an abstract idea. Furthermore, Examiner respectfully notes that it is determined that the claim recites an abstract idea and the additional limitations of a computer device does not necessarily restrict the claim from reciting an abstract idea. The recited steps, as amended, are abstract in nature as there are no technical/technology improvements as a result of these steps. Thus, the claim recites an abstract idea. Whether the claim integrates the abstract idea into a practical application by providing technical/technology improvements are considered under Step 2A-Prong II.
Applicant argues that “the amended claims employ the information provided by the allegedly judicial exception in order to provide a current borrowing basis, a future borrowing basis, and an updated borrowing basis. Specifically, the borrowing base computer program receives documents for collateral, assets, and liabilities from a borrowing system for a borrower, and uses information from those documents to calculate a borrowing base. Then, using historical documents for the borrower, the borrowing base computer program forecasts a future borrowing base. And, after receiving updated documents for the borrower, the borrowing base computer program calculates an updated borrowing base. These calculations prevent the borrowing base from being inaccurate because it is based on current documents. See, e.g., Appl'n, 0003. Thus, these elements together recite a meaningful way of using the alleged judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. ….. Applicant respectfully submits that, when the claims are interpreted properly, this is not a close call, and the claims are directed to statutory subject matter. For at least these reasons, Applicant respectfully submits that the claims are directed to statutory subject matter, and respectfully requests that this rejection be withdrawn.”
Examiner respectfully disagrees.
Under Step 2A: Prong II, Examiner respectfully notes that there is no improved technology in simply receiving, extracting, accessing, calculating, forecasting, predicting, updating, setting, sending, and outputting data (i.e., collateral, assets, and liabilities data, borrowing base data, future borrowing base, historical document data, borrower data, borrowing limit data, updated information, updated borrowing limit data, and etc.). The disclosed invention simply cannot be equated to improvement to technological practices or computers. There is no technical improvement at all. Examiner respectfully notes that Applicant recites features in the limitations do not result in computer functionality or technical improvement. Furthermore, Examiner respectfully notes that Applicant is simply using a computer to input, process, and output data. The recited features in the limitations does not disclose a technical solution to technical problem, but simply a business solution.
Furthermore, the recited steps, as amended, are merely managing/processing data (MPEP 2106.05(d)(II)) and does not result in computer functionality or technical improvement. Moreover, Applicant has simply provided a business method practice of processing data (collateral, assets, and liabilities data, borrowing base data, future borrowing base, historical document data, borrower data, borrowing limit data, updated information, updated borrowing limit data, and etc.), and no technical solution or improvement has been disclosed. There is no technology/technical improvement as a result of implementing the abstract idea. The recited limitations in the pending claims simply amount to the abstract idea of determining a borrowing limit based on a borrowing base. There is no computer functionality improvement or technology improvement. The claim does not provide a technical solution to a technical problem. If there is an improvement, it is to the abstract idea and not to technology. Additionally, Examiner notes that it is important to keep in mind that an improvement in the judicial exception itself (e.g., recited fundamental economic principle or practice and/or commercial interaction and/or mental process and/or mathematical calculation) is not an improvement in technology (See, MPEP 2106.05(a)(II)). Moreover, the claims, as amended, recites steps at a high level of generality. In addition, all uses of the recited judicial exceptions require such data gathering, processing, and outputting; therefore, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and output. See MPEP 2106.05. The claim simply makes use of a computer as a tool to apply the abstract idea without transforming the abstract idea into a patent eligible subject matter. Thus, the claim does not integrate the abstract idea into a practical application; and these arguments are not persuasive.
Additionally, these steps, as amended, are recited as being performed by a borrowing base computer program, and borrower electronic device (independent claim 1); and a borrower electronic device, document source, lender electronic device, and loan servicing system (independent claim 13). The additional elements (as recited in independent claims 1 and 13) are recited at a high level of generality, and are used as a tool to perform the generic computer function of receiving, inputting, processing, and outputting data. See MPEP 2106.05(f). For example, the claims, as amended, recite a borrower electronic device, document source, lender electronic device, and loan servicing system, (independent claim 13), which are simply used to perform an abstract idea, as discussed above in Step 2A, Prong I, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Furthermore, the recitation of “a borrower electronic device, document source, lender electronic device, and loan servicing system (independent claim 13)” in the limitations merely indicates a field of use or technological environment in which the judicial exception is performed. Therefore, Examiner respectfully notes that the claims, as amended, merely confines the use of the abstract idea to a particular technological environment; and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. Hence, the claims, as amended, do not integrate the abstract idea into a practical application. Thus, these arguments are not persuasive.
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 101 rejection of claims 1-8, 10-17, and 19-20.
With respect to the rejection of claims 1-20 under 35 U.S.C. 103, Applicant arguments are moot in view of cited language in previously used prior art, as presented above in this office action. The arguments are addressed to the extent they apply to the amended claims
Applicant argues that “without conceding that the proposed combination of Walter and Diriye is proper, Applicant respectfully submits that the proposed combination does not disclose all elements of amended claim 1….For at least these reasons, the proposed combination of Walter and Diriye does not disclose all elements of amended independent claim 1. While of a different scope, independent claim 13 has been amended to recite similar elements and is allowable for at least these reasons. Therefore, Applicant respectfully requests that the rejection of independent claims 1 and 13, and of all claims dependent thereon, be withdrawn.” Examiner respectfully disagrees and notes that Applicant's arguments are moot in view of new grounds of rejection presented above based on newly cited portions of paragraphs 12-19, 20-23, 147-155; Fig. 4, 9; and abstract of the disclosed prior art: Walter, which reads on the amended language.
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 103 rejection of claims 1-8, 10-17, and 19-20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure are the following:
Bodenheim (U.S. Patent Application Publication No. US 2006/0047600-A1) “Method and system for borrowing base certificate administration”
Vicente (U.S. Patent No. US 8,706,625-B2) “System and method for providing borrowing schemes”
Barnes (Patent Application Publication No. US 2007/0214077-A1) “Systems and methods for asset based lending (abl) valuation and pricing”
Lensink (U.S. Patent Application Publication No. US 2012/0191593-A1) “Providing an interface to loan information”
Tran (U.S. Patent Application Publication No. US 2013/0332337-A1) “Systems and methods for enabling trusted borrowing and lending using electronic funds”
Jain (U.S. Patent Application Publication No. US 2019/0080399-A1) “Automated collateral risk and business performance assessment system”
Benton (U.S. Patent Application Publication No. US 2023/0088229-A1) “Peer-to-peer borrowing and lending systems and methods”
Ott (U.S. Patent Application Publication No. US 2023/0316104-A1) “Machine learning systems and methods for document recognition and analytics”
Melton (U.S. Patent Application Publication No. US 20240412283-A1) “Analysis of energy reserves”
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H MUSTAFA whose telephone number is (571)270-7978. The examiner can normally be reached M-F 8:00 - 5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael W Anderson can be reached on 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MOHAMMED H MUSTAFA/Examiner, Art Unit 3693
/ELIZABETH H ROSEN/Primary Examiner, Art Unit 3693
Prosecution Insights