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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “optionally” in claims 1, 9 and 17 is a relative term which renders the claim indefinite. The term “optionally” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Limitations “a debtor database comprising, for each debtor, the debtor’s data, the debt amount, and optionally personal’s debtor’s conditions set by the landlord for negotiating the debt” and “automatically issuing one or more settlement proposal communications with each debtor based on selectable predefined communication contents, predefined rules, optionally debtor’s proposal, and data acquired from said debtor database” are interpreted as best understood.
Claim 1 recites the limitation "the landlord side" in line 2. There is insufficient antecedent basis for this limitation in the claim. For examination purposes the claim is interpreted as best understood.
Claim 1 recites the limitation "the debtor’s data" in line 4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes the claim is interpreted as best understood.
Claims 1, 9, 17 recites the limitation "the debt amount" in line 4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes the claim is interpreted as best understood.
Claims 1, 9, 17 recites the limitation "the same" in line 4. It is unclear the scope of what it is communicated to the debtor. For examination purposes the claim is interpreted as best understood.
Claims 1, 9, 17 recites the limitation "the debtor’s agreement" in line 13. There is insufficient antecedent basis for this limitation in the claim. For examination purposes the claim is interpreted as best understood.
Claims 1, 9, 17 recites the limitation "the proposal" in line 14. It is unclear if the Applicant is referring to the “settlement proposal” or to the term “debtor’s proposal”. For examination purposes the claim is interpreted as best understood.
Claims 2, 10 recites the limitation "the type of communication preference" in line 2. There is insufficient antecedent basis for this limitation in the claim. For examination purposes the claim is interpreted as best understood.
Claims 3 and 11 recites the limitation "the debtor’s communication" in line 1. There is insufficient antecedent basis for this limitation in the claim. It is unclear if the Applicant refers to “one or more settlement proposal communications”, “selectable predefined communication contents”, “each communication” as introduced in claim 1 and 9 respectively, or if this is different type of communication. For examination purposes the claim is interpreted as best understood.
Claims 5, 13 recites the limitation "the debt settlement" in line 2. There is insufficient antecedent basis for this limitation in the claim. For examination purposes the claim is interpreted as best understood.
Claims 5, 13 recites the limitation "the debt settlement" in line 2. There is insufficient antecedent basis for this limitation in the claim. The term was not introduced properly. For examination purposes the claim is interpreted as best understood.
Claim 6 discloses the limitation “wherein the negotiator is implemented generative artificial intelligence (GenAI) algorithms to generate a dynamic negotiation strategy”. The claim appears to have a grammatical error. For examination purposes the claim is interpreted as best understood.
Claims 7, 15, 19 recites the limitation "the GenAI negotiation algorithms" in line 2. There is insufficient antecedent basis for this limitation in the claim. For examination purposes the claim is interpreted as best understood.
Claims 8, 16, 20 recites the limitation "the past resident’s sentiment" in line 5. There is insufficient antecedent basis for this limitation in the claim. For examination purposes the claim is interpreted as best understood.
Claims 8, 16, 20 recites the limitation "the likelihood" in line 9. There is insufficient antecedent basis for this limitation in the claim. For examination purposes the claim is interpreted as best understood.
Claims 8, 16, 20 recites the limitation "the property owner" in line 11. There is insufficient antecedent basis for this limitation in the claim. For examination purposes the claim is interpreted as best understood.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the claims are directed to at least one potentially eligible category of subject matter (i.e., process and machine, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-20 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls under the “Certain Methods Of Organizing Human Activity” group within the enumerated groupings of abstract ideas set forth in the MPEP 2106 since the claims set forth steps that recite commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
Claims 1 and 9 and 17 recites the abstract idea of enabling a property manager and a tenant to settle a tenant’s debt upon termination or end of a rental contract [002]. In claim 1, this idea is described by the following claim steps:
interfacing with a landlord to update a debtor’s database,
collecting in a debtor database, for each debtor, the debtor’s data, the debt amount, and optionally personal’s debtor’s conditions set by the landlord for negotiating the debt, and
negotiating the debt payment by:
issuing one or more settlement proposal communications with each debtor based on selectable predefined communication contents, predefined rules, optionally debtor’s proposal, and data acquired from said debtor database, and communicating the same to the debtor,
receiving a response from the debtor to each communication,
analyzing the received response to determine either the debtor’s agreement to the proposal and execution of a respective payment or a debtor’s response proposal,
upon determination of a debtor’s agreement and fulfilling a proposal’s payment, automatically issuing a release document, and
sending the release document to the debtor.
This idea falls within the certain methods of organizing human activity grouping of abstract ideas because it is directed towards commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The noted abstract idea is also directed to managing interactions between people such as that required during communications when negotiating a debt payment and sending a release document conforms to the requirements of more than one party.
Because the above-noted limitations recite steps falling within the Certain Methods Of Organizing Human Activity abstract idea groupings of the MPEP 2106, they have been determined to recite at least one abstract idea when evaluated under Step 2A Prong One of the eligibility inquiry.
Therefore, because the limitations above set forth activities falling within the Certain Methods Of Organizing Human Activity abstract idea groupings described in the MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below. Claim 9 and 17 recites similar limitations as claim 1 and is therefore determined to recite the same abstract idea.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements that fail to integrate the abstract idea into a practical application are:
database;
automatically performing steps;
a computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therewith;
However, using a computer environment such as a database, and other recited computer elements amounts to no more than generally linking the use of the abstract idea to a particular technological environment. Enabling a property manager and a tenant to settle a tenant’s debt upon termination or end of a rental contract can reasonably be performed by pencil and paper until limited to a computerized environment by requiring a database and a computer readable program to perform the steps.
These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and alternatively serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As noted above, the claims as a whole merely describes a method, computer system, and computer program product that generally “apply” the concepts discussed in prong 1 above. (See MPEP 2106.05 f (II)) In particular applicant has recited the computing components at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. As the court stated in TLI Communications v. LLC v. AV Automotive LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) merely invoking generic computing components or machinery that perform their functions in their ordinary capacity to facilitate the abstract idea are mere instructions to implement the abstract idea within a computing environment and does not add significantly more to the abstract idea. Accordingly, these additional computer components do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea and as a result the claim is not patent eligible.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
For the reasons identified with respect to Step 2A, prong 2, claims 1, 9 and 17 fail to recite additional elements that amount to an inventive concept. For example, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a commercial or legal interaction or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(g)). In addition, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (see MPEP 2106.05(h)).
Dependent claims 2-8, 10-16 and 18-20 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above.
Dependent claims 2, 4-5, 10 and 12-13 further limits the abstract idea by introducing a description of the debtor’s data and the release document. Further embellishing the invention by describing the specific type of data does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. Therefore the claims are also non-statutory subject matter.
Dependent claims 3 and 11 further limits the abstract idea by linking the judicial exception to a particular field of use by introducing the limitation wherein the debtor’s communication is selectable from an email, messaging, or calls. Further embellishing that the invention is capable of transmitting information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. The recitation of a user device merely links the abstract idea of to a technological environment. Therefore the claims are also non-statutory subject matter.
Dependent claims 6-8, 14-16 and 18-20 further limits the abstract idea by linking the judicial exception to a particular field of use by introducing limitations directed to the use of generative artificial intelligence (GenAI). However, the examiner views these additional elements as results-oriented steps given that there is no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result are currently present such that this is viewed as equivalent to “apply it” for merely implementing the abstract idea using generic computing components (See Id.). Therefore the claims are also non-statutory subject matter.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide high level of generality computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information see MPEP 2106.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 9-13, and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Appel (US Patent Publication 2024/0078597).
Regarding claims 1, 9 and 17, Appel discloses an automatic landlord system for settling debtors’ debts following termination or end of a property contract, (see abstract),
a computer-implemented automatic landlord method of settling debtors’ debts following termination or end of a property contract (see abstract)
a computer program product for settling debtors’ debts automatic to a landlord system following termination or end of a property contract, the computer program product comprising a non-transitory computer readable storage medium having computer readable program embodied therewith ([004-005]), the computer readable program comprising:
a landlord interface for updating a debtor’s database ([0048] FIG. 4 shows an example environment 405 comprising an AI debt renegotiation system 400 and example users 410A, 410B (“users 410”) with example user devices 412A, 412B (“user devices 412”), and showing example components of AI debt renegotiation system 400, in accordance with various examples of this disclosure. Users 410 may be business owners, business managers, or individual consumers, who may be interested in renegotiating a debt with a financial institution, for example. [0051] In method 501, a user initiates accessing and using AI debt renegotiation system 500. AI debt renegotiation system 500 may communicate with a user device 512 of the user via an AI intelligent dialogue agent 522, with communications and data transmitted via a network system 519, for example.),
a debtor database comprising, for each debtor, the debtor’s data, the debt amount, and optionally personal’s debtor’s conditions set by the landlord for negotiating the debt ([0052] AI debt renegotiation system 500 compiles data relevant to the user from all of the applicable data sources to create a compiled user profile (e.g., summary) 507. AI debt renegotiation system 500 saves this user profile 507, including applicable information, which may include in forms such as variables and/or a knowledge graph 505 (further explained below). AI debt renegotiation system 500 may use the user profile 507 and knowledge graph 505 in interacting with the user to renegotiate the user's debts (508), and may apply all applicable such data and information, which may be collectively referred to as user profile data, to one or more machine learning (ML) models (510). AI debt renegotiation system 500 may apply such user profile data to one or more ML models, and may thereby generate probabilities or ensemble predictions of particular users successfully following through on renegotiated payment plans, in some examples. ), and
a negotiator for:
automatically issuing one or more settlement proposal communications with each debtor based on selectable predefined communication contents, predefined rules, optionally debtor’s proposal, and data acquired from said debtor database, and communicating the same to the debtor ([0056] AI debt renegotiation system 500 may perform these or further forms of analysis, and may initiate a response to the user. AI debt renegotiation system 500 may interact with the user, via an AI intelligent dialogue agent 522 for instance, and ask about how the user would like to negotiate a new debt servicing agreement. AI debt renegotiation system 500 may ask questions to check and verify correct user information and identity credentials, and may generate a new proposed payment plan with new debt servicing terms (526) based on predictions, for terms such as a best day of each month for payments to be due, preference of payment installment value, and/or a number of installment payments. If the user has previously accessed AI debt renegotiation system 500, AI debt renegotiation system 500 may access and use previous information as may still be applicable about value and payment installments. AI debt renegotiation system 500 may also pose questions to the user about why the user would like to renegotiate servicing terms for the applicable debt, such as whether the value of existing installment payments is currently too high for the user's budget, and/or whether the user has recently experienced a job loss, health issues, or issues servicing other debts, for example. ),
receiving a response from the debtor to each communication ([0058] AI debt renegotiation system 500 may receive and process aspects of the user's reactions to the proposal, such as questions about the proposal, time to react, language, and wording. If the user poses questions about the proposal rather than agreeing to the proposal, AI debt renegotiation system 500 may process the user's reactions, together with other applicable data, and determine whether and with what terms to generate another new proposal, with one or more variations to the terms relative to the prior proposal, to adapt to the user's requests and/or other reactions (528).),
analyzing the received response to determine either the debtor’s agreement to the proposal and execution of a respective payment or a debtor’s response proposal ([0059] AI debt renegotiation system 500 may also generate communications to prompt the user to close an agreement on the proposal (530). In one example, AI debt renegotiation system 500 may prompt the user by explaining that accepting the proposal and paying on its terms may result in positive reporting for the user by credit reporting bureaus. In another example, AI debt renegotiation system 500 may prompt the user by offering incentives in exchange for forming an agreement on the proposal, such as an opportunity to participate in a financial education program and/or a loyalty rewards program, and/or to receive compensation or a reward, such as a gift, or cash back, in exchange for finalizing agreement on the proposal. [0060] AI debt renegotiation system 500 may record a user finalization of agreement on the proposal, such as by receiving the user's electronic signature on an electronic contract document reciting the terms of the proposal. AI debt renegotiation system 500 may transmit the finalized, signed electronic contract document to any applicable entities, such as a financial institution or other entity that holds the debt and/or that services processing payments on the debt, and in association with the user's account or accounts with such financial institutions or other entities.),
upon determination of a debtor’s agreement and fulfilling a proposal’s payment, automatically issuing a release document ([0060] AI debt renegotiation system 500 may record a user finalization of agreement on the proposal, such as by receiving the user's electronic signature on an electronic contract document reciting the terms of the proposal. AI debt renegotiation system 500 may transmit the finalized, signed electronic contract document to any applicable entities, such as a financial institution or other entity that holds the debt and/or that services processing payments on the debt, and in association with the user's account or accounts with such financial institutions or other entities. [0067] AI debt renegotiation system 600 may generate and transmit an agreement proposal message to user device 650, to be rendered in the client-side AI debt renegotiation system app UI as agreement proposal message 668, as shown in the depiction of user device 650B (e.g., “Do you accept this proposal?”), and enabling the user to e-sign or otherwise accept the agreement for the proposed installment payment plan (630), such as with an agreement e-signature solicitation message 672 rendered in the UI as shown in user device 650C (e.g., “Now you just need to sign to sign the offer, and the payment will be settled from your account, starting next month on the 6th”). AI debt renegotiation system 600 may thus enable a lawfully valid execution of the proposed payment plan via user device 650. AI debt renegotiation system 600 may receive a lawfully valid execution of the proposed payment plan, and record a payment plan corresponding to the proposed payment plan in a corresponding user account in a financial institution processing system, such as with a creditor entity or an intermediary or servicing entity.), and
sending the release document to the debtor ([0060] AI debt renegotiation system 500 may record a user finalization of agreement on the proposal, such as by receiving the user's electronic signature on an electronic contract document reciting the terms of the proposal. AI debt renegotiation system 500 may transmit the finalized, signed electronic contract document to any applicable entities, such as a financial institution or other entity that holds the debt and/or that services processing payments on the debt, and in association with the user's account or accounts with such financial institutions or other entities.).
Regarding claims 2, and 10 Appel discloses
wherein the debtor’s data includes the debtor’s personal data, the type of communication preference, and debtor’s communication address ([0025] It should be understood that, to the extent implementations of the invention collect, store, or employ personal information provided by, or obtained from, individuals (for example, financial account login credentials, information about personal debts, other personal financial information, social media identification and information), such information shall be used in accordance with all applicable laws concerning protection of personal information. Additionally, the collection, storage, and use of such information may be subject to consent of the individual to such activity, for example, through “opt-in” or “opt-out” processes as may be appropriate for the situation and type of information. Storage and use of personal information may be in an appropriately secure manner reflective of the type of information, for example, through various encryption and anonymization techniques for particularly sensitive information. ([0052] AI debt renegotiation system 500 compiles data relevant to the user from all of the applicable data sources to create a compiled user profile (e.g., summary) 507. AI debt renegotiation system 500 saves this user profile 507, including applicable information, which may include in forms such as variables and/or a knowledge graph 505 (further explained below). AI debt renegotiation system 500 may use the user profile 507 and knowledge graph 505 in interacting with the user to renegotiate the user's debts (508), and may apply all applicable such data and information, which may be collectively referred to as user profile data, to one or more machine learning (ML) models (510). AI debt renegotiation system 500 may apply such user profile data to one or more ML models, and may thereby generate probabilities or ensemble predictions of particular users successfully following through on renegotiated payment plans, in some examples.), and in addition, one or more of:
(a) time since the debtor moved out,
(b) time since a last interaction with the debtor about the debt,
(c) debtor’s credit score,
(d) debtor’s payment history at the property,
(e) negotiation flexibility of the landlord,
(f) cost of a debt collection action,
(g) probability of succeeding in a debt collection action, and
(h) previous text conversations made with the debtor.
The Examiner notes that the type of data within the debtor database constitute non-functional descriptive material in view of the fact that it does not affect the database ability of store information. The type of data stored within the system does not alter or change the database and it is not further used in any functions of the system, thereby have little to no patentable weight.
Regarding claims 3 and 11, Appel discloses
wherein the debtor’s communication is selectable from an email, messaging, or calls (See Fig. 6B).
Regarding claims 4 and 12, Appel discloses
wherein said release document includes an irrevocable landlord’s obligation that the debtor has fully settled his debt and will not face any further legal action ([0060] AI debt renegotiation system 500 may record a user finalization of agreement on the proposal, such as by receiving the user's electronic signature on an electronic contract document reciting the terms of the proposal. AI debt renegotiation system 500 may transmit the finalized, signed electronic contract document to any applicable entities, such as a financial institution or other entity that holds the debt and/or that services processing payments on the debt, and in association with the user's account or accounts with such financial institutions or other entities.).
The Examiner notes that the specific type of document transmitted by the system constitute non-functional descriptive material in view of the fact that it does not affect the system ability of transmitting a document. The type of document transmitted by the system does not alter or change the system, thereby have little to no patentable weight.
Regarding claim 5, and 13 Appel discloses
wherein an example of a release document is attached to a communication with the debtor before the debt settlement ([0063] Making reference to both FIGS. 6A and 6B, a user may initiate contact with AI debt renegotiation system 600 by logging onto a client UI app or web page associated with AI debt renegotiation system 600 on mobile device 650 and selecting or entering and sending an initiating message 652 (e.g., “I want to pay my debts”). AI debt renegotiation system 600 may respond by initiating a conversation on debt renegotiation (612), which may include initiating applicable software processes and/or settings; requesting and/or confirming a validation that the user has selected all applicable opt-ins (614), along with an opt-in confirmation message 654 to be rendered in the client-side AI debt renegotiation system app UI).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 6-7, 14-15 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Appel (US Patent Publication 2024/0078597) in view of HENRY-PARDIAK (WO 2024/121728).
Regarding claims 6, 14 and 18, Appel discloses
wherein the negotiator is implemented with artificial intelligence algorithms to generate a dynamic negotiation strategy ([0049] AI debt renegotiation system 400 may include modules, which may each include software, hardware, and computing resources of any kind, including a customer classifying module 422, a user behavior predicting module 424, a debt proposal generating module 426, and a conversation agent module 428... AI debt renegotiation system 400 may retrieve data from external data sources 490 and use it, consistent with applicable data privacy laws, regulations, and permissions, to enrich existing client data. Example of such external data sources 490 may include open banking and/or credit score reporting companies. Debt proposal generating module 426 may include a machine learning model to create personalized proposals for the customers based on the outputs of user behavior predicting module 424 and debt proposal generating module 426.).
However Appel does not explicitly disclose:
generative artificial intelligence (GenAI).
HENRY-PARDIAK which similarly discloses a system and method for managing debt further teaches:
Implement a generative artificial intelligence (GenAI) to generate a dynamic negotiation strategy ([083] The computing system 100 may be a computer specifically designed to operate a machine learning algorithm (MLA) and/or a deep learning algorithm (DLA), and/or Large Language Model (LLM) and/or generative Al. [118] The debt portfolio management application 216 integrated with MLAs and generative Al, may be the primary analytics tool that determines portfolio valuations prior to acquisition (as a management negotiation tool) and post-acquisition in real time. In certain embodiments, the debt portfolio management application 216 integrated with MLAs and generative Al may also generate an individual best-case debt recovery scenario, facilitate on the collaborative client portal (for example, the self-serve UI/UX 222) during online negotiations, make an offer, and/or schedule payment processes. [124] In certain embodiments, the debt portfolio management application 216 integrated with MLAs and generative Al may utilize meta learning assessment, algorithm ensemble stacking, voting, boosting, and bagging techniques to determine a task-specific application. Some of the non-limiting examples of the task may include pre-acquisition negotiation of the portfolio, client user self-serving pricing negotiation, operation pricing negotiations, write-offs, forward-flow agreements, or the like. [125] In certain embodiments, the debt portfolio management application 216 integrated with MLAs and generative Al may rely on transfer learning, a technique that includes using knowledge from previous prediction tasks to acquire new knowledge in the current task. It is to be noted that previously acquired knowledge may be helpful for future prediction. Such consideration may provide additional latent information that may be transferred during the training process and the transfer learning method may be used to improve performance in the debt portfolio appraisal, purchase price negotiation, individual client debt settlement domain, or the like.).
Therefore, it would have been obvious to one of ordinary skill in the art before the filling of the invention to implement a generative artificial intelligence (GenAI) to generate a dynamic negotiation strategy since such improvement in the system of Appel provides the known benefit of generate an individual best-case debt recovery scenario, facilitate on the collaborative client portal (for example, the self-serve UI/UX 222) during online negotiations, make an offer, and/or schedule payment processes as disclosed by HENRY-PARDIAK, par. 118.
Regarding claims 7, 15 and 19 HENRY-PARDIAK further teaches
wherein the GenAI negotiation algorithms implement a multi-agent architecture orchestrated in a ‘group chat’-like architecture, with each agent represented by a corresponding GenAI module (See Figs. 9-15 [152] FIG. 9 illustrates a process 900 of creation of and/or addition of a debt portfolio to a distributed ledger 50, in accordance with various embodiments of the present disclosure. As shown, the process 900 commences at step 902, where the corporate user 212 provides a request to the debt portfolio management system 200 for creation of and/or addition of a debt portfolio to a distributed ledger 50. In certain embodiments, the request may be provided using the user device 20.).
Therefore, it would have been obvious to one of ordinary skill in the art before the filling of the invention to implement multi-agent architecture orchestrated in a ‘group chat’-like architecture, with each agent represented by a corresponding GenAI module since such improvement in the system of Appel provides the known benefit of generate an individual best-case debt recovery scenario, facilitate on the collaborative client portal (for example, the self-serve UI/UX 222) during online negotiations, make an offer, and/or schedule payment processes as disclosed by HENRY-PARDIAK, par. 118.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Phillips, L. and Moggridge, P., 2019. Artificial intelligence in debt collection. Credit Control Journal and Asset & Risk Review, 40(2).
A. P. Desai, T. Ravi, M. Luqman, G. Mallya, N. Kota and P. Yadav, "Opportunities and Challenges of Generative-AI in Finance," 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA, 2024, pp. 4913-4920, doi: 10.1109/BigData62323.2024.10825658.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA C SANTOS-DIAZ whose telephone number is (571)272-6532. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM.
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/MARIA C SANTOS-DIAZ/Primary Examiner, Art Unit 3629