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 11/28/2025.
Claims 1, 5, 6, 11, and 18 have been amended and are hereby entered.
Claim 10 has been canceled.
Claims 1-9 and 11-20 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 Objections
Claims 18-20 are objected to because of the following informalities:
Claim 18: lines 7-8 recite the limitations “selecting tools and prompts based on the conversation context to guide the user through a loan process.” A “user” has not been previously recited in Claim 18. It appears there is a typographical mistake. For compact examination purposes, Examiner interpreted the instances recited in Claim 18: lines 7-8 as “selecting tools and prompts based on the conversation context to guide a user through a loan process,” respectively. Appropriate correction is required.
Claim 19 has been misnumbered as dependent claim 18. Furthermore, claim 20 has been misnumbered as dependent claim 19. Claim 18 is an independent claim. The misnumbered dependent claims 18-19 have been marked “Original,” and were originally filed on 09/04/2024 as dependent claims 19 and 20. It appears that this is a typographical mistake. For compact examination purposes, Examiner interpreted the misnumbered dependent claims 18 and 19 as dependent claims 19 and 20. Appropriate correction is required.
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 1-9 and 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of processing a loan using extracted loan information and borrower data, without significantly more.
Claim 1 is directed to a method, which is one of the statutory categories of invention. (Step 1: YES).
Claim 1 is directed to a method for managing a loan origination process, the method comprising: by a chat manager, in response to receiving messages from a user conversation of a user, extracting borrower details and loan conditions; using a task library to generate tasks based on a current loan status; selecting a set of tools and prompts based on a context of the user conversation to guide the user through a loan process; communicating context information and the set of tools to a generative artificial intelligence (AI) system; receiving and storing a response from the generative AI system, the response comprising system tools and instructions; executing the system tools to extract loan information and borrower data, and mapping Automated Underwriting System (AUS) results to system conditions; iteratively updating system conditions and the task library based on updates generated by the generative AI system; and displaying a system-generated response to the user that directs subsequent steps in the loan process; wherein the task library generates new tasks based on results and findings provided by the AUS. These series of steps describe the abstract idea of processing a loan using extracted loan information and borrower data (with the exception of the italicized and bolded terms above), which is mitigating risk by updating conditions based on user and loan data to properly process and complete a loan process; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing of a loan by identifying borrower and loan data and applying the loan conditions to complete a loan transaction, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., a chat manager, user interface (UI), generative artificial intelligence (AI) system, task library, system tools, instructions, and automated underwriting system (AUS) 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 chat manager, user interface (UI), generative artificial intelligence (AI) system, task library, system tools, instructions, and automated underwriting system (AUS), 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 chat manager, user interface (UI), generative artificial intelligence (AI) system, task library, system tools, instructions, and automated underwriting system (AUS), 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 2-9 are directed to a method, which recites the steps that describe the abstract idea of processing a loan using extracted loan information and borrower data. These series of steps describe the abstract idea of processing a loan using extracted loan information and borrower data, which is mitigating risk by updating conditions based on user and loan data to properly process and complete a loan process; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing of a loan by identifying borrower and loan data and applying the loan conditions to complete a loan transaction, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Thus, claims 2-10 recite an abstract idea. The additional limitations of a chat manager, user interface (UI), generative artificial intelligence (AI) system, task library, system tools, instructions, and automated underwriting system (AUS), 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 chat manager, user interface (UI), generative artificial intelligence (AI) system, task library, system tools, instructions, and automated underwriting system (AUS), 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 11 is directed to a system, which is one of the statutory categories of invention. (Step 1: YES).
Claim 11 is directed to a system for managing a loan origination process, the system comprising: a non-transitory computer-readable medium or media comprising instructions that, when executed by one or more processors, causes the steps to be performed comprising: receiving at a chat manager messages from a user via a user interface (UI) and processing by the chat manager the messages to extract borrower details and loan conditions; generating, by a task library communicatively coupled to the chat manager, tasks based on a current loan status; analyzing, by a generative artificial intelligence (AI) system communicatively coupled to the chat manager, context information and provide one or more instructions and guidance for using system tools; and evaluating, by an automated underwriting system (AUS) communicatively coupled to the chat manager, borrower data and loan-related information to assess loan eligibility; wherein the task library generates new tasks based on results and findings provided by the AUS, the task library is iteratively updated based on updates generated by the generative Al system; a database communicatively coupled to the chat manager, the database configured to store relevant data including user interaction history, borrower profiles, financial documents, and AUS findings; and wherein the UI is communicatively coupled to the chat manager, the UI configured to display results, recommendations, instructions, and updates to the user. These series of steps describe the abstract idea of processing a loan using extracted loan information and borrower data (with the exception of the italicized and bolded terms above), which is mitigating risk by updating conditions based on user and loan data to properly process and complete a loan process; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing of a loan by identifying borrower and loan data and applying the loan conditions to complete a loan transaction, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., a system, non-transitory computer-readable medium or media, one or more processors, chat manager, task library, generative artificial intelligence (AI) system, one or more instructions, system tools, user interface (UI), Automated Underwriting System (AUS), and database, do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 11 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 system, non-transitory computer-readable medium or media, one or more processors, chat manager, task library, generative artificial intelligence (AI) system, one or more instructions, system tools, user interface (UI), Automated Underwriting System (AUS), and database, 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 11 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 11 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a system, non-transitory computer-readable medium or media, one or more processors, chat manager, task library, generative artificial intelligence (AI) system, one or more instructions, system tools, user interface (UI), Automated Underwriting System (AUS), and database, 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 11 is not patent eligible.
Dependent claims 12-17 are directed to a system, which performs a series of steps that describe the abstract idea of processing a loan using extracted loan information and borrower data. Specifically, dependent claim 16 is directed to a system, which performs a series of steps, e.g., wherein the database serves as a centralized data repository that provides the chat manager and the generative AI system access to current and accurate information to guide user decision-making. These series of steps describe the abstract idea of processing a loan using extracted loan information and borrower data (with the exception of the italicized and bolded terms above), which is mitigating risk by updating conditions based on user and loan data to properly process and complete a loan process; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing of a loan by identifying borrower and loan data and applying the loan conditions to complete a loan transaction, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Thus, claims 12-17 recite an abstract idea. The additional limitations of a system, non-transitory computer-readable medium or media, one or more processors, chat manager, task library, generative artificial intelligence (AI) system, one or more instructions, system tools, user interface (UI), Automated Underwriting System (AUS), database, and centralized data repository, 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 system, non-transitory computer-readable medium or media, one or more processors, chat manager, task library, generative artificial intelligence (AI) system, one or more instructions, system tools, user interface (UI), Automated Underwriting System (AUS), database, and centralized data repository, 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 18 is directed to a non-transitory computer-readable medium, which is one of the statutory categories of invention. (Step 1: YES).
Claim 18 is directed to a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause steps for managing a loan origination process to be performed, the steps comprising: receiving, from a chat manager, messages from a user conversation, and extracting borrower details and loan conditions; using a task library to generate tasks based on a current loan status; selecting tools and prompts based on the conversation context to guide the user through a loan process; communicating context information and the set of tools to a generative artificial intelligence (AI) system; receiving and storing a response from the generative AI system, the response comprising system tools and instructions; executing the system tools to extract loan information and borrower data, and mapping Automated Underwriting System (AUS) results to system conditions; iteratively updating the system conditions and the task library based on updates generated by the generative AI system; and displaying a system-generated response to the user that directs subsequent steps in the loan process; wherein the task library generates new tasks based on results and findings provided by the AUS. These series of steps describe the abstract idea of processing a loan using extracted loan information and borrower data (with the exception of the italicized and bolded terms above), which is mitigating risk by updating conditions based on user and loan data to properly process and complete a loan process; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing of a loan by identifying borrower and loan data and applying the loan conditions to complete a loan transaction, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., a processor, chat manager, task library, generative artificial intelligence (AI) system, system tools, and instructions, do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 18 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 processor, task library, chat manager, generative artificial intelligence (AI) system, system tools, and instructions, 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 18 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 18 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a processor, chat manager, task library, generative artificial intelligence (AI) system, system tools, and instructions, 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 computer network limitations are a field of use limitations (MPEP 2106.05(h)). 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 18 is not patent eligible.
Dependent claims 19-20 are directed to a non-transitory computer-readable medium, which recites the steps that describe the abstract idea of processing a loan using extracted loan information and borrower data. These series of steps describe the abstract idea of processing a loan using extracted loan information and borrower data, which is mitigating risk by updating conditions based on user and loan data to properly process and complete a loan process; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing of a loan by identifying borrower and loan data and applying the loan conditions to complete a loan transaction, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Thus, claims 19-20 recite an abstract idea. The additional limitations of a processor, chat manager, task library, generative artificial intelligence (AI) system, system tools, and instructions, 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 processor, chat manager, task library, generative artificial intelligence (AI) system, system tools, and instructions, 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 2-10, 12-17, and 19-20 have further defined the abstract idea that is present in their respective independent claim: Claims 1, 11, and 18; and thus correspond to Certain Methods of Organizing Human Activity, and hence are abstract in nature for the reason presented above. The dependent claims 2-10, 12-17, 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, dependent claims 2-10, 12-17, and 19-20 are directed to an abstract idea without significantly more.
Thus, claims 1-9 and 11-20 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 1-9 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal (U.S. Patent Application Publication No. US 2023/0105825 A1; hereinafter “Agarwal”), in view of Masson (U.S. Patent Publication No. US 2023/0186385 A1; hereinafter “Masson”).
Regarding Claims 1 and 18:
Agarwal teaches:
A method for managing a loan origination process, the method comprising: by a chat manager, in response to receiving messages from a user conversation of a user, extracting borrower details and loan conditions; (Agarwal, a system and method for providing a natural language interface or conversation/chat interface for interacting with an automated software assistant (AA) and/or a human assistant (HA) when completing an electronic form is disclosed. For example, the electronic form may be a mortgage application soliciting input from the user in the form of questions wishing to obtain a mortgage loan. (See, Abstract; Para. 43, 54-59; Fig. 1));
A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause steps for managing a loan origination process to be performed, the steps comprising: receiving, from a chat manager, messages from a user conversation, and extracting borrower details and loan conditions; (Agarwal, a system and method for providing a natural language interface or conversation/chat interface for interacting with an automated software assistant (AA) and/or a human assistant (HA) when completing an electronic form is disclosed. For example, the electronic form may be a mortgage application soliciting input from the user in the form of questions wishing to obtain a mortgage loan. (See, Abstract; Para. 43, 54-59; Fig. 1));
using a task library to generate tasks based on a current loan status; (Agarwal, The system may also generate quick requests, which may include individual questions or requests that user must provide when completing the form. ….. in other words, the system may recognize that one conversation may be associated with an identifier, e.g., a loan number. In the event the system cannot recognize the identifier, the user may drag an icon of a previously submitted document related to that loan number to the conversation to form the association. Alternatively, the user may enter a text or voice command to make the association. Additionally, the conversation may include folders or sub-folders within the conversation which may contain information related to one particular identifier. For example, a loan number may be a parent folder and a bank account may be a folder or a sub-folder. The system may identify the folder using the identifier (See, Abstract; Para. 43, 46-48));
selecting tools and prompts based on the conversation context to guide the user through a loan process; (Agarwal, the AA may use machine learning algorithms trained on existing HA and user interaction data to predict how the user is likely to feel about any particular topic or line of questioning. For example, questions related to producing tax forms may always cause stress to users. By receiving the prediction of emotional state before the user actually experiences these emotions allows the system to prevent potential conflicts, provide improved customer experience, and successfully complete the task of submitting a form. That is, by sending a signal to the HA, HA may adjust the way they converse with the user to prevent potential difficulties (See, Abstract; Para. 43-52));
communicating context information and the set of tools to a generative artificial intelligence (AI) system; (Agarwal, the chat interface provides user with AA configured to use a conversation (i.e., natural language questions and answers) format to elicit user reposes required to complete the form. Further, the AA is configured to “invite” HA based on user's needs, the HA's level of skill, the HA's availability, and/or other such factors. In essence, AA may act like an “intelligent concierge” or a go-between the HA and the user. The user may converse with both the AA and the HA within the chat interface. By having the ability to interact with both the AA and HA, the user will be in the “company” of two helpful guides, never left alone to complete the arduous task of filling out an online form. (See, Abstract; Para. 43-48));
receiving and storing a response from the generative AI system, the response comprising system tools and instructions; (Agarwal, Each workflow will initiate a sequence of automatically generated questions to the user on a particular subject or an area of the form. That is, each workflow may group related questions together. By sending a particular workflow to the user, the HA avoids asking specific questions manually. The system may also generate quick requests, which may include individual questions or requests that user must provide when completing the form. For example, age, income, property address, and so on. Each quick request element may include a unique icon. User's response to a quick request or any other question may be tagged with an icon thus making it easier for the user to recognize which response is related to which data element. Further still, the user may drag the quick request links to a particular response to categorize their response. For example, when the user provides a response out of sequence or when either AA or HA do not understand user's response, tagging the response with a quick request category allows the user to communicate the purpose of the response with greater efficiency (See, Abstract; Para. 43-46));
executing the system tools to extract loan information and borrower data, and [mapping Automated Underwriting System (AUS) results to system conditions]; (Agarwal, To further complicate the issue associated with updated user input, is the fact that a modification may trigger a divergent workflow thereby causing the AA to ask downstream questions that were not previously required by the workflow. The present embodiments provide a solution by associating the information extracted from the original branch of the conversation with a particular transaction identifier (e.g., a loan application number). Each time a new branch is generated, including a modified user response and new AA questions, that branch is associated with a previous branch by utilizing the same identifier. For example, the information extracted from each branch may be stored as a single “conversation log” in a database utilizing the online transactional processing or OLTP processing. By virtue of associating the branches with a common transaction identifier, the present embodiments can sequester individual branch data which allows the system to maintain interdependencies between user responses in separate branches. This allows the system to generate graphical visualizations of each branch resulting in practically infinite branching capabilities. (See, Abstract; Para. 43-48, 54, 65, 97));
iteratively updating the system conditions and the task library based on updates generated by the generative AI system; and (Agarwal, While modifying the response immediately after providing it is not an issue, the client chat interface 127 is also configured to accommodate user modifications after having provided subsequent responses to questions generated based on their original answer they now wish to modify. Unlike the interaction via a GUI, which allows users to quickly modify a previously entered response (e.g., by clicking a particular field in a form), the modification of user provided input via a conversation interface may not be as straightforward. In essence, in an effort to change a response buried in a multi-message exchange, the user would have to scroll to find a relevant response and then modify it. As a result, the AA would generate a “new” set of downstream answers or commands corresponding with the updated user input (e.g., as a separate branch). These downstream answers in a new branch may be generated by reusing the information provided by the user in the original branch. (See, Abstract; Para. 96-97));
displaying a system-generated response to the user that directs subsequent steps in the loan process; (Agarwal, any request that AA has determined to require the HA's attention will be displayed in area 430 of window 412. For example, after the HA sends the user a quick request (e.g., from a list of quick requests 443), the AA may determine that certain information and/or documents are missing (e.g., user authentication credentials or loan amount) and were not requested via one of the quick requests 443. For example, illustrated in FIG. 4F, the AA may generate and display reminders in area 430 informing HA that the user still needs to submit authentication credentials by highlighting the corresponding icon which is ready to be clicked by the HA to be sent to directly the user requesting additional details. (See, Abstract; Para. 82, 83, 86, 106-108));
wherein the task library generates new tasks based on results and findings provided by [the AUS]. (Agarwal, the client chat interface 127 is also configured to accommodate user modifications after having provided subsequent responses to questions generated based on their original answer they now wish to modify. Unlike the interaction via a GUI, which allows users to quickly modify a previously entered response (e.g., by clicking a particular field in a form), the modification of user provided input via a conversation interface may not be as straightforward. In essence, in an effort to change a response buried in a multi-message exchange, the user would have to scroll to find a relevant response and then modify it. As a result, the AA would generate a “new” set of downstream answers or commands corresponding with the updated user input (e.g., as a separate branch). These downstream answers in a new branch may be generated by reusing the information provided by the user in the original branch…… the present embodiments can sequester individual branch data which allows the system to maintain interdependencies between user responses in separate branches. This allows the system to generate graphical visualizations of each branch resulting in practically infinite branching capabilities. (See, Abstract; Para. 96-97)).
Agarwal does not specifically teach Automated Underwriting System (AUS) and mapping Automated Underwriting System (AUS) results to system conditions.
However, Masson further teaches the following limitations:
Automated Underwriting System (AUS); (Masson, The cloud-based platform 730 may output the results of the lending strategies and business revenue management system 102 in different formats. One example of output may be directly to the Financial Institution Operating Systems (e.g., Loan Origination System or Automated Underwriting Systems) through API 742 and following a strategy validation protocol from the system user on the interfaces 230. This may require an initial technology setup but may enhance the Financial Institution Quality Control strategy implementation results and speed to market to instantly deploy any strategies built and validated in the system 102 by the user. (See, Para. 34-36; Abstract)).
mapping Automated Underwriting System (AUS) results to system conditions; (Masson, The cloud-based platform 730 may output the results of the lending strategies and business revenue management system 102 in different formats. One example of output may be directly to the Financial Institution Operating Systems (e.g., Loan Origination System or Automated Underwriting Systems) through API 742 and following a strategy validation protocol from the system user on the interfaces 230. This may require an initial technology setup but may enhance the Financial Institution Quality Control strategy implementation results and speed to market to instantly deploy any strategies built and validated in the system 102 by the user. (See, Para. 34-36; Abstract)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Agarwal with the features of Masson’s system because “while conventional systems may be capable of providing optimization models and reporting, these systems, however, are unable to capture industry knowledge and, instead, require a team of lending industry experts and statisticians to review manually or in separate systems the lending product characteristics (e.g. credit card debt to income, mortgage loan to value), and lending function and program characteristics (e.g. marketing prescreen, underwriting eligibility criteria, account line management). Such limitation either reduces the performance of lending strategies if implemented solely based on the system output or requires the work and interaction of several teams to produce a higher quality strategy that may answer the business needs and revenue targets.” (Masson, Para. 16).
Regarding Claims 2 and 19:
Agarwal teaches:
further comprising, in response to determining that an execution of the system tools fails, sending updated context information to the generative AI system to obtain further instructions. (Agarwal, For example, messages from known scammers, may be identified as such. A machine learning classifier, a neural network, or other artificial intelligence model may be trained using these labeled pairs to identify potential bad actor user input and/or confirm identity of a known user.….. User satisfaction is also a function of the chat interface guidance provided by AA or HA. User satisfaction can be increased by: (1) eliminating repeat questions, (2) having empathetic and supportive AA or HA, (3) providing user friendly instruction on how to submit and subsequently access documents, (4) the opportunity to adaptively modify answers, and (5) keeping the user informed of the progress and time necessary to complete the form….. the user may receive additional instruction from AA or HA which will then be incorporated into the response. The user may continue responding to the remaining questions and finish the multi-step process. By having AA explain to the user the question results in a timelier response and higher customer satisfaction. (See, Abstract; Para. 43, 65, 86, 115)).
Regarding Claim 3:
Agarwal teaches:
wherein the chat manager, in response to determining that a text response is available when the execution of the system tool fails, displays the text response to the user. (Agarwal, Additionally, most users prefer to exchange text messages via SMS (short message service) associated with their native messaging application rather than installing an additional component (e.g., a mobile app). However, sending confidential information via SMS messages (e.g., SSN, DOB, address, etc.) may pose security risks. By sending users an SMS message with a link via to a secure chat application running inside a standard mobile web browser allows the users to utilize their native messaging application and provide sensitive data without any potential security risks. Furthermore, the conversation inside the chat application will be reproduced for user's review as a series of text message with the exception of the sensitive data, which will be masked or obscured….. the automated assistant or bot may interact with users through text, e.g., via chat interface of client chat interface 127. In some embodiments, an automated assistant may be implemented by an automated assistant provider such that it is not the same as the provider of distributed chat application 126. (See, Abstract; Para. 42, 62, 78)).
Regarding Claims 4 and 13:
Agarwal teaches:
wherein the task library comprises one or more predefined tasks that each correspond to one or more stages in the loan process. (Agarwal, HA-only view 440 will include a set of workflows generated by computer component 102 and/or chat interface application 126 (illustrated in FIG. 1 ) to provide HA more efficient collection information form the user. In particular, by selecting a particular workflow, HA automatically engages AA's help to gather responses to questions in a particular category or phase of the process (e.g., mortgage loan application process). For example, the client chat interface 127 may generate workflows corresponding to a group of questions within an electronic form the user is attempting to complete. As illustrated in FIG. 4A, HA view 440 of the chat interface application 126 may include workflows 441 (also illustrated in FIG. 4B) and quick requests 443 (also illustrated in FIG. 4C). Each workflow 441, e.g., “Apply Now”, “Getting Started”, “Customize Quote,” and “Authenticate”, may include questions to be provided by the user which are grouped based on a particular process. For example, the questions from “Getting Started” may include all questions related to the user (e.g., name, dob, address,) that can also be relevant to “Apply Now” workflow. (See, Abstract; Para. 78, 81)).
Regarding Claim 5:
Agarwal teaches:
wherein the generative AI system analyzes the context information to generate one or more instructions for using the system tools. (Agarwal, The application 126 or computing components 102 operating the AA may utilize machine learning classifier that is trained based at least in part on known bad actor statements expressing interest in utilizing the services provided by the chat interface and statements of similar contextual value extracted from the prior message exchange threads of the present user…… A machine learning classifier, a neural network, or other artificial intelligence model may be trained using these labeled pairs to identify potential bad actor user input and/or confirm identity of a known user. (See, Abstract; Para. 48-50, 65)).
Regarding Claims 6 and 12:
Agarwal teaches:
wherein the chat manager enqueues and dequeues the messages to maintain a proper processing order. (Agarwal, the user may be accessing the history of messages exchanged by accessing it in the native messaging application as it will be replicated there with the exception of any confidential information which will be obscured or masked, as discussed below. The user may re-activate the conversation and re-start form completion upon sending another message via the native messaging application…. the AA may determine that based on user's questions, the conversation is moving into a new area of specialization. For example, the system may use a machine-learning algorithm trained on previous interactions with users applying for a mortgage loan to determine that the user is looking for assistance beyond standard data collection. (See, Abstract; Para. 73, 76, 77));
Regarding Claims 7, 15, and 20:
Agarwal teaches:
wherein the chat manager, after each execution of the system tools, determines whether further processing is required, and if so, re-evaluates the current loan status and updates the task library. (Agarwal, the client chat interface 127 may generate workflows corresponding to a group of questions within an electronic form the user is attempting to complete. As illustrated in FIG. 4A, HA view 440 of the chat interface application 126 may include workflows 441 (also illustrated in FIG. 4B) and quick requests 443 (also illustrated in FIG. 4C). Each workflow 441, e.g., “Apply Now”, “Getting Started”, “Customize Quote,” and “Authenticate”, may include questions to be provided by the user which are grouped based on a particular process. For example, the questions from “Getting Started” may include all questions related to the user (e.g., name, dob, address,) that can also be relevant to “Apply Now” workflow (See, Abstract; Para. 81)).
Regarding Claim 8:
Agarwal teaches:
wherein the generative AI system provides text instructions and guidance for use of the system tools based on a current scenario and a current user context. (Agarwal, User satisfaction is also a function of the chat interface guidance provided by AA or HA. User satisfaction can be increased by: (1) eliminating repeat questions, (2) having empathetic and supportive AA or HA, (3) providing user friendly instruction on how to submit and subsequently access documents, (4) the opportunity to adaptively modify answers, and (5) keeping the user informed of the progress and time necessary to complete the form. (See, Para. 86)).
Regarding Claims 9 and 14:
Agarwal teaches:
wherein the chat manager iteratively communicates updated system conditions and context information to the generative AI system to enable the system to adapt to updated borrower data. (Agarwal, the client chat interface 127 is also configured to accommodate user modifications after having provided subsequent responses to questions generated based on their original answer they now wish to modify. Unlike the interaction via a GUI, which allows users to quickly modify a previously entered response (e.g., by clicking a particular field in a form), the modification of user provided input via a conversation interface may not be as straightforward. In essence, in an effort to change a response buried in a multi-message exchange, the user would have to scroll to find a relevant response and then modify it. As a result, the AA would generate a “new” set of downstream answers or commands corresponding with the updated user input (e.g., as a separate branch). These downstream answers in a new branch may be generated by reusing the information provided by the user in the original branch…… the present embodiments can sequester individual branch data which allows the system to maintain interdependencies between user responses in separate branches. This allows the system to generate graphical visualizations of each branch resulting in practically infinite branching capabilities. (See, Abstract; Para. 96-97)).
Regarding Claim 10:
(Canceled).
Regarding Claim 11:
Agarwal teaches:
A system for managing a loan origination process, the system comprising: a non-transitory computer-readable medium or media comprising instructions that, when executed by one or more processors, causes the steps to be performed comprising: receiving at a chat manager messages from a user via a user interface (UI) and processing by the chat manager the messages to extract borrower details and loan conditions; (Agarwal, a system and method for providing a natural language interface or conversation/chat interface for interacting with an automated software assistant (AA) and/or a human assistant (HA) when completing an electronic form is disclosed. For example, the electronic form may be a mortgage application soliciting input from the user in the form of questions wishing to obtain a mortgage loan. (See, Abstract; Para. 43, 54-59; Fig. 1));
generating, by a task library communicatively coupled to the chat manager, tasks based on a current loan status; (Agarwal, The system may also generate quick requests, which may include individual questions or requests that user must provide when completing the form. ….. in other words, the system may recognize that one conversation may be associated with an identifier, e.g., a loan number. In the event the system cannot recognize the identifier, the user may drag an icon of a previously submitted document related to that loan number to the conversation to form the association. Alternatively, the user may enter a text or voice command to make the association. Additionally, the conversation may include folders or sub-folders within the conversation which may contain information related to one particular identifier. For example, a loan number may be a parent folder and a bank account may be a folder or a sub-folder. The system may identify the folder using the identifier (See, Abstract; Para. 43, 46-48));
analyzing, by a generative artificial intelligence (AI) system communicatively coupled to the chat manager, context information and provide one or more instructions and guidance for using system tools; Agarwal, the chat interface provides user with AA configured to use a conversation (i.e., natural language questions and answers) format to elicit user reposes required to complete the form. Further, the AA is configured to “invite” HA based on user's needs, the HA's level of skill, the HA's availability, and/or other such factors. In essence, AA may act like an “intelligent concierge” or a go-between the HA and the user. The user may converse with both the AA and the HA within the chat interface. By having the ability to interact with both the AA and HA, the user will be in the “company” of two helpful guides, never left alone to complete the arduous task of filling out an online form. (See, Abstract; Para. 43-48));
and evaluating, by [an automated underwriting system (AUS)] communicatively coupled to the chat manager, borrower data and loan-related information to assess loan eligibility; (Agarwal, To further complicate the issue associated with updated user input, is the fact that a modification may trigger a divergent workflow thereby causing the AA to ask downstream questions that were not previously required by the workflow. The present embodiments provide a solution by associating the information extracted from the original branch of the conversation with a particular transaction identifier (e.g., a loan application number). Each time a new branch is generated, including a modified user response and new AA questions, that branch is associated with a previous branch by utilizing the same identifier. For example, the information extracted from each branch may be stored as a single “conversation log” in a database utilizing the online transactional processing or OLTP processing. By virtue of associating the branches with a common transaction identifier, the present embodiments can sequester individual branch data which allows the system to maintain interdependencies between user responses in separate branches. This allows the system to generate graphical visualizations of each branch resulting in practically infinite branching capabilities. (See, Abstract; Para. 43-48, 54, 65, 97));
wherein the task library generates new tasks based on results and findings provided by [the AUS], (Agarwal, the client chat interface 127 is also configured to accommodate user modifications after having provided subsequent responses to questions generated based on their original answer they now wish to modify. Unlike the interaction via a GUI, which allows users to quickly modify a previously entered response (e.g., by clicking a particular field in a form), the modification of user provided input via a conversation interface may not be as straightforward. In essence, in an effort to change a response buried in a multi-message exchange, the user would have to scroll to find a relevant response and then modify it. As a result, the AA would generate a “new” set of downstream answers or commands corresponding with the updated user input (e.g., as a separate branch). These downstream answers in a new branch may be generated by reusing the information provided by the user in the original branch…… the present embodiments can sequester individual branch data which allows the system to maintain interdependencies between user responses in separate branches. This allows the system to generate graphical visualizations of each branch resulting in practically infinite branching capabilities. (See, Abstract; Para. 96-97));
the task library is iteratively updated based on updates generated by the generative Al system; (Agarwal, While modifying the response immediately after providing it is not an issue, the client chat interface 127 is also configured to accommodate user modifications after having provided subsequent responses to questions generated based on their original answer they now wish to modify. Unlike the interaction via a GUI, which allows users to quickly modify a previously entered response (e.g., by clicking a particular field in a form), the modification of user provided input via a conversation interface may not be as straightforward. In essence, in an effort to change a response buried in a multi-message exchange, the user would have to scroll to find a relevant response and then modify it. As a result, the AA would generate a “new” set of downstream answers or commands corresponding with the updated user input (e.g., as a separate branch). These downstream answers in a new branch may be generated by reusing the information provided by the user in the original branch. (See, Abstract; Para. 96-97));
a database communicatively coupled to the chat manager, the database configured to store relevant data including user interaction history, borrower profiles, financial documents, and [AUS findings]; and (Agarwal, To further complicate the issue associated with updated user input, is the fact that a modification may trigger a divergent workflow thereby causing the AA to ask downstream questions that were not previously required by the workflow. The present embodiments provide a solution by associating the information extracted from the original branch of the conversation with a particular transaction identifier (e.g., a loan application number). Each time a new branch is generated, including a modified user response and new AA questions, that branch is associated with a previous branch by utilizing the same identifier. For example, the information extracted from each branch may be stored as a single “conversation log” in a database utilizing the online transactional processing or OLTP processing. By virtue of associating the branches with a common transaction identifier, the present embodiments can sequester individual branch data which allows the system to maintain interdependencies between user responses in separate branches. This allows the system to generate graphical visualizations of each branch resulting in practically infinite branching capabilities. (See, Abstract; Para. 43-48, 54, 65, 97));
wherein the UI is communicatively coupled to the chat manager, the UI configured to display results, recommendations, instructions, and updates to the user. (Agarwal, any request that AA has determined to require the HA's attention will be displayed in area 430 of window 412. For example, after the HA sends the user a quick request (e.g., from a list of quick requests 443), the AA may determine that certain information and/or documents are missing (e.g., user authentication credentials or loan amount) and were not requested via one of the quick requests 443. For example, illustrated in FIG. 4F, the AA may generate and display reminders in area 430 informing HA that the user still needs to submit authentication credentials by highlighting the corresponding icon which is ready to be clicked by the HA to be sent to directly the user requesting additional details. (See, Abstract; Para. 82, 83, 86, 106-108)).
Agarwal does not specifically teach an automated underwriting system and AUS findings.
However, Masson further teaches the following limitation:
an automated underwriting system; (Masson, The cloud-based platform 730 may output the results of the lending strategies and business revenue management system 102 in different formats. One example of output may be directly to the Financial Institution Operating Systems (e.g., Loan Origination System or Automated Underwriting Systems) through API 742 and following a strategy validation protocol from the system user on the interfaces 230. This may require an initial technology setup but may enhance the Financial Institution Quality Control strategy implementation results and speed to market to instantly deploy any strategies built and validated in the system 102 by the user. (See, Para. 34-36; Abstract));
AUS findings (Masson, The cloud-based platform 730 may output the results of the lending strategies and business revenue management system 102 in different formats. One example of output may be directly to the Financial Institution Operating Systems (e.g., Loan Origination System or Automated Underwriting Systems) through API 742 and following a strategy validation protocol from the system user on the interfaces 230. This may require an initial technology setup but may enhance the Financial Institution Quality Control strategy implementation results and speed to market to instantly deploy any strategies built and validated in the system 102 by the user. (See, Para. 34-36; Abstract)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Agarwal with the features of Masson’s system because “while conventional systems may be capable of providing optimization models and reporting, these systems, however, are unable to capture industry knowledge and, instead, require a team of lending industry experts and statisticians to review manually or in separate systems the lending product characteristics (e.g. credit card debt to income, mortgage loan to value), and lending function and program characteristics (e.g. marketing prescreen, underwriting eligibility criteria, account line management). Such limitation either reduces the performance of lending strategies if implemented solely based on the system output or requires the work and interaction of several teams to produce a higher quality strategy that may answer the business needs and revenue targets.” (Masson, Para. 16).
Regarding Claim 16:
Agarwal teaches:
wherein the database serves as a centralized data repository that provides the chat manager and the generative AI system access to current and accurate information to guide user decision-making. (Agarwal, To further complicate the issue associated with updated user input, is the fact that a modification may trigger a divergent workflow thereby causing the AA to ask downstream questions that were not previously required by the workflow. The present embodiments provide a solution by associating the information extracted from the original branch of the conversation with a particular transaction identifier (e.g., a loan application number). Each time a new branch is generated, including a modified user response and new AA questions, that branch is associated with a previous branch by utilizing the same identifier. For example, the information extracted from each branch may be stored as a single “conversation log” in a database utilizing the online transactional processing or OLTP processing. By virtue of associating the branches with a common transaction identifier, the present embodiments can sequester individual branch data which allows the system to maintain interdependencies between user responses in separate branches. This allows the system to generate graphical visualizations of each branch resulting in practically infinite branching capabilities. (See, Abstract; Para. 96-97)).
Regarding Claim 17:
Agarwal teaches:
wherein the AUS provides data that informs system decisions to enable an integration of AI-driven decision-making with loan processing tasks. (Agarwal, To further complicate the issue associated with updated user input, is the fact that a modification may trigger a divergent workflow thereby causing the AA to ask downstream questions that were not previously required by the workflow. The present embodiments provide a solution by associating the information extracted from the original branch of the conversation with a particular transaction identifier (e.g., a loan application number). Each time a new branch is generated, including a modified user response and new AA questions, that branch is associated with a previous branch by utilizing the same identifier. For example, the information extracted from each branch may be stored as a single “conversation log” in a database utilizing the online transactional processing or OLTP processing. By virtue of associating the branches with a common transaction identifier, the present embodiments can sequester individual branch data which allows the system to maintain interdependencies between user responses in separate branches. This allows the system to generate graphical visualizations of each branch resulting in practically infinite branching capabilities. (See, Abstract; Para. 96-97)).
Response to Arguments
With respect to the objection of claims 1, 5, 6, and 11 the objections are withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 11/28/2025. However, the objection of claim 18 is not withdrawn because the noted typographical mistake was not appropriately corrected; and new claim objections have been given with regards to Claims 18-20. In view of the grounds for the claim objections presented above in this office action, appropriate correction is required.
Applicant's arguments filed on 11/22/2025 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 “the claims involve a practical application….Claims 1 and 18 are clearly related to a practical application of managing a loan origination process. Implementation of the claims may increase efficiency and transparency to the borrower, while simultaneously reducing costs to lenders…..Applicant respectfully asserts that such integration of the AUS with the chat manager and generative Al system, together with other limitations make claims, as a whole, related to a practical application of managing a loan origination process…By automating complex tasks and ensuring real-time responsiveness to user inputs, the system not only reduces processing time but also enhances accuracy and transparency. These improvements enhance the overall goal of making the loan process more accessible and less burdensome for borrowers, while also providing lenders with a more efficient and reliably repeatable process."
Examiner respectfully disagrees.
Under Step 2A: Prong II, Examiner respectfully notes that there is no improved technology in simply receiving, extracting, generating, selecting, communicating, storing, mapping, executing, processing, updating, displaying, and outputting data (i.e., user conversations, borrower details, loan conditions, current loan status, context information, loan information, conditions, responses, new tasks, results, findings, and etc.). The disclosed invention cannot be equated to improvement to technological practices or computers. There is no technical improvement at all. Instead, Applicant recites “by a chat manager, in response to receiving messages from a user conversation of a user, extracting borrower details and loan conditions; using a task library to generate tasks based on a current loan status; selecting a set of tools and prompts based on a context of the user conversation to guide the user through a loan process; communicating context information and the set of tools to a generative artificial intelligence (AI) system; receiving and storing a response from the generative AI system, the response comprising system tools and instructions; executing the system tools to extract loan information and borrower data, and mapping Automated Underwriting System (AUS) results to system conditions; iteratively updating system conditions and the task library based on updates generated by the generative AI system; and displaying a system-generated response to the user that directs subsequent steps in the loan process; wherein the task library generates new tasks based on results and findings provided by the AUS.” The recited features in the limitations do not result in computer functionality or technical improvement. Examiner respectfully notes that Applicant is simply using a computer to input, process, and output data. As previously discussed, the recited features in the limitations, as amended, does not disclose a technical solution to technical problem, but simply a business solution. Specifically, the recited steps, as amended, are merely managing/processing data (MPEP 2106.05(d)(II)) and do not result in computer functionality or technical improvement. Thus, Applicant has simply provided a business method practice of processing data (user conversations, borrower details, loan conditions, current loan status, context information, loan information, conditions, responses, new tasks, results, findings, and etc.), and no technical solution or improvement has been disclosed.
Moreover, 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 processing a loan using extracted loan information and borrower data. 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) is not an improvement in technology (See, MPEP 2106.05(a)(II)). Thus, the claim does not integrate the abstract idea into a practical application; and these arguments are not persuasive.
Additionally, Claims 1, 11, and 18, as amended, recites steps at a high level of generality. In addition, all uses of the recited judicial exceptions require such data gathering and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering, processing, and outputting. 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, these arguments are not persuasive. The recited steps in claim 1, as amended, are recited as being performed by a chat manager, user interface (UI), generative artificial intelligence (AI) system, task library, system tools, instructions, and automated underwriting system (AUS). The additional elements: a chat manager, user interface (UI), generative artificial intelligence (AI) system, task library, system tools, instructions, and automated underwriting system (AUS) are recited at a high level of generality, and are used as a tool to perform the generic computer function of receiving, processing, and outputting data. See MPEP 2106.05(f). Additionally, the claims, as amended, recites a chat manager, user interface (UI), generative artificial intelligence (AI) system, task library, system tools, instructions, and automated underwriting system (AUS), which are used to perform an abstract idea, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Specifically, the recitation of “a chat manager, user interface (UI), generative artificial intelligence (AI) system, task library, system tools, instructions, and automated underwriting system (AUS)” in the limitations of the claims, as amended, merely indicates a field of use or technological environment in which the judicial exception is performed. Specifically, the additional elements, as listed above, are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking the above listed additional elements is similar to invoking software and software components. 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. The claim, 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 (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). Hence, claims 1-9 and 11-20, as amended, do not integrate the abstract idea into a practical application. Thus, these arguments are not persuasive.
Applicant argues that “the claim recite "significantly more" than merely a judicial exception…. the claims 1 and 18, as amended, comprise at least the following elements: executing the system tools to extract loan information and borrower data, and mapping Automated Underwriting System (A US) results to system conditions; iteratively updating the system conditions and the task library based on updates generated by the generative AI system...wherein the task library generates new tasks based on results and findings provided by the AUS. Such a combination of elements, 1) mapping Automated Underwriting System (AUS) results to system conditions, 2) iteratively updating the system conditions and the task library based on updates generated by the generative AI system, and 3) the task library generates new tasks based on results and findings provided by the AUS is NOT well-understood routine, or conventional, evidenced by distinction from prior art with details presented in the following argument with respect to the claim rejection under 35 U.S.C. § 103.”
Examiner respectfully disagrees.
Under Step 2B, Examiner respectfully notes that all of Applicant's arguments have been reviewed, and the inventive concept cannot be furnished by a judicial exception. The improvements argued are to the abstract idea and not to technology. The technical limitations are simply utilized as a tool to implement the abstract idea without adding significantly more. Thus, the claim is directed to an abstract idea, and hence these arguments are not persuasive. The presence of a computer does not make the claimed solution necessarily rooted in computer technology. As noted above, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a chat manager, user interface (UI), generative artificial intelligence (AI) system, task library, system tools, instructions, and automated underwriting system (AUS) (Claim 1) 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. Furthermore, as explained above with respect to Step 2A, Prong II, the additional elements: a chat manager, user interface (UI), generative artificial intelligence (AI) system, task library, system tools, instructions, and automated underwriting system (AUS) (Claim 1), are at best mere instructions to “apply” the abstract idea, which cannot provide an inventive concept. See MPEP 2106.05(f). As discussed in Step 2A, Prong II above, the claims’ limitations are recited at a high level of generality. These elements simply amount to receiving and outputting data and are well-understood, routine, conventional activity. See MPEP 2106.05(d)(II). As discussed in Step 2A, Prong II above, the recitation of a computer/processor to perform recited limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept.
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 101 rejection of claims 1-9 and 11-20.
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 “Applicant respectfully asserts that the claims, as amended, are patentable over Agarwal in view of Masson. 1. None of Agarwal and Masson teaches the element of "the task library generates new tasks based on results and findings provided by the AUS;" None of Agarwal in view of Masson teaches the element of "iteratively updating the system conditions and the task library based on updates generated by the generative AI system"…. Based on at least the above remarks, the Applicant respectfully asserts that claims 1, 11, and 18, as amended, are patentable over Agarwal in view of Masson and requests withdrawn of the rejections under 35 USC 103.”
Examiner respectfully disagrees.
Examiner respectfully notes that Agarwal teaches “wherein the task library generates new tasks based on results and findings provided by [the AUS].” Specifically Agarwal recites that “the client chat interface 127 is also configured to accommodate user modifications after having provided subsequent responses to questions generated based on their original answer they now wish to modify. Unlike the interaction via a GUI, which allows users to quickly modify a previously entered response (e.g., by clicking a particular field in a form), the modification of user provided input via a conversation interface may not be as straightforward. ….As a result, the AA would generate a “new” set of downstream answers or commands corresponding with the updated user input (e.g., as a separate branch). These downstream answers in a new branch may be generated by reusing the information provided by the user in the original branch;” which teaches “generating new tasks based on results and findings.” (See, Abstract; Para. 96-97). As noted in the 35 U.S.C. rejection, Agarwal does not specifically teach an Automated Underwriting System (AUS) and mapping Automated Underwriting System (AUS) results to system conditions. However, Masson further teaches these limitations. Specifically Masson recites that “the cloud-based platform 730 may output the results of the lending strategies and business revenue management system 102 in different formats. One example of output may be directly to the Financial Institution Operating Systems (e.g., Loan Origination System or Automated Underwriting Systems) through API 742 and following a strategy validation protocol from the system user on the interfaces 230. This may require an initial technology setup but may enhance the Financial Institution Quality Control strategy implementation results and speed to market to instantly deploy any strategies built and validated in the system 102 by the user;” which teaches “Automated Underwriting System (AUS)” and “mapping Automated Underwriting System (AUS) results to system conditions.” (See, Para. 34-36; Abstract)). It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Agarwal with the features of Masson’s system because “while conventional systems may be capable of providing optimization models and reporting, these systems, however, are unable to capture industry knowledge and, instead, require a team of lending industry experts and statisticians to review manually or in separate systems the lending product characteristics (e.g. credit card debt to income, mortgage loan to value), and lending function and program characteristics (e.g. marketing prescreen, underwriting eligibility criteria, account line management). Such limitation either reduces the performance of lending strategies if implemented solely based on the system output or requires the work and interaction of several teams to produce a higher quality strategy that may answer the business needs and revenue targets.” (Masson, Para. 16).
Furthermore, Examiner respectfully notes that Agarwal teaches “iteratively updating the system conditions and the task library based on updates generated by the generative AI system.” Specifically Agarwal recites that “while modifying the response immediately after providing it is not an issue, the client chat interface 127 is also configured to accommodate user modifications after having provided subsequent responses to questions generated based on their original answer they now wish to modify. Unlike the interaction via a GUI, which allows users to quickly modify a previously entered response (e.g., by clicking a particular field in a form), the modification of user provided input via a conversation interface may not be as straightforward. In essence, in an effort to change a response buried in a multi-message exchange, the user would have to scroll to find a relevant response and then modify it. As a result, the AA would generate a “new” set of downstream answers or commands (generative AI system) corresponding with the updated user input (e.g., as a separate branch). These downstream answers in a new branch may be generated by reusing the information provided by the user in the original branch;” which teaches iteratively updating the system conditions and the task library based on updates generated by the generative AI system. (See, Abstract; Para. 96-97).
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 103 rejection of claims 1-9 and 11-20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure are the following:
Hsieh (U.S. Patent Application Publication No. US 2019/0295107 A1) - “Lead management system and methods thereof”
Wood (U.S. Patent Application Publication No. US 2022/0129890 A1) “Compliance controller for the integration of legacy systems in smart contract asset control”
Gueye (U.S. Patent Application Publication No. US 2019/0303807 A1) “Method and system for facilitating provisioning of social networking data to a mobile device”
McDonald (U.S. Patent Application Publication No. US 2017/0221142 A1) “Mortgage loan data processing system and method for a loan broker”
Arnall (U.S. Patent Application Publication No. US 2017/0337628 A1) “Automated consumer-facing mortgage processing system”
Hansen (U.S. Patent No. US 11,238,075 B1) “Systems and methods for providing inquiry responses using linguistics and machine learning”
Goel (U.S. Patent No. US 11,568,482 B1) “Systems and methods for detecting and linking data objects across distributed platforms”
Hepp (U.S. Patent Application Publication No. US 2024/0257236 A1) “Real estate finance exchange”
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
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/MOHAMMED H MUSTAFA/Examiner, Art Unit 3693
/ELIZABETH H ROSEN/Primary Examiner, Art Unit 3693