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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 July 22, 2026 has been entered.
Status of Claims
Claims 1, 3, 7, 9 – 11, 15 – 16, 18 and 20 have been amended and are hereby entered.
Claims 2, 8, 12 and 17 were cancelled.
Claims 1, 3 - 7, 9 - 11, 13 - 16 and 18 - 20 are pending and have been examined.
This action is made NON-FINAL.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on: 5 May 2025, 13 August 2026 and 18 August 2026 are in compliance with the provisions of 37 CFR 1.97 and 1.98. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed July 22, 2026 have been fully considered but they are not persuasive.
Regarding the applicant's arguments for Double-Patenting Rejection in p. 8: Applicant did not take any action, did not significantly amended the claims for at least one the previous ODP, did not submit persuasive arguments against the rejection and/or did not file an Electronic-Terminal Disclosure or e-td to at least obviate the Obviousness type of Double Patenting (ODP) rejections. Thus, at least the pending ODP will be maintained.
Arguments regarding the 112(f) interpretation have been considered and acknowledged. Therefore, this particular interpretation has been maintained due to the lack of Applicant's amendments for claim 19 and to provide clarity of record for claim interpretation.
Amendments regarding the 112(a) rejection have been entered and its respective arguments in pp. 9 - 12 from Remarks have been considered. Thus, this particular rejection has been withdrawn mainly due to Applicant amendments to claims 10 and 18.
Regarding to Applicant's arguments against the 101 rejection of pending claims on pages 12-26: Applicant’s arguments directed to 101 analysis were considered. However, these arguments are not persuasive and the examiner respectfully disagrees for the following reasons:
For Step 2A-Prong 1 starting in p. 12: The Applicant argues that the pending claims are not directed to any of the abstract ideas identified but further focus solely on arguing the abstract idea of mental process previously identified, but these arguments are now moot due to the amendments. However, considering and upon re-evaluation of the amendments, the Examiner find these arguments unpersuasive and respectfully disagrees. Because the amended claims are still reciting an abstract idea of a certain method of organizing human activity encompassing “commercial or legal interactions” in the form of agreements in the form of contracts or legal obligations (i.e handling and receiving contractor information via the determination of “project objectives”, as claimed) and advertising (i.e providing business consulting services) as the claimed system outputs “optimization recommendations” and simulated plans of action to facilitate the user to complete the business project efficiently. Additionally, the claim limitations also encompass “managing personal behavior” in the form of “following rules or instructions” specifically, when “simulating” the plan of action in a “virtual reality or an augmented reality” to “enable performance of the at least one intended task” wherein such simulation requires “superimpose[ing] a real-time visual data feed of a site”, meaning a user will need to follow the instructions provided in the “plan of action” recommended by the computer to execute the task.
For Step 2A-Prong 2 and Step 2B starting in p. 17: The Applicant alleges that the claims integrate, the judicial exception identified, into a practical application and further alleges that claim 1 is similar to claim 3 of Example 47 (Anomaly Detection) of the July 2024 SME Examples because claim 1 discloses steps that “tie the generated feature set to a particular technological application, the rendering of a virtual or augmented reality simulation that enables performance of the intended task, in precisely the manner that the post-analysis steps of Example 47 Claim 3”. However, the Examiner finds these arguments unpersuasive and respectfully disagrees. Because the instant claims are still invoking “apply it” (see MPEP 2016.05 (f) and MPEP 2106/o5 (f)(2)) by using a general computer as a tool to determine optimization recommendations based on project objectives to generate feature sets (i.e. feature vector with factors being considered) that are processed and simulated as a virtual representation in a VR/AR environment in the GUI (i.e. the computer is performing display functions while further using VR/AR technology for the superimposition of real-time data feed rendering of a visual representation of a site, as claimed, which is directed to “field of use”; MPEP 2016.05 (h)). Thus, this last step is considered data display and is not necessarily a “concrete, technologically grounded use”, as alleged by the Applicant. Rather, these claims are improving of the abstract idea itself and further narrows the drafted abstract ideas identified and their specificity in their limitations which does not necessarily equate to eligibility. For instance, In buySAFE, Inc. v. Google, Inc. (Fed. Cir. 2014), the court stated that "abstract ideas, no matter how groundbreaking, innovative, or even brilliant, are outside what the statute means by "new and useful process, machine, manufacture, or composition of matter", and reference is made to Myriad by the court for this position. Also stated in buySAFE is "In defining the excluded categories, the Court has ruled that the exclusion applies if a claim involves a natural law or phenomenon or abstract idea, even if the particular natural law or phenomenon or abstract idea at issue is narrow. Mayo, 132 S. Ct. at 1303. The Court in Mayo rejected the contention that the very narrow scope of the natural law at issue was a reason to find patent eligibility, explaining the point with reference to both natural laws and one kind of abstract idea, namely, mathematical concepts.
In p.18 from Remarks, Applicants further compare certain steps from claim 1 to claim 1 of Example 42 (Method for Transmission of Notifications When Medical Records Are Updated) of the July 2024 SME Examples. However, the Examiner finds these arguments and respectfully disagrees. Because the argued steps of “parsing and processing” input data to “generate” intent-based data sub-units, “converting” the intent-based data sub-units into machine executable instructions and “generate” multi-dimensional design vectors from the received input data are improving the abstract idea itself, as discussed above for the previous analysis that applies herein. Thus, these claims are not integrated into a practical application because claims are not meaningful limitation steps as these are recited in a very general or broad manner that do not reflects the disclosed improvement of the “transformation” of data for “reliability of downstream actions” and its identified judicial exception does not “meaningfully limits the claim by going beyond generally linking the use of the judicial exception to a particular technological environment” (i.e. computer environments) and merely claims “the idea of a solution or outcome” of generating “a structured, machine-readable artefact (i.e., the feature set corresponding to the multi-dimensional design vector)” which is further improving the abstract idea itself (See MPEP 2106.04(d)(1) and MPEP 2106.05(a)). Thus, for these same reasons stated above, the claims are not providing an inventive concept at Step 2B (in response to Applicant arguments in p. 23 from Remarks). Thus, for all the reasons stated above, the Examiner respectfully disagrees, and maintains 35 USC § 101 rejection for these pending claims.
Regarding to Applicant's arguments of rejection under 35 USC § 102 for the pending claims on pages 26 – 31: Applicant arguments against Cami reference teachings for claim 1 limitations, including the step for “generating one or more intent-based data sub-units by parsing and processing the received input” and the step of “converting the one or more intent-based data sub-units to machine executable instructions” have been considered (see p. 28 from Remarks). Upon re-evaluation of the claim limitations, new ground of rejection has been reflected, and the argued steps are now taught by Fenton (WO Pub No. 2021050131 A1) and thus, Applicant’s arguments are deemed moot. Please, refer to the Claim Rejections - 35 USC § 103 section which have been issued in this OA for further details.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
At least claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of Co-pending Application No. 18/847,868 (reference application referred as ‘868 hereafter; Examiner notes that this app was recently issued with a NOA) in view of Cami (U.S. Pub No. 20230229986 A1) in further view of Fenton (WO Pub No. 2021050131 A1). Although, the claims at issue are not identical, they are not patentably distinct from each other because the differences between the claims are considered to be obvious as set forth below:
Instant claims
Co-pending or reference claims
(18/847,868)
Claim 1: A method, executed by at least one processor, for analyzing an intent of a user in a computing environment, the method comprising:
receiving an input from a user for executing at least one intended task by the user;
generating one or more intent-based data sub-units by parsing and processing the received input;
converting the one or more intent-based data sub-units to machine executable instructions;
analyzing the machine executable instructions based on one or more ecosystem influencers, wherein the one or more ecosystem influencers comprise at least one of a project phase, supply constraints, and a quality impact;
determining an intent of the user based on the analyzed machine executable instructions and one or more project objectives associated with the at least one intended task;
determining system optimization recommendations based on the one or more project objectives;
generating a feature set corresponding to a multi-dimensional design vector by correlating the intent of the user and the determined system optimization recommendations.
processing at least one data feed received from a knowledge database based on the determined intent of the user to select at least one plan of action for executing the at least one intended task;
and simulating, in a graphical user interface, the at least one plan of action as a virtual reality or an augmented reality based on the generated feature set, the simulation rendering a virtual representation of the at least one intended task to enable performance of the at least one intended task according to the determined intent of the user, wherein to simulating the at least one plan of action, the computer-executable program further causes the at least one processor to superimpose a real-time visual data feed of a site associated with the at least one intended task over the virtual representation.
Claim 1: A computer-implemented method for generating[[ a]] an intent- based model recommendation for a physical construction project an Architecture, Engineering, and Construction (AEC) environment, the method comprising:
receiving, by a server computer from a client computer, a plurality of input streams associated with the physical construction project, the plurality of input streams comprising at least one non-textual input stream selected from an acoustic input captured by an acoustic sensor, an image input captured by an imaging sensor, and a gesture input;
converting, by respective format converters, each non-textual input stream of the at least one non-textual input stream into a textual input stream;
determining, by a controller, a user intent associated with at least one construction task by parsing the textual input stream, converted from each of the at least one non-textual input stream, through intent-based processing units to generate intent-based data sub- units, and combining the intent-based data sub-units through a combinatorial module into a combined machine executable instruction for executing the at least one construction task by a user;
classifying, by the controller, the determined user intent as at least one of a temporal intent associated with construction timelines, a spatial intent associated with construction location, a fiscal intent associated with construction budget, and a societal intent associated with environmental or societal impact of the physical construction project;
processing, by a model ensemble comprising a plurality of machine learning modules, one or more data feeds received from a knowledge database based on the classified user intent and on real-time data feeds received from at least one on-site sensor monitoring the physical construction project;
generating, by the model ensemble, a plurality of construction scenarios by varying one or more construction-specific parameters comprising at least one of supply chain constraints, factory schedules, material procurement timing, worker resource allocation, and equipment Internet of Things (IoT) metrics;
evaluating an outcome of each of the plurality of construction scenarios against one or more project objectives comprising at least one of a schedule objective, a budget objective, a carbon footprint objective, a worker health objective, and a material quality objective, by generating a set of construction constraints from the one or more project objectives, mapping the outcome of each of the plurality of construction scenarios to the set of construction constraints, and shortlisting one or more scenarios from the plurality of construction scenarios based on the mapping; and
generating, by a notifier, at least one model recommendation associated with the user intent based on the evaluation and the processing of the one or more data feeds, the at least one model recommendation simulating a plan of action for the at least one construction task, wherein simulating the plan of action comprises rendering the plan of action as an augmented reality overlay superimposing a virtual representation of the physical construction project onto a real- time visual data feed of a physical construction site, associated with the physical construction project, captured by the imaging sensor.
Consequently, for pending claim 1 in view of App ‘868 and its reference claim 1, the differences between the claims are the recitation of the following underlined limitations:
analyzing the machine executable instructions based on one or more ecosystem influencers,
wherein the one or more ecosystem influencers comprise at least one of a project phase, supply constraints, and a quality impact;
determining system optimization recommendations based on the one or more project objectives;
generating a feature set corresponding to a multi-dimensional design vector by correlating the intent of the user and the determined system optimization recommendations.
The App ‘868 and its reference claims did not explicitly teach the steps indicated above. However, these instant application’s limitations were further evaluated by Cami which taught certain steps from the ones disclosed above. Specifically, the step directed in part to the one or more ecosystem influencers comprise at least one of a project phase, supply constraints, and a quality impact was taught as “completion metric” including factors such as “(i) the scheduled sequence of tasks, (ii) the current condition of the construction project, and (iii) at least one task from the schedule of tasks” as well as “time to complete, cost to complete, material cost to complete, labor cost to complete, and completion risk” and these are used as a basis (see ¶0032 – 33; Cami) Also, Cami teaches in ¶0072 another example wherein the “data analysis can be done to refine in relationship to the cost per task UOM”, but also considering the “worker's cost rate and the actual time taken to complete the UOMs” which are assigned (i.e. constraints). Then, for the steps of determining system optimization recommendations based on the one or more project objectives and generating a feature set corresponding to a multi-dimensional design vector, these were directed to an example wherein “during the learning process vectors will be created where the algorithm will execute all actions from a starting state and add up the rewards/penalties to project completion for each action” and the “vectors will be analyzed and the best result will be saved to the policy”. Also, Cami teaches in ¶0044 an algorithm that can “be customized, tweaked, or be trained to produce more specific or tailored recommendations based on local legal, construction, or requirements made by the company performing the construction or receipt of a construction project” and in ¶0148 Cami further teaches an example when generating “task suggestion policy”, “multiple paths can be generated and then screened using other machine learning techniques”. Finally, the step of correlating the intent of the user and the determined system optimization recommendations for generating multi-dimensional vector feature sets was taught as the example wherein the user “natural language comments” provided can be “interpreted by a language interpretation algorithm to provide an indication of what is left, what sub-task is not yet completed (e.g., “sink installation,”) and also quantify what percentage of the task (e.g., complete bathroom) is left” and this “quantification can be based on any algorithm”. For instance, “the expected number of hours left to complete the task versus the total number of hours allocated or expected for the task can be used to determine what percentage of the “complete bathroom” task is left. Such “status can be provided or obtained from a machine vision algorithm” to further use the “expected status” that “can be provided from data obtained from smarttools and later verified by a human operator or secondary review by a machine vision module before the task is considered to be complete” wherein such quantification of the tasks is interpreted as the correlation of the user intent and determined system recommendations for the generation of the feature set, as defined in ¶00107 and ¶00122 from Applicant disclosure (see ¶0030; Cami)
Therefore, it would have been obvious to one skilled in the art at the time of filing because would have provided to the reference App ‘868 with the abilities of having one or more ecosystem influencers comprise at least one of a project phase, supply constraints, and a quality impact, the abilities of determining system optimization recommendations based on the one or more project objectives and generating a feature set corresponding to a multi-dimensional design vector by correlating the intent of the user and the determined system optimization recommendations, as taught by Cami in order to “improve workflow related to construction projects, including but not limited to, creation of schedules, verification of construction progress, estimation of costs, updating of schedules, creation and estimation of construction status, use of information generated by construction equipment, and the use of granular information to continually update or tune the same” invention provided (¶0007; Cami).
Further, neither App ‘868 or Cami taught the remaining claim steps, but Fenton, taught the step of analyzing the machine executable instructions based on one or more ecosystem influencers which was interpreted as “the user may select “a task-query” and once selected, “the conversational assistant services 272 may be used to request additional information or clarification relating to the task-query from the user. For instance, a task-query for “attach a file from David” may be selected, and the conversational assistant services 272 may recognize that additional information may be needed (such as David’s surname, a date range, or type of file) to effectively execute the task-query.” (see ¶0104; Fenton).
Thus, it would have been obvious to one skilled in the art at the time of filing because would have provided to the reference App ‘868 and Cami with the ability of analyzing the machine executable instructions based on one or more ecosystem influencers, as taught by Fenton in order to “enable a digital assistant or computer service to more quickly traverse a large set of information by using natural language received by observing the user’s composition of a communication” and thus, “the user can quickly manage what tasks, such as attaching a file and/or scheduling a meeting, may be needed to achieve the intended communication content in an automated fashion” (¶0026 and ¶0030; Fenton), see also MPEP 2143.I.G.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier (based on the 3-prong analysis; see MPEP 2181(I)). Specifically, claim 19 and their respective limitation(s) recite(s) the generic placeholder of (at Prong one): “an ensemble learning unit” wherein this claim limitation is reciting the term “configured to” as a “means plus function” or recitation of modified functional language wherein such claim limitations is (at Prong two): “wherein the model ensemble for processing the data feed is configured to process one or more data feeds by an ensemble learning unit based on the determined intent”
However, the structure of this placeholder is not modified by sufficient structure, material, or acts for performing the claimed function and thus, this structure is not disclosed in the claims (at Prong three). Further, the “ensemble learning unit”, is being considered hardware since Applicant specification at least in ¶0059 seems to not define or specify if this “ensemble learning unit” is part of or is derived from the “model ensemble” as well as shown in Fig. 3, element 114 from the disclosure. Rather, it is claimed, as underlined above, as the “model ensemble” implements the “ensemble learning unit” externally (i.e. suggests that the “ensemble learning unit” is an external element being implemented by the “model ensemble”) which is why it is interpreted as a hardware component. Finally, if Applicant intends to refer to the “ensemble learning unit” as part of the “model ensemble” claimed (i.e. as being required as part of the invention), an amendment is suggested to further clarify or change “…by an ensemble learning unit” to “wherein the model ensemble for processing the data feed, further comprises of a model ensemble module to process one or more data feeds based on the determined intent” (see ¶0062 from Applicant disclosure for support of the underlined element) or to “wherein the model ensemble for processing the data feed, further comprises of ensemble learning instructions to process one or more data feeds based on the determined intent” (see ¶0059 from Applicant disclosure for support of the underlined element) to indicate that this element is a software component.
Because these claims limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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, 3 - 7, 9 - 11, 13 - 16 and 18 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of this claimed invention recited in the claims begins in view of independent claim 20, the most representative claim of the independent claims set 1, 11 and 20, as follows:
At Step 1: Claims 1 - 10 falls under statutory category of a process, claims 11 – 19 are directed to a machine and claim 20 is directed to an article of manufacture.
At Step 2A Prong 1: Claim 20 (representative of claims 1 and 11) recites an abstract idea in the following limitations:
…receive an input from a user for executing at least one intended task by the user;
…
analyze the machine executable instructions based on one or more ecosystem influencers…
determine an intent of the user based on the analyzed machine executable instructions and one or more project objectives associated with the at least one intended task
determine system optimization recommendations based on the one or more project objectives; and
…
process at least one data feed received…based on the determined intent of the user to select at least one plan of action for executing the at least one intended task; …
Generally, and as disclosed in the specification in ¶0007, this claimed invention “intelligently infer user intent and automatically formulate optimal solution for any project or activity” and “automatically and intelligently extract a user intent from a given set of considerations and accordingly customize, adjust or fine tune further actions based on processing of said intent.” However, the abstract idea(s) of a certain method of organizing human activity (See MPEP 2106.04(a)(2), subsection II) are/is recited in claim 20 in the form of “commercial or legal interactions”. Specifically, the abstract idea is recited in at least the limitations for “receiving” user input (i.e. as queries or instructions) related to “intended tasks” that are directed to a business project (i.e. construction projects and their respective tasks required; see ¶0081 from Applicant disclosure) and are being “parsed”/”processed” and “converted” to “machine executable instructions” to “analyze” them based on “project phase, supply constraints, and a quality impact” and “determine” user’s intent as well as “optimization recommendations” that will satisfy the corresponding “project objectives” (i.e. cost-effective ways, time-sensitive, favorable location, projection and any other kind of solutions to complete a business project; see ¶0122 from Applicant disclosure) to further correlate the user intent with the “optimization recommendations” determined to “select” a “plan of action” and “simulate” it. Thus, these limitations recite legal interactions that encompass agreements in the form of contracts or legal obligations (i.e handling and receiving contractor information via the determination of “project objectives”, as claimed) and advertising (i.e providing business consulting services) as the system outputs “optimization recommendations” and simulated plans of action to facilitate the user to complete the business project efficiently in terms of design, sustainability, safety, lean construction standards, financial factors (see ¶00023 – 25 and ¶0122 from Applicant disclosure), among other factors that might be required and/or considered. Similarly, these steps also at least encompass “managing personal behavior” in the form of “following rules or instructions” specifically, when “simulating” the plan of action in a “virtual reality or an augmented reality” to “enable performance of the at least one intended task” wherein such simulation requires “superimpose[ing] a real-time visual data feed of a site”, meaning a user will need to follow the instructions provided in the “plan of action” recommended by the computer to execute the task.
At Step 2A Prong 2: For independent claims 1, 11 and 20, The judicial exception(s) or abstract idea previously identified is not integrated into a practical application (see MPEP 2106.04 (d)). The claims recite the additional element(s) of a server computer (from claim 11); at least one processor (from claim 20) and a knowledge database, a graphical user interface (from claims 1, 11 and 20), including the steps of “parsing and processing the received input” to generate “intent-based data sub-units” and “converting” them to “machine executable instructions”. These additional elements, individually and in combination, and while considering the claims as a whole, are merely used as a tool to perform the abstract idea (See MPEP 2106.05(f)). Specifically, the limitation steps are recited as being performed by the computer. The computer and its GUI using VR/AR technology are recited at a high level of generality that is being used as a tool to perform the generic computer functions for determine optimization recommendations based on project objectives to generate feature sets (i.e. feature vector with factors being considered) that are processed and simulated as a virtual representation in a VR/AR environment. Also, the functions of “parsing and processing” input data to “generate” intent-based data sub-units, “converting” the intent-based data sub-units into “machine executable instructions” and “generate” multi-dimensional design vectors from the received input data are also broadly claimed and not further limits as to how these functions are performed from other general computer functions. Thus, all these steps mentioned above are further describing and applying the abstract idea without placing any limits on how the technological components are being improved, while distinguishing in the claim language, the performing limitations from functions that generic computer components can perform.
Finally, the step of “simulate” the “plan of action as a virtual reality or an augmented reality” to “enable performance of the at least one intended task” wherein such simulation requires “superimpose[ing] a real-time visual data feed of a site” is also “merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” (MPEP 2106.05(h)). In this case, the AR/VR technology used for the GUI that “simulate” the plan of action data to perform an intended task is broadly recited and lacks details on how this “simulation” is specifically performed and simply is limited to applying the data (i.e. data superimposed in the metaverse and/or applying “multi contour terrain navigation” visuals; see ¶0063, ¶0082 and ¶0092 from Applicant disclosure) and presenting visual data on a AR/VR environments or with AR/VR technological field that attempts to limit the use of the abstract idea to computer environments (see MPEP 2106.05(h) for examples (vi) and (viii)). Therefore, this is indicative of the fact that the claim set has not integrated the abstract idea into a practical application and therefore, the claims are found to be directed to the abstract idea identified by the Examiner.
Step 2B: For independent claims 1, 11 and 20, these claims do not provide an inventive concept. The recited additional elements of the claim(s) are the following: a server computer (from claim 11); at least one processor (from claim 20) and a knowledge database, a graphical user interface (from claims 1, 11 and 20), including the steps of “parsing and processing the received input” to generate “intent-based data sub-units” and “converting” them to “machine executable instructions”. These additional elements are not sufficient to amount significantly more than the judicial exception or abstract idea (see MPEP 2106.05). Because, as indicated in Step 2A Prong 2, these additional element(s) claimed are merely, instructions to “apply” the abstract ideas, which cannot provide an inventive concept. Also, the recitation of a computer to perform the claim limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Thus, even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept at Step 2B.
For dependent claims 2-10 and 12-19, the same analysis is incorporated. Due to their dependency to the independent claims analyzed, these claims cover or fall under the same abstract idea(s) of a method of organizing human activity. They describe additional limitations steps of:
Claims 2-10 and 12-19: further describes the abstract idea of the method of analyzing user’s intent and generating “intent-based data sub-units” from textual and non-textual format input streams that are converted into “machine executable instructions” to be further analyzed based on “ecosystem influencers” and these instructions are further combined into one “combined machine executable instruction”. The claims further describe the types of inputs the basis of user interests which are derived from the user intent, classifying determined intents into different types, generate intermediate outputs that are determined via objective analysis to determine “system optimization recommendations”, the types of “ecosystem influencers”, generating feature sets from “multi-dimensional design vector” that include different factors. Thus, being directed to the abstract idea group of “commercial or legal interactions” as agreements in the form of contracts or legal obligations are processed and analyzed/determined to output or advertise “optimization recommendations” to facilitate the user to complete the business project efficiently in terms of design, sustainability, safety, lean construction standards, financial factors, among other factors that might be required and/or considered which also falls under managing personal behavior in the form of following instructions.
Step 2A Prong 2 and Step 2B: For dependent claims 2 - 3, 7, 10, 12, 15 – 16 and 18 – 19, these claims recite the additional elements of: at least one Artificial Intelligence (AI) model (from claim 7 and 16); a model ensemble, a notifier (from claim 18); an ensemble learning unit (from claim 19). These additional elements recited are invoking computers merely used as a tool to perform or “apply” the abstract idea(s) to the existing process of performing an “objective analysis”, process “data feed” to further simulate a “plan of action” as a virtual/augmented reality environment and perform an “intended task”. Moreover, the “model ensemble” and its “ensemble learning unit” from claims 10 and 18 – 19 that are configured to process data are broadly recited without limiting how such “processing” of data feeds is achieved. Thus, amounting to no more than mere instructions to “apply” the exception using a generic computer component (MPEP 2106.05(f) and (f)(2)). Similarly, the step of “a notifier configured to provide a notification to the user, the notification indicating the simulation the at least one plan of action” in claim 18 for displaying notification data which is generally linked to computer general functions (e.g. invoking “apply it” by a generic computer). Accordingly, for the same reasons stated above, these additional element(s) claimed cannot provide an inventive concept at Step 2B.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 3 -7, 9-11, 13-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cami (U.S. Pub No. 20230229986 A1) in further view of Fenton (WO Pub No. 2021050131 A1).
Regarding claims 1, 11 and 20:
This independent claim set is represented by claim 20
Cami discloses “intelligent workflow generation and metrics related to the workflow and construction projects based on updatable parameters (e.g., tasks, sub-tasks, weather, workers, resources, tools)” related to construction (see ¶0026 – 27; Cami) thus, teaches:
when executed by at least one processor, causes the at least one processor to: (See Fig. 3 (310): Refer to ¶0054 – 55 for more details.)
receive an input from a user for executing at least one intended task by the user; (In ¶0055; Fig. 2A (205 – 215); Fig. 2B: teaches that “user interface may receive inputs from a user through the user's interactions with the electronic device and then can convert those inputs for use by the processor” (see ¶0066 for more details of types of inputs). Refer to ¶0078 – 80 for an example wherein “a client for a construction project can describe what needs to be built” and select “Work Blocs” that “can be a discrete construction task which can be described in human interpretable language, such as “add bathroom,” “install generator,” “finish flooring,” “install bathtub,” “replace vanity”, etc. wherein the “Work Bloc” further “includes tasks such as installing water supply pipes, drain pipes, sink, toilet, bathtub, tiles, and painting the bathroom”)
…wherein the one or more ecosystem influencers comprise at least one of a project phase, supply constraints, and a quality impact; (In ¶0038: teaches an example wherein “Workers can also upload a picture or video when they think a task is complete which can be analyzed by a supervisor or through a machine vision algorithm” and subsequently a “worker can also receive reminders about his or her progress or report delays which can be used to adjust or update an expected completion metric” wherein the “completion metric” includes factors such as “(i) the scheduled sequence of tasks, (ii) the current condition of the construction project, and (iii) at least one task from the schedule of tasks” as well as “time to complete, cost to complete, material cost to complete, labor cost to complete, and completion risk” and these are used as a basis (see ¶0032 – 33) and are directed to the ecosystem influencers claimed. See ¶0072 for another example wherein the “data analysis can be done to refine in relationship to the cost per task UOM”, but also considering the “worker's cost rate and the actual time taken to complete the UOMs” which are assigned (i.e. constraints) and are interpreted as the ecosystem influencers that are defined in ¶0024 from Applicant disclosure. Refer to ¶0117 for details of the “Machine vision module 410” analyzing data.)
determine an intent of the user based on the analyzed [machine executable instructions] and one or more project objectives associated with the at least one intended task; (In ¶0147; Fig. 2A (205 – 210): teaches an example wherein “a framing task needs to be put on hold for inspection” and a trained and “reinforced learning algorithm” will “look at the remaining tasks on the project and suggest to the supervisor that a plumbing task can be moved up in the schedule while the framing inspection is completed” (i.e. determining “a path forward to completing the project in cases where work is stalled”) wherein suggestions are based on a “policy created from reinforced learning on prior experiences and experimentation by the algorithm itself” and the “object of the algorithm is to reduce down time on the project” which is directed to determining the user intent based on the input analyzed and the project objectives, in accordance to the examples given in ¶0081 and ¶0084 from Applicant disclosure. Refer to ¶0117 for details of the “Machine vision module 410” analyzing data to determine “which type of information or characteristics to search for based on the provided metadata” and refer to ¶0032 wherein “a natural language interpret can be used to interpret comments or description related to a task by a human operator, such as “task is complete,” or “double check drywall fit.”)
determine system optimization recommendations based on the one or more project objectives; generate a feature set [corresponding to a multi-dimensional design vector] (In ¶0150; Figs.5 – 6: teaches an example that “during the learning process vectors will be created where the algorithm will execute all actions from a starting state and add up the rewards/penalties to project completion for each action” and the “vectors will be analyzed and the best result will be saved to the policy” which is directed to determining system optimization recommendations based on project objectives as defined in ¶0121 from Applicant disclosure as well as generating multi-dimensional vector feature sets. Refer to ¶0044 wherein the algorithm can “be customized, tweaked, or be trained to produce more specific or tailored recommendations based on local legal, construction, or requirements made by the company performing the construction or receipt of a construction project” and refer to ¶0148 that when generating “task suggestion policy”, “multiple paths can be generated and then screened using other machine learning techniques”. See ¶0094 – 95 for more details of the creation of “project baseline” at block 245 from Fig. 2A, based on project objectives which is also interpreted as determining system optimization recommendations.)
by correlating the intent of the user and the determined system optimization recommendations. (In ¶0030; Fig. 2A (215, 230 and 250 – 255): teaches that user “natural language comments” provided can be “interpreted by a language interpretation algorithm to provide an indication of what is left, what sub-task is not yet completed (e.g., “sink installation,”) and also quantify what percentage of the task (e.g., complete bathroom) is left” and this “quantification can be based on any algorithm”. For instance, “the expected number of hours left to complete the task versus the total number of hours allocated or expected for the task can be used to determine what percentage of the “complete bathroom” task is left. Such “status can be provided or obtained from a machine vision algorithm” to further use the “expected status” that “can be provided from data obtained from smarttools and later verified by a human operator or secondary review by a machine vision module before the task is considered to be complete” wherein such quantification of the tasks is interpreted as the correlation of the user intent and determined system recommendations for the generation of the feature set, as defined in ¶00107 and ¶00122 from Applicant disclosure. Refer to ¶0089 wherein the system can “learn to schedule tasks and have suggestions ready if tasks are blocked or delayed to keep available resources being utilized towards completing the project.”)
process at least one data feed received from a knowledge database based on the determined intent of the user to select at least one plan of action for executing the at least one intended task; (In ¶0066: teaches “server 12 can receive images, videos, scans, or other information related to construction sites. Server 12 can contain algorithms within instructions to process such information to use as input to the methods described herein”. Further in ¶0117 “a machine vision module can be configured to take a picture of a particular area of a construction site and process the image to provide information related to the job site. In some examples, the machine vision module can be provided with metadata related to the visual information it is being provided, such as a stage in construction or a related work block. Machine vision module 410 can use the metadata to determine which type of information or characteristics to search for based on the provided metadata” which is directed to selecting a plan of action for the user intended task. See ¶0116 wherein “Database 405 can contain information related to one or more construction projects, tasks to be completed, equipment available, materials, workers, historical information about projects, worker efficiencies, or any other data which can be used by a simulation module or other trained machine learning or other algorithmic model to predict information about related to a construction project”.)
and simulate, in a graphical user interface, the at least one plan of action as a virtual reality or an augmented reality based on the generated feature set, (In ¶0120 – 121; Fig.4 (425): teaches “Simulation module 425 can obtain information from various modules and simulate a workflow” and “provide as an output any of (i) work blocks, (ii) financial estimations for a construction project, (iii) summary information related to current and anticipated timelines for a construction project”. But also, the “simulation module 425 can contain trained machine learning modules which can take multiple inputs to simulate or generate information related to the construction project”. See ¶0126 “user output module 445” details.)
the simulation rendering a virtual representation of the at least one intended task to enable performance of the at least one intended task according to the determined intent of the user, wherein to simulate the at least one plan of action, the computer-executable program further causes the at least one processor to superimpose a real-time visual data feed of a site associated with the at least one intended task over the virtual representation. (In ¶0055 – 56; Figs. 2F and 2G: teaches an example wherein the user “prompt to turn on or perform a certain action on a device may appear on the display. The user may tap on the displayed button with a finger or stylus, and the tap can then be converted for use by a processor. The processor may then run an application related to changing the device status of the electronic device using instructions and data stored in the memory” to perform operations such as displaying on the user interface “an augmented reality overlay (such as a task verification overlay, upcoming tasks overlay, etc.)”, in accordance with the example of superimposition/overlay of the virtual environment on a real-time visual data feed given in ¶0064 from Applicant disclosure. As for simulating plans of action by superimposing a real-time visual data feed of a site, this is taught in ¶0041 as another example wherein the system can provide functionality for the “use of blueprints for a project in a user application and the use of augmented reality or virtual reality overlays on the blueprint to provide functionality related to the progress of a project”.)
Cami does not explicitly teach the abilities of specifically, parse received input, convert the intent-based data sub-units into machine executable instructions and analyze them based on goals/objectives and the user intent. However, Fenton which directed to enabling “relate to technology for enriching user- authored content through actionable context-based suggestions” wherein “improved computer- communications technology in which a computerized service proactively generates and suggests questions or commands (referred to herein as task-queries)” (see ¶0003; Fenton), thus teaches:
generate one or more intent-based data sub-units by parsing and processing the received input; (In ¶0068 - 69; Fig. 3 (230, 282 and 284): teaches that “Task-intent logic 230” can infer or predict “a task-intent based on user-composed communication information. For example, task-intent logic 230 may determine, from the body of an email being composed by a user stating “as referenced in the picture below,” a probability that the user intends to insert a picture within the body of the email” and the “Task-intent logic 230 may take different forms depending on the mechanism(s) used to determine user intents”. Thus, the “Task-intent logic 230” can include machine learning models (i.e. “support vector machine”) that can be “trained offline to generate character and word embeddings using large corpora of textual content, webpages, and articles. The embeddings act as representations for word and character tokens with contextual and semantic information. The content input by a user (e.g., text) may be transformed to representations using the embeddings, and the representations may be then fed to bidirectional long short-term memory (LSTM) layers of a neural network, such as a recurrent neural network (RNN)”, in accordance to parsing examples given in ¶0084 from Applicant disclosure.)
convert the one or more intent-based data sub-units to machine executable instructions; (In ¶0095; Figs. 4A and 5A: teaches that the “task-query suggestion interface 292 may be utilized to suggest a task-query formulated from a predicted task-intent and/or to receive user selections of suggested task-queries. For instance, where task-intent detector 262 predicts an attach intent and task-query formulator creates related task-queries for attaching a file, task-query suggestion interface 292 may present graphic user interface components suggesting the task-queries to a user as selectable (e.g., clickable) options so that a user may identify which task-queries the user would like to perform. FIGS. 4A and 5A, discussed below, illustrate example embodiments utilizing task-query suggestion interface 292.”)
analyze the machine executable instructions based on one or more ecosystem influencers… (In ¶0104; Fig. 2 (272): teaches that the user may select “a task-query” and once selected, “the conversational assistant services 272 may be used to request additional information or clarification relating to the task-query from the user. For instance, a task-query for “attach a file from David” may be selected, and the conversational assistant services 272 may recognize that additional information may be needed (such as David’s surname, a date range, or type of file) to effectively execute the task-query.”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Cami to provide the abilities of specifically, parse received input, convert the intent-based data sub-units into machine executable instructions and analyze them based on goals/objectives and the user intent, as taught by Fenton in order to “enable a digital assistant or computer service to more quickly traverse a large set of information by using natural language received by observing the user’s composition of a communication” and thus, “the user can quickly manage what tasks, such as attaching a file and/or scheduling a meeting, may be needed to achieve the intended communication content in an automated fashion” (¶0026 and ¶0030; Fenton), see also MPEP 2143.I.G.
Regarding claims 3 and 15:
The combination of Cami and Fenton, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively.
Cami further teaches:
further comprising: receiving the input from the user through a plurality of input streams, at least one of the plurality of input streams corresponding to a non-textual format; (In In ¶0055: teaches that the “user interface may receive inputs from a user through the user's interactions with the electronic device and then can convert those inputs for use by the processor” wherein the inputs can be “received by any one, or combination, of a variety of input devices, such as a touch screen, mouse, key input, stylus, microphone, or the like”. Moreover, the system’s “server 12 can receive images, videos, scans, or other information related to construction sites” wherein the “server 12 can contain algorithms within instructions to process such information to use as input” (see ¶0066).)
processing the plurality of input streams including converting the non-textual format of the at least one of the plurality of input streams to a textual format; (In ¶0117: teaches that the “machine vision module 410 can contain software which can process image or video information which may be obtained from a construction site”. For example, “a machine vision module can be configured to take a picture of a particular area of a construction site and process the image to provide information related to the job site”.)
generating the one or more intent-based data sub-units by parsing and processing each of the plurality of input streams; (In ¶0030: teaches an example wherein a “supervisor can provide natural language comments such as “bathroom complete except for finish on sink” and the language can be interpreted by a language interpretation algorithm to provide an indication of what is left, what sub-task is not yet completed (e.g., “sink installation,”) and also quantify what percentage of the task (e.g., complete bathroom) is left”.)
generating the machine executable instructions corresponding to the one or more intent-based data sub-units; and (In ¶0055: teaches that the “user interface may receive inputs from a user through the user's interactions with the electronic device and then can convert those inputs for use by the processor” wherein the inputs can be “received by any one, or combination, of a variety of input devices, such as a touch screen, mouse, key input, stylus, microphone, or the like”)
combining the generated machine executable instructions corresponding to each of the plurality of processed input streams to a combined machine executable instruction. (In ¶0120: teaches that “simulation module 425 can take as inputs any combination of as input work blocks, smart contracts related to work blocks, and construction data related to various modules described with respect to FIG. 4 and provide as an output any of (i) work blocks, (ii) financial estimations for a construction project, (iii) summary information related to current and anticipated timelines for a construction project” which is directed to combining machine executable instructions into one for further processing. Also, in ¶0066, the system’s “server 12 can receive images, videos, scans, or other information related to construction sites” which has the capacity to combine instructions as the system contains “algorithms within instructions to process such information to use as input”.)
Regarding claims 4 and 13:
The combination of Cami and Fenton, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively.
Cami further teaches:
further comprising receiving the input from the user as at least one of a text, image, video, gesture, and audio format. (In ¶0066: teaches that the system’s “server 12 can receive images, videos, scans, or other information related to construction sites” wherein the “server 12 can contain algorithms within instructions to process such information to use as input”. See ¶0055 which discloses that the “user interface may receive inputs from a user through the user's interactions with the electronic device and then can convert those inputs for use by the processor” wherein the inputs can be “received by any one, or combination, of a variety of input devices, such as a touch screen, mouse, key input, stylus, microphone, or the like”)
Regarding claim 5:
The combination of Cami and Fenton, as shown in the rejection above, discloses the limitations of claims 1, 8 and 15, respectively.
Cami further teaches:
further comprising determining the intent of the user corresponding to a plurality of preferences of the user pertaining to execution of the at least one intended task. (In ¶0030: teaches an example for considering user intent and preferences wherein a “supervisor can provide natural language comments such as “bathroom complete except for finish on sink” and the language can be interpreted by a language interpretation algorithm to provide an indication of what is left, what sub-task is not yet completed (e.g., “sink installation,”) and also quantify what percentage of the task (e.g., complete bathroom) is left”. Further, the system considers user preferences when “multiple potential sets of work blocks can be generated” as the “work block module 430 can interact with simulation module 425 to choose a specific set of work blocks based on user preferences”, thus “work blocks can also contain timing, priority, or relate to other work blocks to determine an order in which work blocks can be completed” (see ¶0122) which is interpreted to determine the user intent and preferences pertaining to an intended task that is to be executed.)
Regarding claims 6 and 14:
The combination of Cami and Fenton, as shown in the rejection above, discloses the limitations of claims 5 and 11, respectively.
Cami further teaches:
further comprising classifying the determined intent as at least one of a temporal intent, a spatial intent, a fiscal intent, and a societal intent. (In ¶0030: teaches an example of this descriptive material as a “supervisor can provide natural language comments such as “bathroom complete except for finish on sink” and the language can be interpreted by a language interpretation algorithm to provide an indication of what is left, what sub-task is not yet completed (e.g., “sink installation,”) and also quantify what percentage of the task (e.g., complete bathroom) is left” which can be interpreted as classifying determined intent as at least a temporal intent. Refer to ¶0063 wherein “construction device 25, such as for example a communication connected tool or smarttool, as described herein, can also include an identification of a user using the device through biometrics, a pin code, or a job code, to indicate the user using the device, the type of task, sub-task, or job the device is being used for”, which is another example of classifying user intent. Also, see ¶0071 and the table that shows user “inputs” being collected and considered such as “task type” or “job type”.)
Regarding claims 7 and 16:
The combination of Cami and Fenton, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively.
Cami further teaches:
further comprising: generating an intermediate output comprising one or more model parameters by analyzing the machine executable instructions based on the ecosystem influencers; (In ¶0117; Fig. 4 (410); Fig. 5: teaches “a machine vision module can be configured to take a picture of a particular area of a construction site and process the image to provide information related to the job site” or the module can “be provided with metadata related to the visual information it is being provided, such as a stage in construction or a related work block” to further “use the metadata to determine which type of information or characteristics to search for based on the provided metadata” directed to the model parameters, in accordance to intermediate output example at least given in ¶0023 from Applicant disclosure. Refer to ¶0027 and ¶0037 for general details of “intelligent workflow generation and metrics related to the workflow and construction projects based on updatable parameters (e.g., tasks, sub-tasks, weather, workers, resources, tools)” that are used as input to dynamically track “the progress of the project and updates the completion costs, time or other metrics that may be used to monitor progress or goals”.)
providing the generated intermediate output to at least one Artificial Intelligence (AI) model for an objective analysis; and (In ¶0117; Fig. 4 (410); Fig. 5 (520 – 530): teaches that the determined metadata for searching can be further included or used with “tile installation error detection algorithm and “on-site” material counting algorithm” to obtain “information about errors in tile installation and available materials on-site for planning purposes”. Also, refer to ¶0128 – 129 and Fig. 5 wherein a “convoluted neural network (CNN) 500” is used and contains “a number of inputs in an input layer 500, such as for example, inputs 501-505, an output layer, 530, with a number of outputs, such as outputs 531-532, and a number of middle layers, such as middle layer 520” (i.e. directed to the generated intermediate outputs). Moreover, and as another example of generated intermediate outputs, “the information obtained in output layer 530 can be used by other algorithms to determine a cost to complete” which is further used in the “reinforcement learning the weights between the layers of CNN 500 can be updated to determine the most crucial inputs and provide accurate results”.)
determining the system optimization recommendations based at least one [of] the objective analysis. (In ¶0117; Fig. 4 (410); Fig. 5: teaches that based on provided metadata, the “machine vision module 410 can analyze the video for completeness of tiling within the shower, the type of tile, and the fit and finish of the tile”. See ¶0128 – 129 for the use of “reinforcement learning” in the CNN to “determine the most crucial inputs and provide accurate results” from updated weights of the CNN outputs.)
Regarding claim 9:
The combination of Cami and Fenton, as shown in the rejection above, discloses the limitations of claim 1.
Cami further teaches:
wherein the multi-dimensional design vector comprising at least one of: a position coordinate system, cost, sustainability, safety, facility management, a construction principle, and an industry standard. (In ¶00150: teaches that “during the learning process vectors will be created where the algorithm will execute all actions from a starting state and add up the rewards/penalties to project completion for each action” and “vectors will be analyzed and the best result will be saved to the policy” wherein the policy is the “strategy that will be developed by the algorithm during the exploration phase to be used in real life practice (exploitation) phase” (see ¶0143; directed to the design vector). As for the dimensions of the design vector, refer to ¶0040 wherein “cost-to-completion algorithm can be implemented using machine learning, clustering, generative adversarial network, neural networks, deep learning, gaussian predictors, or other related artificial intelligence techniques” and can “provide one or more metrics related to completing a construction project based on at least a current time” or the “metrics can be cost, efficiency, total time, total manpower, or other parameters of the construction project.” Also, refer to ¶0134 wherein dimensionality reduction method such as “principle component analysis (PCA)” can be used to “remove the amount of information which is least impactful or statistically least significant” and “reduce the dimensions or number of variables of a “space” by finding new vectors which can maximize the linear variation of the data”)
Regarding claims 10 and 18:
The combination of Cami and Fenton, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively.
This dependent claim set is represented by claim 18
Cami further teaches:
further comprising: a model ensemble configured to process the at least one data feed to select the at least one plan of action for executing the at least one intended task; and (In ¶0116; Fig. 4 (405, 410 and 425); Fig. 5: teaches that the “Database 405 can contain information related to one or more construction projects, tasks to be completed, equipment available, materials, workers, historical information about projects, worker efficiencies, or any other data which can be used by a simulation module or other trained machine learning or other algorithmic model to predict information about related to a construction project”, in accordance to the model ensemble examples given in ¶0059 and ¶0062 from Applicant disclosure. Further, the “simulation module 425 can contain trained machine learning modules which can take multiple inputs to simulate or generate information related to the construction project” (see ¶0121) and the “output module 445 can summarize information related to the construction project which is obtained or derived from any of the blocks described with respect to FIG. 4” such as from the “simulation module 425” wherein such information generated include plan of action such as “current construction status of the project” or other project schedules/summary requested by the user or based on the determined user intent(see ¶0126).)
a notifier configured to provide a notification to the user, the notification indicating the simulation of the at least one plan of action wherein the at least one plan of action is simulated to enable at least one of responding an additionally received input from the user, (In ¶0055 – 56; Fig. 4 (425 and 445); Figs. 2D, 2F and 2G: teaches that an example, “the prompt to turn on or perform a certain action on a device may appear on the display” and right after the user has entered their input such as tapping “on the displayed button with a finger or stylus”, the device status can be changed or the user interface can “display one or more of the following: a cost to complete table, working hours remaining per task, current tasks a project baseline, upcoming project tasks, an augmented reality overlay (such as a task verification overlay, upcoming tasks overlay, etc.)” which are directed to a plan of action as VR/AR, in accordance to the notifier examples given in ¶0055, ¶0064 and ¶108 – 109 from Applicant disclosure. Refer to ¶0043 for providing notifications to a user example wherein “alerts can be chosen or acted upon by project managers or end users. In some cases, the alerts can provide a plurality of choices to a user. The choices, and the alerts, can be actionable. In some examples, the actionable alert can be integrated or cause an autonomous or semi-autonomous construction equipment to take action. In other examples, aspects related to the construction project, such as scheduling of labor, hiring of more workers, or ordering of materials or equipment can be performed automatically based on the generated alerts.” See ¶0038 wherein users can receive reminders that can be sent remotely for a project and receive indications that their tasks were complete.)
and the performance of the at least one intended task. (In ¶105 – 106; Fig. 2D, 2F and 2G: describes “a schematic view of a user viewing a work site through a virtual reality headset” and the user can interact with a “Tool 284” to remotely control it as shown in Figs. 2D and 2F which is an example of performance of an intended task based on user’s intent. Also, refer to ¶0038 wherein “projects can also be managed remotely by using a digital schedule and assigning the generated schedule of tasks to workers manually or remotely” and the workers can receive their tasks and further provide information (i.e. inputs) that are “analyzed using natural language interpretation algorithms” and can receive indications of tasks being verified as completed, new tasks and “reminders about his or her progress or report delays which can be used to adjust or update an expected completion metric”. All of these data can be “provided by a worker through a smart glasses or wearable devices” which is directed to performing an intended task based on the user intent through a VR/AR environment.)
Regarding claim 19:
The combination of Cami and Fenton, as shown in the rejection above, discloses the limitations of claim 18.
Cami further teaches:
wherein the model ensemble for processing the data feed is configured to process one or more data feeds by an ensemble learning unit based on the determined intent. (In ¶0135; Fig. 5: teaches that “Ensemble methods can be used, which primarily use the idea of combining several predictive models, which can be supervised ML or unsupervised ML to get higher quality predictions than each of the models could provide on their own”. Also, with the use of “Neural networks” they can “generally attempt to replicate the behavior of biological brains in turning connections between an input and output “on” or “off” in an attempt to maximize a chosen objective.” Refer to ¶0117 for data processing by the “machine vision module” and its algorithm.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Unnikrishnan (U.S. Pub No. 20220067626 A1) is pertinent because it “relates generally to machine learning, and more particularly to optimization related to enterprise performance management.”
VIJAYARAGHAVAN (U.S. Pub No. 20230215061 A1) is pertinent because it “relate[s] to a project visualization system that may be used to graphically depict project data (e.g., project elements and links, dependencies, relationships, or other associations among project elements) in a way that may enable project managers to understand complex multi-level dependencies between various project elements.”
Zass (U.S. Pub No. 20200413011 A1) is pertinent because it “relates generally to a task recommendation system and more particularly to task recommendation system configured to predict tasks recommended for users that can be executed by third party services.”
Matsuoka (U.S. Pub No. 20230057896 A1) is pertinent because it “relates generally to a task recommendation system and more particularly to task recommendation system configured to predict tasks recommended for users that can be executed by third party services.”
Takahashi (U.S. Pub No. 20220058552 A1) is pertinent because it “relates to a system that provides a mechanism for achieving a relatively large-scale single project or relatively large-scale multiple projects with relatively less manpower and a mechanism for inputting and managing information for efficient use of resources, and further a system that can enhance the project achievement ability of the members involved in the project”
Sheikh (U.S. Pub No. 20220067748 A1) is pertinent because it is “relate to systems and methods for advisor interfaces, classification, electronic document management and collaboration for advisors and clients.”
Hartung (U.S. Pub No. 20160132828 A1) is pertinent because it “relates to providing interactive real-time analysis and recommendations to distributed project teams.”
Wang (U.S. Pub No. 20110231353 A1) is pertinent because it “relates to unique artificial intelligence (AI) application in that through a specially designed user interface technology, the application system help users clarify, analyze, judge, make decisions on their needs such as sophisticated information, objectives to be fulfilled, tasks to be executed through AI interface communication, and help provide appropriate answers, approaches or procedures.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ivonnemary Rivera Gonzalez whose telephone number is (571)272-6158. The examiner can normally be reached Mon - Fri 9:00AM - 5:30PM.
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/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626 /DENNIS W RUHL/Primary Examiner, Art Unit 3626
