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 .
Status of Claims
Claims 1, 7 - 12 and 15 - 18 have been amended and are hereby entered.
Claims 1-20 are pending and have been examined.
This action is made FINAL.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on: 11 December 2025, 14 January 2026 and 09 March 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 March 16, 2026 have been fully considered but they are not persuasive.
Regarding the applicant's arguments for Double-Patenting Rejection in p. 7: Applicant did not take any action, did not significantly amended the claims, submitted persuasive arguments against the rejection and/or did not file an Electronic-Terminal Disclosure or e-td to obviate the Obviousness and Anticipatory types of Double Patenting (OADP) rejections. Thus, the OADPs 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 p. 8 from Remarks have been considered. However, this particular rejection has been withdrawn/maintained due to the nominal amendments made by Applicant. This is because the Applicant cited paragraphs ¶0090 – 92, these embodiments are directed to “actions” being simulated in virtual/augmented reality as a response to user inputs or to perform intended task(s) based on user intent. However, in these cited paragraphs the “notifier” is nowhere mentioned or even linked as to how it is configured or even how the “notifier” is specifically executing (i.e. simulating) such simulation examples for plans of action and “enabling” the performance of the intended tasks as a function. Moreover, the “notifier” is programmed to “provide notifications to the user” which are received as “audio, visual, or textual notifications in the form of indications or prompts” as well as “recommended actions” (see ¶0063 from Applicant disclosure) which further proves that the “notifier” does not actually “simulates” any actions, but simply outputs or “indicate” the simulations. Finally, these examples cited do not cure the deficiencies of how are these claimed functions are specifically achieved.
Regarding to Applicant's arguments against the 101 rejection of pending claims on pages 9-17: 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. 9: The Applicant argues that the pending claims are not directed to any of the abstract ideas identified because “Claim 1 includes computations that are not abstract, but are instead applied in a structured and integrated way that results in an improvement to computer-related technology”. However, the Examiner finds this argument is unpersuasive and respectfully disagrees. Because the pending claims were analyzed and “evaluated after determining what [the] applicant has invented by reviewing the entire application disclosure and construing the claims in accordance with their broadest reasonable interpretation (BRI)” for Step 2A-Prong 1 and 2 (See MPEP § 2106, subsection II, for more information about the importance of understanding what the applicant has invented, and MPEP § 2111 for more information about the BRI). Further, each claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim (see MPEP 2106.04, subsection II) which recited steps of “analyzing” user input (i.e. as queries or instructions) related to “intended tasks” of a business project (i.e. construction projects and their respective tasks required) to “determine” user’s intent as well as “optimization recommendations” that will satisfy the corresponding “project objectives” (i.e. cost-effective, time-sensitive, favorable location, projection and any other kind of solutions to complete a business project) which 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 “optimization recommendations” outputted. Also, the claim steps directed to “analyzing” user inputs and “determining” the user intent and optimization recommendation(s) based on objectives to further “correlate” user’s intent and the optimization recommendations to generate a “feature set” can recite a mental process even if they are claimed as being performed on a computer”. Because under the “broadest reasonable interpretation of the claim in light of the specification” it was determined that the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed for analyzing and determining user data and its intent to determine project optimization recommendations and generate feature set(s) which “is merely using a computer as a tool to perform the concept” that is recited in a high level of generality and merely uses the computer as a tool to perform the claimed functions (see MPEP 2106.04(a)(2)(III)(C) and 2106.05 (f)). Also, in response to Applicant arguments in p.11 from Remarks, these claimed functions identified as mental processes are not further limited from not being performed mentally and the use of a physical aid such as a computer does not negate the mental nature of the limitation(s) (see MPEP 2106.04(a)(2)(III)(B & C)). Thus, these claims and their additional elements, when evaluated, individually and in combination, under the broadest reasonable interpretation and their specification (see MPEP 2111 and 2106.04(II)), were still directed to the abstract idea without reciting significantly more than the judicial exception.
For Step 2A-Prong 2 and Step 2B starting in p. 12: The Applicant alleges that the claims integrate, the judicial exception identified, into a practical application and further alleges that is similar to the hypothetical claim 1 of Example 42 from the 2019 PEG because claim 1 discloses “an analysis is performed in a particular sequential manner, as claimed, to automatically and intelligently determine the intent of the user, regardless of the diverse nature of the received input”. However, the Examiner finds these arguments unpersuasive and respectfully disagrees since the 2019 PEG is an outdated analysis. Regardless, 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” which is further improving the abstract idea itself (See MPEP 2106.04(d)(1) and MPEP 2106.05(a)). Also, the claim limitations invoked the use of a computer as a tool to perform an abstract idea of analyzing user inputs to determine its intents and recommendations for feature set generation (see MPEP 2106.04(d)(I) and MPEP 2106.05(f)). Thus, for these same reasons, the claims are not providing an inventive concept at Step 2B (in response to Applicant arguments in pp. 15 – 17 from Remarks). Therefore, these claims, contrary to Applicant assertions. Finally, this claimed invention in its entirety, is still focused on the result of its application, which merely generates a feature set as a recommendation based on an analysis or correlation from the gathered and determined data and no other significant steps would amount more than the judicial exception or abstract idea to be eligible at Step 2A-Prong 2 and Step 2B (see MPEP 2106.05(f)(1)).
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 18 – 20: Applicant is focusing on each prior art teaching, rather than focusing on the actual language claimed in each claim limitation and how their corresponding limitation steps are different (i.e. distinguishable) from the prior art teachings. Also, Applicant fails to recognize that the claim language recited is broad and under the broadest reasonable interpretation (BRI). Therefore, Cami still reasonably satisfies the generally recited functions in the claims, specifically, contrary to Applicant arguments in p. 9 from Remarks, regarding the steps of “determining an intent of the user” input and project objectives.
As for Cami failing to disclose the step of “generate a feature set by correlating the intent of the user and the determined system optimization recommendations” in pp. 9 – 10 from Remarks, this argument amounts to a general allegation that the claims are different from the claim language, when in fact, under BRI are still reasonable since Cami teaches “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” (see ¶0150; Cami) which include the analysis or “quantification” of a task in terms of time completion/cost via the “language interpretation algorithm” and the “machine vision algorithm” (see ¶0030; Cami). Finally, even if Applicant does not concede with these final reasons, the Applicant is welcomed to further specify the claims to distinguish the claim language from the Cami reference, although the limitations of user intent determinations and generation of feature vectors based on data correlation, as currently and broadly claimed, is well known in this field of endeavor. For example, in Matsuoka (U.S. Pub No. 20230057896 A1), its “task-facilitation service generates a feature vector from the member data and the user model (and/or any data associated with or derived from the user model) using a feature-selection process. The feature-selection process may weight features of the feature vector according to a value in which the feature contributes to a likelihood of the feature vector being associated with a particular task” (see ¶0020, ¶0163 – 166 and ¶0208; Matsuoka). Also, Matsuoka teaches that the data collected can be evaluated with “natural language processing (NLP) or other artificial intelligence” to “identify an intent” based on “on a semantic analysis of a communication… user input…message-associated statistics…and/or the like” (see ¶0056; Matsuoka). Please, refer to the Claim Rejections - 35 USC § 102 section which are maintained herein 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 the instant independent claims 1, 11 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 7, 16 and 25 of U.S. Patent No. 11907885 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because they are not patentably distinct from each other because the differences between the claims are considered to be anticipated as set forth below:
Instant claims
Co-pending or reference claims
(US 11907885 B1)
Claims 1, 11 and 20: A non-transitory computer-readable storage medium, having stored thereon a computer-executable program which, (claim 20)
when executed by at least one processor, causes the at least one processor to:
receive an input from a user for executing at least one intended task by the user;
… [refer to next row]
determine an intent of the user based on the analyzed input 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
generate a feature set by correlating the intent of the user and the determined system optimization recommendations.
Claims 1, 11 and 20: A non-transitory computer-readable storage medium, having stored thereon a computer-executable program which, (claim 20)
when executed by at least one processor of a server device, causes the at least one processor to:
train a machine learning model using training data including data relating to previous user inputs received from a user and information about the user;
apply the trained machine learning model to a current user input received from the user to determine a user intent of the user for executing at least one intended task by the user, wherein the user intent is classified as at least one of: a temporal intent, a spatial intent, a fiscal intent, and a societal intent;
generate a feature set based on the user intent;
process, based on the user intent, at least one data feed received from a database of the user to select at least one plan of action for executing the at least one intended task, wherein the at least one processor for processing the at least one data feed is configured to process, based on the user intent, one or more data feeds by implementing an ensemble learning for the one or more data feeds; and
simulate, in a graphical user interface, the at least one plan of action as virtual or augmented reality based on the feature set to enable at least one of responding an additionally received input from the user and perform the at least one intended task according to the user intent.
(Claim 20 cont.): analyze the received input based on one or more ecosystem influencers;
Claim 12: The system of claim 11, wherein the current user input is received from the user as at least one of: a text, an image, a video, and an audio format, and wherein the at least one intended task is determined from the current user input based on subjecting the current user input to at least one of: a natural language processing technique, an audio processing technique, and an image processing technique.
Consequently, for instant claims 1, 11 and 20 are covered by US 11907885 B1 and its claims 1, 11 – 12 and 20. Thus, these instant claims are anticipated by the patent reference, 1, 11 – 12 and 20 because both applicant’s pending application and the reference patent cover every feature claim in which the instant claims are broadly recited and encompass the same disclosed technology. Moreover, both the instant claims and the reference claims share similar invention titles which are directed to a method and a system that generally “related to artificial intelligence (Al) and machine learning (ML) in a construction environment.” in order to “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.” (see ¶0002 and ¶0007 from the instant specification and ¶0003 and ¶006 – 8 from the reference specification). Thus, under the broadest reasonable interpretation (BRI), this invention scope in the instant claims 1, 11 and 20 are covered by the independent reference claims 1, 11 – 12 and 20 from US 11907885 B1 (see MPEP 804 (II)(B)(2) for more details).
At least claims 1, 11 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 10 and 18 – 19 of Co-pending Application No. 18/847,868 (reference application referred as ‘868 hereafter). 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)
Claims 1, 11 and 20: A non-transitory computer-readable storage medium, having stored thereon a computer-executable program which, (claim 20)
when executed by at least one processor, causes the at least one processor to:
… [refer to next row]
analyze the received input based on one or more ecosystem influencers;
determine an intent of the user based on the analyzed input 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
generate a feature set by correlating the intent of the user and the determined system optimization recommendations.
Claims 1, 10 and 18: A non-transitory computer-readable storage medium, having stored thereon a computer-executable program which, (claim 18)
when executed by at least one processor, causes the at least one processor to:
determine a user intent based on a user input for executing at least one intended task by a user;
convert the determined user intent to one or more machine executable instructions;
generate a plurality of scenarios based on the one or more machine executable instructions;
evaluate an outcome of each of the plurality of scenarios by mapping it to one or more project objectives associated with the at least one intended task; and
generate at least one model recommendation associated with the user intent based on the evaluation.
(Claim 20 cont.): receive an input from a user for executing at least one intended task by the user;
Claim 19: the computer-executable program further causes the at least one processor to: receive the user input through a plurality of input streams, at least one of the plurality of input streams corresponding to a non-textual format…
Consequently, for instant claims 1, 11 and 20 are covered by reference claims 1, 10 and 18 – 19 of ‘868 application. Thus, these instant claims are obvious by the reference claims 1, 10 and 18 – 19 because both applicant’s pending application and the reference cover every feature claim in which the instant claims are broadly recited and encompass the same disclosed technology. Moreover, both the instant claims and the reference claims share similar invention titles which are directed to a method and a system that generally “related to artificial intelligence (Al) and machine learning (ML) in a construction environment.” in order to “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.” (see ¶0002 and ¶0007 from both the instant and reference specifications). Thus, under the broadest reasonable interpretation (BRI), this invention scope in the instant claims 1, 11 and 20 are covered by the independent reference claims 1, 10 and 18 – 19 from ‘868 application (see MPEP 804 (II)(B)(2) for more details).
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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 10 and 18 - 19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Because claims 10 and 18 are directed in part to the limitation step of “simulating the at least one plan of action as a virtual or augmented reality…” to “enable at least one of…performance of the at least one intended task according to the determined user intent”. However, for these particular functions which are executed by a “notifier”, an "algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function(s) to be performed” (See MPEP 2161.01). However, the specifications, lack sufficient details of how the simulation of plan of action as virtual/augmented reality (VR or AR) and the enablement of the “performance” step is implemented and what the “intended task” is. Applicant disclosure merely discloses in ¶0055, ¶0064 and ¶108 – 109 this enabling “performance” function executed by the “notifier” as well as other examples and does not further describe what the respective “intended task” is and how the enabling “performance” function occurs with respect to this “intended task”. Although, Applicant cited paragraphs ¶0090 – 92, these embodiments are directed to “actions” being simulated in virtual/augmented reality (VR or AR) as a response to user inputs or to perform intended task(s) based on user intent. However, in these cited paragraphs the “notifier” is nowhere mentioned or even linked to these claimed functions. Specifcially, how is the “notifier” configured or even how it is specifically executing (i.e. simulating) such simulation examples for plans of action and “enabling” the performance of the intended tasks as a function. Moreover, the “notifier” is programmed to “provide notifications to the user” which are received as “audio, visual, or textual notifications in the form of indications or prompts” as well as “recommended actions” (see ¶0063 from Applicant disclosure) which further proves that the “notifier” does not actually “simulates” any actions, but simply outputs or “indicate” the simulations. Finally, these examples cited do not cure the deficiencies of how are the claimed functions are specifically achieved. Hence, one of skill in the art would not be able to determine and understand how to simulate plan of action as VR/AR and enable the “performance” of the “at least one intended task” if not further specified since specification does not have adequate disclosure to show the inventor had possession of the invention. Therefore, the disclosed claim limitation is considered to be lacking possession of the subject matter claimed and indicated above which is not sufficiently supported by the specifications as originally filed. For purposes of providing clarity, the limitation of “simulating the at least one plan of action as a virtual or augmented reality…” to “enable at least one of…performance of the at least one intended task according to the determined user intent” was interpreted as the computer system converting user inputs and displaying, via the computer interface, prompts and appearing actions as well as simulated outputs related to controlling a tool remotely and/or managing projects remotely with tasks that involve digital scheduling and assigning tasks to workers based on user interactions and natural language analysis of user input. For the same reasons stated above, claim 19 is interpreted under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ) based on their dependency to claim 18.
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 - 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:
…analyze the received input based on one or more ecosystem influencers;
determine an intent of the user based on the analyzed input 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
generate a feature set by correlating the intent of the user and the determined system optimization recommendations.
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 the limitations for “analyzing” user input (i.e. as queries or instructions) related to “intended tasks” of a business project (i.e. construction projects and their respective tasks required; see ¶0081 from Applicant disclosure) to “determine” user’s intent as well as “optimization recommendations” that will satisfy the corresponding “project objectives” (i.e. cost-effective, time-sensitive, favorable location, projection and any other kind of solutions to complete a business project). Thus, these limitations that recite 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” to facilitate the user to complete the business project efficiently in terms of design, sustainability, safety, lean construction standards, financial factors (see ¶0122 from Applicant disclosure) among other factors that might be required and/or considered.
The steps of “analyze” received inputs and “determine” user’s intent and “system optimization recommendations” based on the inputs and “project objectives” and “correlating” user intents and “system optimization recommendations” fall under the abstract idea of mental processes that can be practically be performed in the human mind or in pen and paper (See MPEP 2106.04(a)(2), subsection III). Because analyzing user inputs and determining the user intent and optimization recommendation based on objectives to further “correlate” user’s intent and the optimization recommendations to generate a “feature set” encompass evaluation, judgement and opinion. Also, these steps can either be done with the help of physical aid such as pen and paper or can be performed by humans without or with the assistance (e.g. tool) a computer. Thus, the steps do not negate and further still reads in the mental nature of the limitation(s), when analyzing, determining and correlating such user and optimization information, as well as the concept is merely claimed to be performed on a generic computer and is merely using a computer as a tool to perform the concept of providing project/business recommendations (see MPEP 2106.04(a)(2)(III)(B & C)).
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 controller (from claim 11) and at least one processor (from claim 20). 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 controller used are recited at a high level of generality that is being used as a tool to perform the generic computer functions for generating feature sets (i.e. feature vector with factors being considered) and determine optimization recommendations based on project objectives. Thus, 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 “generate a feature set by correlating the intent of the user and the determined system optimization recommendations” in the representative claim is really nothing more than links to computer for implementing the use of ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components (refer to MPEP 2106.05 f (2)). Thus, in these limitation steps, the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer.
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 controller (from claim 11) and at least one processor (from claim 20). 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 and mental processes. 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” and mental processes 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.
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 knowledge database and 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 – 91 that are configured to process data, this function is broadly recited without limiting how such “processing” 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)). Accordingly, for the same reasons stated above, these additional element(s) claimed cannot provide an inventive concept at Step 2B.
Also, claims 2 – 3, 12 and 15 recite the functions of “parsing and processing” data from the received input data that is broadly claimed and not further limited as to how these functions are performed from other general computer components executing this same function which is generally applied by the computer.
The step of “receive an input from a user for executing at least one intended task by the user” in the representative claim is really nothing more than links to computer for implementing the use of ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components (refer to MPEP 2106.05 f (2)). Thus, in these limitation steps, the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer.
As for the steps of “converting” “one or more intent-based data sub-units to machine executable instructions” (in claims 2 and 12) and “non-textual” input streams to a “textual format” (in claims 3 and 15), are also broadly recited that are performed generally to apply the abstract idea without placing any limits on how the “converting” the data either to textual formats and into instructions are performed distinctively from generic computer components and without the function being generally be invoked as an “apply it” to a computer.
The step of “a notifier configured to simulate the at least one plan of action as virtual or augmented reality” in claim 18 , is also broadly recited which is generally performed to apply the abstract idea without actively reciting and placing any limits on how the “simulation” for the “at least one plan of action” would be performed distinctively as “virtual or augmented reality” (VR/AR) from other generic computer components (e.g. invoking “apply it” by a generic computer) using VR/AR technology to achieve this same “simulation” data result.
But also, this same step for simulating action plans 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 VR/AR technology for the function of “simulating” data that 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) in VR/AR environments that attempts to limit the use of the abstract idea to computer environments (see MPEP 2106.05(h) for examples (ix) and (x)). 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.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cami (U.S. Pub No. 20230229986 A1).
Regarding claims 1, 11 and 20:
This independent claim set is represented by claim 20
Cami 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”)
analyze the received input based on one or more ecosystem influencers; (In ¶0072; Fig. 2A (210): teaches “At block 210, data analysis can be performed on data obtained at block 205” 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 input 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; and (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. 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.)
generate a feature set 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. See ¶0150 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” which is directed to the generation of the feature set. 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.”)
Regarding claims 2 and 12:
Cami, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively.
Cami further teaches:
further comprising: generating one or more intent-based data sub-units by parsing and processing the received input; (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”, in accordance to examples given in ¶00101 – 102 from Applicant disclosure.)
converting the one or more intent-based data sub-units to machine executable instructions; 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”, in accordance to the example given in ¶00113 from Applicant disclosure. Refer to ¶0032 wherein “a list or sequence of tasks” can be generated from information related to “a construction project” received and to ¶0039 wherein “work blocs can be further broken down into assignable actions or sub-tasks, which can be assigned resources and scheduled with start and end dates”.)
analyzing the machine executable instructions based on the one or more ecosystem influencers. (In ¶0055: teaches an example wherein “the 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”, that way 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 cause the device to perform aspects of this disclosure” which is interpreted as the system analyzing machine executable instructions based on the one or more ecosystem influencers. See ¶0038 for another 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 refer to ¶0117 for more details of the “Machine vision module 410” and its algorithm.)
Regarding claims 3 and 15:
Cami, 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 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 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:
Cami, 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:
Cami, 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:
Cami, 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:
Cami, 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 received input 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 claims 8 and 17:
Cami, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively.
Cami further teaches:
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.)
Regarding claim 9:
Cami, as shown in the rejection above, discloses the limitations of claim 1.
Cami further teaches:
further comprising generating the feature set corresponds to a 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:
Cami, 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 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 (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 simulate the at least one plan of action as virtual or augmented reality based on the feature set 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 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”. For 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 ¶0120 – 121 wherein “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.”)
and performance of the at least one intended task according to the determined user intent. (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, 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.
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”
Fenton (WO Pub No. 2021050131 A1) is pertinent because it “relate to technology for enriching user- authored content through actionable context-based suggestions” wherein “User-composed content within an electronic communication is observed and used to predict one or more intents each associated with a task related to the communication and to be completed in creating the communication”
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.”
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
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