DETAILED ACTION
The following is a Non-Final Office Action in response to communications filed September 29, 2025. Claims 1, 3, 8–13, 15, 23–24, and 26 are amended. Currently, claims 1–19 and 21–26 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 September 29, 2025 has been entered.
Response to Amendment/Argument
Applicant’s Response is sufficient to overcome the previous rejection of claims 1–19 and 21–26 under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Accordingly, the previous rejection of claims 1–19 and 21–26 under 35 U.S.C. 112(b) is withdrawn.
With respect to the previous rejection of claims under 35 U.S.C. 101, Applicant’s remarks have been fully considered but are not persuasive.
Applicant first argues that the claims include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two because the claims include elements that embody an improvement in technology. Examiner disagrees.
As an initial matter, Examiner notes that claim elements reciting an abstract idea under Step 2A Prong One are not additional elements capable of integrating the abstract idea into a practical application under Step 2A Prong Two. MPEP 2106.07(a) sets forth the process for formulating a rejection under 35 U.S.C. 101 and states:
For Step 2A Prong Two, the rejection should identify any additional elements (specifically point to claim features/limitations/steps) recited in the claim beyond the identified judicial exception; and evaluate the integration of the judicial exception into a practical application by explaining that 1) there are no additional elements in the claim; or 2) the claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application.
MPEP 2106.07(a) indicates that additional elements are claim elements “recited in the claim beyond the identified judicial exception” and further states that a claim may not include any additional elements when the recited claim elements are entirely abstract. Here, Applicant’s remarks rely on considering all claim elements as additional elements under Step 2A Prong Two. However, Examiner maintains that the only additional elements of claim 1 include a computer and steps for receiving, training, and storing, which neither integrate the abstract idea into a practical application under Step 2A Prong Two nor amount to significantly more than the recited abstract idea under Step 2B for the reasons asserted below.
To the extent that Applicant asserts that the claims are patent-eligible because the claims include elements that are not well-understood, routine, and conventional, Examiner disagrees. More particularly, Examiner submits that the algorithmic portions of claim 1 are not identified as well-understood, routine, and conventional claim elements. As a result, Applicant’s remarks are not commensurate with either the process for formulating a rejection under 35 U.S.C. 101, as set forth under MPEP 2106.07(a), or the rejection of record.
Further, although the claims include a physical step for “constructing the construction,” which may provide advantages, such as construction quality, safety, and construction time, Examiner maintains that such improvements are improvements to the business process rather than improvements to any technology or technical field. MPEP 2106.04(d)(1) details the evaluation of improvements in the functioning of a computer, or an improvement to any other technology or technical field in Step 2A Prong Two and, Examiner submits that the examples of technical improvements are not analogous to Applicant’s asserted improvements in safety. In contrast, any improvements in safety stem from scheduling improvements associated with abstract business or mathematical processes rather than improvements in any associated technology.
Still further, Examiner notes that commonplace business methods embodied on generic computing technology do not show an improvement to technology (see e.g., MPEP 2106.05(a)(II)). Here, the claims address commonplace business practices for improving safety at a worksite using generic computing technology, as supported by FIG. 1 and paragraphs 274–277 of Applicant’s Specification, which do not identify any technical improvements associated with the recited computer technology. Instead, Applicant’s Specification describes the recited computer technology as generic computer technology. As a result, Applicant’s argument is not persuasive.
Accordingly, Applicant’s remarks are not persuasive, and the previous rejection is maintained and reasserted below.
With respect to the previous rejection of claims under 35 U.S.C. 103, Applicant’s remarks have been fully considered, but are not persuasive. More particularly, Applicant’s remarks address the references individually without regard for the asserted combination. For example, Applicant asserts that Strachan does not disclose element (c) of independent claim 1 despite element (c) being rejected over the combination of references, generally, and in view of the combined disclosures of Strachan and Runkler, specifically. As a result, Applicant’s remarks are not persuasive because “[o]ne cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references.” In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., Inc., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Accordingly, Applicant’s remarks are not persuasive, and the previous rejection is asserted below.
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 1–19 and 21–26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1–19 and 21–26 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claim 1 recites an abstract idea. Claim 1 includes limitations for “(a) using a construction-related training dataset …”; “(d) generating a first plurality of task vectors, each task vector corresponding to the one or more construction-related training tasks, using the first trained algorithm …”; “(e) for each task vector of the first plurality of task vectors, using a second algorithm, different to the first algorithm, which receives the task vector of the first plurality of task vectors as input and generates data relating to a predicted duration time for the one or more construction-related training tasks”; “(i) for the plurality of construction tasks, receiving task data corresponding to each construction task of the plurality of construction tasks…”; “(ii) generating a second plurality of task vectors, each task vector of the second plurality of tsk vectors corresponding to one or more corresponding construction tasks of the plurality of construction tasks, using the first trained algorithm which processes the received task data corresponding to the one or more corresponding construction tasks, such that each construction task has a corresponding task vector of the second plurality of vectors”; “(iii) for each task vector of the second plurality of task vectors, using the second trained algorithm which receives each task vector of the second plurality of task vectors as input and generates data relating to a predicted duration time for the one or more corresponding construction tasks …”; “(iv) generating a construction schedule including data relating to a predicted construction schedule duration time, using the data relating to the predicted duration times of the plurality of construction tasks, the construction schedule including the plurality of construction tasks, which are ordered in the construction schedule”; “(v) identifying risky schedule items in the construction schedule, wherein risk is characterized by a time dimension and receiving input from a planner, and using the input to modify the construction schedule, to reduce the risk characterized by time dimension on identified risky schedule items, to produce a modified construction schedule”; and “(vi) constructing the construction using the modified construction schedule generated in step (v), the modified schedule including ordered tasks which are ordered in the modified construction schedule, and respective predicted duration times of the ordered tasks.”
The elements above recite an abstract idea. More particularly, the elements above recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because the elements recite a process for generating and implementing a construction schedule based on task data. Additionally, the steps for “using a construction-related training dataset”, “generating a first plurality of task vectors … using the first trained algorithm”, “using a second algorithm”, “generating a second plurality of task vectors … using the first trained algorithm”, and “for each task vector of the plurality of task vectors, using the second trained algorithm” recite mathematical concepts because, when considered in view of Applicant’s Specification, the elements describe mathematical relationships of the task data and/or algorithmic mathematical calculations. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claim 26 includes substantially similar limitations to those recited with respect to claim 1. As a result, claim 26 recites an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1.
Claims 2–19 and 21–25, which depend from claim 1, further describe the process for generating and implementing a construction schedule based on task data and recite certain methods of organizing human activity for the same reasons as stated above with respect to claim 1. Further, claims 8–9, 15, 17–19, 21, and 23–25 recite mathematical concepts because, when considered in view of Applicant’s Specification, the claims recite mathematical relationships, formulas, and/or calculations. As a result, claims 2–19 and 21–25 similarly recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claim 1 include a computer and steps for “receiving” data, “training a first algorithm”, “training the second algorithm”, and “storing the modified construction schedule”. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the computer is a generic computer component that is merely used as a tool to perform the recited abstract idea; the steps for “training” algorithms do no more than generally link the use of the recited abstract idea to a particular technological environment; and the steps for receiving and storing are insignificant extrasolution activities to the recited abstract idea. As a result, claim 1 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claim 26 includes substantially similar limitations to those included with respect to claim 1. Although claim 26 additionally recites a computer system including instructions embodied on a non-transitory storage medium, the additional elements, when considered in view of the claims as a whole, do not integrate the abstract idea into a practical application because the additional computer elements are generic computer components that are merely used as a tool to perform the recited abstract idea. As a result, claim 26 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2–19 and 21–22, which depend from claim 1, do not recite any additional elements. As a result, claims 2–19 and 21–22 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 23–25 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 23–25 include a display. When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computer elements are generic computer components that are merely used as a tool to perform the recited abstract idea. As a result, claims 23–25 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional element that do not recite an abstract idea under Step 2A Prong One. The additional elements of claim 1 include a computer and steps for “receiving” data, “training a first algorithm”, “training the second algorithm”, and “storing the modified construction schedule”. The additional elements do not amount to significantly more than the abstract idea because the computer is a generic computer component that is merely used as a tool to perform the recited abstract idea; the steps for “training” algorithms do no more than generally link the use of the recited abstract idea to a particular technological environment; and the steps for receiving and storing are well-understood, routine, and conventional computer functions in view of MPEP 2106.05(d)(II), which identifies receiving data and storing information in memory as conventional computer functions. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 1 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
As noted above, claim 26 includes substantially similar limitations to those included with respect to claim 1. Although claim 26 additionally recites a computer system including instructions embodied on a non-transitory storage medium, the additional elements do not amount to significantly more than the abstract idea because the additional computer elements are generic computer components that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 26 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2–19 and 21–22, which depend from claim 1, do not recite any additional elements. As a result, claims 2–19 and 21–22 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 23–25 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 23–25 include a display. The additional elements do not amount to significantly more than the abstract idea because the additional computer elements are generic computer components that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 23–25 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1–19 and 21–26 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1–8, 12–18, 22–23, and 25–26 are rejected under 35 U.S.C. 103 as being unpatentable over Dumont et al. (U.S. 2015/0339619) in view of Strachan et al. (U.S. 2018/0088939), and in further view of Hildebrand et al. (U.S. 2015/0294258) and Runkler et al. (U.S. 2019/0362239).
Claims 1 and 26: Dumont discloses a method of constructing a construction, the constructing including a plurality of construction tasks (See paragraph 2), the method including computer-implemented (See FIG. 1) steps of:
(b) receiving construction-related task data corresponding to each construction-related task in the construction-related dataset, the construction-related task data including: data identifying task type; data relating to a planned task duration, and data relating to any relationships to one or more other construction-related tasks (See paragraphs 36, 47, and 28, wherein task type, duration-related data, and dependency-related data is disclosed, and wherein the data is associated with each task);
the method further comprising the computer-implemented steps of: (i) for the plurality of construction tasks, receiving task data corresponding to each construction task of the plurality of construction tasks, the received task data including: data identifying task type: data relating to a planned task duration, and data relating to any relationships to one or more other tasks of the plurality of construction tasks (See paragraphs 36, 47, and 28, wherein task type, duration-related data, and dependency-related data is disclosed, and wherein the data is associated with each task);
(iii) for each task, generates data relating to a predicted duration time for the one or more corresponding construction tasks, to generate predicted duration times of the plurality of construction tasks (See paragraph 28 and 64, wherein task durations are estimated);
(iv) generating a construction schedule including data relating to a predicted construction schedule duration time, using the data relating to the predicted duration times of the plurality of construction tasks, the construction schedule including the plurality of construction tasks, which are ordered in the construction schedule (See paragraphs 61 and 64, wherein a work schedule is generated based on updated duration estimates and dependencies);
(v) identifying items in the construction schedule and receiving input from a planner, and using the input to modify the construction schedule to produce a modified construction schedule (See FIG. 2 and paragraphs 27 and 32, wherein user inputs modify schedule parameters; and paragraph 35, wherein work schedules are automatically updated), and storing the modified construction schedule (See FIG. 1 and paragraph 26, wherein a database stores work schedules and relevant information); the method further including the step of:
(vi) constructing the construction using the modified construction schedule generated in step (v), the modified construction schedule including ordered tasks which are ordered in the modified construction schedule, and respective predicted duration times of the ordered tasks (See FIG. 2 and paragraph 48, in view of paragraphs 17–18 and 35, wherein work is performed based on initial and updated work schedules). Dumont does not expressly disclose the remaining claim elements.
Strachan discloses (a) using a construction-related (See paragraph 8, wherein the disclosure relates to projects, including construction projects) training dataset comprising a plurality of construction-related training tasks and data relating to actual duration times for one or more construction-related training tasks (See FIG. 2 and paragraphs 21–22, wherein a training dataset is disclosed with respect to completed work items, and wherein the completed work item information includes work items, open dates, and state transition dates; see also paragraphs 24 and 30);
(b) receiving construction-related training task data corresponding to each construction-related training task in the construction-related training dataset (See FIG. 2 and paragraphs 21–22, wherein the completed work item data is retrieved);
(c) a first algorithm to generate task vectors, using the construction-related training task data (See FIG. 2 and paragraphs 26–27 and 29, wherein the feature extraction engine uses text mining, keyword analysis, sentiment analysis, and the like and/or may self-adjust or self-configure selected features to extract feature values and generate feature vectors, and paragraph 13, in view of paragraph 22, wherein feature values are supplied using machine learning, and wherein feature values are extracted using data records for completed work items);
(d) generating a first plurality of task vectors, each task vector corresponding to the one or more construction-related training tasks, using the first trained algorithm which processes the construction-related training task data corresponding to the one or more construction-related training tasks, such that each construction-related training task has a corresponding task vector (See FIG. 2 and paragraphs 24–25 and 29, wherein feature vectors are generated for each completed work item by extracting features from the work item data);
(e) for each task vector of the first plurality of task vectors, using a second algorithm, different to the first algorithm, which receives the task vector of the first plurality of task vectors as input and generates data relating to a predicted duration time for the one or more construction-related training tasks (See FIG. 2 and paragraphs 29–30 and 35–36, wherein feature vectors of the completed work items are used as inputs to machine learning models to predict timing data for states of transition; see also paragraph 39);
(f) training the second algorithm using the actual duration times (See FIG. 2 and paragraphs 29–30 and 35–36, wherein predictor models are trained to predict timing data for states of transition; see also paragraphs 38–39);
(ii) generating a second plurality of task vectors, each task vector of the second plurality of task vectors corresponding to one or more corresponding construction tasks of the plurality of construction tasks, using the first trained algorithm which processes the received task data corresponding to the one or more corresponding construction tasks, such that each construction task has a corresponding task vector of the second plurality of task vectors (See paragraphs 49–50, wherein feature vectors are generated for each uncompleted work item by extracting features from the work item data; and see paragraphs 13 and 17, wherein data extraction is performed using machine learning); and
(iii) for each task vector of the second plurality of task vectors, using the second trained algorithm which receives each task vector of the second plurality of task vectors as input and generates data relating to a predicted duration time for the one or more corresponding construction tasks, to generate predicted duration times of the plurality of construction tasks (See paragraphs 49–50, in view of FIG. 2, wherein feature vectors of the uncompleted work items are used as inputs to a predictive machine learning model trained on completed work items to predict timing data for states of transition; see also paragraph 39).
Dumont discloses a system directed to updating work schedules based on task attributes. Strachan discloses a system directed to estimating state transition timing for work items. Each reference discloses a system directed to managing task timing. The technique of utilizing task vectors to predict task timing is applicable to the system of Dumont as they each share characteristics and capabilities; namely, they are directed to managing task timing.
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Strachan to the teachings of Dumont would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate task timing management into similar systems. Further, applying task vectors for task timing predictions to Dumont would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results. Dumont and Strachan do not expressly disclose the remaining claim elements.
Hildebrand discloses (v) identifying risky schedule items in the construction schedule, wherein risk is characterized by a time dimension and receiving input from a planner, and using the input to modify the construction schedule to reduce the risk characterized by time dimension on identified risky schedule items, to produce a modified construction schedule (See paragraphs 30 and 41–42, wherein modified well schedules are generated in response to identified task risks that are characterized by chronological order and time window dimensions, and wherein the modified schedule is managed by an authorized user).
As disclosed above, Dumont discloses a system directed to updating work schedules based on task attributes, and Strachan discloses a system directed to estimating state transition timing for work items. Hildebrand discloses a system directed to managing well operating tasks and schedules. Each reference discloses a system directed to managing tasks. The technique of utilizing risk considerations is applicable to the systems of Dumont and Strachan as they each share characteristics and capabilities; namely, they are directed to managing tasks.
One of ordinary skill in the art would have recognized that applying the known technique of Hildebrand would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Hildebrand to the teachings of Dumont and Strachan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate task management into similar systems. Further, applying risk considerations to Dumont and Strachan would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results. Dumont, Strachan, and Hildebrand do not expressly disclose the remaining elements of claim 1.
Runkler discloses (c) training a first algorithm to generate vectors, using the training data (See FIG. 7 and paragraphs 106 and 183, wherein preparation and/or vectorization functions are performed by neural networks using a configuration dataset, and wherein a vectorized configuration dataset is used to train a neural network; see also FIG. 4 and paragraphs 85, 160, and 162, wherein the neural network includes a trained auto-encoder and other trained network structures); and
using a second algorithm, different to the first algorithm; and training the second algorithm (See FIG. 7 and paragraphs 106 and 183, wherein preparation and/or vectorization functions are performed by neural networks using a configuration dataset, and wherein a vectorized configuration dataset is used to train a neural network; see also FIG. 4 and paragraphs 85, 160, and 162, wherein the neural network includes a trained auto-encoder and other trained network structures).
As disclosed above, Dumont discloses a system directed to updating work schedules based on task attributes, Strachan discloses a system directed to estimating state transition timing for work items, and Hildebrand discloses a system directed to managing well operating tasks and schedules. Runkler discloses a system directed to planning assistance in project automation. Each reference discloses a system directed to managing a project. The technique of utilizing first and second trained algorithms is applicable to the systems of Dumont, Strachan, and Hildebrand as they each share characteristics and capabilities; namely, they are directed to managing a project.
One of ordinary skill in the art would have recognized that applying the known technique of Runkler would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Runkler to the teachings of Dumont, Strachan, and Hildebrand would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate project management into similar systems. Further, applying first and second trained algorithms to Dumont, Strachan, and Hildebrand would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
With respect to claim 26, Dumont further discloses a computer system (See FIG. 1); and with respect to claim 27, Dumont further discloses a computer program product embodied on a non-transitory storage medium (See paragraphs 24–25).
Claim 2: Dumont discloses the method of Claim 1, wherein the construction is a building, a bridge, a tunnel, a sewer, a railway, an airport, a port, a dam or a road (See paragraphs 17–18, wherein types of construction are disclosed).
Claim 3: Dumont discloses the method of Claim 1, the method including the computer-implemented step of: storing the modified construction schedule including data relating to a predicted modified construction schedule duration time, and including the ordered tasks which are ordered in the modified construction schedule, and the respective predicted duration times of the ordered tasks (See FIG. 1 and paragraph 26, wherein a database stores work schedules and relevant information).
Claim 4: Dumont discloses the method of Claim 1, wherein the respective predicted duration times of the ordered tasks comprise adjusted values of planned task durations (See paragraph 64, wherein updated task duration estimates are generated).
Claim 5: Dumont discloses the method of Claim 1, wherein the data relating to any relationships to one or more other tasks includes a number of other tasks having a relationship to a task (See paragraphs 28 and 64, wherein dependency parameters implicitly include a number of dependencies).
Claim 6: Dumont does not expressly disclose the elements of claim 6.
Strachan discloses wherein the generated data relating to a predicted duration time includes a probability distribution (See paragraphs 11 and 64, wherein probability distributions for work item completions and transitions are disclosed).
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 7: Dumont does not expressly disclose the elements of claim 7.
Strachan discloses wherein the generated data relating to a predicted duration time includes a time value derived from the probability distribution, e.g. a mean, a mode, a median or a cumulative probability time value (See paragraphs 11 and 64, wherein probability distributions for work item completions and transitions are disclosed).
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 8: Dumont discloses the method of Claim 1, wherein generating the data relating to the predicted construction schedule duration time comprises: applying the duration values to the construction schedule to generate a plurality of duration values corresponding to the construction schedule. Dumont does not expressly disclose the remaining claim elements (See paragraph 29, wherein work schedules include time-based plans for instructing work crew work; see also paragraph 64, wherein each task is associated with a duration). Dumont does not expressly disclose the remaining claim elements.
Strachan discloses sampling a respective probability distribution relating to each task vector of the second plurality of task vectors a plurality of times, to generate a plurality of duration values corresponding to each task vector of the second plurality of task vectors (See paragraphs 11 and 64, wherein a probability distribution is durations is generated for each uncompleted work item).
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 12: Dumont does not expressly disclose the elements of claim 12.
Strachan discloses wherein the data relating to the predicted construction schedule duration time includes a probability distribution (See paragraphs 11 and 64, wherein probability distributions for work item completions and transitions are disclosed).
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 13: Dumont does not expressly disclose the elements of claim 13.
Strachan discloses wherein the data relating to the predicted construction schedule duration time includes a time value derived from the probability distribution, e.g. a mean, a mode, a median or a cumulative probability time value (See paragraphs 11 and 64, wherein probability distributions for work item completions and transitions are disclosed, and wherein the data includes time values).
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 14: Dumont does not expressly disclose the elements of claim 14.
Strachan discloses wherein for a task including the data identifying the task type, the data relating to a planned task duration, and/or the data relating to any relationships to one or more other tasks, the data does not comprise a null value (See Table 1, in view of paragraph 52, wherein uncompleted work items in an “open” state do not have any associated null values).
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 15: Dumont does not expressly disclose the elements of claim 15.
Strachan discloses wherein if a task where data identifying the task type, the data relating to a planned task duration, and/or the data relating to any relationships to one or more other tasks includes a null value (See Table 1, in view of paragraph 52, wherein uncompleted work items in an “new” state have associated null values for previous state transition data):
a task vector is generated using the first trained algorithm; a plurality of other task vectors in the construction schedule which are similar to the task vector are identified; task data for the other task vectors corresponding to the data comprising a null value for the task vector is identified; task data for the task vector is generated using the task data for the other task vectors; an updated task vector is generated for the task using the first trained algorithm taking the task data as input (See paragraph 29, wherein work items with similar or identical feature values are aggregated for analysis).
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 16: Dumont does not expressly disclose the elements of claim 16.
Strachan discloses wherein a number of tasks corresponding to a task vector of the second plurality of task vectors is defined by a fixed window size, and wherein the received task data corresponding to two or more tasks is combined into a one-dimensional vector for input to the first trained algorithm (See paragraph 29, wherein work items with similar or identical feature values are aggregated for analysis, and wherein the window is fixed according to identical feature values).
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 17: Although Strachan discloses the first trained algorithm (See citations above), Dumont, Strachan, and Hildebrand do not expressly disclose the remaining elements of claim 17.
Runkler discloses wherein the first trained algorithm is an encoder part of a neural network based auto-encoder (See paragraph 142, wherein an auto-encoder of a neural network is disclosed).
One of ordinary skill in the art would have recognized that applying the known technique of Runkler would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 18: Although Dumont discloses data relating to a predicted duration time (See citations above), Dumont does not expressly disclose the remaining elements of claim 18.
Strachan discloses wherein the second trained algorithm comprises a plurality of models, and wherein the data relating to a predicted duration time is generated by combining output of the plurality of models (See paragraphs 13 and 29, wherein machine learning predictor models are associated with different work item characteristics, and wherein, in view of paragraph 53, the model outputs are combined to produce data relating to a predicted duration for a set of uncompleted work items; see also paragraph 39).
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Dumont, Strachan, and Hildebrand do not expressly disclose the remaining claim elements.
Runkler discloses a neural network (See paragraph 142).
One of ordinary skill in the art would have recognized that applying the known technique of Runkler would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 22: Dumont discloses the method of Claim 1, (i) wherein the constructing the construction comprises a group of projects, or a portfolio of projects, or (ii) wherein the constructing the construction comprises a subproject of a larger project (See paragraphs 26 and 30, wherein schedules are associated with one or more projects).
Claim 23: Dumont discloses the method of Claim 1, wherein the predicted duration times for the plurality of construction tasks is presented on a display using a histogram, where each bucket of the histogram represents a time-dimension prediction of a respective task, and a height of the histogram represents the outcome (See paragraphs 70–71, wherein reports are presented using bar charts, graphs, plots, text descriptions, etc.). Dumont does not expressly disclose the remaining claim elements.
Strachan disclose a probability of this outcome (See paragraphs 11 and 64, wherein probability distributions for work item completions and transitions are disclosed).
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 25: Dumont discloses the method of Claim 1, wherein an array of tiles is presented on a display with respect to two axes, a first axis representing respective portions of the construction of the construction, and a second axis representing respective outcomes of respective portions of the construction of the construction, wherein each tile includes a presentation (See paragraphs 70–71, wherein reports are presented using bar charts, graphs, plots, text descriptions, etc.). Dumont does not expressly disclose the remaining claim elements.
Strachan discloses ranges of completion outcomes of the respective portions, wherein each element includes how likely a respective range of completion outcomes is to occur (See paragraphs 11 and 64, wherein probability distributions for work item completions and transitions are disclosed).
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claims 9–11, 21, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Dumont et al. (U.S. 2015/0339619) in view of Strachan et al. (U.S. 2018/0088939), and in further view of Hildebrand et al. (U.S. 2015/0294258), Runkler et al. (U.S. 2019/0362239), and LOPES et al. (U.S. 2017/0091688).
Claim 9: As disclosed above, Dumont, Strachan, Hildebrand, and Runkler disclose the elements of claim 1.
Dumont discloses the method of Claim 1, wherein a duration of the one or more corresponding construction tasks corresponding to a task vector is determined by: applying the duration values to the schedule to generate a plurality of duration values corresponding to the schedule (See paragraph 29, wherein work schedules include time-based plans for instructing work crew work; see also paragraph 64, wherein each task is associated with a duration). Dumont does not expressly disclose the remaining claim elements.
Strachan discloses sampling a probability distribution relating to the task vector of the second plurality of task vectors a plurality of times, to generate a plurality of duration values corresponding to the task vector of the second plurality of task vectors (See paragraphs 11 and 64, wherein a probability distribution is durations is generated for each uncompleted work item).
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Dumont, Strachan, Hildebrand, and Runkler do not expressly disclose an impact.
Lopes discloses an impact of the one or more corresponding construction tasks (See paragraphs 84–85, wherein project and task sequencers are disclosed; and see paragraphs 103 and 125, wherein constraint impacts on schedule solutions are disclosed).
As disclosed above, Dumont discloses a system directed to updating work schedules based on task attributes, Strachan discloses a system directed to estimating state transition timing for work items, Hildebrand discloses a system directed to managing well operating tasks and schedules, and Runkler discloses a system directed to planning assistance in project automation. Lopes discloses a system directed to scheduling optimization for maintenance services. Each reference discloses a system directed to managing tasks. The technique of utilizing task impact is applicable to the systems of Dumont, Strachan, Hildebrand, and Runkler as they each share characteristics and capabilities; namely, they are directed to managing tasks.
One of ordinary skill in the art would have recognized that applying the known technique of Lopes would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Lopes to the teachings of Dumont, Strachan, Hildebrand, and Runkler would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate task management into similar systems. Further, applying task impact to Dumont, Strachan, Hildebrand, and Runkler would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claim 10: Dumont does not expressly disclose the elements of claim 10.
Strachan discloses determining a duration of the one or more corresponding construction tasks corresponding to each task vector of the second plurality of task vectors (See paragraphs 35 and 50, wherein feature vectors of the uncompleted work items are used as inputs to machine learning models to predict timing data for states of transition; see also paragraph 39).
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Dumont, Strachan, Hildebrand, and Runkler do not expressly disclose the remaining elements of claim 10.
Lopes discloses wherein an impact is determined (See paragraphs 84–85, wherein project and task sequencers are disclosed; and see paragraphs 103 and 125, wherein constraint impacts on schedule solutions are disclosed).
One of ordinary skill in the art would have recognized that applying the known technique of Lopes would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 9.
Claim 11: Dumont does not expressly disclose the elements of claim 11.
Strachan discloses for each task vector of the second plurality of task vectors, data is generated relating to the predicted construction schedule duration time using a third trained algorithm, using a respective task vector of the second plurality of task vectors as an input (See paragraph 29, wherein predictive models are employed based on work item characteristics, and paragraphs 35 and 50, wherein feature vectors of the uncompleted work items are used as inputs to machine learning models to predict timing data for states of transition; see also paragraph 39).
One of ordinary skill in the art would have recognized that applying the known technique of Strachan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Dumont, Strachan, Hildebrand, and Runkler do not expressly disclose the remaining elements of claim 11.
Lopes discloses wherein data is generated relating to an impact on the schedule (See paragraphs 84–85, wherein project and task sequencers are disclosed; and see paragraphs 103 and 125, wherein constraint impacts on schedule solutions are disclosed).
One of ordinary skill in the art would have recognized that applying the known technique of Lopes would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 9.
Claim 21: Dumont discloses the method of Claim 1, the method further including the computer implemented steps of: applying the duration values to the schedule to generate a plurality of duration values corresponding to the schedule (See paragraph 29, wherein work schedules include time-based plans for instructing work crew work; see also paragraph 64, wherein each task is associated with a duration). Dumont does not expressly disclose the remaining claim elements.
Strachan discloses sampling a probability distribution relating to the task vector of the second plurality of task vectors a plurality of times, to generate a plurality of duration values corresponding to the task vector of the second plurality of task vectors (See paragraphs 11 and 64, wherein a probability distribution is durations is generated for each uncompleted work item);
determining a timing of the one or more tasks corresponding to the task vector of the second plurality of task vectors and training a third algorithm to generate timing data, using the determined timing (See paragraphs 29–30, in view of paragraphs 38–39, wherein timing data for completed items is used to train