DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/8/2026 has been entered.
Status of Claims
This is in reply to the claim amendments and remarks of the RCE filed 5/8/2026.
Claims 1-2, 4, 6, 8-9, 11-12, 14-15, 17-18, and 20 have been amended, claims 7, 10, 16, and 21 have been cancelled, and claim 23-24 have been added new.
Claims 1-6, 8-9, 11-15, 17-20, and 22-24 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.
Response to Amendments
The previously pending double patenting rejections have been withdrawn in response to Applicant’s filing of a terminal disclaimer that has been approved.
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.
The Examiner notes that Applicant’s amendments are broader than the previous version of claims. Broader claims do not help overcome the 101 rejections.
With regard to the limitations of claims 1-6, 8-9, 11-15, 17-20, and 22-24, 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 claims recite generic use of machine learning for implementing the abstract idea (See MPEP 2106.05). Applicant’s arguments are not persuasive.
Applicant argues the claims improve the technology. The Examiner respectfully disagrees. The Examiner 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 by carrying out a first machine-learning process on a first training data set; train a second machine-learning model by carrying out a second machine-learning process on a second training data set; stored within the SaaS application; inputting the obtained first set of project data into the first machine-learning model; cause a first client device to present, to a first user via front-end software of the SaaS software application that is installed on the first client device, stored within the SaaS application, cause a second client device to present, to a second user via the front-end software of the SaaS software application that is installed on the second 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). There are not specifics on how the machine learning models are trained beyond generic recitation of with X data. Applicant’s arguments are not persuasive.
Applicant argues the claims improve SaaS applications. The Examiner respectfully disagrees. The Applicant’s claims recite “stored within the SaaS application; cause a first client device to present, to a first user via front-end software of the SaaS software application that is installed on the first client device, stored within the SaaS application, cause a second client device to present, to a second user via the front-end software of the SaaS software application that is installed on the second 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. No technical details of the SaaS software is claimed, with generic use of machine learning. The Examiner notes that the predictions are part of the abstract idea and are not additional elements (See MPEP 2106). The Examiner notes that inputting data into a machine learning model as it become available (e.g. over time) is a generic machine learning process (See MPEP 2106.05) and cited prior art. 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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 23-24 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 23-24: Claims 23-24 recite “wherein the second client device comprises the first client device; and wherein the second user comprises the first user”. This limitation does not make sense. If the first client device is the second client device and the first user is the second user then these steps do not need to be claimed as they do not make any sense and cause confusion. With that wording the independent claims do not make sense. For the purposes of examination, the Examiner interprets the first client device to be separate from the second client device. The claims are rejected as being indefinite.
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, 11-15, 17-20, and 22-24 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 1-6, 8-9, 11-15, 17-20, and 22-24 are directed toward a process, a product, and 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 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 comprising values for a first set of input data variables, wherein the first machine-learning model is configured to (i) receive, for a construction project, input data for the first set of input data variables, and (ii) based on an evaluation of the input data for the first set of input data variables, output a prediction of a subset of the first set of reference construction projects that are likely to be similar to the construction project; train a second machine-learning model 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 comprising values for a second set of input data variables that differs from the first set of input data variables, wherein the second machine-learning model is configured to (i) receive, for a construction project, input data for second set of input data variables, and (ii) based on an evaluation of the input data for the second set of input data variables, output a prediction of a subset of the second set of reference construction projects that are likely to be similar to the construction project; predict, for a given construction project, an initial value for at least one parameter at a first time when values are available for every one of the first set of input data variables but are not available for every one of the second set of input data variables by; obtaining a first set of project data for the given construction project that is stored within the SaaS application, wherein the first set of project data comprises values for the first set of input data variables; inputting the obtained first set of project data into the first machine-learning model and thereby predicting a first subset of reference construction projects that are likely to be similar to the given construction project at the first time; and based on historical data for the predicted first subset of reference construction projects, predicting the initial value for the at least one parameter for the given construction project; cause a first client device to present, to a first user via front-end software of the SaaS software application that is installed on the first client device, the initial value for the at least one parameter of the given construction project; and an indication of additional data variables that (i) are included in the second set of data variables but not the first set of data variables and (ii) require input of values in order to predict an updated value for the at least one parameter; receive user-input values for the additional data variables; after receiving the user-input values for the additional values, predict, for the given construction project, the updated value for the at least one parameter at a second time when values are available for every one of the second set of input data variables by: obtaining a second set of project data for the given construction project that is stored within the SaaS application, wherein the second set of project data comprises values for the second set of input data variables; inputting the obtained second set of project data into the second machine- learning model and thereby predicting a second subset of the reference construction projects that are likely to be similar to the given construction project at the second time; and based on historical data for the predicted second subset of reference construction projects, predicting the updated value for the at least one parameter for the given construction project; and cause a second client device to present, to a second user via the front-end software of the SaaS software application that is installed on the second client device, the updated value for the at least one parameter (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 additionally 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 by carrying out a first machine-learning process on a first training data set; train a second machine-learning model by carrying out a second machine-learning process on a second training data set; stored within the SaaS application; inputting the obtained first set of project data into the first machine-learning model; cause a first client device to present, to a first user via front-end software of the SaaS software application that is installed on the first client device, stored within the SaaS application, cause a second client device to present, to a second user via the front-end software of the SaaS software application that is installed on the second 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, 12-15, 18-20, and 22-23 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)”; “wherein the second client device comprises the first client device; and wherein the second user comprises the first user (claims 23-24)” 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 by carrying out a first machine-learning process on a first training data set; train a second machine-learning model by carrying out a second machine-learning process on a second training data set; stored within the SaaS application; inputting the obtained first set of project data into the first machine-learning model; cause a first client device to present, to a first user via front-end software of the SaaS software application that is installed on the first client device, stored within the SaaS application, cause a second client device to present, to a second user via the front-end software of the SaaS software application that is installed on the second 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. 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, 12-15, 18-20, and 22-23 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)”; “wherein the second client device comprises the first client device; and wherein the second user comprises the first user (claims 23-24)” 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.
Allowable over 35 USC 103
Claims 1-6, 8-9, 11-15, 17-20, and 22-24 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, 11-15, 17-20, and 22-24 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, 11-15, 17-20, and 22-24. 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 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 comprising values for a first set of input data variables, wherein the first machine-learning model is configured to (i) receive, for a construction project, input data for the first set of input data variables, and (ii) based on an evaluation of the input data for the first set of input data variables, output a prediction of a subset of the first set of reference construction projects that are likely to be similar to the construction project; train a second machine-learning model 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 comprising values for a second set of input data variables that differs from the first set of input data variables, wherein the second machine-learning model is configured to (i) receive, for a construction project, input data for second set of input data variables, and (ii) based on an evaluation of the input data for the second set of input data variables, output a prediction of a subset of the second set of reference construction projects that are likely to be similar to the construction project; predict, for a given construction project, an initial value for at least one parameter at a first time when values are available for every one of the first set of input data variables but are not available for every one of the second set of input data variables by; obtaining a first set of project data for the given construction project that is stored within the SaaS application, wherein the first set of project data comprises values for the first set of input data variables; inputting the obtained first set of project data into the first machine-learning model and thereby predicting a first subset of reference construction projects that are likely to be similar to the given construction project at the first time; and based on historical data for the predicted first subset of reference construction projects, predicting the initial value for the at least one parameter for the given construction project; cause a first client device to present, to a first user via front-end software of the SaaS software application that is installed on the first client device, the initial value for the at least one parameter of the given construction project; and an indication of additional data variables that (i) are included in the second set of data variables but not the first set of data variables and (ii) require input of values in order to predict an updated value for the at least one parameter; receive user-input values for the additional data variables; after receiving the user-input values for the additional values, predict, for the given construction project, the updated value for the at least one parameter at a second time when values are available for every one of the second set of input data variables by: obtaining a second set of project data for the given construction project that is stored within the SaaS application, wherein the second set of project data comprises values for the second set of input data variables; inputting the obtained second set of project data into the second machine- learning model and thereby predicting a second subset of the reference construction projects that are likely to be similar to the given construction project at the second time; and based on historical data for the predicted second subset of reference construction projects, predicting the updated value for the at least one parameter for the given construction project; and cause a second client device to present, to a second user via the front-end software of the SaaS software application that is installed on the second client device, the updated value for the at least one parameter”, which is not taught by the prior art. Therefore the claims are allowed over the 35 USC 103 rejections.
Conclusion
The prior art made of record, but not relied upon is considered pertinent to Applicant's disclosure is listed on the attached PTO-892 and should be taken into account / considered by the Applicant upon reviewing this office action.
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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BRIAN EPSTEIN can be reached on (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
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