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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/10/2025 has been entered.
Response to Arguments
Argument:
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Response:
The examiner disagrees. Desjardins contains specific limitations related to a technical problem in machine learning (catastrophic forgetting). The decision was calling it overbroad to equate those limitations with any high level machine learning algorithm. The instant application does not contain any limitations related to technological improvements to machine learning and as such is more similar to the facts of Example 47 claim 2 of the July 2024 examples (https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf)
Argument:
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Response:
The examiner disagrees. The broadest reasonable interpretation of the claimed steps encompasses a construction accounting process which falls within the method of organizing human activity group. Program instructions, CRMs storing the instructions and processors are a high level computer-implementation that is described in MPEP 2106.05(f) as not providing a practical application. The use of a conventional data gathering/output tool like a GUI to gather/output data is insignificant extra-solution activity per MPEP 2106.05(g). Furthermore the broadest reasonable interpretation of the training of a machine learning model encompasses mathematical concepts (See discussion in July 2024 Example 47 claim 2).
Argument:
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Response:
The examiner disagrees. Per MPEP 2106.05(f) high level recitations of computer components do not provide a practical application or significantly more. The use of a conventional data gathering/output tool like a GUI to gather/output data is insignificant extra-solution activity per MPEP 2106.05(g)
And again Desjardins contains specific limitations related to a technical problem in machine learning (catastrophic forgetting). The decision was calling it overbroad to equate those limitations with any high level machine learning algorithm. The instant application does not contain any limitations related to technological improvements to machine learning
Argument:
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Response:
The examiner disagrees. Improving construction-project data processes is an improvement in an abstract idea, which does not provide an improvement in technology. See MPEP 2106.05(a), which states: However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.
Argument:
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Response:
The examiner disagrees. Improving construction-project data processes is an improvement in an abstract idea, which does not provide an improvement in technology. See MPEP 2106.05(a), which states: However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.
The recited software in the claim is recited at a high level and per MPEP 2106.05(f) that’s not sufficient to provide a practical application.
Argument:
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Response:
The examiner disagrees. With respect to the machine learning elements, the more recent 2024 guidance suggests that training a machine learning is a recitation of mathematical concepts and high level application of machine learning does not provide a practical application. See https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf and in particular Example 47 Claim 2.
Regarding the software and computer elements, these elements are used in their ordinary capacity and MPEP 2106.05(f) states: Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
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 1-5, 7-14 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-5, 7-14 and 16-20 recite:
1. (Currently Amended) A computing system comprising: a network interface; at least one processor; at least one non-transitory computer-readable medium; and program instructions stored on the at least one non-transitory computer-readable medium that are executable by the at least one processor such that the computing system is configured to:
engage in one or more machine-learning processes to train a machine learning model to evaluate (i) user-defined information comprising information identifying an individual who performed work on a given construction project and (ii) given contextual data information related to the individual's work on the given construction project, and produce, for each of a possible set of cost code options, a given respective likelihood of suitability;
cause a client device associated with a user to present a GUI view for receiving a user input comprising an initial set of user-defined information for a new time entry related to a construction project;
receive, from the client device associated with the user via the GUI view, a first communication comprising the initial set of user-defined information for the new time entry related to a construction project, wherein the initial set of user-defined information includes (i) a date for the new time entry and (ii) identifying information for at least one other individual who performed work for the construction project;
after receiving the initial set of user-defined information for the new time entry, obtain contextual information related to the at least one other individual's work on the construction project;
provide at least a portion of the initial set of user-defined information and the contextual information as input to the machine learning model thereby causing the machine learning model to (i) evaluate the initial set of user-defined information and the contextual information and (ii) output, for each possible cost code option, a respective likelihood of suitability with respect to the new time entry;
based at least on the machine learning models’ output of respective likelihoods of suitability of the possible cost code options, the initial set of user-defined information and the contextual information related to the at least one other individual's work on the construction project, identify a set of one or more recommended cost codes for the new time entry;
cause the client device to present an updated GUI view to display a visualization of the set of one or more recommended cost codes for selection of a given cost code by the user;
receive, from the client device, via the updated GUI view, a second communication comprising an additional set of user-defined information for the time entry that includes an indication of the given cost code that has been selected from the one or more recommended cost codes;
based at least on the initial set of user-defined information and the additional set of user-defined information, create the new time entry comprising the given cost code, and
utilize the additional set of user-defined information to engage in another machine-learning process to recurrently train the machine learning model to evaluate (i) user-defined information comprising information identifying an individual who performed work on a given construction project and (ii) given contextual data information related to the individual's work on the given construction project, and produce, for each of a possible set of cost code options, a given respective likelihood of suitability.
2. (Currently Amended) The computing system of claim 1, wherein the identifying information for the at least one other individual who performed work for the construction project includes one or more of:(i) an identifier of the at least one other individual or (ii) an identifier of a construction crew with which the at least one other individual is associated.
3. (Currently Amended) The computing system of claim 1, wherein the contextual information includes one or more of: (i) schedule information for the construction project, (ii) phase information for the construction project, (iii) budget information for the construction project, (iv) work breakdown structure information for the construction project, (v) location information for the at least one other individual, or (vi) previously-entered information for the construction project or the at least one other individual.
4. (Previously Presented) The computing system of claim 1, wherein the program instructions that are executable by the at least one processor such that the computing system is configured to identify the set of one or more recommended cost codes comprise program instructions that are executable by the at least one processor such that the computing system is configured to: apply one or more data analytics operations to at least a portion of the initial set of user- defined information and the contextual information and thereby identify the set of one or more recommended cost codes.
5. (Currently Amended) The computing system of claim 4, wherein the program instructions that are executable by the at least one processor such that the computing system is configured to apply the one or more data analytics operations comprise program instructions that are executable by the at least one processor such that the computing system is configured to: provide at least a portion of the initial set of user-defined information and the contextual information as input for a rules engine that is configured to apply a set of one or more user- defined rules for determining cost codes and output a recommendation of one or more cost codes for the new time entry.
7. (Previously Presented) The computing system of claim 1, wherein the machine-learning model comprises a classification model and is further configured to apply one or more post- processing operations and thereby determine the set of one or more recommended cost codes that is to be presented to the user.
8. (Currently Amended) The computing system of claim 1, wherein the additional set of user-defined information further includes: a quantity of time for the new time entry; information about a billing rate for the new time entry; and an indication of whether or not the new time entry is billable against a budget for the construction project.
9. (Currently Amended) The computing system of claim 1, wherein the at least one other individual includes the user.
10. (Currently Amended) At least one non-transitory computer-readable medium, wherein the at least one non-transitory computer-readable medium is provisioned with program instructions that, when executed by at least one processor, cause a computing system to: engage in one or more machine-learning processes to train a machine learning model to evaluate (i) user-defined information comprising information identifying an individual who performed work on a given construction project and (ii) given contextual data information related to the individual's work on the given construction project, and produce, for each of a possible set of cost code options, a given respective likelihood of suitability; cause a client device associated with a user to present a GUI view for receiving a user input comprising an initial set of user-defined information for a new time entry related to a construction project;
receive, from the client device associated with the user via the GUI view, a first communication comprising the initial set of user-defined information for the new time entry related to a construction project, wherein the initial set of user-defined information includes (i) a date for the new time entry and (ii) identifying information for at least one other individual who performed work for the construction project; after receiving the initial set of user-defined information for the new time entry, obtain contextual information related to the at least one other individual's work on the construction project; provide at least a portion of the initial set of user-defined information and the contextual information as input to the machine learning model thereby causing the machine learning model
to (i) evaluate the initial set of user-defined information and the contextual information and (ii) output, for each possible cost code option, a respective likelihood of suitability with respect to the new time entry; based at least on the machine learning model’s output of respective likelihoods of suitability for the possible cost code options, the initial set of user-defined information and the contextual information related to the at least one other individual's work on the construction project, identify a set of one or more recommended cost codes for the new time entry; cause the client device to n updated GUI view updated GUI view that displays a visualization of the set of one or more recommended cost codes to-for selection of a given cost code by the user; receive, from the client device, via the updated GUI view, a second communication comprising an additional set of user-defined information for the time entry that includes an indication of the given cost code that has been selected from the one or more recommended cost codes; based at least on the initial set of user-defined information and the additional set of user-defined information, create the new time entry comprising the given cost code, and utilize the additional set of user-defined information to engage in another machine- learning process to recurrently train the machine learning model to evaluate (i) user-defined information comprising information identifying an individual who performed work on a given construction project and (ii) given contextual data information related to the individual's work on the given construction project, and produce, for each of a possible set of cost code options, a given respective likelihood of suitability.
11. (Currently Amended) The at least one non-transitory computer-readable medium of claim 10, wherein the identifying information for the at least one other individual who performed work for the construction project includes one or more of: (i) an identifier of the at least one other individual or (ii) an identifier of a construction crew with which the at least one other individual is associated.
12. (Currently Amended) The at least one non-transitory computer-readable medium of claim 10, wherein the contextual information includes one or more of: (i) schedule information for the construction project, (ii) phase information for the construction project, (iii) budget information for the construction project, (iv) work breakdown structure information for the construction project, (v) location information for the at least one other individual, or (vi) previously-entered information for the construction project or the at least one other individual.
13. (Previously Presented) The at least one non-transitory computer-readable medium of claim 10, wherein the program instructions that, when executed by the at least one processor cause the computing system to identify the set of one or more recommended cost codes comprise program instructions that, when executed by the at least one processor cause the computing system to: apply one or more data analytics operations to at least a portion of the initial set of user- defined information and the contextual information and thereby identify the set of one or more recommended cost codes.
14. (Previously Presented) The at least one non-transitory computer-readable medium of claim 13, wherein the program instructions that, when executed by the at least one processor cause the computing system to apply the one or more data analytics operations comprise program instructions that, when executed by the at least one processor cause the computing system to: provide at least a portion of the initial set of user-defined information and the contextual information as input for a rules engine that is configured to apply a set of one or more user- defined rules for determining cost codes and output a recommendation of one or more cost codes for the new time entry.
16. (Previously Presented) The at least one non-transitory computer-readable medium of claim 15, wherein the machine learning model comprises a classification model and is further configured to apply one or more post-processing operations and thereby determine the set of one or more recommended cost codes that is to be presented to the user.
17. (Currently Amended) The at least one non-transitory computer-readable medium of claim 10, wherein the additional set of user-defined information further includes: a quantity of time for the new time entry; information about a billing rate for the new time entry; and an indication of whether or not the new time entry is billable against a budget for the construction project.
18. (Currently Amended) A method carried out by a computing system, the method comprising: engaging in one or more machine-learning processes to train a machine learning model to evaluate (i) user-defined information comprising information identifying an individual who performed work on a given construction project and (ii) given contextual data information related to the individual's work on the given construction project, and produce, for each of a possible set of cost code options, a given respective likelihood of suitability; causing a client device associated with a user to present a GUI view for receiving user input comprising an intial set of user-defined information for a new time entry related to a construction project; receiving, from the client device associated with the user via the GUI view, a first communication comprising the initial set of user-defined information for the new time entry related to the construction project, wherein the initial set of user-defined information includes (i) a date for the new time entry and (ii) identifying information for at least one other individual who performed work for the construction project; after receiving the initial set of user-defined information for the new time entry, obtaining contextual information related to the at least one other individual's work on the construction project; providing at least a portion of the initial set of user-defined information and the contextual information as input to the machine learning model thereby causing the machine learning model to (i) evaluate the initial set of user-defined information and the contextual information and (ii) output, for each possible cost code option, a respective likelihood of suitability with respect to the new time entry; based at least on the machine-learning model’s output of respective likelihoods of suitability for the possible cost code options, the initial set of user-defined information and the contextual information related to the at least one other individual's work on the construction project, identifying a set of one or more recommended cost codes for the new time entry; causing the client device to present an updated GUI view that displays a visualization of the set of one or more recommended cost codes for selection of a given cost code by the user; receiving, from the client device via the updated GUI view, a second communication comprising an additional set of user-defined information for the time entry that includes an indication of the given cost code that has been selected from the one or more recommended cost codes; based at least on the initial set of user-defined information and the additional set of user-defined information, creating the new time entry comprising the given cost code, and
utilizing the additional set of user-defined information to engage in another machine- learning process to recurrently train the machine learning model to evaluate (i) user-defined information comprising information identifying an individual who performed work on a given construction project and (ii) given contextual data information related to the individual's work on the given construction project, and produce, for each of a possible set of cost code options, a given respective likelihood of suitability.
19. (Currently Amended) The method of claim 18, wherein the identifying information for the at least one other individual who performed work for the construction project includes one or more of: (i) an identifier of the at least one other individual or (ii) an identifier of a construction crew with which the at least one other individual is associated.
20. (Currently Amended) The method of claim 18, wherein the contextual information includes one or more of: (i) schedule information for the construction project, (ii) phase information for the construction project, (iii) budget information for the construction project, (iv) work breakdown structure information for the construction project, (v) location information for the at least one other individual, or (vi) previously-entered information for the construction project or the at least one other individual.
Claims 1-5, 7-14 and 16-20 all recite subject matter falling in one of the four categories of invention (Step 1). But for the recitation of the above underlined additional elements, the claims recite concepts for properly accounting for time worked by a worker. Accounting/billing is a fundamental economic practice. Thus the claims recite a method of organizing human activity. Additionally the broadest reasonable interpretation of the steps related to machine learning training/retraining encompasses mathematical calculations and therefore encompass mathematical concepts. See also specification paragraph 74 listing various mathematical ways of training. Thus claims 1-5, 7-14 and 16-20 recite an abstract idea (Step 2A_1).
The computing additional elements (computing system, network interface, medium, processor, client device, instructions, machine learning model) are all recited a high degree of generality such that they amount to mere instructions to implement an abstract idea. Per MPEP 2106.05(f) they therefore do not provide a practical application or significantly more (Step 2A_2 and Step 2B). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to 8 perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Using the trained machine model is used to generally apply the abstract idea without placing any limits on how the trained model functions. Therefore the usage of trained machine learning models is at the "apply it" level described in MPEP 2106.05(f).
Regarding the use of a GUI view to perform data gathering/output, per MPEP 2106.05(g) necessary data gathering or output is insignificant extra-solution activity that does not provide a practical application or significantly more. Furthermore, the use of a GUI to collect/output information is conventional. See MPEP 2106.05(d) citing Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016). Thus the GUI view does not provide significantly more because it is well-understood, routine and conventional activity.
Thus claims 1-5, 7-14 and 16-20 recite an abstract idea without a practical application or significantly more and are ineligible.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHAN A MITCHELL whose telephone number is (571)270-3117. The examiner can normally be reached M-F 9-5.
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/NATHAN A MITCHELL/ Primary Examiner, Art Unit 3627