DETAILED ACTION
This communication is a Non-Final Office Action on the merits in response to communications received on 06/29/2026. Claims 1, 10, and 21 have been amended. Therefore, claims 1, 3, 5, 7-15, and 21-28 are pending and have been addressed below. The present application is being examined under the pre-AIA first to invent provisions.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/29/2026 has been entered.
Claim Objections
Claim 26 is objected to because of the following informalities:
Claim 26 recites “The system of claim 1, wherein receiving the first metric”. This is a minor typo-graphical error and needs to be replaced with “mining” to remove the claim objection. Appropriate correction is required.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1, 3, 5, 7-15, and 21-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
5. Under Step 1 of the two-part analysis from Alice Corp, Claim 1 recites a machine (i.e., a thing, consisting of parts, or of certain devices and combination of devices), Claim 10 recites a manufacture (i.e., "an article that is given a new form, quality, property, or combination through man-made or artificial means."), Claim 21 recites a process (i.e., a series of acts or steps). Thus, each of the claims fall within one of the four statutory categories.
6. Under Step 2A – Prong One of the two-part analysis from Alice Corp, the claimed invention recites an abstract idea.
7. Claims 1, 10, and 21 recite:
“first data associated with a first loan officer and second data associated with a second loan officer, the first data including a current expertise set;”, “compliance data associated with qualifications, compliance or non-compliance with regulations, and industry customs of the first loan officer;”, “determining based at least in part on the first data and the compliance data, at least a first transaction associated with the first loan officer;”, “receiving…a first metric in response to inputting the first transaction and a performance review criteria;”, “determining based at least in part on the second data, at least a second transaction associated with the second loan officer;”, “receiving…a second metric in response to inputting the second transaction and the performance review criteria; generating, based at least in part on first metric and the second metric, a comparison between the first metric associated with the first loan officer and the second metric associated with the second loan officer;”, “comparing the first metric with a loan service provider metric for a loan service provider to determine a match between the first loan officer and the loan service provider;”, “the comparison providing an indication of a compatibility of the first loan officer with the loan service provider;”
Under the broadest reasonable interpretation, the limitations recite an abstract idea for collecting and analyzing certain data related to loan officers associated with loan service providers which encompasses fundamental economic practices, i.e., mitigating risks, commercial interactions, (i.e., marketing or sales activities, business relations), managing personal behavior or interactions (i.e., social activities), and mental processes, (i.e., observations, evaluations, opinions, judgment), that fall within the certain methods of organizing human activity and mental processes groupings of abstract ideas. See MPEP 2106.04
The Applicant’s Specification at [0002] While technology has driven process automation in the mortgage lending industry, business is fueled by strong client relationships. As a result, the entire food chain is largely controlled by the loan officers and the loans they produce.
Consistent with the disclosure the series of steps cover tasks necessary to evaluate and compare loan officer(s) compatibility with loan service providers which involves concepts relating to sales/marketing activities and/or business relations. The limitations also cover tasks for protecting potential clients against risk before contacting a loan officer and/or tasks a financial institution may perform when monitoring performance or coaching their personnel. The limitations of “determining” and “generating” in the context of the claim are mental processes for collecting data and recognizing certain data within the data, which are evaluations that may be performed in the human mind or by a human with pen and paper. For example, a manager could look over information provided from loan officers and loan service providers, compare transactions, compliance data, and performance metrics, to produce reports with trends comparing the loan officers with the loan service providers in his/her mind or with use of a pen or paper. Accordingly, the claim recites an abstract idea.
8. Under Step 2A – Prong Two of the two-part analysis from Alice Corp, this judicial exception is not integrated into a practical application because the additional elements of: “a system comprising: at least one processor; a display; a non-transitory machine-readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:”, “from a plurality of loan information sources”, “from one or more regulatory servers”, “from one or more machine learned networks”, “into the one or more machine learned networks”, “graphical user interface that includes at least a graphical”, “the graphical user interface”, “on the display, a second user interface” – see claims 1, 10, and 21 are all recited at a high-level of generality in light of the specification [¶ 0024, 0051-0052]. Thus, because the specification describes the additional elements in general terms without describing the particulars the additional elements may be broadly but reasonably construed as reciting generic computer components performing ordinary computer functions in light of the applicant’s specification. Therefore, the additional elements recited in the claim add the words “apply it” with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely use a computer processor as a tool to perform the abstract idea as discussed in MPEP 2106.05 (f).
The other additional elements of: “mining”, “presenting…on the display”, “generating…presenting…” merely add insignificant extra-solution activity, i.e., data gathering, data output, to the judicial exception, as discussed in MPEP 2106.05(g).
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
9. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of: “a system comprising: at least one processor; a display; a non-transitory machine-readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:”, “from a plurality of loan information sources”, “from one or more regulatory servers”, “from one or more machine learned networks”, “into the one or more machine learned networks”, “graphical user interface that includes at least a graphical”, “the graphical user interface”, “on the display, a second user interface” – see claims 1, 10, and 21 at best amounts to nothing more than mere instructions in which to apply the judicial exception and cannot provide an inventive concept at Step 2B.
The other additional elements of: “mining”, “presenting…on the display”, “generating…presenting…” were considered insignificant extra-solution activity. Below, the additional elements have been re-evaluated to determine whether it is considered well-understood, routine, and/or conventional. MPEP 2106.05(d)(II) discusses the Symantec, TLI Communications, and OIP Techs court decisions which indicate “receiving and transmitting data over a network” and “presenting offers and gathering statistics” are well-understood, routine, and/or conventional activities and/or computer functions when they are claimed in a generic manner. Thus, at Step 2B the claim(s) are ineligible.
10. Claims 3, 5, 7-9, 11-15, 22-28 are dependent claims of 1, 10, and 21.
Claim 3 recites “wherein the first data includes a rating of the first loan officer and a satisfaction measure associated with the first loan officer.” which further describes the type of data/information that may be recited within the abstract idea, but does not make the abstract idea any less abstract. Claims 5 and 13 recite “determining a user of the system has permission to view the graphical user interface, prior to presenting the graphical user interface on the display.” which recites steps for authenticating a user that further narrow how the abstract idea may be performed, but do not make the claim any less abstract, claims 7, 14, and 22 recite “wherein the operations further comprise: receiving first additional data associated with the first loan officer and second additional data associated with the second loan officer; generating a first modified metric based at least in part on the first additional data and the first metric; generating a second modified metric based at least in part on the second additional data and the second metric; generating, based at least in part on first modified metric and the second modified metric, a second graphical user interface that includes at least a graphical comparison between the first modified metric associated with the first loan officer and the second modified metric associated with the second loan officer; and presenting the second graphical user interface on the display.” which recites the same abstract idea identified in claim 1 and further narrows how the abstract idea may be performed, but does not make the claim any less abstract, claims 8, 15, and 23 recites “wherein: the operations further comprise: determining a first confidence factor associated with the first data; determining a second confidence factor associated with the second data; and generating the graphical user interface is based at least in part on the first confidence factor and the second confidence factor.” which further narrows how the abstract idea may be performed, but does not make the claim any less abstract, claims 9 and 24 recite “wherein the first confidence factor represents a veracity score associated with the first data.” which further describes the data/information recited in the abstract idea, but does not make the claim any less abstract, claim 11 recite “gathering, via one or more networks, the first data and the second data from one or more data stores.” which further narrows how the abstract idea may be performed, but does not make the claim any less abstract. Here, the additional elements of “one or more data stores” are recited at a high-level of generality and operate in their normal capacity to retrieve and/or store data. See MPEP 2106.05(f) claim 12 recite “wherein the operations further comprise: causing the graphical user interface to be presented on a second display of a user device.” which is recited at a high-level of generality and adds use of generic interface functionality to the claim. See MPEP 2106.05(f) claim 25 recites “further comprising: determining a user has permission to view the graphical user interface; and responsive to determining that the user has permission to view the graphical user interface, causing the graphical user interface to be presented on a second display of a user device.” which recites steps for authenticating a user that further narrow how the abstract idea may be performed, but do not make the claim any less abstract., claim 26 recites “wherein receiving the first metric further comprises receiving one or more vectors for the first loan officer, the one or more vectors including a confidence factor, a veracity score, an amount of data associated with the first loan officer collected by the system, and one or more data types associated with the first data” which further describes the type of data/information that may be used within the abstract idea, but does not make the claim any less abstract, claim 27 recites “generating an interview schedule including the first loan officer and the second loan officer based at least in part on the first metric and the second metric; and presenting the interview schedule on the display as part of the graphical user interface that includes at least the graphical comparison between the first metric associated with the first loan officer and the second metric associated with the second loan officer.” which further narrows how the abstract idea may be performed, but does not make the claim any less abstract., claim 28 recites “wherein the graphical user interface includes photographs of the first loan officer, photographs of the second loan officer, expert analysis, estimated compensation for the first loan officer, estimated compensation for the second loan officer, an organization projection including the first loan officer, and an organization projection including the second loan officer” which further describes the type of data or information that may be presented. Here, the graphical user interface operates in its normal capacity and is merely being used as a tool to aid in performing the abstract idea. See MPEP 2106.05(f) Accordingly, when the limitations above are considered individually and as a whole with the judicial exception, the limitations from the dependent claims fail to integrate the judicial exception into a practical application or provide an inventive concept.
Claim Rejections - 35 USC § 103
11. 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.
12. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
13. Claim(s) 1, 3, 5, 7, 10-14, 21-22, and 25-27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Polston (US 2006/0184448 A1) in view of Yoo (US 2015/0006259 A1) in further view of Corr (US 2006/0004651 A1).
With respect to claims 1, 10, and 21, Polston discloses
a system, one or more machine-readable medium, and method (¶ 0027: discloses computerized system 10) comprising:
at least one processor (¶ 0027: at least one processor);
a display (Fig. 11);
a machine-readable medium (¶ 0027: discloses a computer readable medium) storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
first data associated with a first loan officer and second data associated with a second loan officer(¶ 0078-0082, 0091, 0093: discloses the system 10 is designed to monitor the activities of individual users and track statistics associated with specific entities within the hierarchy of the financial institution. The database contains loan officers that are not currently employed by the financial institution but have been selected as recruiting targets by the manager.);
generating, based at least in part on first metric and the second metric, a graphical user interface that includes at least a graphical comparison between the first metric associated with the first loan officer and the second metric associated with the second loan officer (¶ 0088, See at least Fig. 13 and associated text, Loan officer list with calculated metrics such as buyer count, buyer pipeline $ and seller count); and
presenting the graphical user interface on the display. (¶ 0088, See Fig. 11, 502, Lender metrics i.e., loan officers of all institutions are compared with lenders of north division, see ‘This Year vs Last Year’ comparison of Lenders at a financial institution.)
The Polston reference does not explicitly disclose the following limitations.
In the same field of endeavor, the Yoo reference is related to a methods and systems for providing feedback to professionals. (0002-0004)
the first data including a current expertise set (¶ 0034, 0036, 0041, 0049: discloses the profile generator 202 accesses local and remotes databases to retrieve data associated with a professional or entity. The professional is an individual working in a professional services environment such as financial services professional, i.e., bankers. A profiled individual or entity has a level of domain expertise. A level of expertise refers to a level of familiarity with a particular subject.)
determining based at least in part on the first data, at least a first transaction associated with the first loan officer (¶ 0050-0051: discloses the profile generator 202 analyzes accessed data including level of education, user generated data, interaction data to determine what procedures they followed and lifecycle data.);
determining based at least in part on the second data, at least a second transaction associated with the second loan officer (¶ 0050-0051: discloses the profile generator 202 analyzes accessed data including level of education, user generated data, interaction data to determine what procedures they followed and lifecycle data.);
receiving from one or more machine learned networks a first metric in response to inputting the first transaction into the one or more machine learned networks (¶ 0043-0044, 0065-0066, 0069-0072, 0089: discloses the prediction engine 208 executes algorithms on the data set. The prediction engine accesses a neural network to generate the prediction. Also, the method includes generating a performance metric for the professional. A performance metric may have any form that allows the performance metric of a first profiled professional to be compared to a second performance metric of a second profiled professional. The analysis engine selects a subset of features to characterize the professional including number/types of cases seen, cases seen or worked on per day);
receiving from the one or more machine learned networks a second metric in response to inputting the second transaction and the performance review criteria into the one or more machine learned networks (¶ 0043-0044, 0065-0066, 0069-0072, 0089: discloses generating a performance metric for the professional. A performance metric may have any form that allows the performance metric of a first profiled professional to be compared to a second performance metric of a second profiled professional. The analysis engine selects a subset of features to characterize the professional including number/types of cases seen, cases seen or worked on per day. The method including generating and comparing a second performance metric generated for a second professional.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in Polston’s method and system for recruiting loan officers, the techniques for the first data including a current expertise set; determining based at least in part on the first data, at least a first transaction associated with the first loan officer; determining based at least in part on the second data, at least a second transaction associated with the second loan officer; receiving from one or more machine learned networks a first metric in response to inputting the first transaction into the one or more machine learned networks; receiving from the one or more machine learned networks a second metric in response to inputting the second transaction and the performance review criteria into the one or more machine learned networks, as disclosed by Yoo to achieve the claimed invention.
As disclosed by Yoo, the motivation for the combination would have been to provide advantages that enable professionals to understand the heterogeneity in practice styles across their industries and learn from outliers as well as from averages. (¶ 0002-0004)
The combination of Polston and Yoo does not explicitly disclose the following limitations. In the same field of endeavor, the Corr reference is related to using a common database and interfaces to facilitate the generation of reports to aid a manager in the operation of the brokerage (¶ 0078) and teaches:
mining, from a plurality of loan information sources (¶ 0011, 0036, 0041, 0043, 0062: discloses the current status of all loan applications is stored in database 210. During the course of the loan application process, various items of information are transmitted among the parties and maintained in databases stored in the broker computer. The loan origination software system and centralized database are executed and maintained on a dedicated server computer that is coupled to one or more computers operated by the loan broker. The system is a comprehensive software program that interconnects an entire mortgage origination enterprise comprising the borrower, loan officer, processer, broker, lender, underwriter, and service provider.)
mining, from one or more regulatory servers, compliance data associated with qualifications, compliance or non-compliance with regulations, and industry customs of the first loan officer (¶ 0043, 0052, 0057-0058: discloses the database stores government regulation information. Data can be stored for each of the broker entities such as loan officer client. Loan officers each perform different tasks.);
determining based at least in part on the first data and the compliance data, at least a first transaction associated with the first loan officer (¶ 0043, 0052, 0057-0058: discloses the highest priority tasks for loan officers is to manage borrower relationships, pre-qualify borrowers, initiate the loan process, and the monitor the pipeline. The broker manager is concerned with monitoring productivity and compliance with regulatory requirements of loan officers.);
comparing the first metric with a loan service provider metric for a loan service provider to determine a match between the first loan officer and the loan service provider (¶ 0011, 0079-0087: discloses the central database provides the ability to provide accurate business metrics in real time including loan officer pipeline summary and lender overview and trends.); and
generating, on the display, a second graphical user interface presenting the comparison between the first metric and the loan service provider metric (¶ 0011, 0079-0087: discloses the brokage manager interface includes several report generating capabilities to compile statistics associated with individual users and third party service providers. For example, the brokage manager uses an “executive dashboard” that provides several graphic views of real-time business information including activities and trends of loan officers, lenders, and settlement providers.),
the comparison providing an indication of a compatibility of the first loan officer with the loan service provider. (¶ 0011, 0079-0087: discloses the trends from the loan officers and lenders allow a manager to monitor the performance of personnel within the brokerage and view particular activities or trends related to specific user which helps the manager to optimize the brokerage.)
As such, the passages in at least [¶ 0058, 0079-0087] from the Corr reference teach or suggest techniques for mining loan and compliance information, comparing business metrics and generating interfaces that depict real-time business information including activities and trends from loan officers and lenders were known in the state of the art and previously performed in the industry to aid managers that needed a tool to allow better monitoring of performance and business activities of all the personnel to optimize the brokerage.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Polston and Yoo, to include, the techniques for mining, from a plurality of loan information sources; mining, from one or more regulatory servers, compliance data associated with qualifications, compliance or non-compliance with regulations, and industry customs of the first loan officer; determining based at least in part on the first data and the compliance data, at least a first transaction associated with the first loan officer; comparing the first metric with a loan service provider metric for a loan service provider to determine a match between the first loan officer and the loan service provider; generating, on the display, a second graphical user interface presenting the comparison between the first metric and the loan service provider metric, the comparison providing an indication of a compatibility of the first loan officer with the loan service provider, as disclosed by Corr to achieve the claimed invention. As disclosed by Corr, the motivation for the combination would have been to incorporate the executive dashboard tool in order to provide advantages for managers concerned with monitoring productivity of loan officers and business provided to particular lenders and third party service providers. (¶ 0058, 0080)
With respect to claim 3, the combination of Polston, Yoo, and Corr discloses the system of claim 1,
wherein the first data includes a rating of the first loan officer (¶ 0091, 0093: Polston discloses maintains a list of ten potential loan officers. The system is designed to monitor the activities of individual users and to track statistics associated with specific entities within the institution.) and a satisfaction measure associated with the first loan officer. (¶ 0091: Polston discloses the data contains the loan officer’ priority, i.e., hot, warm, cold.)
With respect to claim 11, the combination of Polston, Yoo, and Corr discloses the one or more non-transitory machine-readable medium,
wherein the operations further comprise:
gathering, via one or more networks, the first data and the second data from one or more data stores. (¶ 0076: Polston discloses the computerized system 10 is provided to multiple lending institutions 300 which represent one or more data stores and maintains the related data for each of the institutions.)
With respect claim 12, the combination of Polston, Yoo, and Corr discloses the one or more non-transitory machine-readable medium,
wherein the operations further comprise:
causing the graphical user interface to be presented on a second display of a user device. (¶ 0007, 0101: Polston discloses the organizational chart tool 500 is a useful tool to reach other information about the performance of a financial institution. By clicking on “Lenders” in the organizational chart. The lender list contains for each lender 40, total count of buyers in the system associated with that lender 40, the pipeline amount 20, the count of sellers 30 associated with that lender 40.)
With respect to claims 5 and 13, the combination of Polston, Yoo, and Corr discloses the system and one or more non-transitory machine-readable medium, wherein the operations further comprise:
determining a user of the system has permission to view the graphical user interface, prior to presenting the graphical user interface on the display. (¶ 0088-0089: Polston discloses the system allows a manager to track the goals directly against real-world values relating to the use of the system. For example, feedback on meeting plan goals can be provided every time a manager logs into the system 10.)
With respect to claims 7, 14, and 22, the combination of Polston, Yoo, and Corr discloses the system, method, and one or more machine-readable medium, wherein the operations further comprise:
receiving first additional data associated with the first loan officer and second additional data associated with the second loan officer (¶ 0091, 0093: Polston discloses the system 10 is designed to monitor the activities of individual users and to track statistics associated with specific entities.);
generating a first modified metric based at least in part on the first additional data and the first metric (¶ 0104: Polston discloses active lenders recruited and active lender percentages.); generating a second modified metric based at least in part on the second additional data and the second metric (¶ 0104 – see Polston);
generating, based at least in part on first modified metric and the second modified metric, a second graphical user interface that includes at least a graphical comparison between the first modified metric associated with the first loan officer and the second modified metric associated with the second loan officer (¶ 0007, 0101, 0104-0105 – see Polston); and
presenting the second graphical user interface on the display. (¶ 0007, 0101, 0104: Polston discloses the organizational chart tool 500 is a useful tool to reach other information about the performance of a financial institution. By clicking on “Lenders” in the organizational chart. The lender list contains for each lender 40, total count of buyers in the system associated with that lender 40, the pipeline amount 20, the count of sellers 30 associated with that lender 40.)
With respect to claim 25, the combination of Polston, Yoo, and Corr discloses the method in accordance with claim 21, further comprising:
determining a user has permission to view the graphical user interface (¶ 0088-0089 – see Polston); and responsive to determining that the user has permission to view the graphical user interface, causing the graphical user interface to be presented on a second display of a user device. (¶ 0088-0089: Polston discloses the system allows a manager to track the goals directly against real-world values relating to the use of the system. For example, feedback on meeting plan goals can be provided every time a manager logs into the system 10.)
With respect to claim 26, the combination of Polston, Yoo, and Corr discloses the system of claim 1,
wherein receiving the first metric further comprises receiving one or more vectors for the first loan officer, the one or more vectors including a confidence factor, a veracity score, an amount of data associated with the first loan officer collected by the system, and one or more data types associated with the first data. (¶ 0069: Yoo discloses a performance metric may be a number and/or category. A performance metric may have any form that allows the performance metric to be compared to a second performance metric.)
With respect to claim 27, the combination of Polston, Yoo, and Corr discloses the system of claim 1, wherein the operations further comprise:
generating an interview schedule including the first loan officer and the second loan officer (¶ 0092-0093: Polston discloses contact prompting tool 440 is a tool designed to centralize all of the contact responsibilities of the manager. The system manages contacts by tracking when the next contact is due and then prompting the responsible party to make the contact at the appropriate time.) based at least in part on the first metric and the second metric (¶ 0092-0093: Polston discloses the system can automatically trigger certain communications from managers based upon the monitored activities and statistics. A branch manager may be responsible for contacting existing loan officers every week.); and
presenting the interview schedule on the display as part of the graphical user interface that includes at least the graphical comparison between the first metric associated with the first loan officer and the second metric associated with the second loan officer. (¶ 0089-0093: Polston discloses the system presents information and resources to managers through a management portal. There are four tools presented through the management page including loan officer recruiting tool 420 and contact prompting tool 440 for contacts to be made by the manager. Each of the tools can be shown on the main management page with some summary information. Although the tools may be described separately it would be well within the scope of the invention to combine some or all of the information and options into a single common interface. For example, while prompts and prospective loan officer information are shown in the recruiting tool it would be possible to combine this information and prompts within the prompts for contacts tool.)
14. Claim(s) 8-9, 15, 23, and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Polston in view of Yoo in view of Corr in further view of Bettios (US 2014/0040111 A1).
With respect to claims 8, 15, and 23, the combination of Polston, Yoo, and Corr discloses the system, method, and one or more non-transitory machine-readable medium, However, Bettios is related to systems and methods for analyzing loan acquisitions (abstract) and teaches:
wherein: the operations further comprise:
determining a first confidence factor associated with the first data (abstract, ¶ 0032, 0051, 0070: discloses generating a set of scores for the loan based on the set of analysis rules. The set of analysis rules are configured to evaluate the loan, i.e., from a lender originating the loan.); determining a second confidence factor associated with the second data (abstract, ¶ 0032, 0051, 0070: discloses the system may generate detailed findings as well as confidence scores and may prepare a formatted report.); and
generating the graphical user interface is based at least in part on the first confidence factor and the second confidence factor. (¶ 0033, 0070: discloses the system provides access to the findings and scores through a web-based portal.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system and methods of Polston, Yoo, and Corr to have included the ability for determining a first confidence factor associated with the first data; determining a second confidence factor associated with the second data; and generating the graphical user interface is based at least in part on the first confidence factor and the second confidence factor, as disclosed by Bettios, to achieve the claimed invention. As disclosed by Bettios, the motivation for the combination would have been to quickly communicate or flag quality issues to parties. (¶ 0032)
With respect to claims 9 and 24, the combination of Polston, Yoo, Corr, and Bettios discloses the system and method,
wherein the first confidence factor represents a veracity score associated with the first data. (¶ 0070: Bettios discloses the set of scores may include a score that corresponds with each analytical rule applied to the loan closing data or with each compliance /risk category. As scoring may associate each rule with a designated score…a red score may indicate one or more errors.)
15. Claim(s) 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Polston in view of Yoo in view of Corr in further view of Calman (US 2014/0104372 A1).
With respect to claim 28, Polston and Corr discloses the system of claim 1,
wherein the graphical user interface includes the first loan officer and the second loan officer (Figs. 11, 13, ¶ 0073-0074, 0088-0091 – see Polston);
However, Yoo discloses:
wherein the graphical user interface includes (¶ 0048: discloses user interface 310 displays a listing of profiled professionals), expert analysis (¶ 0048: discloses a summary of professional specialties.), estimated compensation for the first loan officer (¶ 0099: discloses identifies a fair market value for compensating a profiled professional), estimated compensation for the second loan officer (¶ 0099: discloses identifies a fair market value for compensating a profiled professional), an organization projection including the first loan officer (¶ 0098: discloses identifies a future match between a professional and an industry opportunity.), and an organization projection including the second loan officer. (¶ 0098: discloses identifies a future match between a professional and an industry opportunity.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to have modified Polston’s and Corr’s graphical user interface to include information such as expert analysis, estimated compensation for the first loan officer, estimated compensation for the second loan officer, an organization projection including the first loan officer, and an organization projection including the second loan officer to achieve the claimed invention. As disclosed by Yoo, the motivation for the combination would have been to provide advantages that enable professionals to understand the heterogeneity in practice styles across their industries and learn from outliers as well as from averages. (¶ 0002-0004)
The combination of Polston, Yoo, and Corr does not explicitly disclose the following limitations. In the same field of endeavor, the Calman reference is related to a system and methods for identifying experts, i.e., financial institution associates, (¶ 0065) and teaches:
wherein the graphical user interface includes photographs of the first loan officer, photographs of the second loan officer (Fig. 7, ¶ 0065-0066: discloses the system is configured to search a database to identify financial institution associates that have work experience dealing with loans. Various different experts 710 are provided to the user. The list of experts in the GUI 700 includes a picture of the loan experts 710. Further a descriptive outline may include a description of expert’s specialties, years of experience, and the like.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to have modified the graphical user interface from the combination of Polston, Yoo, and Corr to include the photographs of the first loan officer, photographs of the second loan officer to achieve the claimed invention. As disclosed by Calman, the motivation for the combination would have been to provide advantages that enable personal connection with one or more financial institution representative who may be able to provide expert advice regarding products. (¶ 0019-0021)
Response to Arguments
Applicant's arguments filed 29 June 2026 have been fully considered but they are not persuasive.
With Respect to Rejections Under 35 USC 101
Applicant argues “However, a person would not be able to perform the claimed steps as a mathematical concept, organizing human activity, nor as a mental process. Specifically, a person could not "min[e], from a plurality of loan information sources, first data associated with a first loan officer," as this feature requires automated data extraction from multiple disparate electronic sources. Nor could a person "min[e], from one or more regulatory servers, compliance data associated with qualifications, compliance or non-compliance with regulations, and industry customs of the first loan officer," as this feature requires accessing and extracting data from regulatory server infrastructure. Nor could a person "receiv[e] from one or more machine learned networks a first metric in response to inputting the first transaction and a performance review criteria into the one or more machine learned networks," as these features require the use of trained machine learning models that cannot be replicated mentally. Nor could a person "compar[e] the first metric with a loan service provider metric for a loan service provider to determine a match between the first loan officer and the loan service provider" at the scale and with the standardization contemplated by the specification.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. The remarks here do not make the claimed invention any less abstract or preclude the limitations from being within the certain methods of organizing human activity or mental processes groupings. The original Specification makes clear in at least [¶ 0004, 0018, 0035-0040] the claimed invention helps compare loan officers with loan service providers so users can quickly evaluate compatibility. Thus, the Specification confirms the limitations constitute processes that relate to sales or marketing activities and business relations that fall within the certain methods of organizing human activity. As for the remarks directed towards mining loan and compliance information of a loan officer, these limitations at best are performing the data gathering tasks necessary to aid in performing the abstract idea. As for the remarks directed towards using machine learned networks and comparing steps recited, it is important to note claims can recite mental process even if they are claimed as being performed on a computer. See MPEP 2106.04(a)(2) For these reasons, the rejections under 101 are being maintained.
Applicant further argues “The instant claimed techniques improve the technological process of aggregating and normalizing fragmented data from disparate sources in the mortgage lending industry and thereby integrate any alleged abstract idea into a practical application of the alleged abstract idea at least because they contain elements that reflect an improvement to other technology or technical field. The Federal Circuit has articulated that claims related to improvements in computer-related technology are not abstract when "the focus of the claims is on the specific asserted improvement" and not merely using computers as a tool to help with an abstract process. Enfish, 822 F.3d at 1338. The claims should recite a "specific technique" (SRI Int'l, Inc. V. Cisco Systems, Inc., 930 F.3d 1295, 1303-04 (Fed. Cir. 2019)), a "specific solution" (Koninklijke KPN N.V. V. Gemalto M2M GmbH, 942 F.3d 1143, 1150 (Fed. Cir. 2019)) to a "specific technological problem" (Packet, 965 F.3d at 1299), or a "new way" of doing something (Finjan, Inc. V. Blue Coat Systems, Inc., 879 F.3d 1299, 1303 (Fed. Cir. 2018)). Amended claim 1 recites a specific technical architecture for solving the industry-specific problem of normalizing fragmented data from disparate sources. Specifically, amended claim 1 recites: (1) mining data from a plurality of loan information sources; (2) mining compliance data from one or more regulatory servers; (3) using the compliance data in the determining step to identify transactions; (4) processing the data through machine learned networks to generate standardized metrics; (5) comparing loan officer metrics with loan service provider metrics to determine compatibility; and (6) generating a second GUI presenting the compatibility indication. This is not generic computer components performing generic functions, but rather a specific technical solution to the problem of normalizing custom recruiting methods and compensation packages across different loan service providers.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. The Examiner asserts that the mere combination of information from disparate data sources does not make the claims patent eligible. See also - Fairwarning IP, LLC v. Iatric Systems, Inc In the instant case, the Specification and remarks do not support a finding that the claims reflect a technological improvement to mining, normalizing, and/or comparing data from disparate data sources or using machine learning technology. Rather, the ordered combination of limitations are directed to collecting and analyzing information and activities from loan officers to help compare loan officers metrics with loan providers metrics to determine their compatibility. At most, the claims require that these processes be executed on or by a generic computer with generic machine learning techniques. See Spec [¶ 0024, 0051-0052] The courts have previously held recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible." See Alice, 134 S. Ct. at 2358 Thus, while the invention may in fact require that the claimed data relate to "transactions or activities that are executed in a computer environment, limiting the claims to the loan processing/compliance computer field does not alone transform them into a patent-eligible application. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “The Federal Circuit's decision in DDR Holdings, LLC V. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014), supports eligibility. In DDR Holdings, the court found claims eligible because "[t]he claimed solution is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks." Id. at 1257. The court emphasized that the claims did not "merely recite the performance of some business practice known from the pre-Internet world along with the requirement to perform it on the Internet." Id. Instead, the claims addressed "a business challenge (retaining website visitors) that is particular to the Internet" and provided a technical solution that could not exist outside of the computer network context. Id.”
“Similarly, amended claim 1 addresses a problem specific to the fragmented mortgage lending industry that arises from the existence of disparate electronic data sources, thereby, normalizing data from multiple loan information sources and regulatory servers to determine compatibility between loan officers and loan service providers. This problem could not exist without the claimed computer technology, as it requires: (1) mining data from a plurality of loan information sources; (2) mining compliance data from one or more regulatory servers; (3) processing the mined data through machine learned networks to generate standardized metrics; and (4) comparing metrics to determine compatibility matches. Just as the claims in DDR Holdings addressed a challenge particular to the Internet by providing a solution rooted in computer technology, amended claim 1 addresses a challenge particular to the fragmented mortgage industry's disparate data sources by providing a solution rooted in data mining, machine learning, and networked computing technology.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. In the instant case, the Examiner asserts the recited claims do not attempt to solve a challenge particular to the Internet as in DDR, therefore, the claims do not recite a comparable technological solution. See Intellectual Ventures I, 792 f.3d at 1371 (because the patent claims at issue did not “address problems unique to the Internet,…DDR has no applicability.”) The ordered combination of limitation provide vague and functional descriptions of computing components such as a plurality of loan information sources, one or more regulatory servers, machine learned networks which insufficient to transform the abstract idea into a patent-eligible invention. Using result-focused functional claim language is a frequent feature of ineligible claims, especially those that claim the use of generic computer and network technology to carry out economic transactions or business relations. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “The USPTO's Subject Matter Eligibility Example 42 (Medical Record Updates) confirms that the claims are eligible. In Example 42, Claim 1 was found eligible because it recited "a specific improvement over prior art systems by allowing remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user." Specifically, Example 42's eligible Claim 1 included: (a) storing information in a standardized format about a patient's condition in a plurality of network-based non-transitory storage devices; (b) providing remote access to users over a network so any one of the users can update the information in a non-standardized format; (c) converting, by a content server, the non-standardized updated information into the standardized format; (d) automatically generating a message containing the updated information whenever updated information has been stored; and (e) transmitting the message to all of the users over the computer network in real time. Amended claim 1 recites analogous features that provide a specific improvement over prior systems in the fragmented mortgage lending industry.”
“First, just as Example 42 recites collecting data from multiple remote medical providers, claim 1 recites "mining, from a plurality of loan information sources, first data associated with a first loan officer" and "mining, from one or more regulatory servers, compliance data associated with qualifications, compliance or non-compliance with regulations, and industry customs of the first loan officer." Second, just as Example 42 recites "converting, by a content server, the non-standardized updated information into the standardized format," claim 1 recites processing the mined data through "one or more machine learned networks" to generate standardized metrics. Third, just as Example 42 recites automatically generating and transmitting messages with updated information to users, claim 1 recites "generating, on the display, a second graphical user interface presenting the comparison between the first metric and the loan service provider metric, the comparison providing an indication of the first loan officer's compatibility with the loan service provider." The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. In Example 42, the additional elements recited a specific improvement over prior art systems by allowing remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user. The presently recited claims for mining loan and compliance information of loan officers and then comparing loan officers with loan service providers does not recite a comparable technological improvement. There are not technical details related to how the information or data being mined in the claim is formatted. Using off-the-shelf computer, network, and display technology to gather, analyze, send, and present data is not inventive. See Elec. Power Grp., 830 F.3d at 1355 For these reasons, the rejections under 101 are being maintained.
Applicant further argues “The USPTO's Subject Matter Eligibility Example 39 (Facial Detection) further supports eligibility. In Example 39, the claim was found eligible because "the claim does not recite a mental process because the steps are not practically performed in the human mind." The USPTO explained that while humans can detect and recognize faces, the specific claim limitations which involved storing information about a first training set of facial images, training a neural network using the first training set, and using the trained neural network to perform facial detection could not practically be performed in the human mind due to the complexity and computational nature of the operations. Similarly, amended claim 1's steps cannot practically be performed in the human mind. First, the claim recites "mining, from a plurality of loan information sources, first data associated with a first loan officer and second data associated with a second loan officer." This mining operation requires automated extraction and aggregation of data from multiple disparate electronic databases and sources, e.g., an operation that cannot be performed mentally given the volume, variety, and velocity of data involved. Second, the claim recites "mining, from one or more regulatory servers, compliance data associated with qualifications, compliance or non-compliance with regulations, and industry customs of the first loan officer." Accessing regulatory server infrastructure and extracting compliance data requires electronic communication protocols and data parsing that are inherently computational in nature. Third, the claim recites "receiving from one or more machine learned networks a first metric in response to inputting the first transaction and a performance review criteria into the one or more machine learned networks." Machine learned networks involve complex mathematical operations across potentially millions of parameters, e.g., operations that are fundamentally impossible for a human to perform mentally. Fourth, the claim recites "comparing the first metric with a loan service provider metric for a loan service provider to determine a match between the first loan officer and the loan service provider." At the scale contemplated by the specification, which envisions processing data for numerous loan officers across the fragmented mortgage industry, this comparison operation requires computational resources that far exceed human cognitive capabilities. Accordingly, just as Example 39's facial detection claim was found not to recite a mental process because the steps could not practically be performed in the human mind, amended claim 1 does not recite a mental process because its steps of mining data from multiple sources, processing through machine learned networks, and determining compatibility matches cannot be performed mentally or with pen and paper.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. In Example 39 the claim recites details for training a neural network and was held eligible because it did not recite any of the enumerated judicial exceptions from MPEP 2106.04(a)(2). That is not the case here. The presently recited limitations of claim 1 do not recite any details for training a neural network, thus Example 39 has no applicability. The inability for the human mind to perform each claim step does not alone confer patentability. In the Trinity Info Media, LLC v. Covalent, Inc., (Fed. Cir. 2023) court decision, the courts held although a human could not "detect events on an interconnected electric power grid in real time over a wide area and automatically analyze the events on the interconnected electric power grid," we nevertheless found such claims to be directed to an abstract idea in Electric Power Group. 830 F.3d at 1351, 1353-54. Similarly, a human could not communicate over a computer network without the use of a computer, yet we held that claims directed to enabling "communication over a network" were focused on an abstract idea in ChargePoint. 920 F.3d at 766-67. Thus, Applicant’s claims for mining and comparing loan officers with loan service provider can be directed to an abstract idea even if the claims require generic computer components or require operations that a human could not perform as quickly as a computer. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “The recent Appeals Review Panel decision in Ex parte Desjardins (Appeal 2024-000567, Application 16/319,040, decided 2025), convened by the USPTO Director, further supports eligibility. In Ex parte Desjardins, the ARP vacated a § 101 rejection of machine learning claims, holding that "although independent claim 1 may recite an abstract idea, it is not directed to an abstract idea. Instead, we determine that independent claim 1, when considered as a whole, integrates an abstract idea into a practical application." Critically, the ARP admonished that "[c]ategorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable even if they are adequately described and nonobvious because the panel essentially equated any machine learning with an unpatentable 'algorithm' and the remaining additional elements as 'generic computer components,' without adequate explanation." The ARP instructed that "[e]xaminers and panels should not evaluate claims at such a high level of generality." The Examiner here has done exactly what the ARP cautioned against equating the machine learned networks in claim 1 with generic computer components and evaluating the claims at a high level of generality. The amended claims recite a specific technical architecture that goes beyond merely applying machine learning to a new data environment, including: (1) mining compliance data from regulatory servers; (2) using the compliance data in the determining step not merely receiving it; (3) comparing loan officer metrics with loan service provider metrics to determine compatibility; and (4) generating a second GUI presenting the compatibility indication. This is not generic computer components performing generic functions, but rather a specific technical solution to the industry-specific problem of normalizing fragmented data from disparate sources.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. In Ex Parte Dejardins, the claims improved the functioning of the computer itself. The panel explained that where a claimed invention improves the operation of a machine learning system, such as by enhancing its training efficiency or preserving prior learning, it is not “directed to” an abstract idea under Alice Step 1. In the instant case, the presently recited claims, remarks, and Specification do not recite a comparable technological improvement. The Specification [¶ 0024] discusses the machine learning techniques that may be used in a generic manner without any specific training details. As previously explained, using result-focused functional claim language is a frequent feature of ineligible claims, especially those that claim the use of generic computers, machine learning, and graphical user interfaces to carry out economic transactions or business relations. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “The claims also integrate any alleged abstract idea into a practical application when viewed as an ordered combination. The MPEP instructs that "[w]hen evaluating whether additional elements meaningfully limit the judicial exception, it is particularly critical that examiners consider the additional elements both individually and as a combination." MPEP § 2106.05(e). Further, "even in the situation where the individually-viewed elements do not add significantly more or integrate the exception, those additional elements when viewed in combination may render the claim eligible." See Diamond v. Diehr, 450 U.S. 175, 188 (1981) ("a new combination of steps in a process may be patentable even though all the constituents of the combination were well known and in common use before the combination was made").”
“Amended claim 1 recites an integrated system comprising: (1) mining data from multiple loan information sources; (2) mining compliance data from regulatory servers; (3) determining transactions based on the first data and compliance data; (4) receiving metrics from machine learned networks; (5) generating a GUI with graphical comparisons; (6) comparing metrics with loan service provider metrics to determine compatibility; and (7) generating a second GUI presenting the compatibility indication. These elements form an integrated data processing pipeline where each step feeds into the next, culminating in actionable compatibility determinations between loan officers and loan service providers.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. It is important for Applicant to note that a claim for a new abstract idea is still an abstract idea. Here, the Applicant merely restates the series of steps recited by the claim but does not provide anything inventive about the ordered combination of elements. The additional elements including the computer components and machine learned networks are discussed at a high-level of generality in light of the Specification. Such generic descriptions of the additional elements evidences [see Applicant’s Spec, [¶ 0024, 0051-0052] that the claims do not provide an inventive concept. See MPEP 2106.05(f); See also Beteiro, LLC v. DraftKings Inc., 104F.4th 1350, 1358 (Fed. Cir. 2024) (a generic description of claimed components and features indicates they are conventional) (citing Weisner V. Google LLC, 51 F.4th 1073, 1083-84 (Fed. Cir. 2022)); see Elec. Power Grp., 830 F.3d at 1355 (using off-the-shelf computer, network, and display technology to gather, analyze, send, and present data is not inventive.) Thus, whether viewing the claim limitations individually or as an ordered combination, the asserted claims do not add an inventive concept that would be sufficient to confer patent eligibility. For these reasons, the rejections under 101 are being maintained.
With Respect to Rejections Under 35 USC 103
Applicant' s arguments and amendments with respect to claim(s) 1, 10, and 21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EHRIN PRATT whose telephone number is (571)270-3184. The examiner can normally be reached 8-5 EST Monday-Friday.
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/EHRIN L PRATT/Examiner, Art Unit 3629
/ANDREW B WHITAKER/Primary Examiner, Art Unit 3629
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