DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6/20/2025 has been entered.
Status of Claims
This action is a Final Action on the merits in response to the communications filed on
12/03/2024.
Applicant has amended claims 1 – 7, 9 – 16, 18 – 20, and 22.
Claim 21 has been canceled.
Claim 23 is a new claims.
Claims 1 – 7, 9 – 16, 18 – 20, and 22 – 23, are pending this application.
Response to Rejections:
Response to Rejections Under 35 U.S.C. § 101 – Subject Matter Patentability
Response to Rejections under 35 U.S.C. 102 – Anticipation
Response to Rejections Under 35 U.S.C. § 103 – Obviousness
Examiner’s Response to Rejections Under 35 U.S.C. § 101 – Subject Matter Patentability.
Applicant argues claims 1, 11, and 20 are patent eligible under 35 U.S.C. § 101.
Examiner respectfully disagrees. First, Applicant’s amended claim 1 falls within a statutory category. However, amended claim 1 recites an abstract idea, mathematical relationships. Claim 1 evaluates current information associated with the identified contractor, and evaluates the quality score for the identified contractor based on the current information and these are mathematical relationship calculations. Further training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon, where the model uses correlation analysis and is trained calculate the mathematical relationships of the one or more subsets of the attributes to a quality score. Claim 1 further calculates the mathematical relationships of weights corresponding to connections between neurons; calculates the mathematical relationships of an adjustment to an offer with the project that is based on the quality score of the contractor; calculates mathematical relationships of weights of the model based on a quality score for the contractor. A claim recites a judicial exception when the judicial exception sets forth in the claim mathematical concepts. See MPEP § 2106.04(a)(2). Accordingly, claim 1 recites an abstract idea. Claims 11 and 20, are substantially similar to claim 1 and recites the same abstract idea.
Claim 1 recites the abstract idea, mental processes but for the recitation of generic computer components (e.g., system, memory, contractors, a processor, an interactive interface, and a trained machine learning model); and uses a computer as a tool to perform mental processes. For example, evaluating the current information associated with the identified contractor that generates an update to the quality score based on the current information; observing the update to the quality score to generate an updated quality score; evaluating an adjustment to a numerical based on the updated quality score; evaluating the numerical offer in accordance with the adjustment; and evaluating at least a subset of the plurality of numerical weights based on an indicator of a level of accuracy of the updated quality score are all evaluation and observation of data. The step of observing current information on a project and wherein a quality score is based on information other than the current information is insignificant extra-solution activity that is pre-solution activity; the step of an update at least a subset of the plurality of numerical weights based on an indicator of a level of accuracy of the updated quality score is also insignificant extra-solution activity that is post-solution activity. Accordingly, claim 1 recites mental processes.
Applicant argues the currently amended claims are non-abstract and patent eligible for the same reasons as in Core Wireless Licensing SARL v. LG Electronics, 880 F.3d 1356, 1362 (Fed. Cir. 2018), hereinafter “CoreWireless”.
Examiner respectfully disagrees. Core Wireless Licensing S.A.R.L. v. LG Elecs., Inc., 880 F.3d 1356, 1359 (Fed. Cir. 2018) concerns display interfaces being improved, and “particularly for electronic devices with small screens like mobile telephones. The improved interfaces allow a user to more quickly access desired data stored in, and functions of applications included in, the electronic devices. Id. at 2:20-44. Whereas Applicant’s amended claims merely performs calculations that produces a contractor quality score based on the contractor information, creates account credit offer based off of the contractor information and adjusts the account credit offer based off of the contractor quality score. Applicant’s claims are contrastingly different than CoreWireless; and CoreWireless provides a technological improvement.. However, Applicant’s claims do not provide a technological improvement nor is there an inventive concept.
Applicant argues for the same reasons as Example 39, the currently amended claims do not recite any mathematical relationships, formulas, or calculations; and do not recite a mental process because the steps are not practically performed in the human mind.
Examiner respectfully disagrees. Example 39 is different than Applicant’s instant claims. Example 39 concerns pixel by pixel analysis. Applicant’s claim 1 merely performs calculations to obtain a quality score for contractors using a trained machine learning model; Applicant further uses the machine learning model for correlation analysis and provides an updated quality score and adjusts the numerical offer. Applicant’s claim 1 further performs post solution action that is extra-solution activity that performs updating. Accordingly, Applicant’s claim 1 recites an abstract idea, mathematical concepts.
Applicant argues the currently amended claims are non-abstract, incorporate a practical application, and are patent-eligible for at least the same reasons recited in claims 1 and 3 of Example 47.
Examiner respectfully disagrees. Example 47 claims recites an artificial neural network (ANN) that identifies or detects anomalies and is contrastingly different than Applicant’s claims. Applicant’s claims recitation of neurons are merely recited to signify a data point and there is zero description as to how neurons and layers are used in Applicant’s claims. Applicant’s claim 1 may be performed with simple regression analysis and observing a data analysis output table. Example 47 cannot be performed with simple regression analysis and requires specially trained artificial neural networks to detect anomalies; and Applicant’s claims do not. For example, Applicant’s claim 1 receives data and uses the data as input into a machine learning model; and uses a machine learning model to perform data analysis that includes correlating data; This data analysis provides a resulting output that may be used as feedback, such as a recommendation which is what a machine learning model is designed to do; and the model is trained on this validation data. In Applicant’s claims, there is zero requirement of an artificial neural network, zero applicability of an artificial neural network, and zero need for an artificial neural network because Applicant’s claims can be performed using simple regression analysis. Accordingly, Applicant’s amended claim 1 recites an abstract idea.
Although Applicant amends claim 1 to include training a machine learning model to generate an update to the quality score the amendments to independent claim 1 do not take the claim beyond an abstract idea to place the claim into allowance; as this is merely rerunning the model with an update to the machine learning model. Applicant merely performs calculations of mathematical relationships, correlation analysis with subset(s) of an attribute(s) and a score using the model and uses a computer as a tool to perform generic computer functions, and is linking the use of the judicial exception to a field of use. The judicial exception of amended claim 1 as a whole is not integrated into a practical application; and the claim is not significantly more than the judicial exception; and there is no improvement to the computer nor to any technology or technical field. See MPEP 2106.05(a). Claim 1 only recites a processor and memory for performing the recited steps. These elements are recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amount to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). Dependent claims 2 – 7, 9 – 10, 12 – 16, 18 – 20, and 22 - 23, include the abstract ideas of the amended independent claims. The limitations of the dependent claims further limit the abstract idea. The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation. Accordingly, amended independent claims 1, 11, and 20 and the claims that depend therefrom are rejected as ineligible for patenting under 35 U.S.C. § 101 based upon the same analysis applied to claim 1 above. Therefore claims 1 – 7, 9 – 16, and 18 – 20, and 22 – 23, are ineligible under 35 U.S.C. § 101.
Examiner’s Response to Rejections Under 35 U.S.C. § 102 – Anticipation
First, for clarification, yes, claim 20 is rejected under 35 U.S.C. § 102 in the previous Office Action dated 3/20/2025.
Applicant argues that Allin and Waslander fail to teach or suggest all features of currently amended claim 1, individually or in combination. Applicant argues Waslander fails to disclose the processing the current information associated with the identified contractor using a trained machine learning model that generates an update to the quality score for the contractor based on current information or generate an adjustment to a numerical offer associated with the project on the updated quality score for the identified contractor, wherein an adjustment direction of the adjustment is based on an update direction of the update to the quality score.
Examiner respectfully disagrees. Applicant’s amended independent claims 1, 11, and 20 are anticipated by Waslander, Fiona Lake et al. (U.S. Publication No. 2021/035,0481) and are rejected under 35 U.S.C. § 102. Waslander teaches in ¶ 0526, The CASP may be configured to use any suitable automated machine learning to interpret the tuning and improve the results over time by feeding back the machine learning intelligence into the algorithm(s) and is likened to a trained machine learning model that generates an update to the quality score for the contractor based on current information or generate an adjustment to a numerical offer associated with the project on the updated quality score for the identified contractor, wherein an adjustment direction of the adjustment is based on an update direction of the update to the quality score.
Applicant argues Waslander also fails to disclose update at least a subset of the plurality of numerical weights of the trained machine learning model based on an indicator of a level of accuracy of the updated quality score for the identified contractor.
Examiner respectfully disagrees. Waslander teaches in ¶ 0540, the model includes one or more sets of weights that are adaptive weights and numerical parameters that can be tuned by one or more learning algorithms or training methods and sets of weights that are adaptive weights and numerical parameters is likened to update at least a subset of the plurality of numerical weights of the trained machine learning model based on an indicator of a level of accuracy; Waslander teaches in ¶¶ 0142 – 0143, update general contractor ranking; and input data into the general contractor rating system that scores contractors based on performance and adherence to procedures; and is likened to updated quality score for the identified contractor.
Applicant argues Waslander does not appear to disclose any indicator of a level of accuracy of the updated quality score, used in this way or otherwise.
Examiner respectfully disagrees. Waslander teaches several paragraphs reciting similarities to indicators of a level of accuracy of the updated quality score, for example, ¶ 0540, adaptive weights, which may be numerical parameters where adaptive weights may be likened to indicator of a level of accuracy; tuned by one or more learning algorithms or training methods or other suitable processes may be likened to indicator of a level of accuracy; making the neural network adaptive to inputs and capable of learning for example a learning pattern recognition may be likened to indicator of a level of accuracy. Claims 11 and 20 are similar to claim 1, and are rejected for the same reasons. The dependent claims are rejected by virtue of dependence on independent claims 1, 11, and 20. Accordingly, the pending claims are anticipated by Waslander and are rejected under 35 U.S.C. § 102.
Examiner’s Response to Rejections Under 35 U.S.C. § 103 – Obviousness.
Applicant argues the claims are allowable over Waslander, Allin, Kiat, and Yadav, individually or in any combination. Applicant further argues independent claims 1, 11, and 20 are believed to be allowable over Waslander. Claims 2-5 and 12-15 are dependent from one of the independent claims discussed above, and are therefore believed to be allowable over Waslander for at least the same reasons.
Examiner respectfully disagrees. Examiner has set forth above Waslander teaches claims 1, 11, and 20. The dependent claims 2 – 5, and 12 – 15, are rejected under 35 U.S.C. § 102 by virtue of dependence on independent claims 1, 11, and 20.
Claim Rejections – 35 U.S.C. § 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 – 7, 9 – 16, 18 – 20, and 22 – 23, are rejected under 35 U.S.C. §101 because the claimed invention is directed towards an abstract idea without significantly more.
receive, through an interactive user interface, current information associated with an identified contractor on a project wherein the identified contractor is one of the plurality of contractors, and wherein a quality score for the identified contractor is based on information other than the current information;
process the current information associated with the identified contractor using a trained machine learning model that generates an update to the quality score for the identified contractor based on the current information, wherein the trained machine learning model is trained to correlate one or more subsets of the one or more attributes to a respective quality score, wherein the trained machine learning model includes a plurality of numerical weights corresponding to connections between neurons of the trained machine learning model, wherein the update to the quality score is generated based on the plurality of numerical weights of the trained machine learning model;
apply the update to the quality score to generate an updated quality score;
generate an adjustment to a numerical offer associated with the project based on the updated quality score for the identified contractor, wherein an adjustment direction of the adjustment is based on an update direction of the update to the quality score;
automatically adjust the numerical offer as presented within the interactive user interface in accordance with the adjustment;
and update at least a subset of the plurality of numerical weights of the trained machine learning model based on an indicator of a level of accuracy of the updated quality score for the identified contractor.
The limitations of claim 1, under its broadest reasonable interpretation, recites certain methods of organizing human activity, but for the recitation of generic computer components (e.g., system, memory, contractors, a processor, an interactive interface, and a trained machine learning model); and uses a computer as a tool to perform certain methods of organizing human activity. For example, steps of evaluating the current information associated with the identified contractor that generates an update to the quality score based on the current information; observing the update to the quality score to generate an updated quality score; evaluating an adjustment to a numerical based on the updated quality score; evaluating the numerical offer in accordance with the adjustment; and evaluating at least a subset of the plurality of numerical weights based on an indicator of a level of accuracy of the updated quality score are all commercial interactions that are agreements in the form of contracts and business relations. Accordingly, amended claim recites certain methods of organizing human activity.
Claim 1 recites an abstract idea, mathematical concepts, but for the recitation of generic computer components. For example, claim 1 recites evaluating the current information associated with the identified contractor that generates an update to the quality score based on the current information recites mathematical relationships where the mathematical relationship is calculated with the contractor and calculated quality score; and correlating one or more subsets of the one or more attributes to a respective quality score. Further, training a machine learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. A claim recites a judicial exception when the judicial exception sets forth in the claim mathematical concepts or mental processes. See MPEP § 2106.04(a). Accordingly, Applicant’s amended independent claim 1 recites mathematical concepts.
The limitations of claims 11, and 20, substantially recite the same subject matter of claim 1 and also include the abstract ideas identified above, where claim 11, recites the additional elements of an interactive user interface, contractor, and a trained machine learning model; and claim 20, recites the additional elements of a non-transitory computer-readable storage medium, a processor, an interactive user interface, contractor, and a trained machine learning model. However, these additional elements are generic computer components as per Applicant’s Specifications shown below:
“[0058] A computing device such as a PC, laptop, smartphone, or tablet is used by every individual and organization. The use of computing devices is vast, and its functionality fits in all the organizational work. The computing devices run on different operating Systems (OS) that makes them dominant in the surrounding. Several computing devices and peripherals perform data transfer by using infrared signals such as the signals used by a TV remote control device. Several laptops are equipped with IR transmitters and receivers for data transfer. Computers are used as a control system in several industries such as industrial robots and computer-aided designs. The computing devices have different configurations, models, designs, shapes, textures, weights, temperatures. The computing device has Internet connectivity and also have different software to perform connectivity between different devices to share information.”
and thus are not practically integrated nor significantly more.
The dependent claims encompass the same abstract ideas as well. For instance, claims 2 and 12 are directed towards observing within the current information, data indicative of a change to at least one of a general contractor; claims 3 and 13 are directed towards observing the updated quality score is indicative of at least one of a repayment risk… identified contractor; claims 4 and 14 are directed towards observing lien information associated with the identified contractor… and the lien information; claims 5 and 15 are directed towards observing the adjustment to the numerical offer includes an adjustment multiple that is based on the updated quality score… causes the processor to multiply the numerical offer by the adjustment multiple; claims 6 and 16 are directed towards evaluating a profile of the identified contractor, and observing the profile includes… to generate the update to the quality score based on the profile of the identified contractor; claim 7 is directed towards observing a contractor quality score is based on at least one of a revenue growth score…, or information indicative of payment performance on one or more previous credit offers; claims 9 and 18 are directed towards observing a trained model evaluating the quality score includes inputting data tracking the current information over time into the model to evaluate the quality score; claims 10 and 19 are directed towards evaluating the model based on the indicator of the level of accuracy of the evaluated quality score for the identified contractor… based on feedback associated with the updated quality score; claim 22 is directed towards observing the quality score is based on the plurality of the weights of the model, and observing the level of accuracy of the evaluated quality score exceeds a threshold, and observing the evaluating of the model includes reinforcing at least the subset of the plurality of numerical weights of the model; and claim 23 is directed towards observing the quality score is based on the plurality of the weights of the model… includes weakening or removing at least the subset of the plurality of numerical weights of the model; and all of the dependent claims involve are observing and evaluating data. Thus, the dependent claims further limit the abstract concepts found in the independent claims.
These judicial exceptions are not integrated into a practical application. Claim 1 recites a memory that stores instructions, contractors, a processor coupled to the memory, interactive user interface, and a trained machine learning model; claim 11 recites the additional elements of an interactive user interface, a trained machine learning model, contractor, and neurons; and 20 recite the additional elements of a non-transitory computer-readable storage medium, a processor, an interactive user interface, contractor, and a trained machine learning model; however these devices are considered a generic computer component (see at least Applicant Spec. ¶¶ 0058 – 0059), as in “computing devices” performing generic computer functions. Each of the additional limitations are no more than mere instructions to apply the exception using generic computer components (e.g., processor). The combination of these additional elements are no more than mere instructions to apply the exception using generic computer components (e.g., processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because the additional elements do not impose meaningful limits on practicing the idea. Thus, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount significantly more than the judicial exception. As stated above, the additional elements of a non-transitory computer-readable storage medium, system, processor, a trained machine learning model, an interactive user interface, a contractor, and a memory, are considered generic computer components performing generic computer functions and amount to no more than mere instructions using generic computer components to implement the judicial exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Dependent claims 2 – 7, 9 – 10, 12 – 16, 18 – 20, and 22 – 23 when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1 – 7, 9 – 16, 18 – 20, and 22 – 23, are not patent eligible.
Claim Rejections – 35 U.S.C. § 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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, 3, 6 – 13, 16, 18 – 20, and 22 – 23, are rejected under 35 U.S.C. § 102(a)(2) as anticipated by Waslander, Fiona Lake et al. (U.S. Publication No. 2021/035,0481) hereinafter “Waslander”.
Claims 1, 11, and 20:
A system for contractor analysis, the system comprising: Waslander teaches in 0005, a construction analysis system is provided;
a memory that stores instructions and one or more attributes of a plurality of contractors that are respectively associated with one or more projects, wherein each of the plurality of contractors is associated with a corresponding quality score; Waslander teaches in ¶ 0006, a non-transitory computer-readable storage medium storing at least one program, the at least one program including instructions, which when executed by at least one processor of a construction analysis system; and Waslander teaches in ¶ 0527 the CASP may be operative to enable at least one contractor to maintain a cost type of work information and/or a schedule type of work information for each construction activity (e.g., through any suitable tool(s) (e.g., an online tool)). For example, at operation, the CASP may be operative to enable one or more contractors to maintain data/knowledge of costs and/or timeline schedules by construction activity.
and a processor coupled to the memory, wherein execution of the instructions by the processor causes the processor to: Waslander teaches in ¶ 0007, a processor communicatively coupled to the memory and the communications component;
receive, through an interactive user interface, current information associated with an identified contractor on a project, wherein the identified contractor is one of the plurality of contractors, and wherein a quality score for the identified contractor is based on information other than the current information; Examiner defines current information as current project progress data. Waslander teaches in ¶ 0044, a Project Dashboard page that displays an overview of a project's current status, as well as displays an updated summary of the user's budget overrun, schedule overrun, and whether or not the general contractor is in compliance with predefined best practices. Waslander further teaches in ¶ 0044, the project dashboard includes a project delivery window that displays an updated forecast for the budget and schedule based on current project progress data. Waslander teaches in ¶ 0529, the CASP may be operative to receive cost and/or schedule work information from one or more contractors for one or more construction activities.
process the current information associated with the identified contractor using a trained machine learning model that generates an update to the quality score for the identified contractor based on the current information, wherein the trained machine learning model is trained to correlate one or more subsets of the one or more attributes to a respective quality score, wherein the trained machine learning model includes a plurality of numerical weights corresponding to connections between neurons of the trained machine learning model, wherein the update to the quality score is generated based on the plurality of numerical weights of the trained machine learning model; Waslander teaches in ¶ 0129, project process operations includes input data into the general contractor rating system that scores contractors. Waslander teaches in ¶ 0528, at operation of process, a CAS subsystem of the CASP (e.g., subsystem) may be operative to automatically determine, based on the received work information (e.g., as received at operation), a work amount that may be required by at least one property service provider for the received scope elements (e.g., as received at operation) for the received renovation objective (e.g., as received at operation) in any suitable manner using any suitable tools (e.g., using any suitable algorithm(s) and/or automated neural networks and/or the like (e.g., of any suitable data structure(s))) where a property service provider is a contractor. For example, the CASP may be configured to include any suitable cost and/or schedule calculation engine(s) to calculate a particular contractor's cost and/or schedule (e.g., cost work amount and/or schedule work amount) for a particular renovation objective with particular scope elements, such as by using the contractor work information as may be received by the CASP (e.g., at operation) and the amounts of the construction activities as may be automatically translated by the CASP from the scope elements of the objective (e.g., at operation). Waslander teaches in ¶ 0540, A neural network or neuronal network or artificial neural network or any suitable artificial intelligence, machine learning, and or computer algorithm may be hardware-based, software-based, or any combination thereof, such as any suitable model (e.g., a computational model);
apply the update to the quality score to generate an updated quality score; Waslander teaches in ¶ 0142, update general contractor ranking;
generate an adjustment to a numerical offer associated with the project based on the updated quality score for the identified contractor, wherein an adjustment direction of the adjustment is based on an update direction of the update to the quality score; Waslander teaches in ¶ 0139, remove underperforming or non-performing contractors; Waslander teaches in ¶ 0143, a. input data into the general contractor rating system that scores contractors based on performance and adherence to procedures; Waslander teaches in ¶ 0510, a budget and scope phase of an HV app may be provided that may be operative to tailor an estimate and/or a proposed scope to a client's budget, function, and/or style objective, where tailor an estimate may be likened to generate an adjustment to a numerical offer associate with the project. Waslander teaches in ¶ 0513, a change in the project budget or cost such as the change order may be sent back to the contractor as rejected and the contractor may be held responsible for the increased costs and is likened to wherein an adjustment direction of the adjustment is based on an update direction of the update to the quality score. For example, the PESP (e.g., app and/or agent) may recap the proposed scope of the project, share the cost estimate, work with client to tune or otherwise adjust the scope and cost estimate, discuss options to explore in follow up, and/or the like. Waslander teaches in ¶ 0540, tuning the one or more learning algorithms or training methods or other suitable processes) and/or may be capable of approximating one or more functions (e.g., non-linear functions or transfer functions) of its inputs (e.g., to predict or estimate certain outcomes based on any suitable input data).
automatically adjust the numerical offer as presented within the interactive user interface in accordance with the adjustment; Waslander teaches in ¶ 0532, the CASP may be operative to enable bidding by one or more contractors (e.g., through any suitable tool(s) (e.g., an online tool)); Waslander further teaches in ¶ 0532, the CASP may be opened up to contractors on a renovation-specific basis to input/adjust their cost or schedule work information and submit their offer in a competitive bidding environment.
and update at least a subset of the plurality of numerical weights of the trained machine learning model based on an indicator of a level of accuracy of the updated quality score for the identified contractor. Waslander teaches in ¶ 0540, the machine learning model may include one or more sets or matrices of weights (e.g., adaptive weights which may be numerical parameters that may be tuned by one or more learning algorithms or training methods or other suitable processes), where adaptive is likened to based on an indicator of a level of accuracy.
Claims 3 and 13:
Waslander teaches claims 1 and 11. Waslander further teaches the following:
wherein the updated quality score is indicative of at least one of a repayment risk associated with the identified contractor or a collections risk associated with the identified contractor; Examiner understands the limitation as updating the model with a risk variable; Waslander teaches in ¶ 0144, feed project data into learning model for project risk and contractor performance; Waslander further teaches in ¶ 0519, Skylight may pay the refundable amount to any suitable contractor(s) to cover any overrun invoices that may have been approved as refundable, while no funds is debited from the homeowner's bank account for such a payment process; Waslander teaches in ¶ 0526, using any suitable automated machine learning to interpret the tuning and improve the results over time by feeding back the machine learning intelligence into the algorithm(s); Waslander teaches in ¶ 0534, machine learning or any other procedures are used by the CASP so that risks can automatically be applied to projects based on their characteristics where risks are modeled and automatically applied to projects; Waslander teaches above in claim 1 updating the model providing updates to contractor ranking and score.
Claims 6 and 16:
wherein the processor is configured to: create a profile of the identified contractor, wherein the profile includes at last one of a name of the identified contractor, an address of the identified contractor, a website of the identified contractor, client information associated with the identified contractor, property information of a property associated with the identified contractor, general current information of a general contractor associated with the identified contractor, financial information about the identified contractor, business information about the identified contractor, or the current information, wherein, to generate the update to the quality score, the execution of the instructions by the processor causes the processor to generate the update to the quality score based on the profile of the identified contractor; Waslander teaches in ¶ 0064, creating a new user and user profile; Waslander teaches in ¶ 0220 the signup information for the contractor may include sign up may be name, email address, and zip code and/or the like that may be likened to client information associated with the identified contractor and one of a name of the identified contractor; Waslander teaches above in claim 1 updating the model providing updates to contractor ranking and score.
Claim 7:
wherein the updated quality score is based on at least one of a revenue growth score, a capital equipment score, a quality sub score, a credit score, a general contractor quality score, a quality percentage, an amount of credit offers completed, or information indicative of payment performance on one or more previous credit offers; Waslander teaches in ¶ 0456, PESP platform determines that one such as a contractor or property manager has a low credit score, the PESP platform may then treat that contractor differently than someone with an adequate credit score such as not servicing the party with the low credit score and blocking them from being able to finalize an agreement; Waslander teaches above in claim 1 updating the model providing updates to contractor ranking and score.
Claims 9 and 18:
wherein using the trained machine learning model to generate the update to the quality score includes inputting data tracking the current information over time into the trained machine learning model to generate the update to the quality score based on the current information; Waslander teaches in ¶ 0264, the PESP may allow a PM to have a dashboard or display where the PM may track some or all communications it has had with one or more PSPs over time with respect to a particular project or multiple projects. Waslander teaches above in claim 1 updating the model providing updates to contractor ranking and score.
Claims 10 and 19:
wherein, to update the trained machine learning model based on the indicator of the level of accuracy of the updated quality score for the identified contractor, the execution of the instructions by the processor causes the processor to: update the trained machine learning model based on feedback associated with the updated quality score; Waslander teaches in ¶ 0260 ii. The PESP may use various components of the bullet point list above and potentially other inputs and feed it into any suitable AI/machine learning capabilities to produce an implicit “contractor score” or “PSP score” for a results ranking. The machine learning may also take PM preferences and/or PM history into account so that the PSP score may be personalized to an individual PM; Waslander further teaches in ¶ 0255, PSP ratings for completed projects, including sentiment analysis on free-form text feedback (e.g., identifying positive or negative language in free text, determining what language may be related to positive or negative reviews, determining what language may be tied to PSPs that do work on time versus not on time, on budget versus not on budget, clean work sites versus not clean work sites, etc.—and then processing such data to provide suitable sentiment analysis on such media for use by any suitable user of the PESP to aid in making a decision or recommendation). Waslander teaches communicate in real-time, statistics about the contractor and project in ¶¶ 0381 – 0382 iii, communicate with the PM in real-time or otherwise using the PESP (e.g., send instant messages back and forth with the PM). The dashboard may include a set of statistics for the PSP about the bidding process and/or the project execution process, where real-time is likened to update; Waslander teaches above in claim 1 updating the model providing updates to contractor ranking and score.
Claim 22:
wherein the update to the quality score is based on the plurality of numerical weights of the trained machine learning model, wherein the level of accuracy of the updated quality score exceeds a threshold, and wherein the updating of the trained machine learning model includes reinforcing at least the subset of the plurality of numerical weights of the trained machine learning model; Waslander teaches in ¶ 0026, the term “contractor” may be used to generically refer to a PSP of the PESP (e.g., a construction professional who may ultimately lead an actual construction or renovation process); Waslander further teaches in ¶ 0439, PSPs may be chosen if they have a rating that exceeds a certain threshold; Waslander teaches in ¶ 0439, PMs may be required to have completed a minimum number of projects on the platform and received a minimum rating; Waslander teaches above in ¶ 0540, the computer algorithm may include one or more sets of weights that are adaptive weights of numerical parameters, where one or more sets of weights that are adaptive weights of numerical parameters is likened to at least the subset of the plurality of numerical weights; Waslander teaches above in claim 11 updating the model providing updates to contractor ranking and score.
Claim 23
wherein the update to the quality score is based on the plurality of numerical weights of the trained machine learning model, wherein the level of accuracy of the updated quality score is less than a threshold, and wherein the updating of the trained machine learning model includes weakening or removing at least the subset of the plurality of numerical weights of the trained machine learning model; Waslander teaches above in ¶ 0540, the computer algorithm may include one or more sets of weights that are adaptive weights of numerical parameters, where one or more sets of weights that are adaptive weights of numerical parameters is likened to at least the subset of the plurality of numerical weights; Waslander teaches above in claim 11 updating the model providing updates to contractor ranking and score.
Claim Rejections – 35 U.S.C. § 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2 and 12, are rejected under 35 U.S.C. § 103 as being unpatentable over Waslander, Fiona Lake et al. (U.S. Patent Publication No. 2021/035,0481) hereinafter “Waslander” in view of Allin, Patrick J. et al. (U.S. Patent Publication No. 2006/017,3706) hereinafter “Allin”.
Claims 2 and 12:
Waslander disclose claims 1, 11, and 20. Waslander further teaches the following:
The system of claim 1, wherein the processor is configured to: identify, within the current information, data indicative of a change to at least one of a general contractor associated with the identified contractor or a property owner associated with the identified contractor, wherein the adjustment to the numerical offer is based on the change; Waslander teaches in ¶ 0532, the contractor is enabled by the CASP to make adjustments to any of its costs or schedule work information to make it more or less competitive for a particular project where the adjustments to costs or schedule work information is likened to current information data indicative of a change to at least one of a general contractor. The contractor may thus be able to determine the resulting total cost/schedule of its bid for the renovation and is likened to adjustment to the numerical offer based on the change; Waslander teaches above in claim 1 updating the model providing updates to contractor ranking and score.
Waslander teaches claims 1, 11, and 20. Waslander does not explicitly teach terminating a particular line item. However, Allin teaches the following:
data indicative of a change to at least one of a general contractor associated with the identified contractor or a property owner associated with the identified contractor; Allin teaches in ¶ 0221, a top level budget form that can be associated with the enter top level budget task; the General Contractor or subcontractor may change the information and can also choose to add new line items or to terminate a particular line item. Allin teaches in ¶ 0222, a terminate budget screen pay appear as a result of the change associated with the General Contractor’s changing information such as terminating a line item.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method for managing property renovations using a construction analysis system of Waslander with a system and method for managing a construction payment process that evaluates at least one of either the metrics or the project data to determine financial risk of Allin to assist businesses and contractors with change requests such as adjusting budget tasks and adding or terminating line items (Allin, Spec., ¶ 0274).
Claims 4 – 5, and 14 – 15, are rejected under 35 U.S.C. § 103 as being unpatentable over Waslander, Fiona Lake et al. (U.S. Patent Publication No. 2021/035,0481) in view of Yadav-Ranjan, Rani (U.S. Patent Publication No. 2004/005,9592).
Claims 4 and 14:
Waslander disclose claims 1, 11, and 20. Waslander further teaches the following:
wherein the processor is configured to: receive lien information associated with the identified contractor; Waslander teaches in ¶ 0052, payment details such as copies of relevant documents (invoices, lien waivers etc.) are accessible under each line item where the lien waivers are beneficial for the contractor and are likened to lien information associated with the identified contractor; Waslander further teaches in ¶ 0052, when a payment line is fully displayed, as shown in expanded line, additional sections displayed include uploaded documents, comments, and a full description of the transaction; Waslander teaches in ¶ 0131, a. update budget and schedule in response to receipt of invoices and documents; Waslander teaches in ¶ 0132, 2. invoice submission and review; Waslander teaches in 0135, a. evaluate each change order and determine appropriate cost increase and who is responsible for the cost; Waslander teaches in ¶ 0136, 5. dispute resolution for change order or inappropriate invoice; Waslander teaches in ¶ 0137, 6. payment experience an operation, including payout; Waslander teaches in ¶ 0138, a. facilitate payments between homeowner and contractor;
Waslander disclose claims 1, 11, and 20, and Waslander does not explicitly disclose the score depends on liens. However, Yadav-Ranjan discloses the following:
wherein, the update to the quality score is, based on the current information and the lien information; Yadav-Ranjan teaches in ¶ 0004, the score may depend on how many liens the Contractor has outstanding versus liens settled.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method for managing property renovations using a construction analysis system of Waslander with a system and method with the process of automatically assessing the risk associated with construction contractors of Yadav-Ranjan to assist businesses with creating a predictive model that generates a score representative of a Contractor’s worthiness (Yadav-Ranjan, Spec. ¶ 0017).
Claims 5 and 15:
Waslander disclose claims 1, 11, and 20. Waslander further teaches the following:
wherein the adjustment to the numerical offer includes an adjustment multiple that is based on the updated quality score, and wherein, to adjust the numerical offer in accordance with the adjustment; Waslander teaches in ¶ 0454 iii., The PESP may be operative to utilize one or more application program interfaces (“APIs”) (e.g., of one or more third party enabler subsystems) to get credit scoring information and/or other information related to PM ability to pay or PSP financial stability returned to the PESP; and Waslander teaches in ¶ 0455, based on credit scoring information, such as lowest scoring credit, will be treated different than someone adequate credit score.
Waslander discloses claims 1, 11, and 20, and Waslander further discloses credit score and contractor score, Wa