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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/15/2026 has been entered.
Status of Claims
This action is a Final Action on the merits in response to the communications filed on
04/15/2026.
Applicant has amended claims 1 – 7, 9 – 16, 18 – 20, and 22;
Claims 1 – 7, 9 – 16, 18 – 20, and 22 – 23 are pending this application.
Response to Remarks
Examiner’s 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 claim 1 is not directed to mathematical concepts, mental processes, and or certain methods of organizing human activity, and are patent eligible.
Examiner respectfully disagrees. Applicant’s claim 1 recites certain methods of organizing human activity and mathematical concepts. Claim 1 particularly recites commercial interactions. For instance, claim 1 recites the 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. However, the claim is merely commercial interactions that are agreements and business relations where we have a contractor information being processed regarding a contractors performance with a lender and capacity to receive credit based on the contractor’s performance; and the relationship is between the contractor and a funding source that also adjusts the credit offer based on the contractor’s credit performance or behavior. Accordingly, claim 1 recites the abstract idea of commercial interactions.
Claim 1 also recites the abstract idea of mathematical concepts where the claim particularly recites mathematical relationships; as 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 with a trained learning model using the contractor data. 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. Accordingly, claim 1 recites mathematical relationships.
Applicant argues the currently amended claims qualify as non-abstract for reasons similar to Ex parte Desjardins. Examiner respectfully disagrees. Applicant’s claims are not similar to the claims discussed in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) Memorandum December 5, 2025, hereinafter “Desjardins Memorandum”. Desjardins Memorandum describes the claimed invention where there is a method of training a machine learning model on a series of tasks. However, in Applicant’s instant claims there is no training of a machine learning model on a series of tasks, and Applicant merely uses one or more trained machine learning models of a machine learning engine to generate a contractor quality score for a contractor based on input data about the contractor (Applicant Spec. ¶ 0018). Desjardins recites claims to a method of training a machine learning model were directed to improvements in the machine learning technology itself and additionally included data structure elements reciting adjustments in values to plurality of performance parameters while preserving prior values. Particularly Desjardins’ specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Likewise, Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), recites an improvement to technology, where Enfish provides a self-referential database table and is contrasting to the standard relational database table; as the first the self-referential model can store all entity types in a single table, and second the self-referential model can define the table’s columns by rows in that same table, and thus providing an improvement. Applicant’s Specification ¶ 0025, recites “Such systems and methods provide various technical improvements, such as improved accuracy in generating or adjusting a credit offer to an appropriate level for a given contractor and/or project, improved flexibility to accommodate contractors and/or projects of different types (e.g., including those that might otherwise be rejected), and/or improved efficiency to more quickly generate or adjust credit offers for contractors and/or projects by using trained machine learning models and/or other techniques described herein;” however, this is merely manipulation of data to obtain a contractor quality for a credit offer and the credit offer to the contractor can be adjusted. There is no technical improvement and Applicant is merely resolving a business problem.
Applicant argues the currently amended claims are patent-eligible for similar reasons as those recited in Example 47. Examiner respectfully disagrees. The claims of Example 47 represent a complex task for detecting anomalies, where the artificial neural network detects potential network intrusions or malicious attacks. Applicant’s claim 1 merely uses machine learning modeling for analyzing contractor credit behavior to adjust a credit offer. Furthermore, in Applicant’s case, a neuron is merely a circle; and a circle can represent a node, a perception, a layer, or the circle can be a combination thereof; and thus is generic and contrastingly different than an Example 47 neuron comprising a register, a microprocessor, and at least one input.
Applicant argues its claims are similar to the informative Ex Parte Hannun (Appeal 2018-003323) “Hannun”. Examiner respectfully disagrees. In Hannun, the claims are directed to speech transcription; in the instant Applicant’s case, the claims are directed to contractor analysis for a credit offer. In Hannun, the claims recite steps of normalizing an input file, generating a jitter set of audio files, generating a set of spectrogram frames, obtaining predicted character probabilities using a trained neural network, and decoding a transcription of the input audio using the predicted character probability outputs; In Applicant’s case, the claims recite steps of receive, 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; apply the update to the quality score to generate an updated quality score; automatically an adjustment to a numerical offer associated with the project based on the updated quality score for the identified contractor and a plurality of tier thresholds, wherein an adjustment direction of the adjustment is based on an update direction of the update to the quality score, and wherein an amount of the adjustment is based on a comparison between the updated quality score and the plurality of tier thresholds; adjust the numerical offer in accordance with the adjustment to generate an adjusted numerical offer; an adjustment to a numerical offer associated with the project based on the updated quality score for the identified contractor and a plurality of tier thresholds, wherein an adjustment direction of the adjustment is based on an update direction of the update to the quality score, and wherein an amount of the adjustment is based on a comparison between the updated quality score and the plurality of tier thresholds; the numerical offer in accordance with the adjustment to generate an adjusted numerical offer; generate based on use of the updated quality score to generate the adjustment, a level of accuracy of the updated quality score; and adjust at least one weight of the plurality of numerical weights based on the level of accuracy of the updated quality score. In Hannun the recited steps do not recite certain methods of organizing human activity; in Applicant’s case, claim 1 recites certain methods of organizing human activity and particularly recites commercial interactions where the claim recites business relations, as we have contractor information being processed regarding a contractors performance with a lender and capacity to receive credit based on the contractor’s performance; and the relationship is between the contractor and a funding source that also adjusts the credit offer based on the contractor’s credit performance or behavior. Thus Applicant’s instant claims are contrastingly different than Hannun.
Applicant argues Applicant’s claims qualify as non-abstract for similar reasons to the claims in Example 39. Examiner respectfully disagrees. Example 39 and its claim limitations are not similar to Applicant’s instant claims. Example 39 recites claim limitations that train a neural network for facial detection, where the limitations apply mathematical transformation functions on an acquired set of facial images. Example 39 collects digital facial images and uses an expanded training set of facial images to train the neural network and then retrain the neural network; the claim limitations further recite creating a second training set for a second stage of training comprising the first training set and digital non-facial images that are incorrectly detected as facial images after the first stage of training; and none of these limitations recite an abstract idea. However, Applicant’s claim 1 recites the abstract ideas of certain methods of organizing human activity and mathematical concepts; particularly recites commercial interactions where the claim recites business relations, as we have contractor information being processed regarding a contractors performance with a lender and capacity to receive credit based on the contractor’s performance; and the relationship is between the contractor and a funding source that also adjusts the credit offer based on the contractor’s credit performance or behavior; and 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. Thus Example 39 and its claim limitations are not similar to Applicant’s instant claims.
Claim 1 judicial exceptions are not integrated into a practical application. The additional elements of a memory that stores instructions, contractors, a processor coupled to the memory, interactive user interface, and a trained machine learning model however these devices are considered a generic computer component (see at least Applicant Spec. ¶¶ 0058 – 0059), performing generic computer functions. these additional elements are no more than mere instructions to apply the exception using generic computer components (e.g., processor) and are not significantly more than the abstract ideas, as the claim is merely resolving a business problem of credit offering to contractors. Claims 11 and 20, are substantially similar and recite the same subject matter as claim 1 and also include the abstract ideas identified above; the claims that depend therefrom inherit the same deficiencies as the independent claims. Thus all pending claims are rejected under 35 U.S.C. § 101.
Examiner’s Response to Rejections Under 35 U.S.C. § 102 – Anticipation
Applicant’s arguments are persuasive. Examiner’s cited art, Waslander, Fiona Lake et al. (U.S. Publication No. 2021/035,0481) hereinafter “Waslander” fails to teach generate an update to the quality score of the identified contractor based on an analysis of the current information using a trained machine learning model, the trained machine learning model previously 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 a plurality of connections between a plurality of neurons of the trained machine learning model, and wherein the update to the quality score is generated based on the plurality of numerical weights of the trained machine learning model; automatically generate, using an adjustment subsystem, an adjustment to a numerical offer associated with the project based on the updated quality score for the identified contractor and a plurality of tier thresholds, wherein an adjustment direction of the adjustment is based on an update direction of the update to the quality score, and wherein an amount of the adjustment is based on a comparison between the updated quality score and the plurality of tier thresholds; generate, using the adjustment subsystem and based on use of the updated quality score to generate the adjustment, a level of accuracy of the updated quality score; and adjust at least one weight of the plurality of numerical weights of the trained machine learning model based on the level of accuracy of the updated quality score to adjust one of the plurality of connections between two of the plurality of neurons and to update the trained machine learning model, the at least one weight contributing to generation of the updated quality score. Accordingly, rejection under 35 U.S.C. § 102 is removed for claims 1, 3, 6 – 13, 16, 18 – 20, and 22 – 23.
Examiner’s Response to Rejections Under 35 U.S.C. § 103 – Obviousness.
Applicant’s arguments are persuasive and claims 2, 4 – 5, 12, and 14 – 15 are allowable over Waslander in view of Allin, Patrick J. et al. (U.S. Publication No. 2006/0173706) hereinafter “Allin”; and Waslander in view of Yadav-Ranjan, Rani (U.S. Publication No. 2004/0059592) hereinafter Yadav. Independent claims 1, 11, and 20 are allowable over Waslander. Claims 2, 4 – 5, 12, and 14 – 15 are dependent from the independent claims, and are allowable over Waslander for the same reasons; in addition, Waslander in view of Allin in view of Yadav-Ranjan do not cure the deficiencies of Waslander. Accordingly, rejection under 35 U.S.C. § 103 is removed for claims 2, 4 – 5, 12, and 14 – 15.
Claim Rejections – 35 U.S.C. § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 – 7, 9 – 16, 18 – 20, and 22 – 23 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 has been amended to recite “accuracy-based learning”; “automatically generate, using an adjustment subsystem”; “automatically adjust, using the adjustment subsystem”; “wherein an amount of the adjustment is based on a comparison between the updated quality score and the plurality of tier thresholds; and “generate, using the adjustment subsystem and based on use of the updated quality score to generate the adjustment, a level of accuracy of the updated quality score.”
Although Applicant’s Specification teaches in ¶¶ 0224 – 0225, in some examples, the contractor 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. In some examples, the analysis system is configured to, and can, input the contractor information (e.g., as part of the input data 1205) into a trained machine learning model (e.g., one of the trained ML model(s) 1225) to generate the contractor quality score (e.g., contractor quality score 1230) based on the contractor information. In some examples, inputting the contractor information into the trained machine learning model includes inputting data tracking the contractor information over time (e.g., as part of the input data 1205) into the trained machine learning model to generate the contractor quality score based on the contractor information. In some examples, the analysis system is configured to, and can, use the contractor quality score as training data (e.g., validation data from validation 1275 and/or training data 1270) to update the trained machine learning model (e.g., as in the further training 1255 and/or the initial training 1265); there is no discussion of “accuracy-based learning” or the like within Applicant’s Specification.
Although Applicant’s Specification teaches in ¶ 00216, in some examples, the feedback 1250 can be received from another component or subsystem, for instance based on whether the component or subsystem successfully uses the contractor quality score 1230, whether use the contractor quality score 1230 causes any problems for the component or subsystem, whether use the contractor quality score 1230 are accurate, or a combination thereof. If the feedback 1250 is positive (e.g., expresses, indicates, and/or suggests approval of the contractor quality score 1230, success of the contractor quality score 1230, and/or accuracy the contractor quality score 1230), then the ML engine 1220 performs further training 1255 of the one or more ML models 1225 by updating the one or more ML models 1225 to reinforce weights and/or connections within the one or more ML models 1225 that contributed to the identification of the contractor quality score 1230, encouraging the one or more ML models 1225 to make similar contractor quality score determinations given similar inputs. If the feedback 1250 is negative (e.g., expresses, indicates, and/or suggests disapproval of the contractor quality score 1230, failure of the contractor quality score 1230, and/or inaccuracy of the contractor quality score 1230) then the ML engine 1220 performs further training 1255 of the one or more ML models 1225 by updating the one or more ML models 1225 to weaken, remove, and/or replace weights and/or connections within the one or more ML models that contributed to the identification of the contractor quality score 1230, discouraging the one or more ML models 1225 to make similar contractor quality score determinations given similar inputs; there is no discussion of automatically adjust, using the adjustment subsystem; nor is there any discussion of generate, using the adjustment subsystem and based on use of the updated quality score to generate the adjustment, a level of accuracy of the updated quality score, and particularly there is no discussion of an adjustment subsystem, automatically generate, using an adjustment subsystem, or the like within Applicant’s Specification.
Although Applicant’s Specification in ¶ 00191, teaches based on the contractor quality score, term and the base interest rate, the system 100 may update the credit offer, at operation 908. It can be noted that a good contractor quality score may result in a good credit offer or a good interest rate. For example, for very high contractor quality score of AB Flooring, i.e. 824, the updated credit offer is 1,00,000 at a reduced base interest rate of 2.8%. Further, the system 100 may compare the contractor quality score to score tiers, at operation 514. For example, the system 100 compares the contractor quality score of AB Flooring, to tiers, like Darwin Electric, who has a similar financial related information but different previous client references, thus leading to higher quality score. Finally, the system 100 may update the credit offer adjustment, at operation 516. In one embodiment, the system may update the credit offer adjustment based on the tiers. For example, when comparing to Darwin Electric with an asking credit for 1,50,000 and contractor quality score of 914, the system 100 updates the credit offer for AB Flooring, to 1,10,000, for overall score of 824; there is no discussion of wherein an amount of the adjustment is based on a comparison between the updated quality score and the plurality of tier thresholds or the like within Applicant’s Specification.
Claims 11 and 20 recite substantially similar limitations as claim 1, and thus are rejected for the same reasons set forth above. Additionally, claims 2 – 7, 9 – 10, 12 – 16, 18 – 19, and 22 – 23, depend on claims 1, 11 and 20 and inherit the same deficiencies. For the reasons above, claims 1 – 7, 9 – 16, 18 – 20, and 22 – 23 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112.
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, 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;
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 and a plurality of tier thresholds, wherein an adjustment direction of the adjustment is based on an update direction of the update to the quality score, and wherein an amount of the adjustment is based on a comparison between the updated quality score and the plurality of tier thresholds;
adjust the numerical offer in accordance with the adjustment to generate an adjusted numerical offer;
generate, and based on use of the updated quality score to generate the adjustment, a level of accuracy of the updated quality score;
and adjust at least one weight of the plurality of numerical weights based on the level of accuracy of the updated quality score.
The limitations of claim 1, under its broadest reasonable interpretation, recites certain methods of organizing human activity, where the claim recites commercial interactions. For instance, claim 1 recites receive, 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; apply the update to the quality score to generate an updated quality score; automatically an adjustment to a numerical offer associated with the project based on the updated quality score for the identified contractor and a plurality of tier thresholds, wherein an adjustment direction of the adjustment is based on an update direction of the update to the quality score, and wherein an amount of the adjustment is based on a comparison between the updated quality score and the plurality of tier thresholds; adjust the numerical offer in accordance with the adjustment to generate an adjusted numerical offer; an adjustment to a numerical offer associated with the project based on the updated quality score for the identified contractor and a plurality of tier thresholds, wherein an adjustment direction of the adjustment is based on an update direction of the update to the quality score, and wherein an amount of the adjustment is based on a comparison between the updated quality score and the plurality of tier thresholds; the numerical offer in accordance with the adjustment to generate an adjusted numerical offer; generate based on use of the updated quality score to generate the adjustment, a level of accuracy of the updated quality score; and adjust at least one weight of the plurality of numerical weights based on the level of accuracy of the updated quality score are all commercial interactions that are agreements in the form of contracts and business relations. Claims 11 and 20 are substantially similar and recite the same subject matter as claim 1. Accordingly, claims recites certain methods of organizing human activity.
Claim 1 recites an abstract idea, mathematical concepts, and particularly recites mathematical relationships. For example, claim 1 recites receiving a quality score for the identified contractor, generate an update to the quality score using a trained machine learning model; training a machine learning model trained to correlate subsets of the attributes to a quality score wherein the trained machine learning model includes a plurality of numerical weights corresponding to a plurality of connections between a plurality of neurons of the trained machine learning model, and 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; automatically generate, using an adjustment subsystem, an adjustment to a numerical offer associated with the project based on the updated quality score for the identified contractor and a plurality of tier thresholds, wherein an adjustment direction of the adjustment is based on an update direction of the update to the quality score, and wherein an amount of the adjustment is based on a comparison between the updated quality score and the plurality of tier thresholds; automatically adjust, using the adjustment subsystem, the numerical offer in accordance with the adjustment to generate an adjusted numerical offer; generate, using the adjustment subsystem and based on use of the updated quality score to generate the adjustment, a level of accuracy of the updated quality score; and adjust at least one weight of the plurality of numerical weights of the trained machine learning model based on the level of accuracy of the updated quality score to adjust one of the plurality of connections between two of the plurality of neurons and to update the trained machine learning model, the at least one weight contributing to generation of the updated quality score all recite 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; in addition, 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. Accordingly, claims 1, 11, and 20 recite mathematical concepts.
The dependent claims encompass the same abstract ideas as well. For instance, claims 2 and 12 are directed toward observing 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; claims 3 and 13 are directed toward observing 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; claims 4 and 14, rare directed toward receiving lien information associated with the identified contractor, wherein the update to the quality score is based on the current information and the lien information; claims 5 and 15 are directed toward observing 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; claims 6 and 16 are directed toward creating 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, 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, or business information about the identified contractor, wherein, to generate the update to the quality score; claim 7 is directed towards observing 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; claim 9 is directed towards evaluating the update to the quality score; claim 18 is directed towards evaluating the update to the quality score; claims 10 and 19 are directed toward observing the level of accuracy of the updated quality score is based on the adjustment subsystem successfully generating the adjustment to the numerical offer and the adjusted numerical offer; claim 22 is directed towards observing the level of accuracy of the updated quality score indicates that the updated quality score is accurate, and wherein adjusting the at least one weight includes reinforcing the at least one weight; and claim 23 is directed towards observing the level of accuracy of the updated quality score indicates that the updated quality score is inaccurate, and wherein adjusting the at least one weight includes weakening or removing the at least one weight all involve 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 the additional elements of a system for contractor analysis and accuracy-based learning, 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, a processor coupled to the memory, wherein execution of the instructions by the processor causes the processor to, automatically generate, using an adjustment subsystem, and to adjust one of the plurality of connections between two of the plurality of neurons and to update the trained machine learning model, the at least one weight contributing to generation of the updated quality score; claim 11 recites the additional elements of claim 1 and a computer-implemented method for contractor analysis and accuracy-based learning; and in addition to reciting the additional elements of claim 1, claim 20 recites the additional elements of a non-transitory computer readable storage medium having embodied thereon a program, wherein the program is executable by a processor to perform a method of contractor analysis and accuracy-based learning. However, the additional elements of a system for contractor analysis and accuracy-based learning, 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, a processor coupled to the memory, wherein execution of the instructions by the processor causes the processor to, automatically generate, using an adjustment subsystem, and to adjust one of the plurality of connections between two of the plurality of neurons and to update the trained machine learning model, the at least one weight contributing to generation of the updated quality score, a computer-implemented method for contractor analysis and accuracy-based learning, the execution of the instructions by the processor causes the processor to multiply the numerical offer by the adjustment multiple and a non-transitory computer readable storage medium having embodied thereon a program, wherein the program is executable by a processor to perform a method of contractor analysis and accuracy-based learning, the execution of the instructions by the processor causes the processor to input data tracking the current information over time into the trained machine learning model to cause the trained machine learning model to generate the update to the quality score, a website of the identified contractor, the execution of the instructions by the processor causes the processor to generate the update to the quality score based on the current information and the profile of the identified contractor 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 claims do not include additional elements that are sufficient to amount significantly more than the judicial exception. 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). the additional elements do not impose meaningful limits on practicing the idea. Thus, the claims are directed to an abstract idea. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Dependent claims 2 – 7, 9 – 10, 12 – 16, 18 – 19, 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 under 35 U.S.C. § 101.
Conclusion
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
07/10/2026
/ROBERT D RINES/Primary Examiner, Art Unit 3625
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