DETAILED ACTION
This communication is a Non-Final Office Action rejection on the merits. Claims 1-13, 15, and 20-25 are currently pending and have been addressed below.
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 .
Information Disclosure Statement
The information disclosure statements filed on 07/02/2025 and 03/20/2026 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/20/2026 has been entered.
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.
The term "appropriate" in claims 1 and 20 is a relative term which renders the claim indefinite. The term " appropriate" 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. In a project management, an appropriate offer may be a line of credit that covers all the owner expenses. For examination purposes the term “appropriate” has been construed to be an offer for a specific stage/milestone and a specific team based on historical project data.
Claims 2-13, 15, and 21-25 are rejected for having the same deficiencies as those set forth with respect to the claims that they depend from, independent claim 1.
Response to Arguments
Applicant's arguments filed 03/20/2026 (related to the 103 Rejection) have been fully considered and are persuasive. Examiner agrees that the combination of Cobb, Konson, Harrison, and Cella individually or in combination, fail to teach or suggest at least: processing the project data, the stage timing, the at least one recommended offer, at least one action taken at a user device, and a plurality of possible presentment timings using the trained machine learning model to generate a presentation time to present the at least one recommended offer and at least one interface setting customized for an interface of the user device through which the at least one recommended offer is to be presented, wherein the presentation time is based on the stage timing and the at least one action and is one of the plurality of possible presentment timings; receiving feedback from the user device through the interface, the feedback indicative of an alignment score between the recommended offer and a purpose associated with the next stage; and updating at least one weight of the plurality of numeric weights based on the feedback to update the trained machine learning model to affect the alignment score, the at least one weight associated with identification of the recommended offer and generation of both the presentation time and the at least one interface setting. Therefore claim 1 has potential allowable subject matter.
Claim 20 recites similar limitations and therefore has Potential Allowable Subject Matter for the same reasons as claim 1. Claims 2-13, 15, and 20-25 have Potential Allowable Subject Matter because of their dependency from independent claim 1.
Applicant's arguments filed 03/20/2026 (related to the 101 Rejection) have been fully considered but they are not persuasive.
Applicant states, on pages 9-14, that the "plurality of neurons arranged in a plurality of layers including an input, output, and hidden layer that includes an activation function to transform input received through the input layer" in currently amended claim 1 is analogous in some respects to the "neurons organized in an array" in USPTO Example 47 claim 1. Furthermore, the "numeric weights [that] represent strengths of connections between the plurality of neurons of the trained machine learning model" in currently amended claim 1 is analogous in some respects to the "synaptic weight[s]" in USPTO Example 47 claim 1. Currently amended claim 1 also improves the "trained machine learning model" itself, by reciting "updating at least one weight of the plurality of numeric weights based on the feedback to update the trained machine learning model to affect the alignment score, the at least one weight associated with identification of the recommended offer and generation of both the presentation time and the at least one interface setting." Because the claimed subject matter qualifies non-abstract for similar reasons to those at issue in USPTO Example 47 claim 1, the claimed subject matter qualifies as patent-eligible without any further analysis.
Also, the currently amended claims incorporate a practical application and are patent-eligible for similar reasons as those recited in Appeals Review Panel decision Ex parte Desjardins (Sep. 26, 2025) (precedential), in which the opinion is written by USPTO Director John Squires. See https://www.uspto.gov/patents/ptab/precedential-informative-decisions, https://www.uspto.gov/sites/default/files/documents/202400567-arp-rehearing-decision-20250926.pdf. The claims at issue in Ex parte Desjardins concern a "machine learning model" that is "trained on a first machine learning task using first training data," and "training the machine learning model" "on a second, different machine learning task" using "second training data to adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." USPTO Director Squires identifies that the claims at issue in Ex parte Desjardins provide a technical improvement by "effectively learn[ing] new tasks in succession whilst protecting knowledge about previous tasks," which is "an improvement to how the machine learning model itself operates." Ex parte Desjardins, 9. USPTO Director Squires also cautions against "[c]ategorically excluding AI innovations from patent protection." Id.
The claimed "updating [of] at least one weight of the plurality of numeric weights based on the feedback to update the trained machine learning model to affect the alignment score, the at least one weight associated with identification of the recommended offer and generation of both the presentation time and the at least one interface setting" further improves the "trained machine learning model" itself For instance, the specification discusses "feedback indicating whether the recommended offer is a good or bad recommended offer given the project data" and/or "feedback indicating whether the presentment configuration is a good or bad presentment configuration given the project data and/or recommended offer(s)." The specification adds that, "[i]n response to positive feedback [ ... ] the ML engine 520 [ ... ] update[s] the ML model(s) 525 to strengthen and/or reinforce weights associated with generation of the output(s) 530 to encourage the ML engine 520 to generate similar output(s) 530 given similar input(s) 505"- or "[i]n response to negative feedback[ ... ] the ML engine 520 [ ... ] updat[es] the ML model(s) 525 to weaken and/or remove weights associated with generation of the output(s) 530 to discourage the ML engine 520 from generating similar output(s) 530 given similar input(s) 505." See specification, para. [0062], [0064].
The claimed concept is also "use[d] [ ... ] in conjunction with a particular machine or manufacture that is integral to the claim," which is another of the "considerations" indicative of a practical application under MPEP § 2106.04(d)(I). For instance, currently amended independent claim 1 integrally relies on the "trained machine learning model," the "plurality of neurons arranged in a plurality of layers" the "connections between the plurality of neurons," the "user device," and the "interface."
Lastly, Applicant submits that the currently amended independent claims recite significant detail as to how the claimed solutions are accomplished, including, inter alia, details as to the structure of the "trained machine learning model" and how the "trained machine learning model" is "update[d]."
Examiner respectfully disagrees with Applicant. Examiner notes that Applicant's currently amended independent claims are not analogous to Ex parte Hannun (Feb. 1, 2019), USPTO Example 47 claim 1, or Ex parte Desjardins. Rather, independent claims 1 and 20 are analogous to USPTO Example 47 claim 2. These claim elements are considered to be abstract ideas because they are directed to “certain methods of organizing human activity” which include “fundamental economic principles or practices.” In this case, “providing credit offer recommendations based on a project status/stage” is a mitigating risk activity (see Applicant’s specification, Paragraph 0032). Also, “presenting information at a specified time” is still an abstract idea (MPEP 2106.04a2, controlling the timing of the display of acquired content). If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
For the Ex parte Hannun, the additional limitations recite a specific timing mechanism in which the execution of a matching order is delayed for a specific period of time. This delay allows for other matching orders to be received from the in-market participants so that the order can be allocated between the first and second and executed upon expiration of the delay period. As explained in the Specification, "the purpose of the temporary restraint on execution is to allow a preset grace period within which other in crowd market participant quotes or orders may be submitted at the best price represented by the new in-crowd market participant quote." Spec., ¶55. The Specification further explains "advantages of temporarily restraining this type of trade include encouraging more in-crowd market participants to quote at the best price and the removal of any communication or computer hardware advantage among the in-crowd market participants." Id. Thus, the use of the claimed timing mechanisms and the associated temporary restraints on execution of trades provide a specific technological improvement over prior derivatives trading systems.
In contrast, Applicant’s currently amended claims recite to present the at least one recommended offer based on the stage timing and the at least one action and is one of the plurality of possible presentment timings. The presentment configuration can identify timings with which to present the at least one recommended offer, for instance timings associated with the stage that the project is in along a project timeline, timings associated with a start (e.g., predicted start or actual start) of the next stage of the project along the project timeline, timings associated with a user (e.g., the at least one contractor) opening an application on a user device, timings associated with a user (e.g., the at least one contractor) performing an action (e.g., submitting a request) in an application on a user device, or a combination thereof (see ¶78). Also, the interface setting is merely used to identify fonts, font sizes, font colors, location(s) on a screen at which to display an alert identifying the recommended offer(s), image(s) to display with the alert, text to output with the alert, or a combination thereof (Paragraph 0078). At step 2A, Prong 2 - Examiner notes that the interface is considered “field of use” since it’s just used to receive a presentment configuration and setting information (e.g., timings associated with a start of the next stage, timings associated with a request, and/or setting for a font color or display location), but does not improve the interface (MPEP 2106.05h). At step 2B - Instructions to display and/or arrange information in a graphical user interface may not be sufficient to show an improvement in computer-functionality (MPEP 2106.05a). Further, the step of “presenting the at least one recommended offer at the start of the next stage” is considered a well-known activity of “performing repetitive calculations” since it’s just readjusting a credit offer at the predicted stage timing of the next stage in the project timeline (MPEP 2106.05(d)). Lastly, the specification is silent of any advantage of presenting the offer at the specific timing (see ¶ 0077-0083).
The trained machine learning is merely used to generate a model output indicative of the at least one recommended offer (Paragraph 0074). Merely stating that the step is performed by a computer component (e.g., machine learning) results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. In this case, the limitations are similar to example 47, claim 2 of the 2024 AI Guidance. For example, “training by receiving interactions with the recommended offer” is merely updating parameters of the machine learning (e.g., updating recommendations based on an alignment score feedback), which uses mathematical calculations to iteratively adjust the values. Also, the claim does not provide any details about how the trained machine learning operates or how the recommendations are generated (see Page 6 of 2024 AI Guidance). Lastly, the limitation of “wherein the trained machine learning model includes a plurality of neurons arranged in a plurality of layers including an input, output, and hidden layer that includes an activation function to transform input received through the input layer, and wherein a plurality of numeric weights represent strengths of connections between the plurality of neurons of the trained machine learning model” is merely describing the machine learning structure, but the claims and specification do not state how the structure improves the accuracy of the machine learning. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claims fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim amounts to significantly more than the abstract idea itself. Thus, the claim is ineligible.
Claims 2-13, 15, and 21-25 are rejected for having the same deficiencies as those set forth with respect to the claims that they depend from, independent claims 1 and 20.
Examiner recommends to follow Example 47, claim 3 of the 2024 AI Guidance, if supported by the specification.
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 1-13, 15, and 20-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without reciting significantly more.
Independent Claim 1
Step One - First, pursuant to step 1 in the January 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”) on 84 Fed. Reg. 53, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - Claim 1 recites: A method of situationally-responsive presentment of recommendations, the method comprising: receiving project data associated with a project, wherein the project data includes timeline information indicative of a current stage that the project is in along a project timeline and identifies a next stage along the project timeline that is after the current stage, wherein the project data includes team information associated with a team working on at least the current stage and the next stage of the project, and wherein the team includes at least one contractor; analyzing the project data to identify, at a first time, at least one recommended offer that is appropriate for the next stage and that is appropriate for the team; predicting, based on the project data, a stage timing of the next stage in the project timeline; processing the project data, the stage timing, the at least one recommended offer, at least one action taken, and a plurality of possible presentment timings to generate a presentation time to present the at least one recommended offer, wherein the presentation time is based on the stage timing and the at least one action and is one of the plurality of possible presentment timings; sending, after the stage timing and at the presentation time, an alert with the at least one recommended offer and present the at least one recommended offer according to the at least one interface setting and at the presentation time; receiving feedback from the user, the feedback indicative of an alignment score between the at least one recommended offer and a purpose associated with the next stage; and updating at least one weight of the plurality of numeric weights based on the feedback to update the model to affect the alignment score, the at least one weight associated with identification of the at least one recommended offer and generation of both the presentation time and the at least one interface setting. These claim elements are considered to be abstract ideas because they are directed to “certain methods of organizing human activity” which include “fundamental economic principles or practices.” In this case, “providing credit offer recommendations based on a project status/stage” is a mitigating risk activity (see Applicant’s specification, Paragraph 0032). Also, “presenting information at a specified time” is still an abstract idea (MPEP 2106.04a2, controlling the timing of the display of acquired content). If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 - The judicial exception is not integrated into a practical application. Claim 1 includes additional elements: using a trained machine learning model, wherein the trained machine learning model includes a plurality of neurons arranged in a plurality of layers including an input, output, and hidden layer that includes an activation function to transform input received through the input layer, and wherein a plurality of numeric weights represent strengths of connections between the plurality of neurons of the trained machine learning model; an interface; a presentment configuration associated with the at least one recommended offer; at least one interface setting for an interface through which the at least one recommended offer is to be presented; and a user device.
The trained machine learning is merely used to generate a model output indicative of the at least one recommended offer (Paragraph 0074). Within FIG. 5, a graphic representing the ML model(s) 525 illustrates a set of circles connected to one another. Each of the circles can represent a node, a neuron, a perceptron, a layer or a portion thereof, or a combination thereof. The circles are arranged in columns, each column of which can represent a layer (e.g., a fully connected layer, a convolutional layer). The leftmost column of white circles represent an input layer. The rightmost column of white circles represent an output layer. Two columns of shaded circled between the leftmost column of white circles and the rightmost column of white circles each represent hidden layers. An ML model can include more or fewer hidden layers than the two illustrated, but includes at least one hidden layer. In some examples, the layers and/or nodes represent interconnected filters, and information associated with the filters is shared among the different layers with each layer retaining information as the information is processed (Paragraph 0052). The interface is merely used to: activate and present the at least one recommended offer according to the at least one interface setting and at the presentation time associated with the presentment configuration (Paragraph 0077); and receive feedback from a user (Paragraph 0061). The presentment configuration is merely used to identify timings with which to present the at least one recommended offer, for instance timings associated with the stage that the project is in along a project timeline, timings associated with a start (e.g., predicted start or actual start) of the next stage of the project along the project timeline, timings associated with a user (e.g., the at least one contractor) opening an application on a user device, timings associated with a user (e.g., the at least one contractor) performing an action (e.g., submitting a request) in an application on a user device, or a combination thereof (Paragraph 0078). The interface setting is merely used to identify fonts, font sizes, font colors, location(s) on a screen at which to display an alert identifying the recommended offer(s), image(s) to display with the alert, text to output with the alert, or a combination thereof (Paragraph 0078). The user device is merely used to receive the at least one recommended offer (Paragraph 0004). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). These elements of “machine learning,” “interface,” “presentment configuration,” “interface setting,” and “user device” are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element. Also, the interface is considered “field of use” since it’s just used to receive an input of a presentment configuration and setting information (e.g., timings associated with a start of the next stage and/or font color), but does not improve the interface (MPEP 2106.05h). Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B - The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” the concept of sending at least one recommended offer to a user based on a predefined timing. The specification shows that the trained machine learning is merely used to generate a model output indicative of the at least one recommended offer (Paragraph 0074). Within FIG. 5, a graphic representing the ML model(s) 525 illustrates a set of circles connected to one another. Each of the circles can represent a node, a neuron, a perceptron, a layer or a portion thereof, or a combination thereof. The circles are arranged in columns, each column of which can represent a layer (e.g., a fully connected layer, a convolutional layer). The leftmost column of white circles represent an input layer. The rightmost column of white circles represent an output layer. Two columns of shaded circled between the leftmost column of white circles and the rightmost column of white circles each represent hidden layers. An ML model can include more or fewer hidden layers than the two illustrated, but includes at least one hidden layer. In some examples, the layers and/or nodes represent interconnected filters, and information associated with the filters is shared among the different layers with each layer retaining information as the information is processed (Paragraph 0052). The interface is merely used to: activate and present the at least one recommended offer according to the at least one interface setting and at the presentation time associated with the presentment configuration (Paragraph 0077); and receive feedback from a user (Paragraph 0061). The presentment configuration is merely used to identify timings with which to present the at least one recommended offer, for instance timings associated with the stage that the project is in along a project timeline, timings associated with a start (e.g., predicted start or actual start) of the next stage of the project along the project timeline, timings associated with a user (e.g., the at least one contractor) opening an application on a user device, timings associated with a user (e.g., the at least one contractor) performing an action (e.g., submitting a request) in an application on a user device, or a combination thereof (Paragraph 0078). The interface setting is merely used to identify fonts, font sizes, font colors, location(s) on a screen at which to display an alert identifying the recommended offer(s), image(s) to display with the alert, text to output with the alert, or a combination thereof (Paragraph 0078). The user device is merely used to receive the at least one recommended offer (Paragraph 0004). In this case, “training by receiving interactions with the recommended offer” is merely updating parameters of the machine learning (e.g., updating recommendations based on an alignment score feedback), which uses mathematical calculations to iteratively adjust the values. Also, the claim does not provide any details about how the trained machine learning operates or how the recommendations are generated (see Page 6 of 2024 AI Guidance). The limitation of “wherein the trained machine learning model includes a plurality of neurons arranged in a plurality of layers including an input, output, and hidden layer that includes an activation function to transform input received through the input layer, and wherein a plurality of numeric weights represent strengths of connections between the plurality of neurons of the trained machine learning model” is merely describing the machine learning structure, but the claims and specification do not state how the structure improves the accuracy of the machine learning. Further, the interface and the user device are considered a conventional computer function of “receiving and transmitting over a network” and “performing repetitive calculations” (see MPEP 2106.05d). Lastly, instructions to display and/or arrange information in a graphical user interface may not be sufficient to show an improvement in computer-functionality (MPEP 2106.05a). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Independent claim 20 is directed to a system at step 1, which is a statutory category. Claim 20 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 20 further recites: a processor and a memory – which are treated as just an explicit “processor/computer” for executing the operations and are treated under MPEP 2106.05f in the same manner as claim 1. Accordingly, these additional elements are viewed as “apply it on a computer” at step 2a, prong 2 and step 2b.
Dependent claims 2-9, 11-13, and 15 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of the abstract idea mentioned above - such as: adjusting the at least one recommended offer based on the change to the project data; wherein the change to the project data includes a change to the project from the current stage to the next stage along the project timeline; wherein the change to the project data includes at least one of an addition to the team or a removal from the team; wherein the change to the project data includes a change to the financial information, and wherein the team also identifies an owner associated with the property; wherein the at least one recommended offer is an offer for pre-qualification for credit; wherein the at least one recommended offer is an offer for materials financing; wherein the identifying of the at least one recommended offer includes identifying the at least one recommended offer for the general contractor based on a history associated with the general contractor; wherein the identifying of the at least one recommended offer includes identifying the at least one recommended offer for the subcontractor based on a history associated with the subcontractor; wherein the identifying of the at least one recommended offer includes identifying the at least one recommended offer based on the project history of the at least one contractor; wherein the stage timing of the next stage is a predicted start of the next stage. These processes are similar to the abstract idea noted in the independent claim because they further the limitations of the independent claim which are directed to “certain methods of organizing human activity” which include “fundamental economic principles or practices.” In this case, “providing recommendations based on other parameters in the project data” is still a mitigating risk activity (see Applicant’s specification, Paragraph 0032). In addition, there are no additional elements to consider at Step 2A Prong 2 and Step 2B. Therefore, the claims still recite an abstract idea that can be grouped into certain methods of organizing human activity.
Dependent claim 10 is directed to an additional element such as: a software platform. The software platform is merely used to present a recommended offer to a general contractor or sub-contractor (Paragraph 0005). This is considered “field of use” (MPEP 2106.05h) at step 2A, Prong 2; since it’s just used to provide recommendations, but the technology is not improved. At Step 2B, this is considered a conventional computer function of “receiving or transmitting data over a network” (MPEP 2106.05d). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 21-25 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above - such as: training a machine learning model using training data to generate the trained machine learning model; wherein the training data includes examples of projects, recommended offers, project stage timings, presentation times to present the recommended offers, and interface settings; wherein the updating of the at least one weight based on the feedback includes at least one of strengthening or reinforcing the at least one weight; wherein the updating of the at least one weight based on the feedback includes at least one of weakening or removing the at least one weight; and generating customized content using the trained machine learning model, wherein the alert includes the customized content. Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In this case, the claims do not provide any specific details about how the recommendations are generated of how the customized content is created. See 2024 AI Guidance, example 47, claim 2. Further, the step of “updating of the at least one weight based on the feedback” is considered a well-understood, routing, and conventional function since it's just “receiving or transmitting data over a network” and “performing repetitive calculations” (MPEP 2106.05(d)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Potential Allowable Subject Matter
The closest prior art is Cobb et al. (US 2010/0094748 A1). Cobb et al. discloses a method of situationally-responsive presentment of recommendations, the method comprising (Paragraph 0002, This invention relates to a comprehensive fully-integrated adjustable local area network (LAN), wide area network (WAN) and web-based communication network computer system and process for managing risk related to construction mortgage loans ("construction loans") for residential and commercial construction projects and more particularly, but not by way of limitation, to an adjustable risk mitigation system and process for overall management of construction loans; Paragraph 0032, Upon completion of the construction site inspections and draw requests, the RMG makes recommendations to the client for modifying the construction loan to permanent financing (or otherwise closing the permanent financing), closing the permanent financing with exceptions or not closing the permanent financing subject to applicable statutory requirements, client policy and agreed upon permanent financing closing business practices, shown in Box 70; In this case, Examiner notes that the draw request is a formal document submitted by a borrower or contractor after completing a project milestone. Therefore, based on broadest reasonable interpretation in light of the specification, Cobb et al. discloses to identify at least one recommended offer that is appropriate for the next stage and that is appropriate for the teams since it provides recommendations for modifying the construction loan responsive to submitting a draw request, wherein the draw request includes completion/updates of the construction project milestones):
receiving project data associated with a project, wherein the project data includes timeline information indicative of a current stage that the project is in along a project timeline and identifies a next stage along the project timeline that is after the current stage, wherein the project data includes team information associated with a team working on at least the current stage and the next stage of the project, and wherein the team includes at least one contractor (Paragraph 0002, The construction loans can be traditional construction mortgage loans or permanent mortgage loans that are modified after closing to interim construction mortgage loans. The risk mitigation system is characterized by a variety of processes and systems including without limitation a process for providing continuous status updates of the construction project, performing building contractor, subcontractor and material supplier (collectively "contractor") review services, performing budget and project review services, reviewing and processing loan draw requests, preparing and obtaining construction lien waivers, processing and performing visual site inspections, and performing a variety of other key risk mitigation features inherent with any construction project, all with the ability to adjust for changes in circumstances in the construction project and market conditions that may occur before, during or after completion of the construction project; Paragraph 0011, The subject system greatly enhances and supplements the ability of the construction lender's staff and specialists to manage, control and understand the progress of a construction project while minimizing inherent risks associated with the construction loan; Examiner notes that a construction project consists of multiple phases and/or milestones);
analyzing the project data … to identify, at a first time, at least one recommended offer that is appropriate for the next stage and that is appropriate for the team, … (Paragraph 0002, The construction loans can be traditional construction mortgage loans or permanent mortgage loans that are modified after closing to interim construction mortgage loans. The risk mitigation system is characterized by a variety of processes and systems including without limitation a process for providing continuous status updates of the construction project, performing building contractor, subcontractor and material supplier (collectively "contractor") review services, performing budget and project review services, reviewing and processing loan draw requests, preparing and obtaining construction lien waivers, processing and performing visual site inspections, and performing a variety of other key risk mitigation features inherent with any construction project, all with the ability to adjust for changes in circumstances in the construction project and market conditions that may occur before, during or after completion of the construction project; Paragraph 0032, Upon completion of the construction site inspections and draw requests, the RMG makes recommendations to the client for modifying the construction loan to permanent financing (or otherwise closing the permanent financing), closing the permanent financing with exceptions or not closing the permanent financing subject to applicable statutory requirements, client policy and agreed upon permanent financing closing business practices, shown in Box 70; In this case, Examiner notes that the draw request is a formal document submitted by a borrower or contractor after completing a project milestone. Therefore, based on broadest reasonable interpretation in light of the specification, Cobb et al. discloses to identify at least one recommended offer that is appropriate for the next stage and that is appropriate for the teams since it provides recommendations for modifying the construction loan responsive to submitting a draw request, wherein the draw request includes completion/updates of the construction project milestones);
processing the project data, the stage timing, the at least one recommended offer, at least one action taken at a user device, and a plurality of possible presentment timings … to generate a presentation time to present the at least one recommended offer and at least one interface setting customized for an interface of the user device through which the at least one recommended offer is to be presented, wherein the presentation time is based on the stage timing and the at least one action and is one of the plurality of possible presentment timings (Paragraph 0032, Upon completion of the construction site inspections and draw requests, the RMG makes recommendations to the client for modifying the construction loan to permanent financing (or otherwise closing the permanent financing), closing the permanent financing with exceptions or not closing the permanent financing subject to applicable statutory requirements, client policy and agreed upon permanent financing closing business practices, shown in Box 70; In this case, Examiner notes that the timing for presenting the at least one offer is after evaluating completion/updates of the construction project, wherein the completion/update information is obtained from the draw requests);
sending, after the stage timing and at the presentation time, an alert with the at least one recommended offer to the user device to cause the user device to activate the interface and present the at least one recommended offer through the [web application] according to … and at the presentation time (Paragraph 0019, The organizations networking with the system are shown as "clients". Clients are construction lenders that include financial lending institutions, banks, saving and loans, investors, mortgage companies and other entities engaged in construction lending. Borrowers, contractors, inspectors and others related to the construction loan process can also access and network with the system. The term "stakeholders" as used herein, can apply to anyone actively engaged in the loan process and having a need to know and includes borrowers, contractors and the like. The network computers in the computer system include proprietary data, proprietary documents and programmed software instrumental in the management of a construction loan; Paragraph 0032, Upon completion of the construction site inspections and draw requests, the RMG makes recommendations to the client for modifying the construction loan to permanent financing (or otherwise closing the permanent financing), closing the permanent financing with exceptions or not closing the permanent financing subject to applicable statutory requirements, client policy and agreed upon permanent financing closing business practices, shown in Box 70; As previously stated, the timing for presenting the at least one offer is after evaluating completion/updates of the construction project, wherein the completion/update information is obtained from the draw requests); …
Although Cobb et al. discloses sending and presenting the at least one recommended offer to a user device after evaluating completion/progress of the construction project (e.g., recommendations to the client for modifying the construction loan based on completion status specified in the draw request), Cobb et al. does not specifically disclose wherein the presentation is in according to the at least one interface setting (e.g., font, font sizes, location on a screen, etc.).
Konson et al. (US 11,132,749 B1). Konson et al. discloses sending, after the [threshold], an alert with the at least one recommended offer to the user device to cause the user device to activate the interface and present the at least one recommended offer through the interface according to the at least one interface setting and at the presentation time (Column 23, lines 39-54, In FIG. 13, the tiles 1305 are configured in the tile display area 1350 on the left side of the graphic user interface. A tile 1305 may be minimized so that it either is not displayed at all, or is represented as an icon, box, or any other shape on a portion of the dashboard 1300 that allows the user to see which tiles are not being displayed (e.g., in the loan adjustments and tile control area 1360). This may be done to allow the user to view those tiles or categories of tiles that they wish to view, either individually or as a group. For example, the user may wish to view on the display only those tiles that indicate that attention is needed in a particular area. The tiles 1305 may be programmed to indicate attention is needed based on either system parameters or user-generated parameters, and may display such an indication in any number of ways, including for example color, shape, size, location, font, flashing, blinking, etc.; Column 24, lines 34-41, In an embodiment, the tiles 1305 in the graphical user interface are continuously active and updating based on the collection of information with processing being performed in the background. The data that is fed to the tile 1305, supporting data and/or raw data may be accessed by selecting an information icon that will display the documents, API-collected information, and any other available data for the user; Column 34, lines 25-34, As can be seen, the system generates a first offer (e.g., $75,000) for a maximum amount of money that a borrower is authorized to borrow under the risk thresholds established by the underwriter. As additional offers, the system is also configured to provide an offer at approximate 75% of the maximum amount (potentially rounded to a next lower “rounded” dollar amount) and an offer at approximate 50% of the maximum amount (potentially rounded to a next lower “rounded” dollar amount; Column 40, lines 1-8, As with any other changes to the risk calculations herein, this also causes the corresponding tiles (and details pages) in various portions of the dashboard 1300 to be updated (in real-time) as well, such as by updating the Offers and Risk modifiers; As previously stated, the timing for providing a recommendation is when the risk exceeds a threshold. Also, the interface setting includes at least a font and/or location used in the generated offer, see at least Paragraphs 0055-0056 in Applicant’s specification).
Harrison (US 2008/0040266 A1). Harrison discloses wherein the project data includes timeline information indicative of a current stage that the project is in along a project timeline and identifies a next stage along the project timeline that is after the current stage, …; analyzing the project data … to identify, at a first time, at least one recommended offer that is appropriate for the next stage … (Paragraph 0021, The results of an inspection are not always predictable. For example, a draw may be scheduled based on an assumption that the work at that time will be 50% complete, and that the lender will provide the borrower with 50% of the project's available funds at that time. However, the borrower may have arranged for the completion of a larger portion of the project at that time, and may want access to funds that reflect the additional progress. For example, if the project at the time of the draw is not just 50% complete, but actually 75% complete, the borrower may want access to up to 75% of the project's available funds. Likewise, the lender may be willing to provide more than 50% of the available funds to both encourage quicker project completion (which may reduce project risk) as well as to begin earning interest on those funds more quickly. Accordingly, in a reallocate draw schedule act 240, the lender overrides the draw schedule and allows the borrow to receive more than 50% of the funds. The lender need not allow access to 75% of the available funds, and in some cases, may actually allow access to more than 75% of the available funds; Paragraph 0022, This decision affects the available draw at each of the remaining draws. Accordingly the lender reallocates the amount available for each of the remaining draws. Furthermore, the estimated construction schedule will likely also change. Each of these changes can be encompassed in the reallocate draw schedule act 240. Furthermore, the reallocations may be made linearly, based on regression, interpolation, or may depend on a lender preferences, such as the nature of the project, a particular phase of project completion, and/or past experience with the borrower, for example; Examiner interprets “reallocation to receive more funds from a loan for the next phase of the project” as the “recommended offer that is appropriate for the next stage along the project timeline and that is appropriate for the team”); …;
…, wherein the presentation time is based on the stage timing and the at least one action and is one of the plurality of possible presentment timings (Paragraph 0021, The results of an inspection are not always predictable. For example, a draw may be scheduled based on an assumption that the work at that time will be 50% complete, and that the lender will provide the borrower with 50% of the project's available funds at that time. However, the borrower may have arranged for the completion of a larger portion of the project at that time, and may want access to funds that reflect the additional progress. For example, if the project at the time of the draw is not just 50% complete, but actually 75% complete, the borrower may want access to up to 75% of the project's available funds. Likewise, the lender may be willing to provide more than 50% of the available funds to both encourage quicker project completion (which may reduce project risk) as well as to begin earning interest on those funds more quickly. Accordingly, in a reallocate draw schedule act 240, the lender overrides the draw schedule and allows the borrow to receive more than 50% of the funds. The lender need not allow access to 75% of the available funds, and in some cases, may actually allow access to more than 75% of the available funds; Paragraph 0022, This decision affects the available draw at each of the remaining draws. Accordingly the lender reallocates the amount available for each of the remaining draws. Furthermore, the estimated construction schedule will likely also change. Each of these changes can be encompassed in the reallocate draw schedule act 240. Furthermore, the reallocations may be made linearly, based on regression, interpolation, or may depend on a lender preferences, such as the nature of the project, a particular phase of project completion, and/or past experience with the borrower, for example; Examiner notes that the reallocation to receive more funds from a loan for the next phase of the project is presented to a user after the completion/progress of the first phase is evaluated); …
Although Cobb et al. discloses a risk management system for providing recommendation to the client for modifying the construction loan (e.g., recommendations to the client for modifying the construction loan based on completion status specified in the draw request), the combination of Cobb et al., Konson et al., and Harrison does not specifically disclose wherein the recommended offer is provided using at least one trained machine learning model.
Cella (US 2020/0294128 A1) discloses analyzing the … data using a trained machine learning model to identify, at a first time, at least one recommended offer that is appropriate for the [construction loan], wherein the trained machine learning model includes a plurality of neurons arranged in a plurality of layers including an input, output, and hidden layer that includes an activation function to transform input received through the input layer, and wherein a plurality of numeric weights represent strengths of connections between the plurality of neurons of the trained machine learning model (Paragraph 0564, The RPA system 154 may provide automation for one or more aspects of a brokering solution 244 that enables automated brokering and/or provides a recommendation or plan for a brokering activity relevant to a lending transaction, such as for brokering a set of mortgage loans, home loans, lines of credit, automobile loans, construction loans, or other loans of any of the types described herein. The brokering solution 244 and/or RPA system 154 for brokering may include a set of interfaces, workflows, and models (which may include, use or be enabled by various adaptive intelligent systems 158) and other components that are configured to enable automation of one or more aspects of a brokering action or a brokering process for a lending transaction, such as based on a set of conditions, which may include smart contract terms and conditions, marketplace conditions (of platform marketplaces and/or external marketplaces 188, conditions monitored by monitoring systems 164 and data collection systems 166, and the like (such as of entities 198, including without limitation parties 210, collateral 102 and assets 218, among others, as well as of interest rates, available lenders, available terms and the like). For example, a user of the brokering solution 244 may create, configure (such as using one or more templates or libraries), modify, set or otherwise handle (such as in a user interface of the brokering solution 244 and/or RPA system 154) various rules, thresholds, conditional procedures, workflows, model parameters, and the like that determine, or recommend, a brokering action or plan for brokering a set of loans of a given type or types based on one or more events, conditions, states, actions, or the like, where the brokering plan may be based on various factors, such as the interest rates of the set of loans available from various primary and secondary lenders, permitted attributes of borrowers (e.g., based on income, wealth, location, or the like) prevailing interest rates in a platform marketplace or external marketplace, the status of the borrowers of a set of loans, the status or other attributes of collateral 102 or assets 218, risk factors of the borrower, the lender, one or more guarantors, market risk factors and the like (including predicted risk based on one or more predictive models using artificial intelligence 156), status of debt, condition of collateral 102 or assets 218 available to secure or back a set of loans, the state of a business or business operation (e.g., receivables, payables, or the like), conditions of parties 210 (such as net worth, wealth, debt, location, and other conditions), behaviors of parties (such as behaviors indicating preferences, behaviors indicating debt preferences), and many others. Brokering may include brokering with respect to terms and conditions of sets of loans, selection of appropriate loans, configuration of payment terms for consolidated loans, configuration of payoff plans for pre-existing loans, communications to encourage borrowing, and the like. In embodiments the brokering solution 244 may automatically recommend or set rules, thresholds, actions, parameters and the like (optionally by learning to do so based on a training set of outcomes over time), resulting in a recommended brokering plan, which may specify a series of actions required to accomplish a recommended or desired outcome of brokering (such as within a range of acceptable outcomes), which may be automated and may involve conditional execution of steps based on monitored conditions and/or smart contract terms, which may be created, configured, and/or accounted for by the brokering plan. Brokering plans may be determined and executed based at least one part on market factors (such as competing interest rates offered by other lenders, property values, attributes of borrowers, values of collateral, and the like) as well as regulatory and/or compliance factors. Brokering plans may be generated and/or executed for creation of new loans, for secondary loans, for modifications of existing loans, for refinancing terms, for situations involving market changes (e.g., changes in prevailing interest rates or property values) and others. In embodiments, adaptive intelligent systems 158, including artificial intelligence 156 may be trained on a training set of brokering activities by experts and/or on outcomes of brokering actions to generate a set of predictions, classifications, control instructions, plans, models, or the like for automated creation, management and/or execution of one or more aspects of a brokering plan; Paragraph 1072, The input neurons may then feed the values to each of the neurons in the hidden layer. In the hidden layer, a variable number of neurons may be used (determined by the training process). Each neuron may consist of a radial basis function that is centered on a point with as many dimensions as a number of predictor variables); …
However, the cited art, alone or in any combination, fails to teach or suggest at least: a method of situationally-responsive presentment of recommendations, the method comprising: receiving project data associated with a project, wherein the project data includes timeline information indicative of a current stage that the project is in along a project timeline and identifies a next stage along the project timeline that is after the current stage, wherein the project data includes team information associated with a team working on at least the current stage and the next stage of the project, and wherein the team includes at least one contractor; analyzing the project data using a trained machine learning model to identify, at a first time, at least one recommended offer that is appropriate for the next stage and that is appropriate for the team, wherein the trained machine learning model includes a plurality of neurons arranged in a plurality of layers including an input, output, and hidden layer that includes an activation function to transform input received through the input layer, and wherein a plurality of numeric weights represent strengths of connections between the plurality of neurons of the trained machine learning model; predicting, based on the project data, a stage timing of the next stage in the project timeline; processing the project data, the stage timing, the at least one recommended offer, at least one action taken at a user device, and a plurality of possible presentment timings using the trained machine learning model to generate a presentation time to present the at least one recommended offer and at least one interface setting customized for an interface of the user device through which the at least one recommended offer is to be presented, wherein the presentation time is based on the stage timing and the at least one action and is one of the plurality of possible presentment timings; sending, after the stage timing and at the presentation time, an alert with the at least one recommended offer to the user device to cause the user device to activate the interface and present the at least one recommended offer through the interface according to the at least one interface setting and at the presentation time; receiving feedback from the user device through the interface, the feedback indicative of an alignment score between the at least one recommended offer and a purpose associated with the next stage; and updating at least one weight of the plurality of numeric weights based on the feedback to update the trained machine learning model to affect the alignment score, the at least one weight associated with identification of the at least one recommended offer and generation of both the presentation time and the at least one interface setting.
Nor does the remaining prior art of record remedy the deficiencies found in the cited prior art. Furthermore, neither the prior art, the nature of the problem, nor knowledge of a person having ordinary skill in the art provides for any predictable or reasonable rationale to combine prior art teachings.
Claim 20 recites similar limitations and therefore have Potential Allowable Subject Matter for the same reasons as claim 1. Claims 2-13, 15, and 21-25 have Potential Allowable Subject Matter because of their dependency from independent claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Allin et al. (US 2006/0173706 A1) – discloses a financial institution may forecast revenue and costs for the general contractor, identify business risks for the general contractor (e.g., two projects for the same customer are both running behind and over-budget), or recognize significant relationships between General Contractors and subs (e.g., on 80 percent of its projects, the General Contractor uses the same electrician). The financial institution may also evaluate data including budgets of the participants, change orders submitted, current draw status, past draw status, and invoice data, and may use these and other data to calculate metrics with which to evaluate risk. Some of these metrics may include progress against budget, project status versus schedule comparisons, project percentage complete calculations and task percentage complete calculations (see at least Paragraph 0314).
Zhang et al. (TW M606543 U) – discloses after the loan project is established, the commissioner regularly tracks the progress of the project and inputs the progress of the project through the input device 110. The tracking module 148 obtains the corresponding loan case data from the database 141 and compares the actual progress with the scheduled progress in the record. The comparison result is provided to the input device 110 so that the commissioner can refer to the difference between the actual progress and the reservation record. For example, the construction progress is lagging behind, the allocation rate is too low, or the quota has not been used yet. The commissioner can further evaluate whether to modify the loan conditions (see at least Page 3).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARJORIE PUJOLS-CRUZ whose telephone number is (571)272-4668. The examiner can normally be reached Mon-Thru 7:30 AM - 5:00 PM.
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/MARJORIE PUJOLS-CRUZ/ Examiner, Art Unit 3624
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