DETAILED ACTION
Status of Claims
This Final Office Action is responsive to Applicant's reply filed 11/21/2025.
Claims 1, 5-6, 8, 11, 15-17, and 20 have been amended, claims 7 and 10 have been cancelled, and claim 21-22 has been added new.
Claims 1-6, 8-9, and 11-22 are currently pending and have been examined.
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 .
Priority
This application claims priority of Application 17/521447 filed on 11/8/2021. Applicant's claim for the benefit of this prior-filed application is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/16/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendments
The previously pending 35 USC 112 rejections have been withdrawn in response to Applicant’s claim amendments.
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
With regard to the limitations of claims 1-6, 8-9, and 11-22, Applicant argues that the claims are patent eligible under 35 USC 101 because the pending claims are not directed toward an abstract idea. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. Applicant’s arguments are not persuasive.
The Examiner further points to Example 47 claim 2 of the 2024 AI SME Update, which shows how claiming of a neural network without more specific technical features is not enough to make the claims eligible, where claiming “train a first machine-learning model; carrying out a first machine-learning process on a first training data set; train a second machine-learning model; carrying out a second machine-learning process on a second training data set; stored within the SaaS application, stored within the SaaS application; inputting the obtained given set of project data into the given machine-learning model; cause a client device to present the predicted value for the at least one parameter of the given construction project to a user via front-end software of the SaaS software application that is installed on the client device (claims 1, 11, and 17)” provides nothing more than mere instructions to implement the abstract idea on a generic computer because they are recited at such a high level of generality. The “machine learning models” are used generically to apply the abstract idea without limiting how the trained machine learning functions. The “machine learning models” are described at a high level such that it amounts to using a computer with generic machine learning to apply the abstract idea without any details about how the outcomes are accomplished (See MPEP 2106.05). Applicant’s arguments are not persuasive.
Applicant argues the claims improve SaaS applications. The Examiner respectfully disagrees. The Applicant’s claims recite “for a software as a service (SaaS) application”, “within the Saas application”, and “via front-end software of the Saas software application that is installed on the client device”. These limitations are recited at such a high level of generality that they merely add the words apply it with the judicial exception. There is no specific use of the SaaS application beyond generic use in analyzing construction projects. The Examiner notes that the predictions are part of the abstract idea and are not additional elements (See MPEP 2106). Applicant’s arguments are not persuasive.
Regarding the Commissioner’s Memorandum, the Examiner asserts that case/application is unrelated to Applicant’s claims. That case had no 101 rejection given for rounds of prosecution before the PTAB decided to throw a 101 rejection in. The 101 rejection given by the PTAB was very poor and hard to follow and was not warranted. That is why the PTAB 101 rejection was over turned. In this application the Applicant can see below a well drawn out and explained 101 rejections. Please see the rejection below. Applicant’s arguments are not persuasive.
Applicant argues the claims amount to significantly more. The Examiner respectfully disagrees. Page 2 of the McRO-Bascom Memo from December 2016, "The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation "that improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process." The Applicants’ claims are geared toward making predictions on variables of construction projects to display to a human user to make further determinations, where these techniques are merely being applied/calculated in a computing environment (e.g. a computing platform; processor; non-transitory computer readable media; a SaaS application). Simply applying these known concepts to a specific technical environment (e.g. the computers/Internet) does not account for significantly more than the abstract idea because it does not solve a problem rooted in computer technology nor does it improve the functioning of the computer itself because it is merely making a determination based on rules and/or mathematical relationships to output to a user. The Applicant’s claimed limitations do not appear to bring about any improvement in the operation or functioning of a computer per se, or to improve computer-related technology by allowing computer performance of a function not previously performable by a computer (see page 2 of the McRo-Bascom memo). The solution appears to be more of a business-driven solution rather than a technical one. In addition, McRO had no evidence that the process previously used by animators is the same as the process required by the claims. The Applicant’s claimed limitations and originally filed specification provide no evidence that the claimed process/functions are any different than what would be done without a computer, where there are no adjustments to the mental process to accommodate implementation by computers. Applicant’s arguments are not persuasive.
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 1-6, 8-9, and 11-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 17-20 and 22 are directed toward a process, claims 11-16 are directed toward a product, and claims 1-6, 9, and 21 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a computing platform comprising: at least one processor; at least one non-transitory computer-readable medium; and program instructions for a software as a service (SaaS) application for managing construction projects that are stored on the at least one non-transitory computer-readable medium, wherein the program instructions, when executed by the at least one processor, cause the computing platform to: train a first machine-learning model associated with a first construction-project phase by carrying out a first machine-learning process on a first training data set that includes historical data for a first set of reference construction projects that indicates a status of each of the first set of reference construction projects as of the first construction-project phase, wherein the first machine-learning model is configured to (i) receive, for a construction project, input data for a first set of input data variables that indicate a status of the construction project as of the first construction-project phase, and (ii) based on an evaluation of the input data, output a prediction of a subset of the first set of reference construction projects that are likely to be similar to the construction project as of the first construction-project phase; train a second machine-learning model associated with a second construction-project phase that is later in time than the first construction-project phase by carrying out a second machine-learning process on a second training data set that includes historical data for a second set of reference construction projects that indicates a status of each of the second set of reference construction projects as of the second construction-project phase, wherein the second machine-learning model is configured to (i) receive, for a construction project, input data for a second set of input data variables that indicate a status of the construction project as of the second construction-project phase, wherein the second set of input data variables differs from the first set of input data variables, and (ii) based on an evaluation of the input data, output a prediction of a subset of the second set of reference construction projects that are likely to be similar to the construction project as of the second construction-project phase; determine, for a given construction project, a predicted value for at least one parameter; determining a current construction-project phase of the given construction project of the given construction project based on an analysis of project data for the given construction project that is stored within the SaaS application, wherein the current construction-project phase comprises either the first construction-project phase or the second construction-project phase; based on determining the current construction-project phase of the given construction project, (i) selecting a given machine-learning model to use from either the first machine-learning model associated with the first construction-project phase or the second machine-learning model associated with the second construction-project phase and (ii) obtaining a given set of project data for the given construction project that is stored within the SaaS application, wherein the given set of project data comprises values for either the first set of input data variables or the second set of input data variables; inputting the obtained given set of project data into the given machine-learning model and thereby predicting a subset of reference construction projects that are likely to be similar to the given construction project as of the current construction-project phase; and based on historical data for the predicted subset of reference construction projects, determining the predicted value for the at least one parameter for the given construction project; and cause a client device to present the predicted value for the at least one parameter of the given construction project to a user via front-end software of the SaaS software application that is installed on the client device (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106.05). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing construction project data based on determining if projects are similar and analyzing historical construction project data to determine if construction projects are similar to determine a predicted value, which is output to a human for interpretation, which is managing how humans interact for the commercial purpose of managing/planning construction projects.
Dependent claims 2-6, 8-9, 12-16, and 18-22 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the independent claims further recite “present the predicted value for the at least one parameter of the given construction project to a user (claims 1, 11, and 17)” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because displaying data merely adds insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the independent claims recite “a computing platform comprising: at least one processor; at least one non-transitory computer-readable medium; and program instructions for a software as a service (SaaS) application; are stored on the at least one non-transitory computer-readable medium, wherein the program instructions, when executed by the at least one processor, cause the computing platform to (claim 1)”; “non-transitory computer-readable medium having stored thereon program instructions for a software as a service (SaaS) application for managing construction projects, wherein the program instructions, when executed by at least one processor, cause a computing platform to (claim 11)”; “implemented by a computing platform that hosts a software as a service (SaaS) application (claim 17)”; “train a first machine-learning model; carrying out a first machine-learning process on a first training data set; train a second machine-learning model; carrying out a second machine-learning process on a second training data set; stored within the SaaS application, stored within the SaaS application; inputting the obtained given set of project data into the given machine-learning model; cause a client device to present the predicted value for the at least one parameter of the given construction project to a user via front-end software of the SaaS software application that is installed on the client device (claims 1, 11, and 17)”, which are additional elements that do not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 2-6, 8-9, 12-16, and 18-22 further narrow the abstract idea and dependent claims 4-5, 14-15, and 22 additionally recite “a first unsupervised machine-learning technique; a second unsupervised machine-learning technique (claims 4, 14, and 22); a k-means clustering technique (claim 5); input by a user of the SaaS application (claim 15)” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed machine learning is recited at such a high level of generality that it merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05). Further, method; System; and Product independent claims 1, 11, and 17 recite “a computing platform comprising: at least one processor; at least one non-transitory computer-readable medium; and program instructions for a software as a service (SaaS) application; are stored on the at least one non-transitory computer-readable medium, wherein the program instructions, when executed by the at least one processor, cause the computing platform to (claim 1)”; “non-transitory computer-readable medium having stored thereon program instructions for a software as a service (SaaS) application for managing construction projects, wherein the program instructions, when executed by at least one processor, cause a computing platform to (claim 11)”; “implemented by a computing platform that hosts a software as a service (SaaS) application (claim 17)”; “train a first machine-learning model; carrying out a first machine-learning process on a first training data set; train a second machine-learning model; carrying out a second machine-learning process on a second training data set; stored within the SaaS application, stored within the SaaS application; inputting the obtained given set of project data into the given machine-learning model; cause a client device to present the predicted value for the at least one parameter of the given construction project to a user via front-end software of the SaaS software application that is installed on the client device (claims 1, 11, and 17)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0045-0048 and Figures 1-2. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the independent claims recite “present the predicted value for the at least one parameter of the given construction project to a user (claims 1, 11, and 17)” steps/functions of the independent claims would not account for significantly more than the abstract idea because displaying/presenting data on a general purpose computer (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-6, 8-9, 12-16, and 18-22 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 4-5, 14-15, and 22 additionally recite “a first unsupervised machine-learning technique; a second unsupervised machine-learning technique (claims 4, 14, and 22); a k-means clustering technique (claim 5); input by a user of the SaaS application (claim 15)” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure and generic machine learning is recited at such a high level of generality it merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-6, 8-9, and 11-22 are provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-6, 8-9, and 11-22 of Patent No. 11,983,653 B2. Although the conflicting claims are not identical, they are not patentably distinct from each other because both sets of claims are generally directed toward similar details related to a system, product, and method for analyzing data variables of a construction project to make predictions for project management purposes to output predictions of the parameters for the construction projects using historical and similarity data. The details of training a first machine learning model using construction data to determine similar projects, training a second machine learning model using historical data to determine more similar projects, based on obtained input variables, and determining predicted values based on parameters to present to a human user using general purpose computer hardware and generic machine learning, are recited in both applications, but in different claims. The related application presents these details mainly in its independent claims, while these details are recited throughout both the independent claims and the dependent claims of the instant application. Differences in the claims are addressed by the prior art applied in the rejections above (namely features that may be recited in the instant claims, but are not specifically presented in the claims of the related application). Also, elimination of an element or its functions is deemed to be obvious in light of prior art teachings of at least the recited element or its functions (see In re Karlson, 136 USPQ 184, 186; 311 F2d 581 (CCPA 1963)).
This is a provisional obviousness-type double patenting rejection because the conflicting claims have not in fact been patented.
Allowable over 35 USC 103
Claims 1-6, 8-9, and 11-22 are allowable over the prior art, but remain rejected under §101 and double patenting for the reasons set forth above. Reasons the 103 rejection is overcome: Independent claims 1-6, 8-9, and 11-22 disclose a system, product, and method for analyzing data variables of a construction project to make predictions for project management purposes to output predictions of the parameters for the construction projects using historical and similarity data using machine learning to analyze different phases of the project to determine which projects are similar via a SaaS application.
The closest prior art of record is:
Jermann et al. (US 2020/0241490 A1) – which discloses analyzing data variables using historical data that is transformed and filtered to make predictions.
Cantor et al. (US 2013/0325763 A1) – which discloses predicting likelihood of on time product deliveries based on likely outcomes of predicted solutions.
Bailey et al. (US 11,468,379 B2) – which discloses evaluation of projects by determining probability of successful completion.
Al Qady et al. (Mohammed Al Qady, Amr Kandil, Automatic clustering of construction project documents based on textual similarity, Automation in Construction, Volume 42, 2014, Pages 36-49, ISSN 0926-5805, https://doi.org/10.1016/j.autcon.2014.02.006) – which discloses automatic clustering of construction documents based on textual similarity.
The prior art of record neither teaches nor suggests all particulars of the limitations as recited in claims 1-6, 8-9, and 11-22. While individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight. Specifically the claimed “a computing platform comprising: at least one processor; at least one non-transitory computer-readable medium; and program instructions for a software as a service (SaaS) application for managing construction projects that are stored on the at least one non-transitory computer-readable medium, wherein the program instructions, when executed by the at least one processor, cause the computing platform to: train a first machine-learning model associated with a first construction-project phase by carrying out a first machine-learning process on a first training data set that includes historical data for a first set of reference construction projects that indicates a status of each of the first set of reference construction projects as of the first construction-project phase, wherein the first machine-learning model is configured to (i) receive, for a construction project, input data for a first set of input data variables that indicate a status of the construction project as of the first construction-project phase, and (ii) based on an evaluation of the input data, output a prediction of a subset of the first set of reference construction projects that are likely to be similar to the construction project as of the first construction-project phase; train a second machine-learning model associated with a second construction-project phase that is later in time than the first construction-project phase by carrying out a second machine-learning process on a second training data set that includes historical data for a second set of reference construction projects that indicates a status of each of the second set of reference construction projects as of the second construction-project phase, wherein the second machine-learning model is configured to (i) receive, for a construction project, input data for a second set of input data variables that indicate a status of the construction project as of the second construction-project phase, wherein the second set of input data variables differs from the first set of input data variables, and (ii) based on an evaluation of the input data, output a prediction of a subset of the second set of reference construction projects that are likely to be similar to the construction project as of the second construction-project phase; determine, for a given construction project, a predicted value for at least one parameter; determining a current construction-project phase of the given construction project of the given construction project based on an analysis of project data for the given construction project that is stored within the SaaS application, wherein the current construction-project phase comprises either the first construction-project phase or the second construction-project phase; based on determining the current construction-project phase of the given construction project, (i) selecting a given machine-learning model to use from either the first machine-learning model associated with the first construction-project phase or the second machine-learning model associated with the second construction-project phase and (ii) obtaining a given set of project data for the given construction project that is stored within the SaaS application, wherein the given set of project data comprises values for either the first set of input data variables or the second set of input data variables; inputting the obtained given set of project data into the given machine-learning model and thereby predicting a subset of reference construction projects that are likely to be similar to the given construction project as of the current construction-project phase; and based on historical data for the predicted subset of reference construction projects, determining the predicted value for the at least one parameter for the given construction project; and cause a client device to present the predicted value for the at least one parameter of the given construction project to a user via front-end software of the SaaS software application that is installed on the client device”, which is not taught by the prior art. Therefore the claims are allowed over the 35 USC 103 rejections.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record, but not relied upon is considered pertinent to applicant's disclosure is listed on the attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625